Search Results (67)

Background/Objectives: Benign paroxysmal positional vertigo (BPPV) is one of the most common vestibular disorders and is characterized by transient but very severe vertigo, increasing fall risk, especially in older people. While many risk factors have been reported, there are still contradicting papers and evidence from large-scale studies remains limited. Methods: This nationwide, nested case–control study utilized Korean National Health Insurance Service-Health Screening Cohort data to investigate possible risk factors for BPPV. In particular, it examined the association between prior head trauma and BPPV, proposing prior head trauma as a plausible and clinically relevant risk factor. From an initial cohort of 514,866 participants, 29,467 BPPV cases were matched 1:4 with 117,868 controls based on age, sex, income, region, and index date. Conditional logistic regression, with overlap weighting, assessed the risk of BPPV associated with head trauma and other potential factors. Results: Head trauma was modestly more prevalent in the BPPV group (2.29% vs. 1.83%) and was significantly associated with BPPV (adjusted OR 1.28, 95% CI 1.17–1.40, p < 0.001). The corresponding Absolute Risk Increase (ARI) was 0.82 percentage points over the entire follow-up and 0.66 percentage points within 1 year. The association persisted across most subgroups including both demographic and clinical factors except underweight individuals and those with high comorbidity scores. Conclusions: This large-scale analysis reinforces head trauma as a significant risk factor for BPPV, providing population-level evidence that may guide clinical assessment and prevention strategies. Full article

Automated parcel lockers (APLs) are transforming urban last-mile delivery by reducing failed distributions, decoupling delivery from recipient availability, optimizing carrier routes, reducing carbon foot-print and mitigating traffic congestion. The paper investigates the optimal design of APLs systems under stochastic demand and operational constraints, formulating the problem as a resource allocation optimization with service-level guarantees. We proposed a data-driven discrete-event simulation (DES) model implemented in ARENA to (i) determine optimal locker configurations that ensure customer satisfaction under stochastic parcel arrivals and dwell times, (ii) examine utilization patterns and spatial allocation to enhance system operational efficiency, and (iii) characterize inventory dynamics of undelivered parcels and evaluate system resilience. The results show that the configuration of locker types significantly influences the system’s ability to maintain high customers service levels. While flexibility in locker allocation helps manage excess demand in some configurations, it may also create resource competition among parcel types. The heterogeneity of locker utilization gradients underscores that optimal APLs configurations must balance locker units with their size-dependent functional interdependencies. The Dickey–Fuller GLS test further validates that postponed parcels exhibit stationary inventory dynamics, ensuring scalability for logistics operators. As a theoretical contribution, the paper demonstrates how DES combined with time-series econometrics can address APLs capacity planning in city logistics. For practitioners, the study provides a decision-support framework for locker sizing, emphasizing cost–service trade-offs. Full article

Heilongjiang Province, as China’s principal gateway for Russian timber imports, faces structural inefficiencies in the localization of wood-processing enterprises—characterized by ecological sensitivity, resource–industry mismatches, and uneven spatial distribution. To address these challenges, this study proposes a two-stage site selection framework that integrates Geographic Information Systems (GIS) with an enhanced Non-dominated Sorting Genetic Algorithm II (NSGA-II). The model aims to reconcile ecological protection with industrial efficiency by identifying optimal facility locations that minimize environmental impact, reduce construction and logistics costs, and enhance service coverage. Using spatially resolved multi-source datasets—including forest resource distribution, transportation networks, ecological redlines, and socioeconomic indicators—the GIS-based suitability analysis (Stage I) identified 16 candidate zones. Subsequently, a multi-objective optimization model (Stage II) was applied to minimize carbon intensity and cost while maximizing service accessibility. The improved NSGA-II algorithm achieved convergence within 700 iterations, generating 124 Pareto-optimal solutions and enabling a 23.7% reduction in transport-related CO₂ emissions. Beyond carbon mitigation, the model spatializes policy constraints and economic trade-offs into actionable infrastructure plans, contributing to regional sustainability goals and transboundary industrial coordination with Russia. It further demonstrates methodological generalizability for siting logistics-intensive and policy-sensitive facilities in other forestry-based economies. While the model does not yet account for temporal dynamics or agent behaviors, it provides a robust foundation for informed planning under China’s dual-carbon strategy and offers replicable insights for the global forest products supply chain. Full article

Electric vehicle (xEV) battery durability significantly impacts the long-term operation, consumer satisfaction, and market adoption of xEVs. As driving range diminishes over time, it affects vehicle service life and lifecycle GHG emissions. Measuring the full service life of xEV batteries in laboratory tests presents technical and logistical challenges, necessitating representative measurements for parameterizing numerical models. These models are crucial for predicting long-term performance and rely on high-quality experimental data. While performance and aging trends under extreme temperatures are documented, cell thermal contact conditions suitable for direct model input are not well characterized. This study investigates lithium-ion cells from three xEV types, cycled at constant currents from C/40 to 1C, at temperatures between −15 °C and +45 °C, over 1000 cycles in a multi-year campaign. Stable isothermal cell temperatures were achieved using custom-built liquid immersion baths with forced convection, highlighting fundamental electrochemical behaviors by decoupling complex self-heating not typically monitored in air environments. The data inform and validate physics-based models on temperature-dependent performance and durability, providing operational limits to enhance cell and battery thermal management design and educate xEV consumers about conditions affecting performance, range, and durability. Full article

Background: Rare diseases are defined as clinical conditions that affect only a small number of persons in a population, considered fewer than 1 per 2000 in the European Union or fewer than 1 per 1600 in the United States They are serious, often chronic and progressive conditions, characterized by a pronounced clinical polymorphism that crosses all medical specialties. Multiple areas of life beyond just physical health are affected with significant impact on patients, families, and healthcare systems. Objective: To analyze the socio-demographic, medical, and vocational characteristics that correlate with functional status and work disability as a measure of quality of life in rare diseases. Methods: An observational retrospective study of adults with rare diseases evaluated for eligibility for social insurance rights in the National Institute of Medical Assessment and Work Capacity Rehabilitation Bucharest (INEMRCM, the Romanian abbreviation) over a 5-year period was made. Descriptive analysis was used to present sample characteristics. Means and standard deviations (SD) were calculated to describe numerical variables, frequencies were used to describe categorical variables, and logistic regression analysis was conducted to evaluate potential predictors of work capacity. All statistical analyses were performed by PSPP.3 software. p < 0.05 was the cut-off for statistical significance with a 95% confidence interval. Results: 90 consecutive persons were included in the survey. The mean age of the group was 44.5 years ± SD 10.61 years, with a female/male ratio of 48/42 persons. The mean disease duration was 10.61 years ± SD 9.76 years. Men had more severe disease (73.81%); p = 0.018 and significantly younger retirement age, M/F = 39.10 ± 12.26/43.06 ± 9.32; p = 0.037. Less disabling diseases were predominant autoimmune conditions (85.71% of cases); genetic conditions had a more severe functional impact in 63.75% of cases; p = 0.037. People with multisystem diseases but with specific or targeted treatment can work more frequently (76.19%); those with visual impairment have more severe impairments (73.77%); p < 0.001. All individuals who received specific therapy had a better functional status, unlike only 37.21% of those who received symptomatic treatment or treatment for complications; p = 0.023. Logistic regression analysis indicated that the type of impairment and the availability of specific treatments could serve as predictors of a reduced likelihood of employment in rare disease cases. Education level and occupation were not correlated with functional impairment and work disability (NS). Conclusions: Several factors, including some that are modifiable, were associated with better outcomes, such as reduced disability and an increased potential for work participation. Sex, disease etiology, type of impairment, and treatment were all significantly linked to functional capacity. Among these, the type of impairment and the availability of specific treatments might be predictors of employment. Addressing these parameters requires a multidisciplinary team, involving specialized care and comprehensive support services to improve the overall quality of life of individuals affected by rare diseases. Full article

Background and Aims: Alopecia areata (AA) is a non-scarring inflammatory hair loss condition associated with various immune-mediated comorbidities. Prediabetes, characterized by elevated blood glucose levels not yet high enough to be classified as diabetes, significantly increases the risk of developing type 2 diabetes mellitus (T2DM) and cardiovascular complications. The associations between AA obesity and prediabetes have long been investigated in an attempt to identify preventable risk factors, yet the literature is relatively scarce and inconclusive. This study aimed to explore the association between AA, prediabetes, obesity, and T2DM in a large population cohort. Methods: All patients diagnosed with AA between 2005 and 2019 within Maccabi Healthcare Services (MHS) in Israel were compared with age-matched and gender-matched healthy controls for prediabetes, T2DM and obesity, using logistic regression models for all analyses. A total of 33,401 patients with AA and 66,802 controls were included in the analysis. Results: The prevalence of prediabetes was significantly higher in AA patients (26.3%) compared to controls (18.1%), with an odds ratio (OR) of 1.62. Obesity prevalence was also higher in AA patients (17.2% vs. 13.3%, OR 1.35). T2DM prevalence was similar between groups. Prediabetes prevalence notably increased with age in AA patients, especially in those aged 40 and older (OR 2.02). Discussion: The study highlights a significant association between prediabetes and alopecia areata, with prediabetes risk emerging prominently in AA patients. Obesity also showed a strong link with AA. These findings suggest the need for regular screening and early management of prediabetes and obesity in patients with AA to potentially mitigate associated health risks. Full article

Considering the growing consumer demand for local food products in the United States, several restaurants are seeking to include locally sourced meat and seafood products on their menus. Despite this trend, limited research has examined what factors encourage or discourage restaurants from purchasing or purchasing more local proteins. This study extends the literature by investigating what traits are desired and what barriers exist when purchasing local proteins for restaurant procurers (those tasked with purchasing ingredients), utilizing data from an online survey. The results indicate that the top three desired traits for locally raised meats (beef, pork, chicken) are naturally grown, hormone-free, and organic. The most important traits for seafood (fish, crab, shrimp, and oysters) are the different certifications (i.e., Marine Stewardship Council, state/local certification, and Aquaculture Stewardship Council). The most common barriers are inconsistent quality, high cost, limited availability, and further processing needs. Results from a logistic regression suggest that restaurants characterized by fewer seats, higher entrée prices, and longer tenure are more likely to purchase local proteins. Restaurants willing to pay more for local proteins tend to be full-service types, have fewer seats, have higher entrée prices, serve other local foods, and cater to both tourists and residents. Full article

Background/Objectives: Yellow fever (YF) outbreaks continue to affect populations that are not reached by routine immunization services, such as workers at a high risk of occupational exposure to YF. In the Central African Republic (CAR), YF cases were detected in districts characterized by the presence of workers in forest areas. We developed an innovative approach based on a local partnership with private companies of the extractive industry to administer YF vaccine to workers in remote areas during the response to an outbreak. Methods: The planning stage of the campaign included the mapping of forestry and mining companies through the involvement of national and/or local representatives of companies from both the formal and informal sectors. Information sessions and mobilization targeted the heads of operating companies. Advanced and mobile strategies were used to target workers on their work site. Companies provided logistical support including transportation and communication and set up temporary vaccination posts. Results: Using this local partnership, it was possible to vaccinate over 70,000 workers (5.8% of the entire vaccinated population) in hard-to-reach areas, protecting them from YF. This represented around 47% of the estimated number of workers and dependents. The partnership with the private sector also contributed to increasing knowledge on the risk of YF and means of protection among a high-risk community. Conclusions: Private companies represent potentially useful actors that can contribute to the protection of high-risk workers and to the prevention and control YF outbreaks. The experience in the CAR has demonstrated that it is possible to obtain support from private companies, including informal ones, for a vaccination campaign. Full article

Understanding activity-travel patterns and their determinants with regard to multi-purpose commuters is essential for enhancing commuting efficiency and ensuring equal participation in activities. This study applies sequence analysis and hierarchical clustering to identify distinct activity-travel patterns of Kunming commuters using 2016 Household Travel Survey data. Subsequently, a multinomial logistic regression model (MNL) examines the factors influencing these patterns. The results reveal significant heterogeneity across four activity-travel patterns: the fixed commuter pattern (FCP), characterized by pronounced morning and evening peaks with minimal non-commuting activities; the balanced commuter pattern (BCP), where commuters participate in non-commuting activities after afternoon work; the restricted commuter pattern (RCP), with non-commuting activities occurring after midday work; and the flexible commuter pattern (FLCP), featuring a late-start work pattern where some commuters go to work after 5 pm. Additionally, the study finds that female commuters and those with longer commuting and working hours tend to have simpler time allocation. Conversely, male commuters, those from complex family structures, car-owning households, and residents in areas with abundant activity opportunities actively engage in non-commuting activities. These findings can help policymakers optimize travel services and develop heterogeneous commuting and transportation policies. Full article

Municipal solid waste (MSW) management poses substantial challenges in rapidly urbanizing areas, with implications for both the environment and public health. This study focuses on the city of Kinshasa in the Democratic Republic of Congo, investigating whether the presence or absence of solid waste collection services results in varying health and economic impacts, and additionally, seeking to establish a correlation between residing in proximity to dumpsites and the prevalence of diseases like malaria and typhoid, thereby providing a comprehensive understanding of the health implications tied to waste exposure. Health data were collected through survey questionnaires, and the geospatial distribution of 19 dumpsites was analyzed using Google Earth Pro 7.3.1 for satellite imagery and GIS software 10.3.1 to map dumpsites and define 1 km buffer zones around the largest dumpsites for household sampling. Statistical analyses were conducted using R Version 4.2.3, employing Chi-square tests for disease prevalence and logistic regression to assess associations between waste management practices and health outcomes. A multivariate regression was used to evaluate correlations between discomfort symptoms (e.g., nasal and eye irritation) and waste activities. The geospatial analysis revealed significant variation in dumpsite size and location, with larger dumpsites near water bodies and flood-prone areas. The study contributes valuable insights into waste-related health risks, emphasizing the need for improved waste management policies in rapidly urbanizing areas like Kinshasa. The socio-demographic analysis reveals distinct traits within the surveyed populations of two communes, Bandalungwa (Bandal) and Bumbu. Bumbu, characterized by larger open dumpsites and limited waste collection services, exhibits a higher prevalence of certain diseases, particularly typhoid fever, and malaria. This discrepancy is statistically significant (p < 2.2 × 10⁻¹⁶), suggesting a potential link between waste exposure and disease prevalence. In Bandal, self-waste collection is a high risk of exposure to typhoid (OR = 4.834 and p = 0.00001), but the implementation of a waste collection service shows protective effect (OR = 0.206 and p = 0.00001). The lack of waste collection services in Bumbu increases the risk of exposure, although not significantly (OR = 2.268 and p = 0.08). Key findings indicate that waste disposal methods significantly differ between Bandal and Bumbu. Bumbu’s reliance on burning and dumping creates environments conducive to disease vectors, contributing to elevated disease transmission risks. However, an in-depth correlation analysis reveals that specific waste management practices, such as burning, burying, and open dumping, do not exhibit statistically significant associations with disease prevalence, underlining the complexity of disease dynamics. This study contributes valuable insights into the importance for urban public health, particularly in rapidly urbanizing cities like Kinshasa, where inadequate waste management exacerbates health risks. By investigating the correlation between proximity to unregulated dumpsites and the prevalence of diseases such as malaria and typhoid fever, the research underscores the urgent need for targeted waste management policies. The stark health disparities between Bandal, with better waste services, and Bumbu, where services are lacking, highlight the protective effect of organized waste collection. These findings suggest that expanding public waste services and enforcing stricter regulations on waste disposal could reduce disease prevalence in vulnerable areas. Additionally, the study supports integrating waste management into urban planning as a critical public health measure. Its evidence-based approach offers valuable insights for policymakers in Kinshasa and other cities facing similar challenges, emphasizing the broader health implications of environmental governance in urban settings. Full article

Afforestation (AF) in farmland has been widely used as an alternative and sustainable land-use practice to address socioeconomic and environmental challenges. The aim of this study is to estimate farmers’ willingness to accept (WTA) compensation and land, both of which are equally significant for policymakers to ensure the effective implementation of AF and achieve desired outcomes. This topic has not been sufficiently explored in previous research. This study focused on areas characterized by insecure farming conditions, backward economies, and fragile landscapes, where farmers are generally unfamiliar with AF or compensation for ecosystem services under payment for ecosystem services programs. It assessed their attitudes towards the WTA AF, compensation, and land as an alternative practice, which remains under-researched. This is crucial for designing effective AF programs in the future to improve livelihood and enhance the quantity and quality of the environment. This study used the contingent valuation method to estimate the minimum WTA compensation and maximum land for the forgone loss and alternative land-use practices. A questionnaire survey was conducted in Hupsekot municipality, Nepal, with 232 farmer households. The ordinal logistic regression model was used to analyze influencing factors of WTA compensation and land. The result showed that farmers’ average WTA compensation was NPR 1268.67 (USD 9.76)/Kattha/year, with 2.64 Kattha land available for AF. The factors, including socioeconomic characters and attitudes toward the environmental situation and forests, significantly influenced WTA values and provided potential target factors to achieve maximum AF land within a lower budget. Full article

Laboratory medicine has undergone a deep and multifaceted revolution in the course of human history, in both organizational and technical terms. Over the past century, there has been a growing recognition of the need to centralize numerous diagnostic activities, often similar or identical but located in different clinical departments, into a common environment (i.e., the medical laboratory service), followed by a progressive centralization of tests from smaller laboratories into larger diagnostic facilities. Nevertheless, the numerous technological advances that emerged at the beginning of the new millennium have helped to create a new testing culture characterized by a countervailing trend of decentralization of some tests closer to patients and caregivers. The forces that have driven this (centripetal) counter-revolution essentially include a few key concepts, namely “home testing”, “portable or even wearable devices” and “remote patient monitoring”. By their very nature, laboratory medical services and remote patient testing/monitoring are not contradictory, but may well coexist, with the choice of one or the other depending on the demographic and clinical characteristics of the patient, the type of analytical procedure and the logistics and local organization of the care system. Therefore, this article aims to provide a general overview of patient self-testing, with a particular focus on portable and wearable (including implantable) devices. Full article

Nowadays, the focus of flow shops is the adoption of customized demand in the context of service-oriented manufacturing. Since production tasks are often characterized by multi-variety, low volume, and a short lead time, it becomes an indispensable factor to include supporting logistics in practical scheduling decisions to reflect the frequent transport of jobs between resources. Motivated by the above background, a hybrid method based on dual back propagation (BP) neural networks is proposed to meet the real-time scheduling requirements with the aim of integrating production and transport activities. First, according to different resource attributes, the hierarchical structure of a flow shop is divided into three layers, respectively: the operation task layer, the job logistics layer, and the production resource layer. Based on the process logic relationships between intra-layer and inter-layer elements, an operation task–logistics–resource supernetwork model is established. Secondly, a dual BP neural network scheduling algorithm is designed for determining an operations sequence involving the transport time. The neural network 1 is used for the initial classification of operation tasks’ priority; and the neural network 2 is used for the sorting of conflicting tasks in the same priority, which can effectively reduce the amount of computational time and dramatically accelerate the solution speed. Finally, the effectiveness of the proposed method is verified by comparing the completion time and computational time for different examples. The numerical simulation results show that with the increase in problem scale, the solution ability of the traditional method gradually deteriorates, while the dual BP neural network has a stable performance and fast computational time. Full article

In 2018, an outbreak of human rabies caused by the hematophagous bat Desmodus rotundus hit the Brazilian Amazon Basin community of Melgaço, Brazil, resulting in the death of 10 people, 9 of them children. The incidence of rabies has been on the rise among populations in conditions of vulnerability in this ecosystem due to human expansion into sylvatic environments and limited access to public health services. To address this issue, in September 2019, a collaborative effort from national, local, and international institutions promoted and executed a pilot for pre-exposure prophylaxis of a population in high-risk areas for hematophagous bat-mediated rabies. This measure is usually only implemented in response to outbreaks. The pilot was conducted in Portel, in a nearby location to the previous outbreak, with the use of fluvial transportation, and 2987 individuals in 411 dwellings were successfully vaccinated. It established a methodology for pre-exposure prophylaxis for populations in conditions of vulnerability, identifying logistics and costs, as well as characterizing the target riverine population regarding risk factors associated with bites by hematophagous bats. This approach offers a proactive measure to prevent future outbreaks and provides valuable insights into how to address the issue of rabies in remote and difficult-to-reach areas. Full article

As a representative of potential “hidden champions”, a concept originating in Germany, specialized and innovative Little Giant Enterprises (LGEs) have become exemplary models for small and medium-sized enterprises (SMEs) in China. These enterprises are regarded as crucial support for realizing the strategy of building a strong manufacturing country and addressing the weaknesses in key industrial areas. This paper begins by examining urban agglomerations, which serve as the main spatial carriers for industrial restructuring and high-quality development in manufacturing. Based on data from LGEs in the Yangtze River Delta (YRD) and Pearl River Delta (PRD) urban agglomerations from 2019 to 2023, the study employs the Random Forest (RF) and Multi-scale Geographically Weighted Regression (MGWR) methods to conduct a comparative analysis of their spatial patterns and influencing factors. The results are as follows: (1) LGEs exhibit spatial clustering in both the YRD and PRD regions. Enterprises in the YRD form a “one-axis-three-core” pattern within a distance of 65 km, while enterprises in the PRD present a “single-axis” pattern within a distance of 30 km, with overall high clustering intensity. (2) The YRD is dominated by traditional manufacturing and supplemented by high-tech services. In contrast, the PRD has a balanced development of high-tech manufacturing and services. Enterprises in different industries are generally characterized by a “multi-point clustering” characteristic, of which the YRD displays a multi-patch distribution and the PRD a point–pole distribution. (3) Factors such as industrial structure, industrial platforms, and logistics levels significantly affect enterprise clustering and exhibit scale effects differences between the two urban clusters. Factors such as industrial platforms, logistics levels, and dependence on foreign trade show positive impacts, while government fiscal expenditure shows a negative impact. Natural geographical location factors exhibit opposite effects in the two regions but are not the primary determinants of enterprise distribution. Each region should leverage its own strengths, improve urban coordination and communication mechanisms within the urban cluster, strengthen the coordination and linkage of the manufacturing industry chain upstream and downstream, and promote high-tech industries, thereby enhancing economic resilience and regional competitiveness. Full article