Journal articles on the topic 'Self-driving databases'
To see the other types of publications on this topic, follow the link: Self-driving databases.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Self-driving databases.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Thompson, Trevor, Damian Poulter, Clare Miles, Marco Solmi, Nicola Veronese, André F. Carvalho, Brendon Stubbs, and Ergun Y. Uc. "Driving impairment and crash risk in Parkinson disease." Neurology 91, no. 10 (August 3, 2018): e906-e916. http://dx.doi.org/10.1212/wnl.0000000000006132.
Full textAbstract:
ObjectivesTo provide the best possible evidence base for guiding driving decisions in Parkinson disease (PD), we performed a meta-analysis comparing patients with PD to healthy controls (HCs) on naturalistic, on-the-road, and simulator driving outcomes.MethodsSeven major databases were systematically searched (to January 2018) for studies comparing patients with PD to HCs on overall driving performance, with data analyzed using random-effects meta-analysis.ResultsFifty studies comprising 5,410 participants (PD = 1,955, HC = 3,455) met eligibility criteria. Analysis found the odds of on-the-road test failure were 6.16 (95% confidence interval [CI] 3.79–10.03) times higher and the odds of simulator crashes 2.63 (95% CI 1.64–4.22) times higher for people with PD, with poorer overall driving ratings also observed (standardized mean differences from 0.50 to 0.67). However, self-reported real-life crash involvement did not differ between people with PD and HCs (odds ratio = 0.84, 95% CI 0.57–1.23, p = 0.38). Findings remained unchanged after accounting for any differences in age, sex, and driving exposure, and no moderating influence of disease severity was found.ConclusionsOur findings provide persuasive evidence for substantive driving impairment in PD, but offer little support for mandated PD-specific relicensure based on self-reported crash data alone, and highlight the need for objective measures of crash involvement.
2
Pawłowska, Justyna, Artur Gądek, and Ewa Wodka-Natkaniec. "Return of TKA knee arthroplasty patients to driving a car. Comparative systematics." Polish Journal of Sports Medicine 37, no. 1 (February 28, 2021): 23–28. http://dx.doi.org/10.5604/01.3001.0014.8688.
Full textAbstract:
Background. Despite the availability of numerous articles and analyses carried out in many healthcare centres, there is still no specific guidelines for both orthopaedic surgeons, and physical therapists for patients preparation after total knee arthroplasty to return to driving a car. The influence of many factors should be taken into account, such as: the operated side, the pain associated with the injury and surgery, the lack of sufficient force to slow down in extreme conditions, the patient’s reaction time and the lack of self-confidence when making such an important decision. Both the patient and the doctor who decides to let the patient drive a car must be aware that this is a very complex and responsible action. This may have an impact on the safety of the patient himself as well as other road users. The aim of the study was to analyse the research carried out so far and to help doctors and physiotherapists in taking the decision of returning the patient to driving a car. Most of the available studies are based on driving simulators and automatic gearboxes where a safe braking element is achieved in three to six weeks, where left-sided TKA patients can return to driving after 4 weeks. The most promising research seems to be in real driving, which allows them to return to driving just 3 weeks after the procedure. Material and metods. The medical databases, i.e. PubMed and Medline were searched for articles concerning the studied issue. The search have covered all databases until April 2020. Most of the work presented is based on a 1994 study and article made by Spalding, who was the first who conduct a study concerning the return of TKA patients to driving. Results and Conclusions. Safe, possible return to driving in patients after TKA is possible 6 weeks after the procedure.
3
Unger, Astrid, Margrit Gelautz, and Florian Seitner. "A Study on Training Data Selection for Object Detection in Nighttime Traffic Scenes." Electronic Imaging 2020, no. 16 (January 26, 2020): 203–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.16.avm-202.
Full textAbstract:
With the growing demand for robust object detection algorithms in self-driving systems, it is important to consider the varying lighting and weather conditions in which cars operate all year round. The goal of our work is to gain a deeper understanding of meaningful strategies for selecting and merging training data from currently available databases and self-annotated videos in the context of automotive night scenes. We retrain an existing Convolutional Neural Network (YOLOv3) to study the influence of different training dataset combinations on the final object detection results in nighttime and low-visibility traffic scenes. Our evaluation shows that a suitable selection of training data from the GTSRD, VIPER, and BDD databases in conjunction with selfrecorded night scenes can achieve an mAP of 63,5% for ten object classes, which is an improvement of 16,7% when compared to the performance of the original YOLOv3 network on the same test set.
4
Pál, Márton, Fanni Vörös, István Elek, and Béla Kovács. "Possibilities of high precision GPS data in autonomous driving." Abstracts of the ICA 1 (July 15, 2019): 1–2. http://dx.doi.org/10.5194/ica-abs-1-286-2019.
Full textAbstract:
<p><strong>Abstract.</strong> A self-driving car is a vehicle that is able to perceive its surroundings and navigate in it without human action. Radar sensors, lasers, computer vision and GPS technologies help it to drive individually (Figure 1). They interpret the sensed information to calculate routes and navigate between obstacles and traffic elements.</p><p>Sufficiently accurate navigation and information about the current position of the vehicle are indispensable for transport. These expectations are fulfilled in the case of a human driver: the knowledge on traffic rules and signs make possible to navigate through even difficult situations. Self-driving systems substitute humans by monitoring and evaluating the surrounding environment and its objects without the background information of the driver. This analysing process is vulnerable. Sudden or unexpected situations may occur but high precision navigation and background GPS databases can complement sensor-detected data.</p><p>The assistance of global navigation has been used in cars for decades. Drivers can easily plan their routes and reach their destination by using car GPS units. However, these devices do not provide accurate positioning: there may be a difference of several metres from the real location. Self-driving cars also use navigation to complement sensor data. Although there are already autonomous system tests on motorways and countryside roads, in densely built-in areas this technology faces complications due to accuracy problems. The dilution of precision (DOP) values can be extremely high in larger settlements because high buildings may hide southern sky (where satellite signs are sensed from on our latitude).</p><p>We can achieve centimetre-level accuracy (if the conditions are ideal) with geodesic RTK (real-time kinematic) GPS systems. This high-precision position data is derived from satellite-based positioning systems. Measurements of the phase of the signal’s carrier wave are real-time corrected by a single reference or an interpolated virtual station.</p><p>In this research we use RTK GPS technology in order to work out a spatial database. These measurements can also be less precise in dense cities, but there is time during fieldwork to try to eliminate inaccuracy. We have chosen a sample area in the inner city of Budapest, Hungary where we located all traffic signs, pedestrian crossings and other important elements. As self-driving cars need precise position data of these terrain objects, we have tried to work with a maximum error of a few decimetres.</p><p>We have examined online map providers if they have feasible data structure and some base data. The implemented structure is similar to OpenStreetMap DB, in which there are already some traffic lights in important crossings. With this preliminary test database, we would like to filter out dangerous situations. If the camera of the car does not see a traffic sign because of a tree or a truck, information about it will be available from the database. If a pedestrian crossing is hardly visible and the sensor does not recognize it, the background GIS data will warn the car that there may be inattentive people on the road.</p><p>A test application has also been developed (Figure 2.), in which our Postgres/Postgis database records have been inserted. In the next phase of the project we try to test our database in the traffic. We plan to drive through the sample area and observe the GPS accuracy in the recognition of the located signs.</p><p>This research aims to achieve higher safety in the field of autonomous driving. By having a refreshable cartographic GIS database in the memory of a self-driving car, there is a smaller chance of risking human life. However, the maintenance demands a high amount of work. Because of this we should concentrate only on the most important signs. Even the cars can be able to supervise the content of the database if there is a large number of them on the road. The frequent production and analysis of point clouds is also an option to get nearer to safe automatized traffic.</p>
5
Holló, Péter, Diána Henézi, and Tamás Berta. "Comparison of Self-reported and Observed Road Safety Performance Indicators." Periodica Polytechnica Transportation Engineering 46, no. 3 (March 20, 2018): 117–21. http://dx.doi.org/10.3311/pptr.12127.
Full textAbstract:
The first concept of road safety performance indicators was published by European Transport Safety Council, Brussels in 2001. Hungary has long and reliable time series of safety belt wearing and child safety seat usage rates in passenger cars. These rates are based on real roadside observations and representative sample, meeting the requirements of international road accident databases. The methodology of the data collection has been further developed during the recent years, but we paid attention to the possibility of comparison with the earlier data and consistent analysis of time series. Although the roadside observations could not have been organized in each year, the trends can be seen relatively well. Our initial hypothesis was that the self-reported data always show a bit "better" picture about the driving behaviour than the observed ones, since the people are not sure that their data will be handled in an anonym way. Based on these results we can say that the ESRA results are relatively good estimations of the real safety belt wearing rates.
6
van der Lijn, Iris, Gera A. de Haan, Famke Huizinga, Fleur E. van der Feen, A. Wijnand F. Rutgers, Catherina Stellingwerf, Teus van Laar, and Joost Heutink. "Self-Reported Visual Complaints in People with Parkinson’s Disease: A Systematic Review." Journal of Parkinson's Disease 12, no. 3 (April 5, 2022): 785–806. http://dx.doi.org/10.3233/jpd-202324.
Full textAbstract:
Background: Scientific research increasingly focuses on visual symptoms of people with Parkinson’s disease (PD). However, this mostly involves functional measures, whereas self-reported data are equally important for guiding clinical care. Objective: This review provides an overview of the nature and prevalence of self-reported visual complaints by people with PD, compared to healthy controls. Methods: A systematic literature search was performed. Studies from three databases (PubMed, PsycInfo, and Web of Science) were screened for eligibility. Only studies that reported results of visual self-reports in people with idiopathic PD were included. Results: One hundred and thirty-nine eligible articles were analyzed. Visual complaints ranged from function-related complaints (e.g., blurred vision, double vision, increased sensitivity to light or changes in contrast sensitivity) to activity-related complaints (e.g., difficulty reading, reaching, or driving). Visual complaints were more prevalent in people with PD compared to healthy controls. The presence of visual complaints leads to a reduced quality of life (QoL). Increased prevalence and severity of visual complaints in people with PD are related to longer disease duration, higher disease severity, and off-state. Conclusion: A large proportion of people with PD have visual complaints, which negatively affect QoL. Complaints are diverse in nature, and specific and active questioning by clinicians is advised to foster timely recognition, acknowledgement, and management of these complaints.
7
Chukwuedozie Francis Nwachukwu. "Inflammatory reaction - A posit to the double-edged sword." International Journal of Biological and Pharmaceutical Sciences Archive 1, no. 2 (May 30, 2021): 197–209. http://dx.doi.org/10.30574/ijbpsa.2021.1.2.0036.
Full textAbstract:
Response by inflammation is triggered by arrays of causes, which include disrupted cells, toxins, germs, and others. It underlies a wide variety of pathophysiological changes. Many aspects of inflammation as it relates to the pathology of various inflammations are very much understood. Yet the healthy roles of inflammation are widely unknown. Inflammation has a controversial role in health and its meanings are, a matter of viewpoint. It has critical roles in protecting organisms from pathogens and injurious substances likewise causing a driving variety of disease progression. On this ground the research aimed at prescribing the essential needs for effective regulations of inflammatory responses. Efficient control of the inflammatory process will avert a plethora of diseases. Articles used for this review were obtained using appropriate keywords on six electronic databases including nature, advantage, disadvantage, and immune response regarding inflammation and immunological response. Inflammation is self-perpetuating though no disease is caused by inflammation as it is not self-triggering. Additionally, the research did weigh up the merits alongside the demerit of inflammation to advocate for effective regulation of inflammation. Essentially, inflammation is a required mechanism in healthy and unhealthy status in humans hence there is a need for importunate reconsideration, exploring its therapeutic benefits.
8
Orlov, Sergey P., Elizaveta E. Bizyukova, and Anastasia E. Iakovleva. "Virtual tests of robotic vehicle units for virtual commissioning." Vestnik of Samara State Technical University. Technical Sciences Series 29, no. 1 (April 23, 2021): 46–57. http://dx.doi.org/10.14498/tech.2021.1.4.
Full textAbstract:
The creation of robotic vehicles for agricultural purposes is a promising direction in the automotive industry. The complexity of the self-driving truck's design, work in difficult operating conditions, and a large number of measuring devices and processing subsystems determine the relevance of creating a virtual test system. These tests are part of the overall virtual commissioning process for a robotic vehicle. The article discusses a set of basic subsystems that provide virtual tests based on a model-based approach: mathematical modeling, measurement modeling, information subsystem with databases, visualization and documentation subsystem. Metrological models of measuring channels for virtual tests have been developed, allowing simulating random vehicle parameter changes. The testing process covers all the most essential components of a robotic vehicle. For example, the article presents a dynamic model of the braking system of a robotic chassis and shows the results of braking modes' virtual tests. The developed virtual test system is used to create a KAMAZ truck as part of a robotic system for agricultural vehicles.
9
Almeida, João, João Rufino, Muhammad Alam, and Joaquim Ferreira. "A Survey on Fault Tolerance Techniques for Wireless Vehicular Networks." Electronics 8, no. 11 (November 16, 2019): 1358. http://dx.doi.org/10.3390/electronics8111358.
Full textAbstract:
Future intelligent transportation systems (ITS) hold the promise of supporting the operation of safety-critical applications, such as cooperative self-driving cars. For that purpose, the communications among vehicles and with the road-side infrastructure will need to fulfil the strict real-time guarantees and challenging dependability requirements. These safety requisites are particularly important in wireless vehicular networks, where road traffic presents several threats to human life. This paper presents a systematic survey on fault tolerance techniques in the area of vehicular communications. The work provides a literature review of publications in journals and conferences proceedings, available through a set of different search databases (IEEE Xplore, Web of Science, Scopus and ScienceDirect). A systematic method, based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) Statement was conducted in order to identify the relevant papers for this survey. After that, the selected articles were analysed and categorised according to the type of redundancy, corresponding to three main groups (temporal, spatial and information redundancy). Finally, a comparison of the core features among the different solutions is presented, together with a brief discussion regarding the main drawbacks of the existing solutions, as well as the necessary steps to provide an integrated fault-tolerant approach to the future vehicular communications systems.
10
Kossmann, Jan, and Rainer Schlosser. "Self-driving database systems: a conceptual approach." Distributed and Parallel Databases 38, no. 4 (March 16, 2020): 795–817. http://dx.doi.org/10.1007/s10619-020-07288-w.
Full textAbstract:
Abstract Challenges for self-driving database systems, which tune their physical design and configuration autonomously, are manifold: Such systems have to anticipate future workloads, find robust configurations efficiently, and incorporate knowledge gained by previous actions into later decisions. We present a component-based framework for self-driving database systems that enables database integration and development of self-managing functionality with low overhead by relying on separation of concerns. By keeping the components of the framework reusable and exchangeable, experiments are simplified, which promotes further research in that area. Moreover, to optimize multiple mutually dependent features, e.g., index selection and compression configurations, we propose a linear programming (LP) based algorithm to derive an efficient tuning order automatically. Afterwards, we demonstrate the applicability and scalability of our approach with reproducible examples.
11
Gerten, Dieter, Martin Schönfeld, and Bernhard Schauberger. "On deeper human dimensions in Earth system analysis and modelling." Earth System Dynamics 9, no. 2 (June 14, 2018): 849–63. http://dx.doi.org/10.5194/esd-9-849-2018.
Full textAbstract:
Abstract. While humanity is altering planet Earth at unprecedented magnitude and speed, representation of the cultural driving factors and their dynamics in models of the Earth system is limited. In this review and perspectives paper, we argue that more or less distinct environmental value sets can be assigned to religion – a deeply embedded feature of human cultures, here defined as collectively shared belief in something sacred. This assertion renders religious theories, practices and actors suitable for studying cultural facets of anthropogenic Earth system change, especially regarding deeper, non-materialistic motivations that ask about humans' self-understanding in the Anthropocene epoch. We sketch a modelling landscape and outline some research primers, encompassing the following elements: (i) extensions of existing Earth system models by quantitative relationships between religious practices and biophysical processes, building on databases that allow for (mathematical) formalisation of such knowledge; (ii) design of new model types that specifically represent religious morals, actors and activities as part of co-evolutionary human–environment dynamics; and (iii) identification of research questions of humanitarian relevance that are underrepresented in purely economic–technocratic modelling and scenario paradigms. While this analysis is by necessity heuristic and semi-cohesive, we hope that it will act as a stimulus for further interdisciplinary and systematic research on the immaterial dimension of humanity's imprint on the Earth system, both qualitatively and quantitatively.
12
Buntin, Kathrin, Peter Ertl, Dominic Hoepfner, Philipp Krastel, Edward J. Oakeley, Dominik Pistorius, Tim Schuhmann, Joanne Wong, and Frank Petersen. "Deliberations on Natural Products and Future Directions in the Pharmaceutical Industry." CHIMIA International Journal for Chemistry 75, no. 7 (August 25, 2021): 620–33. http://dx.doi.org/10.2533/chimia.2021.620.
Full textAbstract:
Natural Products (NPs) are molecular' special equipment ' that impart survival benefits on their producers in nature. Due to their evolved functions to modulate biology these privileged metabolites are substantially represented in the drug market and are continuing to contribute to the discovery of innovative medicines such as the recently approved semi-synthetic derivative of the bacterial alkaloid staurosporin in oncology indications. The innovation of low molecular weight compounds in modern drug discovery is built on rapid progress in chemical, molecular biological, pharmacological and data sciences, which together provide a rich understanding of disease-driving molecular interactions and how to modulate them. NPs investigated in these pharmaceutical research areas create new perspectives on their chemical and biological features and thereby new chances to advance medical research. New methods in analytical chemistry linked with searchable NP-databases solved the issue of reisolation and enabled targeted and efficient access to novel molecules from nature. Cheminformatics delivers high resolution descriptions of NPs and explores the substructures that systematically map NP-chemical space by sp3-enriched fragments. Whole genome sequencing has revealed the existence of collocated gene clusters that form larger functional entities together with proximate resistance factors thus avoiding self-inhibition of the encoded metabolites. The analysis of bacterial and fungal genes provides tantalizing glimpses of new compound-target pairs of therapeutic value. Furthermore, a dedicated investigation of structurally unique, selectively active NPs in chemical biology demonstrates their extraordinary power as shuttles between new biological target spaces of pharmaceutical relevance.
13
Khouja, Jasmine N., Steph F. Suddell, Sarah E. Peters, Amy E. Taylor, and Marcus R. Munafò. "Is e-cigarette use in non-smoking young adults associated with later smoking? A systematic review and meta-analysis." Tobacco Control 30, no. 1 (March 10, 2020): 8–15. http://dx.doi.org/10.1136/tobaccocontrol-2019-055433.
Full textAbstract:
ObjectiveThe aim of this review was to investigate whether e-cigarette use compared with non-use in young non-smokers is associated with subsequent cigarette smoking.Data sourcesPubMed, Embase, Web of Science, Wiley Cochrane Library databases, and the 2018 Society for Research on Nicotine and Tobacco and Society for Behavioural Medicine conference abstracts.Study selectionAll studies of young people (up to age 30 years) with a measure of e-cigarette use prior to smoking and an outcome measure of smoking where an OR could be calculated were included (excluding reviews and animal studies).Data extractionIndependent extraction was completed by multiple authors using a preprepared extraction form.Data synthesisOf 9199 results, 17 studies were included in the meta-analysis. There was strong evidence for an association between e-cigarette use among non-smokers and later smoking (OR: 4.59, 95% CI: 3.60 to 5.85) when the results were meta-analysed in a random-effects model. However, there was high heterogeneity (I2=88%).ConclusionsAlthough the association between e-cigarette use among non-smokers and subsequent smoking appears strong, the available evidence is limited by the reliance on self-report measures of smoking history without biochemical verification. None of the studies included negative controls which would provide stronger evidence for whether the association may be causal. Much of the evidence also failed to consider the nicotine content of e-liquids used by non-smokers meaning it is difficult to make conclusions about whether nicotine is the mechanism driving this association.
14
Tsai, G. J., K. W. Chiang, and N. El-Sheimy. "KINEMATIC CALIBRATION USING LOW-COST LiDAR SYSTEM FOR MAPPING AND AUTONOMOUS DRIVING APPLICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1 (September 26, 2018): 445–50. http://dx.doi.org/10.5194/isprs-archives-xlii-1-445-2018.
Full textAbstract:
<p><strong>Abstract.</strong> More recently, mapping sensors for land-based Mobile Mapping Systems (MMSs) have combined cameras and laser scanning measurements defined as Light Detection and Ranging (LiDAR), or laser scanner together. These mobile laser scanning systems (MLS) can be used in dynamic environments and are able of being adopted in traffic-related applications, such as the collection of road network databases, inventory of traffic sign and surface conditions, etc. However, most LiDAR systems are expensive and not easy to access. Moreover, due to the increasing demand of the autonomous driving system, the low-cost LiDAR systems, such as Velodyne or SICK, have become more and more popular these days. These kinds of systems do not provide the total solution. Users need to integrate with Inertial Navigation System/ Global Navigation Satellite System (INS/GNSS) or camera by themselves to meet their requirement. The transformation between LiDAR and INS frames must be carefully computed ahead of conducting direct geo-referencing. To solve these issues, this research proposes the kinematic calibration model for a land-based INS/GNSS/LiDAR system. The calibration model is derived from the direct geo-referencing model and based on the conditioning of target points where lie on planar surfaces. The calibration parameters include the boresight and lever arm as well as the plane coefficients. The proposed calibration model takes into account the plane coefficients, laser and INS/GNSS observations, and boresight and lever arm. The fundamental idea is the constraint where geo-referenced point clouds should lie on the same plane through different directions during the calibration. After the calibration process, there are two evaluations using the calibration parameters to enhance the performance of proposed applications. The first evaluation focuses on the direct geo-referencing. We compared the target planes composed of geo- referenced points before and after the calibration. The second evaluation concentrates on positioning improvement after taking aiding measurements from LiDAR- Simultaneously Localization and Mapping (SLAM) into INS/GNSS. It is worth mentioning that only one or two planes need to be adopted during the calibration process and there is no extra arrangement to set up the calibration field. The only requirement for calibration is the open sky area with the clear plane construction, such as wall or building. Not only has the contribution in MMSs or mapping, this research also considers the self-driving applications which improves the positioning ability and stability.</p>
15
Pavlo, Andrew, Matthew Butrovich, Lin Ma, Prashanth Menon, Wan Shen Lim, Dana Van Aken, and William Zhang. "Make your database system dream of electric sheep." Proceedings of the VLDB Endowment 14, no. 12 (July 2021): 3211–21. http://dx.doi.org/10.14778/3476311.3476411.
Full textAbstract:
Database management systems (DBMSs) are notoriously difficult to deploy and administer. Self-driving DBMSs seek to remove these impediments by managing themselves automatically. Despite decades of DBMS auto-tuning research, a truly autonomous, self-driving DBMS is yet to come. But recent advancements in artificial intelligence and machine learning (ML) have moved this goal closer. Given this, we present a system implementation treatise towards achieving a self-driving DBMS. We first provide an overview of the NoisePage self-driving DBMS that uses ML to predict the DBMS's behavior and optimize itself without human support or guidance. The system's architecture has three main ML-based components: (1) workload forecasting, (2) behavior modeling, and (3) action planning. We then describe the system design principles to facilitate holistic autonomous operations. Such prescripts reduce the complexity of the problem, thereby enabling a DBMS to converge to a better and more stable configuration more quickly.
16
Reeves, Aaron, Rachel Loopstra, and David Stuckler. "The growing disconnect between food prices and wages in Europe: cross-national analysis of food deprivation and welfare regimes in twenty-one EU countries, 2004–2012." Public Health Nutrition 20, no. 8 (March 20, 2017): 1414–22. http://dx.doi.org/10.1017/s1368980017000167.
Full textAbstract:
AbstractObjectiveFood insecurity has been rising across Europe following the Great Recession, but to varying degrees across countries and over time. The reasons for this increase are not well understood, nor are what factors might protect people’s access to food. Here we test the hypothesis that an emerging gap between food prices and wages can explain increases in reported inability to afford protein-rich foods and whether welfare regimes can mitigate its impact.DesignWe collected data in twenty-one countries from 2004 to 2012 using two databases: (i) on food prices and deprivation related to food (denoted by reported inability to afford to eat meat, chicken, fish or a vegetarian equivalent every second day) from EuroStat 2015 edition; and (ii) on wages from the Organisation for Economic Co-operation and Development 2015 edition.ResultsAfter adjusting for macroeconomic factors, we found that each 1 % rise in the price of food over and above wages was associated with greater self-reported food deprivation (β=0·060, 95 % CI 0·030, 0·090), particularly among impoverished groups. However, this association also varied across welfare regimes. In Eastern European welfare regimes, a 1 % rise in the price of food over wages was associated with a 0·076 percentage point rise in food deprivation (95 % CI 0·047, 0·105) while in Social Democratic welfare regimes we found no clear association (P=0·864).ConclusionsRising prices of food coupled with stagnating wages are a major factor driving food deprivation, especially in deprived groups; however, our evidence indicates that more generous welfare systems can mitigate this impact.
17
Shaw, M., H. Caci, P. Hodgkins, J. Kahle, N. Callamaras, and A. Woods. "Review of studies of ADHD: Long-term outcomes with and without treatment." European Psychiatry 26, S2 (March 2011): 579. http://dx.doi.org/10.1016/s0924-9338(11)72286-6.
Full textAbstract:
IntroductionAs awareness of ADHD has increased worldwide, interest has grown beyond the constellation of ADHD symptoms, to include long-term impact on people's lives and society in general.ObjectivesExamine the results of studies of long-term life consequences of ADHD.AimsTo identify areas of life affected long-term by ADHD and differences in outcomes with and without ADHD treatment.MethodsFollowing Cochrane guidelines, 12 databases were searched for studies published in English (1980–2010). Limiting criteria maximized study inclusion while maintaining high study rigor: (1) peer-reviewed, (2) primary study reports, (3) including a comparator condition, and (4) reporting long-term outcomes (mean 8 years, range 6 months-40 years from study start for prospective studies; subjects in adolescence or adulthood for retrospective or cross-sectional studies). The fully-defined electronic search yielded 4615 citations. Manual review based on titles and abstracts yielded 340 studies included in this analysis of outcomes.ResultsThe majority of studies (86%, 243 of 281; studies of untreated ADHD only) showed that untreated ADHD has substantial negative long-term outcomes, encompassing nine broad-ranging areas of life: non-medicinal drug use/addictive behaviour, antisocial behaviour, academic achievement, occupational achievement, public services use, self-esteem, social function, obesity, and driving outcomes. In contrast, most studies including ADHD pharmacotherapy and/or non-pharmacotherapy (94%, 46 of 49) showed that compared with baseline or untreated ADHD, long-term outcomes improved or stabilized with treatment of ADHD.ConclusionsADHD has notable negative long-term consequences, and this negative impact may be reduced with treatment of ADHD. Supported by Shire Development Inc.
18
Zourlidou, Stefania, and Monika Sester. "Traffic Regulator Detection and Identification from Crowdsourced Data—A Systematic Literature Review." ISPRS International Journal of Geo-Information 8, no. 11 (October 31, 2019): 491. http://dx.doi.org/10.3390/ijgi8110491.
Full textAbstract:
Mapping with surveying equipment is a time-consuming and cost-intensive procedure that makes the frequent map updating unaffordable. In the last few years, much research has focused on eliminating such problems by counting on crowdsourced data, such as GPS traces. An important source of information in maps, especially under the consideration of forthcoming self-driving vehicles, is the traffic regulators. This information is largely lacking in maps like OpenstreetMap (OSM) and this article is motivated by this fact. The topic of this systematic literature review (SLR) is the detection and recognition of traffic regulators such as traffic lights (signals), stop-, yield-, priority-signs, right of way priority rules and turning restrictions at intersections, by leveraging non imagery crowdsourced data. More particularly, the aim of this study is (1) to identify the range of detected and recognised regulatory types by crowdsensing means, (2) to indicate the different classification techniques that can be used for these two tasks, (3) to assess the performance of different methods, as well as (4) to identify important aspects of the applicability of these methods. The two largest databases of peer-reviewed literature were used to locate relevant research studies and after different screening steps eleven articles were selected for review. Two major findings were concluded—(a) most regulator types can be identified with over 80% accuracy, even using heuristic-driven approaches and (b) under the current progress on the field, no study can be reproduced for comparative purposes nor can solely rely on open data sources due to lack of publicly available datasets and ground truth maps. Future research directions are highlighted as possible extensions of the reviewed studies.
19
Danubianu, Mirela, Dragos Mircea Danubianu, Cristian Teodorescu, and Lucian Constantin. "Data-Mining – A Valuable Managerial Tool for Improving Power Plants Efficiency." Present Environment and Sustainable Development 8, no. 1 (May 1, 2014): 205–16. http://dx.doi.org/10.2478/pesd-2014-0018.
Full textAbstract:
Abstract Energy and environment are top priorities for the EU’s Europe 2020 Strategy. Both fields imply complex approaches and consistent investment. The paper presents an alternative to large investments to improve the efficiencies of existing (outdated) power installations: namely the use of data-mining techniques for analysing existing operational data. Data-mining is based upon exhaustive analysis of operational records, inferring high-value information by simply processing records with advanced mathematical / statistical tools. Results can be: assessment of the consistency of measurements, identification of new hardware needed for improving the quality of data, deducing the most efficient level for operation (internal benchmarking), correlation of consumptions with power/ heat production, of technical parameters with environmental impact, scheduling the optimal maintenance time, fuel stock optimization, simulating scenarios for equipment operation, anticipating periods of maximal stress of equipment, identification of medium and long term trends, planning and decision support for new investment, etc. The paper presents a data mining process carried out at the TERMICA - Suceava power plant. The analysis calls for a multidisciplinary approach, a complex team (experts in power&heat production, mechanics, environmental protection, economists, and last but not least IT experts) and can be carried out with lower expenses than an investment in new equipment. Involvement of top management of the company is essential, being the driving force and motivation source for the data-mining team. The approach presented is self learning as once established, the data-mining analytical, modelling and simulation procedures and associated parameter databases can adjust themselves by absorbing and processing new relevant information and can be used on a long term basis for monitoring the performance of the installation, certifying the soundness of managerial measures taken and suggesting further adjustments
20
Abdi, Sarah, Luc de Witte, and Mark Hawley. "Emerging Technologies With Potential Care and Support Applications for Older People: Review of Gray Literature." JMIR Aging 3, no. 2 (August 11, 2020): e17286. http://dx.doi.org/10.2196/17286.
Full textAbstract:
Background The number of older people with unmet care and support needs is increasing substantially due to the challenges facing the formal and informal care systems. Emerging technological developments have the potential to address some of the care and support challenges of older people. However, limited work has been done to identify emerging technological developments with the potential to meet the care and support needs of the aging population. Objective This review aimed to gain an overview of emerging technologies with potential care and support applications for older people, particularly for those living at home. Methods A scoping gray literature review was carried out by using the databases of 13 key organizations, hand searching reference lists of included documents, using funding data, and consulting technology experts. A narrative synthesis approach was used to analyze and summarize the findings of the literature review. Results A total of 39 documents were included in the final analysis. From the analysis, 8 emerging technologies were identified that could potentially be used to meet older people’s needs in various care and support domains. These emerging technologies were (1) assistive autonomous robots; (2) self-driving vehicles; (3) artificial intelligence–enabled health smart apps and wearables; (4) new drug release mechanisms; (5) portable diagnostics; (6) voice-activated devices; (7) virtual, augmented, and mixed reality; and (8) intelligent homes. These emerging technologies were at different levels of development, with some being trialed for care applications, whereas others being in the early phases of development. However, only a few documents mentioned including older people during the process of designing and developing these technologies. Conclusions This review has identified key emerging technologies with the potential to contribute to the support and care needs of older people. However, to increase the adoption of these technologies by older people, there is a need to involve them and other stakeholders, such as formal and informal carers, in the process of designing and developing these technologies.
21
Zhou, Xuanhe, Lianyuan Jin, Ji Sun, Xinyang Zhao, Xiang Yu, Jianhua Feng, Shifu Li, Tianqing Wang, Kun Li, and Luyang Liu. "DBMind." Proceedings of the VLDB Endowment 14, no. 12 (July 2021): 2743–46. http://dx.doi.org/10.14778/3476311.3476334.
Full textAbstract:
We demonstrate a self-driving system DBMind, which provides three autonomous capabilities in database, including self-monitoring, self-diagnosis and self-optimization. First, self-monitoring judiciously collects database metrics and detects anomalies (e.g., slow queries and IO contention), which can profile database status while only slightly affecting system performance (<5%). Then, self-diagnosis utilizes an LSTM model to analyze the root causes of the anomalies and automatically detect root causes from a pre-defined failure hierarchy. Next, self-optimization automatically optimizes the database performance using learning-based techniques, including deep reinforcement learning based knob tuning, reinforcement learning based index selection, and encoder-decoder based view selection. We have implemented DBMind in an open source database openGauss and demonstrated real scenarios.
22
Elsey, Helen, Rachel Bragg, Marjolein Elings, Cathy Brennan, Tracey Farragher, Sandy Tubeuf, Rochelle Gold, et al. "Impact and cost-effectiveness of care farms on health and well-being of offenders on probation: a pilot study." Public Health Research 6, no. 3 (February 2018): 1–190. http://dx.doi.org/10.3310/phr06030.
Full textAbstract:
Background Care farms (CFs), in which all or part of the farm is used for therapeutic purposes, show potential for improving well-being for disadvantaged groups. We assessed the feasibility of determining the cost-effectiveness of CFs in improving quality of life compared with comparator sites among probationers undertaking community orders (COs). Objectives (1) To conduct a systematic review of CF impacts and mechanisms in improving health and logic model development; (2) to inform future studies by estimating differences in quality of life and other outcomes, identifying factors driving CO allocation and ways to maximise recruitment and follow-up; and (3) to assess feasibility of cost-effectiveness analysis. Review methods A mixed-methods synthesis following Campbell Collaboration guidelines. Pilot study: three probation service regions in England, each with a CF and a comparator CO site. Participants were adult offenders (aged ≥ 18 years) serving COs. The primary outcome was quality of life [as measured via the Clinical Outcome in Routine Evaluation–Outcome Measure (CORE-OM)]. Other outcomes were health behaviours, mental well-being, connectedness to nature and reconvictions. Data sources In November 2014, we searched 22 health, education, environmental, criminal justice and social science electronic databases, databases of grey literature and care farming websites across Europe. There were no language restrictions. A full list of databases searched is given in Appendix 1; some examples include Web of Science, Cumulative Index to Nursing and Allied Health Literature (via EBSCOhost), The Campbell Library, Criminal Justice Abstracts (via EBSCOhost), MEDLINE (via Ovid) and Scopus (Elsevier B.V., Amsterdam, the Netherlands). Results Our systematic review identified 1659 articles: 14 qualitative, 12 quantitative and one mixed-methods study. Small sample sizes and poor design meant that all were rated as being at a high risk of bias. Components of CFs that potentially improve health are being in a group, the role of the farmer and meaningful work, and interaction with animals. There was a lack of quantitative evidence indicating that CFs improve quality of life and there was weak evidence of improved mental health, self-efficacy, self-esteem, affect and mood. In the pilot study we recruited 134 respondents, and only 21 declined; 37% were allocated to three CFs and the remainder to comparators. This was below our recruitment target of 300. Recruitment proved challenging as a result of the changes in probation (probation trusts were disbanded in 2014) and closure of one CF. We found significant differences between CFs and comparator users: those at CFs were more likely to be male, smokers, substance users, at higher risk of reoffending (a confounder) and have more missing CORE-OM questions. Despite these differences, the use of propensity analysis facilitated comparison. Participants consented to our team accessing, and we were able to link, probation and police reconviction data for 90% of respondents. We gained follow-up questionnaire data from 52% of respondents, including health and social care use cost data. We transformed CORE-OM into CORE-6D, allowing derivation of quality-adjusted life-years. As a pilot, our study was not powered to identify significant differences in outcomes. Qualitatively, we observed that within COs, CFs can be formally recognised as rehabilitative but in practice can be seen as punitive. Limitations Changes in probation presented many challenges that limited recruitment and collection of cost data. Conclusions Recruitment is likely to be feasible in a more stable probation environment. Retention among probationers is challenging but assessing reconvictions from existing data is feasible. We found worse health and risk of reoffending among offenders at CFs, reflecting the use of CFs by probation to manage challenging offenders. Future work A sufficiently powered natural experiment is feasible and of value. Using reconvictions (from police data) as a primary outcome is one solution to challenges with retention. Propensity analysis provides a viable method for comparison despite differences in participants at CFs and comparator sites. However, future work is dependent on stability and support for CFs within probation services. Study registration This study is registered as PROSPERO CRD42014013892 and SW2013–04 (the Campbell Collaboration). Funding details The National Institute for Health Research Public Health Research programme.
23
Chen, Kuan-Ting, and Huei-Yen Winnie Chen. "Modeling the Impact of Driving Styles on Crash Severity Level Using SHRP 2 Naturalistic Driving Data." Safety 8, no. 4 (November 5, 2022): 74. http://dx.doi.org/10.3390/safety8040074.
Full textAbstract:
Previous studies have examined driving styles and how they are associated with crash risks relying on self-report questionnaires to categorize respondents based on pre-defined driving styles. Naturalistic driving studies provide a unique opportunity to examine this relationship differently. The current study aimed to study how driving styles, derived from real-road driving, may relate to crash severity. To study the relationship, this study retrieved safety critical events (SCEs) from the SHRP 2 database and adopted joint modelling of the number of the aggregated crash severity levels (crash vs. non-crash) using the Diagonal Inflated Bivariate Poisson (DIBP) model. Variables examined included driving styles and various driver characteristics. Among driving styles examined, styles of maintenance of lower speeds and more adaptive responses to driving conditions were associated with fewer crashes given an SCE occurred. Longer driving experiences, more miles driven last year, and being female also reduced the number of crashes. Interestingly, older drivers were associated with both an increased number of crashes and increased number of non-crash SCEs. Future work may leverage more variables from the SHRP 2 database and widen the scope to examine different traffic conditions for a more complete picture of driving styles.
24
Kim, Tae-Yeun, Hoon Ko, and Sung-Hwan Kim. "Data Analysis for Emotion Classification Based on Bio-Information in Self-Driving Vehicles." Journal of Advanced Transportation 2020 (January 16, 2020): 1–11. http://dx.doi.org/10.1155/2020/8167295.
Full textAbstract:
All persons in self-driving vehicle would like to receive each service. To do it, the system has to know the person’s state from emotion or stress, and to know the person’s state, it has to catch by analyzing the person’s bio-information. In this paper, we propose a system for inferring emotion using EEG, pulse, blood pressure (systolic and diastolic blood pressure) of user, and recommending color and music according to emotional state of user for a user service in self-driving vehicle. The proposed system is designed to classify the four emotional information (stability, relaxation, tension, and excitement) by using EEG data to infer and classify emotional state according to user’s stress. SVM algorithm was used to classify bio information according to stress index using brain wave data of the fuzzy control system, pulse, and blood pressure data. When 80% of data were learned according to the ratio of training data by using the SVM algorithm to classify the EEG, blood pressure, and pulse rate databased on the biometric emotion information, the highest performance of 86.1% was shown. The bio-information classification system based on the stress index proposed in this paper will help to study the interaction between human and computer (HCI) in the 4th Industrial Revolution by classifying emotional color and emotional sound according to the emotion of the user it is expected.
25
Desai, Janvi. "Self-Optimizing Database Architecture." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 1675–78. http://dx.doi.org/10.22214/ijraset.2021.39071.
Full textAbstract:
Abstract: Over the most recent decades, analysts and database service providers have fabricated devices to help DBAs (Database Administrators) in various parts of framework tuning and the actual design of the database. Most of this past work, regardless, is fragmented on the grounds that it expects people to come up with an official agreement or judgement about any modifications to the data in the database and fix issues after they happen rather than preventing such cases from taking place or adjusting to these changes automatically. What is required for a really "self-driving" database management system (DBMS) is another way of approaching this that is intended for independent activity and automatic decision making. This is different from prior endeavors since all angles of this framework are constrained by a coordinated arranging part that not just enhance the framework for the current responsibility, but in addition to this, it also predicts future responsibility that might take place and prepares itself for such not-so-common occurrences and adjusts to them as required while keeping the efficiency of the operations as close to normal as possible. With this, the DBMS can uphold all the past tuning procedures without requiring a human to decide the right way and proper opportunity to use them. It likewise empowers new advancements that are significant for current DBMSs (Database Management System), which are impractical today because of the fact that the intricacy of overseeing these frameworks has outperformed the abilities of human specialists who are supposed to tune them and make changes when required. Keywords: Database Management System, Database Administrator, Forecasting, Long Short-Term Memory, Recurrent Neural Networks
26
Shi, Dongmei, and Hongyu Tang. "Research on Safe Driving Evaluation Method Based on Machine Vision and Long Short-Term Memory Network." Journal of Electrical and Computer Engineering 2021 (April 14, 2021): 1–13. http://dx.doi.org/10.1155/2021/9955079.
Full textAbstract:
The rapid development of transportation industry has brought some potential safety hazards. Aiming at the problem of driving safety, the application of artificial intelligence technology in safe driving behavior recognition can effectively reduce the accident rate and economic losses. Based on the presence of interference signals such as spatiotemporal background mixed signals in the driving monitoring video sequence, the recognition accuracy of small targets such as human eyes is low. In this paper, an improved dual-stream convolutional network is proposed to recognize the safe driving behavior. Based on convolutional neural networks (CNNs), attention mechanism (AM) is integrated into a long short-term memory (LSTM) neural network structure, and the hybrid dual-stream AM-LSTM convolutional network channel is designed. The spatial stream channel uses the CNN method to extract the spatial characteristic value of video image and uses pyramid pooling instead of traditional pooling, normalizing the scale transformation. The time stream channel uses a single-shot multibox detector (SSD) algorithm to calculate the adjacent two frames of video sequence for the detection of small objects such as face and eyes. Then, AM-LSTM is used to fuse and classify dual-stream information. The self-built driving behavior video image set is built. ROC, accuracy rate, and loss function experiments are carried out in the FDDB database, VOT100 data set, and self-built video image set, respectively. Compared with CNN, SSD, IDT, and dual-stream recognition methods, the accuracy rate of this method can be improved by at least 1.4%, and the average absolute error in four video sequences can be improved by more than 2%. On the contrary, in the self-built image set, the recognition rate of doze reaches 68.3%, which is higher than other methods. The experimental results show that this method has good recognition accuracy and practical application value.
27
Naqvi, Rizwan Ali, Muhammad Arsalan, Abdul Rehman, Ateeq Ur Rehman, Woong-Kee Loh, and Anand Paul. "Deep Learning-Based Drivers Emotion Classification System in Time Series Data for Remote Applications." Remote Sensing 12, no. 3 (February 10, 2020): 587. http://dx.doi.org/10.3390/rs12030587.
Full textAbstract:
Aggressive driving emotions is indeed one of the major causes for traffic accidents throughout the world. Real-time classification in time series data of abnormal and normal driving is a keystone to avoiding road accidents. Existing work on driving behaviors in time series data have some limitations and discomforts for the users that need to be addressed. We proposed a multimodal based method to remotely detect driver aggressiveness in order to deal these issues. The proposed method is based on change in gaze and facial emotions of drivers while driving using near-infrared (NIR) camera sensors and an illuminator installed in vehicle. Driver’s aggressive and normal time series data are collected while playing car racing and truck driving computer games, respectively, while using driving game simulator. Dlib program is used to obtain driver’s image data to extract face, left and right eye images for finding change in gaze based on convolutional neural network (CNN). Similarly, facial emotions that are based on CNN are also obtained through lips, left and right eye images extracted from Dlib program. Finally, the score level fusion is applied to scores that were obtained from change in gaze and facial emotions to classify aggressive and normal driving. The proposed method accuracy is measured through experiments while using a self-constructed large-scale testing database that shows the classification accuracy of the driver’s change in gaze and facial emotions for aggressive and normal driving is high, and the performance is superior to that of previous methods.
28
Silver, Daniel, and Diana Miller. "Cultural Scenes and Voting Patterns in Canada." Canadian Journal of Political Science 47, no. 3 (September 2014): 425–50. http://dx.doi.org/10.1017/s0008423914000778.
Full textAbstract:
AbstractExtending recent social science work using the concept of “scene” into politics, this paper investigates connections between cultural variation and political variation across Canadian localities. First, we introduce the notion of “scene.” Then, using a national database of local amenities (with some 1800 categories and 1.6 million data points), we show that key dimensions of cultural meaning account for significant differences in voting patterns in recent Canadian elections. In particular, electoral districts with scenes that suggest themes of self-expression are associated with support for left-leaning parties, while scenes that support locality and corporateness are associated with the right. We conclude with suggestions for pursuing hypotheses about potential mechanisms driving these associations.
29
Perera, R. Malinga, Bastian Oetomo, Benjamin I. P. Rubinstein, and Renata Borovica-Gajic. "HMAB." Proceedings of the VLDB Endowment 16, no. 2 (October 2022): 216–29. http://dx.doi.org/10.14778/3565816.3565824.
Full textAbstract:
Effective physical database design tuning requires selection of several physical design structures (PDS), such as indices and materialised views, whose combination influences overall system performance in a non-linear manner. While the simplicity of combining the results of iterative searches for individual PDSs may be appealing, such a greedy approach may yield vastly suboptimal results compared to an integrated search. We propose a new self-driving approach (HMAB) based on hierarchical multi-armed bandit learners, which can work in an integrated space of multiple PDS while avoiding the full cost of combinatorial search. HMAB eschews the optimiser cost misestimates by direct performance observations through a strategic exploration, while carefully leveraging its knowledge to prune the less useful exploration paths. As an added advantage, HMAB comes with a provable guarantee on its expected performance. To the best of our knowledge, this is the first learned system to tune both indices and materialised views in an integrated manner. We find that our solution enjoys superior empirical performance relative to state-of-the-art commercial physical database design tools that search over the integrated space of materialised views and indices. Specifically, HMAB achieves up to 96% performance gain over a state-of-the-art commercial physical database design tool when running industrial benchmarks.
30
Na, Annalisa, Kacy Richburg, and Zbigniew Gugala. "Clinical Considerations for Return to Driving a Car following a Total Knee or Hip Arthroplasty: A Systematic Review." BioMed Research International 2020 (July 7, 2020): 1–10. http://dx.doi.org/10.1155/2020/8921892.
Full textAbstract:
Aim. The purpose of this study is to systematically review patient characteristics and clinical determinants that may influence return to driving status and time frames following a primary TKA or THA and provide an update of the current literature. Methods. This review was completed per the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Final electronic database searches were completed in October 2019 in Medline/PubMed, Medline/OVID, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Cochrane Library using preselected search terms. Manuscripts of prospective and nonrandomized studies that examined the return to driving a car after a primary knee or hip arthroplasty patients were included. The Methodological Index for Non-Randomized Studies was used to measure study quality. Two authors selected studies and assessed their qualities. All disagreements were resolved through discussion and, as needed, a third reviewer. Data on study title, author(s), country, year, study design, sample size, inclusion and exclusion criteria, age, BMI, gender, statistical analyses, driving measure, follow-up time, surgical approach, laterality, and postoperative management were extracted from each study. Results. A total of 23 studies were eligible, including 12 TKA studies (n=654) with mean ages between 43 and 82 years, 9 THA studies (n=922) with mean ages between 34 and 85 years, and 2 combined TKA and THA (TKA, n=815; THA, n=685), yielded MINORS scores between 6 and 12. Most patients achieved or exceeded preoperative response times between 1 and 8 weeks following a TKA and 2 days to 8 weeks following a THA, and/or self-reported return to driving between 1 week and 6 months. Influences on return to driving time included laterality and pain, but gender was mixed. Discussion/Conclusions. Study results were consistent with previous systematic reviews in that return to driving a car after a primary TKA or THA is highly variable, and most commonly occurs around 4 weeks, but can range between 2 and 8 weeks. While various patient and clinical factors can influence return to driving for a TKA or THA, the most common contributing facts were pain and laterality. The heterogeneous nature of the studies prevented a meta-analysis for determining contributions of return to driving following a primary TKA or THA. Regardless, this study updates previous systematic reviews and presents insight on patient and clinical factors beyond generalized timeframes for return to driving a car. This information and results from future studies are essential to guide clinical recommendations and patient and clinician expectations for return to driving a car after a primary TKA or THA.
31
Kherraki, Amine, Muaz Maqbool, and Rajae El Ouazzani. "Efficient lightweight residual network for real-time road semantic segmentation." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 1 (March 1, 2023): 394. http://dx.doi.org/10.11591/ijai.v12.i1.pp394-401.
Full textAbstract:
<span lang="EN-US">Intelligent transportation system (ITS) is currently one of the most discussed topics in scientific research. Actually, ITS offers advanced monitoring systems that include vehicle counting, pedestrian detection. Lately, convolutional neural networks (CNNs) are extensively used in computer vision tasks, including segmentation, classification, and detection. In fact, image semantic segmentation is a critical issue in computer vision applications. For example, self-driving vehicles require high accuracy with lower parameter requirements to segment the road scene objects in real-time. However, most related work focus on one side, accuracy or parameter requirements, which make CNN models difficult to use in real-time applications. In order to resolve this issue, we propose the efficient lightweight residual network (ELRNet), a novel and ELRNet, which is an asymmetrical encoder-decoder architecture. Indeed, in this network, we compare four varieties of the proposed factorized block, and three loss functions to get the best combination. In addition, the proposed model is trained from scratch using only 0.61M parameters. All experiments are evaluated on the popular public the cambridge-driving labeled video database (CamVid) road scene dataset and reached results show that ELRNet can achieve better performance in terms of parameters requirements and precision compared to related work.</span>
32
Bazin, Patrick. "The Guichet du Savoir." Library Management 27, no. 6/7 (July 1, 2006): 423–29. http://dx.doi.org/10.1108/01435120610702413.
Full textAbstract:
PurposeThe purpose of this paper is to intoduce the Guichet du Savoir, an online information service proposed by the Lyon Municipal Library.Design/methodology/approachA description of its originality is provided, which is based on three principles: any kind of question is accepted, any question and its answer are immediately published and later archived in a knowledge database easily accessible online; the entire library staff cooperates in providing the answers.FindingsThe Guichet du Savoir is therefore a means of exchange with the public, a dynamic encyclopaedia, as well as a means for managing and emphasizing librarian skills. Since its opening, in March 2004, this service has published more than 13,000 answers and represents one of the main driving forces for the library's modernization.Originality/valueOf value to librarians who will see how the Guichet du Savoir comes within the scope of a self‐asserting strategy for the library itself, which is no longer perceived as a book sanctuary only, but also as an intermediating force for the circulation and the sharing of knowledge in the information society.
33
Giorgi, Salvatore, David B. Yaden, Johannes C. Eichstaedt, Robert D. Ashford, Anneke E. K. Buffone, H. Andrew Schwartz, Lyle H. Ungar, and Brenda Curtis. "Cultural Differences in Tweeting about Drinking Across the US." International Journal of Environmental Research and Public Health 17, no. 4 (February 11, 2020): 1125. http://dx.doi.org/10.3390/ijerph17041125.
Full textAbstract:
Excessive alcohol use in the US contributes to over 88,000 deaths per year and costs over $250 billion annually. While previous studies have shown that excessive alcohol use can be detected from general patterns of social media engagement, we characterized how drinking-specific language varies across regions and cultures in the US. From a database of 38 billion public tweets, we selected those mentioning “drunk”, found the words and phrases distinctive of drinking posts, and then clustered these into topics and sets of semantically related words. We identified geolocated “drunk” tweets and correlated their language with the prevalence of self-reported excessive alcohol consumption (Behavioral Risk Factor Surveillance System; BRFSS). We then identified linguistic markers associated with excessive drinking in different regions and cultural communities as identified by the American Community Project. “Drunk” tweet frequency (of the 3.3 million geolocated “drunk” tweets) correlated with excessive alcohol consumption at both the county and state levels (r = 0.26 and 0.45, respectively, p < 0.01). Topic analyses revealed that excessive alcohol consumption was most correlated with references to drinking with friends (r = 0.20), family (r = 0.15), and driving under the influence (r = 0.14). Using the American Community Project classification, we found a number of cultural markers of drinking: religious communities had a high frequency of anti-drunk driving tweets, Hispanic centers discussed family members drinking, and college towns discussed sexual behavior. This study shows that Twitter can be used to explore the specific sociocultural contexts in which excessive alcohol use occurs within particular regions and communities. These findings can inform more targeted public health messaging and help to better understand cultural determinants of substance abuse.
34
Park, Sung Ho, Hyo Sik Yoon, and Kang Ryoung Park. "Faster R-CNN and Geometric Transformation-Based Detection of Driver’s Eyes Using Multiple Near-Infrared Camera Sensors." Sensors 19, no. 1 (January 7, 2019): 197. http://dx.doi.org/10.3390/s19010197.
Full textAbstract:
Studies are being actively conducted on camera-based driver gaze tracking in a vehicle environment for vehicle interfaces and analyzing forward attention for judging driver inattention. In existing studies on the single-camera-based method, there are frequent situations in which the eye information necessary for gaze tracking cannot be observed well in the camera input image owing to the turning of the driver’s head during driving. To solve this problem, existing studies have used multiple-camera-based methods to obtain images to track the driver’s gaze. However, this method has the drawback of an excessive computation process and processing time, as it involves detecting the eyes and extracting the features of all images obtained from multiple cameras. This makes it difficult to implement it in an actual vehicle environment. To solve these limitations of existing studies, this study proposes a method that uses a shallow convolutional neural network (CNN) for the images of the driver’s face acquired from two cameras to adaptively select camera images more suitable for detecting eye position; faster R-CNN is applied to the selected driver images, and after the driver’s eyes are detected, the eye positions of the camera image of the other side are mapped through a geometric transformation matrix. Experiments were conducted using the self-built Dongguk Dual Camera-based Driver Database (DDCD-DB1) including the images of 26 participants acquired from inside a vehicle and the Columbia Gaze Data Set (CAVE-DB) open database. The results confirmed that the performance of the proposed method is superior to those of the existing methods.
35
Cuyvers, Katrien, Vincent Donche, and Piet Van den Bossche. "Unravelling the process of self-regulated learning of medical specialists in the clinical environment." Journal of Workplace Learning 33, no. 5 (February 8, 2021): 375–400. http://dx.doi.org/10.1108/jwl-09-2020-0151.
Full textAbstract:
Purpose This study aims to unravel the dynamic nature of the process of self-regulated learning (SRL) of medical specialists as it actually unfolds over time in the authentic clinical environment. Design/methodology/approach A longitudinal multiple case-study design was used, combining multiple data-collection techniques. Long-term observations offered evidence on overt SRL strategies. Physicians’ observed behaviours were used as cues for in loco stimulated recall interviews, asking about covert SRL strategies and their thoughts regarding a situation at hand. Field notes and audiotaped stimulated recall interviews were transcribed verbatim and integrated in a longitudinal database to map SRL as it actually unfolds moment-by-moment. The transcripts were analysed from an inter- and intra-individual perspective using Nvivo 12. Findings Results show a variety of strategies that initiate, advance and evaluate the process of SRL. Different SRL strategies not included in contemporary frameworks on SRL are found and classified as a new category which the authors labelled “learning readiness”. Exemplary for an SRL strategy in this category is awareness of learning needs. Results show that SRL in the clinical environment is found as an interrelated, dynamic process unfolding in time with feedback loops between different SRL strategies. Performance is found to play a leading role in driving SRL. Originality/value This study contributes empirically to the conceptual understanding of SRL in the clinical environment. The use of a situated, longitudinal methodology, which goes beyond the common path of retrospective self-report questionnaires, adds to the disentanglement of the process of SRL as it actually unfolds in the work environment.
36
Galetskyi, Sergii. "E-LEARNING AS A LEADING COMPONENT OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN THE EDUCATIONAL PROCESS." Academic Notes Series Pedagogical Science 1, no. 194 (June 2021): 84–88. http://dx.doi.org/10.36550/2415-7988-2021-1-194-84-88.
Full textAbstract:
The evolution of E-learning has been investigated in this article. In all historical epochs, education played an important role in a society. In the conditions of dynamic and ambitious socio-economic transformation of Ukrainian society education becomes the object of new growing requirements of the individual, society and state. These requirements are a major factor of the driving force behind the development of the education system. In this article we outlined new challenges to the qualification of the working force in information society and E-learning, among which are: the need for dynamic adaptation to rapidly changing knowledge and technologies, the acquisition of new skills. The last two decades have been marked by a significant increase in the emphasis on teaching on the basis of information and computer technology. A withdrawal from traditional training was based on the rapid development of the Internet and Covid-19 pandemic. The use of information and communication technologies in education makes it possible to increase its efficiency and accessibility. In addition, some aspects of the implementation of E-learning in the university have been reviwed. Many universities are actively developing a range of technologies, including mixed learning, with the aim of implementing E-learning at the university. Main features of E-learning, as a method for increasing motivation and self-study skills and as a way to improve an access to education, were investigated in the article. It is pointed out that in the structure of E-learning in educational establishments it is necessary to have such elements as: 1) E-learning system environment with the necessary means for communication of participants of E-learning; 2) electronic database of educational material; 3) virtual laboratory; 4) E-learning participants (teachers, students) and technicians (programmers, system administrators, web designers, animators). E-Learning supports self-directed types of learning. High motivation and self-study skills are prerequisites and must be complemented by an appropriately structured didactic learning environment. Self-organization and self-responsibility are important skills within flexible working and learning cultures. An important condition for E-learning: in the E- learning system can learn only highly motivated, focused and organized persons.
37
Devahema, D., S. M. K. Shyaam, M. Karthikeyan, V. S. Vishal, and G. Pushpak. "Object Detection for Blind People Using Faster Region-Based Convolutional Neural Networks." Journal of Computational and Theoretical Nanoscience 17, no. 11 (November 1, 2020): 4915–19. http://dx.doi.org/10.1166/jctn.2020.9206.
Full textAbstract:
Age is not just a number as human body also ages as time pass by. The time passes our vision can also begin to deteriorate as a study suggests 82% of blind people in 39 million blind population are about 50 years and older. So the device suggested can help people to walk without support of others as it uses image recognition by machine learning and informs the user about the obstacle ahead. Such a way of using machine learning has already been applied in self-driving cars and it is quite effective. And also the device can help disable people who were born blind. The camera will be mounted on the user chest and Faster R-CNN will divide the live image into 3 * 3 grid and processes various object in a single grid and compares it with its own database. The algorithm can also calculate the distance from the user to the object like a chair and staircase etc. The device can also read the colour of the traffic lights and can tell the user when the light is green and when the light is red. This device can help many old as well as young people who are blind and reduce the travel difficulties by a large amount.
38
Lagahit, M. L. R., and Y. H. Tseng. "A PRELIMINARY STUDY ON UPDATING HIGH DEFINITION MAPS: DETECTING AND POSITIONING A TRAFFIC CONE BY USING A STEREO CAMERA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W19 (December 23, 2019): 271–74. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w19-271-2019.
Full textAbstract:
Abstract. The concept of Autonomous Vehicles (AV) or self-driving cars has been increasingly popular these past few years. As such, research and development of AVs have also escalated around the world. One of those researches is about High-Definition (HD) maps. HD Maps are basically very detailed maps that provide all the geometric and semantic information on the road, which helps the AV in positioning itself on the lanes as well as mapping objects and markings on the road. This research will focus on the early stages of updating said HD maps. The methodology mainly consists of (1) running YOLOv3, a real-time object detection system, on a photo taken from a stereo camera to detect the object of interest, in this case a traffic cone, (2) applying the theories of stereo-photogrammetry to determine the 3D coordinates of the traffic cone, and (3) executing all of it at the same time on a Python-based platform. Results have shown centimeter-level accuracy in terms of obtained distance and height of the detected traffic cone from the camera setup. In future works, observed coordinates can be uploaded to a database and then connected to an application for real-time data storage/management and interactive visualization.
39
Lógó, J. M., N. Krausz, V. Potó, and A. Barsi. "QUALITY ASPECTS OF HIGH-DEFINITION MAPS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2021 (June 30, 2021): 389–94. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2021-389-2021.
Full textAbstract:
Abstract. A self-driving vehicle is one of the most expected inventions in the near future. These vehicles are enabled by several technological developments, like artificial intelligence, robust control, vehicular sensors, and high-speed communication. But beyond all these elements, the essential component is the knowledge about reality. Our profession has answered that question with the development of high-definition (abbreviated as HD) maps. Fully automated driving (also called driverless transportation) must be reliable enough to entrust our lives to the car. This fact indicates that the applied technology and the used map must be of high quality. But how can the quality of such a map be expressed? We are looking for the answer in the current paper.Following Carlo Batini’s idea, the general approach is based on the triumvirate of data sources – quality dimensions – life cycle phases. Data sources cover aerial, terrestrial and mobile mapping products with the available highest technological care; furthermore, onboard vehicular sensing extends the corresponding data sets. Lifecycle phases focus on the production (data collection and processing technologies) expanded by conceptualization (pre-production) and data delivery and use (post-production). Quality dimensions are strongly related to the dimensionality of the data; they can be measured by dimension metrics.The first part of the paper summarizes the applied data collection methodologies, emphasizing the output data. This description contains a summary of the processing mechanism – inevitably characterized by quality indicators. The paper aims to give a complete outline for the quality dimensions; we do not limit the resolution and accuracy dimensions, but other significant clusters like completeness or consistency are also discussed. Because the reality changes are enormous in transportation (vehicles, pedestrians, etc., are moving – even at higher speed) and the newly developing HD maps are expected to be live, actuality is a cardinal quality dimension as well. Vehicular technologies like SENSORIS give an excellent option to the equipped vehicles to download and use maps from the cloud and upload their field observations, opening a new way to maintain the map database. The so established crowd-sourced data collection intensely influences the map quality; therefore, this method generates quality-related issues that are also to be analyzed.The second part of the paper is a case study, where a pilot site close to the university campus was selected. In this area, thousands of images were captured and uploaded into the Mapillary database. Artificial intelligence processes were applied for segmenting, classifying, and evaluating the content of the georeferenced imagery. The map database stores various object categories in the area, for example, pedestrian crossings, traffic signs, or trash cans. All extracted objects are available in georeferenced format, enabling spatial analyses to derive numeric quality indicators. The paper presents the complete results of this study.
40
de Boer, Jasper, Vanessa Walf-Vorderwuelbecke, Gareth Williams, Sujith Samarasinghe, Phil Ancliff, Nicholas Goulden, and Owen Williams. "Targeted Inactivation of the c-Myb Oncogene through Integrated Network Analysis." Blood 126, no. 23 (December 3, 2015): 2561. http://dx.doi.org/10.1182/blood.v126.23.2561.2561.
Full textAbstract:
Abstract Major progress has been made in the treatment of Acute Myeloid Leukaemia (AML). However, drugs that have been developed to treat leukaemia still fail in about half the patients. New treatments are urgently needed. In the last couple of years we have been working to understand the exact molecular changes in the blood forming cells that cause leukaemia. We, and others, have identified key proteins that these cells need to survive. These are known as the driving oncogenes. Here we describe a pipeline, implemented by us, to inactivate key driving transcription factors through drug repositioning. In this study we focus on the oncogene c-MYB, a transcription factor that has a critical role in AML. To achieve the goal of discovering drugs that pharmacologically target the driving transcription factors for inactivation we set up a technique for integrated analysis of gene expression datasets and transcription factor binding to DNA and combined this with pattern-matching software to mine the connectivity map database. Briefly, inducible expression/knockdown systems and next generation profiling allowed us to integrate cellular output (RNA-Seq or Genechip) and transcription factor binding (ChIP-Seq or ChIPip-on-Chip) of the driving c-MYB oncoprotein in leukaemia. This profile was used to mine the connectivity database for perturbagens of this profile. The top hit, of this integrated network-based analysis of transcription factor behaviour and perturbing agents, was taken forward for analysis. It was found to cause an acute reduction in the protein levels of MYB, preceding changes in the level of its mRNA. This reduction in c-MYB proteins level could be blocked with proteasome inhibitor MG132. Meaning we induced the proteolysis of the c-MYB oncogene. The reduction in c-MYB levels was accompanied by a significant anti-AML activity in an in vitro colony forming assay of both AML cell lines and primary patient samples. Interestingly, the drug had no significant effect on colony formation of CD34 positive cord blood cells. To investigate the specificity we tried to rescue the function of MYB. Through overexpressing a "stabilized" MYB mutant, lacking the negative regulatory domain we could, in large part, rescue the block in leukemic self-renewal. Meaning, its main effect on the block in AML stem cells is through targeting c-MYB oncogene. It is vital to know if this drug would be active in patients and we therefore required a model that reflected, as closely as possible, the disease progression in vivo. In order to achieve this, we established a robust xenograft model with human AML cell lines. Next, we genetically modified these cells with a luciferase-expressing lentiviral vector, so that they could be detected within the xenograft host using the IVIS III pre-clinical imaging system. Using this approach, we have shown that oral administration, even when used alone as a monotherapy, causes a strong block in the leukaemia progression (over a 300 fold difference in leukemic burden at day 17 of treatment vs control), resulting in prolonged survival of xenograft hosts. Here we describe a pipeline to discover drugs that inactivate key transcription factors important in AML. By combining our knowledge of transcription factor behaviour and drug repositioning, we discovered candidate drugs that inactivate key oncogenes. It's important to note that repositioning candidates have been through several stages of clinical development and therefore have well-known safety and pharmacokinetic profiles. Therefore, we hope that these candidates can be taken forward rapidly into clinical trials to improve the patient outcome in AML. Disclosures No relevant conflicts of interest to declare.
41
Oppersma, Eline, Wolfgang Ganglberger, Haoqi Sun, Robert Thomas, and Michael Westover. "475 Automatic detection of self-similarity and prediction of CPAP failure." Sleep 44, Supplement_2 (May 1, 2021): A187. http://dx.doi.org/10.1093/sleep/zsab072.474.
Full textAbstract:
Abstract Introduction Sleep disordered breathing is a significant risk factor for cardiometabolic and neurodegenerative diseases. Tolerance and efficacy of continuous positive airway pressure (CPAP), the primary form of therapy for sleep apnea, is often poor. High loop gain (HLG) is a driving mechanism of central sleep apnea or periodic breathing. The current study aimed to develop a computational approach to detect HLG based on self-similarity in respiratory oscillations during sleep solely using breathing patterns, measured via Respiratory Inductance Plethysmography (RIP). To quantify the potential utility of the developed similarity metric, the presented algorithm was used to predict acute CPAP failure. Methods We developed an algorithm for detecting apneas as periods with reduced breathing effort, manifested in the RIP signal as low signal amplitude. Our algorithm calculates self-similarity in breathing patterns between consecutive periods of apnea or hypopnea. Working under the assumption that high loop gain induces self-similar respiratory oscillations and increases the risk of failure during CPAP, the full night similarity, computed during diagnostic non-CPAP polysomnography (PSG), was used to predict failure of CPAP, which we defined as titration central apnea index (CAI)>10. Central apnea labels are obtained both from manual scoring by sleep technologists, and from an automated algorithm developed for this study. The Massachusetts General Hospital (MGH) sleep database was used, including 2466 PSG pairs of diagnostic and CPAP titration PSG recordings. Results Diagnostic CAI based on technologist labels predicted failure of CPAP with an AUC of 0.82 ±0.03. Based on automatically generated labels, the combination of full night similarity and automatically generated CAI resulted in an AUC of 0.85 ±0.02. A subanalysis was performed on a population with technologist labeled diagnostic CAI>5. Full night similarity predicted failure with an AUC of 0.57 ±0.07 for manual and 0.65 ±0.06 for automated labels. Conclusion This study showed that central apnea labels can be derived in an automated way. The proposed self-similarity feature, as a surrogate estimate of expressed respiratory high loop gain and computed from easily accessible effort signals, can detect periodic breathing regardless of admixed obstructive features such as flow-limitation, and can aid prediction of CPAP failure or success. Support (if any):
42
Lin, Yu Jen, Yue Liang Guo, and Saou-Hsing Liou. "O7B.3 A cohort study of sleep on health psychology among professional drivers." Occupational and Environmental Medicine 76, Suppl 1 (April 2019): A63.1—A63. http://dx.doi.org/10.1136/oem-2019-epi.169.
Full textAbstract:
BackgroundLong-term effects of sleep-related factors on risk of psychiatric disorders among professional drivers have not been conclusive. A cohort study was used to evaluate the effectiveness of subjective and objective sleep assessment tools to assess for both the 7 year risk of psychiatric disorders events. Methods: Taiwan Bus Driver Cohort Study (TBDCS) recruited 1650 professional drivers from a large bus company in Taiwan in 2005. The subjects were interviewed in person, completed the sleep assessment questionnaires (Pittsburg sleeping quality score (PSQI), Epworth daytime sleepiness score (ESS), Snore Outcomes Survey score(SOS)), and had an overnight pulse oximeter survey. Psychiatric diseases as diagnosed in the National Health Research Database were the outcomes of this study, including substance abuse, anxiety, mood, and sleep disorders. Cox proportional hazards model was performed to estimate the hazard ratio for psychiatric disorders. Results: Between 2006 and 2012, 102 bus drivers were diagnosed as having psychiatric disorders. Psychiatric disorders were related to higher PSQI score, SOS score, ODI4 levels, and ODI3 levels. The relation between PSQI and psychiatric disorders remains robust after adjusting for age, education, drinking, smoking, refreshing drinks, exercise, bus driving experience, and shift modes. Conclusion: This study showed increased psychiatric disorders in the 7 year follow-up after self-reported poor sleeping quality. Further research is warranted to develop strategies for preventing sleep-related psychiatric disorders among professional drivers.
43
Heinze, Christian, Constantin Hütterer, Thomas Schnupp, Gustavo Lenis, and Martin Golz. "Drowsiness discrimination in an overnight driving simulation on the basis of RR and QT intervals." Current Directions in Biomedical Engineering 3, no. 2 (September 7, 2017): 563–67. http://dx.doi.org/10.1515/cdbme-2017-0117.
Full textAbstract:
AbstractWe examined if ECG-based features are discrimi-native towards drowsiness. Twenty-five volunteers (19–32 years) completed 7×40 minutes of monotonous overnight driving simulation, designed to induce drowsiness. ECG (512 s-1) was recorded continuously; subjective ratings of drowsiness on the Karolinska sleepiness scale (KSS) were polled every five minutes. ECG recordings were divided into 5-min segments, each associated with the mean of one self- and two observer-KSS ratings. Those mean KSS values were binarized to obtain two classes not drowsy and drowsy. The Q-, R- and T-waves in the recordings were detected; R-peak positions were manually reviewed; the Q- and T-detection method was tested against the manual annotations of Physio-net’s QT database. Power spectral densities of RR intervals (RR-PSD) and quantiles of the empirical distribution of heart-rate corrected QTc intervals were estimated. Support-vector machines and random-holdout cross-validation were used for the estimation of the classification error. Using either RR-PSD or QTc features yielded mean test errors of 79.3 ± 0.3 % and 82.7 ± 0.5 %, respectively. Merging RR and QTc features improved the accuracy to 88.3 ± 0.2 %. QTc intervals of the class drowsy were generally prolonged com-pared to not drowsy. Our findings indicate that the inclusion of QT intervals contribute to the discrimination of driver sleepiness.
44
Nguyen, Khuong An, You Wang, Guang Li, Zhiyuan Luo, and Chris Watkins. "Realtime Tracking of Passengers on the London Underground Transport by Matching Smartphone Accelerometer Footprints." Sensors 19, no. 19 (September 26, 2019): 4184. http://dx.doi.org/10.3390/s19194184.
Full textAbstract:
Passengers travelling on the London underground tubes currently have no means of knowing their whereabouts between stations. The challenge for providing such service is that the London underground tunnels have no GPS, Wi-Fi, Bluetooth, or any kind of terrestrial signals to leverage. This paper presents a novel yet practical idea to track passengers in realtime using the smartphone accelerometer and a training database of the entire London underground network. Our rationales are that London tubes are self-driving transports with predictable accelerations, decelerations, and travelling time and that they always travel on the same fixed rail lines between stations with distinctive bumps and vibrations, which permit us to generate an accelerometer map of the tubes’ movements on each line. Given the passenger’s accelerometer data, we identify in realtime what line they are travelling on and what station they depart from, using a pattern-matching algorithm, with an accuracy of up to about 90% when the sampling length is equivalent to at least 3 station stops. We incorporate Principal Component Analysis to perform inertial tracking of passengers’ positions along the line when trains break away from scheduled movements during rush hours. Our proposal was painstakingly assessed on the entire London underground, covering approximately 940 km of travelling distance, spanning across 381 stations on 11 different lines.
45
Kvetny, R. N., R. V. Masliy, A. M. Kyrylenko, and V. V. Shcherba. "Research of Neural Network Approach of Objects Detection in the Images." Metrology and instruments, no. 6 (January 11, 2020): 15–21. http://dx.doi.org/10.33955/2307-2180(6)2019.15-21.
Full textAbstract:
The article is devoted to the study of object detection in images using neural networks. The structure of convolutional neural networks used for image processing is considered. The formation of the convolutional layer (Fig. 1), the sub-sampling layer (Fig. 2) and the fully connected layer (Fig. 3) are described in detail. An overview of popular high-performance convolutional neural network architectures used to detect R-FCN, Yolo, Faster R-CNN, SSD, DetectNet objects has been made. The basic stages of image processing by the DetectNet neural network, which is designed to detect objects in images, are discussed. NVIDIA DIGITS was used to create and train models, and several DetectNet models were trained using this environment. The parameters of experiments (Table 1) and the comparison of the quality of the trained models (Table 2) are presented. As training and validation data, we used an image of the KITTI database, which was created to improve self-driving systems that do not go without built-in devices, one of which could be the Jetson TX2. KITTI’s images feature several object classes, including cars and pedestrians. Model training and testing was performed using a Jetson TX2 supercomputer. Five models were trained that differed in the Base learning rate parameter. The results obtained make it possible to find a compromise value for the Base learning rate parameter to quickly obtain a model with a high mAP value. The quality of the best model obtained on the KITTI validation dataset is mAP = 57.8%.
46
Brunetti, Pietro, Raffaele Giorgetti, Adriano Tagliabracci, Marilyn Huestis, and Francesco Busardò. "Designer Benzodiazepines: A Review of Toxicology and Public Health Risks." Pharmaceuticals 14, no. 6 (June 11, 2021): 560. http://dx.doi.org/10.3390/ph14060560.
Full textAbstract:
The rising use of designer benzodiazepines (DBZD) is a cat-and-mouse game between organized crime and law enforcement. Non-prohibited benzodiazepines are introduced onto the global drug market and scheduled as rapidly as possible by international authorities. In response, DBZD are continuously modified to avoid legal sanctions and drug seizures and generally to increase the abuse potential of the DBZD. This results in an unpredictable fluctuation between the appearance and disappearance of DBZD in the illicit market. Thirty-one DBZD were considered for review after consulting the international early warning database, but only 3-hydroxyphenazepam, adinazolam, clonazolam, etizolam, deschloroetizolam, diclazepam, flualprazolam, flubromazepam, flubromazolam, meclonazepam, phenazepam and pyrazolam had sufficient data to contribute to this scoping review. A total of 49 reports describing 1 drug offense, 2 self-administration studies, 3 outpatient department admissions, 44 emergency department (ED) admissions, 63 driving under the influence of drugs (DUID) and 141 deaths reported between 2008 and 2021 are included in this study. Etizolam, flualprazolam flubromazolam and phenazepam were implicated in the majority of adverse-events, drug offenses and deaths. However, due to a general lack of knowledge of DBZD pharmacokinetics and toxicity, and due to a lack of validated analytical methods, total cases are much likely higher. Between 2019 and April 2020, DBZD were identified in 48% and 83% of postmortem and DUID cases reported to the UNODC, respectively, with flualprazolam, flubromazolam and etizolam as the most frequently detected substances. DBZD toxicology, public health risks and adverse events are reported.
47
Cheung, Susan, Stefan Pasiakos, Harris Lieberman, Victor Fulgoni, and Claire Berryman. "Associations between Essential Amino Acids and Functional Health Outcomes in Older Adults: Analysis of the National Health and Nutrition Examination Survey, 2001–2018." Current Developments in Nutrition 6, Supplement_1 (June 2022): 891. http://dx.doi.org/10.1093/cdn/nzac067.011.
Full textAbstract:
Abstract Objectives Little is known about the relationships between habitual essential amino acid (EAA) intake and functional health in older US adults. This cross-sectional study investigates associations between usual EAA intakes and body composition, muscle strength, and physical function in US adults ≥65 y. Methods The Food and Nutrient Database for Dietary Studies (FNDDS) 2001–2018 was linked to USDA FoodData Central to access existing EAA composition data for FNDDS ingredients. FNDDS ingredients without existing EAA data were matched to similar ingredient codes with available EAA data. Usual intakes of EAA, leucine, lysine, and sulfur-containing AAs (SAA; methionine + cysteine) from NHANES 2001–2018 were calculated as relative [mg/kg ideal body weight (IBW)/d] and absolute (g/d) intakes for individuals ≥65 y (n = 10,843). Dependent variables were muscle strength measured by isometric grip test, BMI, waist circumference (WC), DXA-measured appendicular lean mass and whole-body fat mass, and self-reported physical function. Regression analyses were used to determine covariate-adjusted relationships between EAA, leucine, lysine, and SAA intake and functional health outcomes. P < 0.0013 was considered significant. Results Absolute and relative EAA, leucine, lysine, and SAA intakes were not associated with muscle strength or self-reported physical function in males or females or with body composition in males. Absolute EAA intakes (per g) were associated with WC in females (β ± SEM, 2.1 ± 0.6 cm, P = 0.0007). Absolute lysine intakes (per g) were associated with BMI (3.0 ± 0.7 kg/m2, P < 0.0001) and WC (7.0 ± 1.7 cm, P = 0.0001) in females. Relative EAA, leucine, and lysine intakes (per mg/kg IBW) were associated with BMI (0.07 ± 0.02, 0.26 ± 0.07, and 0.25 ± 0.04 kg/m2, respectively; P ≤ 0.0004 for all) and WC (0.18 ± 0.03, 0.81 ± 0.17, and 0.64 ± 0.10 cm, respectively; P < 0.0001 for all) in females. Relative lysine intakes (per mg/kg IBW) were associated with whole body fat mass (0.24 ± 0.07 kg, P = 0.0006) in females. Conclusions EAA intakes, particularly lysine, were positively associated with measures of adiposity in women ≥65 y. Investigating sources of lysine intake may provide insight about which foods or food groups are driving this relationship. Funding Sources IAFNS Protein Committee, USAMRDC, DoD Center Alliance for Nutrition and Dietary Supplements Research.
48
Hsiao, Wei-Ting, Yao-Chiang Kan, Chin-Chi Kuo, Yu-Chieh Kuo, Sin-Kuo Chai, and Hsueh-Chun Lin. "Hybrid-Pattern Recognition Modeling with Arrhythmia Signal Processing for Ubiquitous Health Management." Sensors 22, no. 2 (January 17, 2022): 689. http://dx.doi.org/10.3390/s22020689.
Full textAbstract:
We established a web-based ubiquitous health management (UHM) system, “ECG4UHM”, for processing ECG signals with AI-enabled models to recognize hybrid arrhythmia patterns, including atrial premature atrial complex (APC), atrial fibrillation (AFib), ventricular premature complex (VPC), and ventricular tachycardia (VT), versus normal sinus rhythm (NSR). The analytical model coupled machine learning methods, such as multiple layer perceptron (MLP), random forest (RF), support vector machine (SVM), and naive Bayes (NB), to process the hybrid patterns of four arrhythmia symptoms for AI computation. The data pre-processing used Hilbert–Huang transform (HHT) with empirical mode decomposition to calculate ECGs’ intrinsic mode functions (IMFs). The area centroids of the IMFs’ marginal Hilbert spectrum were suggested as the HHT-based features. We engaged the MATLABTM compiler and runtime server in the ECG4UHM to build the recognition modules for driving AI computation to identify the arrhythmia symptoms. The modeling extracted the crucial data sets from the MIT-BIH arrhythmia open database. The validated models, including the premature pattern (i.e., APC–VPC) and the fibril-rapid pattern (i.e., AFib–VT) against NSR, could reach the best area under the curve (AUC) of the receiver operating characteristic (ROC) of approximately 0.99. The models for all hybrid patterns, without VPC versus AFib and VT, achieved an average accuracy of approximately 90%. With the prediction test, the respective AUCs of the NSR and APC versus the AFib, VPC, and VT were 0.94 and 0.93 for the RF and SVM on average. The average accuracy and the AUC of the MLP, RF, and SVM models for APC–VT reached the value of 0.98. The self-developed system with AI computation modeling can be the backend of the intelligent social-health system that can recognize hybrid arrhythmia patterns in the UHM and home-isolated cares.
49
Schram, Ben, Robin Orr, Timothy Rigby, and Rodney Pope. "An Analysis of Reported Dangerous Incidents, Exposures, and Near Misses amongst Army Soldiers." International Journal of Environmental Research and Public Health 15, no. 8 (July 28, 2018): 1605. http://dx.doi.org/10.3390/ijerph15081605.
Full textAbstract:
Occupational health and safety incidents occurring in the military context are of great concern to personnel and commanders. Incidents such as “dangerous incidents”, “exposures”, and “near misses” (as distinct from injuries, illnesses, and fatalities) indicate serious health and safety risks faced by military personnel, even if they do not cause immediate harm. These risks may give rise to harm in the future, if not adequately addressed, and in some cases the incidents may cause latent harm. The purpose of this study was to ascertain the rates and patterns of incidents of these types reported by full time (ARA) and part time (ARES) Australian Army personnel. A retrospective cohort study was performed using self-reported incident data from the Workplace Health, Safety, Compensation and Reporting (WHSCAR) database over a two-year period. Data were analysed descriptively. Of 3791 such incidents, 3636 (96 percent) occurred in ARA and 155 (4 percent) in ARES personnel, somewhat consistent with the proportions of total army person-years served in each (ARA 93 percent; ARES 7 percent). In ARA, 84 percent of these incident types were exposures, 14 percent near misses, and 2 percent dangerous incidents. In ARES, 55 percent of incidents were exposures, 38 percent near misses, and 7 percent dangerous incidents. Soldiers at the rank of ‘private’ experienced the highest rates of these incident types, in both ARA and ARES. Driving gave rise to more near misses than any other activity, in both populations. Exposures to chemicals and sounds were more common in the ARA than ARES. The ARES reported higher proportions of vehicle near misses and multiple mechanism dangerous incidents than the ARA. The findings of this study can usefully inform development of risk mitigation strategies for dangerous incidents, exposures, and near misses in army personnel.
50
Giezendanner, R., P. Weigand, X. R. Duan, W. Meier, U. Meier, M. Aigner, and B. Lehmann. "Laser-Based Investigations of Periodic Combustion Instabilities in a Gas Turbine Model Combustor." Journal of Engineering for Gas Turbines and Power 127, no. 3 (June 24, 2005): 492–96. http://dx.doi.org/10.1115/1.1850498.
Full textAbstract:
The driving mechanism of pulsations in gas turbine combustors depends on a complex interaction between flow field, chemistry, heat release, and acoustics. Experimental data on all these factors are therefore required to obtain insight into the coupling mechanisms during a pulsation period. In order to develop a comprehensive experimental database to support a phenomenological understanding and to provide validation data for numerical simulation, a standard burner for optical investigations was established that exhibits strong self-excited oscillations. The burner was a swirl-stabilized nonpremixed model combustor designed for gas turbine applications and operated using methane as fuel at atmospheric pressure. It was mounted in a combustion chamber, which provides almost unobstructed optical access. The periodic combustion instabilities were studied by a variety of phase-resolved laser-based diagnostic techniques, locked to the frequency of the dominant pressure oscillation. Measurement techniques used were LDV for velocity measurements, planar laser-induced fluorescence for imaging of CH and OH radicals, and laser Raman scattering for the determination of the major species concentrations, temperature, and mixture fraction. The phase-resolved measurements revealed significant variations of all measured quantities in the vicinity of the nozzle exit, which trailed off quickly with increasing distance. A strong correlation of the heat release rate and axial velocity at the nozzle was observed, while the mean mixture fraction as well as the temperature in the periphery of the flame is phase shifted with respect to axial velocity oscillations. A qualitative interpretation of the experimental observations is given, which will help to form a better understanding of the interaction between flow field, mixing, heat release, and temperature in pulsating reacting flows, particularly when accompanied by corresponding CFD simulations that are currently underway.