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Kulsha, A. Y., M. A. Klimovich, M. V. Sterjanov, V. N. Tesluk, and N. G. Egorova. "Mechatronic device of AI systems." Doklady BGUIR 18, no. 4 (June 25, 2020): 28–35. http://dx.doi.org/10.35596/1729-7648-2020-18-4-28-35.
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The purpose of this work is to demonstrate the phased development of a mechatronic device, to describe the development process of the device design and software, to demonstrate the application of the theory of selected sections of mathematics and physics in robotics, and, in particular, linear algebra, geometry, computational mathematics, discrete mathematics and mechanics. The study was based on the mechatronic device, which had been designed by the youth team of the Republic of Belarus in preparation for the international robotics competition “First Global Challenge 2019”, which became the winner of this event. The article describes in detail the statement of the problem at this competition, identifies the basic requirements for the robot being built, provides a general description of the stages of building the device both during the design process and during the immediate implementation of the project, as well as substantiates the engineering decisions that were made during the design process. The stages that are of the greatest interest in terms of applying the theory of applied physics and mathematics are described in more detail. Particular attention is paid to the design and development of structural modules, as well as to the development of software for controlling the device. The robot is an experimental model that can be used in further research in the field of artificial intelligence, machine learning, automation systems, and is also a potential platform for teaching robotics at the level of specialized secondary and higher education.
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Mohd Shith Putera, Nurus Sakinatul Fikriah, Sarah Munirah Abdullah, Noraiza Abdul Rahman, Rafizah Abu Hassan, Hartini Saripan, and Imam Haryanto. "Malaysian Medical Device Regulation for Artificial Intelligence in Healthcare: Have all the pieces fallen into position?" Environment-Behaviour Proceedings Journal 6, no. 16 (March 28, 2021): 137–44. http://dx.doi.org/10.21834/ebpj.v6i16.2635.
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Artificial Intelligence (AI) ability of self-learning and adaptation has challenged the medical device regulation in overseeing the safety and effectiveness of medical devices. Thus, this research aims to evaluate the adequacy of the pre-market requirements under the Medical Device Act 2012 in governing AI modification. Employing the doctrinal research methodology, systematic means of legal reasoning pertinent to AI for healthcare applications are produced. An effective medical device regulation is pivotal to foster trustworthiness in the governance and adoption of AI. However, the research findings indicate the deficiency of the current conformity assessment for medical devices in addressing AI modifications. Keywords: Artificial Intelligence and Law, Artificial Intelligence and Medical Device Regulation, Malaysian Medical Device Regulation eISSN: 2398-4287© 2021. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v6i16.2635
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Hernandez-Boussard, Tina, Matthew P. Lundgren, and Nigam Shah. "Conflicting information from the Food and Drug Administration: Missed opportunity to lead standards for safe and effective medical artificial intelligence solutions." Journal of the American Medical Informatics Association 28, no. 6 (March 1, 2021): 1353–55. http://dx.doi.org/10.1093/jamia/ocab035.
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Abstract The Food & Drug Administration (FDA) is considering the permanent exemption of premarket notification requirements for several Class I and II medical device products, including several artificial Intelligence (AI)–driven devices. The exemption is based on the need to rapidly more quickly disseminate devices to the public, estimated cost-savings, a lack of documented adverse events reported to the FDA’s database. However, this ignores emerging issues related to AI-based devices, including utility, reproducibility and bias that may not only affect an individual but entire populations. We urge the FDA to reinforce the messaging on safety and effectiveness regulations of AI-based Software as a Medical Device products to better promote fair AI-driven clinical decision tools and for preventing harm to the patients we serve.
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Gutierrez, Gregory M., and Thomas Kaminski. "A Novel Dynamic Ankle-Supinating Device." Journal of Applied Biomechanics 26, no. 1 (February 2010): 114–21. http://dx.doi.org/10.1123/jab.26.1.114.
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Lateral ankle sprains (LAS) are among the most common joint injuries, and although most are resolved with conservative treatment, others develop chronic ankle instability (AI). Considerable attention has been directed toward understanding the underlying causes of this pathology; however, little is known concerning the neuromuscular mechanisms behind AI. A biomechanical analysis of the landing phase of a drop jump onto a device that simulates the mechanism of a LAS may give insight into the dynamic restraint mechanisms of the ankle by individuals with AI. Furthermore, work evaluating subjects who have a history of at least one lateral ankle sprain, yet did not develop AI, may help elucidate compensatory mechanisms following a LAS event. Identifying proper neuromuscular control strategies is crucial in reducing the incidence of AI.
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Kohno, Hiroki, Goro Matsumiya, Yoshiki Sawa, Norihide Fukushima, Yoshikatsu Saiki, Akira Shiose, and Minoru Ono. "Can the intermittent low-speed function of left ventricular assist device prevent aortic insufficiency?" Journal of Artificial Organs 24, no. 2 (January 9, 2021): 191–98. http://dx.doi.org/10.1007/s10047-020-01234-4.
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AbstractAortic insufficiency (AI) is known to associate with a persistently closed aortic valve during continuous-flow ventricular assist device support. Some devices carry an intermittent low-speed (ILS) function, which facilitates aortic valve opening, but whether this function prevents AI is unknown. In this study, the Jarvik 2000 device, which is programmed to reduce the pump speed each minute for 8 s, was chosen to examine this potential effect. Prospectively collected data of 85 heart transplant-eligible Jarvik 2000 recipients who met the study criteria (no pre-existing AI and aortic valve surgery) were retrospectively analyzed for the incidence, correlating factors, and clinical outcomes of de novo AI. All data were provided by the Japanese Registry for Mechanically Assisted Circulatory Support. De novo AI occurred in 58 patients, 23 of whom developed at least moderate AI during a median support duration of 23.5 months. Freedom from moderate or greater AI was 84.4%, 66.1% and 60.2% at 1, 2 and 3 years, respectively. Multivariate analyses revealed that progressive AI was correlated with decreased pulse pressure after implantation (hazard ratio 1.060, 95% confidence interval 1.001–1.127, p = 0.045). No correlation was found for mortality or other adverse events, including stroke, bleeding, infection, pump failure, hemolysis, and readmission. The benefit of the Jarvik 2000′s current ILS mode against AI appears to be minimal. However, in this limited cohort where all recipients underwent implantation as a bridge to transplantation, the impact of de novo progressive AI on other clinical adversities was also minimal.
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Zoppo, Gianluca, Francesco Marrone, Monica Pittarello, Marco Farina, Alberto Uberti, Danilo Demarchi, Jacopo Secco, Fernando Corinto, and Elia Ricci. "AI technology for remote clinical assessment and monitoring." Journal of Wound Care 29, no. 12 (December 2, 2020): 692–706. http://dx.doi.org/10.12968/jowc.2020.29.12.692.
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Objective: To report the clinical validation of an innovative, artificial intelligence (AI)-powered, portable and non-invasive medical device called Wound Viewer. The AI medical device uses dedicated sensors and AI algorithms to remotely collect objective and precise clinical data, including three-dimensional (3D) wound measurements, tissue composition and wound classification through the internationally recognised Wound Bed Preparation (WBP) protocol; this data can then be shared through a secure General Data Protection Regulation (GDPR)- and Health Insurance Portability and Accountability Act (HIPAA)-compliant data transfer system. This trial aims to test the reliability and precision of the AI medical device and its ability to aid health professionals in clinically evaluating wounds as efficiently remotely as at the bedside. Method: This non-randomised comparative clinical trial was conducted in the Clinica San Luca (Turin, Italy). Patients were divided into three groups: (i) patients with venous and arterial ulcers in the lower limbs; (ii) patients with diabetes and presenting with diabetic foot syndrome; and (iii) patients with pressure ulcers. Each wound was evaluated for area, depth, volume and WBP wound classification. Each patient was examined once and the results, analysed by the AI medical device, were compared against data obtained following visual evaluation by the physician and research team. The area and depth were compared with a Kruskal–Wallis one-way analysis of variations in the obtained distribution (expected p-value>0.1 for both tests). The WBP classification and tissue segmentation were analysed by directly comparing the classification obtained by the AI medical device against that of the testing physician. Results: A total of 150 patients took part in the trial. The results demonstrated that the AI medical device's AI algorithm could acquire objective clinical parameters in a completely automated manner. The AI medical device reached 97% accuracy against the WBP classification and tissue segmentation analysis compared with that performed in person by the physician. Moreover, data regarding the measurements of the wounds, as analysed through the Kruskal–Wallis technique, showed that the data distribution proved comparable with the other methods of measurement previously clinically validated in the literature (p=0.9). Conclusion: These findings indicate that remote wound assessment undertaken by physicians is as effective through the AI medical device as bedside examination, and that the device was able to assess wounds and provide a precise WBP wound classification. Furthermore, there was no need for manual data entry, thereby reducing the risk of human error while preserving high-quality clinical diagnostic data.
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Zwiefelhofer, E. M., S. X. Yang, M. Asai-Coakwell, M. G. Colazo, J. Hellquist, M. L. Zwiefelhofer, M. Anzar, and G. P. Adams. "118 A comparison of intravaginal progesterone devices for fixed-time artificial insemination in beef cattle." Reproduction, Fertility and Development 33, no. 2 (2021): 167. http://dx.doi.org/10.1071/rdv33n2ab118.
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Intravaginal progesterone (P4) devices used for ovarian synchronization before fixed-time AI (FTAI) differ in drug release, which may influence fertility outcome. A 2×2 study was designed to determine the effects of different intravaginal devices (PRID Delta, 1.55g of P4 vs. CIDR, 1.38g of P4) and parity (heifers vs. cows) on follicular dynamics, expression of oestrus, and pregnancy per AI (P/AI). At random stages of the oestrous cycle, nulliparous beef heifers and lactating cows were given 100µg of gonadorelin (gonadotrophin-releasing hormone, GnRH) intramuscularly (IM) and assigned randomly to either the PRID (n=76 heifers, 76 multiparous, 27 primiparous) or CIDR (n=76 heifers, 73 multiparous, 32 primiparous) group. Devices were removed 5 days later, an oestrus-detection patch was applied just cranial to the tail head, and 500µg of cloprostenol was given IM at the time of device removal and again 24h later. At 72h after device removal, cattle were inseminated and given 100µg of GnRH IM. Transrectal ultrasonography was used to determine the diameter of the largest follicle on the day of device removal and at FTAI, ovulation time, and pregnancy status 30 days after FTAI. A colour change of ≥50% of the oestrus-detection patch at FTAI was taken as expression of oestrus. Data were compared among groups by 2-way ANOVA using MIXED and GLIMMIX procedures. There were no interactions between P4 device and parity for any endpoint. The diameter of the largest follicle (mean±s.e.m.) was not different between PRID and CIDR groups on either the day of device removal (10.6±0.1 vs. 10.9±0.1mm) or the day of FTAI (13.7±0.1 vs. 13.9±0.1mm). The proportion displaying oestrus did not differ between P4 device groups, but was greater in heifers than in cows [121/152 (79.6%) vs. 135/207 (65.2%); P<0.01], and the interval from FTAI to ovulation was shorter in heifers than in cows (27.8±1.2 vs. 32.0±1.1 h; P=0.01). The P/AI was not different between P4 device groups or parity groups (overall 67.0%, 238/355). However, among lactating cows, the P/AI tended to be greater in the PRID vs. CIDR group [75/102 (73.5%) vs. 64/105 (61.0%); P=0.10], and was greater in multiparous vs. primiparous cows [106/148 (71.6%) vs. 33/59 (55.9%); P=0.04]. Among cattle that displayed oestrus, the P/AI tended to be greater in the PRID vs. CIDR group [92/123 (74.8%) vs. 85/131 (64.9%); P=0.09]. Among lactating cows that displayed oestrus, the P/AI was greater in multiparous vs. primiparous cows [74/94 (78.2%) vs. 24/42 (57.1%); P<0.01]. In summary, follicular dynamics and expression of oestrus did not differ between PRID and CIDR groups, but the P/AI tended to be greater in PRID-treated lactating cows and in cattle that displayed oestrus. This research was supported by CEVA Animal Health, Saskatchewan ADF, Agriculture and Agri-Foods Canada, and Rockway Inc.
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Zwiefelhofer, E. M., S. X. Yang, M. Asai-Coakwell, M. G. Colazo, J. Hellquist, M. L. Zwiefelhofer, M. Anzar, and G. P. Adams. "118 A comparison of intravaginal progesterone devices for fixed-time artificial insemination in beef cattle." Reproduction, Fertility and Development 33, no. 2 (2021): 167. http://dx.doi.org/10.1071/rdv33n2ab118.
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Intravaginal progesterone (P4) devices used for ovarian synchronization before fixed-time AI (FTAI) differ in drug release, which may influence fertility outcome. A 2×2 study was designed to determine the effects of different intravaginal devices (PRID Delta, 1.55g of P4 vs. CIDR, 1.38g of P4) and parity (heifers vs. cows) on follicular dynamics, expression of oestrus, and pregnancy per AI (P/AI). At random stages of the oestrous cycle, nulliparous beef heifers and lactating cows were given 100µg of gonadorelin (gonadotrophin-releasing hormone, GnRH) intramuscularly (IM) and assigned randomly to either the PRID (n=76 heifers, 76 multiparous, 27 primiparous) or CIDR (n=76 heifers, 73 multiparous, 32 primiparous) group. Devices were removed 5 days later, an oestrus-detection patch was applied just cranial to the tail head, and 500µg of cloprostenol was given IM at the time of device removal and again 24h later. At 72h after device removal, cattle were inseminated and given 100µg of GnRH IM. Transrectal ultrasonography was used to determine the diameter of the largest follicle on the day of device removal and at FTAI, ovulation time, and pregnancy status 30 days after FTAI. A colour change of ≥50% of the oestrus-detection patch at FTAI was taken as expression of oestrus. Data were compared among groups by 2-way ANOVA using MIXED and GLIMMIX procedures. There were no interactions between P4 device and parity for any endpoint. The diameter of the largest follicle (mean±s.e.m.) was not different between PRID and CIDR groups on either the day of device removal (10.6±0.1 vs. 10.9±0.1mm) or the day of FTAI (13.7±0.1 vs. 13.9±0.1mm). The proportion displaying oestrus did not differ between P4 device groups, but was greater in heifers than in cows [121/152 (79.6%) vs. 135/207 (65.2%); P<0.01], and the interval from FTAI to ovulation was shorter in heifers than in cows (27.8±1.2 vs. 32.0±1.1 h; P=0.01). The P/AI was not different between P4 device groups or parity groups (overall 67.0%, 238/355). However, among lactating cows, the P/AI tended to be greater in the PRID vs. CIDR group [75/102 (73.5%) vs. 64/105 (61.0%); P=0.10], and was greater in multiparous vs. primiparous cows [106/148 (71.6%) vs. 33/59 (55.9%); P=0.04]. Among cattle that displayed oestrus, the P/AI tended to be greater in the PRID vs. CIDR group [92/123 (74.8%) vs. 85/131 (64.9%); P=0.09]. Among lactating cows that displayed oestrus, the P/AI was greater in multiparous vs. primiparous cows [74/94 (78.2%) vs. 24/42 (57.1%); P<0.01]. In summary, follicular dynamics and expression of oestrus did not differ between PRID and CIDR groups, but the P/AI tended to be greater in PRID-treated lactating cows and in cattle that displayed oestrus. This research was supported by CEVA Animal Health, Saskatchewan ADF, Agriculture and Agri-Foods Canada, and Rockway Inc.
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López-Helguera, Irene, Fernando López-Gatius, Irina Garcia-Ispierto, Beatriz Serrano-Perez, and Marcos G. Colazo. "Effect of PRID-Delta devices associated with shortened estrus synchronization protocols on estrous response and fertility in dairy cows." Annals of Animal Science 17, no. 3 (July 26, 2017): 757–70. http://dx.doi.org/10.1515/aoas-2016-0083.
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Abstract This study evaluated the effect of a once-used progesterone (P4) intravaginal device (PRID) associated with four different shortened P4-based estrus synchronization (ES) protocols on estrous response (ER) and pregnancy per AI (P/AI) in cyclic and acyclic lactating dairy cows. Cows (n=465) were randomly assigned to one of the following protocols: 1) 2PGG, cows were given a PRID-Delta and 100 μg GnRH i.m. at PRID insertion (day 0). The PRID was left for 5 d, and 25 mg of dinoprost (PGF2α) i.m. given twice at PRID removal and 24 h later; 2) 2PGGe, same treatments as 2PGG plus 500 IU of eCG i.m. at PRID removal; 3) 2PGe, same treatments as 2PGGe, except GnRH was not given at PRID insertion; 4) PGe, same treatments as 2PGe, except PGF was only given at PRID removal. A total of 258 cows received a new PRID-Delta containing 1.55 g of P4, whereas 207 cows received a once-used PRID. Estrus was determined from P4 device removal until 96 h after using an automated heat detection system. Cows in estrus were given a second GnRH at AI and those without signs of estrus by 96 h after PRID removal were given GnRH and timed-AI (TA I). All inseminations were performed by one technician with commercially available frozen-thawed semen. Ultrasonography was performed at initiation of protocol and 28-34 days post AI to determine cyclicity and pregnancy status, respectively. Cows receiving once-used P4 devices had greater ER than cows receiving a new device (59.9 vs. 50.0; P=0.029), but P/AI did not differ between P4 devices, respectively (P>0.1; 40.6 vs 40.7%). Cyclic cows were less likely to display estrus than acyclic cows by a factor of 0.66 (P=0.036). Cows subjected to the 2PGe (2.41; P<0.01) protocol were more likely to display estrus than cows subjected to the 2PGG, whereas cows subjected to the PGe protocol did not differ (0.94; P=0.8) from those in the 2PGG group. Despite differences in ER, neither cyclicity nor estrus synchronization protocol affected P/AI (overall 40.6%). In summary, cyclic cows, those given a new P4 device and those subjected to either 2PGG or PGe protocol had reduced ER. However, all the factors examined had no significant effect on P/AI. All the estrus synchronization protocols resulted in acceptable fertility.
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Park, Seong Ho, Jaesoon Choi, and Jeong-Sik Byeon. "Key principles of clinical validation, device approval, and insurance coverage decisions of artificial intelligence." Journal of the Korean Medical Association 63, no. 11 (November 10, 2020): 696–708. http://dx.doi.org/10.5124/jkma.2020.63.11.696.
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Artificial intelligence (AI) will likely affect various fields of medicine. This article aims to explain the fundamental principles of clinical validation, device approval, and insurance coverage decisions of AI algorithms for medical diagnosis and prediction. Discrimination accuracy of AI algorithms is often evaluated with the Dice similarity coefficient, sensitivity, specificity, and traditional or free-response receiver operating characteristic curves. Calibration accuracy should also be assessed, especially for algorithms that provide probabilities to users. As current AI algorithms have limited generalizability to real-world practice, clinical validation of AI should put it to proper external testing and assisting roles. External testing could adopt diagnostic case-control or diagnostic cohort designs. A diagnostic case-control study evaluates the technical validity/accuracy of AI while the latter tests the clinical validity/accuracy of AI in samples representing target patients in real-world clinical scenarios. Ultimate clinical validation of AI requires evaluations of its impact on patient outcomes, referred to as clinical utility, and for which randomized clinical trials are ideal. Device approval of AI is typically granted with proof of technical validity/accuracy and thus does not intend to directly indicate if AI is beneficial for patient care or if it improves patient outcomes. Neither can it categorically address the issue of limited generalizability of AI. After achieving device approval, it is up to medical professionals to determine if the approved AI algorithms are beneficial for real-world patient care. Insurance coverage decisions generally require a demonstration of clinical utility that the use of AI has improved patient outcomes.
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Morvan, Y., and J. D. Zeitoun. "SAT0634-HPR PATIENT-REPORTED OUTCOMES REGARDING TWO FORMS OF METHOTREXATE AUTOINJECTORS IN RHEUMATOID ARTHRITIS: AN INTERNATIONAL CROSS-OVER SURVEY." Annals of the Rheumatic Diseases 79, Suppl 1 (June 2020): 1277.1–1277. http://dx.doi.org/10.1136/annrheumdis-2020-eular.3669.
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Background:Several types of methotrexate (MTX) autoinjectors (AI) are currently marketed in rheumatoid arthritis (RA), yet comparative data are scarce.Objectives:Investigate respective perceptions of patients regarding two marketed forms of MTX AIviaa survey conducted by a global market research company.Methods:Patients with moderate to severe RA treated by one of the two forms of MTX AI were recruited. In each participating country (France, Ireland, United-Kingdom, Spain), the respective proportions of recruited patients were approximately aligned on local market shares. The two investigated devices were: A-AI/ The first MTX AI marketed in Europe: bigger size, with an activation button, without double injection sound-control, with a larger window; B-AI/ The second MTX AI commercialized in Europe: smaller and thinner size, without activation button, with double injection sound-control and a smaller window. Each patient was interviewed during 30 minutes on his or her satisfaction level with the currently used device. Then, they were presented the alternative AI and they could test it on skin-mimicking pads. After this step, the patients were interviewed on the alternative device.Results:100 patients were enrolled over one-month period (A-AI users, n=65; B-AI users, n=35). Overall, 61% of A-AI users reported that B-AI was “better” or “much better” whereas 43% of B-AI users judged A-AI as “better” or “much better”. When B-AI users were asked to evaluate convenience elements of A-AI, recognition of injection ending, general design and ease of use were the indicators that were the most poorly judged (60%, 54%, and 46% respectively). When A-AI users were cross-tested for B-AI, injection mode, general feeling, and ease of use were the three items providing the greatest satisfaction (80%, 77%, and 75%, respectively). When they were asked about the characteristics of their usual device, the button, the design of the device and discomfort associated with the injection were the most dissatisfactory elements (30%, 31%, 34% respectively). Also, 73% of A-AI users reported being interested in trying B-AI while 26% of B-AI users replied being so.Last, 95% of B-AI users declared being “very satisfied” or “totally satisfied”, with ease of use and recognition of injection ending being the most attractive items (94% and 95% of high or full satisfaction respectively).Conclusion:In this international cross-over survey, the newest autoinjector on the market, B-AI has shown to exhibit better reported outcomes with respect to ease of use and recognition of the end of the injection and other tested indicators.Treatment of patients with RA should aim at the best care and must be based on a shared decision between the patient and the rheumatologist.1In this scope, the shared decision making is permitted when the patient agrees with the choice of treatment (medication, administration route, device, etc.). Patient’s involvement in decision making is assumed to lead to improvement in health outcomes such as a better adherence to the treatment.2References:[1]Smolen JS, Landewé R, Bijlsma J, et al EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs: 2016 update Annals of the Rheumatic Diseases 2017;76:960-977[2]Nota,I.; Drossaert, C.H.; Taal,E.; Vonkeman, H.E.; van de Laar, M.A. Patient participation in decisions about disease modifying anti-rheumatic drugs: A cross-sectional survey. BMC Musculoskelet. Disord. 2014, 15, 333.Disclosure of Interests:None declared
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Yoon, Young Hyun, Dong Hyun Hwang, Jun Hyeok Yang, and Seung Eun Lee. "Intellino: Processor for Embedded Artificial Intelligence." Electronics 9, no. 7 (July 18, 2020): 1169. http://dx.doi.org/10.3390/electronics9071169.
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The development of computation technology and artificial intelligence (AI) field brings about AI to be applied to various system. In addition, the research on hardware-based AI processors leads to the minimization of AI devices. By adapting the AI device to the edge of internet of things (IoT), the system can perform AI operation promptly on the edge and reduce the workload of the system core. As the edge is influenced by the characteristics of the embedded system, implementing hardware which operates with low power in restricted resources on a processor is necessary. In this paper, we propose the intellino, a processor for embedded artificial intelligence. Intellino ensures low power operation based on optimized AI algorithms and reduces the workload of the system core through the hardware implementation of a neural network. In addition, intellino’s dedicated protocol helps the embedded system to enhance the performance. We measure intellino performance, achieving over 95% accuracy, and verify our proposal with an field programmable gate array (FPGA) prototyping.
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S Jeyabalan. "Wildfire Identification using AI Techniques." ACS Journal for Science and Engineering 1, no. 1 (March 12, 2021): 17–23. http://dx.doi.org/10.34293/acsjse.v1i1.3.
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An essential inspiration for building up an online device is to help general well being specialists and crisis responders in settling on educated choices previously, during, and after rapidly spreading fire crises. Need for such frameworks are a lot of critical to dodge the exercise in futility and to lessen the human passings while fierce blaze happens. More automation and inferior extremity activity salvage monetary system on work cost and asset fatigue. It is a practical method of hazard expectation in wildfire.. Toward that end, CDC is building up an online apparatus that uses transient expectations and estimates of smoke fixations and incorporates them with proportions of populace level weakness to help recognize in danger populaces to rapidly spreading fire smoke perils. The instrument will be operationalized on a public scale, looking for information and help from a few scholastic, government, and SLTT Partners. We anticipate that this device will diminish an opportunity to recognize affected networks, help to distinguish and specify weak populaces, better describe populace level openness, and educate execution regarding fitting mediations for those territories influenced by out of control fire smoke perils.
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刘, 峰. "Research and Practice of “Device-Device” Security Attack and Defense Based on IoT + AI." Computer Science and Application 10, no. 03 (2020): 464–70. http://dx.doi.org/10.12677/csa.2020.103048.
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VanEtten, T., F. Ireland, D. Vandever, D. Kesler, and M. Wheeler. "193 EVALUATION OF A NOVEL ELECTRONIC ESTRUS DETECTION DEVICE IN RECIPIENTS SYNCHRONIZED FOR EMBRYO TRANSFER." Reproduction, Fertility and Development 18, no. 2 (2006): 204. http://dx.doi.org/10.1071/rdv18n2ab193.
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The ability to detect estrus in cattle is an important aspect for both successful artificial insemination (AI) and successful embryo transfer (ET). The use of estrus detection devices has allowed producers to more precisely determine when to breed their cattle. There have been many types of devices including chin-ball marking harnesses, dye patches, and even electronic devices that monitor the animal's activity. With the growing popularity in timed AI and ET, these devices have again become of great interest. The latest tool for estrus detection is the multi-use TattleTale" Heat Detector (Microdyne Co., LLC., St. Joseph, MO, USA). The TattleTale" is an electronic device that is placed in a patch and affixed to the tail head. It is activated by a 3-s mount with subsequent mounts recorded per h through 12 h. Twelve hours after activation the device flashes indicating that the animal is ready for breeding. Such devices may allow breeders to determine exactly when estrus began as well as identify the peak estrus period, resulting in more accurate determination of optimal time for AI or ET. The objective of this study was to compare the accuracy of estrus detection devices when analyzing detection records and pregnancy rates. Cows from the Dixon Springs Agricultural Center (Simpson, IL, USA) were randomly separated into two groups based on estrus detection devices, one group receiving Kamar� patches (Kamor, Inc., Steamboat Springs, CO, USA) and the other receiving TattleTales. Both groups were synchronized using the OvSynch protocol (Syntex, Buenos Aires, Argentina). Cows were checked twice each day for four consecutive days, with a fifth day occurring a week later when the embryos were transferred. Estrus results were analyzed using a PROC UNIVARITE and GLM analyses with differences between treatment means determined by the LSD method of SAS (SAS Institute, Inc., Cary, NC, USA). The TattleTale detected a higher percentage (P < 0.05) of the cows in estrus (92 vs. 70%) than the Kamar� patch. Further, the TattleTale detected more asynchronous recipients (in estrus >48 h before expected) and synchronous recipients (in estrus <24 h than expected), although not significant with this sample size. In the present study, the TattleTale was more accurate in detecting estrus (i.e. fewer pregnancies were observed without estrus detected). Using the TattleTale information, a breeder would be able detect more animals in estrus and determine the most accurate time to breed or transfer embryos. Practically, the use of estrus detection devices in combination with timed AI and ET could allow for the exclusion of animals not in estrus in the optimal time windows for AI or ET, thus saving valuable semen and embryos that would be otherwise wasted on asynchronous females. The advantages of this device to producers include improved pregnancy rates based on timed breeding, lower herd management costs due to reduced quantities of wasted semen or embryos and fewer cows being resynchronized. This device also records any mounting activity missed by visual observation alone. This work was supported by USDA RRF W-1171.
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Chang, C. Y., B. C. Fang, Y. D. Wang, and F. C. Tzeng. "Isolated AI-SiN-(p)Si-(n)Si MISS device." IEE Proceedings I Solid State and Electron Devices 133, no. 5 (1986): 189. http://dx.doi.org/10.1049/ip-i-1.1986.0040.
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La Torraca, Paolo, Francesco Maria Puglisi, Andrea Padovani, and Luca Larcher. "Multiscale Modeling for Application-Oriented Optimization of Resistive Random-Access Memory." Materials 12, no. 21 (October 23, 2019): 3461. http://dx.doi.org/10.3390/ma12213461.
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Memristor-based neuromorphic systems have been proposed as a promising alternative to von Neumann computing architectures, which are currently challenged by the ever-increasing computational power required by modern artificial intelligence (AI) algorithms. The design and optimization of memristive devices for specific AI applications is thus of paramount importance, but still extremely complex, as many different physical mechanisms and their interactions have to be accounted for, which are, in many cases, not fully understood. The high complexity of the physical mechanisms involved and their partial comprehension are currently hampering the development of memristive devices and preventing their optimization. In this work, we tackle the application-oriented optimization of Resistive Random-Access Memory (RRAM) devices using a multiscale modeling platform. The considered platform includes all the involved physical mechanisms (i.e., charge transport and trapping, and ion generation, diffusion, and recombination) and accounts for the 3D electric and temperature field in the device. Thanks to its multiscale nature, the modeling platform allows RRAM devices to be simulated and the microscopic physical mechanisms involved to be investigated, the device performance to be connected to the material’s microscopic properties and geometries, the device electrical characteristics to be predicted, the effect of the forming conditions (i.e., temperature, compliance current, and voltage stress) on the device’s performance and variability to be evaluated, the analog resistance switching to be optimized, and the device’s reliability and failure causes to be investigated. The discussion of the presented simulation results provides useful insights for supporting the application-oriented optimization of RRAM technology according to specific AI applications, for the implementation of either non-volatile memories, deep neural networks, or spiking neural networks.
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Bo, G. A., A. Cedeño, R. Maingón, J. P. Cedeño, H. Gamboa, J. Avellan, J. Bravo, C. Rivera, and I. Macias. "172 Effect of period of insertion of a progesterone-releasing device and pro-oestrus length on follicular and luteal characteristics and pregnancy rates to fixed-time AI in Bos indicus heifers." Reproduction, Fertility and Development 32, no. 2 (2020): 213. http://dx.doi.org/10.1071/rdv32n2ab172.
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An experiment was designed to evaluate the effect of the length of insertion of a progesterone (P4)-releasing device and the length of pro-oestrus on follicular and luteal characteristics and pregnancy rates to AI (P/AI) in Bos indicus heifers treated with oestradiol/P4-based treatments. Bos indicus beef heifers (n=374), 22-26 months of age, with a corpus luteum (CL) or at least one follicle ≥8mm in diameter and with a body condition score between 2.5 and 3.5 (1-to-5 scale) were synchronised using three treatments for fixed-time AI (FTAI). On Day 0, all heifers received 2mg of oestradiol benzoate (Sincrodiol, Ourofino) and an intravaginal device with 1g of P4 (Sincrogest, Ourofino). The P4 device was removed on Day 6 in heifers in the J-Synch 6 group (n=120) and on Day 7 in heifers in the J-Synch 7 group (n=105) and conventional group (n=165). All heifers received 500μg of cloprostenol (Sincrocio, Ourofino) and 300IU of equine chorionic gonadotrophin (SincroeCG 6000UI, Ourofino) at the time of P4 device removal. Furthermore, heifers in the conventional treatment group received 0.5mg of oestradiol cypionate (SincroCP, Ourofino) at the same time. In addition, all heifers were tail-painted for oestrus detection (CeloTest, Biotay). Heifers that had lost ≥50% of the tail paint by 70-74h (J-Synch groups) or 48-52h (conventional group) after device removal were FTAI at that time. Heifers not showing oestrus by 70-74h (J-Synch groups) or 48-52h (conventional group) received 10μg of gonadotrophin-releasing hormone (Sincroforte, Ourofino) at the same time and were FTAI 8h later. All heifers were also examined using ultrasonography (Mindray DP50 Vet) every 12h from the time of device removal to determine the time of ovulation, 6 days after ovulation to determine the diameter of the CL, and 28 days after FTAI for P/AI determination. Data were analysed using the MLGM procedure (InfoStat) for normal data families (follicular dynamics) and binary data family (P/AI). The results are shown in Table 1. The diameter of the dominant preovulatory follicle and the CL did not differ among groups (P>0.12). However, the interval from device removal to ovulation was longer in heifers in the J-Synch groups than in heifers in the conventional group (P<0.05). Furthermore, P/AI was not different among groups. In conclusion, although the J-Synch protocols delayed the interval from P4 device removal to ovulation, the three protocols evaluated in the present study were equally effective in Bos indicus heifers. Table 1.Mean (±s.e.m.) diameter of the preovulatory follicle (P/Foll) and corpus luteum (CL), interval from progesterone (P4) device removal to ovulation, and pregnancy rates to AI (P/AI) in Bos indicus heifers Treatment P/Foll, mm Interval to ovulation, h CL diameter, mm P/AI,% (n) J-Synch 6 10.5±0.7 101.4±2.3a 18.6±1.0 52.0 (62/120) J-Synch 7 10.6±0.7 96.0±2.2a 16.5±0.9 39.0 (41/105) Conventional 9.4±0.7 73.0±1.9b 16.8±0.9 45.0 (74/165) a,bDifferent superscripts denote differences between groups in the interval from P4 device removal to ovulation.
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Becker, Kurt, Myriam Lipprandt, Rainer Röhrig, and Thomas Neumuth. "Digital health – Software as a medical device in focus of the medical device regulation (MDR)." it - Information Technology 61, no. 5-6 (October 25, 2019): 211–18. http://dx.doi.org/10.1515/itit-2019-0026.
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Abstract The Medical Device Directive (MDD) will be replaced on 26.05.2020 by the new Medical Device Regulation (MDR). The European Parliament wants to create a transparent, solid, predictable and sustainable legal framework. One of the major upcoming changes effects the perspective on software as a medical device. This paper describes the fundamental relationships between the policy framework and the challenges faced by manufacturers and operators of medical devices, in particular medical software and artificial intelligence (AI) systems, who need to verify compliance. To address this topic, we review and discuss the main implications of medical device regulations on software as a medical device and digital health applications along the MDR structure. Furthermore, we address practical limitations of the implementation, such as the availability of notified bodies and costs of the approval procedure.
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Gollasch, Stephan. "A new ballast water sampling device for sampling organisms above 50 micron." Aquatic Invasions 1, no. 1 (2006): 46–50. http://dx.doi.org/10.3391/ai.2006.1.1.12.
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Pitaluga, P. C. S. F., M. F. Sá Filho, J. N. S. Sales, P. S. Baruselli, and L. Vincenti. "17 MANIPULATION OF PROESTRUS PERIOD BY EXOGENOUS GONADOTROPIN AND ESTRADIOL DURING TIMED ARTIFICIAL INSEMINATION PROTOCOL IN SUCKLED BEEF COWS." Reproduction, Fertility and Development 25, no. 1 (2013): 155. http://dx.doi.org/10.1071/rdv25n1ab17.
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The objective of this study was to evaluate the effect of eCG or estradiol cypionate (ECP) during the proestrus period on estrus occurrence, ovarian responses, and pregnancy per AI (P/AI) in suckled Bos indicus beef cows subjected to timed AI (TAI) protocols. A total of 393 cows in random stages of estrus (Day 0), received an intravaginal progesterone (P4) device (1.9 g of P4; CIDR®, Pfizer Animal Health, New York, NY, USA) and 2.0 mg of estradiol benzoate IM (EB; Gonadiol®, MDS Animal Health). Eight days later, the P4 devices were removed, and cows were given 0.15 mg of d-cloprostenol (Prostaglandina Tortuga®, Tortuga Companhia Zootécnica Agrária, São Paulo, Brazil). At this time, cows were randomly assigned to 1 of 3 treatments: 300 IU of eCG (Group eCG; n = 138), 300 IU of eCG plus 1 mg of ECP IM (Group eCG+ECP; n = 124), or no additional treatment (control; n = 131). Cows were timed inseminated 48 h after P4 device removal and were administrated simultaneously 100 µg of gonadorelin IM (Profertil®, Tortuga Companhia Zootécnica Agrária). A subset of cows (n = 98) were evaluated according to the occurrence of estrus between the P4 device removal and TAI and their ovarian follicles were evaluated by ultrasound at P4 device removal and corpus luteum (5 days after TAI). The data were analyzed using the GLIMMIX procedure of SAS (SAS Institute Inc., Cary, NC, USA) and differences with P < 0.05 were considered statistically significant. A greater pregnancy rate (P/AI; P = 0.04) was observed in cows receiving the eCG treatment at P4 device removal [eCG = 42% (58/138) and eCG+ECP = 50.8% (63/124)] than cows from the control group [29.8% (39/131)]. There was no additive effect of ECP supplementation on P/AI. Cows that received ECP [eCG+ECP = 56.3% (18/32)] displayed more estrus (P = 0.002) compared with those receiving eCG [eCG = 23.5% (8/34)] or only GnRH at TAI [control = 15.6% (5/32)]. In addition, regardless of the ECP supplementation, cows receiving the eCG treatment at P4 device removal [eCG = 85.3% (29/34) and eCG+ECP = 90.1% (29/32)] presented a greater ovulation rate than cows from the control group [65.6% (21/32)]. In conclusion, exogenous estradiol administrated at device removal increased the proportion of suckled Bos indicus cows that displayed estrus. Cows receiving eCG treatment (with or without estradiol supplementation) had greater ovulatory and pregnancy responses after the estradiol/P4-based synchronization protocol.
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Khan, Tareq. "An Intelligent Baby Monitor with Automatic Sleeping Posture Detection and Notification." AI 2, no. 2 (June 18, 2021): 290–306. http://dx.doi.org/10.3390/ai2020018.
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Artificial intelligence (AI) has brought lots of excitement to our day-to-day lives. Some examples are spam email detection, language translation, etc. Baby monitoring devices are being used to send video data of the baby to the caregiver’s smartphone. However, the automatic understanding of the data was not implemented in most of these devices. In this research, AI and image processing techniques were developed to automatically recognize unwanted situations that the baby was in. The monitoring device automatically detected: (a) whether the baby’s face was covered due to sleeping on the stomach; (b) whether the baby threw off the blanket from the body; (c) whether the baby was moving frequently; (d) whether the baby’s eyes were opened due to awakening. The device sent notifications and generated alerts to the caregiver’s smartphone whenever one or more of these situations occurred. Thus, the caregivers were not required to monitor the baby at regular intervals. They were notified when their attention was required. The device was developed using NVIDIA’s Jetson Nano microcontroller. A night vision camera and Wi-Fi connectivity were interfaced. Deep learning models for pose detection, face and landmark detection were implemented in the microcontroller. A prototype of the monitoring device and the smartphone app were developed and tested successfully for different scenarios. Compared with general baby monitors, the proposed device gives more peace of mind to the caregivers by automatically detecting un-wanted situations.
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Lim, Geunsik, MyungJoo Ham, and Jaeyun Jung. "VTS: Virtual Testbed System for Cloud-based On-Device AI Application." KIISE Transactions on Computing Practices 26, no. 1 (January 31, 2020): 12–19. http://dx.doi.org/10.5626/ktcp.2020.26.1.12.
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Butler, A., H. Butler, G. Cesaroni, R. Alberio, S. Perez Wallace, and A. Garcia-Guerra. "163 Treatment with gonadotrophin-releasing hormone at the time of AI in beef heifers that fail to express oestrus after an estradiol-based synchronisation protocol improves pregnancies per AI." Reproduction, Fertility and Development 32, no. 2 (2020): 208. http://dx.doi.org/10.1071/rdv32n2ab163.
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Oestrus expression between progesterone (P4) withdrawal and fixed-time AI (FTAI) has been shown to improve pregnancies per AI (P/AI) by 27% in beef cattle. As a result, cattle that do not express oestrus after P4 withdrawal present a challenge to the efficiency of FTAI. The present study was designed to test the hypothesis that administration of gonadotrophin-releasing hormone (GnRH) at the time of AI in heifers that do not express oestrus can improve fertility. Two-year-old Angus heifers (n=1032) with a condition score of 2.75 to 3.5 at two locations in Argentina were used. On Day 0, heifers received an intravaginal device containing 0.5g of P4 (Cronipres, Biogenesis) and 2mg of oestradiol benzoate intramuscularly (Bioestrogen, Biogenesis). On Day 8, devices were removed and heifers received 150µg of d-cloprostenol intramuscularly (Enzaprost, Biogenesis), 0.5mg of oestradiol cipionate intramuscularly (Croni-Cip, Biogenesis), and an oestrus detection patch (Estrotect, Rockway Inc.). On Day 10, AI was performed in all heifers 50 to 54h after device removal. Heifers were categorised based on oestrus expression, and those that did not express oestrus were randomised to receive 100µg of gonadorelin acetate intramuscularly (Gonasyn, Syntex; n=158) concurrent with AI or remain as untreated controls (n=151). Pregnancy was determined using ultrasonography at Days 38 and 111 after AI. Differences in fertility were evaluated using generalised linear mixed models, and the results are shown in Table 1. Oestrus expression between device removal and FTAI was 70.1% (723/1032). Pregnancies per AI at Days 38 and 111 were different between groups (P<0.01). Heifers that expressed oestrus had greater P/AI than those that did not regardless of treatment (P<0.01). However, in heifers that did not express oestrus, treatment with GnRH at the time of AI resulted in greater P/AI than in control heifers (P=0.004). Similarly, P/AI at Day 111 were greater in heifers that expressed oestrus than in those that did not regardless of treatment (P<0.01). Heifers that did not express oestrus and were treated with GnRH had greater P/AI than those that did not express oestrus and remained as controls (P=0.02). Pregnancy loss between Days 38 and 111 was not different between groups; however, there was a tendency (P=0.06) for greater pregnancy loss in heifers that did not express oestrus and were treated with GnRH compared with heifers that expressed oestrus. In summary, treatment with GnRH at the time of AI is a suitable strategy to improve P/AI in heifers that fail to exhibit oestrus after an oestradiol-based synchronisation protocol. However, this strategy appears to increase pregnancy loss, and thus further research is needed to confirm this observation and the mechanisms underlying it. Table 1.Pregnancies per AI (P/AI) and pregnancy loss (%; no. in parentheses) in Angus heifers based on oestrus expression and treatment with GnRH Group P/AI Day 38 P/AI Day 111 Pregnancy loss Oestrus 68.9 (498/723)A 65.2 (471/723)A 5.4 (27/498) No oestrus control 29.8 (45/151)B 28.5 (43/151)B 4.4 (2/45) No oestrus + GnRH 45.6 (72/158)C 40.5 (64/158)C 11.1 (8/72) P-value <0.001 <0.001 0.15 A-CValues within a column with different superscripts differ significantly (P<0.05).
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Kar, Anup, Aradhana Misra, Kandarpa Kumar Sarma, and Nikos E. Mastorakis. "Data Recovery through Modulation Identification in Dense Wireless Networks." MATEC Web of Conferences 210 (2018): 03009. http://dx.doi.org/10.1051/matecconf/201821003009.
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With rise in device complexity and transmission rates, reliability in data recovery has become another critical issue requiring costly and computationally demanding mechanism. The popularity of artificial intelligence (AI) and its ubiquitousness have established the usefulness of design of data recovery schemes where device level complexity is less. Lower device complexity is being ensured by the use of AI driven data recovery. In this work, we focus on the design of such a mechanism where traditional process are replaced by a neuro-computing structure. The advantage is lower levels of device complexity but incorporation of a training latency. Experimental results have established the reliability of the proposed system.
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Ferreira, R. M., H. Ayres, L. U. Gimenes, F. P. Torres, F. A. Lima, M. B. Veras, T. G. Guida, R. V. Sala, and P. S. Baruselli. "Inducing ovulation with oestradiol cypionate allows flexibility in the timing of insemination and removes the need for gonadotrophin-releasing hormone in timed AI protocols for dairy cows." Reproduction, Fertility and Development 29, no. 3 (2017): 468. http://dx.doi.org/10.1071/rd15270.
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The effects of addition of gonadotrophin-releasing hormone (GnRH) to a progesterone plus oestradiol-based protocol and timing of insemination in Holstein cows treated for timed AI (TAI) were evaluated. Cows (n = 481) received a progesterone device and 2 mg oestradiol benzoate. After 8 days, the device was removed and 25 mg dinoprost was administered. Cows were allocated to one of three (Study 1; n = 57) or four (Study 2; n = 424) groups, accordingly to ovulation inducer alone (Study 1; oestradiol cypionate (EC), GnRH or both) or ovulation inducer (EC alone or combined with GnRH) and timing of insemination (48 or 54 h after device removal; Study 2). In Study 1, the diameter of the ovulatory follicle was greater for GnRH than EC. Oestrus and ovulation rates were similar regardless of ovulatory stimuli. However, time to ovulation was delayed when GnRH only was used. In Study 2, cows treated with GnRH or not had similar pregnancy per AI (P/AI) 30 days (41.5% vs 37.3%; P = 0.28) and 60 days (35.9% vs 33.0%; P = 0.61) after TAI. TAI 48 and 54 h after device removal resulted similar P/AI at 30 days (40.3% vs 38.5%; P = 0.63) and 60 days (33.8% vs 35.1%; P = 0.72). Thus, adding GnRH at TAI does not improve pregnancy rates in dairy cows receiving EC. The flexibility of time to insemination enables TAI of a large number of cows using the same protocol and splitting the time of AI.
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Lin, Weison, Adewale Adetomi, and Tughrul Arslan. "Low-Power Ultra-Small Edge AI Accelerators for Image Recognition with Convolution Neural Networks: Analysis and Future Directions." Electronics 10, no. 17 (August 25, 2021): 2048. http://dx.doi.org/10.3390/electronics10172048.
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Edge AI accelerators have been emerging as a solution for near customers’ applications in areas such as unmanned aerial vehicles (UAVs), image recognition sensors, wearable devices, robotics, and remote sensing satellites. These applications require meeting performance targets and resilience constraints due to the limited device area and hostile environments for operation. Numerous research articles have proposed the edge AI accelerator for satisfying the applications, but not all include full specifications. Most of them tend to compare the architecture with other existing CPUs, GPUs, or other reference research, which implies that the performance exposé of the articles are not comprehensive. Thus, this work lists the essential specifications of prior art edge AI accelerators and the CGRA accelerators during the past few years to define and evaluate the low power ultra-small edge AI accelerators. The actual performance, implementation, and productized examples of edge AI accelerators are released in this paper. We introduce the evaluation results showing the edge AI accelerator design trend about key performance metrics to guide designers. Last but not least, we give out the prospect of developing edge AI’s existing and future directions and trends, which will involve other technologies for future challenging constraints.
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Sreeraj, M., Jestin Joy, Alphonsa Kuriakose, M. R. Sujith, P. K. Vishnu, and Haritha Unni. "CLadron*: AI assisted device for identifying artificially ripened climacteric fruits." Procedia Computer Science 171 (2020): 635–43. http://dx.doi.org/10.1016/j.procs.2020.04.069.
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Baxter, Ronald D., Jeffrey E. Carter, Christopher K. Craig, Jeffrey W. Williams, and James H. Holmes. "92 Improved Burn Healing Classification via Artificial Intelligence." Journal of Burn Care & Research 41, Supplement_1 (March 2020): S60—S61. http://dx.doi.org/10.1093/jbcr/iraa024.095.
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Abstract Introduction Current methods of assessment for burn severity and healing potential are insufficient but this determination remains critical in providing optimal burn care. A novel device using multispectral imaging and artificial intelligence (AI) has shown prior success aiding in this process. Promising initial results of this proof-of-concept case series have been presented previously, invoking much interest from the burn care community. These results reflect the final results of this study and provide support for continued investigation with this device for the treatment of burn wounds. Methods Subjects with thermal burns were enrolled within 72 hours of injury. The goal was to enroll at least 12 burn wounds from each category of severity: 1°; superficial 2°; deep 2°; and 3°. Upon enrollment burns were imaged with the study device serially for up to 7 days post-burn. The true healing potential of each imaged burn area was determined using either healing assessment at 21 days post-burn or multiple punch biopsies of the burn wound taken at time of excision and grafting. This ground truth was used to train the AI algorithm to automatically identify the margins of non-healing burn tissue within device images. Accuracy of the study device to identify and differentiate healing and non-healing burn tissue within the captured wound area was computed. Results Data from 38 subjects with 58 total burns and 393 wound images were obtained during this study. Following completion of training, AI predictions achieved 90.7% sensitivity with 87.5% specificity in predicting non-healing burn tissue using cross-validation. Results improved throughout the study as more wound images were used in AI training (Figure 1). Conclusions The AI algorithm trained on 58 burn wounds (38 subjects) had an area under the curve of 0.955 and has continued to improve with the addition of more burn wounds. Expansion of this study has been initiated at multiple burn centers to continue collecting data for AI algorithm training. Applicability of Research to Practice These results demonstrate feasibility of utilizing AI-assisted diagnosis in burn wounds and possible application in the management of burn patients.
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Hamamoto, Ryuji. "SS1-KL-1 APPLICATION OF AI TECHNOLOGIES FOR MEDICAL CARE." Neuro-Oncology Advances 1, Supplement_2 (December 2019): ii2. http://dx.doi.org/10.1093/noajnl/vdz039.007.
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Abstract On the basis of progress of the Machine Learning algorithm mainly on the Deep Learning, improvement of the GPU performance, the large-scale public database such as TCGA is available, big attention recently gathers in the AI technology. While large countries such as the United States or China vigorously promote AI research and development by a national policy, Cabinet Office, Government of Japan, also emphasized the importance of AI technologies in the 5th Science and Technology Basic Plan in 2016. As for the AI development, it is wrestled relatively for a long time; the word “Artificial Intelligence” was firstly used in the Dartmouth workshop in 1956. However, the AI development has not been promoted smoothly until now and repeats the active state period and the period of depression. As the current active state period of AI is called as the third AI boom, the most different point of this boom and the other booms is that AI technologies have already been involved in our social life such as the AI-based face authentication device in this period. Indeed, The US Food and Drug Administration (FDA) has already authorized around 30 AI-based medical instruments, and the Pharmaceuticals and Medical Devices Agency (PMDA) in Japan also authorized the first AI-based medical instrument last year. Therefore, now is the important time that we need to consider deeply for the creation of an affluent society, which enables coexistence of human being and AI. In this lecture, I particularly focus on medical imaging analysis using AI technologies and, would like to lecture on an action to the medical care application of the AI technology based on the experience that promoted medical AI research as the leader of two national projects relevant to medical AI called CREST and PRISM, and RIKEN AIP center.
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Liang, Yuli, Seung-Hee Lee, and Jane E. Workman. "Implementation of Artificial Intelligence in Fashion: Are Consumers Ready?" Clothing and Textiles Research Journal 38, no. 1 (September 3, 2019): 3–18. http://dx.doi.org/10.1177/0887302x19873437.
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Given the growing interest in combinations of fashion and digital innovations, it is critical for both researchers and retailers to understand how consumers respond to new technologies, especially artificial intelligence (AI). The purpose of the study was to examine consumers’ attitudes and purchase intention toward an AI device. By adapting the technology acceptance model, a conceptual model was constructed and tested related to consumers’ attitudes and purchase intention toward an AI device—Echo Look. A total of 313 subjects (61% female) between 18 and 65 years old in the top 10 metropolitan areas in the United States participated in the study. The results indicated that perceived usefulness, perceived ease of use, and performance risk were significant in consumers’ attitude toward AI. Positive attitudes toward technology positively influenced the purchase intention. Based on these results, theoretical and practical implications are discussed.
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Canadas, E. R., B. Duran, G. Machado, A. Nall, S. E. Battista, M. Mussard, P. S. Baruselli, and A. Garcia-Guerra. "115 Presynchronization and reutilization of progesterone devices during a 6-day CO-Synch protocol for fixed-time artificial insemination in beef heifers." Reproduction, Fertility and Development 33, no. 2 (2021): 165. http://dx.doi.org/10.1071/rdv33n2ab115.
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Ovulatory response to the initial gonadotrophin-releasing hormone (GnRH) of the CO-Synch protocol is affected by circulating progesterone (P4) and follicle size. In addition, heifers that ovulate to initial GnRH treatment have greater fertility after AI. Thus, the aim of this study was to assess the effect of (1) presynchronization (Presynch) before a 6-day CO-Synch protocol and (2) P4 device reuse (new vs. second use) on ovulatory response, oestrous expression, and pregnancy per AI (P/AI) in beef heifers. Yearling beef heifers (n=233) were randomly assigned in a 2×2 factorial design to the following treatments: (1) Treatment (Presynch): (1a) Presynch+6-day CO-Synch with new P4 device; (1b) Presynch+6-days CO-Synch with a once-used P4 device; (2) Control (no Presynch): (2a) 6-day CO-Synch with new P4 device; (2b) 6-day CO-synch with once-used P4 device. Presynch consisted of insertion of a new P4 intravaginal device (CIDR, 1.38g of P4; Zoetis) on Day −17 and removal of the CIDR on Day −11 concurrently with 500µg of cloprostenol sodium. On Day −9, all heifers received either a new or once-used (used) CIDR and 100µg of gonadorelin acetate (GnRH, Parnell Inc.). Six days later (Day −3) CIDRs were removed, 1000µg of PGF was administered, and an oestrous detection patch applied (Estrotect, Rockway Inc.). At 72h after CIDR removal, 100µg of GnRH and AI using 3 different sires was performed. Pregnancy was determined by ultrasonography 31 days after AI. A subset of heifers (n=151) were examined on Day −9 and Day −3 by ultrasonography to assess ovulation to Day −9 GnRH. Data were analysed using GLIMMIX (SAS 9.4; SAS Institute Inc.). Presynch heifers had larger follicle diameter on Day −9 (12.7±0.3 vs. 10.4±0.3 mm; P<0.0001), greater ovulatory response (84.6%; 66/78 vs. 52.05%; 38/73; P<0.0001), greater oestrus expression (90.6%; 106/117 vs. 78.4%; 91/116; P=0.03), and expressed oestrus earlier (49.8±1.0 vs. 53.1±1.1 h; P=0.01) compared with controls. There was an interaction for treatment and CIDR on oestrous expression, whereas a lesser (P=0.003) percentage of control heifers with new CIDR showed oestrus compared with all other groups (Table 1). Heifers treated with a used P4 device tended (P=0.08) to have greater P/AI (52.1%; 61/117) than those with a new CIDR (40.5%; 47/116). In conclusion, presynchronization before initiation of a 6-day CO-Synch increased follicle diameter, ovulatory response, and oestrous expression, but did not affect fertility. The earlier onset of oestrus in Presynch heifers warrants further study on timing of AI. Table 1. Oestrous expression and pregnancy per AI (P/AI) in beef heifers with or without presynchronization and treated with a new or used CIDR Treatment CIDR Oestrus (%; n/n) Time of oestrus (h) P/AI (%; n/n) Control New 67.8a (40/59) 53.7±1.5a 33.9 (20/59) Used 89.5b (51/57) 52.7±1.6a 50.9 (29/57) Presynch New 94.7b (54/57) 50.9±1.4b 47.4 (27/57) Used 86.7b (52/60) 48.7±1.3b 53.3 (32/60) P-value Treatment 0.03 0.01 0.21 CIDR 0.62 0.18 0.08 Interaction 0.003 0.78 0.38 a,bValues with different superscripts differ (P < 0.05).
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Canadas, E. R., B. Duran, G. Machado, A. Nall, S. E. Battista, M. Mussard, P. S. Baruselli, and A. Garcia-Guerra. "115 Presynchronization and reutilization of progesterone devices during a 6-day CO-Synch protocol for fixed-time artificial insemination in beef heifers." Reproduction, Fertility and Development 33, no. 2 (2021): 165. http://dx.doi.org/10.1071/rdv33n2ab115.
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Ovulatory response to the initial gonadotrophin-releasing hormone (GnRH) of the CO-Synch protocol is affected by circulating progesterone (P4) and follicle size. In addition, heifers that ovulate to initial GnRH treatment have greater fertility after AI. Thus, the aim of this study was to assess the effect of (1) presynchronization (Presynch) before a 6-day CO-Synch protocol and (2) P4 device reuse (new vs. second use) on ovulatory response, oestrous expression, and pregnancy per AI (P/AI) in beef heifers. Yearling beef heifers (n=233) were randomly assigned in a 2×2 factorial design to the following treatments: (1) Treatment (Presynch): (1a) Presynch+6-day CO-Synch with new P4 device; (1b) Presynch+6-days CO-Synch with a once-used P4 device; (2) Control (no Presynch): (2a) 6-day CO-Synch with new P4 device; (2b) 6-day CO-synch with once-used P4 device. Presynch consisted of insertion of a new P4 intravaginal device (CIDR, 1.38g of P4; Zoetis) on Day −17 and removal of the CIDR on Day −11 concurrently with 500µg of cloprostenol sodium. On Day −9, all heifers received either a new or once-used (used) CIDR and 100µg of gonadorelin acetate (GnRH, Parnell Inc.). Six days later (Day −3) CIDRs were removed, 1000µg of PGF was administered, and an oestrous detection patch applied (Estrotect, Rockway Inc.). At 72h after CIDR removal, 100µg of GnRH and AI using 3 different sires was performed. Pregnancy was determined by ultrasonography 31 days after AI. A subset of heifers (n=151) were examined on Day −9 and Day −3 by ultrasonography to assess ovulation to Day −9 GnRH. Data were analysed using GLIMMIX (SAS 9.4; SAS Institute Inc.). Presynch heifers had larger follicle diameter on Day −9 (12.7±0.3 vs. 10.4±0.3 mm; P<0.0001), greater ovulatory response (84.6%; 66/78 vs. 52.05%; 38/73; P<0.0001), greater oestrus expression (90.6%; 106/117 vs. 78.4%; 91/116; P=0.03), and expressed oestrus earlier (49.8±1.0 vs. 53.1±1.1 h; P=0.01) compared with controls. There was an interaction for treatment and CIDR on oestrous expression, whereas a lesser (P=0.003) percentage of control heifers with new CIDR showed oestrus compared with all other groups (Table 1). Heifers treated with a used P4 device tended (P=0.08) to have greater P/AI (52.1%; 61/117) than those with a new CIDR (40.5%; 47/116). In conclusion, presynchronization before initiation of a 6-day CO-Synch increased follicle diameter, ovulatory response, and oestrous expression, but did not affect fertility. The earlier onset of oestrus in Presynch heifers warrants further study on timing of AI. Table 1. Oestrous expression and pregnancy per AI (P/AI) in beef heifers with or without presynchronization and treated with a new or used CIDR Treatment CIDR Oestrus (%; n/n) Time of oestrus (h) P/AI (%; n/n) Control New 67.8a (40/59) 53.7±1.5a 33.9 (20/59) Used 89.5b (51/57) 52.7±1.6a 50.9 (29/57) Presynch New 94.7b (54/57) 50.9±1.4b 47.4 (27/57) Used 86.7b (52/60) 48.7±1.3b 53.3 (32/60) P-value Treatment 0.03 0.01 0.21 CIDR 0.62 0.18 0.08 Interaction 0.003 0.78 0.38 a,bValues with different superscripts differ (P < 0.05).
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Huguenine, E., J. de la Mata, A. Menchaca, R. L. R. de Carneiro, and G. A. Bo. "173 Pregnancy rates in suckled beef cows synchronised with progesterone/oestradiol-based protocol and inseminated with conventional or sexed-sorted semen." Reproduction, Fertility and Development 32, no. 2 (2020): 214. http://dx.doi.org/10.1071/rdv32n2ab173.
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An experiment was designed to evaluate pregnancy rates per AI (P/AI) in suckled beef cows synchronised with a progesterone (P4)/oestradiol-based protocol and AI with non-sexed (conventional) semen and with a sexed-sorted semen with 65% X-bearing sperm and 35% Y-bearing sperm that was named SuperConventionalTM. Angus and Hereford suckled cows (n=558), 60-90 days postpartum, with a body condition score of 2 to 3.5 (1-to-5 scale) and 48% (269/558) with a corpus luteum, were randomly allocated to be AI with non-sexed (conventional) or SuperConventional semen. The experiment was performed on 3 farms using straws with sexed-sorted SuperConventional semen containing 6 or 8 million sperm and non-sorted conventional semen with 25 million sperm per straw. Semen was from split ejaculates from two Angus bulls. All cows received a P4 device with 0.6g of P4 (Pluselar, Calier) and 2mg of oestradiol benzoate (Calier) on Day 0. All P4 devices were removed on Day 8, and all cows received 400IU of equine chorionic gonadotrophin (Vetegon, Calier), 150µg of d+cloprostenol (Veteglan, Calier), and 1mg of oestradiol cipionate (Calier) at the same time. All cows were also tail-painted and observed for signs of oestrus. Cows with >30% of the tail paint rubbed off by 48-50h after P4 device removal were AI at that time with either SuperConventional or conventional semen. Those not showing oestrus by 48-50h received 10µg of buserelin (Pluserelina, Calier) at that time and were AI with either SuperConventional or conventional semen 50-52h after P4 device removal. Pregnancy was diagnosed using ultrasonography 60 days after AI, and all pregnancies were sexed to determine the proportion of female pregnancies. Data were analysed using GLM for binary data. The P/AI were different between cows showing or not showing oestrus (P<0.01) but did not differ among the three types of semen used (Table 1). The percentage of cows pregnant with female fetuses was 51% (50/98) for cows AI with conventional semen and 65.5% (55/84) and 65.3% (51/78) for those AI with SuperConventional semen with 8 million or 6 million sperm per straw, respectively. In conclusion, sexed-sorted semen with 65% of X-bearing sperm and 6 or 8 million sperm per straw can be successfully used to inseminate suckled beef cows. Furthermore, results confirm those of previous studies that cows in oestrus at the time of fixed-time AI have higher pregnancy rates than cows not in oestrus in P4/oestradiol-based synchronisation programmes. Table 1.Effect of semen type and oestrus expression on pregnancy rates per AI in suckled beef cows synchronised with a progesterone/oestradiol-based protocol Semen type In oestrus,% (n) Not in oestrus,% (n) Total,% (n) Conventional (25 million sperm) 59.8 (82/137)a 32.0 (16/50)b 52.4 (98/187) SuperConventional (8 million sperm) 53.7 (73/136)a 20.0 (10/50)b 44.6 (83/186) SuperConventional (6 million sperm) 50.4 (63/125)a 26.7 (16/60)b 42.7 (79/185) Total 54.8 (218/398)a 26.3 (42/160)b a,bDifferent superscripts denote differences in pregnancy rates per AI between cows in oestrus and not in oestrus by the time of fixed-time AI (P<0.01). Research was supported by Fondo Nacional de Ciencia y Tecnología (FONCYT PICT 2017-4550) and UNVM.
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Choi, Inyeop, and Hyogon Kim. "An On-Device Deep Learning Approach to Battery Saving on Industrial Mobile Terminals." Sensors 20, no. 14 (July 21, 2020): 4044. http://dx.doi.org/10.3390/s20144044.
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The mobile terminals used in the logistics industry can be exposed to wildly varying environments, which may hinder effective operation. In particular, those used in cold storages can be subject to frosting in the scanner window when they are carried out of the warehouses to a room-temperature space outside. To prevent this, they usually employ a film heater on the scanner window. However, the temperature and humidity conditions of the surrounding environment and the temperature of the terminal itself that cause frosting vary widely. Due to the complicated frost-forming conditions, existing industrial mobile terminals choose to implement rather simple rules that operate the film heater well above the freezing point, which inevitably leads to inefficient energy use. This paper demonstrates that to avoid such waste, on-device artificial intelligence (AI) a.k.a. edge AI can be readily employed to industrial mobile terminals and can improve their energy efficiency. We propose an artificial-intelligence-based approach that utilizes deep learning technology to avoid the energy-wasting defrosting operations. By combining the traditional temperature-sensing logic with a convolutional neural network (CNN) classifier that visually checks for frost, we can more precisely control the defrosting operation. We embed the CNN classifier in the device and demonstrate that the approach significantly reduces the energy consumption. On our test terminal, the net ratio of the energy consumption by the existing system to that of the edge AI for the heating film is almost 14:1. Even with the common current-dissipation accounted for, our edge AI system would increase the operating hours by 86%, or by more than 6 h compared with the system without the edge AI.
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Canadas, E. R., B. J. Duran, G. Machado, A. Nall, S. E. Battista, M. L. Mussard, P. S. Baruselli, and A. Garcia-Guerra. "109 Pre-synchronization and reutilization of progesterone devices during a 6-day CO-Synch protocol for fixed-time artificial insemination in beef heifers." Reproduction, Fertility and Development 33, no. 2 (2021): 161. http://dx.doi.org/10.1071/rdv33n2ab109.
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Ovulatory response to the initial gonadotrophin-releasing hormone (GnRH) of the CO-Synch protocol is affected by circulating progesterone (P4) and follicle size. In addition, heifers that ovulate to the initial GnRH treatment have greater fertility after AI. Thus, this study determined the effect of (1) presynchronization (Presynch) before a 6-day CO-Synch protocol and (2) circulating [RCE1] (P4) on ovulatory response, oestrus expression, and pregnancies per AI (P/AI) in beef heifers. Yearling beef heifers (n=233) at three locations were randomly assigned in a 2×2 factorial design to the following treatments: (1) Presynch+6-day CO-Synch with a new P4 device; (2) Presynch+6-day CO-Synch with a once-used P4 device; (3) 6-day CO-Synch with a new P4 device; and (4) 6-day CO-Synch with a once-used P4 device. Presynch consisted of insertion of a new P4 intravaginal device (1.38g of P4) on Day −17 and removal of P4 device on Day −11 concurrently with 500µg of cloprostenol sodium (PGF). On Day −9, all heifers received either a new (New) or once-used (Used) CIDR and 100µg of gonadorelin acetate (GnRH). Six days later (Day −3), CIDRs were removed, 1000µg of PGF was administered and an oestrous detection patch applied (Estrotect). At 72h after CIDR removal, AI was performed concurrently with administration of 100µg of GnRH. Pregnancy was determined by transrectal ultrasonography 31 days after AI. A subset of heifers (n=155) was examined on Day −9 and Day −3 by ultrasonography to determine ovulation to Day −9 GnRH. Data were analysed using generalized linear mixed models (SAS 9.4; SAS Institute Inc.). Presynch heifers had larger follicle diameter on Day −9 (12.7±0.3 vs. 10.1±0.3 mm; P<0.001), greater ovulatory response to Day −9 GnRH (82.5%; 66/80 vs. 56%; 42/75; P<0.001), greater expression of oestrus (90.6%; 106/117 vs. 78.4%; 91/116; P<0.02), and earlier oestrus (49.8±1 vs. 53.1±1 h; P<0.01) compared with controls. There was a treatment×CIDR interaction on oestrous expression, such that a lesser (P<0.05) percentage of control heifers with new CIDR expressed oestrus compared with all other groups (Table 1). Heifers with a used CIDR during the 6-day CO-Synch tended (P=0.08) to have greater P/AI (52.1%; 61/117) than those with a new CIDR (40.5%; 47/116). In conclusion, presynchronization before initiation of a 6-day CO-Synch increased follicle diameter, enhanced ovulatory response and oestrous expression, but did not affect fertility. The earlier onset of oestrus in presynchronized heifers suggests that the timing of AI may need to be modified. Table 1. Oestrous expression and pregnancy per AI (P/AI) in beef heifers with or without presynchronization and treated with a new or used CIDR during a 6-day CO-Synch Treatment CIDR Oestrus (%; n/n) Time of oestrus (h) P/AI (%; n/n) Control New 67.8a (40/59) 53.7±1.5a 33.9 (20/59) Used 89.5b (51/57) 52.7±1.6a 50.9 (29/57) Presynch New 94.7b (54/57) 50.9±1.4b 47.4 (27/57) Used 86.7b (52/60) 48.7±1.3b 53.3 (32/60) P-value Treatment 0.03 0.01 0.21 CIDR 0.62 0.19 0.08 Interaction 0.003 0.75 0.38 a,bValues with different superscripts differ (P<0.05).
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Canadas, E. R., B. J. Duran, G. Machado, A. Nall, S. E. Battista, M. L. Mussard, P. S. Baruselli, and A. Garcia-Guerra. "109 Pre-synchronization and reutilization of progesterone devices during a 6-day CO-Synch protocol for fixed-time artificial insemination in beef heifers." Reproduction, Fertility and Development 33, no. 2 (2021): 161. http://dx.doi.org/10.1071/rdv33n2ab109.
Full textAbstract:
Ovulatory response to the initial gonadotrophin-releasing hormone (GnRH) of the CO-Synch protocol is affected by circulating progesterone (P4) and follicle size. In addition, heifers that ovulate to the initial GnRH treatment have greater fertility after AI. Thus, this study determined the effect of (1) presynchronization (Presynch) before a 6-day CO-Synch protocol and (2) circulating [RCE1] (P4) on ovulatory response, oestrus expression, and pregnancies per AI (P/AI) in beef heifers. Yearling beef heifers (n=233) at three locations were randomly assigned in a 2×2 factorial design to the following treatments: (1) Presynch+6-day CO-Synch with a new P4 device; (2) Presynch+6-day CO-Synch with a once-used P4 device; (3) 6-day CO-Synch with a new P4 device; and (4) 6-day CO-Synch with a once-used P4 device. Presynch consisted of insertion of a new P4 intravaginal device (1.38g of P4) on Day −17 and removal of P4 device on Day −11 concurrently with 500µg of cloprostenol sodium (PGF). On Day −9, all heifers received either a new (New) or once-used (Used) CIDR and 100µg of gonadorelin acetate (GnRH). Six days later (Day −3), CIDRs were removed, 1000µg of PGF was administered and an oestrous detection patch applied (Estrotect). At 72h after CIDR removal, AI was performed concurrently with administration of 100µg of GnRH. Pregnancy was determined by transrectal ultrasonography 31 days after AI. A subset of heifers (n=155) was examined on Day −9 and Day −3 by ultrasonography to determine ovulation to Day −9 GnRH. Data were analysed using generalized linear mixed models (SAS 9.4; SAS Institute Inc.). Presynch heifers had larger follicle diameter on Day −9 (12.7±0.3 vs. 10.1±0.3 mm; P<0.001), greater ovulatory response to Day −9 GnRH (82.5%; 66/80 vs. 56%; 42/75; P<0.001), greater expression of oestrus (90.6%; 106/117 vs. 78.4%; 91/116; P<0.02), and earlier oestrus (49.8±1 vs. 53.1±1 h; P<0.01) compared with controls. There was a treatment×CIDR interaction on oestrous expression, such that a lesser (P<0.05) percentage of control heifers with new CIDR expressed oestrus compared with all other groups (Table 1). Heifers with a used CIDR during the 6-day CO-Synch tended (P=0.08) to have greater P/AI (52.1%; 61/117) than those with a new CIDR (40.5%; 47/116). In conclusion, presynchronization before initiation of a 6-day CO-Synch increased follicle diameter, enhanced ovulatory response and oestrous expression, but did not affect fertility. The earlier onset of oestrus in presynchronized heifers suggests that the timing of AI may need to be modified. Table 1. Oestrous expression and pregnancy per AI (P/AI) in beef heifers with or without presynchronization and treated with a new or used CIDR during a 6-day CO-Synch Treatment CIDR Oestrus (%; n/n) Time of oestrus (h) P/AI (%; n/n) Control New 67.8a (40/59) 53.7±1.5a 33.9 (20/59) Used 89.5b (51/57) 52.7±1.6a 50.9 (29/57) Presynch New 94.7b (54/57) 50.9±1.4b 47.4 (27/57) Used 86.7b (52/60) 48.7±1.3b 53.3 (32/60) P-value Treatment 0.03 0.01 0.21 CIDR 0.62 0.19 0.08 Interaction 0.003 0.75 0.38 a,bValues with different superscripts differ (P<0.05).
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Gams, Matjaž, and Tine Kolenik. "Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules." Electronics 10, no. 4 (February 22, 2021): 514. http://dx.doi.org/10.3390/electronics10040514.
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This paper presents relations between information society (IS), electronics and artificial intelligence (AI) mainly through twenty-four IS laws. The laws not only make up a novel collection, currently non-existing in the literature, but they also highlight the core boosting mechanism for the progress of what is called the information society and AI. The laws mainly describe the exponential growth in a particular field, be it the processing, storage or transmission capabilities of electronic devices. Other rules describe the relations to production prices and human interaction. Overall, the IS laws illustrate the most recent and most vibrant part of human history based on the unprecedented growth of device capabilities spurred by human innovation and ingenuity. Although there are signs of stalling, at the same time there are still many ways to prolong the fascinating progress of electronics that stimulates the field of artificial intelligence. There are constant leaps in new areas, such as the perception of real-world signals, where AI is already occasionally exceeding human capabilities and will do so even more in the future. In some areas where AI is presumed to be incapable of performing even at a modest level, such as the production of art or programming software, AI is making progress that can sometimes reflect true human skills. Maybe it is time for AI to boost the progress of electronics in return.
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Gursoy, Dogan, Oscar Hengxuan Chi, Lu Lu, and Robin Nunkoo. "Consumers acceptance of artificially intelligent (AI) device use in service delivery." International Journal of Information Management 49 (December 2019): 157–69. http://dx.doi.org/10.1016/j.ijinfomgt.2019.03.008.
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Kommu, Gangadhara Rao. "An AI(Artificial Intelligence) based Device for Covid-19 Fever Detection." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 25, 2021): 2745–48. http://dx.doi.org/10.22214/ijraset.2021.35548.
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Coronavirus disease (covid-19) is a global pandemic, and every country is actively fighting against the virus. It is an effective way to prevent the spread of the virus in finding the person with abnormal temperature promptly to perform the further medical observation. However, the traditional method of temperature measurement has low efficiency and accuracy. Body temperature acting as important role in medicine, several diseases is characterized by a change in human body temperature. Monitoring body temperature also allows the doctor to track the effectiveness of treatments. But current continuous body temperature measurement system is mainly limited by reaction time, movement noise, and labour requirement. In addition, the traditional contact body temperature measurement has the problem of wasting consumables and causing discomfort. To address the above issues, we present a non contact, automatic system using a single thermal non-contact sensor. The Proposed Covid prevention method scans body temperature through MLX9014 Contactless Temperature Sensor and sends the data to Raspberry pi model 3+ architecture. Our application takes data from MLX9014 and analyzes it to see whether the temperature is greater than 370 Celsius , which then captures the image through pi camera.
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Glassman, Jill, Kathryn Humphreys, Serena Yeung, Michelle Smith, Adam Jauregui, Arnold Milstein, and Lee Sanders. "Parents’ Perspectives on Using Artificial Intelligence to Reduce Technology Interference During Early Childhood: Cross-sectional Online Survey." Journal of Medical Internet Research 23, no. 3 (March 15, 2021): e19461. http://dx.doi.org/10.2196/19461.
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Background Parents’ use of mobile technologies may interfere with important parent-child interactions that are critical to healthy child development. This phenomenon is known as technoference. However, little is known about the population-wide awareness of this problem and the acceptability of artificial intelligence (AI)–based tools that help with mitigating technoference. Objective This study aims to assess parents’ awareness of technoference and its harms, the acceptability of AI tools for mitigating technoference, and how each of these constructs vary across sociodemographic factors. Methods We administered a web-based survey to a nationally representative sample of parents of children aged ≤5 years. Parents’ perceptions that their own technology use had risen to potentially problematic levels in general, their perceptions of their own parenting technoference, and the degree to which they found AI tools for mitigating technoference acceptable were assessed by using adaptations of previously validated scales. Multiple regression and mediation analyses were used to assess the relationships between these scales and each of the 6 sociodemographic factors (parent age, sex, language, ethnicity, educational attainment, and family income). Results Of the 305 respondents, 280 provided data that met the established standards for analysis. Parents reported that a mean of 3.03 devices (SD 2.07) interfered daily in their interactions with their child. Almost two-thirds of the parents agreed with the statements “I am worried about the impact of my mobile electronic device use on my child” and “Using a computer-assisted coach while caring for my child would help me notice more quickly when my device use is interfering with my caregiving” (187/281, 66.5% and 184/282, 65.1%, respectively). Younger age, Hispanic ethnicity, and Spanish language spoken at home were associated with increased technoference awareness. Compared to parents’ perceived technoference and sociodemographic factors, parents’ perceptions of their own problematic technology use was the factor that was most associated with the acceptance of AI tools. Conclusions Parents reported high levels of mobile device use and technoference around their youngest children. Most parents across a wide sociodemographic spectrum, especially younger parents, found the use of AI tools to help mitigate technoference during parent-child daily interaction acceptable and useful.
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Huang, Haichao, Zhouzhenyan Hong, Huiming Zhou, Jiaxian Wu, and Ning Jin. "Knowledge Graph Construction and Application of Power Grid Equipment." Mathematical Problems in Engineering 2020 (October 9, 2020): 1–10. http://dx.doi.org/10.1155/2020/8269082.
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Recent development of artificial intelligence (AI) technology enquires the traditional power grid system involving additional information and connectivity of all devices for the smooth transit to the next generation of smart grid system. In an AI-enhanced power grid system, each device has its unique name, function, property, location, and many more. A large number of power grid devices can form a complex power grid knowledge graph through serial and parallel connection relationships. The scale of power grid equipment is usually extremely large, with thousands and millions of power devices. Finding the proper way of understanding and operating these devices is difficult. Furthermore, the collection, analysis, and management of power grid equipment become major problems in power grid management. With the development of AI technology, the combination of labeling technology and knowledge graph technology provides a new solution understanding the internal structure of a power grid. As a result, this study focuses on knowledge graph construction techniques for large scale power grid located in China. A semiautomatic knowledge graph construction technology is proposed and applied to the power grid equipment system. Through a series of experimental simulations, we show that the efficiency of daily operations, maintenance, and management of the power grid can be largely improved.
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Alizadeh, Fatemeh, Gunnar Stevens, and Margarita Esau. "I Don’t Know, Is AI Also Used in Airbags?" i-com 20, no. 1 (April 1, 2021): 3–17. http://dx.doi.org/10.1515/icom-2021-0009.
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Abstract In 1991, researchers at the center for the Learning Sciences of Carnegie Mellon University were confronted with the confusing question of “where is AI?” from users, who were interacting with artificial intelligence (AI) but did not realize it. After three decades of research, we are still facing the same issue with the unclear understanding of AI among people. The lack of mutual understanding and expectations among AI users and designers and the ineffective interactions with AI that result raises the question of “how AI is generally perceived today?” To address this gap, we conducted 50 semi-structured interviews on perception and expectations of AI. Our results revealed that for most, AI is a dazzling concept that ranges from a simple automated device up to a full controlling agent and a self-learning superpower. We explain how these folk concepts shape users’ expectations when interacting with AI and envisioning its current and future state.
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Rojas Canadas, E., S. E. Battista, J. Kieffer, S. Wellert, and A. Garcia Guerra. "160 Increasing gonadotrophin-releasing hormone dose at initiation of a 5-day CO-Synch protocol increases ovulatory response but not fertility in yearling beef heifers." Reproduction, Fertility and Development 32, no. 2 (2020): 206. http://dx.doi.org/10.1071/rdv32n2ab160.
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Heifers typically have a reduced ovulation rate following gonadotrophin-releasing hormone (GnRH) application at initiation of a CO-Synch + controlled internal drug release (CIDR) protocol. Thus, the objective of the present study was to determine whether increasing the dose of GnRH at initiation of a 5-day CO-Synch protocol in beef heifers would improve ovulation rate and therefore increase pregnancies per AI (P/AI). Angus yearling heifers (n=299) at five locations in Ohio (United States) were randomised to receive either 100µg (single; n=149) or 200µg (double; n=150) of gonadorelin acetate (Gonabreed, Parnell) at initiation of a 5-day CO-Synch. On Day −8, heifers received a new intravaginal progesterone-releasing device (1.38g of progesterone; CIDR, Zoetis) and either a single or double dose of GnRH as described above. Five days later (Day −3), devices were removed, 1000µg of cloprostenol sodium (Estroplan, Parnell) was administered, and an oestrous detection patch was applied (Estrotect, Rockway Inc.). Sixty hours after device removal, AI was performed concurrently with the administration of 100µg of GnRH. Pregnancy was determined using ultrasonography 35 days after AI. Ovaries from a subset of animals (n=178) were examined on Days −8 and −3 using ultrasonography to determine the presence of corpora lutea (CL) and the size of the largest follicle. Data were analysed using the GLIMMIX procedure of SAS ver. 9.4 (SAS Institute Inc.). Oestrous expression was similar (P=0.50) between heifers treated with a single (49.0%) or double (52.7%) dose of GnRH. Overall, P/AI was similar (P=0.35) between heifers receiving a single (43.6%; 65/149) or double (38.7%; 58/150) dose of GnRH at initiation of the protocol. However, increasing the dose of GnRH resulted in a greater (P=0.04) ovulation rate in heifers in the double-dose group (40.9%; 36/88) compared with those in the single-dose group (26.1%; 23/88). In addition, heifers with a CL at the time of treatment had reduced ovulatory response to GnRH treatment (16.0%) compared with heifers without a CL (53.7%; P=0.001); however, there was no treatment×CL presence interaction (P=0.69). Heifers that did not ovulate to the initial GnRH treatment had a greater (P=0.0008) diameter of the largest follicle on Day −3 compared with heifers that did ovulate (11.4±0.2 vs. 10.0±0.3). Furthermore, heifers that did ovulate after the initial GnRH had greater (P=0.04) P/AI (52.5%) than heifers that did not ovulate (40.2%), and heifers with a CL on Day −8 tended (P=0.07) to have greater P/AI (47.9%) than heifers without a CL (40.2%). In addition, heifers with a CL present on Day −3 had greater (P=0.04) P/AI (48.2%) than heifers without a CL (31.7%). In summary, increasing the dose of GnRH at initiation of a 5-day CO-Synch did not affect fertility to fixed-time AI but enhanced ovulation rate in beef heifers. Furthermore, heifers that did ovulate at initiation of the protocol or that had a CL at device insertion or removal had greater fertility to fixed-time AI. Thus, alternative strategies that maximise ovulation at initiation of the synchronisation protocol are needed.
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Menchaca, A., M. Vilariño, and E. Rubianes. "29 SHORT-TERM v. LONG-TERM PROGESTERONE PROTOCOL USING CERVICAL OR INTRAUTERINE FIXED-TIME INSEMINATION IN SHEEP." Reproduction, Fertility and Development 22, no. 1 (2010): 172. http://dx.doi.org/10.1071/rdv22n1ab29.
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The short-term protocol with progesterone, prostaglandin F2α (PGF2α), and eCG is used to control follicular dynamics and luteal activity synchronizing the ovulation for fixed-time AI in sheep. The objective of this experiment was to compare the pregnancy rate obtained with short-term protocol (6 d) and long-term protocol (14 d) using cervical or intrauterine fixed-time AI in sheep. Three hundred fifty-two Merino ewes with a body condition score of 2.9 ± 0.3 (mean ± SD; scale 0 to 5) were used during the breeding season (April, 33S, Uruguay). All the females received a CIDR-G (0.3 g of progesterone, InterAg, Hamilton, New Zealand) for 6 d (short-term protocol; n = 178) or 14 d (long-term protocol, n = 174). One imdose of eCG (300 IU, Novormon, Syntex, BA, Argentina) was given at the moment of device withdrawal for the both protocols, and one imdose of PGF2α (10 mg of dinoprost, Lutalyse, Pfizer, New York, NY, USA) was given at the end of the short-term protocol to ensure luteolysis. Cervical AI (short-term protocol, n = 85; long-term protocol, n = 104) or intrauterine AI (short-term protocol, n = 93; long-term protocol, n = 70) was performed 48 or 54 h after device withdrawal, using 200 × 106 or 100 × 106 spermatozoa per ewe, respectively. Fresh semen was extended in UHT skim milk (1000 × 106 spermatozoa mL-1) and used within 1 h of collection. Estrus was recorded twice a day for 4 days after device withdrawal using vasectomized males. Pregnancy diagnosis was performed by transrectal ultrasonography 40 d after AI (5.0 MHz, Aloka, Tokyo, Japan). Logistic regression was used to analyze the effect of the treatment (P < 0.05), the AI technique (P < 0.05), and their interaction (P = NS). Pregnancy rate was higher for the short-term than for the long-term protocol, and for intrauterine than for cervical AI (Table 1). The highest pregnancy rate was achieved with short-term protocol using intrauterine AI (54.8%, 51/93), and the lowest response was obtained with long-term protocol using cervical AI (33.7%, 35/104; P < 0.05). These data were not different from data of short-term protocol using cervical AI or long-term protocol using intrauterine AI (42.4%, 36/85; and 44.3% 31/70, respectively). Ewes in estrus/treated ewes was not different among short-term and long-term protocols (83.7%, 149/178; and 82.8%, 144/174, respectively; P = NS). In summary, regardless of insemination technique, short-term protocol of 6 d enhances pregnancy rate in fixed-time AI programs in sheep. Table 1.Main effects of short-term (6 d) v. long-term (14 d) protocol using cervical or intrauterine fixed-time AI on pregnancy rate in sheep Financially supported by Pfizer, SP, Brazil.
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Prabhakar, Bala, Rishi Kumar Singh, and Khushwant S. Yadav. "Artificial intelligence (AI) impacting diagnosis of glaucoma and understanding the regulatory aspects of AI-based software as medical device." Computerized Medical Imaging and Graphics 87 (January 2021): 101818. http://dx.doi.org/10.1016/j.compmedimag.2020.101818.
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Goshev, Georgi. "The Transition to Democracy in Bulgaria: Much-Needed Reforms, Showed by AI-Approach, AI-Methodology and AI-Cognitive G-Space Architecture." European Journal of Interdisciplinary Studies 2, no. 3 (August 30, 2016): 80. http://dx.doi.org/10.26417/ejis.v2i3-80-91.
Full textAbstract:
In this paper we review the leading barrier to democratic change - societies' limited command of democratic principles and practices and the lack of built-in device in the authority for mechanisms of civil control in democratic rule of law. Main contribution of this work is the illustrative example of the capabilities developed by Goshev - Goshev AI-tools: AI-approach, AI-methodology and AI-cognitive G-space architecture to improve the legal and statutory mechanisms of power. Below are elaborated comprehensive measures focused on the success of the transition to democracy. These measures include: a. A complete overhaul of the status-quo in education in democracy. Particularly, the existing cursory, unsystematic, and primarily targeted to children and youth educational patch-work would be replaced by a structured, comprehensive and all-inclusive progressively graduated educational system b. An exhaustive reform of the legislative base. This reform would be more comprehensive and rigorous than reforms mandated as part of EU integration/membership. Specifically, the reform would involve development of logically complete and consistent context-specific designs of democratic legal systems and institutions, their testing and writing into legislation c. Development of mechanisms for permanent monitoring and improvement of the legal system and state governance. Mechanisms of such type would provide for early detection and swift rectification of practices inconsistent with the values and norms of democracy. Keywords: Ttransition to democracy; AI-approach; AI-methodology; and AI-cognitive G-space architecture
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Goshev, Georgi. "The Transition to Democracy in Bulgaria: Much-Needed Reforms, Showed by AI-Approach, AI-Methodology and AI-Cognitive G-Space Architecture." European Journal of Interdisciplinary Studies 2, no. 3 (August 30, 2016): 80. http://dx.doi.org/10.26417/ejis.v2i3.80-91.
Full textAbstract:
In this paper we review the leading barrier to democratic change - societies' limited command of democratic principles and practices and the lack of built-in device in the authority for mechanisms of civil control in democratic rule of law. Main contribution of this work is the illustrative example of the capabilities developed by Goshev - Goshev AI-tools: AI-approach, AI-methodology and AI-cognitive G-space architecture to improve the legal and statutory mechanisms of power. Below are elaborated comprehensive measures focused on the success of the transition to democracy. These measures include: a. A complete overhaul of the status-quo in education in democracy. Particularly, the existing cursory, unsystematic, and primarily targeted to children and youth educational patch-work would be replaced by a structured, comprehensive and all-inclusive progressively graduated educational system b. An exhaustive reform of the legislative base. This reform would be more comprehensive and rigorous than reforms mandated as part of EU integration/membership. Specifically, the reform would involve development of logically complete and consistent context-specific designs of democratic legal systems and institutions, their testing and writing into legislation c. Development of mechanisms for permanent monitoring and improvement of the legal system and state governance. Mechanisms of such type would provide for early detection and swift rectification of practices inconsistent with the values and norms of democracy. Keywords: Ttransition to democracy; AI-approach; AI-methodology; and AI-cognitive G-space architecture
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Goshev, Georgi. "The Transition to Democracy in Bulgaria: Much-Needed Reforms, Showed by AI-Approach, AI-Methodology and AI-Cognitive G-Space Architecture." European Journal of Interdisciplinary Studies 2, no. 3 (August 30, 2016): 80. http://dx.doi.org/10.26417/ejis.v2i3.p80-91.
Full textAbstract:
In this paper we review the leading barrier to democratic change - societies' limited command of democratic principles and practices and the lack of built-in device in the authority for mechanisms of civil control in democratic rule of law. Main contribution of this work is the illustrative example of the capabilities developed by Goshev - Goshev AI-tools: AI-approach, AI-methodology and AI-cognitive G-space architecture to improve the legal and statutory mechanisms of power. Below are elaborated comprehensive measures focused on the success of the transition to democracy. These measures include: a. A complete overhaul of the status-quo in education in democracy. Particularly, the existing cursory, unsystematic, and primarily targeted to children and youth educational patch-work would be replaced by a structured, comprehensive and all-inclusive progressively graduated educational system b. An exhaustive reform of the legislative base. This reform would be more comprehensive and rigorous than reforms mandated as part of EU integration/membership. Specifically, the reform would involve development of logically complete and consistent context-specific designs of democratic legal systems and institutions, their testing and writing into legislation c. Development of mechanisms for permanent monitoring and improvement of the legal system and state governance. Mechanisms of such type would provide for early detection and swift rectification of practices inconsistent with the values and norms of democracy. Keywords: Ttransition to democracy; AI-approach; AI-methodology; and AI-cognitive G-space architecture
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Goshev, Georgi. "The Transition to Democracy in Bulgaria: Much-Needed Reforms, Showed by AI-Approach, AI-Methodology and AI-Cognitive G-Space Architecture." European Journal of Interdisciplinary Studies 5, no. 1 (August 30, 2016): 80. http://dx.doi.org/10.26417/ejis.v5i1.p80-91.
Full textAbstract:
In this paper we review the leading barrier to democratic change - societies' limited command of democratic principles and practices and the lack of built-in device in the authority for mechanisms of civil control in democratic rule of law. Main contribution of this work is the illustrative example of the capabilities developed by Goshev - Goshev AI-tools: AI-approach, AI-methodology and AI-cognitive G-space architecture to improve the legal and statutory mechanisms of power. Below are elaborated comprehensive measures focused on the success of the transition to democracy. These measures include: a. A complete overhaul of the status-quo in education in democracy. Particularly, the existing cursory, unsystematic, and primarily targeted to children and youth educational patch-work would be replaced by a structured, comprehensive and all-inclusive progressively graduated educational system b. An exhaustive reform of the legislative base. This reform would be more comprehensive and rigorous than reforms mandated as part of EU integration/membership. Specifically, the reform would involve development of logically complete and consistent context-specific designs of democratic legal systems and institutions, their testing and writing into legislation c. Development of mechanisms for permanent monitoring and improvement of the legal system and state governance. Mechanisms of such type would provide for early detection and swift rectification of practices inconsistent with the values and norms of democracy. Keywords: Ttransition to democracy; AI-approach; AI-methodology; and AI-cognitive G-space architecture