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Hathal, Hussein M., Ramzy S. Ali, and Abdulkareem S. Abdullah. "A Novel Metaheuristic Moss-Rose-Inspired Algorithm with Engineering Applications." Electronics 10, no. 16 (August 4, 2021): 1877. http://dx.doi.org/10.3390/electronics10161877.
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Every day, a moss rose generates new flowers with variable diameters. Two flowering mechanisms are controlled by exposure to sunlight, namely, a variable concentration of florigen based on photoreceptors called phytochromes, and the biological clock, which is responsible for the changing diameters of the plant flowers at night and some hours during the day. By explaining and idealizing the flowering mechanisms of the moss rose in nature, a new sort of nature-inspired optimization algorithm called the moss rose optimization algorithm (MROA) was proposed in this study. The MROA was benchmarked using three methods. First, 18 benchmark functions were utilized to evaluate the effectiveness of the MROA. Second, the MROA was used for planning a smart antenna system (SA) as an online solution to find unknown weights. Third, the MROA was used to find the optimal dimensions for a microstrip antenna for the frequency (2.4 GHz) as an offline solution. The MROA was compared with other algorithms. The results show the capacities and proficiencies of the proposed algorithm regarding finding the ideal solutions. The promising arrangements for smart antenna identification and microstrip antenna design highlight the importance of this algorithm for resolving current issues with unknown fields of investigation.
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Alluhaidan, Ala Saleh, Mashael Maashi, Munya A. Arasi, Ahmed S. Salama, Mohammed Assiri, and Amani A. Alneil. "Mud Ring Optimization Algorithm with Deep Learning Model for Disease Diagnosis on ECG Monitoring System." Sensors 23, no. 15 (July 26, 2023): 6675. http://dx.doi.org/10.3390/s23156675.
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Due to the tremendous growth of the Internet of Things (IoT), sensing technologies, and wearables, the quality of medical services has been enhanced, and it has shifted from standard medical-based health services to real time. Commonly, the sensors can be combined as numerous clinical devices to store the biosignals generated by the physiological actions of the human body. Meanwhile, a familiar method with a noninvasive and rapid biomedical electrocardiogram (ECG) signal can be used to diagnose and examine cardiovascular disease (CVD). As the growing number of patients is destroying the classification outcome because of major changes in the ECG signal patterns among numerous patients, computer-assisted automatic diagnostic tools are needed for ECG signal classification. Therefore, this study presents a mud ring optimization technique with a deep learning-based ECG signal classification (MROA-DLECGSC) technique. The presented MROA-DLECGSC approach recognizes the presence of heart disease using ECG signals. To accomplish this, the MROA-DLECGSC technique initially preprocessed the ECG signals to transform them into a uniform format. In addition, the Stacked Autoencoder Topographic Map (SAETM) approach was utilized for the classification of ECG signals to identify the presence of CVDs. Finally, the MROA was applied as a hyperparameter optimizer, which assisted in accomplishing enhanced performance. The experimental outcomes of the MROA-DLECGSC algorithm were tested on the benchmark database, and the results show the better performance of the MROA-DLECGSC methodology compared to other recent algorithms.
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Wen, Changsheng, Heming Jia, Di Wu, Honghua Rao, Shanglong Li, Qingxin Liu, and Laith Abualigah. "Modified Remora Optimization Algorithm with Multistrategies for Global Optimization Problem." Mathematics 10, no. 19 (October 2, 2022): 3604. http://dx.doi.org/10.3390/math10193604.
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Remora Optimization Algorithm (ROA) is a metaheuristic optimization algorithm, proposed in 2021, which simulates the parasitic attachment, experiential attack, and host feeding behavior of remora in the ocean. However, the performance of ROA is not very good. Considering the habits of the remora that rely on the host to find food, and in order to improve the performance of the ROA, we designed a new host-switching mechanism. By adding new a host-switching mechanism, joint opposite selection, and restart strategy, a modified remora optimization algorithm (MROA) is proposed. We use 23 standard benchmark and CEC2020 functions to test the performance of MROA and compare them with eight state-of-art optimization algorithms. The experimental results show that MROA has better-optimized performance and robustness. Finally, the ability of MROA to solve practical problems is demonstrated by five classical engineering problems.
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TILLMAN, GLENN E., JAMIE L. WASILENKO, MUSTAFA SIMMONS, TODD A. LAUZE, JOSEPH MINICOZZI, BRIAN B. OAKLEY, NEELAM NARANG, PINA FRATAMICO, and WILLIAM C. CRAY. "Isolation of Shiga Toxin–Producing Escherichia coli Serogroups O26, O45, O103, O111, O121, and O145 from Ground Beef Using Modified Rainbow Agar and Post–Immunomagnetic Separation Acid Treatment†." Journal of Food Protection 75, no. 9 (September 1, 2012): 1548–54. http://dx.doi.org/10.4315/0362-028x.jfp-12-110.
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It is estimated that at least 70% of human illnesses due to non-O157 Shiga toxin–producing Escherichia coli (STEC) in the United States are caused by strains from the top six serogroups (O26, O45, O103, O111, O121, and O145). Procedures for isolating STEC from food products often use plating media that include antimicrobial supplements at concentrations that inhibit background microflora growth but can also inhibit target STEC growth. In this study, an agar medium with lower supplement concentrations, modified Rainbow agar (mRBA), was evaluated for recovery of STEC serogroups O26, O45, O103, O111, O121, and O145 from ground beef enrichments. A post–immunomagnetic separation (IMS) acid treatment step was additionally used to reduce background microflora and increase recovery of target STEC strains. Ground beef samples (325 g) were artificially contaminated with STEC and confounding organisms and enriched for 15 h. Recovery of the target STEC was attempted on the enrichments using IMS and plating onto mRBA and Rainbow agar (RBA). Additionally, acid treatment was performed on the post-IMS eluate followed by plating onto mRBA. Using the combination of mRBA and acid treatment, target STEC were isolated from 103 (85.8%) of 120 of the low-inoculated samples (1 to 5 CFU/325-g sample) compared with 68 (56.7%) of 120 using no acid treatment and plating onto RBA with higher levels of novobiocin and potassium tellurite. The combination of acid treatment and mRBA provides a significant improvement over the use of RBA for isolation of STEC serogroups O26, O45, O103, O111, O121, and O145 from raw ground beef.
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Martynova, T. A., N. I. Maksimov, and M. M. Glavatskikh. "Relationship between electrokinetic parameters of red blood cells and acute coronary syndrome." Perm Medical Journal 35, no. 6 (December 30, 2018): 29–35. http://dx.doi.org/10.17816/pmj35629-35.
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Aim. To assess the state of electrokinetic properties of red blood cells in different variants of the course of acute coronary syndrome (ACS).
Materials and methods. The study deals with the assessment of clinical and laboratory-instrumental data from 201 patients with ACS, the results of electrokinetic parameters of red blood cells, their predictors.
Results. According to electrokinetic indices of erythrocytes, no reliable differences between the groups were found out. The mean red blood cell oscillation amplitude (MRBCOA) in group 1 was 1.83 ± 0.05, in group 2 – 1.75 ± 0.06 (t = 0.955, p = 0.341). Stationary red blood cell percentage (SRBCP) in patients with NSTE-ACS was 15.6 ± 1.45, in patients with STE-ACS – 13.54 ± 1.64 (t = 0.957, p = 0.339). The methods of multiple disperse and regressive analysis were used. Predictors of SRBCP indices in NSTE-ACS group appeared to be intima-media complex thickness (IMCT), left ventricle diastolic dysfunction, coronary insufficiency index (CII); in STE-ACS group – IMCT, MRBCOA, troponin I and AST levels. In STE-ACS group, predictors of MRBCOA were SRBCP and troponin I level, in NSTE-ACS – no predictors, influencing MRBCOAwere revealed.
Conclusions. In NSTE-ACS group, the following predictors, influencing SRBCP were detected:IMCT, CII, LV diastolic dysfunction. In patients of STE-ACS group, there were found the predictors, influencing electrokinetic properties of red blood cells: IMCT, AST, troponin I. In acute coronary syndrome, changes in electrokinetic parameters of red blood cells are mainly influenced by atherosclerotic process, however, in NSTE-ACS, structural-functional status of myocardium is of primary significance, and in STE-ACS – the degree of manifestation of resorptive-necrotic syndrome.
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LEWIS, GENTRY L., NATALIA CERNICCHIARO, and RODNEY A. MOXLEY. "Performance of Chromogenic Agar Media for Isolation of Shiga Toxin–Producing Escherichia coli from Ground Beef." Journal of Food Protection 83, no. 7 (June 22, 2020): 1149–54. http://dx.doi.org/10.4315/jfp-19-585.
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ABSTRACT The performance of three chromogenic agar media for detection of the “top seven” Shiga toxin–producing Escherichia coli (STEC) in beef was compared. Samples of retail ground beef were inoculated with STEC O26, O45, O103, O111, O121, O145, or O157 at geometric mean (±standard error of the mean) levels of 0, 48 (±1), 420 (±1), 4,100 (±1), or 45,000 (±1) CFU/10 g and enriched 1:10 (90 mL) in EC broth (40°C for 6 h). Following enrichment, aliquots of broth culture were treated by immunomagnetic separation with one of three pools of beads against the seven STEC serogroups: pool I, O26, O45, and O121; pool II, O103, O111, and O145; and pool III, O157. After immunomagnetic separation, 50 μL of washed bead suspensions in buffered peptone water were spiral plated onto modified Rainbow Agar O157 (mRBA), CHROMagar STEC (CS), or modified Possé differential medium (mPossé2) and incubated at 37°C for 18 h. Up to six isolated colonies were picked from each spiral plate based on expected colony phenotypes for STEC on the respective media, and isolate identity was confirmed with an 11-plex PCR assay targeting the O serogroups and virulence genes. Overall, mRBA had the highest sensitivity (99.2%), correctly detecting a significantly higher proportion of STEC serogroups than either CS (79.4%; P < 0.05) or mPossé2 (91.7%; P < 0.05). mRBA also had the highest negative predictive value (90.0%), correctly identifying a significantly higher proportion of true-negative samples compared with CS (25.7%; P < 0.05) and mPossé2 (46.2%; P < 0.05). However, mRBA also had the lowest analytical specificity of 83.2% (P < 0.05), yielding the lowest proportion of colonies tested that were STEC positive (3,548 of 4,263) compared with 97.7% (3,607 of 3,693) for mPossé2 and 98.0% (2,875 of 2,935) for CS. Reduced specificity results in more work and higher expense due to the increased number of colonies that must be tested. Further improvements in agar culture media for non-O157 STEC isolation are needed. HIGHLIGHTS
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Hathal, Hussein, Abdulkareem Abdullah, and Ramzy Ali. "Design and Optimizing of Compact Ultra-Wide Band Printed Patch Antenna Employing Different Optimization Algorithms Based on Plant Inspiration." Basrah journal for engineering science 22, no. 1 (April 24, 2022): 86–92. http://dx.doi.org/10.33971/bjes.22.1.10.
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In this paper, a compact ultra-wide band (UWB) printed patch antenna is designed and optimized using four biologically and plant inspired optimization algorithms. These algorithms are the newly adopted Moss Rose Optimization Algorithm (MROA), Runner Root Algorithm (RRA), Sunflower Optimization Algorithm (SFOA) and Particle Swarm Optimization (PSO). These algorithms are modified in an optimizer software, which merges the attributes of the design of electromagnetic environment of CST Microwave Studio with those of the technical programming environment of MATLAB. A compact (12 × 21.5) mm2 printed patch antenna has been proposed and simulated over the whole UWB frequency range using these four optimization algorithms. The simulation results show the superiority of the antenna design using MROA, which has the widest covered frequency range, the lowest reflection coefficient and the lowest standing wave ratio.
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Barik, Ranjan Kumar, Manoranjan Pradhan, and Rutuparna Panda. "Efficient Conversion Technique from Redundant Binary to NonRedundant Binary Representation." Journal of Circuits, Systems and Computers 26, no. 09 (April 24, 2017): 1750135. http://dx.doi.org/10.1142/s0218126617501353.
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Redundant Binary (RB) to Two’s Complement (TC) converter offers nonredundant representation. However, the sign bit of TC representation has to be handled using nonstandard hardware blocks. The concept of Inverted encoding of negative weighted bits (IEN) eliminates the need of sign extension and offers design only using predefined hardware blocks. NonRedundant Binary (NRB) representation refers to both conventional and IEN representations. The NRB representation is also useful considering problem related to shifting in Carry Save (CS) representation of a RB number. In this paper, we have proposed two new conversion circuits for RB to NRB representation. The proposed circuits of the RB to NRB converter are coded in Verilog Hardware Description language (HDL) and synthesized using the Encounter(R) RTL Compiler RC13.10 v13.10-s006_1 of Cadence tool considering ASIC platform. Considering 64 bits’ operand, the delay power product performances of proposed one-bit and two-bit computations offer improvement of almost 29.9% and 47%, respectively as compared to Carry-Look-Ahead (CLA). The proposed one-bit converter is also applied in the final stage of the Modified Redundant Binary Adder (MRBA). The 32-bit MRBA offers a delay improvement of 7.87% replacing conventional converter with proposed one-bit converter in same FPGA 4vfx12sf363-12 device.
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Song, Danny Y., Doru Petrisor, Michael Muntener, Pierre Mozer, Bogdan Vigaru, Alex Patriciu, Michael Schar, and Dan Stoianovici. "MRI-compatible pneumatic robot (MRBot) for prostate brachytherapy: Preclinical evaluation of feasibility and accuracy." Brachytherapy 7, no. 2 (April 2008): 177–78. http://dx.doi.org/10.1016/j.brachy.2008.02.445.
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Stoianovici, D., D. Y. Song, D. Petrisor, P. Mozer, E. Armour, B. Vigaru, M. Muntener, A. Patriciu, and M. Schar. "MRI-compatible Pneumatic Robot (MRBot) for Prostate Brachytherapy: Preclinical Assessment of Accuracy and Execution of Dosimetric Plans." International Journal of Radiation Oncology*Biology*Physics 72, no. 1 (September 2008): S306—S307. http://dx.doi.org/10.1016/j.ijrobp.2008.06.1071.
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WANG, HONGYE, ZHAO CHEN, and XIUPING JIANG. "Improving the Enrichment and Plating Methods for Rapid Detection of Non-O157 Shiga Toxin–Producing Escherichia coli in Dairy Compost." Journal of Food Protection 79, no. 3 (March 1, 2016): 413–20. http://dx.doi.org/10.4315/0362-028x.jfp-15-249.
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ABSTRACT A culture method to detect non-O157 Shiga toxin–producing Escherichia coli (STEC) was optimized in this study. The finished dairy compost with 30% moisture content was inoculated with a cocktail of six non-O157 STEC serovars at initial concentrations of 1 to 100 CFU/g. Afterward, non-O157 STEC cells in the inoculated dairy compost were enriched by four methods, followed by plating onto cefixime-tellurite sorbitol MacConkey agar supplemented with 5 mg/liter novobiocin (CTN-SMAC) and modified Rainbow agar containing 5 mg/liter novobiocin, 0.05 mg/liter cefixime trihydrate, and 0.15 mg/liter potassium tellurite (mRBA). Immunomagnetic bead separation (IMS) was used to compare the cell concentration of individual non-O157 STEC serotypes after enrichment. There was no significant difference (P > 0.05) between CTN-SMAC and mRBA for non-O157 STEC enumeration. The single-step selective enrichment recovered ca. 0.54 log CFU/g more cells (ca. 0.41 log CFU/g for compost-adapted cells) (P < 0.05) compared with the two-step enrichment. Furthermore, the duration of the process to detect non-O157 STEC from dairy compost by selective enrichment, followed by IMS, was optimized. Among six non-O157 STEC serotypes, serotypes O111, O45, and O145 reached the highest cell density after enrichment in dairy compost, and the cell populations reached 7.3, 7.4, and 7.8 log CFU/g within 16 h of incubation, respectively. In contrast, without an enrichment step, the IMS detection limit of individual non-O157 STEC serovars ranged from 3.15 to 4.15 log CFU/g in dairy compost. These results demonstrate that low levels of non-O157 STEC can be detected within 2 days from dairy compost by using a culture method with an optimized enrichment procedure followed by IMS.
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Chen, Hao, Erdan Wang, Nuo Wang, and Tao Song. "Research on Embodied Carbon Transfer Measurement and Carbon Compensation among Regions in China." International Journal of Environmental Research and Public Health 20, no. 3 (February 3, 2023): 2761. http://dx.doi.org/10.3390/ijerph20032761.
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The existence of interprovincial embodied carbon transfer not only makes it difficult to achieve carbon emission reductions but also exacerbates the inequity, inefficiency, and high costs of interprovincial carbon emission reduction rights and responsibilities. This paper uses multi-regional input–output analysis (MRIOA) to measure the interprovincial embodied carbon transfer in 2017, obtains the net carbon transfer between 30 provinces (municipalities and autonomous regions) and eight regions in 2017, and accounts for the interprovincial carbon compensation amount based on the carbon price in the national carbon market. This study finds that carbon transfer from economically developed provinces to less developed provinces still exists in China, and the overall distribution shows a spatial transfer pattern from south to north and from east to west, with the northwestern region bearing most of the carbon emission pressure for which it should receive corresponding financial compensation. As part of the process to achieve the “dual carbon” target, appropriate emission reduction policies should be formulated according to the characteristics of provincial carbon transfer and the principle of “who benefits, who compensates”, and economically developed regions should give corresponding financial or technical compensation to less developed regions based on net carbon transfer. Compensation and support should be given to less developed regions based on net carbon transfer to prevent further regional development imbalances.
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"Modern Rounding Based Approximate (MROBA) Multiplier for the Design of Fir Filter." International Journal of Engineering and Advanced Technology 8, no. 6S3 (November 22, 2019): 1863–66. http://dx.doi.org/10.35940/ijeat.f1357.0986s319.
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For better power utilization,area,speed which are important criteria in the design of DSP processors multiplier design is main issue.The work of multiplier is rounding the numbers both signed and unsigned.In few applications speed is more important than accuracy so to improve the performance approximate circuits are introduced with reduce energy consumption and increase speed.In this paper,we propose an FIR filter based on Modern Rounding Based Approximate(M ROBA).In this modified rounding based approximate multiplier the numbers are rounded to the nearest exponent irrespective of 2 n .The proposed MROBA multiplier based FIR filter was compared with Rounding Based Approximate (ROBA) Multiplier. In this multiplier the operands are rounded to the nearest exponent of two. This approximation will lead to simplification of multiplication operation thus reducing area and increasing speed. MROBA gives better results with the MROBA MAC unit is implemented.
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Chen, Yourong, Hao Chen, Meng Han, Banteng Liu, Qiuxia Chen, Zhenghua Ma, and Zhangquan Wang. "Miner revenue optimization algorithm based on Pareto artificial bee colony in blockchain network." EURASIP Journal on Wireless Communications and Networking 2021, no. 1 (July 6, 2021). http://dx.doi.org/10.1186/s13638-021-02018-x.
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AbstractIn order to improve the revenue of attacking mining pools and miners under block withholding attack, we propose the miner revenue optimization algorithm (MROA) based on Pareto artificial bee colony in blockchain network. MROA establishes the revenue optimization model of each attacking mining pool and revenue optimization model of entire attacking mining pools under block withholding attack with the mathematical formulas such as attacking mining pool selection, effective computing power, mining cost and revenue. Then, MROA solves the model by using the modified artificial bee colony algorithm based on the Pareto method. Namely, the employed bee operations include evaluation value calculation, selection probability calculation, crossover operation, mutation operation and Pareto dominance method, and can update each food source. The onlooker bee operations include confirmation probability calculation, crowding degree calculation, neighborhood crossover operation, neighborhood mutation operation and Pareto dominance method, and can find the optimal food source in multidimensional space with smaller distribution density. The scout bee operations delete the local optimal food source that cannot produce new food sources to ensure the diversity of solutions. The simulation results show that no matter how the number of attacking mining pools and the number of miners change, MROA can find a reasonable miner work plan for each attacking mining pool, which increases minimum revenue, average revenue and the evaluation value of optimal solution, and reduces the spacing value and variance of revenue solution set. MROA outperforms the state of the arts such as ABC, NSGA2 and MOPSO.
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Smith, Tyler J., Jeesoo Lee, Michael Markl, and James D. Thomas. "Abstract 15470: Mathematical Modeling of the Determinants and Consequences of Mitral Regurgitation: Jacobian Sensitivity Analysis of a Lumped Parameter Model of the Cardiovascular System." Circulation 146, Suppl_1 (November 8, 2022). http://dx.doi.org/10.1161/circ.146.suppl_1.15470.
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Background: Mitral regurgitation (MR) results in complex changes to hemodynamics, which lumped parameter (LP) models of the cardiovascular (CV) system can model. A previously validated LP model using time varying elastance of the atria and ventricles models pressure, volume, and flow in the four chambers and valves of the heart and arterial and venous compartments. Here we model moderate MR and perform a Jacobian matrix sensitivity analysis to explore impact of CV parameters on MR variables. Methods: Using MATLAB app designer, we made an app to give input parameter and output variable specification for the LP model, displaying pressure, volume, and flow curves and specified output variables at equilibrium. Sensitivity analysis was done with a Jacobian matrix, showing the proportional change (Δ) in output variables ( y i ) to change in each input parameter ( x j ), [(Δy i /y i )/(Δx j /x j )]. We assessed the change in these output variables: LA peak pressure (LAP), Forward Stroke Volume (FSV), and MR Volume (MRVol); in response to changes in these input parameters: LV End Systolic Elastance (LVESE), MR Orifice Area (MROA), and peripheral vascular resistance (PVR) in the setting of moderate MR (MROA=0.3 cm 2 ). Results: Pressure curves for the left atrium and ventricle and aorta with and without moderate MR are shown, with the Jacobian sensitivity matrix in Figure 1. To use the Jacobian matrix, multiply a small proportional change in the input parameter (rows) with the value under the output variable (columns). For example, a 10% increase in MROA will cause a 3.7% increase in LAP, 0.8% fall in FSV, and 8.3% increase in MRVol. Conclusion: This analysis shows how small changes in many parameters impact multiple output variables. MRVol is most sensitive to changes in MROA, while the impact of LVESE (contractility) and PVR (afterload) are appropriately discordant. This model warrants further study to predict instantaneous hemodynamic changes in MR and a host of other entities.
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Mahajan, Pranita, and Dipti Rana. "Feature optimization in CNN using MROA for disease classification." Intelligent Decision Technologies, March 1, 2022, 1–15. http://dx.doi.org/10.3233/idt-220097.
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Electronic Medical Records (EMR) carry important information about a patient’s journey. The past decade shows substantial use of Natural Language Processing (NLP)-based Information Retrieval (IR) techniques to extract insights such as symptoms, diseases, and tests from these unstructured records. The state-of-the-art shows that convolutional neural networks (CNN) make a significant contribution to the disease classification task.A significant improvement in precise knowledge mining is possible with precise feature extraction. Feature selection addresses undesirable, unneeded, or irrelevant features. This article proposes a Modified Rider Optimization Algorithm (MROA) to choose important features by selecting optimal weights from a pool of randomly generated weights based on high accuracy and less training time in the CNN algorithm. A modified approach is trained on 114 N2C2 patients’ records to extract symptoms, disease, and tests are performed on them to perform disease classification tasks. The proposed approach is found to be accurate, with 97.77% accuracy in the disease classification and treatment prediction task from EMR.
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Li, Lei, Ming Jeat Foo, Jiaye Chen, Kuan Yuee Tan, Jiaying Cai, Rohini Swaminathan, Karen Sui Geok Chua, et al. "Mobile Robotic Balance Assistant (MRBA): a gait assistive and fall intervention robot for daily living." Journal of NeuroEngineering and Rehabilitation 20, no. 1 (March 1, 2023). http://dx.doi.org/10.1186/s12984-023-01149-0.
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Abstract Background Aging degrades the balance and locomotion ability due to frailty and pathological conditions. This demands balance rehabilitation and assistive technologies that help the affected population to regain mobility, independence, and improve their quality of life. While many overground gait rehabilitation and assistive robots exist in the market, none are designed to be used at home or in community settings. Methods A device named Mobile Robotic Balance Assistant (MRBA) is developed to address this problem. MRBA is a hybrid of a gait assistive robot and a powered wheelchair. When the user is walking around performing activities of daily living, the robot follows the person and provides support at the pelvic area in case of loss of balance. It can also be transformed into a wheelchair if the user wants to sit down or commute. To achieve instability detection, sensory data from the robot are compared with a predefined threshold; a fall is identified if the value exceeds the threshold. The experiments involve both healthy young subjects and an individual with spinal cord injury (SCI). Spatial Parametric Mapping is used to assess the effect of the robot on lower limb joint kinematics during walking. The instability detection algorithm is evaluated by calculating the sensitivity and specificity in identifying normal walking and simulated falls. Results When walking with MRBA, the healthy subjects have a lower speed, smaller step length and longer step time. The SCI subject experiences similar changes as well as a decrease in step width that indicates better stability. Both groups of subjects have reduced joint range of motion. By comparing the force sensor measurement with a calibrated threshold, the instability detection algorithm can identify more than 93% of self-induced falls with a false alarm rate of 0%. Conclusions While there is still room for improvement in the robot compliance and the instability identification, the study demonstrates the first step in bringing gait assistive technologies into homes. We hope that the robot can encourage the balance-impaired population to engage in more activities of daily living to improve their quality of life. Future research includes recruiting more subjects with balance difficulty to further refine the device functionalities.
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Whitlow, S., A. Haughey, J. O’Connor, and RP Killeen. "Prevalence of significant pathology on MRI of brain and orbits in patients presenting with visual disturbance." European Journal of Ophthalmology, March 2, 2023, 112067212311609. http://dx.doi.org/10.1177/11206721231160990.
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Purpose The aim of our study was to determine the frequency and variety of abnormalities detected on MRI studies of the brain and orbits (MRBO), in patients referred for investigation of visual disturbance from a tertiary ophthalmology centre in Ireland. A secondary objective was to assess the various imaging pathologies identified in this cohort of patients. Methods The inclusion criteria were patients who underwent an Magnetic resonance imaging (MRI) brain or MRI brain and orbits over a 12-month period for investigation of first episode of visual disturbance, who were over 18 years of age, with visual disturbance of unknown aetiology. Statistical analysis was performed to calculate the percentage of abnormalities and corresponding 95% confidence interval (CI). Additionally, logistic regression was used to investigate any association between age, gender and the pathologies which presented. Results 135 MRI brain and orbit examinations fulfilled the inclusion criteria. Abnormalities were identified on 86 of the 135 examinations (63.7%; 95% CI: 55.3% to 71.3%). Nonspecific T2 hyperintensities were identified on 28 (20.7%) of the examinations, 13 (9.6%) examinations showed images suggestive of demyelination and 11 (8.1%) showed optic neuropathy. The logistic regression analysis showed no evidence of an association between age (p = 0.223), gender (p = 0.307) and abnormalities in this study. Conclusion This represents a relatively high detection rate of abnormalities on MRBO when compared with similar studies and shows the important role MRI has in patients with a visual disturbance.
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Kamal, Vivek V., Nakul Pradeep, Rani Santhakumari amma, Muhammed Arif, Ananthan D. Thampi, and Sneha Edla. "Tribological property and oxidation stability evaluation of rice bran oil with silicon dioxide nanoparticles and spicy aroma-based herbal oils as additives." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, March 15, 2023, 135065012311623. http://dx.doi.org/10.1177/13506501231162396.
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Around the globe, the majority of industries are utilizing mineral oil-based lubricants because of their cost, availability, and compatibility with different engineering materials. The progressive depletion and negative impacts of mineral oil-based products on the environment have led to the search for some potential alternatives. Biodegradable and nontoxic vegetable oils are considered a potential replacement for mineral oils. However, they exhibit certain limitations such as less oxidation stability, poor cloud, and pour point characteristics. In the present study, herbal oils are used as novel antioxidants, and also the synergetic effect of nanoparticles and herbal oils are studied. The modified rice bran oil (MRBO) was formulated using rice bran oil (RBO) as base oil with optimum concentrations of additives such as ginger oil (GO), black pepper (BP) oil, and silicon dioxide nanoparticles. The wear scar diameter (WSD) was reduced significantly by 18.8% after the addition of 0.05 wt.% of SiO2 to RBO. The synergetic effect of herbal oils and SiO2 has significantly reduced the coefficient of friction (COF) of RBO by 15.5%. The oxidation stability of developed oil was also found to be better than RBO when compared to that of commercial lubricant (COM).
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A, Dinesh, and Rangaraj J. "Energy Efficient Clustering and Routing Using Hybrid Fuzzy with Modified Rider Optimization Algorithm in IoT - Enabled Wireless Body Area Network." Journal of Machine and Computing, April 5, 2023, 171–83. http://dx.doi.org/10.53759/7669/jmc202303016.
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Wireless sensor networks are widely used in various Internet of Things applications, including healthcare, underwater sensor networks, body area networks, and multiple offices. Wireless Body Area Network (WBAN) simplifies medical department tasks and provides a solution that reduces the possibility of errors in the medical diagnostic process. The growing demand for real-time applications in such networks will stimulate significant research activity. Designing scenarios for such critical events while maintaining energy efficiency is difficult due to dynamic changes in network topology, strict power constraints, and limited computing power. The routing protocol design becomes crucial to WBAN and significantly impacts the communication stack and network performance. High node mobility in WBAN results in quick topology changes, affecting network scalability. Node clustering is one of many other mechanisms used in WBANs to address this issue. We consider optimization factors like distance, latency, and power consumption of IoT devices to achieve the desired CH selection. This paper proposes a high-level CH selection and routing approach using a hybrid fuzzy with a modified Rider Optimization Algorithm (MROA). This research work is implemented using MATLAB software. The simulations are carried out under a range of conditions. In terms of energy consumption and network life time, the proposed scheme outperforms current state-of-the-art techniques like Low Energy Adaptive Clustering Hierarchy (LEACH), Energy Control Routing Algorithm (ECCRA), Energy Efficient Routing Protocol (EERP), and Simplified Energy Balancing Alternative Aware Routing Algorithm (SEAR).