Search Results (70)

Background: Overcrowded emergency departments (EDs) create significant challenges for patient management and hospital efficiency. In response, Amiens Picardy University Hospital (APUH) developed the “Prediction of the Patient Pathway in the Emergency Department” (3P-U) model to enhance patient flow management. Objectives: To develop and clinically validate an explainable artificial intelligence (XAI) model for hospital admission predictions, using structured triage data, and demonstrate its real-world applicability in the ED setting. Methods: Our retrospective, single-center study involved 351,019 patients consulting in APUH’s EDs between 2015 and 2018. Various models (including a cross-validation artificial neural network (ANN), a k-nearest neighbors (KNN) model, a logistic regression (LR) model, and a random forest (RF) model) were trained and assessed for performance with regard to the area under the receiver operating characteristic curve (AUROC). The best model was validated internally with a test set, and the F1 score was used to determine the best threshold for recall, precision, and accuracy. XAI techniques, such as Shapley additive explanations (SHAP) and partial dependence plots (PDP) were employed, and the clinical explanations were evaluated by emergency physicians. Results: The ANN gave the best performance during the training stage, with an AUROC of 83.1% (SD: 0.2%) for the test set; it surpassed the RF (AUROC: 71.6%, SD: 0.1%), KNN (AUROC: 67.2%, SD: 0.2%), and LR (AUROC: 71.5%, SD: 0.2%) models. In an internal validation, the ANN’s AUROC was 83.2%. The best F1 score (0.67) determined that 0.35 was the optimal threshold; the corresponding recall, precision, and accuracy were 75.7%, 59.7%, and 75.3%, respectively. The SHAP and PDP XAI techniques (as assessed by emergency physicians) highlighted patient age, heart rate, and presentation with multiple injuries as the features that most specifically influenced the admission from the ED to a hospital ward. These insights are being used in bed allocation and patient prioritization, directly improving ED operations. Conclusions: The 3P-U model demonstrates practical utility by reducing ED crowding and enhancing decision-making processes at APUH. Its transparency and physician validation foster trust, facilitating its adoption in clinical practice and offering a replicable framework for other hospitals to optimize patient flow. Full article

The air quality within urban public transport is a critical determinant of passenger health. In the crowded and poorly ventilated cabins of Almaty’s metro, buses, and trolleybuses, concentrations of CO₂ and PM_2.5 often accumulate, elevating the risk of respiratory and cardiovascular diseases. This study investigates the air quality along three of the city’s busiest transport corridors, analyzing how the concentrations of CO₂, PM_2.5, and PM₁₀, as well as the temperature and relative humidity, fluctuate with the passenger density and time of day. Continuous measurements were collected using the Tynys mobile IoT device, which was bench-calibrated against a commercial reference sensor. Several machine learning models (logistic regression, decision tree, XGBoost, and random forest) were trained on synchronized environmental and occupancy data, with the XGBoost model achieving the highest predictive accuracy at 91.25%. Our analysis confirms that passenger occupancy is the primary driver of in-cabin pollution and that these machine learning models effectively capture the nonlinear relationships among environmental variables. Since the surveyed routes serve Almaty’s most densely populated districts, improving the ventilation on these lines is of immediate importance to public health. Furthermore, the high-temporal-resolution data revealed short-term pollution spikes that correspond with peak ridership, advancing the current understanding of exposure risks in transit. These findings highlight the urgent need to combine real-time monitoring with ventilation upgrades. They also demonstrate the practical value of using low-cost IoT technologies and data-driven analytics to safeguard public health in urban mobility systems. Full article

Background: The Index of Orthodontic Treatment Need (IOTN) is widely used to assess the need for orthodontic treatment. IOTN consists of the Dental Health Component (DHC) and the Aesthetic Component (AC), evaluating malocclusion morphologically and aesthetically, respectively. However, the discriminatory power of individual DHC items and their relationship with AC grades remain unclear. Objective: This study aimed to evaluate the effectiveness of individual DHC items in school dental examinations and investigate their contribution to AC grades among junior high and high school students. Methods: A total of 726 students (443 males, 283 females; aged 12–18 years) from Tsurumi University Junior and Senior High School, excluding 168 students undergoing or having completed orthodontic treatment, were included. Nine calibrated orthodontists assessed DHC and AC using IOTN during standardized school examinations. The discriminatory power and information precision of DHC items were evaluated by Item Response Theory (IRT) analysis using three-, two-, or one-parameter logistic models depending on convergence. Correspondence analysis visualized the correlation between DHC and AC grades. Simple linear regression analyzed the contribution of each DHC item to AC grades. Results: Orthodontic treatment need was identified in 21.1% of students. Females showed a higher rate of treatment need than males. Correspondence analysis suggested that aesthetic evaluations were more lenient than morphological evaluations. IRT and regression analysis revealed that crowding (4.d), increased overjet (2.a), and increased overbite (2.f) demonstrated high discriminatory power and significant contributions to AC grades. Conclusions: Among the DHC items, crowding, increased overjet, and increased overbite had higher discriminatory power for malocclusion and contributed more significantly to AC evaluations compared to other items. Full article

Objectives: This study investigates the presence of crowding-in or crowding-out effects of long-term care insurance (LTCI) on older adults’ care in Korea. Additionally, it examines the influence of old-age income security and private systems, including private transfer income and private health insurance, on these effects. The analysis focuses on three aspects: family-provided care, private non-family care, and total care expenses. Methods: This study conducted logistic and linear regression. Logistic regression was used to examine the likelihood of receiving family-provided and private non-family care, while linear regression analyzed factors associated with total care expenditures. Results: The results reveal a crowding-in effect for family care, as greater utilization of public LTCI is positively associated with family-provided care. However, the relationship between public LTCI and private non-family care was not statistically significant, suggesting that the crowding-in effect on private care systems remains limited. Lastly, LTCI utilization was significantly associated with higher care expenditures. It is noteworthy that the current public LTCI in Korea has low coverage, resulting in insufficient care provision. Consequently, there is growing activity in the private care sector. Conclusions: These findings highlight the need for a more integrated approach to long-term care in Korea, balancing public, private, and family care resources. To achieve quality integrated long-term care for older people, policymakers should focus on expanding public LTCI coverage while fostering coordination between family caregivers and professional care services, ensuring a comprehensive and high-quality care system that meets the diverse needs of Korea’s aging population. Full article

In the context of evolving Smart Cities, the integration of drone technology and distributed computing paradigms presents significant potential for enhancing urban infrastructure and services. This paper proposes a comprehensive approach to optimizing urban delivery logistics through a cloud-based model that employs Ant Colony Optimization (ACO) for planning and Model Predictive Control (MPC) for trajectory tracking within a broader Computing Continuum framework. The proposed system addresses the Capacitated Vehicle Routing Problem (CVRP) by considering both drone capacity constraints and autonomy, using the ACO-based algorithm to efficiently assign delivery destinations while minimizing travel distances. Collision-free paths are computed by using a Visibility Graph (VG) based approach, and MPC controllers enable drones to adapt to dynamic obstacles in real time. Additionally, this work explores how clusters of drones can be deployed as edge devices within the Computing Continuum, seamlessly integrating with IoT sensors and fog computing infrastructure to support various urban applications, such as traffic management, crowd monitoring, and infrastructure inspections. This dual-architecture approach, combining the optimization capabilities of ACO with the flexible, distributed nature of the Computing Continuum, allows for scalable and efficient urban drone deployment. Simulation results validate the effectiveness of the proposed model in enhancing delivery efficiency and collision avoidance while demonstrating the potential of integrating drone technology into Smart City environments for improved data collection and real-time response. Full article

To advance energy conservation and emissions reduction in urban logistics systems, this study focuses on the green vehicle routing problems with time windows (GVRPTWs), which remains underexplored in balancing environmental and service quality objectives. We propose a comprehensive multi-objective optimization framework that addresses this gap by simultaneously minimizing total distribution costs and carbon emissions while maximizing customer satisfaction, quantified based on the vehicle’s arrival time at the customer location. The rationale for adopting this tri-objective formulation lies in its ability to reflect real-world trade-offs between economic efficiency, environmental performance, and service level, which are often considered in isolation in previous studies. To tackle this complex problem, we develop an improved Non-Dominated Sorting Genetic Algorithm III (NSGA-III) that incorporates three key enhancements: (1) an integer-encoded initialization method to enhance solution feasibility, (2) a refined selection strategy utilizing crowding distance to maintain population diversity, and (3) an embedded 2-opt local search operator to prevent premature convergence and avoid local optima. Comprehensive validation experiments using Solomon’s benchmark instances and a real-world case demonstrate that the presented algorithm consistently outperforms several state-of-the-art multi-objective optimization methods across key performance metrics. These results highlight the effectiveness and practical relevance of our approach in advancing energy-efficient, low-emission, and customer-centric urban logistics systems. Full article

Ensuring rapid and efficient evacuation in high-density environments, such as stadiums, is critical for public safety during fire emergencies. Traditional fire alarm systems rely on reactive detection mechanisms, often resulting in delayed response times, increased panic, and overcrowding. This study introduces an AI-driven predictive fire alarm and evacuation model that leverages machine learning algorithms and real-time environmental sensor data to anticipate fire hazards before ignition, improving emergency response efficiency. To detect early fire risk indicators, the system processes data from 62,630 sensor measurements across 15 ecological parameters, including temperature, humidity, total volatile organic compounds (TVOC), CO₂ levels, and particulate matter. A comparative analysis of six machine learning models—Logistic Regression, Support Vector Machines (SVM), Random Forest, and proposed EvacuNet—demonstrates that EvacuNet outperforms all other models, achieving an accuracy of 99.99%, precision of 1.00, recall of 1.00, and an AUC-ROC score close to 1.00. The predictive alarm system significantly reduces false alarm rates and enhances fire detection speed, allowing emergency responders to take preemptive action. Moreover, integrating AI-driven evacuation optimization minimizes bottlenecks and congestion, reduces evacuation times, and improves structured crowd movement. These findings underscore the necessity of intelligent fire detection systems in high-occupancy venues, demonstrating that AI-based predictive modeling can drastically improve fire response and evacuation efficiency. Future research should focus on integrating IoT-enabled emergency navigation, reinforcement learning algorithms, and real-time crowd management systems to further enhance predictive accuracy and minimize casualties. By adopting such advanced technologies, large-scale venues can significantly improve emergency preparedness, reduce evacuation delays, and enhance public safety. Full article

Background: The rapid rise of e-commerce has intensified last-mile logistics challenges, fueling the need for sustainable, efficient solutions. Parcel locker crowdshipping systems, integrated with public transport networks, show promise in reducing congestion, emissions, and delivery costs. However, operational and physical constraints (e.g., crowded stations) and liability complexities remain significant barriers to broad adoption. This study investigates the demographic and operational factors that influence the adoption and scalability of these systems. Methods: A mixed-methods design was employed, incorporating survey data from 368 participants alongside insights from 20 semi-structured interviews. Quantitative analysis identified demographic trends and operational preferences, while thematic analysis offered in-depth contextual understanding. Results: Younger adults (18–34), particularly gig-experienced males, emerged as the most engaged demographic. Females and older individuals showed meaningful potential if safety and flexibility concerns were addressed. System efficiency depended on locating parcel lockers within 1 km of major origins and destinations, focusing on moderate parcel weights (3–5 kg), and offering incentives for minor route deviations. Interviews emphasized ensuring that lockers avoid station congestion, clearly defining insurance/liability protocols, and allowing task refusals during peak passenger hours. Conclusions: By leveraging public transport infrastructure, parcel locker crowdshipping requires robust policy frameworks, strategic station-space allocation, and transparent incentives to enhance feasibility. Full article

Background/Objectives. This study aimed to improve machine learning models for diagnosing interstitial cystitis/bladder pain syndrome (IC/BPS) by comparing classical machine learning methods with newer AutoML approaches, utilizing biomarker data and patient-reported outcomes as features. Methods. We applied various machine learning techniques to biomarker data from the previous IP4IC and ICRS studies to predict the presence of IC/BPS, a disorder impacting the urinary bladder. Data were sourced from two nationwide, crowd-sourced collections of urine samples involving 2009 participants. The models utilized included logistic regression, support vector machines, random forests, k-nearest neighbors, and AutoGluon. Results. Expanding the dataset for model training and evaluation resulted in improved performance metrics compared to previously published findings. The implementation of AutoML methods yielded enhancements in model accuracy over classical techniques. The top-performing models achieved a receiver-operating characteristic area under the curve (ROC-AUC) of up to 0.96. Conclusions. This research demonstrates an improvement in model performance relative to earlier studies, with the top model for binary classification incorporating objective urinary biomarker levels. These advancements represent a significant step toward developing a reliable classification model for the diagnosis of IC/BPS. Full article

More than half of the private forestland in the U.S. is under non-industrial private forest (NIPF) ownership. Understanding NIPF landowners’ decision-making is crucial for developing effective policy that promotes sustainable forest management practices and ensures forest health. This study investigates the factors influencing the adoption of different management practices, with a focus on potential crowding effects among these practices. Drawing on data from over four hundred NIPF landowners in the U.S. central hardwood region, a series of binary logistic regression models were employed to analyze the relationship between landowner and forestland characteristics and the likelihood of adopting various management practices, like invasive plant management, forest stand improvement, and grapevine control. The findings reveal that factors, such as forest acreage, proximity of landowner residence to the forest, and education level, significantly affect the likelihood of adopting management practices. More importantly, this study found evidence of crowding-in effects, where implementing one practice increased the probability of adopting others, suggesting a preference among NIPF landowners for a diverse approach to forest management. Full article

Increasing environmental and economic pressures have led to numerous innovations in the logistics sector, including integrated people and freight transport (IPFT). Despite growing attention from practitioners and researchers, IPFT lacks extensive research coverage. This study aims to bridge this gap by presenting a general framework and making several key contributions. It identifies, researches, and explains relevant terminologies, such as cargo hitching, freight on transit (FoT), urban co-modality, crowd-shipping (CS), occasional drivers (OD), crowdsourced delivery among friends, and share-a-ride, illustrating the interaction of IPFT with different systems like the sharing economy and co-modality. Furthermore, it classifies IPFT-related studies at strategic, tactical, and operational decision levels, detailing those that address uncertainty. The study also analyzes the opportunities and challenges associated with IPFT, highlighting social, economic, and environmental benefits and examining challenges from a PESTEL (political, economic, social, technological, environmental, and legal) perspective. Additionally, it discusses practical applications of IPFT and offers recommendations for future research and development, aiming to guide practitioners and researchers in addressing existing challenges and leveraging opportunities. This comprehensive framework aims to significantly advance the understanding and implementation of IPFT in the logistics sector. Full article

This study examined whether fixed or flexible funding mechanisms influence crowdfunding success. Under the fixed funding mechanism, the pledges contributed to the crowdfunding campaign projects are returned to the backers if the project fails, whereas, under the flexible funding mechanism, the project creator can keep all the raised pledges, irrespective of whether the project succeeds or fails. Secondary data consisted of reward-based crowdfunding projects retrieved from The Crowd Data Centre. Logistic regression was employed to respond to research objectives. The results reveal that the fixed funding mechanism increases the probability of success more than flexible funding. Entrepreneur experience, spelling errors, and project description negatively affect crowdfunding success, and backers positively affect crowdfunding success. The findings guide entrepreneurs seeking financing to design and choose an appropriate funding mechanism that effectively reduces the failure rate. Although many entrepreneurs seek funding in the crowdfunding market, relatively little research has been conducted on the influence of flexible or fixed funding mechanisms on crowdfunding success in Africa. This study provides entrepreneurs with appropriate financing strategies that enhance crowdfunding success. The empirical literature indicates that the flexible funding mechanism creates distrust among backers due to unrealistic target amounts. Full article

Currently, China’s expressway revenue and expenditure imbalance problem is serious. The development of an “Expressway Derivative Economy” (EDE) helps address expressway deficits, ensuring the expressway’s sustainable operation. The “Expressway + Logistics Distribution” (ELD) mode is a crucial form of the EDE and enhances expressway traffic flow and asset income. However, the cooperation mechanism among stakeholders remains unclear, hindering the widespread promotion of this mode. This study designs two ELD modes and elaborates on their respective advantages. Therefore, a three-party evolutionary game model involving the government, expressway groups, and logistics enterprises is established. Government “land-use-right” grant and tax incentive policies are formulated to explore the cooperation mechanism among stakeholders. The results indicate that both government “land-use-right” grant and tax incentive policies positively influence the positive evolution of the system. However, when the government “land-use-right” grants reach a high level, the willingness of logistics enterprises to choose entry will decrease due to resource crowding. Comparatively, a higher-level “land-use-right” grant policy significantly enhances the role of government tax incentive policy in promoting the positive development of the system. During tight government funding, it is a feasible policy to prioritize expressway groups by providing more tax incentives. The findings provide theoretical guidance for promoting the ELD mode. Full article

Developing mmWave radar sensors for indoor crowd motion sensing and tracking faces a critical challenge: the scarcity of large-scale, high-quality training data. Traditional human experiments encounter logistical complexities, ethical considerations, and safety issues. Replicating precise human movements across trials introduces noise and inconsistency into the data. To address this, this study proposes a novel solution: a movable platform equipped with a life-size mannequin to generate realistic and diverse data points for mmWave radar training and testing. Unlike human subjects, the platform allows precise control over movements, optimising sensor placement relative to the target object. Preliminary optimisation results reveal that sensor height impacts tracking performance, with an optimal sensor placement above the test subject yields the best results. The results also reveal that the 3D data format outperforms 2D data in accuracy despite having fewer frames. Additionally, analysing height distribution using 3D data highlights the importance of the sensor angle—15° downwards from the horizontal plane. Full article

Background: Nurses are essential members of the healthcare workforce and were among the first-line carers for patients in community and hospital settings during the COVID-19 pandemic. As a result, they were at a heightened risk of infection, resulting in several reported deaths among nursing staff. Several preventive measures were adopted to contain the spread of the COVID-19 virus. This study aims to explore the knowledge, attitudes, and practices (KAP) of nurses regarding hand hygiene, mask wearing, and social distancing measures in healthcare settings in Barbados during the COVID-19 pandemic. Method: An online survey of nurses working in public hospitals and polyclinics (public primary care clinics) in Barbados from March 2021 to December 2021 was conducted. A nonsystematic convenience sampling method was employed to recruit nurses who were readily available and willing to participate. A questionnaire captured the sociodemographic information and knowledge and practices related to hand hygiene, the use of face masks, and social distancing. Each correct response received one mark. Overall knowledge scores were categorized as poor (<60%), average (60–80%), or good (>80–100%). Results: Of the 192 participants, the majority were female (82.8%) and had >5 years of experience (82%). The findings revealed that 45.8% had poor knowledge of hand hygiene, and that the knowledge of 43.8% of respondents was average. Multivariable logistic regression showed that, after adjustment for age and gender, registered nurses had 2.1 times increased odds (95% confidence interval 1.0, 4.2) of having good knowledge compared to other nursing categories. Regarding mask wearing, 53.6% of nurses had average knowledge, and 27.1% had good knowledge. Multivariable logistic regression showed that, after adjustment for age and gender, registered nurses had 3.3 times increased odds (95% confidence interval 1.5, 7.4) of having good knowledge compared to nursing assistants. A total of 68.6% of respondents followed the correct steps of handwashing every time, and 98.3% wore a mask in public places. More than half of the nurses (51.2%) kept a safe distance from others to avoid spreading SARS-CoV-2; one-third were in a crowded place(s) in the past three months, and 55.8% usually followed guidelines for social isolation as recommended by the WHO. Conclusions: The study identified knowledge deficiencies related to hand hygiene and wearing masks among nurses. It is imperative to provide additional training on infection control measures. Full article