Search Results (716)

This paper proposes the Open Competency Optimization (OCO) approach, based on adaptive combinatorial operators, to solve the Dynamic Green Traveling Salesman Problem (DG-TSP), which extends the classical TSP by incorporating dynamic travel conditions, realistic road gradients, and energy consumption considerations. The objective is to minimize fuel consumption and emissions by reducing the total tour length under varying conditions. Unlike conventional metaheuristics based on real-coded representations, our method directly operates on combinatorial structures, ensuring efficient adaptation without costly transformations. Embedded within a dynamic metaheuristic framework, our operators continuously refine the routing decisions in response to environmental and demand changes. Experimental assessments conducted in practical contexts reveal that our algorithm attains a tour length of 21,059, which is indicative of a 36.16% reduction in fuel consumption relative to Ant Colony Optimization (ACO) (32,994), a 4.06% decrease when compared to Grey Wolf Optimizer (GWO) (21,949), a 2.95% reduction in relation to Particle Swarm Optimization (PSO) (21,701), and a 0.90% decline when juxtaposed with Genetic Algorithm (GA) (21,251). In terms of overall offline performance, our approach achieves the best score (21,290.9), significantly outperforming ACO (36,957.6), GWO (122,881.04), GA (59,296.5), and PSO (36,744.29), confirming both solution quality and stability over time. These findings underscore the resilience and scalability of the proposed approach for sustainable logistics, presenting a pragmatic resolution to enhance transportation operations within dynamic and ecologically sensitive environments. Full article

Floating solar technology has gained significant attention as part of the global expansion of renewable energy due to its potential for installation in underutilized water bodies. Several countries, including the Netherlands, have initiated efforts to extend this technology from inland freshwater applications to open offshore environments, particularly within offshore wind farm areas. This development is motivated by the synergistic benefits of increasing site energy density and leveraging the existing offshore grid infrastructure. The deployment of offshore floating photovoltaic (OFPV) systems involves assembling multiple modular units in a marine environment, introducing operational risks that may give rise to safety concerns. To mitigate these risks, weather windows must be considered prior to the task execution to ensure continuity between weather-sensitive activities, which can also lead to additional time delays and increased costs. Consequently, optimizing marine logistics becomes crucial to achieving the cost reductions necessary for making OFPV technology economically viable. This study employs a simulation-based approach to estimate the installation duration of a 5 MWp OFPV plant at a Dutch offshore wind farm site, started in different months and under three distinct risk management scenarios. Based on 20 years of hindcast wave data, the results reveal the impacts of campaign start months and risk management policies on installation duration. Across all the scenarios, the installation duration during the autumn and winter period is 160% longer than the one in the spring and summer period. The average installation durations, based on results from 12 campaign start months, are 70, 80, and 130 days for the three risk management policies analyzed. The result variation highlights the additional time required to mitigate operational risks arising from potential discontinuity between highly interdependent tasks (e.g., offshore platform assembly and mooring). Additionally, it is found that the weather-induced delays are mainly associated with the campaigns of pre-laying anchors and platform and mooring line installation compared with the other campaigns. In conclusion, this study presents a logistics modeling methodology for OFPV systems, demonstrated through a representative case study based on a state-of-the-art truss-type design. The primary contribution lies in providing a framework to quantify the performance of OFPV installation strategies at an early design stage. The findings of this case study further highlight that marine installation logistics are highly sensitive to local marine conditions and the chosen installation strategy, and should be integrated early in the OFPV design process to help reduce the levelized cost of electricity. Full article

Economic growth, especially in emerging economies, has altered the composition of international tourism. It is therefore essential to possess the skills necessary to understand the influence of culture on human behaviour, thereby enabling an appropriate response to the traveller. This research aims to develop a tool for identifying openness, flexibility, awareness, and intercultural preparedness. It focuses on the metacognitive and cognitive aspects of cultural intelligence that shape the development of empathy in customer service staff in hotels in Quintana Roo. The variables were validated and incorporated into a quantitative study using multivariate analysis and inferential statistics. A sample of 77 questionnaires was analysed using simple random sampling under a proportional design. Multiple Correspondence Analysis (MCA) was employed as a discriminatory technique to identify the most significant independent variables. These were subsequently entered as regressors into ordinal logistic regression (OLR), along with age and work experience, in order to estimate the probabilities associated with each level of the dependent variable. The results indicated that age had minimal influence on the metacognitive and cognitive variables, whereas years of experience among tourism staff exerted a significant effect. Full article

Background: This study aims to compare the logistical challenges of Home Health Care (HHC) services in Portugal and Brazil, highlighting the structural and operational differences between both systems. Methods: Guided by an abductive research approach, data were collected using a semi-structured survey with open-ended questions, applied to 13 HHC teams in Portugal and 18 in Brazil, selected based on national coordination recommendations. The data collection process was conducted in person, and responses were analyzed using descriptive statistics and qualitative content analysis. Results: The results reveal that Portugal demonstrates higher productivity, stronger territorial coverage, and a more integrated inventory management system, while Brazil presents greater multidisciplinary team integration, more flexible fleet logistics, and more advanced digital health records. Despite these strengths, both countries continue to address key logistical aspects, such as scheduling, supply distribution, and data management, largely through empirical strategies. Conclusions: This research contributes to the theoretical understanding of international HHC logistics by emphasizing strategic and systemic aspects often overlooked in operational studies. In practical terms, it offers insights for public health managers to improve resource allocation, fleet coordination, and digital integration in aging societies. Full article

Artificial intelligence (AI) techniques—ranging from hybrid mechanistic–machine learning (ML) ensembles to gradient-boosted decision trees, support-vector machines, and deep neural networks—are transforming the management of seasonal influenza, respiratory syncytial virus (RSV), human immunodeficiency virus (HIV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Symptom-based triage models using eXtreme Gradient Boosting (XGBoost) and Random Forests, as well as imaging classifiers built on convolutional neural networks (CNNs), have improved diagnostic accuracy across respiratory infections. Transformer-based architectures and social media surveillance pipelines have enabled real-time monitoring of COVID-19. In HIV research, support-vector machines (SVMs), logistic regression, and deep neural network (DNN) frameworks advance viral-protein classification and drug-resistance mapping, accelerating antiviral and vaccine discovery. Despite these successes, persistent challenges remain—data heterogeneity, limited model interpretability, hallucinations in large language models (LLMs), and infrastructure gaps in low-resource settings. We recommend standardized open-access data pipelines and integration of explainable-AI methodologies to ensure safe, equitable deployment of AI-driven interventions in future viral-outbreak responses. Full article

Seafood processing byproducts (SB) such as bones and skin can be safely used as food ingredients to increase profitability for the seafood sector and provide nutritional value. An online survey of 716 US adult seafood consumers was conducted to explore SB trial intent, responsiveness to health and safety information, and associated elicited emotions (nine-point Likert scale). Consumers’ SB-elicited emotions were defined as those changing in reported intensity (from a baseline condition) after the delivery of SB-related information (dependent t-tests). As criteria for practical significance, a raw mean difference of >0.2 units was used, and Cohen’s d values were used to classify effect sizes as small, medium, or large. Differences in willingness-to-try, responsiveness to safety and health information, and SB-elicited emotions were found based on self-reported gender and race, with males and Hispanics expressing more openness to SB consumption. SB-elicited emotions were then used to model consumers’ willingness-to-try foods containing SB via logistic regression modeling. Traditional stepwise variable selection was compared to variable selection using raw mean difference > 0.2 units and Cohen’s d > 0.50 constraints for SB-elicited emotions. Resulting models indicated that extrinsic information considered at the point of decision-making determined which emotions were relevant to the response. These new approaches yielded models with increased Akaike Information Criterion (AIC) values (lower values indicate better model fit) but could provide simpler and more practically meaningful models for understanding which emotions drive consumption decisions. Full article

While the explosive growth in e-commerce stresses urban logistics systems, city planners lack of fine-grained data in order to anticipate and manage the resulting freight flows. Using a three-stage analytical approach combining descriptive zonal statistics, hotspot analysis and different regression modeling from univariate to geographically weighted regression, this study integrates one year of parcel deliveries from a leading courier with open spatial layers of land-use zoning, census population, mobile-signal activity and household income to model last-mile demand across different land use types. A baseline linear regression shows that residential population alone accounts for roughly 30% of the variance in annual parcel volumes (2.5–3.0 deliveries per resident) while adding daytime workforce and income increases the prediction accuracy to 39%. In a similar approach where coefficients vary geographically with Geographically Weighted Regression to capture the local heterogeneity achieves a significant raise of the overall R² to 0.54 and surpassing 0.70 in residential and institutional districts. Hot-spot analysis reveals a highly fragmented pattern where fewer than 5% of blocks generate more than 8.5% of all deliveries with no apparent correlation to the broaden land-use classes. Commercial and administrative areas exhibit the greatest intensity (1149 deliveries per ha) yet remain the hardest to explain (global R² = 0.21) underscoring the importance of additional variables such as retail mix, street-network design and tourism flows. Through this approach, the calibrated models can be used to predict city-wide last-mile demand using only public inputs and offers a transferable, privacy-preserving template for evidence-based freight planning. By pinpointing the location and the land uses where demand concentrates, it supports targeted interventions such as micro-depots, locker allocation and dynamic curb-space management towards more sustainable and resilient urban-logistics networks. Full article

Background/Objective: Sarcopenia is a predictor of poor surgical outcomes in older adults. The Psoas Muscle Index (PMI), calculated from routine preoperative CT scans, has been proposed as an imaging-based marker of physiological reserve, but its diagnostic utility in vascular surgery remains unclear. We aimed to assess the predictive value of PMI for early complications following elective abdominal aortic aneurysm (AAA) repair in two European centers. Methods: We retrospectively analyzed 245 patients who underwent open or endovascular AAA repair between 2018 and 2022 in Poland and The Netherlands. PMI was measured at the level of third lumbar vertebrae (L3) level, normalized to height, and stratified into center-specific tertiles. Early complications were compared across tertiles, procedures, and centers. Multivariate logistic regression was used to adjust for age, comorbidities, and procedure type. Results: Low PMI was significantly associated with early complications in EVAR patients at the Polish center (p = 0.004). No associations were found in open repair or at the Dutch center. Mean PMI values did not differ significantly between centers. Conclusions: PMI may serve as a context-dependent imaging biomarker for early risk stratification following AAA repair, particularly in endovascular cases. Its predictive value is influenced by institutional and procedural factors, highlighting the need for prospective validation and standardization before clinical adoption. Full article

Background: We investigated the role of a high-sensitive cardiac troponin T (hsTnT) increase below the upper limit of normal (ULN) in patients with breast cancer (BC). hsTnT assays accurately quantify very low plasma troponin concentrations and enable the early detection of cardiomyocyte injury before a drop in the left ventricular ejection fraction (LVEF). The increase in hsTnT below the ULN in response to chemotherapy has not previously been studied. Method: This was an open-label pilot study. Female patients with newly diagnosed BC scheduled to receive systemic cancer treatment were recruited. Blood sampling and echocardiography were performed at baseline, at 3 and 6 months of cancer treatment. hsTnT concentrations were measured using the Elecsys TnT hs assay (Roche Diagnostics). The limit of blank and 99th percentile cutoff values for the hsTnT assay were 3 and 14 ng/L. We calculated the rise in hsTnT (ΔhsTnT) by the difference (%) between its baseline level and during follow-up (FU) in each patient. Results: Among eligible subjects, we excluded 4 patients before the start of treatment and 17 patients during the follow-up with values for the hsTnT >14 ng/L. Finally, 60 women with a median age of 48.6 ± 1.3 years were included in the study. The median baseline hsTnT concentration was 5.5 ± 1.4 ng/L. During 6 months of cancer treatment, hsTnT increased in all patients by up to 10–305% from baseline, with an average of 94.2%. LV EF was normal at baseline and decreased significantly compared to the value before cancer treatment (61.9 ± 3.3% vs. 56.3 ± 7.0%; p < 0.045). We correlated the hsTnT rise with a drop in LV EF ≥ 10% from its baseline level. Logistic regression analysis showed that Δ hsTnT has a good predictive value for LV dysfunction, 0.78 (p = 0.05), 95% CI (0.67–0.90). The increase in hsTnT > 81% was determined as the optimal threshold value for detecting early biochemical cardiotoxicity. Conclusion: It was investigated that hsTnT rise within the cutoff < 14 ng/L can be used as a marker of early biochemical cardiotoxicity and is valuable for predicting LV drop in 6 months of FU. We conclude that BC patients with increased hsTnT plasma concentration > 81% from the baseline value should be considered as high-risk patients for cardiotoxicity and need more precise cardiac monitoring and early preventive medical intervention strategies. Full article

Background/Objectives: Despite the psychological benefits of breast reconstruction (BR) after mastectomy, uptake remains limited among women with breast cancer. This study explores psychosocial and personality predictors of BR intentions in the pre-mastectomy phase, aiming to inform strategies for mental health promotion in oncology care. Methods: This cross-sectional analysis used preoperative data from a longitudinal study at a university hospital in Greece. Women with primary breast cancer scheduled for mastectomy completed a battery of validated self-report measures, including the International Personality Item Big-Five Factor Markers (IPIP-BFFM), the Hospital Anxiety and Depression Scale (HADS), and the Short Form-36 Health Survey (SF-36). Demographic, clinical, and psychosocial data were also collected. Binary logistic regression was used to examine predictors of (a) BR information-seeking and (b) BR intention. Results: Seventy-four women participated (mean age = 61.1 years). Older age predicted lower BR intention (Exp(b) = 0.897, 95% CI: 0.829–0.970) and information-seeking (Exp(b) = 0.925, 95% CI: 0.859–0.997). Single/divorced status was associated with reduced BR information-seeking (Exp(b) = 0.053, 95% CI: 0.005–0.549). Openness to experience significantly predicted both outcomes (BR information-seeking: Exp(b) = 1.115, 95% CI: 1.028–1.209); BR intention: Exp(b) = 1.095, 95% CI: 1.016–1.181). Higher physical health-related QoL scores were associated with increased BR intention (Exp(b) = 1.039, 95% CI: 1.007–1.072), whereas higher mental health-related QoL (Exp(b) = 0.952, 95% CI: 0.912–0.994) and higher depression scores (Exp(b) = 0.797, 95% CI: 0.638–0.996) were linked to decreased BR intent. No psychological factor significantly predicted information-seeking. Conclusions: These findings underscore the value of psychosocial screening and personality-informed counseling prior to surgery. By identifying individuals less likely to seek information or consider BR, pre-mastectomy assessments can contribute to tailored, mental health-promoting interventions and support informed, patient-centered surgical decision-making. Full article

Background: The increasing complexity of global supply chains and environmental expectations has highlighted the strategic importance of digital transformation in the transport, forwarding, and logistics (TFL) sector. Despite a growing portfolio of available tools, adoption rates—particularly among small and medium-sized enterprises (SMEs) in Central and Eastern Europe—remain low. This study investigates the barriers and motivations related to the implementation of digital planning tools supporting intermodal transport planning. Methods: A structured online survey was conducted among 80 Polish TFL enterprises, targeting decision-makers responsible for operational and digital strategies. The questionnaire included 17 closed and semi-open questions grouped into three thematic sections: tool usage, implementation barriers, and digital readiness. Results: The findings indicate that only 20% of respondents use dedicated route planning tools, and merely 10% report satisfaction with their performance. Key barriers include lack of awareness, organizational inertia, and the prioritization of other initiatives, with financial cost cited less frequently. While environmental sustainability is declared as a priority by most enterprises, digital support for emission tracking is limited. The results highlight the need for targeted education, integration support, and differentiated platform functionalities for SMEs and larger firms. Conclusions: This study offers evidence-based recommendations for developers, policymakers, and logistics managers aiming to accelerate digital adoption in the intermodal logistics landscape. Full article

The COVID-19 pandemic and recent viral outbreaks have highlighted the need for viral diagnostics that balance accuracy with accessibility. While traditional laboratory methods remain essential, point-of-care solutions are critical for decentralized testing at the population level. However, a gap persists between academic proof-of-concept studies and clinically viable tools, with novel technologies remaining inaccessible to clinics due to cost, complexity, training, and logistical constraints. Recent advances in surface functionalization, assay simplification, multiplexing, and performance in complex media have improved the feasibility of both optical and non-optical sensing techniques. These innovations, coupled with scalable manufacturing methods such as 3D printing and streamlined hardware production, pave the way for practical deployment in real-world settings. Additionally, software-assisted data interpretation, through simplified readouts, smartphone integration, and machine learning, enables the broader use of diagnostics once limited to experts. This review explores improvements in viral diagnostic approaches, including colorimetric, optical, and electrochemical assays, showcasing their potential for democratization efforts targeting the clinic. We also examine trends such as open-source hardware, modular assay design, and standardized reporting, which collectively reduce barriers to clinical adoption and the public dissemination of information. By analyzing these interdisciplinary advances, we demonstrate how emerging technologies can mature into accessible, low-cost diagnostic tools for widespread testing. Full article

This study aims to predict the purpose of the use of IC card data in LRT immediately after its opening by means of a questionnaire survey and to understand the changes in the number of commuters to better understand the growth in LRT commuter ridership, which has not been fully clarified in Japan. Furthermore, to assess long-term commuter retention for LRT systems, the analysis revealed the following three points. First, a discriminant analysis based on a national PT survey revealed that commuting and leisure or business activities can be classified with high accuracy. Second, it was found that commuter numbers increased immediately after opening, while the number of leisure or business users decreased in the first few months after opening and then leveled off. Third, the increase in the number of commuters was modeled using a logistic curve, and the annual rate of change in ridership was predicted to be less than 1% in the first three to four years after opening. Full article

Dissolved arsenic in private bedrock drinking water wells is a problem in eastern Wisconsin. Previous studies have identified bedrock sources of arsenic as discrete intervals within the local Paleozoic sedimentary section and have also identified release mechanisms causing arsenic to enter well boreholes. However, widespread contamination modeling is hindered by a lack of 3-D knowledge constraining the depth of the arsenic-bearing units in the subsurface. The growth and improvement of 3-D geologic mapping provides an opportunity to improve predictive models. This study in eastern Wisconsin, USA, uses a multivariate binary logistic regression analysis combined with 3-D geologic mapping to both assess various geologic and well construction factors that impact arsenic occurrence, and improve the ability to predict contamination risk. We find well construction characteristics, the stratigraphic unit within the open interval of a well, and the proximity to fold axes/fault zones are all statistically significant variables that impact the probability of a well exceeding either 2 or 10 µg/L dissolved arsenic. We apply these results by using 3-D mapping to determine the geologic unit present within the open interval of thousands of untested wells and use the logistic regression results to calculate contamination probability. This allows arsenic risk to be rapidly estimated for thousands of individual groundwater wells, and models of potential casing regulations to be assessed. Full article

Background/Objectives: The potential advantages of robotic assistance in spinal procedures are a growing area of interest, and patient perception plays a key role in its broader acceptance. However, public perception of robotic surgery in spinal operations remains unexplored. This study aims to assess the general public’s perceptions, expectations, and concerns regarding robot-assisted spine surgery. Methods: In the fall of 2024, a questionnaire was distributed to attendees at a public open day at the Neurocenter of Southern Switzerland, where the Globus ExcelsiusGPS™ spine surgery robot was demonstrated live on a mannequin. The 15-item questionnaire assessed demographic data, prior knowledge of medical robots, mental representations of surgical robots, expectations, and emotions after witnessing the demonstration. Data were analyzed using descriptive statistics, chi-square, Wilcoxon, McNemar tests, and logistic regression analysis. Results: A total of 109 questionnaires were collected. Most participants were female (64.4%) and had no direct experience with spinal pathology (79.8%). While 87.2% were aware of robotic surgery in general, only 65.1% specifically knew about its use in spine surgery. After witnessing the live demonstration, 81.9% felt reassured by the robot′s presence in surgery, compared to 61.3% before the demonstration (p = 0.007). Preference for robot-assisted surgery increased from 50.5% to 64.5% (p < 0.001). Notably, individuals with back-related issues showed greater confidence in the robot’s capabilities (p = 0.032). Conclusions: The general public perceives robotic spine surgery positively, viewing it as faster, more precise, and capable of performing tasks not readily performed by humans. The study highlights the importance of live demonstrations in enhancing trust and acceptance of robotic systems. Its findings have economic implications, as patients may be more likely to choose hospitals offering robot-assisted spine surgery. However, it is essential to also acknowledge alternative methods, such as computer-assisted navigation, which has demonstrated efficacy in spine surgery. Full article