Search Results (3,164)

The design phase of buildings represents a dynamic and complex process, constantly evolving with modifications and feedback. It involves numerous professionals from various specialties, resulting in a fragmented and iterative trial-and-error process. Analyzing waste is the first step towards increasing the efficiency of the design process for high-rise buildings using Lean methodology. Initially, the design phase was characterized, and processes were classified into productive, contributory, and non-contributory work. Typical waste in building design was identified, analyzed, and ranked based on frequency and impact to facilitate understanding and elimination. Three traditional design stages were identified: Schematic Design (SD), Design Development (DD), and Construction Documentation (CD). A total of 33 typical wastes were classified into the eight Lean categories. Key waste ranked by the Frequency-Adjusted Importance Index (FAII) for cost, schedule, and quality metrics were late-stage design changes, waiting for resources and information, rework, and late-stage clarification of requirements. Full article

The rapid growth of electric vehicles (EVs) has significantly increased the demand for charging infrastructure, posing a challenge in balancing charging demand and infrastructure supply. The development of battery swapping and charging stations (BSCSs) is crucial for addressing these challenges and serves as a fundamental pillar for the sustainable advancement of EVs. This study develops a bi-level optimization model for the location and route planning of BSCSs. The upper-level model optimizes station locations to minimize total cost and service delay, while the lower-level model optimizes driver travel routes to minimize total time. An updated Non-Dominated Sorting Genetic Algorithm (UNSGA) is applied to enhance solution efficiency. The experimental results show that the bi-level model outperforms the single-level model, reducing total cost by 1.5% and travel time by 6.6%. Compared to other algorithms, the UNSGA achieves 9.43% and 8.23% lower costs than MOPSO and MOSA, respectively. Furthermore, BSCSs, despite 15.42% higher construction costs, reduce driver travel time by 22.43% and waiting time by 71.19%, highlighting their operational advantages. The bi-level optimization method provides more cost-effective decision support for EV infrastructure investors, enabling them to adapt to dynamic drivers’ needs and optimize resource allocation. Full article

Background: COVID-19 has limited pregnant and postpartum women’s access to mental health services, leading to the introduction of online interventions. Objectives: This study aims to compare the effectiveness of telepsychotherapy (i.e., psychotherapy provided through digital technology supporting real-time interactivity in the audio or audiovisual modality) with the one yielded by face-to-face interventions in treating perinatal depression and anxiety and to assess the therapist’s perceived alliance in both interventions. Methods: We collected anamnestic information and obstetrical risk factors for 61 women. We evaluated the effectiveness of face-to-face (N = 31) vs. telepsychotherapy (N = 30) interventions on depressive and anxiety symptoms at baseline (T0) and the end of treatment (T1) using the Edinburgh Postnatal Depression Scale (EPDS) and the State-Trait Anxiety Inventory (STAI-Y 1 and 2). We assessed the degree of alliance perceived by therapists with the Working Alliance Inventory (WAI-T). Results: Both groups showed significant decreases in depressive (EPDS face-to-face: T0 12.65 ± 5.81, T1 5.77 ± 4.63, p < 0.001; EPDS remote: T0 11.93 ± 5.24, T1 5.70 ± 4.46, p < 0.001; effect size: 0.002) and state anxiety (STAI-Y 1 face-to-face: T0 51.19 ± 13.73, T1 40.23 ± 12.86, p < 0.001; STAI-Y 1 remote: T0 51.10 ± 11.29, T1 38.00 ± 10.90, p < 0.001; effect size: 0.007//STAI-Y 2 face-to-face: T0 43.13 ± 12.11, T1 41.03 ± 13.06, p = 0.302; STAI-Y 2 remote: T0 44.20 ± 8.70, T1 39.30 ± 9.58, p = 0.003; effect size: <0.001) symptoms by the end of treatment. Women treated remotely also experienced a significant reduction in trait anxiety at T1 (p = 0.003). We found no significant differences in either symptomatology (EPDS; STAI-Y) between the two interventions at baseline or in the therapist-perceived alliance. Conclusions: Synchronous telepsychotherapy for perinatal depression and anxiety showed comparable treatment response to face-to-face interventions, with both modalities associated with significant symptom reduction and the establishment of a working alliance. These findings support the potential of telepsychotherapy as a valuable alternative when in-person services are not accessible, especially during emergency contexts. Full article

Self-medication is increasingly prevalent among healthcare students, raising concerns about the adequacy of current medical education in promoting safe medication practices. This study aimed to assess the frequency, motivations, and perceptions of self-medication among medical and pharmacy students and to identify educational gaps. A cross-sectional survey was conducted using a structured, anonymous questionnaire distributed to medical and pharmacy students at a single academic institution. The questionnaire assessed self-medication frequency, substances used, motivations, perceived risks, confidence in knowledge, sources of information, and attitudes toward curriculum improvements. Over 50% of participants reported practicing self-medication at least once a month. The most commonly used substances were analgesics and dietary supplements. Main motivations included recognition of symptoms, confidence in personal knowledge, and avoidance of waiting times. Despite receiving university instruction on self-medication risks, students continued to self-medicate, with many relying on the internet as a primary source of information. Only 8% felt very confident in counseling patients on self-medication. A majority (over 70%) expressed a strong interest in integrating dedicated educational modules into the curriculum. There is a clear need for improved, practice-oriented education on self-medication. Future interventions should focus on interdisciplinary teaching, digital literacy, and simulation-based training to foster safer medication practices. Full article

For the first time, this study presents a cyber–physical framework that reconciles the long-standing conflict between transparent queue analytics and adaptive machine learning in flexible manufacturing systems. Deterministic indicators, utilization, expected queue length, waiting time, and idle probability, are fused with topological embeddings of the routing graph and ingested by a graph convolutional network that predicts station congestion with calibrated confidence intervals. Shapley additive explanations decompose every forecast into causal contributions, and these vectors, together with a percentile-based risk metric, steer a mixed-integer genetic optimizer toward schedules that lift throughput without breaching statistical congestion limits. A cloud dashboard streams forecasts, risk bands, and color-coded explanations, allowing supervisors to accept or modify suggestions; each manual correction is logged and injected into nightly retraining, closing a socio-technical feedback loop. Experiments on an 8704-cycle production census demonstrate a 38 percent reduction in average queue length and a 12 percent rise in throughput while preserving full audit traceability, enabling one-minute rescheduling on volatile shop floors. The results confirm that transparency and adaptivity can coexist when analytical priors, explainable learning, and risk-aware search are unified in a single containerized control stack. Full article

Urban growth and rising vehicle usage have intensified congestion, accidents, and environmental impact, exposing the limitations of traditional traffic management systems. This study introduces a dual-incident simulation framework to investigate the potential of Vehicle-to-Everything (V2X) technologies in enhancing urban mobility. Using the Eclipse MOSAIC platform integrated with SUMO, a realistic network in Iași, Romania, was modeled under single- and dual-incident scenarios with three V2X penetration levels: 0%, 50%, and 100%. Unlike prior works that focus on single-incident cases or assume full penetration, our approach evaluates cascading disruptions under partial adoption, providing a more realistic transition path for mid-sized European cities. Key performance indicators, i.e., average speed, vehicle density, time loss, and waiting time, were calculated using mathematically defined formulas and validated across multiple simulation runs. Results demonstrate that full V2X deployment reduces average time loss by 18% and peak density by more than 70% compared to baseline conditions, while partial adoption delivers measurable yet limited benefits. The dual-incident scenario shows that V2X-enabled rerouting significantly mitigates cascading congestion effects. These contributions advance the state of the art by bridging microscopic vehicle dynamics with network-level communication modeling, offering quantitative insights for phased V2X implementation and the design of resilient, sustainable intelligent transportation systems. Full article

Background: Port congestion contributes significantly to operational inefficiency and environmental impact in maritime logistics. With tightening EU regulations such as the Emissions Trading System (EU ETS) and FuelEU Maritime, understanding and mitigating the economic and environmental effects of vessel delays is increasingly critical. This study focuses on a single bulk cargo pier at Constanta Port (Romania), which has experienced substantial traffic fluctuations since 2021, and examines operational and environmental performance through a queuing-theoretic lens. Methods: The authors have applied an M/G/1/∞/FIFO/∞ queuing model to vessel traffic and service time data from 2021–2023, supplemented by Monte Carlo simulations to capture variability in maneuvering and service durations. Environmental impact was quantified in CO₂ emissions using standard fuel-based emission factors, and a Cold Ironing scenario was modeled to assess potential mitigation benefits. Economic implications were estimated through operational cost modeling and conversion of CO₂ emissions into equivalent EU ETS carbon costs. Results: The analysis revealed high berth utilization rates across all years, with substantial variability in waiting times and queue lengths. Congestion was associated with considerable CO₂ emissions, which, when expressed in monetary terms under prevailing EU ETS prices, represent a significant financial burden. The Cold Ironing scenario demonstrated a substantial reduction in at-berth emissions and corresponding cost savings, underscoring its potential as a viable mitigation strategy. Conclusions: Results confirm that operational congestion at the studied berth imposes substantial environmental and financial burdens. The analysis supports targeted interventions such as Just-In-Time arrivals, optimized berth scheduling, and Cold Ironing adoption. Recommendations are most applicable to single-berth bulk cargo operations; future research should extend the approach to multi-berth configurations and incorporate additional operational constraints for broader generalizability. Full article

This study investigates the impact of railway transfer penalties on the demographic structure of the Tokyo Metropolitan Area. While previous research has emphasized travel time to the city center as a key determinant of socio-demographic structure, this paper highlights the additional influence of transfer penalties—specifically walking and waiting times—on urban demographic patterns. Using 1 km grids as the unit of analysis, travel time to Tokyo Station is calculated as a measure of accessibility, and the difference in travel time with and without accounting for transfers is defined as the transfer penalty for each grid. The spatial distribution of these penalties is mapped, and their effects on the population are estimated while considering heterogeneity based on distance to the city center. The results indicate that beyond accessibility, higher transfer penalties are associated with lower population densities. Moreover, the negative impact of transfer penalties is observed only in areas located at an intermediate distance from the city center (approximately 26–46 km). Finally, incorporating this spatial heterogeneity, the paper visualizes the projected contribution of transfer penalties to future population distribution. Full article

Federated learning enables collaborative model training across distributed devices while preserving data privacy. However, in real-world environments such as smart cities, heterogeneous and resource-constrained edge devices often render existing methods impractical. Low-power sensors and cameras struggle to complete full-model training, while high-performance devices remain idly waiting for others. Knowledge distillation approaches rely on public datasets that are rarely available or poorly aligned with urban data, which limits their effectiveness in deployment. These limitations lead to inefficiencies, unstable convergence, and poor adaptability in diverse urban networks. Partial training alleviates some challenges by allowing clients to train submodels tailored to their capacity, but existing methods still incur high computational costs for identifying important parameters and suffer from uneven parameter updates, reducing model effectiveness. To address these challenges, we propose Parameter-Level Dynamic Submodel Extraction (PLDSE), a lightweight and adaptive framework for federated learning. PLDSE estimates parameter importance using gradient-based scores on a server-side validation set, reducing overhead while accurately identifying critical parameters. In addition, it integrates a rolling scheduling mechanism to rotate unselected parameters, ensuring full coverage and consistent model updates. Experiments on CIFAR-10, CIFAR-100, and Fashion-MNIST demonstrate superior accuracy and faster convergence, with PLDSE achieving 62.82% on CIFAR-100 under low heterogeneity and 61.51% under high heterogeneity, outperforming prior methods. Full article

Background: Cutaneous melanoma is the leading cause of skin cancer-related mortality, and early detection is crucial to improving prognosis. Teledermatology (TD) has increasingly been adopted in melanoma care to address growing demand and limited access to specialists. This systematic review aimed to evaluate the role of TD in the diagnosis and management of suspected melanoma under real-world clinical conditions. Methods: The review was conducted and reported in accordance with the PRISMA 2020 guidelines. Literature searches were performed in PubMed, Embase, Web of Science, and Scopus up to December 2024. Studies assessing TD use in routine practice were included, focusing on diagnostic accuracy, prognostic indicators, waiting times, satisfaction, and economic outcomes. Results: TD showed high sensitivity and specificity, particularly when dermoscopic images and expert interpretation were available. Some studies reported reduced Breslow thickness and shorter delays compared to face-to-face care. Overall satisfaction was high among both patients and clinicians. Economic evaluations suggested potential cost savings, although formal analyses were limited. The use of artificial intelligence tools yielded mixed results and was generally perceived with caution in the absence of clinical supervision. Conclusions: Teledermatology appears to be a useful approach for improving access and supporting early diagnosis in melanoma care. Its effectiveness depends on proper implementation and integration into clinical workflows. Full article

Background: Enteropathy-associated T-cell lymphoma (EATL) is a rare subtype of mature T-cell lymphoma, accounting for fewer than 5% of peripheral T-cell lymphomas, with an aggressive course and poor prognosis. There are two types of this disease based on morphology and immunophenotype: type I, which is often, but not always, associated with celiac disease (classic EATL), and type 2, monomorphic epitheliotropic intestinal T-cell lymphoma (MEITL). Risk factors for classic EATL are poor adherence to a gluten-free diet, advanced age, male sex, and HLA-DQ2 homozygosity. The treatment options include surgery and various chemotherapy regimens with autologous stem cell transplantation, but the outcomes are discouraging, and clinical trials with targeted and biologic therapies are needed. Case Presentation: We report three cases of type 1 EATL, all with lethal outcomes, with one patient dying during initial treatment, one dying following several surgical interventions and without waiting to start chemotherapy, and one dying following a good treatment response but with severe infection. Full article

Urban traffic congestion remains a major contributor to vehicle emissions and travel inefficiency, prompting the need for adaptive and intelligent traffic management systems. In response, we introduce DeepSIGNAL-ITS (Deep Learning Signal Intelligence for Adaptive Lights in Intelligent Transportation Systems), a unified framework that leverages real-time traffic perception and learning-based control to optimize signal timing and reduce congestion. The system integrates vehicle detection via the YOLOv8 architecture at roadside units (RSUs) and manages signal control using Proximal Policy Optimization (PPO), guided by global traffic indicators such as accumulated vehicle waiting time. Secure communication between RSUs and cloud infrastructure is ensured through Transport Layer Security (TLS)-encrypted data exchange. We validate the framework through extensive simulations in SUMO across diverse urban settings. Simulation results show an average 30.20% reduction in vehicle waiting time at signalized intersections compared to baseline fixed-time configurations derived from OpenStreetMap (OSM). Furthermore, emissions assessed via the HBEFA-based model in SUMO reveal measurable reductions across pollutant categories, underscoring the framework’s dual potential to improve both traffic efficiency and environmental sustainability in simulated urban environments. Full article

This study investigates the impact of reducing waiting times for port berth on improving the Carbon Intensity Indicator (CII) ratings of Korean-flagged container ships. As the International Maritime Organization (IMO)’s CII regulation mandates corrective actions for poorly rated ships for Greenhouse Gas (GHG) reduction in international shipping, the analysis focuses on container ships with projected D or E ratings by 2035. Using Automatic Identification System (AIS) data from ships, this study identifies annual waiting times and simulates changes in CII ratings under scenarios of reduced waiting times (30%, 50%, 70%, and 100%). The relationship between ship speed and fuel consumption was established by analyzing the recent literature, and the CII improvement was evaluated based on IMO Data Collection System (DCS) 2022 data. The results show that a 30% reduction in waiting time can lower CO₂ emissions by 12.18% and improve the CII rating by one or two levels for approximately half of the sample ships. However, a 50% reduction or more is required to maintain improved ratings beyond 2030. The findings highlight the significance of just-in-time (JIT) practices in minimizing latency and enhancing regulatory compliance. The policy recommendations advocate for prioritizing port call optimization and recommend the adoption of JIT as a measure to achieve the IMO’s GHG reduction targets. Full article

Individuals are routinely exposed to traffic-related air pollution on their commutes, which has significant health impacts. Mitigating exposure to traffic-related pollution is a key urban sustainability concern. In Denver, Colorado, low-income Americans are more likely to rely on buses and spend time waiting at bus stops. Evaluating the contribution of traffic emissions at bus stops can provide important information on risks experienced by these populations. We measured PM_2.5 constituents at eight bus stops and one background reference site in Denver, in the summer of 2023. Source profiles, including gasoline emissions from traffic, were estimated using Positive Matrix Factorization (PMF) analysis of PM_2.5 constituents collected at a Chemical Speciation Network site in our study region. The contributions of the different sources at each bus stop were estimated by regressing the vector of species concentrations at each site (dependent variable) on the source-profile matrix from the PMF analysis (independent variables). Traffic-related emissions (~2.5–6.6 μg/m³) and secondary organics (~3–5 μg/m³) contributed to PM_2.5 at the bus stops in our dataset. The highest traffic-related emissions-derived PM_2.5 concentrations were observed at bus stops near local sources: a gas station and a car wash. The contribution of traffic-related emissions was lower at the background site (~1 μg/m³). Full article

Background: The overall incidence of malignancy in patients with end-stage kidney disease (ESKD) is reportedly higher compared to the general population. Cancer remains one of the dominant causes of death in these patients, due in part to uremia-induced impairment of tumor immune surveillance. Malignancy is one of the major limitations in the evaluation of potential kidney transplantation. This study aimed to assess the prevalence of cancer in hemodialysis population, particularly in relation to the waiting list. Materials and Methods: From the population of 5879 prevalent hemodialysis patients (60% men), 757 of them had a history of malignancy. In this population, 449 patients were actively waitlisted, and 4619 were not considered for potential kidney transplantation. Only 54 patients had unclear status in relation to active waiting list (during evaluation/disqualification). We assessed demographic data, basal biochemical data, and comorbidities, including malignancy, in relation to age, sex, presence of metastasis, and being actively waitlisted. Results: Malignancy was reported in 13% of hemodialysis patients, 6% of which had metastatic disease. Patients with malignancy were older (p < 0.001). More cases of cancer were observed in males (p = 0.02), who also had higher Charlson Comorbidity Index scores. Moreover, in patients with cancer, cardiovascular diseases were more common. They were also more malnourished (lower albumin, hemoglobin, lean mass) and more inflamed (higher ferritin, lower phosphorus). Only 27 patients with cancer were actively waitlisted, representing only 3.8% of this population. Patients with prior cancer on the active waiting list constituted 6% of all the waitlisted patients. Patients with a history of malignancy on the active waiting list were significantly younger, healthier, with significantly lower Charlson Comorbidity Index score, significantly lower ferritin, lower prevalence of diabetes, and higher blood pressure when compared to patients with malignancy who not listed for kidney transplantation. Conclusions: As malignancy became a more common comorbidity in dialysis patients, the elderly in particular, standardized cancer screening protocols should be promoted in dialysis units. Modern oncology has made huge progress, enabling the treatment of previously incurable cancers, as malignancy after kidney transplantation is considerably increased either due to de novo cancers or the recurrence of previous malignancy. Therefore, the evaluation of potential kidney transplant recipients, with tailored cancer screening and multidisciplinary evaluation, is strongly recommended. Besides a history of malignancy, the cardiovascular status also determines the eligibility for transplantation in dialysis patients. It is of paramount importance as the main cause of death in transplant recipients is cardiovascular death followed by malignancy. Full article