Search Results (4,005)

Background/ Objectives: Surgical residency is widely recognized as a highly stressful phase due to long working hours and the challenges of managing complex cases. Additionally, family responsibilities, such as being a spouse or parent, can have a positive or negative impact on residents’ well-being. This study aimed to explore the relationship between clinical supervision and mental well-being among otolaryngology residents in Saudi Arabia, focusing on how supervision conditions influence well-being at different stages of training. Methods: This was an analytical cross-sectional correlational study conducted among Saudi otolaryngology head and neck surgery residents. An online survey was used to collect data from 64 residents, utilizing the Dutch Residents Educational Climate Test (D-RECT) to assess clinical supervision and the Warwick–Edinburgh Mental Well-being Scale (WEMWBS) to measure well-being. The data were analyzed to determine the association between supervision conditions and well-being across different residency levels. Results: The results showed that the majority of residents reported higher mean scores for items such as “I’ve been feeling useful” (3.53 ± 1.23), “I’ve been feeling interested in new things” (3.28 ± 1.13), and “I’ve been dealing with problems well” (3.27 ± 1.10). No statistically significant difference in overall WEMWBS scores was found between junior and senior residents. However, mental well-being was significantly associated with all four D-RECT domains (supervision, feedback, coaching assessment, and consultant attitude), with a positive correlation observed between clinical supervision and well-being. Conclusions: This study highlights the critical role of clinical supervision in supporting the mental well-being of otolaryngology residents. Enhanced supervision practices, particularly those emphasizing constructive feedback and supportive consultant attitudes, could mitigate burnout and improve resident outcomes. These findings underscore the need for targeted interventions in residency programs to promote well-being and optimize the learning environment. Full article

The study of the process of laser action on powder materials requires the construction of mathematical models of the interaction of laser radiation with powder particles that take into account the features of energy supply and are applicable in a wide range of beam parameters and properties of the particle material. A model of the interaction of pulsed or pulse-periodic laser radiation with a spherical metal particle is developed. To find the temperature distribution in the particle volume, the non-stationary three-dimensional heat conductivity equation with a source term that takes into account the action of laser radiation is solved. In the plane normal to the direction of propagation of laser radiation, the change in the radiation intensity obeys the Gaussian law. It is possible to take into account changes in the intensity of laser radiation in space due to its absorption by the environment. To accelerate numerical calculations, a computational algorithm is used based on the use of vectorized data structures and parallel implementation of operations on general-purpose graphics accelerators. The features of the software implementation of the method for solving a system of difference equations that arises as a result of finite-volume discretization of the heat conductivity equation with implicit scheme by the iterative method are presented. The model developed describes the heating and melting of a spherical metal particle exposed by multi-pulsed laser radiation. The implementation of the computational algorithm developed is based on the use of vectorized data structures and GPU resources. The model and calculation results are of interest for constructing a two-phase flow model describing the interaction of test particles with laser radiation on the scale of the entire calculation domain. Such a model is implemented using a discrete-trajectory approach to modeling the motion and heat exchange of a dispersed admixture. Full article

Plant colonization patterns on deglaciated terrain give insight into the factors influencing alpine ecosystem development. Our objectives were to use a chronosequence, extending from the Little Ice Age (~1850) terminal moraine to the present glacier terminus, and biophysical predictors to characterize vegetation across Sperry Glacier’s foreland—a mid-latitude cirque glacier in Glacier National Park, Montana, USA. We measured diversity metrics (i.e., richness, evenness, and Shannon’s diversity index), percent cover, and community composition in 61 plots. Field observations characterized drainage, concavity, landform features, rock fragments, and geomorphic process domains in each plot. GIS-derived variables contextualized the plots’ aspect, terrain roughness, topographic position, solar radiation, and curvature. Overall, vegetation cover and species richness increased with terrain age, but with colonization gaps compared to other forelands, likely due to extensive bedrock and slow soil development, potentially putting this community at risk of being outpaced by climate change. Generalized linear models revealed the importance of local site factors (e.g., drainage, concavity, and process domain) in explaining species richness and Shannon’s diversity patterns. The relevance of field-measured variables over GIS-derived variables demonstrated the importance of fieldwork in understanding alpine successional patterns and the need for higher-resolution remote sensing analyses to expand these landscape-scale studies. Full article

Visual programming languages (VPLs) play a significant role in simplifying the process of learning to program and reducing development time. Most VPLs are developed for use in education or specific domains. Recently, some projects have aimed to provide general-purpose VPLs. Among these projects is the Programming Without Coding Technology (PWCT) project, which has been used for several years to develop and maintain the compiler and virtual machine for the Ring programming language. However, PWCT faces several issues related to code generation performance and the operating systems it supports. Additionally, its visual editor lacks many features, such as rich comments, auto-run, and the ability to import textual code, which are highly important in the era of using large language models for generating textual code. In this research, we present the PWCT2 visual programming language, which is distributed on the Steam platform. On Steam, 1772 users have launched the software, and the total usage time recorded exceeds 17,000 h. This generation provides approximately 36 times faster code generation and 20 times lower storage requirements for visual source files. It also allows for the conversion of Ring code into visual code, enabling the creation of a self-hosting VPL. It consists of approximately 92,000 lines of Ring code and comes with 394 visual components. Moreover, using Ring in this project demonstrates the feasibility of utilizing the language for projects of this scale. Ring compiles PWCT2 in less than one second, and the generated bytecode consists of approximately 724,000 instructions. Full article

Background and Objectives: The aim of this study was to determine the effect of immersive virtual reality used as a short-term multifaceted activity with a focus on motor and cognitive function in patients with Parkinson’s Disease. The sub-objective focused on quality of life in the study group of patients. Materials and Methods: Nineteen patients (64.2 ± 12.8 years) were included in this study. Inclusion criteria for this study: adult patients in Hoehn and Yahr’s stage 1–3, cooperative, with stable health status, independent and mobile. IVR therapy was performed twice a week for 20 min for one month. Input and output measurements were taken within 14 days of starting or ending therapy. The 10 Meter Walk test was used to examine and assess both comfortable and fast walking, and the Timed Up and Go (TUG) + s dual task was applied to quickly assess the highest possible level of functional mobility. The Berg Balance Scale test (BBS) was used to assess balance with a 14-item balance scale containing specific movement tasks. The standardized Parkinson’s Disease Questionnaire (PDQ-39) was used to assess quality of life. Data were processed in the PAST program using a nonparametric paired Wilcoxon test. The significance level was set at α = 0.05. The value of the r score was used to evaluate the effect size. Results: A significant reduction in the time in the fast walk 10MWT (p = 0.006; r = 0.63) and TUG (p < 0.001; r = 0.80) parameter were found after therapy. Significant improvement in the BBS score was found after applied therapy (p = 0.016; r = 0.55). In the PDQ-39 questionnaire, significant improvements were found in the study group after therapy in the domains of mobility (p = 0.027; r = 0.51) and emotional well-being (p = 0.011; r = 0.58). Conclusions: The results of this study indicate a positive effect of virtual reality therapy on balance and gait, which is also good in terms of reducing the risk of falls in the study group. Therapy also promoted quality of life in the study group. Full article

Semantic segmentation of remotely sensed images for building footprint recognition has been extensively researched, and several supervised and unsupervised approaches have been presented and adopted. The capacity to do real-time mapping and precise segmentation on a significant scale while considering the intrinsic diversity of the urban landscape in remotely sensed data has significant consequences. This study presents a novel approach for delineating building footprints by utilizing the compressed sensing and radial basis function technique. At the feature extraction stage, a small set of random features of the built-up areas is extracted from local image windows. The random features are used to train a radial basis neural network to perform building classification; thus, learning and classification are carried out in the compressed sensing domain. By virtue of its ability to represent characteristics in a reduced dimensional space, the scheme shows promise in being robust in the face of variability inherent in urban remotely sensed images. Through a comparison of the proposed method with numerous state-of-the-art approaches utilizing remotely sensed data of different spatial resolutions and building clutter, we establish its robustness and prove its viability. Accuracy assessment is performed for segmented footprints, and comparative analysis is carried out in terms of intersection over union, overall accuracy, precision, recall, and F1 score. The proposed method achieved scores of 93% in overall accuracy, 90.4% in intersection over union, and 91.1% in F1 score, even when dealing with drastically different image features. The results demonstrate that the proposed methodology yields substantial enhancements in classification accuracy and decreases in feature dimensionality. Full article

Background/Objectives: Negative symptoms (NSs) significantly impair the outcome of schizophrenia, primarily due to their effect on quality of life and their resistance to pharmacological treatments. Several scales have been developed to assess the various dimensions of NSs, including avolition, anhedonia, alogia, social withdrawal, and blunted affect. While observer-rated scales are the most commonly used, self-assessment tools remain underutilized. However, self-assessments offer a promising approach for gaining insights into the personal experiences of individuals. The objective of this review was to identify and report the psychometric properties of self-assessment scales for NSs that are relevant for both research and clinical practice, with a focus on tools that assess multiple domains of NSs in order to support comprehensive evaluations and tailored therapeutic strategies. Methods: We conducted an exhaustive literature review following PRISMA guidelines to identify self-evaluation scales that evaluate several domains of NSs in the MEDLINE and Web of Science databases. The COSMIN checklist was used to assess the methodological quality of each tool. Results: Our review identified five self-assessment scales. Among these, two scales received a Grade A recommendation for use in clinical or research practice: the Self-evaluation Negative Symptom (SNS), which assesses the five domains of NSs, and the Motivation And Pleasure Scale Self-report (MAP-SR), which evaluates anhedonia, avolition, and social withdrawal. Conclusions: The SNS and the MAP-SR are the only tools with sufficient psychometric properties, making them reliable for use in both research and clinical practice. Despite the development of self-assessment tools for NSs, their integration into research and clinical settings remains limited, highlighting the need for increased utilization to enhance the understanding and management of these symptoms. Full article

The prevalence of infidelity is high, although it can have destructive impacts on marital relationships. Most past research has focused on utilitarian concerns against extramarital behavior, analyzing the motivational forces that either deter or foster infidelity as a function of the rewards and costs that unfaithful behavior would involve for the individual. The present research (total N = 1067 Romanian married participants) aimed to highlight the intrinsic moral concerns that deter infidelity in marital relationships by applying the general framework of the Moral Foundations Theory (MFT). The first study developed a measure of the moral reasons for fidelity and examined its dimensions and psychometric properties. The second study investigated its factorial validity and its relationships with the actual tendency to engage in unfaithful behaviors, the intensity of moral emotions toward infidelity, and the use of different emotion regulation strategies. Overall, the results suggest four types of moral reasons for fidelity: heeding rules, reciprocal ownership, loyalty, and decency and nonmaleficence, and the new scale emerged as having satisfactory psychometric proprieties. Higher scores were positively associated with moral disgust, anger, and contempt toward unfaithful marital partners and compassion toward their spouses, as well as cognitive reappraisal and endorsement of the five moral domains described by MFT. Also, married individuals scoring higher on this measure were also found to have a lower propensity toward infidelity. These findings pinpoint a fine-grained outline of the moral underpinnings of fidelity and indicate their potential relevance for the actual tendency to engage in extramarital relations. Full article

As a pivotal area of research in the field of computer vision, the technology for food identification has become indispensable across diverse domains including dietary nutrition monitoring, intelligent service provision in restaurants, and ensuring quality control within the food industry. However, recognizing food images falls within the domain of Fine-Grained Visual Classification (FGVC), which presents challenges such as inter-class similarity, intra-class variability, and the complexity of capturing intricate local features. Researchers have primarily focused on deep information in deep convolutional neural networks for fine-grained visual classification, often neglecting shallow and detailed information. Taking these factors into account, we propose a Multi-level Attention Feature Fusion Network (MAF-Net). Specifically, we use feature maps generated by the Convolutional Neural Networks (CNNs) backbone network at different stages as inputs. We apply a self-attention mechanism to identify local features on these feature maps and then stack them together. The feature vectors obtained through the attention mechanism are then integrated with the original input to enhance data augmentation. Simultaneously, to capture as many local features as possible, we encourage multi-scale features to concentrate on distinct local regions at each stage by maximizing the Kullback-Leibler Divergence (KL-divergence) between the different stages. Additionally, we present a novel approach called subclass center loss (SCloss) to implement label smoothing, minimize intra-class feature distribution differences, and enhance the model’s generalization capability. Experiments conducted on three food image datasets—CETH Food-101, Vireo Food-172, and UEC Food-100—demonstrated the superiority of the proposed model. The model achieved Top-1 accuracies of 90.22%, 89.86%, and 90.61% on CETH Food-101, Vireo Food-172, and UEC Food-100, respectively. Notably, our method not only outperformed other methods in terms of the Top-5 accuracy of Vireo Food-172 but also achieved the highest performance in the Top-1 accuracies of UEC Food-100. Full article

The complicated electromagnetic couplings between nanostructures present substantial challenges in the design and simulation of metasurfaces, especially large-scale elements. The couplings are typically neglected in a conventional simulation. We introduce a computational framework that includes the electromagnetic coupling effects between meta-atoms. Decomposing the incident field and segmenting the computing range for individual local simulations allows for an effective and accurate simulation of the entire metasurface. Numerical examples of a 2 mm diameter cylindrical metalens with a numerical aperture of 0.9 and a 1 mm aperiodic beam splitter show the deviation from the conventional method is reduced by 97% compared to the rigorous method, while the computation times are 10 times and 4 times faster than the rigorous methods, respectively. Full article

This research study explores the readiness, strengths, and challenges of the district health system and local bodies during Nepal’s COVID-19 vaccination program. The primary aim of this study is to identify gaps in the current health system and provide actionable insights for the effective implementation and management of large-scale health programs in the future. A qualitative approach was employed, gathering perspectives of key stakeholders through twelve key informant interviews of stakeholders involved in the planning, management, and execution of the vaccination program in Makwanpur district, the district with the administrative headquarters of Bagmati Province. The study was conducted in the context of Nepal’s transitioning health system following federalization. The qualitative data were analyzed using thematic analysis, guided by the VIRAT2.0 framework for vaccine readiness provided by the WHO. Seven domains from the framework were used to assess readiness and challenges faced by the newly formed health system. The challenges identified by key informants included insufficient logistics and supply chain management, poor coordination between local and higher levels of government, limited awareness about vaccination among the public, vaccine hesitancy, and lack of a robust system for data management and reporting. These findings highlight critical areas for improvement and can be useful evidence to inform the design and implementation of future health programs and policies. Full article

Simple load forecasting and overload prediction models, such as LSTM and XGBoost, are unable to handle the increasing amount of data in power systems. Recently, various foundation models (FMs) for time series analysis have been proposed, which can be scaled up for large time series variables and datasets across domains. However, the simple pre-training setting makes FMs unsuitable for complex downstream tasks. Effectively handling real-world tasks depends on additional data, i.e., covariates, and prior knowledge. Incorporating these through structural modifications to FMs is not feasible, as it would disrupt the pre-trained weights. To address this issue, this paper proposes a frequency domain mixer, i.e., FreqMixer, framework for enhancing the task-specific analytical capabilities of FMs. FreqMixer is an auxiliary network for the backbone FMs that takes covariates as input. It has the same number of layers as the backbone and communicates with it at each layer, allowing the incorporation of prior knowledge without altering the backbone’s structure. Through experiments, FreqMixer demonstrates high efficiency and performance, reducing MAPE by 23.65%, recall by 87%, and precision by 72% in transformer load forecasting during the Spring Festival while improving precision by 192.09% and accuracy by 14% in corresponding overload prediction, all while processing data from over 160 transformers with just 1M additional parameters. Full article

Slope failures caused by changes in soil moisture content have become a growing global concern, resulting in significant loss of life and economic damage. To ensure the stability of slopes, it is necessary to accurately monitor the moisture content and understand the complex interactions between soil, water, and slope behavior. This paper provides a comprehensive overview of advanced soil moisture detection techniques for unsaturated soil slopes, including point-scale measurements and geophysical methods. It first introduces the fundamental concepts of the soil–water characteristic curve (SWCC) and its influence on the shear strength and stability of unsaturated soil slopes. It then delves into the working principles and applications of various point-scale measurement techniques, such as time-domain reflectometry (TDR), frequency-domain reflectometry (FDR), and neutron probe methods. Additionally, this paper explores the use of geophysiDear Editor: The author has checked that the name and affiliation are accuratecal methods, including ground-penetrating radar (GPR), electrical resistivity tomography (ERT), and electromagnetic induction (EMI), for the non-invasive assessment of soil moisture conditions and slope stability monitoring. This review highlights the advantages of integrating multiple geophysical techniques, combined with traditional geotechnical and hydrological measurements, to obtain a more comprehensive understanding of the subsurface conditions and their influence on slope stability. Several case studies are presented to demonstrate the successful application of this integrated approach in various slope monitoring scenarios. The continued advancement in these areas will contribute to the development of more accurate, reliable, and widely adopted solutions for the assessment and management of slope stability risks. Full article

The growing size and complexity of industrial datasets have intensified the need for efficient fault diagnostics tools. This study addresses the challenge of handling large-scale data by developing a data-driven architecture for fault classification in industrial systems. To extract meaningful insights, 15 time-domain features were combined with 5 Feature Selection Methods to optimize model performance by eliminating redundant features. The sensor data and selected features were analyzed using the Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) algorithms to enable accurate fault detection and prediction. The proposed framework was validated using publicly available datasets, namely the Case Western Reserve University (CWRU) bearing dataset and the National Aeronautics and Space Administration Ames Prognostics Center of Excellence (NASA PCoE) lithium-ion battery dataset. The results demonstrate the framework’s adaptability and high efficacy across diverse scenarios, achieving an average F1-score exceeding 98.40% using only 10 selected features. This finding highlights the effectiveness of embedded Feature Selection Methods in improving classification performance while reducing computational complexity. The study underscores the potential of the proposed framework as a foundational tool in intelligent manufacturing, offering a versatile solution to enhance fault diagnostics in diverse industrial applications. Full article

This comprehensive survey explored the evolving landscape of generative Artificial Intelligence (AI), with a specific focus on the recent technological breakthroughs and the gathering advancements toward possible Artificial General Intelligence (AGI). It critically examined the current state and future trajectory of generative AI, exploring how innovations in developing actionable and multimodal AI agents with the ability scale their “thinking” in solving complex reasoning tasks are reshaping research priorities and applications across various domains, while the survey also offers an impact analysis on the generative AI research taxonomy. This work has assessed the computational challenges, scalability, and real-world implications of these technologies while highlighting their potential in driving significant progress in fields like healthcare, finance, and education. Our study also addressed the emerging academic challenges posed by the proliferation of both AI-themed and AI-generated preprints, examining their impact on the peer-review process and scholarly communication. The study highlighted the importance of incorporating ethical and human-centric methods in AI development, ensuring alignment with societal norms and welfare, and outlined a strategy for future AI research that focuses on a balanced and conscientious use of generative AI as its capabilities continue to scale. Full article