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Updated and expert-quality knowledge bases are fundamental to biomedical research. A knowledge base established with human participation and subject to multiple inspections is needed to support clinical decision making, especially in the growing field of precision oncology. The number of original publications in this field has risen dramatically with the advances in technology and the evolution of in-depth research. Consequently, the issue of how to gather and mine these articles accurately and efficiently now requires close consideration. In this study, we present OncoPubMiner (https://oncopubminer.chosenmedinfo.com), a free and powerful system that combines text mining, data structure customisation, publication search with online reading and project-centred and team-based data collection to form a one-stop 'keyword in–knowledge out' oncology publication mining platform. The platform was constructed by integrating all open-access abstracts from PubMed and full-text articles from PubMed Central, and it is updated daily. OncoPubMiner makes obtaining precision oncology knowledge from scientific articles straightforward and will assist researchers in efficiently developing structured knowledge base systems and bring us closer to achieving precision oncology goals. [ABSTRACT FROM AUTHOR]

Voluntary detection of emerging disease outbreaks is considered essential for limiting their potential impacts on livestock industries. However, many of the strategies employed by animal health authorities to capture data on potential emerging disease threats rely on farmers and veterinarians identifying situations of concern and then voluntarily taking appropriate actions to notify animal health authorities. To improve the performance of these systems, it is important to understand the range of socio‐cultural factors influencing the willingness of individuals to engage with disease reporting such as trust in government, perceived economic impacts, social stigma and perceptions of 'good farming'. The objectives of this systematic review were to assess how different social research methodologies have been employed to understand the role these socio‐cultural dimensions play in voluntary disease reporting and to discuss limitations to address in future research. The review uncovered 39 relevant publications that employed a range of quantitative and qualitative methodologies including surveys, interviews, focus groups, scenarios, observations, mixed‐methods, interventions and secondary data analysis. While these studies provided valuable insights, one significant challenge remains eliciting accurate statements of behaviour and intentions rather than those that reflect desirable social norms. There is scope to develop methodological innovations to study the decision to report animal disease to help overcome the gap between what people say they do and their observable behaviour. A notable absence is studies exploring specific interventions designed to encourage disease reporting. Greater clarity in specifying the disease contexts, behavioural mechanisms and outcomes and the relationships between them would provide a more theoretically informed and policy relevant understanding of how disease reporting works, for which farmers, and in which disease contexts. [ABSTRACT FROM AUTHOR]

In the past four decades, remote-sensing data and methodologies have been increasingly used in many fields to improve the efficiency, accuracy, or safety of data collection, and construction management is no exception. In recent years, unoccupied airborne systems (UAS), commonly referred to as drones, have emerged as an important platform for the collection of remote-sensing data. When coupled with increasingly affordable sensors and automated image processing techniques, UAS have made the application of remote-sensing approaches to myriad construction and civil engineering problems practical and attractive to the point that UAS-based airborne imaging is becoming routine for many construction management tasks. Based on our review of literature, a comprehensive discussion on the use of UAS methodologies for collecting and analyzing data to assist with construction research is much needed. This paper was developed to discuss current best practices in UAS aerial imagery collection and processing for construction research, including defining key terms. In addition, this paper discusses a variety of methods for the analysis of UAS collected aerial imagery for the following tasks: preconstruction planning, material tracking, project progress tracking, safety, as-built documentation, and building/structure inspection. This paper also includes a perspective on the future of UAS for a variety of construction management tasks. This work contributes to the Data Management and Data Science Body of Knowledge by informing construction researchers of the state of the art of UAS data collection and analysis methodologies. This study also provides practitioners with a comprehensive guide to the use of UAS for onsite construction management tasks, which is an essential component of e-Construction. [ABSTRACT FROM AUTHOR]

Recording a stellar occultation is one powerful method that gives direct information about the physical properties of the occulting Solar system object. In order to obtain reliable and accurate results, simultaneous observations from different locations across-track of the projected path are of great importance. However, organizing all the observing stations, aggregating, and analysing the data is time-consuming and not that easy. We have developed a web portal named Occultation Portal (OP) to manage all those occultation observation campaigns from a central server. With this portal, the instrumental and observational information of all observers participating in a stellar occultation campaign and the concerned data are archived systematically in a standard format. The researchers can then visualize the archived data on an event basis. The investigators can also extract the light curve for each data set with the added reduction pipeline to the portal base. This paper describes in detail the portal structure and the developed features. [ABSTRACT FROM AUTHOR]

Soybean production is greatly affected by different types of weeds such as horseweed, kochia, ragweed, redroot pigweed, and waterhemp in the midwestern region of the United States. Identification of the soybean plants and the weeds is crucial to controlling the weeds in precision agriculture. The objective of this study was to classify soybean plants and five weed species where a hyperspectral imaging camera with a spectral range of400 to 1000 nm was used to acquire the images. To acquire the HSI images, a customized robotic hyperspectral data collection scanning platform was developed and used in the greenhouse. A total of 983 hyperspectral data cubes were captured from the greenhouse environment (n = 252, soybean; n = 731, weeds). Spectral information was extracted from the collected images and then a classification model was developed by applying partial least squares discriminant analysis (PLS-DA). To construct the calibration and validation data set, the images were each divided into 70% and 30% ratios for model training and testing, respectively. Seven types of data preprocessing techniques, including mean normalization, maximum normalization, range normalization, multiplicative scatter correction (MSC), standard normal variate (SNV), Savitzky-Golay first derivatives, and Savitzky-Golay second derivatives, were explored individually and accepted as the best preprocessing method based on the highest performance in calibration and validation results. The results showed maximum validation model performance was found at 86.2% by applying the Savitzky-Golay second derivatives preprocessing method for multiclass and 89.4% for binary class. The most important wavelength information was evaluated from the beta coefficient developed using the same preprocessing method. Finally, chemical images were generated using a best-performer model to identify the soybean plants from weeds. The generated images showed a significant difference in chemical composition between soybean and weed plants at 443 nm, 633 nm, 743 nm, and 968 nm. The correlation between these peaks and the chemical components of the plants involves α-carotenoid, chlorophyll, and moisture, respectively. This study shows promising results for the application of HSI in weed control systems for soybeans and relevant weed identification in precision agriculture applications. [ABSTRACT FROM AUTHOR]

With the continuous deepening and development of tobacco manufacturing informatization construction, tobacco manufacturing enterprises have the characteristics of strong production capacity, high degree of production automation, and advanced enterprise-level management information system. In order to cooperate with the market and brand strategy of the tobacco industry, the State Tobacco Monopoly Administration has carried out serious strategic thinking on informatization and customized the overall plan and strategic goals. In recent years, China's informatization construction has made great progress, and both industries and enterprises have felt various benefits from the application of information technology. In the deployment and development of mobile services, it is often local and municipal mobile companies that, in order to meet the different needs of the market, immediately carry out corresponding services, resulting in one service for each municipal subsidiary in the same province or one platform for a class of services. Situation is good for quickly meeting user needs at that time. The exchange and communication of enterprise personnel information are becoming more and more frequent, and the collection of enterprise information through mobile has become a new way of enterprise information collection. The work orders, process parameters, documents, notifications, and instructions generated by the planning layer are sent to the production control layer to respond, guide, and trigger the production site events. The ultimate goal of mobile data collection is to gather the collected data into the enterprise production data center, which plays an important role in mobile devices, mobile performance, mobile planning, mobile balancing, mobile prediction, and so on. Based on the unified mobile application data collection method, this paper analyzed and analyzed the design of application architecture in the tobacco enterprise platform and further described the business functions, security performance, and system characteristics of the platform. [ABSTRACT FROM AUTHOR]

Across-season biomass assessment is crucial in the cultivar selection process to accurately evaluate the yield performance of lines under different growing conditions. However, it has been difficult to have an accurate, reliable, and repeated fresh biomass (FM) estimation of large populations of plants in the field without destructive harvesting, which incurs significant labor and operation costs. Sensorbased phenotyping platforms have advanced in the data collection of structural and vegetative information of plants, but the developed prediction models are still limited by low correlations at different growth stages and seasons. In this study, our objective was to develop and validate the global prediction models for across-season harvested fresh biomass (FM) yield based on the ground- and airbased sensor data including ground-based LiDAR, ground-based ultrasonic, and air-based multispectral camera to extract LiDAR plant volume (LV), LiDAR point density (LV_Den), height, and Normalized Difference Vegetative Index (NDVI). The study was conducted in a row-plot field trial with 480 rows (3 rows in a plot per cultivar) throughout the whole 2020 growing season up to the reproductive stage. We evaluated the performance of each plant parameter, their relationship, and the best subset prediction models using statistical stepwise selection at the row and plot levels through the seasonal and combined seasonal datasets. The best performing model: FM~ LV *LV _Den*NDVI had a determination of coefficient R2 of at least 0.9 in vegetative stages and 0.8 in the reproductive stage. Similar results can be achieved in a simpler model with just two LiDAR variables--FM~ LV *LV _Den. In addition, LV and LV_Den showed a robust correlation with FM on their own over seasons and growth stages, while NDVI only performed well in some seasons. The simpler model based on only LiDAR data can be widely applied over season without the need of additional sensor data and may thus make the in-field acrossseason biomass assessment more feasible and practical for fast and cost-effective development of higher biomass yield cultivars. [ABSTRACT FROM AUTHOR]

In order to quickly and accurately collect the massive commodities and corresponding transaction data of large-scale e-commerce platforms, and improve the ability of data analysis and mining, this paper proposes a platform commodity information collection system based on splash technology. The system prerenders the javascript code in the product page, combined with the Scrapy crawler framework, to realize a system that quickly and effectively collects product data from different platforms, and uses "mobile phone" as the retrieval keyword to verify the designed system, respectively. The experimental results show that the system can effectively collect up to 60,000 comments and 6,000 system requests. Conclusion. The platform commodity information collection system based on splash technology has certain application value and promotion for the commodity data collection of different platforms of e-commerce. [ABSTRACT FROM AUTHOR]

Currently, a large number of mature methods are available for gaze estimation. However, most regular gaze estimation approaches require additional hardware or platforms with professional equipment for data collection or computing that typically involve high costs and are relatively tedious. Besides, the implementation is particularly complex. Traditional gaze estimation approaches usually require systematic prior knowledge or expertise for practical operations. Moreover, they are primarily based on the characteristics of pupil and iris, which uses pupil shapes or infrared light and iris glint to estimate gaze, requiring high-quality images shot in special environments and other light source or professional equipment. We herein propose a two-stage gaze estimation method that relies on deep learning methods and logistic regression, which can be applied to various mobile platforms without additional hardware devices or systematic prior knowledge. A set of automatic and fast data collection mechanism is designed for collecting gaze images through a mobile platform camera. Additionally, we propose a new annotation method that improves the prediction accuracy and outperforms the traditional gridding annotation method. Our method achieves good results and can be adapted to different applications. [ABSTRACT FROM AUTHOR]

In order to solve the problem of data collection of intelligent construction of large-scale projects, the author proposes the application of BIM technology in the collection of intelligent construction data of large-scale projects; this method proposes to take advantage of the technical advantages of BIM; starting from the needs of the commercial complex operation and management stage, a BIM-based commercial complex operation management model is established, and the application content of this model to improve the efficiency of commercial complex operation and management is studied. The experimental results show that according to the design conditions and the depth of BIM application, the standard elements and variable factors can be divided into 70% and 30%, respectively. Conclusion. BIM can realize the refinement, intelligence, and quality of commercial complex operation. [ABSTRACT FROM AUTHOR]

With the development of the times, traditional fitness running data collection methods have shown a series of problems, such as low data accuracy, slow collection speed, and insufficient search ability of modeling algorithms. These problems have led to a sharp rise in the difficulty of building a more accurate fitness running data model. Therefore, this study proposed a set of methods for fitness running data collection and evolution modeling based on digital information technology. First, it filters out the impulse noise during the transmission of the three-dimensional acceleration signal caused by the accidental state change of the technology, by using the data collection method for fitness running based on the digital information platform, relying on a multisensor and median filter algorithm. Second, it filters out the gravitational component of this platform based on the orientation sensor. Then, according to the results of the first step, an algorithm is constructed for the model of fitness running data evolution. The purpose is to expand the search range so that the fitness running data model with better performance can be obtained more effectively. The experimental results show that the method based on digital information technology designed in this study has an overwhelming advantage over the traditional acquisition method, the constructed model is more accurate, and the efficiency of data acquisition is increased by 88%. Therefore, with the aid of digital information technology, the design of the data model can reduce the probability of mold repair and the time consumption of mold trials and reduce the design cost. [ABSTRACT FROM AUTHOR]

In this paper, a multisensor data acquisition system based on FPGA is designed to provide an overview of the current development of the company based on the theoretical knowledge of enterprise development strategy. Through a comprehensive investigation of the company's internal environment, the advantages and disadvantages of the company at this stage are clarified, and the opportunities and threats to the company's development are analyzed in the context of the external macroenvironment and industry development. The coupling coordination degree from 2011 to 2025 was measured using the coupling coordination degree model based on the original data and the prediction results, and the results showed that from 2011 to 2025, the coupling coordination degree of Hebei Province showed a continuous growth and experienced the transition from low coordination, basic coordination, to medium coordination in the coupling coordination stage, aiming at the most innovative fields, mastering the most advanced technologies, cooperating with industry giants, innovating, and building digital ecology, creating, and forming core competitiveness based on core independent products and platform-based solutions, and further promoting transformation and upgrading and high-quality development. In this paper, the data acquisition system is tested by building an experimental platform. The main purpose is to test the stability and accuracy of each module of the data acquisition system. After each module test of the system meets the design requirements, the parallel mechanism attitude transformation test experiment is completed, including the moving platform moving test experiment and the moving platform rotation test experiment. The experimental results show that the designed data acquisition system has stable program and reliable function. [ABSTRACT FROM AUTHOR]

Sleep disturbances are common in older adults and may contribute to disease progression in certain populations (e.g., Alzheimer's disease). Light therapy is a simple and cost-effective intervention to improve sleep. Primary barriers to light therapy are: (1) poor acceptability of the use of devices, and (2) inflexibility of current devices to deliver beyond a fixed light spectrum and throughout the entirety of the day. However, dynamic, tunable lighting integrated into the native home lighting system can potentially overcome these limitations. Herein, we describe our protocol to implement a whole-home tunable lighting system installed throughout the homes of healthy older adults already enrolled in an existing study with embedded home assessment platforms (Oregon Center for Aging & Technology—ORCATECH). Within ORCATECH, continuous data on room location, activity, sleep, and general health parameters are collected at a minute-to-minute resolution over years of participation. This single-arm longitudinal protocol collected participants' light usage in addition to ORCATECH outcome measures over a several month period before and after light installation. The protocol was implemented with four subjects living in three ORCATECH homes. Technical/usability challenges and feasibility/acceptability outcomes were explored. The successful implementation of our protocol supports the feasibility of implementing and integrating tunable whole-home lighting systems into an automated home-based assessment platform for continuous data collection of outcome variables, including long-term sleep measures. Challenges and iterative approaches are discussed. This protocol will inform the implementation of future clinical intervention trials using light therapy in patients at risk for developing Alzheimer's disease and related conditions. [ABSTRACT FROM AUTHOR]

The collection and utilization of huge sensor data from vehicles for visualizing the city is expected to realize various enhanced services for smart cities. A next-generation gigabit vehicle-to-everything (V2X) data collection platform based on 60 GHz millimeter-wave (mmWave) small cell radio access has been proposed in the previous work for enabling efficient and high-capacity data upload from vehicles to the cloud. This paper presents further analysis of the initial link setup delay to evaluate the effectiveness of the fast initial V2X link setup method proposed, which is suitable for 60 GHz communication based on IEEE 802.11ad. This paper also presents the application programming interface (API) design based on the hypertext transport protocol (HTTP) to enable the efficient upload of hundreds of files. Combined with the buffering technique at a multiaccess edge computing (MEC) server, the proposed system successfully utilizes very high bandwidth between a vehicle and a MEC server. The proposed methods achieve a 30 times improvement in delay for establishing the initial link and an 11 times higher average throughput. A prototype system installed in Marysville, Ohio, achieved a peak throughput of 2.8 Gbps and successfully demonstrates the effectiveness of city visualization based on high-capacity data collection and analytics. [ABSTRACT FROM AUTHOR]

Detection of geomorphological changes based on structure from motion (SfM) photogrammetry is highly dependent on the quality of the 3D reconstruction from high-quality images and the correspondingly derived point precision estimates. For long-term monitoring, it is interesting to know if the resulting 3D point clouds and derived detectable changes over the years are comparable, even though different sensors and data collection methods were applied. Analyzing this, we took images of a sinkhole terrestrially with a Nikon D3000 and aerially with a DJI drone camera in 2017, 2018, and 2019 and computed 3D point clouds and precision maps using Agisoft PhotoScan and the SfM_Georef software. Applying the "multiscale model to model cloud comparison using precision maps" plugin (M3C2-PM) in CloudCompare, we analyzed the differences between the point clouds arising from the different sensors and data collection methods per year. Additionally, we were interested if the patterns of detectable change over the years were comparable between the data collection methods. Overall, we found that the spatial pattern of detectable changes of the sinkhole walls were generally similar between the aerial and terrestrial surveys, which were performed using different sensors and camera locations. Although the terrestrial data collection was easier to perform, there were often challenges due to terrain and vegetation around the sinkhole to safely acquire adequate viewing angles to cover the entire sinkhole, which the aerial survey was able to overcome. The local levels of detection were also considerably lower for point clouds resulting from aerial surveys, likely due to the ability to obtain closer-range imagery within the sinkhole. [ABSTRACT FROM AUTHOR]

At times dystopian, at others optimistic, Townsend, like Norton and Bonnefon, pleads for citizen engagement in the process: demand equal access; fight separation of people and technology; don't get demoralized; use vehicles, don't own them; and find your own uses for self-driving technologies. Norton, Bonnefon, and Townsend provide important implications for planning practice advocating for human-centered planning approaches when implementing AVs in our communities. Whereas Norton is wary of history repeating itself, Bonnefon powerfully describes his and his team's academic journey to understand the moral and ethical questions we must face in all their cultural diversity, and Townsend argues that the road ahead is very much yet to be determined. [Extracted from the article]

BACKGROUND Due to ceased appointments, orthodontic emergencies like broken retainers, pricking wires, loose functional appliances, loss of appliances was seen. While handling the emergency cases the orthodontist should follow the guidelines provided by the government. Therefore, due to such reasons, it is important to study the impact of COVID - 19 on orthodontic practice. So, the aim is to study the changes in orthodontic practice during COVID 19 pandemic in central India. METHODS The study population used in the survey consisted of Orthodontists, Orthodontic residents, Dental practitioners in central India. A set of questionnaires was distributed through an online data collection platform (Google forms) between September to November 2020. The participants were reached through various social platforms. The participants were asked their opinion that whether the COVID - 19 will change the future practice in orthodontics. RESULTS According to the survey 62.8 % of practitioners continued their orthodontic practice during the lockdown period. 56.4 %of practitioners had undergone the COVID - 19 training program during the lockdown. To prevent the spread of disease and for the safety of staff and patients, 66.3 % of practitioners preferred teleconsultations. 75.3 % of practitioners used PPE kits for their personal and patient's safety while treating the patients in the dental clinic. Guidelines are given by World Health Organization (WHO) and Ministry of Health and Family Welfare (MoHFW) were followed by 36.2 % and 63.8 % dental clinics respectively. CONCLUSIONS As the world has become more aware and cautious about infection control, dental professionals need to prepare themselves and revise their practice with innovative ideas. A virtual treatment is emerging as a new modality in Orthodontic practice. The only way to combat this situation in crisis is following the strict sterilization protocol, reducing personal contact, and minimizing the amount of aerosol production. [ABSTRACT FROM AUTHOR]

The article provides information about the 2024 ASPRS Annual Workshops at Geo Week. The workshops cover various topics such as geographic object-based object analysis, positional accuracy standards for digital geospatial data, remote sensing data for climate change analysis, geospatial deep learning with ArcGIS, professional mapping using drones, preparation for ASPRS certification, advanced remote sensing data processing and deep learning with PyTorch, and airborne bathymetric lidar. The workshops aim to provide participants with knowledge and skills in these specific areas of study. [Extracted from the article]

The population density in urban areas is rapidly rising, leading to a constant need for new infrastructure and services for citizens. To reduce the time to implementation and optimise the monetary cost of various solutions, the plans and policies of local authorities and stakeholders would benefit from undergoing a series of virtual stress tests. To this end, prescriptive and predictive technologies are widely adopted to optimise city planning and to understand the urban processes and environment such as air pollution and transportation. Nevertheless, holistic sandboxes tightly integrated with cities are still largely lacking. The city digital twin is a promising concept that provides a tool for exploration of new solutions in a controlled environment before their deployment. The digital twin is a virtual replica of the real city, which collects data from the infrastructure, processes and services using not only the available systems, but also purposely built connected devices and sensors. In this context, the establishment of urban living labs facilitates the monitoring and understanding of urban processes and enriches the digital twin with highly-relevant data. This paper presents an urban living lab, under deployment in the district of Lozenets in Sofia, Bulgaria. It is part of a larger initiative for developing a city digital twin of Sofia to support the design, exploration, and experimentation of different solutions. The living lab is equipped with sensors for monitoring air quality, atmospheric parameters, noise pollution and pedestrian flows. In addition, a Light Detection and Ranging (LiDAR) system is realised as an edge computing facility at one of the busiest intersections of the district. Along with the equipment, the paper describes the architecture and components of the platform for data collection, storage, processing, and visualization. Finally, high-priority studies are presented, and their demographic and economic impact is discussed. [ABSTRACT FROM AUTHOR]

Smartphones present many interesting opportunities for survey research, particularly through the use of mobile data collection applications (apps). There is still much to learn, however, about how to integrate apps in general population surveys. Recent studies investigating hypothetical willingness to complete mobile data collection tasks via an app suggest there may be substantial resistance, in particular, due to concerns around data privacy. There is not much evidence about how privacy concerns influence actual decisions to participate in app-based surveys. Theoretical approaches to understanding privacy concerns and survey participation decisions would suggest that the influence of the former over the latter is likely to vary situationally. In this paper, we present results from a methodological experiment conducted in the context of a three-wave probability-based online panel survey of the general population as part of the 2019 Swiss Election Study ("Selects") testing different ways of recruiting participants to an app. Questions included at wave 1 about online data privacy concerns and comfort sharing different types of data with academic researchers allow us to assess their impact on both hypothetical willingness to download a survey app for completing questionnaires, to take and share photos, and to share the smartphone's GPS location and actual completion of these tasks. Our findings confirm that general concerns about online data privacy do influence hypothetical willingness to complete mobile data collection tasks, but may be overridden by how comfortable people feel about sharing specific types of data with researchers. When it comes to actual compliance with task requests, however, neither privacy concerns nor comfort sharing data seem to matter. We conclude with recommendations for exploring these relationships further in future app-based studies. [ABSTRACT FROM AUTHOR]

New players are entering the new and important digital data market for agriculture, increasing power asymmetries and reinforcing their competitive advantages. Although the farmer remains at the heart of agricultural data collection, to date, only a few farmers participate in data platforms. Despite this, more and more decision support systems (DSSs) tools are used in agriculture, and digital platforms as data aggregators could be useful technologies for helping farmers make better decisions. However, as these systems develop, the efficiency of these platforms becomes more challenging (sharing, ownership, governance, and transparency). In this paper, we conduct a case study for an accessible and scalable digital data platform that is focused on adding value to smallholders. The case study research is based on meta-governance theory and multidimensional multilayered digital platform architecture, to determine platform governance and a data development model for the Andalusian (Spain) fruit and vegetable sector. With the information obtained from the agents of this sector, a digital platform called farmdata was designed, which connects to several regional and national, and public and private databases, aggregating data and providing tools for decision making. Results from the interviews reflect the farmer's interests in participating in a centralized cloud data platform, preferably one that is managed by a university, but also with attention being paid toward security and transparency, as well as providing added value. As for future directions, we propose further research on how the benefits should be distributed among end users, as well as for the study of a distributed model through blockchain. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, most education has been conducted through online classes. While feedback and interaction between students and instructors are significant in programming education or engineering practice, online education today cannot satisfy these aspects of learning. Therefore, this study proposes a learning support system for programming education and presents the results of designing and implementing this system. The proposed system consists of an online development environment module, a learning monitoring module, and a learning support module. It also provides a web-based programming environment, real-time chat and code mirroring, error guide messages and related lectures, e-learning quizzes, and learning activity analysis features. The system standardizes the development environment between the instructor and students, helps students take the initiative in solving errors, and enables code-oriented interactions between the instructor and students. It also collects data from all learning situations in the database. Conducting a big data analysis with the collected data will enable individual guidance for students by finding errors that frequently occur in programming and recommending learning materials to solve them. [ABSTRACT FROM AUTHOR]

Human Activity Recognition(HAR) plays an important role in the field of ubiquitous computing, which can benefit various human-centric applications such as smart homes, health monitoring, and aging systems. Human Activity Recognition mainly leverages smartphones and wearable devices to collect sensory signals labeled with activity annotations and train machine learning models to recognize individuals' activity automatically. In order to deploy the Human Activity Recognition model in real-world scenarios, however, there are two major barriers. Firstly, sensor data and activity labels are traditionally collected using special experimental equipment in a controlled environment, which means fitting models trained with these datasets may result in poor generalization to real-life scenarios. Secondly, existing studies focus on single or a few modalities of sensor readings, which neglect useful information and its relations existing in multimodal sensor data. To tackle these issues, we propose a novel activity recognition model for multimodal sensory data fusion: Marfusion, and an experimental data collection platform for HAR tasks in real-world scenarios: MarSense. Specifically, Marfusion extensively uses a convolution structure to extract sensory features for each modality of the smartphone sensor and then fuse the multimodal features using the attention mechanism. MarSense can automatically collect a large amount of smartphone sensor data via smartphones among multiple users in their natural-used conditions and environment. To evaluate our proposed platform and model, we conduct a data collection experiment in real-life among university students and then compare our Marfusion model with several other state-of-the-art models on the collected datasets. Experimental Results do not only indicate that the proposed platform collected Human Activity Recognition data in the real-world scenario successfully, but also verify the advantages of the Marfusion model compared to existing models in Human Activity Recognition. [ABSTRACT FROM AUTHOR]

Dental caries and periodontal disease are very common chronic diseases closely linked to inadequate removal of dental plaque. Powered toothbrushes are viewed as more effective at removing plaque; however, the conflicting evidence and considerable unexplained heterogeneity in their clinical outcomes does not corroborate the relative merits of powered tooth brushing. To explain the heterogeneity of brushing patterns with powered toothbrushes, we conducted a observational study of tooth brushing practices of 12 participants in their naturalistic setting. Integrated brush sensors and a digital data collection platform allowed unobtrusive and accurate capture of habitual brushing patterns. Annotated brushing data from 10 sessions per participant was chosen for scrutiny of brushing patterns. Analysis of brushing patterns from the total 120 sessions revealed substantial between- and within-participant variability in brushing patterns and efficiency. Most participants (91.67%) brushed for less than the generally prescribed two minutes; individual participants were also inconsistent in brushing duration across sessions. The time devoted to brushing different dental regions was also quite unequal. Participants generally brushed their buccal tooth surfaces more than twice as long as the occlusal (2.18 times longer (95% CI 1.42, 3.35; p < 0.001)) and lingual surfaces (2.22 times longer (95% CI 1.62, 3.10; p < 0.001); the lingual surfaces of the maxillary molars were often neglected (p < 0.001). Participants also varied in the epochs of excessive brushing pressure and the regions to which they were applied. In general, the occlusal surfaces were more likely to be brushed with excessive pressure (95% CI 0.10, 0.98; p = 0.015). Our study reveals that users of powered toothbrushes vary substantially in their use of the toothbrushes and diverge from recommended brushing practices. The inconsistent brushing patterns, between and within individuals, can affect effective plaque removal. Our findings underscore the limited uptake of generic oral self-care recommendations and emphasize the need for personalized brushing recommendations that derive from the objective sensor data provided by powered toothbrushes. [ABSTRACT FROM AUTHOR]

Where this has been not possible, we have attempted to coordinate a cross-over model, where a trainee will obtain study approval in one site, and run data collection in another, in which the study has been set-up by the outgoing trainee. Additionally, ensuring user-friendly and secure infrastructure to facilitate trainee data collection and sharing is critical to maintain trainee engagement. The Irish Surgical Research Collaborative In recognition of the tremendous potential demonstrated by trainee-led research collaboratives originating elsewhere, and the absence of a similar model in Ireland, The ISRC was formed in 2015 by a small group of surgical trainees. • One challenge we have come across as a trainee-led collaborative, is the fact that trainees move hospitals at frequent intervals during their training in Ireland and the UK. [Extracted from the article]

Tracking mobile objects in road networks is one of the important applications of wireless sensor networks (WSNs). It includes road safety, smart transportation, vehicle safety, vehicle tracking, traffic monitoring, etc. In this work, we assume that the sensors are installed on vehicles which are moving on the road. These mobile sensors need to be tracked/visited periodically within a pre-defined time interval. In our scenario, the mobility or trajectory of the mobile sensors are unpredictable. In many applications nowadays, the mobile sensors have unpredictable mobility. In this paper, our aim is to collect data by visiting the mobile sensors periodically using fewer number of mobile sinks. The mobile sinks subsequently deliver the collected data to a stationary base station. Time-bound periodic data collection from the mobile sensors along with their unpredictable motion is even more challenging than the data collection from static sensors. Here, we propose a deterministic data gathering algorithm using the solution of Chinese Postman Problem. We measure the performance of the proposed solution. Due to non-availability of any existing solution, we have compared our algorithm with a heuristic algorithm and a variation of an existing solution. The experiment results show that our proposed data gathering algorithm performs well. [ABSTRACT FROM AUTHOR]

• Trellis network data collection platform enables name disambiguation. • Trellis network data collection platform enables multiple language interviews. • Trellis report allows for real-time monitoring of data collection. • Trellis network data collection platform can be used offline in remote areas. Trellis is a mobile platform created by the Human Nature Lab at the Yale Institute for Network Science to collect high-quality, location-aware, off-line/online, multi-lingual, multi-relational social network and behavior data in hard-to-reach communities. Respondents use Trellis to identify their social contacts by name and photograph, a procedure especially useful in low-literacy populations or in contexts where names may be similar or confusing. We use social network data collected from 1,969 adult respondents in two villages in Kenya to demonstrate Trellis ' ability to provide unprecedented metadata to monitor and report on the data collection process including artifactual variability based on surveyors, time of day, or location. [ABSTRACT FROM AUTHOR]

Mobile sensing data processed using machine learning models can passively and remotely assess mental health symptoms from the context of patients' lives. Prior work has trained models using data from single longitudinal studies, collected from demographically homogeneous populations, over short time periods, using a single data collection platform or mobile application. The generalizability of model performance across studies has not been assessed. This study presents a first analysis to understand if models trained using combined longitudinal study data to predict mental health symptoms generalize across current publicly available data. We combined data from the CrossCheck (individuals living with schizophrenia) and StudentLife (university students) studies. In addition to assessing generalizability, we explored if personalizing models to align mobile sensing data, and oversampling less-represented severe symptoms, improved model performance. Leave-one-subject-out cross-validation (LOSO-CV) results were reported. Two symptoms (sleep quality and stress) had similar question-response structures across studies and were used as outcomes to explore cross-dataset prediction. Models trained with combined data were more likely to be predictive (significant improvement over predicting training data mean) than models trained with single-study data. Expected model performance improved if the distance between training and validation feature distributions decreased using combined versus single-study data. Personalization aligned each LOSO-CV participant with training data, but only improved predicting CrossCheck stress. Oversampling significantly improved severe symptom classification sensitivity and positive predictive value, but decreased model specificity. Taken together, these results show that machine learning models trained on combined longitudinal study data may generalize across heterogeneous datasets. We encourage researchers to disseminate collected de-identified mobile sensing and mental health symptom data, and further standardize data types collected across studies to enable better assessment of model generalizability. [ABSTRACT FROM AUTHOR]

Respiratory syncytial virus (RSV) is the most common and critical viral pathogen causing acute lower respiratory tract infection in infants and young children and has a huge disease burden worldwide. At present, there are many studies on RSV transcriptomics exploring the mechanism of disease, but different studies show different gene expression patterns and results due to different sample collection platforms and data analysis strategies. A meta-analysis was performed on eight whole blood transcriptome datasets containing 436 children with acute RSV infection and 241 healthy children. A total of 319 differentially expressed genes (DEGs) (P value <0.0001) were identified in a meta-analysis using a random effect model. Functional enrichment analysis showed that several pathways related to immunity were significantly altered, including the "chemokine signaling pathway", "natural killer cell mediated cytotoxicity" and "cytokine–cytokine receptor interaction". Immune cell type analysis showed that the proportion of neutrophils in most RSV-infected children was higher than that in healthy children. These immune characteristics may help to provide new insights into RSV infection in children. [ABSTRACT FROM AUTHOR]

Background: Spinal muscular atrophy (SMA) is a severe neuromuscular disease that is inherited in an autosomal recessive manner with an estimated incidence of 1 in 10,000 live births. The traditional classification of SMA includes five types (Types 0–4 SMA) based on patient age at disease onset and the highest motor milestone achieved. Spinal muscular atrophy leads to progressive muscle denervation, skeletal muscle atrophy and loss of motor function and ambulation, though phenotypes vary along a disease continuum. Regardless of disease severity, or access to treatment, a multidisciplinary approach to care is required to ease the burden of disease. To date, limited global data exist regarding the cost and resource use associated with SMA management. Objective: We planned to perform a systematic literature review to identify studies on cost and healthcare resource use associated with SMA. Methods: A comprehensive search was conducted in 2019 using several electronic databases in addition to supplementary sources and updated in 2021 in order to capture recently published studies. Electronic searches performed in Embase, MEDLINE, Evidence-Based Medicine Reviews and EconLit via the Ovid platform were supplemented by searches of the grey literature (reference lists, conference proceedings, global Health Technology Assessment body websites and other relevant sources). Study eligibility criteria were based on the population, interventions, comparators and outcomes (PICO) framework. Quality assessment of full-text publications was evaluated with reference to a published checklist. To accommodate heterogeneity across studies including countries, currencies, populations, time units and methods of reporting used, costs were reported in Euros in 2019. Results: A total of 51 publications, comprising 49 unique studies of patients with SMA that met all eligibility criteria were included in the final selection. The publications comprised data from 14 countries and seven additional studies that reported multi-national data. Because of the heterogeneity between the different types of SMA, data were frequently reported separately for individuals with Type 1 or early-onset SMA and for Types 2, 3, and 4 SMA or later-onset SMA. Generally, direct medical costs and resource use were reported to be highest for patients with Type 1 SMA, decreasing incrementally for patients with Type 2 and Type 3 disease. Where cost categories were similar, direct costs were much lower in Europe than in the USA. Indirect costs were primarily associated with informal care, which was a substantial burden on patients and families in terms of both cost and time. Cost drivers were generally found to be dependent on SMA type. Conclusions: Long-term robust studies are required to fully elucidate the economic burden of SMA. Considering that motor function can vary broadly, especially in Type 2 SMA, it would be beneficial to understand how costs and resource use are affected by different degrees of ambulation. Reporting data in terms of achieved motor function could also mitigate the challenges of comparing global data studies of small populations. Global, regional, and/or local data collection platforms and disease registry networks could play an important role in helping to address current data gaps. [ABSTRACT FROM AUTHOR]

Web-based data collection is increasingly popular in both experimental and survey-based research because it is flexible, efficient, and location-independent. While dedicated software for laboratory-based experimentation and online surveys is commonplace, researchers looking to implement experiments in the browser have, heretofore, often had to manually construct their studies' content and logic using code. We introduce lab.js, a free, open-source experiment builder that makes it easy to build studies for both online and in-laboratory data collection. Through its visual interface, stimuli can be designed and combined into a study without programming, though studies' appearance and behavior can be fully customized using html, css, and JavaScript code if required. Presentation and response times are kept and measured with high accuracy and precision heretofore unmatched in browser-based studies. Experiments constructed with lab.js can be run directly on a local computer and published online with ease, with direct deployment to cloud hosting, export to web servers, and integration with popular data collection platforms. Studies can also be shared in an editable format, archived, re-used and adapted, enabling effortless, transparent replications, and thus facilitating open, cumulative science. The software is provided free of charge under an open-source license; further information, code, and extensive documentation are available from https://lab.js.org/. [ABSTRACT FROM AUTHOR]

As a growing number of states legalize the use of cannabinoids for medical and non-medical purposes, there continues to be large gaps in the understanding of appropriate dosing, impact on health, and the state's role in regulation of products. Here, we present a summary of 2022 cannabis regulations by state to evaluate for the presence of THC:CBD ratios, maximum THC concentration or content within products, specific caps for cannabis possession, and requirements for testing for cannabinoid content and/or contaminants such as pesticides and heavy metals. These results are presented in Map 1 and Table 1 and demonstrate substantial variation among product THC content, purchasing limits, and quality measurements across the country. Finally, we note there is currently no centralized data collection platform for this set of information between states as cannabis use evolves, creating poor transparency between consumers and state regulators. [ABSTRACT FROM AUTHOR]

The article focuses on the launch of the Anti-microbial Sales and Use (ASU) Platform by the European Medicines Agency (EMA), mandated under EU regulation to collect annual data from Member States on antimicrobial sales and usage in animals starting January 2024. It mentions this platform replaces the ESVAC project, aiming to enhance data collection and analysis to combat antimicrobial resistance.

• A novel algorithm framework is proposed for object detection in ocean engineering. • Created a dataset of bird droppings on the surface of PV panels. • Constructing YCrS texture space to enhance the texture features of bird droppings. • A case study demonstrates the effectiveness of the algorithm framework. Guano are an important factor affecting the cleanliness of photovoltaic modules on floating solar power plants at sea. It can lead to a decrease in photoelectric conversion efficiency, power loss, and even the occurrence of "hot spots", thereby causing damage to the components. Therefore, the segmentation and detection of guano are crucial for visual automation in the cleaning and inspection processes. However, the composition, density, and thickness of guano naturally vary, leading to inconsistent levels of transparency and color. The uneven intensity of guano images greatly reduces the accuracy of segmentation and detection. Addressing this issue, this study proposes a segmentation algorithm based on combining different color channels to segment guano on the surface of photovoltaic modules. The mean shift method is used for adaptive segmentation to facilitate the detection of guano. Furthermore, the segmentation results obtained by this method are introduced into the input space to improve the traditional Mask RCNN. In addition, this study successfully produced a dataset of guano on the surface of photovoltaic modules using on-site data collection and with the help of a platform built in-house in the laboratory. The experimental results on the self-constructed dataset demonstrate that the enhanced Mask R-CNN model has shown an approximate increase of 5.9% and 6.0% in mAP values for object recognition and segmentation compared to the traditional Mask R-CNN model. This indicates the effectiveness of the methodology proposed in this study. [ABSTRACT FROM AUTHOR]

Since the existing control strategy for electro-hydraulic composite braking (EHB) system is essentially "vehicle-centered", it is liable to cause incompatibility with current driving behavior, torque fluctuation and inconsistent braking feeling occur, which affects braking safety. In view of the above issue, a personalized MPC control strategy in accordance with the design methodology of characteristic E is presented. To do this, the data collection platform for characterizing driving behavior is constructed under typical vehicle-following conditions. Then, a generalized radial basis function (GRBF) neural network is adopted to accurately identify braking intensity of different driving behaviors. Next, an optimization model for the maximum energy recovery of EHB system is established in terms of required braking torque and motor speed, the distribution coefficient of braking torque is optimized by applying the adaptive particle swarm optimization (APSO) algorithm. Finally, the proposed personalized MPC control strategy is verified under different driving behaviors, the results display that: (1) the personalized MPC controller possess superiority of acquiring stable braking feeling, the torque tracking error is decreased by 96.8 %; (2) energy recovery for EHB system with optimized torque distribution is increased by 34.47 % under FTP-75 cycle conditions, and the response amplitude of braking feeling is increased by 5.6 %. • Braking torque distribution coefficients of EHB system is optimized for different driving behaviors. • The parameter setting issue of driving behaviors characteristics and MPC controller is addressed. • A personalized MPC controller in accordance with driving behavior characteristic E is presented. [ABSTRACT FROM AUTHOR]

One of the most significant sources of information from customers is customer complaints. Successful and effective complaint management can end complaint crises and ensure client loyalty, which is a sign of great service performance. In this paper, we proposed a novel customer temperament-centered and e-CCH system-based data collection and data mining method titled "3D" model for customer complaint data analysis. Three phases are (1) Development and launch of e-Customer Complaint Handling system, (2) Data collection and transfer of learning by e-Customer Complaint Handling system, and (3) Data mining by e-Customer Complaint Handling system. An advanced e lectronic C ustomer C omplaint H andling System called the e-CCH system was then developed and launched. This system adapts the seasonal associations model based on Hippocrates's customer temperament theory to the whole stages of customer complaint reporting and handling. With this system, we conducted a dataset collection work from restaurant chains of two brands over four years. As a result, we collect thousands of real-world temperament-centred customer complaint cases by four years to form the one-of-a-kind CCH dataset. This one-of-a-kind CCH dataset was open-sourced with detailed customer complaint attributes and heuristic decision-making for valuable industrial handling manner. After further analysis of this dataset, we found that customers with different temperament types tend to have different types of complaints. In addition, adapting the temperament theory to the e-CCH system can classify customer types better and provide personalized solutions. To our best knowledge, this rich and the one-of-a-kind CCH dataset reported in this paper is the first comprehensive study of customer complaint handling in an industrial service management context. Meanwhile, data mining with cross analysis and correspondence analysis and an ChatGPT experiment for transfer of learning based on this yearly and one-of-a-kind industrial customer complaint dataset was analyzed and discussed. In addition, how this dataset may contribute to more realistic complaint-handling theoretic studies for better service failure recovery and interactive marketing is discussed in-depth. [ABSTRACT FROM AUTHOR]

Biosensors exhibit high potential for the detection of analytes of interest at the point-of-need. Over the past two decades, the combination of novel biosensing systems – such as electrochemiluminescence (ECL) biosensors – and advances in microfluidic techniques has allowed the development of lab-on-a-chip devices with enhanced overall performance and simplified sample handling. However, recording data with conventional platforms requires advanced and complicated instruments, such as sensitive photodetectors coupled to microscopes, to capture the photons from the chemiluminescent reaction. In this work, we integrated microfluidic and luminol/hydrogen peroxide ECL systems on a complementary metal-oxide-semiconductor (CMOS) chip for sample handling and data collection on the same platform. This was achieved by the adaptation of a single electrode as an electrochemical transducer and a CMOS chip as a built-in detector. We demonstrated the application of this platform for the detection of uric acid (UA), a biomarker of gout disease. A linear detection range was observed from 25 to 300 μM, with a detection limit (LOD) as low as 26.09 μM. The device showed high reusability and reproducibility within the linear detection range while maintaining high selectivity for UA detection. The analytical performance has also been evaluated in simulated saliva and urine samples, demonstrating the potential utility in medical diagnosis at the point-of-need. Compared to other ECL imaging platforms, this device showed an eightfold increase in photon collection efficiency. Overall, this approach has promising potential as an inexpensive, portable, and efficient ECL platform for measuring analytes at the point-of-need. [ABSTRACT FROM AUTHOR]

The literature on platform labour has been growing during recent years, encompassing a wide range of themes and perspectives, including gender. Despite efforts to address gender issues in the study of platforms, however, there is still a gap in the relevant literature. This article is an attempt to address this gap by discussing some of the theoretical and methodological implications of gendering platform research. At the same time, it is an attempt to think of gender as a perspective that sheds light on the feminisation and racialisation of precarious labour more broadly and platform labour more specifically. The article explores current research strands on gender and platforms. First, it discusses how gender is and can be integrated into the collection of statistical data on platforms. Then it considers recent research on domestic and care work platforms, which constitutes an area of interest for many researchers. The concept of work-life balance in platforms is raised next and the role of reproductive and affective labour as part of platform labour. The article addresses the question of affect in platforms, especially the ways in which it can provide a valuable theoretical framework for research shedding light on the subjectivity of platform workers. Overall, the article analyses both the recent literature on the topic and possible future research directions on gender in platforms. [ABSTRACT FROM AUTHOR]

The National Library of Scotland's Digital Scholarship Service has been releasing collections as data on its data-delivery platform, the Data Foundry, since September 2019. Following the COVID-19 lockdown, this service experienced significantly higher traffic, as library users increasingly made use of online resources. To ensure that as many users as possible were able to explore the datasets on the Data Foundry, the Library invested in a Digital Research Intern post, with a remit to provide introductory analysis of the Data Foundry collections using Jupyter Notebooks. This article provides a case study of this project, explaining the Library's work to date around its new Digital Scholarship Service and releasing datasets on the Data Foundry; the reasoning behind the decision to begin to provide Jupyter Notebooks; the Notebooks themselves and what types of analysis they contain, as well as the challenges faced in creating them; and the publication and impact of the Notebooks. [ABSTRACT FROM AUTHOR]

In this paper, the operability of a custombuilt differential drive wheel mobile robot was evaluated using a self-driving algorithm in practical application. Several key parameters need to be realized by the WMR to enable succinct self-driving algorithm evaluations and parameter derivations. The main components of the WMR development were examined in this work, specifically parameter derivations, hardware implementation, steering algorithm and WMR operability. The result obtained indicate that with proper design and careful calibration of sensors, the required driving parameters could be realized. This enables the WMR to potentially serve as data collection platform for future evaluation of self-driving algorithms. [ABSTRACT FROM AUTHOR]

Wireless data collection requires a sequence of resource provisioning decisions due to the limited battery capacity of wireless sensors. The corresponding online resource provisioning problem is challenging. Recently, many prediction methods have been proposed that can be used to benefit the performance of various systems through their incorporation. Therefore, in this article, we focus on online resource provisioning problems with short-term predictions motivated by the wireless data collection problem. Specifically, we design separate online algorithms for systems in which the state evolves in either a stationary manner or an arbitrarily determined manner and prove their performance bounds where their bounds improve as the amount of available predictions increases. Additionally, we design a meta-algorithm that can choose which online algorithm to implement at each point in time, depending on the recent behavior of the system environment. The practical performances of the proposed algorithms are corroborated in trace-driven numerical simulations of data collection of shared bikes. Additionally, we show that the performance of our meta-algorithm in various system environments can be better than that of the single best algorithm chosen in hindsight. [ABSTRACT FROM AUTHOR]

'Animals under wheels' is a citizen science driven project that has collected almost 90,000 roadkill records from Flanders, Belgium, mainly between 2008 and 2020. However, until now, the platform and results have never been presented comprehensively to the scientific community and we highlight strengths and challenges of this system. Data collection occurred using the subsite www.dierenonderdewielen.be ('animals under wheels') or the multi-purpose biodiversity platform observation.org and the apps, allowing the registration of roadkill and living organisms alike. We recorded 4,314 citizen scientists who contributed with at least a single roadkill record (207-1,314 active users per year). Non-roadkill records were registered by 85% of these users and the median time between registration of the first and last record was over 6 years, indicating a very high volunteer retention. Based on photographs presented with the roadkill records (n = 7,687), volunteer users correctly identified 98.2% of the species. Vertebrates represent 99% of all roadkill records. Over 145,000 km of transects were monitored, resulting in 1,726 mammal and 2,041 bird victims. Carcass encounter rates and composition of the top 10 detected species list was dependent on monitoring speed. Roadkill data collected during transects only represented 6% of all roadkill data available in the dataset. The remaining 60,478 bird and mammal roadkill records were opportunistically collected. The top species list, based on the opportunistically collected roadkill data, is clearly biased towards larger, enigmatic species. Although indirect evidence showed an increase in search effort for roadkill from 2010-2020, the number of roadkill records did not increase, indicating that roadkills are diminishing. Mitigation measures preventing roadkill could have had an effect on this, but decrease in population densities was likely to (partially) influence this result. As a case study, the mammal roadkill data were explored. We used linear regressions for the 17 most registered mammal species, determining per species if the relative proportion per year changed significantly between 2010 and 2020 (1 significant decrease, 7 significant increases). We investigated the seasonal patterns in roadkill for the 17 mammal species, and patterns per species were consistent over the years, although restrictions on human movement, due to COVID-19, influenced the seasonal pattern for some species in 2020. In conclusion, citizen scientists are a very valuable asset in investigating wildlife roadkill. While we present the results from Flanders, the platform and apps are freely available for projects anywhere in the world. [ABSTRACT FROM AUTHOR]

In mountainous and high latitude regions, migratory animals exploit green waves of emerging vegetation coinciding with rising daily mean temperatures initiating snowmelt across the landscape. Snowmelt also causes rivers and streams draining these regions to swell, a process referred to as to as the 'spring pulse.' Networks of streamgages measuring streamflow in these regions often have long-term and continuous periods of record available in real-time and at the daily time step, and thus produce data with potential to predict temporal migration patterns for species exploiting green waves. We tested the potential of models informed by streamflow data to predict timing of spring migration of mule deer (Odocoileus hemionus) herds in a headwater basin of the Colorado River. Models using streamflow data were compared with those informed by traditional temperature-derived measures of the onset of spring. Non-parametric linear-regression techniques were used to test for temporal stationarity in each variable, and logistic-regression models were used to produce probabilities of migration initiation. Our analysis indicates that models using daily streamflow data can perform as well as those using temperature-derived data to predict past-migration patterns, and nearly as well in potential to forecast future migrations. The best performing model was used to generate probabilities of onset of migration for mule deer herds over the 69-year period-of-record from a streamgage. That model indicated spring migration has been trending toward earlier initiations, with modeled median initiations shifting from a Julian day of 123 in the mid 20th century to Julian day 115 over the most recent two decades. The period of 1960 to 1979 had the latest modeled median initiations with Julian day of 128. The analyses demonstrate promise for merging existing hydrologic and biological data collection platforms in these regions to explore timing of past migration patterns and predict migration onsets in real-time. [ABSTRACT FROM AUTHOR]

Objective: We investigated emotional eating behaviours and perceived stress during COVID-19 partial quarantine according to BMI levels in healthy adults.Design: Cross-sectional study.Setting: An online survey including demographic variables, eating attitude-related questions, Emotional Eater Questionnaire (EEQ) and Perceived Stress Scale-14 (PSS-14) was sent via online data collection platform. Self-reported weight, height and weight changes during the quarantine were also collected.Participants: A total of 506 people aged between 20-65 years who were partially quarantined due to COVID-19 participated in this study.Results: BMI was positively correlated with EEQ (r 0 ·205, P = 0·001). However, BMI was negatively linked with PSS-14 during COVID-19 (r -0·125, P = 0·001), indicating that participants with lower BMI had higher perceived stress during COVID-19. Participants gained weight during the lockdown situation (+1·20 ± 1·70 kg in men; +0·91 ± 1·40 kg in women). EEQ and PSS-14 scores of women found to be significantly higher than men (9·39 ± 5·37 in men v. 11·17 ± 5·85 in women for EEQ; 24·67 ± 8·32 in men v. 27·99 ± 7·34 in women for PSS-14). Obese participants consumed sweetened and carbonated drinks two-fold more in those compared with other participants.Conclusion: These findings suggest that partial quarantine may be closely related to emotional eating and weight gain, and participants with higher BMI showed more emotional eating behaviours. Therefore, certain precautions should be considered beforehand in order not to cause long-term eating disorder problems. [ABSTRACT FROM AUTHOR]

The method is of great importance in systems that include machine learning and classification steps. As a result, academics are constantly working to improve the process. However, the data pertaining to the methodology's performance is equally as valuable as the methodology's creation. While the data is utilized to show the result of the modeling process, it is critical to consider the proper labeling of the data, the technique of acquisition, and the volume. Obtaining data in certain sectors, particularly medical fields, can be costly and time consuming. Thus, data augmenting via classical and synthetic methods has recently gained popularity. Our study uses synthetic data augmentation since it is newer, more efficient, and produces the desired effect. Our study's goal is to classify a data collection of lung sounds into four groups using data augmenting. Obtaining and standardizing the wavelet scatter transformation of each cycle of lung sounds, splitting the transformed data into test and training, augmenting and classifying the training data. In the augmenting stage, we utilized ELM-AE, then ELM-W-AE, with six wavelet functions (Gaussian, Morlet, Mexican, Shannon, Meyer, Ggw) added. The SVM and EBT classifiers improved performance by 4% and 3% in ELM-W-AE compared to the original structure. [ABSTRACT FROM AUTHOR]