Search Results (1,752)

Hospital layout significantly influences hospital service quality, demanding robust tools for informed decision-making during the layout design stage. This study presents a novel Hospital Configuration Model as the foundational component of a Hospital Design Support System, which utilizes simulation modeling to provide evaluation mechanisms on hospital efficiencies and functionalities. The Hospital Configuration Model integrates four critical data types—geometric, topological, semantic, and operational—into a machine-readable digital twin, enabling comprehensive spatial and procedural analyses. The Hospital Configuration Model facilitates simulation modeling to optimize hospital layouts and predict performance metrics such as crowdingness, patient waiting times, patient walking distance, and difficulty in wayfinding. In conclusion, the Hospital Configuration Model is the core and foundation of developing the Hospital Design Support System for evaluating hospital functionalities and efficiencies, and the potential applications of the model include digital twin development, facility management, and safety enhancement. Future research directions should, in particular, include developing the proposed Hospital Design Support System and establishing a standard way of extracting hospital operational information into an industry-standard data model. Full article

Introduction: Tooth shade selection is a fundamental factor in the success of dental restorations, and visual impairment may adversely affect this process. The aim of this cross-sectional clinical study was to determine whether visual impairment influences shade selection using two methods: spectrophotometry and shade guides. Materials and Methods: The sample consisted of 2796 maxillary and mandibular teeth, and shade selection was measured subjectively with a shade guide (VITA Classic, VITA Zahnfabrik) and objectively with a spectrophotometer (VITA Easyshade^® V, VITA Zahnfabrik, Bad Säckingen, Germany). In all cases, three measurements were taken on each tooth, with a waiting time of 15 min between samples. Shade selection was compared between observers with normal vision, myopia, astigmatism, and hyperopia. Results: The results show that myopic subjects perceived the lower central incisors (2.63, p < 0.05), upper lateral incisors (2.42, p < 0.05), lower lateral incisors (2.34, p < 0.05), and lower canines (2.64, p < 0.05) more clearly. Non-astigmatic subjects perceived the lower second premolar as lighter than astigmatic subjects (−2.01, p < 0.05). Conclusions: It can be concluded that myopes see teeth more clearly, but no differences have been found in astigmatism and hyperopes. Full article

The General Transit Feed Specification (GTFS) is an open standard format for recording transit information, utilized by thousands of transit agencies worldwide. In this study, a new tool named GTFS2STN for converting GTFS data into the spatiotemporal networks is introduced. To analyze the travel time variability, it is important to transform a transit network to a spatiotemporal network to enable a comprehensive analysis of transit system accessibility. GTFS2STN is a new tool that converts General Transit Feed Specification (GTFS) data into spatiotemporal networks, addressing the lack of open-source solutions for transit analysis. The tool includes a web application that generates isochrone maps and calculates travel time variability between locations. Validation against Google Maps APIs shows that journey time (i.e., the summation of the transit time, walking time, and waiting time) differences in the Mean Absolute Percentage Error are typically within 12%. A before–after analysis shows that for the transit journey time in 2024 in Nashville, Tennessee, 8 out of 10 pivotal bus stops showed a significantly decreased journey time compared with the case of 2019. A further set of before–after analyses shows that although journey time between transit sites significantly dropped on May 2020 during COVID-19 emergencies, the journey time almost totally recovered to the before-COVID-19 level by November 2020. By supporting any valid GTFS schedule, GTFS2STN enables the analysis of historical and planned transit systems, making it valuable for long-term accessibility assessment and travel time variability studies. Full article

The development of the new energy vehicle (NEV) market in China has promoted the sustainability of the automotive industry, but has also brought pressures to NEV charging infrastructure. This paper aims to determine the strategic role of charging stations, particularly on whether they should provide service for plug-in hybrid electric vehicles (PHEVs) in the highway service area during peak holidays. Firstly, the charging service resource allocation for a charging station that provides services for both electronic vehicles (EVs) and PHEVs is studied. Secondly, different queueing disciplines are compared. At last, a comparison between scenarios where charging services are limited to EVs and those where services extend to both EVs and PHEVs is conducted. A queueing system considering customer balking and reneging is developed. The impacts of parameters, such as the NEV arrival rate and patience degree of different NEV drivers, on the optimal allocation plan, profit, and comparison results are discussed. The main conclusions are as follows: (1) If the EV arrival rate is greater than the charging service rate, the charging station should not provide charging services for PHEVs. Providing service only for EVs derives more revenues and profits and results in a shorter waiting queue. Conversely, if the total arrival rate of NEVs (including EVs and PHEVs) is lower than the charging service rate, then the charging station should also serve PHEVs. (2) If providing service for PHEVs, a mixed queueing discipline should be applied when the total arrival rate approximates the service rate. When the total NEV arrival rate is significantly lower than the charging service rate, the separate queueing discipline should be adopted. (3) When applying a separate queueing discipline, if a certain type of NEV has a higher arrival rate and the drivers exhibit greater patience, then more charging resources should be allocated to this type of NEV. If the charging service is less busy, the more patient the drivers are, the less service resources should be allocated to them, whereas, during peak times, the more patient the drivers are, the more service resources should be allocated to them. Full article

With the escalating demand for automation in chemical laboratories, multi-robot systems are assuming an increasingly prominent role in chemical laboratories, particularly in the task of transporting reagents and experimental materials. In this paper, we propose a multi-robot path planning approach based on the combination of the A* algorithm and the dynamic window algorithm (DWA) for optimizing the efficiency of reagent transportation in chemical laboratories. In environments like chemical laboratories, dynamic obstacles (such as people and equipment) and transportation tasks that demand precise control render traditional path planning algorithms challenging. To address these issues, in this paper, we incorporate the cost information from the current point to the goal point into the evaluation function of the traditional A* algorithm to enhance the search efficiency and add the safety distance to extract the critical points of the paths, which are utilized as the temporary goal points of the DWA algorithm. In the DWA algorithm, a stop-and-wait mechanism and a replanning strategy are added, and a direction factor is included in the evaluation function to guarantee that the robots can adjust their paths promptly in the presence of dynamic obstacles or interference from other robots to evade potential conflicts or traps, thereby reaching the goal point smoothly. Additionally, regarding the multi-robot path conflict problem, this paper adopts a dynamic prioritization method, which dynamically adjusts the motion priority among robots in accordance with real-time environmental changes, reducing the occurrence of path conflicts. The experimental results highlight that this approach effectively tackles the path planning challenge in multi-robot collaborative transportation tasks within chemical laboratories, significantly enhancing transportation efficiency and ensuring the safe operation of the robots. Full article

The purpose of this research is to investigate the organizational reputation of public organizations, along with the views and attitudes of citizens in terms of their satisfaction. More specifically, a survey was carried out with the help of a structured questionnaire, distributed to all the citizens of Serres, regarding the image they have of the services provided by the Citizen Service Center (CSC) of the municipality of Serres, who are selected as research sample. The results show a better level of satisfaction with staff and service and a moderate level with environment. Also, non-significant and significant correlations were found between specific demographic variables and certain occupational characteristics, indicating some certain demographic factors do while others do not have a significant impact on the occupational profile of employees. Regarding the factors that shape a positive reputation, it was found that the main characteristic that citizens believe that the CSC of Serres has is the efficiency of the services offered, while what lags is the lack of bureaucracy. In conclusion, the research suggests that there are areas which must be emphasized and problems to be addressed; these are the attractive facilities, the modern equipment, the full staffing of employees, and the waiting time. Full article

Background: Plexiform neurofibromas (PNs) are histologically benign peripheral nerve sheath tumors associated with neurofibromatosis type 1 (NF1) and often lead to significant morbidity due to growth. Management includes watchful waiting, surgery for partial debulking, and, since recently, systemic treatment with MEK inhibitors. However, due to the scarcity of natural history studies, our understanding of the natural progression of PNs to guide clinicians in deciding in whom and when to intervene is scarce. This study aims to describe the characteristics of NF1 patients with PNs and compare those at high risk for PN progression or experiencing significant morbidity from PN (complex PN) with NF1 patients with PNs of lower complexity. Methods: In this retrospective cohort study using clinical data from hospital records of NF1 patients with PNs seen at the Sophia Children’s Hospital in the Netherlands between 2012 and 2023, we assessed determinants of clinical phenotypes and PN characteristics predictive of outcomes, including PN complexity and the timing of intervention for PN. We assessed the outcomes using logistic regression analysis and Cox regression. Results: Ninety patients with a median age at last evaluation of 15.7 years and a median follow-up duration of 9.8 years were included. Out of 90 individuals with a benign PN, 37 developed plexiform neurofibroma morbidity during follow-up. Older age was (corrected for pathogenic NF1 variant and PN location) significantly associated with plexiform neurofibroma morbidity. Cox regression revealed that craniofacial and trunk PNs were associated with a higher intervention hazard compared to limb PNs. Conclusion: Our pilot multivariate approach identified older age and the location of the PN to be mostly associated with a higher chance of plexiform neurofibroma morbidity and higher intervention hazard. This may contribute to decisions regarding in whom and when to initiate treatment in NF1 patients with PNs. Full article

Autonomous electric vehicle (AEV) services leverage advanced autonomous driving and electric vehicle technologies to provide innovative, driverless transportation solutions. The biggest challenge faced by AEVs is the limited number of charging stations and long charging times. A critical challenge is maximizing passenger travel satisfaction while reducing the AEV idle time. This involves coordinating passenger transport and charging tasks via leveraging the information from charging stations, passenger transport, and AEV data. There are four important contributions in this paper. Firstly, we introduce an integrated scheduling model that considers both passenger transport and charging tasks. Secondly, we propose a multi-level differentiated charging threshold strategy, which dynamically adjusts the charging threshold based on both AEV battery levels and the availability of charging stations, reducing competition among vehicles and minimizing waiting times. Thirdly, we develop a rapid strategy to optimize the selection of charging stations by combining geographic and deviation distance. Fourthly, we design a new evolutionary algorithm to solve the proposed model, in which a buffer space is introduced to promote diversity within the population. Finally, experimental results show that compared to the existing state-of-the-art scheduling algorithms, the proposed algorithm shortens the running time of scheduling algorithms by 6.72% and reduces the idle driving time of AEVs by 6.53%, which proves the effectiveness and efficiency of the proposed model and algorithm. Full article

Traffic congestion at a single intersection can propagate and thus affect adjacent intersections as well, potentially resulting in prolonged gridlock across an entire urban area. Despite numerous research efforts aimed at developing intelligent traffic signal control systems, urban areas continue to experience traffic congestion. This paper presents a novel agent-based fuzzy traffic control system for multiple road intersections. The proposed system is designed to operate in a decentralized manner, with each intersection having its own agent (fuzzy controller) functioning concurrently. The intelligent fuzzy controller of the system can recognize emergency vehicles, assess the queue length and waiting time of vehicles, measure the distance of vehicles from intersections, and consider the cumulated waiting times of short vehicle queues. Two distinct types of agent-based intelligent fuzzy traffic control systems were implemented for comparison: one involving collaboration between an agent and its immediate neighboring agent(s) (where one intersection exchanges traffic data with its immediate neighboring intersection(s)), and the other implementing a non-collaborative agent-based intelligent fuzzy traffic control system (where the individual intersection has no direct communication). Following the experimental simulations, the results were compared with those of existing intelligent fuzzy traffic control systems that lack any module to calculate the distance of the vehicles from the intersection. The results demonstrated that the proposed agent-based system of controllers exhibited superior performance compared with the existing fuzzy controllers in terms of indicators such as average waiting time, fuel consumption, and CO₂ emissions. For instance, the proposed system reduced the average waiting time of vehicles at an intersection by 48.65% compared with the existing three-stage intelligent fuzzy traffic control system. In addition, a comparison was conducted between non-collaborating and collaborating agent-based intelligent fuzzy traffic control systems, where collaboration achieved better results than the non-collaborating system. In the simulation experiments, an interesting new feature emerged: despite any direct communication missing at multiple intersections, green waves evolved with time. This emergent feature suggests that fuzzy controllers have the potential to evolve and adapt to traffic complexity issues in urban environments when operating in an autonomous agent-based mode. This study demonstrates that agent-based fuzzy controllers can effectively communicate with one another to share traffic data and improve the overall system performance. Full article

Container terminals play a crucial role in global logistics and trade, with gate-in operations significantly impacting overall terminal efficiency and cargo turnover speed. This paper analyzes a series of problems caused by the randomness of the arrival of export containers at the container yard, including wastage of yard space, excessive waiting time for external trucks, and conflicts with other production operations. To address these issues, a method based on a decomposed ensemble framework is proposed to predict short-term container quantities for gate-in operations at container terminal gates. The experiment compares the autoregressive integrated moving average (ARIMA) algorithm, the prophet algorithm, and the Long Short-Term Memory (LSTM) algorithm, with results indicating the clear advantage of Long Short-Term Memory in decomposed time series modeling. The introduction of this method is expected to enhance the accuracy and flexibility of terminal production planning, optimizing resource utilization. Contributions of this paper include the proposal of predictive models, a shipping route-based decomposed-ensemble framework, and confirmation of the superiority of Long Short-Term Memory in prediction through comparative analysis. These contributions are expected to improve terminal operational efficiency, reduce resource wastage, and better adapt to the highly stochastic gate-in operation environment. Full article

(1) Background: Targeted alpha therapy is an emerging field in nuclear medicine driven by two advantages: overcoming resistance in cancer-suffering patients to beta therapies and the practical application of lower activities of ²¹²Pb- and ²²⁵Ac-labelled peptides to achieve the same doses compared to beta therapy due to the highly cytotoxic nature of alpha particles. However, quality control of the ²¹²Pb/²²⁵Ac-radiopharmaceuticals remains a challenge due to the low activity levels used for therapy (100 kBq/kg) and the formation of several free daughter nuclides immediately after the formulation of patient doses; (2) Methods: The routine alpha detection on thin-layer chromatograms (TLC) of ²¹²Pb- and ²²⁵Ac-labelled peptides using a MiniScanPRO+ scanner combined with an alpha detector head was compared with detection using an AR-2000 scanner equipped with an open proportional counter tube. Measurement time, resolution and validity were compared for both scanners; (3) Results: For ²²⁵Ac, the quality control values of the radiochemical purity (RCP) were within the acceptance criteria 2 h after TLC development, regardless of when the TLC probe was taken. That is, if the TLC probe was taken 24 h after radiosynthesis, the true value of the RCP was not measured until 5 h after TLC development. For ²¹²Pb-labelled peptides, the probe sampling did not have a high impact on the value of the RCP for the MiniScanPRO+ and AR-2000. A difference was observed when measuring TLC with the AR-2000 in different modes; (4) Conclusions: The MiniScanPRO+ is fast, does not require additional equipment and can also measure the gamma spectrum, which may be important for some radiopharmaceutical production sites and regulatory authorities. The AR-2000 has a better signal-to-noise ratio, and this eliminates the need for additional waiting time after TLC development. Full article

Background: Genomics is a foundational element of precision health and can be used to identify inherited cancers, cancer related risks, therapeutic decisions, and to address health disparities. However, there are structural barriers across the cancer care continuum, including an underprepared nursing workforce, long wait times for service, and inadequate policy infrastructure that limit equitable access to the benefits of genomic discoveries. These barriers have persisted for decades, yet they are modifiable. Two distinct waves of efforts to integrate genomics into nursing practice are analyzed. Drawing on research and observations during these waves, this discussion paper explores additional approaches to accelerate workforce development and health system transformation. Results: Three recommendations for a third wave of efforts to integrate genomics are explored. (1) Collaborate across the domains of nursing practice, professions, and sectors to reset priorities in response to emerging evidence, (2) Education in leadership, policy and practice for rapid scale-up of workforce and health system transformation, and (3) Create a research framework that generates evidence to guide nursing practice. Conclusions: Preparing nurses to lead and practice at the forefront of innovation requires concerted efforts by nurses in all five domains of practice and can optimize health outcomes. Leveraging nursing as a global profession with new strategies can advance genomics-informed nursing. Full article

To achieve a rational allocation of real-time operational equipment, such as excavators and dump trucks, in open-pit mines, and thereby enhance truck–shovel coordination, this paper addresses the challenges posed by unreasonable on-site scheduling, which includes excessive truck waiting times and prolonged excavator boom-and-dipper operations. Ultimately, the paper aims to attain optimal truck–shovel coordination efficiency. To this end, we construct a scheduling optimization model, with the production capacities of trucks and shovels serving as constraints. The objective functions of this model focus on minimizing transportation costs, reducing truck waiting times, and shortening excavator boom-and-dipper operation durations. To solve this model, we have developed an improved genetic algorithm that integrates roulette wheel selection and elite preservation strategies. The experimental results of our algorithm demonstrate that it can provide a more refined operational equipment scheduling scheme, effectively decreasing truck transportation costs and enhancing equipment utilization efficiency in open-pit mines. Full article

This study combines Visible Light Communication (VLC) and Artificial Intelligence (AI) to optimize traffic signal control, reduce congestion, and enhance safety. Utilizing existing road infrastructure, VLC technology transmits real-time data on vehicle and pedestrian positions, speeds, and queues. AI agents, powered by Deep Reinforcement Learning (DRL), process these data to manage traffic flows dynamically, applying anti-bottlenecking and rerouting techniques. A global agent coordinates local agents, enabling indirect communication and a unified DRL model that adjusts traffic light phases in real time using a queue/request/response system. A key focus of this work is the design of reward functions for standard and rerouting scenarios. In standard scenarios, the reward function prioritizes wide green bands for vehicles while penalizing pedestrian rule violations, balancing efficiency and safety. In rerouting scenarios, it dynamically prevents queuing spillovers at neighboring intersections, mitigating cascading congestion and ensuring safe, timely pedestrian crossings. Simulation experiments in the SUMO urban mobility simulator and real-world trials validate the system across diverse intersection types, including four-way crossings, T-intersections, and roundabouts. Results show significant reductions in vehicle and pedestrian waiting times, particularly in rerouting scenarios, demonstrating the system’s scalability and adaptability. By integrating VLC technology and AI-driven adaptive control, this approach achieves efficient, safe, and flexible traffic management. The proposed system addresses urban mobility challenges effectively, offering a robust solution to modern traffic demands while improving the travel experience for all road users. Full article

Background/Objectives: Research has shown that athletes often have poor nutritional knowledge, particularly regarding dietary supplements. The purpose of this study was to investigate the effectiveness of an online nutrition education program in improving nutritional and dietary supplement knowledge among varsity athletes at the University of Guelph in Ontario, Canada. Methods: A total of 30 varsity athletes at the University of Guelph were randomized into experimental [n = 18] and control [n = 12] groups. A randomized wait-list controlled intervention was used, where participants in the experimental group were granted access to an online, 4-week nutrition education program, while no program was provided to the control group within the duration of the study. The program covered nutrition topics and focused on sports nutrition and dietary supplementation. Both groups were administered a validated online nutrition and dietary supplement questionnaire at baseline and post-intervention. Results: Mean baseline knowledge scores for the experimental and control groups were 13.78 ± 2.76 and 13.92 ± 2.39, respectively, and were not significantly different [p = 0.888]. Post-intervention scores were 16.28 ± 1.49 and 14.5 ± 1.88 for the experimental and control groups, respectively, [p < 0.05]. There was a significant interaction between the intervention and time on knowledge. Conclusions: These results indicate that this nutrition education program was successful in significantly improving nutritional and dietary supplement knowledge in varsity athletes. Full article