Search Results (569)

With the growing emphasis on running in urban health initiatives, understanding the spatiotemporal dynamics of running behavior has become essential for smart city development. This study harnesses multi-source big data—including running trajectories, points of interest (POIs), and remote sensing data—to systematically analyze factors influencing running space selection. Through stepwise regression analysis, we identify 16 significant variables encompassing accessibility, diversity, and comfort dimensions. The Geographical and Temporally Weighted Regression (GTWR) model is then employed to uncover distinct spatiotemporal heterogeneity patterns, demonstrating how these factors variably influence running activities across different urban zones and time periods. The methodology and findings contribute to geospatial analysis in urban health studies while providing practical guidance for creating more inclusive, runner-friendly urban environments. Full article

Livability is an important consideration when planning urban public open spaces. To increase urban livability and the potential for a variety of human activities—including necessary, optional, and social activities—in winter cities, climate-responsive urban public open spaces that encourage urban activities must be developed. Erzurum, a winter city, was selected as a case study to evaluate the relationships between climatic conditions and human outdoor activities in urban spaces. This study’s methodological contributions include naturalistic observations and a descriptive examination of urban public open spaces, with a focus on soft mobility within such spaces in a neighborhood area in Erzurum. This study consists of three stages. (1) The first part defines winter cities globally, focusing on livability-related, tangible, and climate-responsive interventions in urban public open spaces. (2) The second part of this study follows the winter observation method, utilizing photographs to investigate how seasonal factors affect various kinds of outdoor activities and pedestrian systems. These photographs are presented and classified based on five key categories: street and walkway design, building access points, parking configurations, material and lighting treatments, and vegetative strategies. (3) Finally, this study uses solution-oriented thinking to provide recommendations informing climate-responsive design principles for urban spaces in Erzurum. Full article

Prognostics and Health Management (PHM), including fault diagnosis and Remaining Useful Life (RUL) prediction, is critical for ensuring the reliability and efficiency of industrial equipment. However, traditional AI-based methods require extensive expert intervention in data preprocessing, model selection, and hyperparameter tuning, making them less scalable and accessible in real-world applications. To address these limitations, this study proposes an autonomous agent powered by Large Language Models (LLMs) to automate predictive modeling for fault diagnosis and RUL prediction. The proposed agent processes natural language queries, extracts key parameters, and autonomously configures AI models while integrating an iterative optimization mechanism for dynamic hyperparameter tuning. Under identical settings, we compared GPT-3.5 Turbo, GPT-4, GPT-4o, GPT-4o-mini, Gemini-2.0-Flash, and LLaMA-3.2 on accuracy, latency, and cost, using GPT-4 as the baseline. The most accurate model is GPT-4o with an accuracy of 0.96, a gain of six percentage points over GPT-4. It also reduces end-to-end time to 1.900 s and cost to $0.00455 per 1 k tokens, which correspond to reductions of 32% and 59%. For speed and cost efficiency, Gemini-2.0-Flash reaches 0.964 s and $0.00021 per 1 k tokens with accuracy 0.94, an improvement of four percentage points over GPT-4. The agent operates through interconnected modules, seamlessly transitioning from query analysis to AI model deployment while optimizing model selection and performance. Experimental results confirmed that the developed agent achieved stable performance under ideal configurations, attaining accuracy 0.97 on FordA for binary fault classification, accuracy 0.95 on CWRU for multi-fault classification, and an asymmetric score of 380.74 on C-MAPSS FD001 for RUL prediction, while significantly reducing manual intervention. By bridging the gap between domain expertise and AI-driven predictive maintenance, this study advances industrial automation, improving efficiency, scalability, and accessibility. The proposed approach paves the way for the broader adoption of autonomous AI systems in industrial maintenance. Full article

This paper presents a flexible approach to a multipurpose, heterogeneous archive and data management system model that merges the robustness of legacy grid-based technologies with modern cloud and edge computing paradigms. It leverages innovations driven by big data, IoT, AI, and machine learning to create an adaptive data storage and processing framework. In today’s digital age, where data are the new intangible gold, the “gold rush” lies in managing and storing massive datasets effectively—especially when these data serve governmental or commercial purposes, raising concerns about privacy and data misuse by third-party aggregators. Astronomical data, in particular, require this same thoughtful approach. Scientific discovery increasingly depends on efficient extraction and processing of large datasets. Distributed archival models, unlike centralized warehouses, offer scalability by allowing data to be accessed and processed across locations via cloud services. Incorporating edge computing further enables real-time access with reduced latency. Major astronomical projects must also avoid common single points of failure (SPOFs), often resulting from suboptimal technological choices driven by collaboration politics or In-Kind Contributions (IKCs). These missteps can hinder innovation and long-term project success. The principal goal of this work is to outline best practices in archival and data management projects—from policy development and task planning to use-case definition and implementation. Only after these steps can a coherent selection of hardware, software, or virtual environments be made. The proposed model—CTAARCHS (Cloud-based Technologies for Astronomical Archiving Research Contents and Handling Systems)—is an open-source, multidisciplinary platform supporting big data needs in astronomy. It promotes broad institutional collaboration, offering code repositories and sample data for immediate use. Full article

Antibiotic resistance is a growing global health threat, with critical care settings representing one of the most vulnerable arenas due to the high burden of infection and frequent empirical antibiotic use. Rapid and precise diagnosis of infectious pathogens is crucial for initiating appropriate therapy, minimizing unnecessary antimicrobial exposure, and supporting effective stewardship programs. This review explores how innovative diagnostic technologies are reshaping infection management and antimicrobial stewardship in critical care. We examine the clinical utility of molecular assays, multiplex PCR, MALDI-TOF mass spectrometry, metagenomic sequencing, point-of-care (POC) diagnostics, and emerging tools like biosensors and AI-powered predictive models. These platforms enable earlier pathogen identification and resistance profiling, facilitating timely and targeted therapy while minimizing unnecessary broad-spectrum antibiotic use. By integrating diagnostics into stewardship frameworks, clinicians can optimize antimicrobial regimens, improve patient outcomes, and reduce resistance selection pressure. Despite their promise, adoption is challenged by cost, infrastructure, interpretation complexity, and inequitable access, particularly in low-resource settings. Future perspectives emphasize the need for scalable, AI-enhanced, and globally accessible diagnostic solutions. In bridging innovation with clinical application, diagnostic advancements can serve as pivotal tools in the global effort to curb antimicrobial resistance in critical care environments. Full article

Background and Objectives: Despite the potential benefits for the patient, aerospace interventions pose significant risks. Pre-hospital triage and patient transport are two essential elements for achieving an organized system of trauma. The advantages and disadvantages of using land transport from the scene of the accident to the trauma centers have been extensively studied, but there are gaps for air transport, and their exact level of efficiency is not known. Materials and Methods: The present study includes a total number of 77 patients, present at SMURD Galați air service for polytraumas caused by various mechanisms, with pluri-regional involvement. The identification of patients, as well as the selection of the most important anamnestic data, was performed after signing a confidentiality agreement; subsequently, all this information was introduced in centralized tables made in the statistical program IBM SPSS Statistics V24. Results: Out of the total of 77 polytraumatized patients who needed air transfer, an average age of 17.3 years will be noted, with a predominance of males in a 2:1 ratio. Most polytraumas are due to road accidents (74%) and patients with minimal tri-regional damage (51.4%). Conclusions: Taking into account the existing statistics in this research, it is important to implement prevention elements, designed based on the profile of the polytraumatized patient. Thus, accessing the most important characteristics of these patients can be an extremely important starting point in reducing the incidence of polytrauma or even patient deaths. Full article

Microglia are central to neuroimmune responses and undergo dynamic structural and functional changes in models of stress and addiction, and in response to pharmacological treatments. While transcriptomic and proteomic assays provide insights into molecular profiles, morphological analysis remains a valuable proxy for assessing region-specific microglial response. However, morphological features alone often fail to capture the full complexity of microglial function, underscoring the need for standardized methods and complementary approaches. Here, we describe a standardized imaging pipeline for analyzing microglia in the nucleus accumbens core (NAcore), integrating unbiased confocal image acquisition with precise anatomical reference points. We compare two widely used image analysis platforms—IMARIS and CellSelect-3DMorph—highlighting their workflows, output metrics, and utility in quantifying microglial morphology following treatment with adenosine triphosphate (ATP). Both tools detect well described features of microglial dynamics, though they differ in automation level, analysis speed, and output types. Our findings demonstrate that both platforms provide reliable morphological data, with CellSelect-3DMorph offering a rapid, open-access alternative for high-throughput analysis. Additionally, using software-derived parameters in principal component analysis clustering has proven useful for identifying distinct subpopulations of microglia separated by their morphology. This work provides a practical framework for morphological analysis and promotes reproducibility in microglial studies under environmental and pharmacological interventions. Full article

Background/Objectives: This study investigated how doctor visit(s), body image discrepancy, and perceived health status are associated with receiving a medical weight problem diagnosis. Methods: The sample included 458 Hispanic adults (366 women, 92 men) who completed a health survey at health fairs. Results: Descriptive analyses indicated that 51.4% of women and 54.3% of men were classified as overweight or obese, yet only 30% received a medical weight problem diagnosis. Most participants selected an ideal body shape that was thinner than their perceived body shape. Separate logistic regression analyses were conducted by gender to assess associations between body image discrepancy, perceived health status, and receiving a medical weight problem diagnosis, controlling for age. Findings revealed that women who had visited a doctor in the past year had 5.02 times the odds (95% CI:1.98–12.73) of receiving a medical weight problem diagnosis compared to those who had not. Each one-point increase in body image discrepancy was associated with a 1.88-fold increase in the odds of receiving a diagnosis (95% CI:1.49–2.37). Conversely, a one-point increase in perceived health status was associated with 1.59 times the odds (95% CI: 0.47–0.83) of not receiving a diagnosis. For men, those who had visited the doctor in the past year had 14.17 times the odds (95% CI:1.53–131.17) of receiving a medical weight problem diagnosis. Each one-point increase in body image discrepancy was associated with 1.60 times the odds of receiving a diagnosis (95% CI:1.01–2.54). However, perceived health status was not a significant predictor of diagnosis among men. Conclusions: Addressing healthcare access barriers and considering the roles of body image and perceived health status could improve obesity diagnosis and treatment in Hispanic populations. Full article

The concept of governance has gained increasing attention across various fields of study. However, its application within the specific context of educational policies, particularly within compulsory public education, remains fragmented and underexplored. To answer the questions “How is governance conceptualized in the context of the compulsory public education system?” and “What contributions to future research emerge from this review?”, 32 peer-reviewed articles published in open-access journals between 2019 and 2023 were extracted from the Web of Science, Scopus, and ERIC databases and selected following PRISMA guidelines. Results from this systematic literature review analysis suggest a sustained yet moderate interest in the field, as evidenced by the reviewed publications, different theoretical and conceptual approaches, and research themes that illustrate different aspects of educational systems. Research gaps include the lack of a consolidated and integrated theoretical–conceptual framework on educational governance; the under-representation of specific actors, contexts, and points of view about how educational policies intentions are interpreted and enacted; insufficient critical analyses of, among others, educational leadership, digital transformation, and non-state actors’ influence in educational governance; and limited discussion of governance’s effects on educational justice, equity and quality. The main limitations relate to geographic, linguistic, and cultural biases of the analyzed studies, the exclusion of non-open-access articles, and the predominance of qualitative methodological approaches, which restrict generalizability. To address these challenges, future research should follow the adoption of interdisciplinary approaches, longitudinal and context-sensitive studies, and the use of mixed methodologies. These findings could contribute to a more informed discussion, avoiding reductionist interpretations and more open and critical perspectives on how educational governance transcends organizational and technical structures by incorporating political, ethical, and contextual dimensions that challenge the quality of educational systems. Full article

The ATP-binding cassette (ABC) gene family represents one of the most extensive and evolutionarily conserved groups of proteins, characterized by ATP-dependent transporters that mediate the movement of substrates across cellular membranes. Despite their well-documented functions in various biological processes, the specific contributions of ABC transporters in eggplant (Solanum melongena L.) remain unexplored. To address this gap, we conducted a comprehensive genome-wide identification and expression profiling of ABC transporter-encoding genes in eggplant. Our investigation identified 159 SmABC genes encoding ABC transporter that were irregularly dispersed across all 12 chromosomes. The encoded proteins exhibited considerable diversity in size, with amino acid lengths varying from 55 to 2628 residues, molecular weights ranging between 4.04 and 286.42 kDa, and isoelectric points spanning from 4.89 to 11.62. Phylogenetic analysis classified the SmABC transporters into eight distinct subfamilies, with the ABCG subfamily being the most predominant. Subcellular localization predictions revealed that most SmABC proteins were localized to the plasma membrane. Members within the same subfamily exhibited conserved motif arrangements and exon–intron structures, suggesting functional and evolutionary conservation. Promoter analysis identified both shared and unique cis-regulatory elements associated with transcriptional regulation. We identified 9 tandem duplication gene pairs and 20 segmental duplication pairs in the SmABC gene family, with segmental duplication being the major mode of expansion. Non-synonymous to synonymous substitutions (Ka/Ks) analysis revealed that paralogs of SmABC family genes underwent mainly purifying selection during the evolutionary process. Comparative genomic analysis demonstrated collinearity between eggplant, Arabidopsis thaliana, and tomato (Solanum lycopersicum), confirming homology among SmABC, AtABC, and SlABC genes. Tissue-specific expression profiling revealed differential SmABC expression patterns, with three distinct genes, SmABCA16, SmABCA17 and SmABCG15, showing preferential expression in purple-peeled fruits (A1, A3, and A5 accessions), implicating their potential involvement in anthocyanin transport. Functional validation via SmABCA16 silencing led to a significant downregulation of SmABCA16 and reduced purple coloration, indicating its regulatory role in anthocyanin transport in eggplant fruit peel. This comprehensive genomic and functional characterization of ABC transporters in eggplant establishes a critical foundation for understanding their biological roles and supports targeted breeding strategies to enhance fruit quality traits. Full article

This study evaluates the performance of U.S. data center Exchange-Traded Funds (ETFs) relative to major equity and technology benchmarks, using monthly returns from January 2000 through December 2024, with particular emphasis on the COVID-19 period and the subsequent post-vaccine era. Data center ETFs have not provided better risk-adjusted returns even though they are often advertised as access points to the digital economy. Digital infrastructure demand increased through the pandemic but did not improve the performance of these funds which stayed weak across both traditional and conditional multi-factor asset pricing models. These ETFs struggle with asset selection and market timing proficiency, which leads to relatively poor performance results during volatile market conditions. The downside risks linked to these funds tend to match or exceed the downside risks of broader indices like the S&P 1500 Information Technology Index. Although these investments are based on strong thematic narratives, they do not achieve returns that align with investor expectations. Full article

This work formulates and solves the actual problem of studying the logistics of unmanned aerial vehicle (UAV) operations in facility security planning. The study is related to security tasks, including perimeter control, infrastructure condition monitoring, prevention of unauthorized access, and analysis of potential threats. Thus, the topic of the proposed publication is relevant as it examines the sequence of logistical actions in the large-scale application of a swarm of drones for facility protection. The purpose of the research is to create a set of mathematical and simulation models that can be used to analyze the capabilities of a drone swarm when organizing security measures. The article analyzes modern problems of using a drone swarm: formation of the swarm, assessment of its potential capabilities, organization of patrols, development of monitoring scenarios, planning of drone routes and assessment of the effectiveness of the security system. Special attention is paid to the possibilities of wave patrols to provide continuous surveillance of the object. In order to form a drone swarm and possibly divide it into groups sent to different surveillance zones, the necessary UAV capacity to effectively perform security tasks is assessed. Possible security scenarios using drone waves are developed as follows: single patrolling with limited resources; two-wave patrolling; and multi-stage patrolling for complete coverage of the protected area with the required number of UAVs. To select priority monitoring areas, the functional potential of drones and current risks are taken into account. An optimization model of rational distribution of drones into groups to ensure effective control of the protected area is created. Possible variants of drone group formation are analyzed as follows: allocation of one priority surveillance zone, formation of a set of key zones, or even distribution of swarm resources along the entire perimeter. Possible scenarios for dividing the drone swarm in flight are developed as follows: dividing the swarm into groups at the launch stage, dividing the swarm at a given navigation point on the route, and repeatedly dividing the swarm at different patrol points. An original algorithm for the formation of drone flight routes for object surveillance based on the simulation modeling of the movement of virtual objects simulating drones has been developed. An agent-based model on the AnyLogic platform was created to study the logistics of security operations. The scientific novelty of the study is related to the actual task of forming possible strategies for using a swarm of drones to provide integrated security of objects, which contributes to improving the efficiency of security and monitoring systems. The results of the study can be used by specialists in security, logistics, infrastructure monitoring and other areas related to the use of drone swarms for effective control and protection of facilities. Full article

The increasing adoption of digital technologies in the agriculture sector has significantly contributed to optimizing on-farm routines, especially in data-driven decision-making. This study aimed to develop an application to determine the slaughter point of lambs by predicting subcutaneous fat thickness (SFT) using pre-slaughter parameters such as body weight (BW), body condition score (BCS), and skinfold measurements at the brisket (BST), lumbar (LST), and tail base (TST), obtained using an adipometer. A total of 45 Pantaneiros lambs were evaluated, finished in feedlot, and slaughtered at different body weights. Each pre-slaughter weight class showed a distinct carcass pattern when all parameters were included in the model. Exploratory analysis revealed statistical significance for all variables (p < 0.001). BW and LST were selected to construct the predictive equation (R² = 55.44%). The regression equations were integrated into the developed application, allowing for in-field estimation of SFT based on simple measurements. Compared to conventional techniques such as ultrasound or visual scoring, this tool offers advantages in portability, objectivity, and immediate decision-making support. In conclusion, combining accessible technologies (e.g., adipometer) with traditional variables (e.g., body weight), represents an effective alternative for production systems aimed at optimizing and enhancing the value of lamb carcasses. Full article

Precision neurosurgery is rapidly evolving as a medical specialty by merging genomic medicine, multi-omics technologies, and artificial intelligence (AI) technology, while at the same time, society is shifting away from the traditional, anatomic model of care to consider a more precise, molecular model of care. The general purpose of this review is to contemporaneously reflect on how these advances will impact neurosurgical care by providing us with more precise diagnostic and treatment pathways. We hope to provide a relevant review of the recent advances in genomics and multi-omics in the context of clinical practice and highlight their transformational opportunities in the existing models of care, where improved molecular insights can support improvements in clinical care. More specifically, we will highlight how genomic profiling, CRISPR-Cas9, and multi-omics platforms (genomics, transcriptomics, proteomics, and metabolomics) are increasing our understanding of central nervous system (CNS) disorders. Achievements obtained with transformational technologies such as single-cell RNA sequencing and intraoperative mass spectrometry are exemplary of the molecular diagnostic possibilities in real-time molecular diagnostics to enable a more directed approach in surgical options. We will also explore how identifying specific biomarkers (e.g., IDH mutations and MGMT promoter methylation) became a tipping point in the care of glioblastoma and allowed for the establishment of a new taxonomy of tumors that became applicable for surgeons, where a change in practice enjoined a different surgical resection approach and subsequently stratified the adjuvant therapies undertaken after surgery. Furthermore, we reflect on how the novel genomic characterization of mutations like DEPDC5 and SCN1A transformed the pre-surgery selection of surgical candidates for refractory epilepsy when conventional imaging did not define an epileptogenic zone, thus reducing resective surgery occurring in clinical practice. While we are atop the crest of an exciting wave of advances, we recognize that we also must be diligent about the challenges we must navigate to implement genomic medicine in neurosurgery—including ethical and technical challenges that could arise when genomic mutation-based therapies require the concurrent application of multi-omics data collection to be realized in practice for the benefit of patients, as well as the constraints from the blood–brain barrier. The primary challenges also relate to the possible gene privacy implications around genomic medicine and equitable access to technology-based alternative practice disrupting interventions. We hope the contribution from this review will not just be situational consolidation and integration of knowledge but also a stimulus for new lines of research and clinical practice. We also hope to stimulate mindful discussions about future possibilities for conscientious and sustainable progress in our evolution toward a genomic model of precision neurosurgery. In the spirit of providing a critical perspective, we hope that we are also adding to the larger opportunity to embed molecular precision into neuroscience care, striving to promote better practice and better outcomes for patients in a global sense. Full article

This study aimed to re-evaluate a historical war supply route within the context of cultural tourism, to revitalize its natural, historical, and cultural values, and to integrate it with existing hiking and trekking routes. Remote sensing (RS) and geographic information system (GIS) technologies were utilized, and land surveys were conducted to support the analysis and validate the existing data. Data for slope, one of the most critical factors for hiking route selection, were generated, and the optimal route between the starting and destination points was identified using least cost path analysis (LCPA). Historical, touristic, and recreational rest stops along the route were mapped with precise coordinates, and both the existing and the newly generated routes were assessed in terms of their accessibility to these points. Field validation was carried out based on the experiences of expert hikers. According to the results, the length of the existing hiking route was determined to be 15.72 km, while the newly developed trekking route measured 17.36 km. These two routes overlap for 7.75 km, with 9.78 km following separate paths in a round-trip scenario. It was concluded that the existing route is more suitable for hiking, whereas the newly developed route is better suited for trekking. Full article