Search Results (841)

The Farmworker Family Health Program (FWFHP) annually supports 600 farmworkers in connectivity-challenged rural areas. Traditional paper-based data collection poses validity concerns, prompting a pilot of direct data entry using tablets and satellite internet to enhance efficiency. The purpose of this article is to describe, using the TIDier checklist, a real-time, live data-entry EMR intervention made possible by satellite internet. Utilizing a customized REDCap database, direct data entry occurred through tablets and satellite internet. Patients received a unique medical record number (MRN) at the mobile health clinic, with an interprofessional team providing care. Medication data, captured in REDCap before the mobile pharmacy visit, exhibited minimal defects at 6.9% of 319 prescriptions. To enhance data collection efficiency, strategies such as limiting free text variables and pre-selecting options were employed. Adequate infrastructure, including tablets with keyboards and barcode scanners, ensured seamless data capture. Wi-Fi extenders improved connectivity in open areas, while backup paper forms were crucial during connectivity disruptions. These practices contributed to enhanced data accuracy. Real-time data entry in connectivity-limited settings is viable. Replacing paper-based methods streamlines healthcare provision, allowing timely collection of occupational and environmental health metrics. The initiative stands as a scalable model for healthcare accessibility, addressing unique challenges in vulnerable communities. Full article

Natural language interfaces can transform the construction industry by enhancing accessibility and reducing administrative workload in the day-to-day operations of project teams. This paper introduces the Voice-Integrated Scheduling Assistant for 4D BIM (VISA4D) tool that integrates speech recognition and Natural Language Processing (NLP) capabilities with Building Information Modeling (BIM) to streamline construction schedule updating and maintenance processes. It accepts voice and text inputs for schedule updates, facilitating real-time integration with Autodesk Navisworks, and eliminates the need for direct access to or advanced knowledge of BIM tools. It also provides visual progress tracking abilities through colour-coded elements within the 4D BIM model for communicating task status updates within the project teams. To demonstrate its capability to enhance schedule updating and maintenance efficiency, the VISA4D tool is implemented in an office building project in Canada and user testing is performed. An overall accuracy of 89% was observed in successfully classifying 71 out of 80 tested construction-specific commands, while the user surveys indicated high usability, with 92% of participants finding VISA4D easy to use and reporting consistent command recognition accuracy. This study advances the existing work on AI-enhanced construction management tools by tackling the challenges associated with their practical implementation in field operations. Full article

This paper addresses the team orienteering problem applied to unmanned aerial vehicles (UAVs), considering obstacle avoidance and environmental factors such as wind conditions and payload weight. The objective is to optimize UAV routes to maximize collected rewards while adhering to operational constraints. To achieve this, we employ a simheuristic algorithm for the overall route optimization, while integrating the A* algorithm to determine feasible paths between nodes that avoid obstacles in a 2D grid-based environment. Then, a feedforward neural network estimates travel time based on UAV speed, wind conditions, trajectory distance, and payload weight. This estimation is incorporated into the optimization process to improve route planning accuracy. Numerical experiments evaluate the impact of various parameters, including obstacle placement, UAV speed, wind conditions, and payload weight. These experiments include maps with 30 to 100 points of interest and varying obstacle densities and show that our hybrid method improves solution quality by up to $15\%$ in total profit compared to a baseline approach. Furthermore, computation times remain within 5–10% of the baseline, showing that the added predictive layer maintains computational efficiency. Full article

Mother’s own milk (MOM) offers the highest protection for preterm and low birth weight infants. However, breastfeeding can be challenging during neonatal hospitalization. When MOM is unavailable, donor human milk (DHM) is the recommended alternative for feeding vulnerable neonates. Human milk banks (HMBs) collect, process, and deliver DHM, playing a key role in lactation support and promoting MOM availability. Although HMBs are expanding globally, scale-up remains hindered, restricting equitable DHM access. In Switzerland, despite the existence of eight HMBs, the western region lacked such a facility until 2022. To address this gap, an interdisciplinary team from the Lausanne University Hospital (CHUV) and the Swiss Red Cross Interregional Blood Transfusion Centre (TIR) collaborated to establish a regional HMB. This partnership leveraged both institutions’ available expertise, infrastructure, and resources. After two years of preparation, the CHUV Lactarium launched in 2022 with the support of the Department of Health and Social Action (DSAS) of the Canton of Vaud. This novel human milk bank–blood bank model is fully integrated into the hospital’s neonatal care, nutrition, and breastfeeding programs, operating under a strict quality and coordination system. Since its implementation, the HMB has met 100% of DHM needs, with an 80% breastfeeding bridging rate. It has had a positive impact on neonatal care, family engagement, professional interest, and community awareness of human milk. This case study illustrates how synergistic collaboration can help bridge gaps in establishing a safe, efficient, and equitable HMB model. It also offers a scalable framework adaptable to other settings. Full article

The fragmented nature of Chinese households’ forestland hinders the realization of economies of scale in forestry production. Understanding the role of forestry service outsourcing organizations in mitigating this fragmentation provides a critical foundation for the exploration of pathways to scaled forestry management. Based on tracking data from 500 households across 10 counties in Fujian Province between 2013 and 2018, this study examines an unbalanced panel containing six periods and 2780 valid observations. It constructs a panel Logit model to examine the influence of forestry service outsourcing organizations on the likelihood of forestland transfer by households, and it employs a panel Tobit model to analyze the relationship between these organizations and the scale of forestland transferred. To capture potential heterogeneity, the analysis incorporates households’ part-time status and the forestland terrain conditions. The results indicate that the duration of establishment of county-level forestry project teams and forestry companies in households’ regions significantly reduces the tendency of households to lease out their forestland, especially for those in plain and hilly regions and part-time forestry producers. Furthermore, the longer the establishment history of township-level forestry project teams, the more inclined households are to retain their family forestland management rights. Our study demonstrates that, when specialized forestry service outsourcing organizations emerge in the market, households are less likely to lease out their forestland, thereby retaining management rights, avoiding the risk of forestland loss, and reducing forestland abandonment. As forestry service outsourcing organizations continue to develop and expand—with improvements in service levels and production efficiency—forestry production is gradually transitioning toward a new stage of service-oriented scale operations. Full article

Hospital length of stay (HLOS) is a critical healthcare metric influencing patient outcomes, resource utilization, and healthcare costs. While reducing HLOS can improve hospital efficiency and patient throughput, it also poses risks such as premature discharge, increased readmission rates, and potential compromise of patient safety. This literature review synthesizes current evidence on the determinants of HLOS, including patient-specific factors such as demographics, comorbidities, and socioeconomic status, as well as hospital-related factors like admission route, resource allocation, and institutional policies. We also examine the relationship between HLOS and key clinical outcomes, including mortality, readmission rates, and healthcare-associated infections. Additionally, we evaluate predictive modeling approaches, including artificial intelligence and machine learning, for forecasting HLOS and guiding early intervention strategies. While interventions such as enhanced recovery after surgery (ERAS) protocols, multidisciplinary care teams, and structured discharge planning have demonstrated efficacy in reducing HLOS, their success varies based on healthcare setting, patient complexity, and resource availability. Predictive analytics, incorporating clinical and non-clinical variables, offer promising avenues for improving hospital efficiency, yet may carry risks related to data quality and model bias. Given the impact of HLOS on clinical and economic outcomes, targeted interventions and predictive models should be applied cautiously, with future research focusing on refining personalized discharge strategies and addressing disparities across diverse patient populations. Full article

The strength and conditioning literature examining neuromuscular physiology, bioenergetics, neuroendocrine factors, nutrition and metabolic factors, and the use of ergogenic aids, as well as physical and physiological responses and adaptations, have clearly identified the benefits of participating in regular resistance training programs for athletic populations, especially as it relates to improving muscular strength. Beyond evidence-based research, models for resistance training program implementation are of considerable value for optimizing athletic performance. In fact, several have been provided that address general to specific characteristics of athleticism (i.e., strength endurance, muscular strength, and muscular power) through resistance training over the decades. For instance, a published model known as the strength–endurance continuum that enhances dynamic correspondence (i.e., training specificity) in athletic populations by developing structural, metabolic, and neural capacities across a high-load, low-repetition and low-load, high-repetition range. Further models have been developed to enhance performance approaches (i.e., optimum performance training model) and outcomes (i.e., performance pyramid), even within specific populations, such as youth (i.e., youth physical development model). The ten, five, three, or 10-5-3 (TFT) model for strength and conditioning professionals synthesizes currently available information and provides a framework for the effective implementation of resistance training approaches to suit the needs of athletes at each stage of development. The model includes three key components to consider when designing strength and conditioning programs, denoted by the acronym TFT (ten, five, three). Over recent years, the model has gained much support from teams, coaches, and athletes, mainly due to the ability to streamline common knowledge within the field into an efficient and effective resistance training system. Furthermore, this model is distinctly unique from others as it prioritizes the development of strength–endurance, muscular strength, and muscular power concurrently. This paper explains the model itself and begins to provide recommendations for those interested in implementing TFT-based approaches, including a summary of points as a brief take-home guide to implementing TFT interventions. It is the author’s hope that this paper encourages other performance professionals to share their models to appreciate human ingenuity and advance our understanding of individualized approaches and systems towards the physical development of the modern-day athlete. Full article

Increasing consumer awareness on significant environmental challenges, in addition to forthcoming regulations, is driving domestic appliance manufacturers to adopt an Ecodesign approach to more effectively and significantly reduce the environmental impacts along all of the life cycle phases of their products, minimising energy and material consumption, optimising the life of the product, facilitating recycling, facilitating disassembly, optimising material conservation/renewability, and minimising toxicity. This paper emphasises and discusses the significance of supporting this process by creating a company-specific handbook of guidelines and checklists to design low-environmental-impact products across an entire company’s appliance range. Checklists are design support tools intended to qualitatively assess whether, and to what extent, an Ecodesign guideline has been applied, enabling the evaluation of existing products or newly developed concepts, while also serving to guide and inspire sustainable design decisions. It is argued that these are effective tools in translating eco-efficient design into practice and guiding the whole of product development organisation through a knowledge-based approach. The Handbook of Guidelines to Design Low Environmental Impact Products is the result of a project commissioned by a home appliance company to the LeNSlab (research group on Design and System Innovation for Sustainability) of the Design Department of Politecnico di Milano, elaborated, after preliminary desk research, through a series of activities, interactions, knowledge exchanges, and operative workshops in cooperation with the company team of experts. The handbook contains 7 Ecodesign strategies, 27 sub-strategies, 157 guidelines, and related checklists, to be specific to such a level that they can effectively be applied to all types of company appliances. Full article

The COVID-19 pandemic accelerated the adoption of virtual multidisciplinary teams (MDTs), transforming healthcare delivery through telemedicine. This review examines the integration of telemedicine into multidisciplinary care across various medical specialties, highlighting its benefits and challenges. A comprehensive literature search was conducted across PubMed, Google Scholar, Scopus, and Web of Science, using keywords related to telemedicine and MDTs. Inclusion criteria focused on studies discussing telemedicine implementation in multidisciplinary care, as well as its effectiveness and impact on patient outcomes, while non-English studies, single-case reports, and articles lacking explicit discussions on MDT integration were excluded. Data extraction covered telemedicine platforms, specialties involved, patient satisfaction, and clinical outcomes. Our findings suggest that virtual MDTs enhance efficiency, accessibility, and patient satisfaction, particularly in remote and underserved areas. However, challenges, such as technological barriers, disparities in digital access, and maintaining effective team communication, persist. Despite these limitations, telemedicine has demonstrated significant potential in improving diagnostic accuracy and treatment coordination. Future efforts should focus on optimizing infrastructure, digital training for healthcare providers, and regulatory frameworks to guarantee long-term sustainability. Full article

This study introduces a novel approach to cyber defense exercises, emphasizing the emulation of technical tasks to create realistic incident response scenarios. Unlike traditional cyber ranges or tabletop exercises, this method enables both management and technical leaders to engage in decision-making processes without requiring a full technical setup. The initial observations indicate that exercises based on the emulation of technical tasks require less preparation time compared to conventional methods, addressing the growing demand for efficient training solutions. This study aims to assist organizations in developing their own cyber defense exercises by providing practical insights into the benefits and challenges of this approach. The key advantages observed include improved procedural compliance, inter-team communication, and a better understanding of the chain of command as participants navigate realistic, organization-wide scenarios. However, new challenges have also emerged, such as managing the simulation tempo and balancing technical complexity—particularly in offense–defense team configurations. This study proposes a structured and scalable approach as a practical alternative to the traditional training methods, aligning better with the evolving demands of modern cyber defense. Full article

It is noted that existing mine emergency-rescue algorithms have overlooked the requirement for multi-route sharing at critical nodes and have offered limited network visualisation. Consequently, a multi-team rescue-path-planning algorithm based on FA-MDPSO (Firefly Algorithm-Multiple Constraints Discrete Particle Swarm Optimisation) was proposed, and a graph-structure optimisation method combining a Force-Directed Layout with Breadth-First Search was introduced for node arrangement and visualisation. Methodologically, the superiority of the improved DPSO (Discrete Particle Swarm Optimisation) in route-planning precision was first validated on the DIMACS dataset. Subsequently, the hyperparameters of MDPSO (Multiple Constraints Discrete Particle Swarm Optimisation) were optimised by means of four intelligent algorithms—ACO (Ant Colony Optimization), FA (Firefly Algorithm), GWO (Grey Wolf Optimizer) and WOA (Whale Optimization Algorithm). Finally, simulations of one to three rescue-team deployments were conducted within a mine-fire scenario, and node-importance analysis was performed. Results indicated that FA-MDPSO achieved comprehensive superiority in route precision, search efficiency and convergence speed, with FA-based hyperparameter optimisation proving most effective in comparative experiments. The graph-structure optimisation was found to substantially reduce crossings and enhance hierarchical clarity. Moreover, the three-team deployment yielded the shortest equivalent path (56,357.02), and node-visitation frequency was observed to be highly concentrated on a small number of key nodes. This not only significantly improves the collaborative rescue efficiency but also provides intuitive and practical technical support for intelligent mine rescue operations. It lays an important foundation for optimising mine emergency rescue plans, ensuring the safety of underground personnel, and promoting the intelligent development of mines. Full article

Background: Prostate cancer is recognised as the second most common diagnosed cancer in men and remains a significant global public health concern. In Ireland, the incidence of prostate cancer continues to rise, with approximately 1 in 6 men being diagnosed in their lifetime. Men’s experiences with prostate cancer are complex, necessitating further research into the factors influencing diagnosis and treatment. Therefore, this study aims to explore men’s experiences with prostate cancer, emphasising the interplay between screening, diagnosis, and the lived experiences of those affected. Methods: A qualitative descriptive study was conducted among men with prostate cancer in Ireland. Using a purposive sampling (n = 11) were interviewed with data saturation guiding sample size determination. A semi-structured interview guide was used for data collection either face-to-face or via Microsoft Teams and phone calls. Data were analysed using Braune and Clarke’s thematic analysis approach after transcription, with NVivo 12.0 software supporting analysis. Results: Thematic analysis identified five themes: systemic obstacle in timely cancer detection, the role of efficient system in cancer care, emotional resilience in cancer recovery, redefining normalcy post treatment and harnessing specialised support network in coping strategies. These themes were examined through the lens of the Biopsychosocial Model to understand their interconnected nature and impact on patient experiences. Conclusions: This study highlights the complex factors affecting prostate cancer patients’ experiences, emphasizing the need for a patient-centred approach, addressing systemic disparities, and promoting multidisciplinary care. It suggests implementing evidence-based survivorship care frameworks to improve quality of life for survivors, with future research exploring long-term effects of integrated care models. Full article

Accurate simulation of extreme precipitation events is crucial for managing climate-vulnerable sectors in Southern Africa, as such events directly impact agriculture, water resources, and disaster preparedness. However, global climate models frequently struggle to capture these phenomena, which limits their practical applicability. This study investigates the effectiveness of three bias correction techniques—scaled distribution mapping (SDM), quantile distribution mapping (QDM), and QDM with a focus on precipitation above and below the 95th percentile (QDM95)—and the daily precipitation outputs from 11 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset was served as a reference. The bias-corrected and native models were evaluated against three observational datasets—the CHIRPS, Multi-Source Weighted Ensemble Precipitation (MSWEP), and Global Precipitation Climatology Center (GPCC) datasets—for the period of 1982–2014, focusing on the December-January-February season. The ability of the models to generate eight extreme precipitation indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI) was evaluated. The results show that the native and bias-corrected models captured similar spatial patterns of extreme precipitation, but there were significant changes in the amount of extreme precipitation episodes. While bias correction generally improved the spatial representation of extreme precipitation, its effectiveness varied depending on the reference dataset used, particularly for the maximum one-day precipitation (Rx1day), consecutive wet days (CWD), consecutive dry days (CDD), extremely wet days (R95p), and simple daily intensity index (SDII). In contrast, the total rain days (RR1), heavy precipitation days (R10mm), and extremely heavy precipitation days (R20mm) showed consistent improvement across all observations. All three bias correction techniques enhanced the accuracy of the models across all extreme indices, as demonstrated by higher pattern correlation coefficients, improved Taylor skill scores (TSSs), reduced root mean square errors, and fewer biases. The ranking of models using the comprehensive rating index (CRI) indicates that no single model consistently outperformed the others across all bias-corrected techniques relative to the CHIRPS, GPCC, and MSWEP datasets. Among the three bias correction methods, SDM and QDM95 outperformed QDM for a variety of criteria. Among the bias-corrected strategies, the best-performing models were EC-Earth3-Veg, EC-Earth3, MRI-ESM2, and the multi-model ensemble (MME). These findings demonstrate the efficiency of bias correction in improving the modeling of precipitation extremes in Southern Africa, ultimately boosting climate impact assessments. Full article

In January of 2019, the University of Iowa Stead Family Children’s Hospital (UI SFCH) formalized their Pediatric Psychosocial Oncology Program by utilizing 15 evidence-based Standards for Psychosocial Care for Children with Cancer and Families as a foundation for program development. The psychosocial oncology clinical team members identified ongoing gaps in care and a need to improve progress toward achieving these standards. Reviewing and analyzing the Pediatric Psychosocial Standard of Care Institutional Assessment Tool further highlighted the need for program development but also demonstrated the need to design institutionally specific objective measures to monitor program improvements over time. The current project focused on the creation of a program evaluation system with objective measures specific to the UI SFCH practice setting. Barriers such as staffing and cost were identified and addressed. Additionally, a REDCap^® database using a structured chart review as its foundation was initiated, which permitted the comprehensive evaluation of the standards of care at UI SFCH. The Matrix and Guidelines included in the Pediatric Psychosocial Standard of Care Institutional Assessment Tool comprised the framework to develop institution specific objective measurements for each standard of care. The objective measures of interest were social work assessments and provider biopsychosocial assessments. Data were exported and uploaded to a statistical program for data analysis. The statistical significance of percentage changes was evaluated with a one-tailed t-test; p values < 0.05 were considered significant. The development of this REDCap^® database project allowed for the evaluation of the program’s current efficiency in implementing the PSCPCC standards of care. Using the database in the future will allow psychosocial oncology team members to easily identify other areas for improvement and to ensure that all 15 standards of psychosocial care are being comprehensively addressed in the care of pediatric oncology patients and interactions with their families. Full article

Systematic reviews require labor-intensive screening processes—an approach prone to bottlenecks, delays, and scalability constraints in large-scale reviews. Large Language Models (LLMs) have recently emerged as a powerful alternative, capable of operating in zero-shot or few-shot modes to classify abstracts according to predefined criteria without requiring continuous human intervention like semi-automated platforms. This review focuses on the central challenges that users in the biomedical field encounter when integrating LLMs—such as GPT-4—into evidence-based research. It examines critical requirements for software and data preprocessing, discusses various prompt strategies, and underscores the continued need for human oversight to maintain rigorous quality control. By drawing on current practices for cost management, reproducibility, and prompt refinement, this article highlights how review teams can substantially reduce screening workloads without compromising the comprehensiveness of evidence-based inquiry. The findings presented aim to balance the strengths of LLM-driven automation with structured human checks, ensuring that systematic reviews retain their methodological integrity while leveraging the efficiency gains made possible by recent advances in artificial intelligence. Full article