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The anomaly or abnormality detection in crowded scenes helps in identifying the violence and protecting the people from severe damage. Thus, there is a need to detect the anomalies with the classifier for learning information along with the usage of huge architectures. A new anomaly detection model is implemented in this model. The collected data is fed to optimal ensemble pattern extraction scheme through techniques like Local binary patterns (LBP), Local Gradient Pattern (LGP), and Local Tetra Pattern (LTrP). The weights are tuned by a new hybrid Spiral Search-based Black Widow Glowworm Swarm Optimization (SS-BWGSO) for getting the optimal ensemble patterns. Next, anomaly frame classification is carried out by optimized VGG16+ResNet technique, where the hyperparameters of VGG16 and ResNet are tuned by SS-BWGSO algorithm. Finally, anomaly detection is performed by the YOLOV3 classifier. Throughout the result analysis the higher performance of the designed technique is observed over the classical methods. [ABSTRACT FROM AUTHOR]

Identifying events using surveillance videos is a major source that reduces crimes and illegal activities. Specifically, abnormal event detection gains more attention so that immediate responses can be provided. Video processing using conventional techniques identifies the events but fails to categorize them. Recently deep learning-based video processing applications provide excellent performances however the architecture considers either spatial or temporal features for event detection. To enhance the detection rate and classification accuracy in abnormal event detection from video keyframes, it is essential to consider both spatial and temporal features. Earlier approaches consider any one of the features from keyframes to detect the anomalies from video frames. However, the results are not accurate and prone to errors sometimes due to video environmental and other factors. Thus, two-stream hybrid deep learning architecture is presented to handle spatial and temporal features in the video anomaly detection process to attain enhanced detection performances. The proposed hybrid models extract spatial features using YOLO-V4 with VGG-16, and temporal features using optical FlowNet with VGG-16. The extracted features are fused and classified using hybrid CNN-LSTM model. Experimentation using benchmark UCF crime dataset validates the proposed model performances over existing anomaly detection methods. The proposed model attains maximum accuracy of 95.6% which indicates better performance compared to state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

According to the critical challenges for accessing streaming multimedia over wireless technologies such as lower ranges of data throughput and unacceptable rates for delay and packet losses, it was necessary to implement a system that makes some processes for these challenges, with considerations of coverage and capacity limitations that definitely will have a direct effect on the overall system quality. In this paper, we discuss the effect of applying radio resource management (RRM) technique and smart antenna modes through the fifth-generation (5G) radio link on the voice streaming packets flow in a network where many scenarios were simulated by OPNET while maintaining the quality of the network, regardless of the data load inside it. Also, we have a proposed algorithm to utilize RRM and smart antenna modes over 5G networks. On the other hand, we make a detailed eleven scenarios divided into two phases to discuss the effect of these parameters on quality as (i) RRM Coverage and system capacity, and (ii) smart antenna modes in terms of coverage and capacity. The results of simulation prove that adding RRM and smart antenna modes to the proposed networks over 5G radio link verifies a considerable evolution in the network on data flow of streaming voice packets, also including that end to end delay, packet delay variations, and throughput realize the overall requirements for quality of service (QoS) to access multimedia streaming services through a wide range of 5G bandwidth. The proposed system support monotonically response of delay all over the time of simulation for all QoS metrics. [ABSTRACT FROM AUTHOR]

The O*NET database is an online repository of detailed information on the knowledge and skill requirements of thousands of jobs across the United States. Thus, it is a valuable resource for test developers who want to target cognitive and other abilities relevant to the contemporary workforce. In this study, we used multidimensional scaling (MDS) to analyze the mean importance ratings of the cognitive abilities and selected skills included in the O*NET database to identify the dimensionality of the data regarding importance and their consistency across job zones. Using the criteria of fit and interpretability, a two-dimensional MDS solution was selected as the best representation of the data. These dimensions reflected Social Interaction/Reasoning and Verbal/Non-Verbal skills and abilities. Interestingly, the dimensionality was not consistent across job zones. Job zones relative to lower education and training requirements were sufficiently represented by the Social Interaction/Reasoning dimension, and the Verbal/Non-Verbal dimension was most relevant to job zones requiring more education and experience. The implications of the results for developing assessments for adult learners are discussed, as is the utility of using MDS for understanding the dimensionality of O*NET data. [ABSTRACT FROM AUTHOR]

Copyright of Acta Scientiarum Naturalium Universitatis Sunyatseni / Zhongshan Daxue Xuebao is the property of Sun-Yat-Sen University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Research studying employment before, during, and after spells of entrepreneurship is growing in both popularity and importance for understanding the antecedents and consequences of entrepreneurship. However, methodological challenges (e.g., retrospective bias, limitations of archival data sources) hinder further development and refinement. The Occupational Information Network, better known as O*Net, is a database of occupational characteristics that is scarcely used in entrepreneurship research, yet can help scholars overcome these challenges. In this article, I provide a brief primer on O*Net, illustrate how it can be used to advance entrepreneurship research, and offer summaries of best practices. [ABSTRACT FROM AUTHOR]

Classification and identification of eye diseases using Optical Coherence Tomography (OCT) has been a challenging task and a trending research area in recent years. Accurate classification and detection of different diseases are crucial for effective care management and improving vision outcomes. Current detection methods fall into two main categories: traditional methods and deep learning-based approaches. Traditional approaches rely on machine learning for feature extraction, while deep learning methods utilize data-driven classification model training. In recent years, Deep Learning (DL) and Machine Learning (ML) algorithms have become essential tools, particularly in medical image classification, and are widely used to classify and identify various diseases. However, due to the high spatial similarities in OCT images, accurate classification remains a challenging task. In this paper, we introduce a novel model called "OCTNet" that integrates a deep learning model combining InceptionV3 with a modified multi-scale attention-based spatial attention block to enhance model performance. OCTNet employs an InceptionV3 backbone with a fusion of dual attention modules to construct the proposed architecture. The InceptionV3 model generates rich features from images, capturing both local and global aspects, which are then enhanced by utilizing the modified multi-scale spatial attention block, resulting in a significantly improved feature map. To evaluate the model's performance, we utilized two state-of-the-art (SOTA) datasets that include images of normal cases, Choroidal Neovascularization (CNV), Drusen, and Diabetic Macular Edema (DME). Through experimentation and simulation, the proposed OCTNet improves the classification accuracy of the InceptionV3 model by 1.3%, yielding higher accuracy than other SOTA models. We also performed an ablation study to demonstrate the effectiveness of the proposed method. The model achieved an overall average accuracy of 99.50% and 99.65% with two different OCT datasets. [ABSTRACT FROM AUTHOR]

The elliptic integrals of the Vinti analytic satellite orbital motion theory are converted to a form suitable for computer evaluation. This advance will facilitate closed-form Vinti algorithms that are more compact, more accurate (i.e., exact to machine accuracy), and valid over a wider range of orbits than series expansion techniques. [ABSTRACT FROM AUTHOR]

PURPOSE: Human resources (HR) analytics systems, powered by big data, AI algorithms, and information technology, are increasingly adopted by organizations to enhance HR's impact on business performance. However, despite the widespread acknowledgment of the importance of "analytical skills" among HR practitioners in successfully implementing HR analytics systems, the specific nature of these skills remains unclear. This paper aims to address this ambiguity by firstly clarifying the concept of "analytical skills," secondly identifying skill gaps that may hinder the effective utilization of computer-assisted analytics among HR practitioners, and thirdly advocating for standardization in the understanding of "analytical skills" within the business context, particularly within HR. METHODOLOGY: We examine business "analytical skills" through the theoretical framework of the knowledge, skills, and abilities (KSA) included in the Occupational Information Network (O*NET) content model. Using data from the O*NET database, occupations were classified into Human Resource Management (HRM) and Analytical occupations. Then, we identified the top highly required KSAs in analytical occupations and compared their levels with those of HRM occupations to pinpoint potential gaps hindering the effective utilization of HR analytics. FINDINGS: Using the O*NET database, which describes work and worker characteristics, we establish the highly required analytical KSAs in the business analytics context that might be labeled "analytical skills". Then, the gap analyses reveal that important analytical KSAs, such as knowledge of sales and marketing, skills in operations analysis, and abilities in mathematical and inductive reasoning, are not expected from HR occupations, creating serious barriers to HR analytics development. In general, we have found that while HR practitioners possess some of the necessary analytical KSAs, they often lack in areas such as mathematics, computers, and complex problem-solving. IMPLICATIONS: Our findings underscore the need for standardization in HR analytics definitions, advocating for the adoption of the O*NET content model as a universal framework for understanding HR analytical knowledge, skills, and abilities (KSAs). By identifying critical analytical KSAs, our research can assist HR departments in improving training, recruitment, and development processes to better integrate HR analytics. Furthermore, we identify significant gaps in analytical skills among HR practitioners, offering potential solutions to bridge these gaps. From a theoretical perspective, our precise definition of HR "analytical skills" in terms of analytic KSAs can enhance research on the effects of HR analytics on organizational performance. This refined understanding can lead to more nuanced and impactful studies, providing deeper insights into how HR analytics contributes to achieving strategic business goals. ORIGINALITY AND VALUE: Our research offers three original insights. First, we establish a standard for HR analyst skills based on the O*NET content model, providing a clear framework for the essential knowledge, skills, and abilities required in HR analytics. Second, we identify significant analytical gaps among HR professionals, highlighting areas that need development and attention. Third, we recognize the necessity for closer cooperation between HR and professional analysts, emphasizing that such collaboration is crucial for maximizing the benefits of computer-assisted HR analytics. These insights ensure that HR analytics can move beyond being a management fad and have a real, lasting impact on business outcomes. [ABSTRACT FROM AUTHOR]

Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: To characterize the burden and social distribution of occupational psychosocial exposures in the United States (US). Methods: We merged 2022 US employment and demographic data from the Current Population Survey (CPS) with occupational characteristic data from the Occupational Information Network (O*NET), wage data from the Occupational Employment and Wage Statistics Survey, and hours worked from the CPS, to estimate the number and proportion of US workers at risk of exposure to 19 psychosocial hazards. We additionally estimated the number and proportion of US workers over‐ or underrepresented in exposure burden. Results: Of the exposures examined, US workers were most commonly employed in occupations with high time pressure (67.5 million US workers exposed; 43.2% US workers exposed), high emotional labor (57.1 million; 36.6%), and low wages (47.8 million; 30.6%). The burden of exposures was uneven across sociodemographic strata, attributable to occupational segregation. The full data set is available online at https://deohs.washington.edu/us-exposure-burden. Conclusions: Work‐related psychosocial exposures are ubiquitous and should be considered in occupational and public health research, policy, and interventions to reduce the burden of disease and health inequities in the United States. [ABSTRACT FROM AUTHOR]

Enhancing road safety globally is imperative, especially given the significant portion of traffic-related fatalities attributed to motorcycle accidents resulting from non-compliance with helmet regulations. Acknowledging the critical role of helmets in rider protection, this paper presents an innovative approach to helmet violation detection using deep learning methodologies. The primary innovation involves the adaptation of the PerspectiveNet architecture, transitioning from the original Res2Net to the more efficient EfficientNet v2 backbone, aimed at bolstering detection capabilities. Through rigorous optimization techniques and extensive experimentation utilizing the India driving dataset (IDD) for training and validation, the system demonstrates exceptional performance, achieving an impressive detection accuracy of 95.2%, surpassing existing benchmarks. Furthermore, the optimized PerspectiveNet model showcases reduced computational complexity, marking a significant stride in real-time helmet violation detection for enhanced traffic management and road safety measures. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the study is to identify the soft skills and abilities that are crucial to success in the fields of operations management (OM) and supply chain management (SCM), using the O*NET database and the classification of a set of professional figures integrating values for task skills and abilities needed to operate successfully in these professions. Design/methodology/approach: The study used the O*NET database to identify the soft skills and abilities required for success in OM and SCM industries. Correlation analysis was conducted to determine the tasks required for the job roles and their characteristics in terms of abilities and soft skills. ANOVA analysis was used to validate the findings. The study aims to help companies define specific assessments and tests for OM and SCM roles to measure individual attitudes and correlate them with the job position. Findings: As a result of the work, a set of soft skills and abilities was defined that allow, through correlation analysis, to explain a large number of activities required to work in the operations and SCM (OSCM) environment. Research limitations/implications: The work is inherently affected by the database used for the professional figures mapped and the scores that are attributed within O*NET to the analyzed elements. Practical implications: The information resulting from this study can help companies develop specific assessments and tests for the roles of OM and SCM to measure individual attitudes and correlate them with the requirements of the job position. The study aims to address the need to identify soft skills in the human sphere and determine which of them have the most significant impact on the OM and SCM professions. Originality/value: The originality of this study lies in its approach to identify the set of soft skills and abilities that determine success in the OM and SCM industries. The study used the O*NET database to correlate the tasks required for specific job roles with their corresponding soft skills and abilities. Furthermore, the study used ANOVA analysis to validate the findings in other sectors mapped by the same database. The identified soft skills and abilities can help companies develop specific assessments and tests for OM and SCM roles to measure individual attitudes and correlate them with the requirements of the job position. In addressing the necessity for enhanced clarity in the domain of human factor, this study contributes to identifying key success factors. Subsequent research can further investigate their practical application within companies to formulate targeted growth strategies and make appropriate resource selections for vacant positions. [ABSTRACT FROM AUTHOR]

The presence of nucleus-shaped anomalous regions in the power spectrum image of the electric field VLF frequency band has been discovered in previous studies. To detect and analyze these nucleus-shaped abnormal areas and improve the recognition rate of nucleus-shaped abnormal areas, this paper proposes a new nucleus-shaped abnormal area detection model ODM_Unet (Omni-dimensional Dynamic Mobile U-net) based on U-net network. Firstly, the power spectrum image data used for training is created and labeled to form a dataset of nucleus-shaped anomalous regions; Secondly, the ODConv (Omni-dimensional Dynamic Convolution) module with embedded attention mechanism was introduced to improve the encoder, extracting nucleus-shaped anomaly region information from four dimensions and focusing on the features of different input data; An SDI (Semantics and Detail Infusion) module is introduced between the encoder and decoder to solve the problem of detail semantic loss in high-level images caused by the reduction of downsampling image size; In the decoder stage, the SCSE (Spatial and Channel Squeeze-and-Excitation) attention module is introduced to more finely adjust the feature maps output through the SDI module. The experimental results show that compared with the current popular semantic segmentation algorithms, the ODM_Unet model has the best detection performance in nucleus-shaped anomaly areas. Using this model to detect data from November 2021 to October 2022, it was found that the frequency of nucleus-shaped anomaly areas is mostly between 0 and 12.5KHz, with geographic spatial distribution ranging from 40° to 70° south and north latitudes, and magnetic latitude spatial distribution ranging from 58° to 80° south and north latitudes. This method has reference significance for detecting other types of spatial electromagnetic field disturbances. [ABSTRACT FROM AUTHOR]

Gender gaps in spatial skills—a domain relevant to STEM jobs—have been hypothesized to contribute to women's underrepresentation in STEM fields. To study emerging adults' beliefs about skill sets and jobs, we asked college students (N = 300) about the relevance of spatial, mathematical, science and verbal skills for each of 82 jobs. Analyses of responses revealed four job clusters—quantitative, basic & applied science, spatial, and verbal. Students' ratings of individual jobs and job clusters were similar to judgments of professional job analysts (O*NET). Both groups connected STEM jobs to science, math, and spatial skills. To investigate whether students' interests in STEM and other jobs are related to their own self-concepts, beliefs about jobs, and spatial performance, we asked students in another sample (N = 292) to rate their self-concepts in various academic domains, rate personal interest in each of the 82 jobs, judge cultural gender stereotypes of those jobs, and complete a spatial task. Consistent with prior research, jobs judged to draw on math, science, or spatial skills were rated as more strongly culturally stereotyped for men than women; jobs judged to draw on verbal skills were more strongly culturally stereotyped for women than men. Structural equation modeling showed that for both women and men, spatial task scores directly (and indirectly through spatial self-concept) related to greater interest in the job cluster closest to the one O*NET labeled "STEM". Findings suggest that pre-college interventions that improve spatial skills might be effective for increasing spatial self-concepts and the pursuit of STEM careers among students from traditionally under-represented groups, including women. [ABSTRACT FROM AUTHOR]

Motivated by the rapid advancement of large language models (LLMs), this study explores the potential impact of them on agricultural labor market. Starting from the task level of each of the 15 selected occupations, their exposure to LLMs was assessed by rating the extent to which the required abilities are aligned with those of LLMs, taking also into account the importance of the abilities in each occupation. Findings indicate that while LLMs can significantly enhance cognitive functions, they cannot fully replace the physical, psychomotor, and sensory abilities. As a consequence, while certain tasks are either partially or highly susceptible to LLM implementation, a considerable proportion, involving manual responsibilities, remains largely unaffected. It was seen that occupations heavily reliant on data are at greater risk of substitution. Conversely, some occupations will probably experience an augmenting effect, as LLMs will automate certain cognitive routine tasks, freeing up human workers to focus on more creative non-routine aspects. Furthermore, a negative correlation between exposure to LLMs and exposure to robotization was found highlighting the interconnected dynamics between these two variables within the analyzed context. In conclusion, although LLMs can offer substantial benefits, their integration necessitates careful consideration of their inherent limitations to maximize efficacy and mitigate risks in the agricultural sector.

Irrigated agriculture is the dominant user of water globally, but most water withdrawals are not monitored or reported. As a result, it is largely unknown when, where, and how much water is used for irrigation. Here, we evaluated the ability of remotely sensed evapotranspiration (ET) data, integrated with other datasets, to calculate irrigation water withdrawals and applications in an intensively irrigated portion of the United States. We compared irrigation calculations based on an ensemble of satellite-driven ET models from OpenET with reported groundwater withdrawals from hundreds of farmer irrigation application records and a statewide flowmeter database at three spatial scales (field, water right group, and management area). At the field scale, we found that ET-based calculations of irrigation agreed best with reported irrigation when the OpenET ensemble mean was aggregated to the growing season timescale (bias = 1.6–4.9 %, R2 = 0.53–0.74), and agreement between calculated and reported irrigation was better for multi-year averages than for individual years. At the water right group scale, linking pumping wells to specific irrigated fields was the primary source of uncertainty. At the management area scale, calculated irrigation exhibited similar temporal patterns as flowmeter data but tended to be positively biased with more interannual variability. Disagreement between calculated and reported irrigation was strongly correlated with annual precipitation, and calculated and reported irrigation agreed more closely after statistically adjusting for annual precipitation. The selection of an ET model was also an important consideration, as variability across ET models was larger than the potential impacts of conservation measures employed in the region. From these results, we suggest key practices for working with ET-based irrigation data that include accurately accounting for changes in soil moisture, deep percolation, and runoff; careful verification of irrigated area and well-field linkages; and conducting application-specific evaluations of uncertainty.

Introduction: The accessibility of websites for blind and visually impaired people results from international regulations (ONZ, 2006) and national regulations (Act, 2019a; Act, 2019b). People with visual disabilities are exposed to digital exclusion related to the lack of access to information resulting from the maladjustment of websites as well as limited access to appropriate software that facilitates the use of the Internet. Research Aim: The aim of the research was to learn the opinions of blind and visually impaired people regarding changes in the accessibility of websites of public and private institutions, to determine the barriers in access to the content posted on websites and to determine the opportunities offered by the Internet for blind and visually impaired people. Method: The diagnostic survey method and a tool – an original survey questionnaire were used. 102 blind and visually impaired people took part in the research. The Kruskal-Wallis H test as well as the Mann-Whitney U test were used in the statistical analysis. Results: The obtained results prove that there are statistically significant differences: in the assessment of the accessibility of websites of public institutions, banking and financial institutions; in using a screen reader (paid, free); in the use of subsidies from PFRON funds for the purchase of equipment/software conditioned by the level of education, degree of disability, gender and place of residence of the surveyed people. Conclusions: The accessibility of websites of public institutions has improved after the adoption of the Accessibility Act of 2019, partly also of private institutions. Blind and visually impaired people encounter financial and digital barriers related to access to the Internet, some of the respondents are exposed to digital exclusion, which makes it difficult to use the opportunities offered by the Internet.

The reason behind brain tumors is the rapid and uncontrolled growth of human cells. From this, the developed model motivated to design of the framework for brain tumor detection. Deep learning-assisted developments help to enhance the detection of tumors. However, it is very complex to estimate manually when there are more MRI images that are constructed in the healthcare sector. Continuous monitoring is necessary for the brain tumor to improve the success rate and also the survival rate of the patient. Thus, various computer-assisted tumor identification techniques are needed. To offer an accurate computer-aided tumor identification approach, the latest deep learning model is recommended in this work. The acquired images are fed to the pre-processing stage. For the proper recognition of the spatial location of a tumor, the tumor segmentation phase is done here with the help of Attention-based Adaptive Swin UNet (AASUNet), where the parameters in AASUNet are optimized using a newly recommended Average Position of Artificial algae and Social Ski-Driver (APASSD) algorithm. Further, the segmented images are subjected to the tumor classification phase, where the Optimized and Cascaded Fully Convolutional DenseNet (OCFCD) is designed with the adoption of the APASSD algorithm for promoting the precise tumor classification by tuning the parameters in the OCFCD network. From the result analysis, the precision and accuracy rate of the designed model is 96.67% and 96.07%. The results from the experimental analysis demonstrate the proposed model can facilitate the automatic detection of brain tumors. [ABSTRACT FROM AUTHOR]

In this study, our aim is to find an effective method to solve the problem of disease similarity caused by multiple diseases occurring on the same leaf. This study proposes the use of an optimized RegNet model to identify seven common apple leaf diseases. We conducted comparisons and analyses on the impact of various factors, such as training methods, data expansion methods, optimizer selection, image background, and other factors, on model performance. The findings suggest that utilizing offline expansion and transfer learning to fine-tune all layer parameters can enhance the model's classification performance, while complex image backgrounds significantly influence model performance. Additionally, the optimized RegNet network model demonstrates good generalization ability for both datasets, achieving testing accuracies of 93.85% and 99.23%, respectively. These results highlight the potential of the optimized RegNet network model to achieve high-precision identification of different diseases on the same apple leaf under complex field backgrounds. This will be of great significance for intelligent disease identification in apple orchards in the future. [ABSTRACT FROM AUTHOR]

Although workplace safety research is common given the frequent occurrence of fatal and nonfatal occupational accidents, it has focused mainly on safety climate and lacks a unified approach when examining predictors of safety outcomes. We argue that adopting an integrated approach with job analysis data and using newer machine learning methods can support and extend findings from cross-sectional research studies using traditional statistical methods. The suggested approach is demonstrated by using three machine learning methods (elastic net, random forest, and gradient boosting) along with publicly available O*NET data to predict annual nonfatal occupational incident rates published by the US Bureau of Labor Statistics. Findings indicate that O*NET descriptors from several subdomains including abilities, work context, and work activities were significant in predicting occupational injury rates. The amount of variance explained by the predictors varied from 54.2% (gradient boosting) to 58.8% (elastic net) with 12 common predictors across the three methods. The exploratory approach with machine learning techniques supports past findings and helps uncover understudied predictors of safety outcomes. This study adds to the literature surrounding person- and situation-based antecedents to workplace safety and has several other implications for research and practice. [ABSTRACT FROM AUTHOR]

Pecan is a major crop in the Mesilla Valley, New Mexico. Due to prolonged droughts, growers face challenges related to water shortages. Therefore, irrigation management is crucial for farmers. Advancements in satellite-derived evapotranspiration (ET) models and accessibility to data from web-based platforms like OpenET provide farmers with new tools to improve crop irrigation management. This study evaluates the evapotranspiration (ET) of a mature pecan orchard using OpenET platform data generated by six satellite-based models and their ensemble. The ET values obtained from the platform were compared with the ET values obtained from the eddy covariance (ETec) method from 2017 to 2021. The six models assessed included Google Earth Engine implementation of the Surface Energy Balance Algorithm for Land (geeSEBAL), Google Earth Engine implemonthsmentation of the Mapping Evapotranspiration at High Resolution with Internalized Calibration (eeMETRIC) model, Operational Simplified Surface Energy Balance (SSEBop), Satellite Irrigation Management Support (SIMS), Priestley–Taylor Jet Propulsion Laboratory (PT-JPL), and Atmosphere–Land Exchange Inverse and associated flux disaggregation technique (ALEXI/DisALEXI). The average growing season ET of mature pecan estimated from April to October of 2017 to 2021 by geeSEBAL, eeMETRIC, SSEBop, SIMS, PT-JPL, ALEXI/DisALEXI, and the ensemble were 1061, 1230, 1232, 1176, 1040, 1016, and 1130 mm, respectively, and 1108 mm by ETec. Overall, the ensemble model-based monthly ET of mature pecan during the growing season was relatively close to the ETec (R2 of 0.9477) with a 2% mean relative difference (MRD) and standard error of estimate (SEE) of 15 mm/month for the five years (N = 60 months). The high agreement of the OpenET ensemble of the six satellite-derived models' estimates of mature pecan ET with the ETec demonstrates the utility of this promising approach to enhance the reliability of remote sensing-based ET data for agricultural and water resource management. [ABSTRACT FROM AUTHOR]

This paper proposes LTH-MAC (Low-Traffic Aware Hybrid MAC), a novel MAC protocol designed to improve energy efficiency and message delivery reliability in Wireless Sensor Networks (WSNs). LTH-MAC achieves this through innovative techniques like flexible timeslots, channel selection, collision-avoiding, parallel transmissions, and efficient backoff schemes. These optimizations lead to reduced idle listening, minimized collisions, and simplified synchronization. Simulations using OPNET environment demonstrate that LTH-MAC significantly reduces energy consumption, especially under light traffic loads. Additionally, LTH-MAC provides lower end-to-end latency and higher message delivery reliability compared to ECoMAC. These advancements position LTH-MAC as a compelling solution for WSN applications demanding efficient and reliable communication. [ABSTRACT FROM AUTHOR]

This paper describes the infiltration of the image from the Internet to the everyday environment, what is beginning to be called the Outernet. However, this shift toward the physical space – architectural, urban, geological – is performed by prioritizing the technical and informative character of the image, which reduces the body to stationary and dissociated situations. From this point of departure we propose a parallel situation, in which we present the body as a transducer between image-providing devices and environments. We will consider the Outernet from its rereading as a low-fidelity Wilderness, a hybrid landscape where the body can hunt and embody image in a wild way – not entirely rational –, here described as performative research. Shifting the intentionality of our movements, we unfold a field of relationships in which the image is distributed along the body, the device, and the environment.

Este ensayo describe la inflitración de la imagen desde Internet hacia el entorno cotidiano, algo que comienza a denominarse Outernet. Sin embargo, esta desplazamiento hacia el espacio físico (arquitectónico, urbano, geológico) se realiza priorizando el carácter técnico-informativo de la imagen, algo que reduce al cuerpo a situaciones estacionarias y disociadas. Desde este punto de partida planteamos una situación paralela en la que presentamos al cuerpo como transductor entre dispositivos y entornos proveedores de imagen. Pensaremos Outernet desde su relectura como un Wilderness de baja fidelidad; un paisaje híbrido donde el cuerpo puede cazar e incorporar imagen de un modo salvaje, no enteramente racional, que aquí denominamos investigación performativa. Virando la intencionalidad de nuestros movimientos, desplegamos un campo de relaciones en donde la imagen es distribuida entre el cuerpo, el dispositivo y el entorno.

The presence of nucleus-shaped anomalous regions in the power spectrum image of the electric field VLF frequency band has been discovered in previous studies. To detect and analyze these nucleus-shaped abnormal areas and improve the recognition rate of nucleus-shaped abnormal areas, this paper proposes a new nucleus-shaped abnormal area detection model ODM_Unet (Omni-dimensional Dynamic Mobile U-net) based on U-net network. Firstly, the power spectrum image data used for training is created and labeled to form a dataset of nucleus-shaped anomalous regions; Secondly, the ODConv (Omni-dimensional Dynamic Convolution) module with embedded attention mechanism was introduced to improve the encoder, extracting nucleus-shaped anomaly region information from four dimensions and focusing on the features of different input data; An SDI (Semantics and Detail Infusion) module is introduced between the encoder and decoder to solve the problem of detail semantic loss in high-level images caused by the reduction of downsampling image size; In the decoder stage, the SCSE (Spatial and Channel Squeeze-and-Excitation) attention module is introduced to more finely adjust the feature maps output through the SDI module. The experimental results show that compared with the current popular semantic segmentation algorithms, the ODM_Unet model has the best detection performance in nucleus-shaped anomaly areas. Using this model to detect data from November 2021 to October 2022, it was found that the frequency of nucleus-shaped anomaly areas is mostly between 0 and 12.5KHz, with geographic spatial distribution ranging from 40° to 70° south and north latitudes, and magnetic latitude spatial distribution ranging from 58° to 80° south and north latitudes. This method has reference significance for detecting other types of spatial electromagnetic field disturbances.

PURPOSE: Human resources (HR) analytics systems, powered by big data, AI algorithms, and information technology, are increasingly adopted by organizations to enhance HR’s impact on business performance. However, despite the widespread acknowledgment of the importance of “analytical skills” among HR practitioners in successfully implementing HR analytics systems, the specific nature of these skills remains unclear. This paper aims to address this ambiguity by firstly clarifying the concept of “analytical skills,” secondly identifying skill gaps that may hinder the effective utilization of computer-assisted analytics among HR practitioners, and thirdly advocating for standardization in the understanding of “analytical skills” within the business context, particularly within HR. METHODOLOGY: We examine business “analytical skills” through the theoretical framework of the knowledge, skills, and abilities (KSA) included in the Occupational Information Network (O*NET) content model. Using data from the O*NET database, occupations were classified into Human Resource Management (HRM) and Analytical occupations. Then, we identified the top highly required KSAs in analytical occupations and compared their levels with those of HRM occupations to pinpoint potential gaps hindering the effective utilization of HR analytics. FINDINGS: Using the O*NET database, which describes work and worker characteristics, we establish the highly required analytical KSAs in the business analytics context that might be labeled “analytical skills”. Then, the gap analyses reveal that important analytical KSAs, such as knowledge of sales and marketing, skills in operations analysis, and abilities in mathematical and inductive reasoning, are not expected from HR occupations, creating serious barriers to HR analytics development. In general, we have found that while HR practitioners possess some of the necessary analytical KSAs, they often lack in areas such as mathematics, computers, and complex problem-solving. IMPLICATIONS: Our findings underscore the need for standardization in HR analytics definitions, advocating for the adoption of the O*NET content model as a universal framework for understanding HR analytical knowledge, skills, and abilities (KSAs). By identifying critical analytical KSAs, our research can assist HR departments in improving training, recruitment, and development processes to better integrate HR analytics. Furthermore, we identify significant gaps in analytical skills among HR practitioners, offering potential solutions to bridge these gaps. From a theoretical perspective, our precise definition of HR “analytical skills” in terms of analytic KSAs can enhance research on the effects of HR analytics on organizational performance. This refined understanding can lead to more nuanced and impactful studies, providing deeper insights into how HR analytics contributes to achieving strategic business goals. ORIGINALITY AND VALUE: Our research offers three original insights. First, we establish a standard for HR analyst skills based on the O*NET content model, providing a clear framework for the essential knowledge, skills, and abilities required in HR analytics. Second, we identify significant analytical gaps among HR professionals, highlighting areas that need development and attention. Third, we recognize the necessity for closer cooperation between HR and professional analysts, emphasizing that such collaboration is crucial for maximizing the benefits of computer-assisted HR analytics. These insights ensure that HR analytics can move beyond being a management fad and have a real, lasting impact on business outcomes.

Traditional grid analytics heavily rely on accurate power system models, especially dynamic ones for generators, controllers, and loads. However, obtaining comprehensive models is impractical in real operations due to inaccessible parameters and consumer privacy. This necessitates dynamic equivalencing for unknown subsystems, which employs physics-informed machine learning and neural ordinary differential equations (ODE-NET) to preserve dynamic behaviors post-disturbances. The contributions include: 1) A neural dynamic equivalence (NeuDyE) formulation enabling continuous-time, data-driven dynamic equivalence, eliminating the need for acquiring inaccessible system details; 2) Introduction of Physics-Informed NeuDyE learning (PI-NeuDyE) to actively control NeuDyE’s closed-loop accuracy; 3) Driving Port NeuDyE (DP-NeuDyE), a practical application of NeuDyE, reducing the number of inputs required for training. Extensive case studies on the 140-bus NPCC system validate the generalizability and accuracy of both PI-NeuDyE and DP-NeuDyE. These analyses cover various scenarios, including limitations in data accessibility. Test results demonstrate the scalability and practicality of NeuDyE, showcasing its potential application in ISO and utility control centers for online transient stability analysis and planning purposes.

The O*NET database is an online repository of detailed information on the knowledge and skill requirements of thousands of jobs across the United States. Thus, it is a valuable resource for test developers who want to target cognitive and other abilities relevant to the contemporary workforce. In this study, we used multidimensional scaling (MDS) to analyze the mean importance ratings of the cognitive abilities and selected skills included in the O*NET database to identify the dimensionality of the data regarding importance and their consistency across job zones. Using the criteria of fit and interpretability, a two-dimensional MDS solution was selected as the best representation of the data. These dimensions reflected Social Interaction/Reasoning and Verbal/Non-Verbal skills and abilities. Interestingly, the dimensionality was not consistent across job zones. Job zones relative to lower education and training requirements were sufficiently represented by the Social Interaction/Reasoning dimension, and the Verbal/Non-Verbal dimension was most relevant to job zones requiring more education and experience. The implications of the results for developing assessments for adult learners are discussed, as is the utility of using MDS for understanding the dimensionality of O*NET data.

Imaging has been an important strategy for exploring space weather. The Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) is a joint Chinese Academy of Sciences (CAS) and European Space Agency (ESA) mission, aiming at studying the interaction between Earth's magnetosphere and solar wind near the subsolar point via soft X-ray imaging. As the boundary of Earth's magnetosphere, magnetopause is a significant detection target to mirror solar wind's change for the SMILE mission. In preparation for inverting three-dimensional magnetopause, we proposed an OESA-UNet model to detect the magnetopause position. The model obtains magnetopause with a U-shaped structure, in an end-to-end manner. Inspired by attention mechanisms, these blocks are integrated into ours. OESA-UNet captures low and high-level feature maps by adjusting the receptive field for precise localization. Adaptively pre-processing the image provides a prior for the network. Availability metrics are designed to determine whether it can serve three-dimensional inversion. Lastly, we provided ablation and comparison experiments by qualitative and quantitative analysis. Our recall, precision, and f1 score are 93.8%, 92.1%, and 92.9%, respectively, with an average angle deviation of 0.005 under the availability metrics. Results indicate that OESA-UNet outperforms other methods. It can better serve the purpose of magnetopause tracing from an X-ray image. [ABSTRACT FROM AUTHOR]

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The increasing impact of human activities on ecosystems is provoking a profound and dangerous effect, particularly in wildlife. Examining the historical migration patterns of quail (Coturnix coturnix) offers a compelling case study to demonstrate the repercussions of human actions on biodiversity. Urbanization trends, where people gravitate toward mega-urban areas, amplify this effect. The proliferation of artificial urban ecosystems extends its influence across every biome, as human reliance on infrastructure and food sources alters ecological dynamics extensively. We examine European quail migrations pre- and post-World War II and in the present day. Our study concentrates on the Italian peninsula, investigating the historical and contemporary recovery of ringed quail populations. To comprehend changes in quail migration, we utilize trajectory analysis, open statistical data, and linear generalized models. We found that while human population and economic growth have shown a linear increase, quail recovery rates exhibit a U-shaped trajectory, and cereal and legume production displays an inverse U-shaped pattern. Generalized linear models have unveiled the significant influence of several key factors—time periods, cereal and legume production, and human demographics—on quail recovery rates. These factors closely correlate with the levels of urbanization observed across these timeframes. These insights underscore the profound impact of expanding human populations and the rise of mega-urbanization on ecosystem dynamics and services. As our planet becomes more urbanized, the pressure on ecosystems intensifies, highlighting the urgent need for concerted efforts directed toward conserving and revitalizing ecosystem integrity. Simultaneously, manage the needs and demands of burgeoning mega-urban areas. Achieving this balance is pivotal to ensuring sustainable coexistence between urban improvement and the preservation of our natural environment. [ABSTRACT FROM AUTHOR]

Existing Optical Mark Recognition (OMR) systems tend to be expensive and rigid in their operation, often resulting in erroneous evaluations due to strict correction protocols. This scenario airs the need for a flexible OMR system. Hence, in this work, we propose a lightweight transfer learning based Convolutional Neural Network (CNN) model, dubbed as OMRNet, which can classify answer boxes on any generalized OMR test sheet. Unlike most existing techniques that rely on image processing algorithms to recognize extracted answer boxes in two classes: confirmed and empty, the OMRNet is designed to classify the answer boxes into confirmed, crossed-out, and empty categories. That is, OMRNet is facilitating the crossing out of previously answered questions and thus removing the rigidity of templates in Multiple Choice Question (MCQ) tests. We have built OMRNet on top of a MobileNetV2 backbone connected to four fully connected layers with appropriate dropouts and activation functions in between. We have evaluated OMRNet on the Multiple Choice Answer Boxes dataset available at https://sites.google.com/view/mcq-dataset. We have performed experiments following a 5 fold cross validation scheme, and OMRNet has achieved accuracies of 95.29%, 95.88%, 93.97%, 97.45%, and 97.20%, with an average accuracy of 95.96%. Also, the experimental results confirm that the present model performs better than the compared state-of-the-art methods and standard CNN models in terms of accuracy, execution time, and memory required to store the trained module. Moreover, we have employed a quantization technique to make the trained module more memory efficient and deployed it to a web app using our own Representational State Transfer Application Programming Interface (REST API). It makes OMRNet available via a Hypertext Transfer Protocol (HTTP) endpoint, allowing potential users to connect to it via the Internet. The source code for the work is available at the following link: https://github.com/sa-y-an/OMRNet. [ABSTRACT FROM AUTHOR]

Patients with lung cancer can only be diagnosed and treated surgically. Early detection of lung cancer through medical imaging could save numerous lives. Adding advanced techniques to conventional tests that offer high accuracy in diagnosing lung cancer is essential. U-Net has excelled in diversified tasks involving the segmentation of medical image datasets. A significant challenge remains in determining the ideal combination of hyper parameters for designing an optimized U-Net for detailed image segmentation. In our work, we suggested a technique for automatically generating evolutionary U-Nets to detect and segregate lung cancer anomalies. We used three distinct datasets, namely the LIDC-IRDC Dataset, Luna 16 Dataset, and Kaggle Dataset, for training the proposed work on lung images. Our results, examined with six distinct evaluation criteria used for medical image segmentation, consistently demonstrated the highest performance. More specifically, the GA-UNet outperforms conventional approaches in terms of an impressive accuracy rate of 97.5% and a Dice similarity coefficient (DSC) of 92.3%. [ABSTRACT FROM AUTHOR]

• Cold air leakage is mainly concentrated in lower areas on both sides of the ORDC opening. • Using long narrow baffles can greatly reduce the heat load of the ORDC. • Baffle improvement factor is defined to measure the energy-saving efficiency of the baffle. • The baffle with small length and moderate width has high baffle improvement factor. • Using baffles can greatly reduce electricity consumption and water-pouring frequency for users. The opening of the open refrigerated display cabinet (ORDC) directly faces the environment and is vulnerable to the invasion of humid and warm ambient air, which causes high power consumption and excessive defrosting water. CFD simulation results show that the cold air leakage of the ORDC is mainly distributed in the lower areas on both edges of the ORDC opening. Limited coverage on the air curtain opening (LCO) is proposed by adding long narrow baffles at critical leakage positions to achieve an effective reduction in the heat load of the cabinet. According to simulation results, the ORDC equipped with a pair of 40 cm × 10 cm baffles can reduce the heat load by up to 33.8 % compared with the baseline prototype. The baffle improvement factor is defined to evaluate the baffles' effect on refrigeration performance. The baffle with a small length (less than 20 cm) and a moderate width (4 cm–8 cm) is preferred to achieve a high baffle improvement factor. Moreover, the experiment on an ORDC is conducted to further investigate the effect of LCO on the refrigeration performance of the ORDC. The experimental results demonstrate that a pair of 40 cm × 10 cm baffles can reduce the 12 h power consumption and 12 h defrosting water weight of the cabinet by 30.1 % and 51.0 %, respectively, compared with the baseline prototype. Therefore, the LCO method has good application potential in saving electricity and reducing the defrost water in ORDCs. [ABSTRACT FROM AUTHOR]

Objective: Personality changes are related to successfully performing adult occupational roles which require teamwork, duty, and managing stress. However, it is unclear how personality development relates to specific job characteristics that vary across occupations. Method: We investigated whether 151 objective job characteristics, derived from the Occupational Information Network (O*NET), were associated with personality levels and changes in a 12‐year longitudinal sample followed over the school to work transition. Using cross‐validated regularized modeling, we combined two Icelandic longitudinal datasets (total N = 1054) and constructed an individual‐level, aggregated job characteristics score that maximized prediction of personality levels at baseline and change over time. Results: The strongest association was found for level of openness (0.25), followed by conscientiousness (0.16) and extraversion (0.14). Overall, aggregated job characteristics had a stronger prediction for personality intercepts (0.14) than slopes (0.10). These results were subsequently replicated in a U.S. sample using levels of the Big Five as the dependent variable. This indicates that associations between job characteristics and personality are generalizable across life stages and nations. Conclusions: Our findings suggest that job titles are a valuable resource that can be linked to personality to better understand factors that influence psychological development. Further work is needed to document the prospective validity of job characteristics across a wider range of occupations and age. [ABSTRACT FROM AUTHOR]

Knee Osteoarthritis (OA) is a destructive joint disease identified by joint stiffness, pain, and functional disability concerning millions of lives across the globe. It is generally assessed by evaluating physical symptoms, medical history, and other joint screening tests like radiographs, Magnetic Resonance Imaging (MRI), and Computed Tomography (CT) scans. Unfortunately, the conventional methods are very subjective, which forms a barrier in detecting the disease progression at an early stage. This paper presents a deep learning-based framework, namely OsteoHRNet, that automatically assesses the Knee OA severity in terms of Kellgren and Lawrence (KL) grade classification from X-rays. As a primary novelty, the proposed approach is built upon one of the most recent deep models, called the High-Resolution Network (HRNet), to capture the multi-scale features of knee X-rays. In addition, an attention mechanism has been incorporated to filter out the counterproductive features and boost the performance further. Our proposed model has achieved the best multi-class accuracy of 71.74% and MAE of 0.311 on the baseline cohort of the OAI dataset, which is a remarkable gain over the existing best-published works. Additionally, Gradient-based Class Activation Maps (Grad-CAMs) have been employed to justify the proposed network learning. [ABSTRACT FROM AUTHOR]

The relationship between life outcomes and an individual's standing in the social and economic hierarchy of society is an important topic across the social sciences. Foundational to this work is assessing an individual's standing in this hierarchy, often referred to as socioeconomic status (SES). One component of an individual's SES, often overlooked in the psychological literature, is occupational prestige - the amount of status accorded to them based on their occupational role. In this research, we collected and validated a new index of occupational prestige for 1029 specific occupations, including all jobs in the US Department of Labor's O*NET database, and 22 broader occupational families. In Study 1, we collected a comprehensive set of occupational prestige ratings from an online convenience sample, and demonstrated their high reliability. In Study 2, we developed a crosswalk between the ratings collected in Study 1 and prior ratings of occupations listed in the US Census and show convergent validity with previous indices. In Studies 3 and 4 we used additional data to evaluate the construct validity of occupational prestige more broadly. In Study 3, we established convergent and discriminant validity with other indicators of SES: income and educational attainment. In Study 4, we use the O*NET database to identify the characteristics of occupations most strongly associated with prestige. These results support the validity of the index and suggest occupations with high prestige require skills traditionally emphasized in liberal arts education (e.g., critical thinking, reading comprehension). [ABSTRACT FROM AUTHOR]

Background: Oriental hornets are large predatory hymenoptera that occur in the southern part of Asia and the southeastern Mediterranean. Among many pests of bee colonies, Vespa orientalis was recorded to be one of the most destructive. Objectives: The aim of this study was to: (1) monitor the presence of pathogens carried by V. orientalis that could potentially threaten honey bees and public health; (2) describe the hornet's predatory behavior on honey bee colonies and (3) collect the medical history of a V. orientalis sting suffered by a 36‐year‐old woman. Methods: Observations of V. orientalis predatory behavior and the catches of hornets for parasitological and microbiological examination, using molecular and bacteriological analyses, were carried out in three experimental apiaries, both in spring in order to capture the foundress queens and during the summer to capture the workers. Furthermore, the medical history and photographic documentation of a V. orientalis sting suffered by a 36‐year‐old woman have been collected. Results: The results obtained highlight that V. orientalis is capable of causing serious damage to beekeeping by killing bees, putting under stress the honey bee colonies and by potentially spreading honey bee pathogens among apiaries. These hornets may also become a public health concern, since they are capable of inflicting multiple, painful stings on humans. Conclusions: Only the development of an Integrated Management Control Program will be able to contain the negative effects of anomalous population growth and the potentially negative impact on honey bees and public health of V. orientalis. [ABSTRACT FROM AUTHOR]

Vespa orientalis L. (Hymenoptera: Vespidae) is a significant threat to beekeeping worldwide. This study aims to study the population dynamics that manage the oriental hornet. The baits used included a pollen substitute, fermented solution, and tuna. The research was conducted from the first week of September until the last week of November 2022 during worker wasp activity. Traps were set up and baited from the first week of March until the last week of May in 2023 during the queen wasp activity. Results indicated that the pollen substitute bait was the most successful in attracting V. orientalis, followed by the fermented solution, while tuna was the least effective. Based on the study, the recommendation is to use a pollen substitute bait for the most effective wasp control method. [ABSTRACT FROM AUTHOR]

We prove a universal approximation property (UAP) for a class of ODENet and a class of ResNet, which are simplified mathematical models for deep learning systems with skip connections. The UAP can be stated as follows. Let $ n $ and $ m $ be the dimension of input and output data, and assume $ m\leq n $. Then we show that ODENet of width $ n+m $ with any non-polynomial continuous activation function can approximate any continuous function on a compact subset on $ \mathbb{R}^n $. We also show that ResNet has the same property as the depth tends to infinity. Furthermore, we derive the gradient of a loss function explicitly with respect to a certain tuning variable. We use this to construct a learning algorithm for ODENet. To demonstrate the usefulness of this algorithm, we apply it to a regression problem, a binary classification, and a multinomial classification in MNIST. [ABSTRACT FROM AUTHOR]