Search Results (330)

Background/Objectives: Patients with diabetes mellitus face increased risk of severe outcomes and mortality from COVID-19. Dipeptidyl peptidase-4 (DPP-4) inhibitors, widely used antidiabetic agents, are hypothesized to affect COVID-19 outcomes via anti-inflammatory and immune-modulating mechanisms. However, real-world evidence, especially in Korean populations, remains limited. Methods: We conducted a retrospective cohort study using Korea’s nationwide Health Insurance Review and Assessment (HIRA) database. Adults with diabetes hospitalized for confirmed COVID-19 between 1 March 2021, and 28 February 2022, were included and stratified by DPP-4 inhibitor use. The primary outcome was 30-day all-cause mortality. Cox proportional hazards models adjusted for age, sex, and comorbidities estimated hazard ratios (HRs). Subgroup analyses examined angiotensin receptor blocker (ARB) and insulin use. Results: Among 16,134 eligible patients, 7082 received DPP-4 inhibitors. The 30-day mortality rate was lower in DPP-4 inhibitor users than non-users (4.3% vs. 10.3%, p < 0.0001). Adjusted analyses showed DPP-4 inhibitor use was associated with reduced mortality risk (adjusted HR: 0.455; 95% CI: 0.414–0.499). Subgroup analyses yielded consistent results across ARB and insulin users. Kaplan-Meier curves demonstrated higher survival probability in the DPP-4 inhibitor group. Conclusions: In this nationwide Korean cohort, DPP-4 inhibitor use was associated with lower mortality among hospitalized diabetic patients with COVID-19. While these findings suggest a potential benefit, causality cannot be confirmed due to the observational design. Prospective studies are needed to verify these associations and explore underlying mechanisms. Full article

This study evaluates the current Spanish insurance framework for catastrophic flood risk, administered by the Consorcio de Compensación de Seguros (CCS), based on nationwide loss data reported by the CCS for the period 1996–2020. The analysis of historical claims data enables a clear differentiation between frequent, low-cost events and infrequent, high-impact catastrophes. While the CCS has fulfilled a critical role in post-disaster compensation, the findings highlight the parallel need for ex ante risk mitigation strategies. The study proposes a more efficient, two-tier risk coverage model. Events whose impacts can be managed through standard insurance mechanisms should be underwritten by private insurers using actuarially fair premiums. In contrast, events with catastrophic implications—due to their scale or financial impact—should be addressed through general solidarity mechanisms, centrally managed by the CCS. Such a risk segmentation would improve the financial sustainability of the system and create fiscal space for prevention-oriented incentives. The current design of the CCS scheme may generate moral hazard, as flood exposure is not explicitly priced into the premium structure. Empirical findings support a shift towards a more transparent, incentive-aligned model that combines collective risk sharing with individual risk responsibility—an essential balance for effective climate adaptation and long-term resilience. Full article

Background: Severe traumatic brain injury (TBI) remains a leading cause of mortality and long-term morbidity, particularly in high-acuity trauma settings. We aim to evaluate the clinical, physiologic, and socioeconomic factors associated with outcomes in patients with severe traumatic brain injury (TBI) at a single urban Level 1 trauma center. Method: This is a single-center, retrospective study of patients presenting with severe TBI between 1 January 2020 and 31 December 2023 at Elmhurst Hospital Center in Queens, New York. Patients were identified using ICD trauma codes and an Abbreviated Injury Severity (AIS) Head score of ≥3. Demographic data, injury characteristics, vital signs, airway interventions, alcohol level, and insurance status were analyzed. Result: A total of 1130 patients met the inclusion criteria. The cohort was predominantly male (76.1%) with a mean age of 52.7 years. Blunt trauma accounted for 97.8% of cases, with a mortality rate of 13.8%, while penetrating trauma comprised 2.2%, with a markedly higher mortality rate of 48%. Patients who died as full code had lower mean systolic blood pressure (82.5 mmHg), oxygen saturation (63%), and shorter emergency department stays (~3.7 h). The mean Glasgow Coma Scale (GCS) score was 12.6, dropping to 6.0 in patients who died. Moreover, higher AIS Head and Injury Severity Score (ISS) values were correlated with worse outcomes. Severely intoxicated patients had higher TBI incidence, with no clear difference observed when compared to normal BAC levels. Self-pay patients exhibited the highest mortality (40%). All associations were statistically significant (p < 0.0001). Conclusions: Severe TBI outcomes are significantly influenced by injury mechanisms, physiologic parameters, and socioeconomic status. These findings emphasize the need for targeted prognostic tools and improved trauma system preparedness for TBI patients at risk of poor outcomes. Full article

Amid the global drive to enhance public sector performance in the digital economy era, improving government spending efficiency has become a critical governance objective. This study investigates the impact of digital inclusive finance on government spending efficiency from a digital finance systems perspective using county-level panel data in China’s Yangtze River Delta for the period 2014–2022 and constructing the fixed-effects model and instrumental variable method to estimate the effect of digital inclusive finance and explore its underlying mechanisms. Heterogeneity across regions with varying economic development levels is analyzed, and fiscal pressure is examined as a potential mediating factor. The results indicate that (1) digital inclusive finance significantly enhances government spending efficiency, primarily through broad service coverage and deep usage of digital financial services such as mobile payments, digital credit, and insurance; (2) the positive effect is more pronounced in counties with lower government spending efficiency and economic development; and (3) fiscal pressure acts as a key transmission channel, with broader digital inclusive finance coverage helping to alleviate fiscal stress and improve government spending efficiency. These findings offer empirical insights into the role of digital finance in promoting effective and adaptive public financial governance. Full article

Farm household pluriactivity has become increasingly prevalent in China; however, its influence on crop structure remains insufficiently explored. This study examines the impact of farm household pluriactivity on crop structure in China, focusing on factor input mechanisms. Based on survey data from 473 farm households in Sichuan Province, this study employs ordinary least squares (OLS), two-stage least squares (2SLS), and mediation analyses to systematically assess the impact of pluriactivity on crop structure through factor input mechanisms. The analysis reveals three key findings. First, rather than reducing the grain planting area, an increase in part-time farming is associated with a significant rise in the proportion of grain cultivation. Second, factor inputs partially mediate this relationship: while pluriactivity tends to reduce staple crop cultivation through mechanisms such as cultivated land transfer-out, land abandonment, and increased non-agricultural labor input, it simultaneously promotes staple crop expansion via enhanced agricultural technical services. Third, heterogeneity tests indicate that the positive effect of pluriactivity on staple crop cultivation is especially pronounced among households in hilly areas and those that have adopted agricultural insurance. These findings provide valuable policy insights for fostering sustainable agricultural transitions and enhancing food security in developing regions. Full article

Background/Objectives: Mild traumatic brain injury (mTBI) presents with persistent, heterogeneous symptoms requiring multifaceted care. Although interdisciplinary rehabilitation is increasingly recommended, implementation remains inconsistent. This study aimed to synthesize existing literature and clinician perspectives to construct a practice-informed conceptual framework for interprofessional mTBI rehabilitation. Methods: Structured interviews were conducted with 94 clinicians—including neurologists, neuropsychologists, optometrists, occupational and physical therapists, speech-language pathologists, neurosurgeons, and case managers—across academic, private, and community settings in the United States. Interviews followed a semi-structured format adapted for the NIH I-Corps program and were analyzed thematically alongside existing rehabilitation literature. Results: Clinicians expressed strong consensus on the value of function-oriented, patient-centered care. Key themes included the prevalence of persistent cognitive and visual symptoms, emphasis on real-world goal setting, and barriers such as fragmented communication, reimbursement restrictions, and referral delays. Disciplinary differences were noted in perceptions of symptom persistence and professional roles. Rehabilitation technologies were inconsistently adopted due to financial, training, and interoperability barriers. Equity issues included geographic and insurance-based disparities. A four-domain conceptual framework emerged: discipline-specific expertise, coordinated training, technological integration, and care infrastructure, all shaped by systemic limitations. Conclusions: Despite widespread clinician endorsement of interprofessional mTBI care, structural barriers hinder consistent implementation. Targeted reforms—such as embedding interdisciplinary models in clinical education, expanding access to integrated technology, and improving reimbursement mechanisms—may enhance care delivery. The resulting framework provides a foundation for scalable, patient-centered rehabilitation models in diverse settings. Full article

Rotator cuff tears are common in individuals over 40, and physical therapy is often prescribed post-surgery. However, access can be limited by cost, convenience, and insurance coverage. CuffLink is a telehealth rehabilitation system that integrates the Strengthening and Stabilization System mechanical exerciser with the interACTION mobile health platform. The system includes a triple-axis accelerometer (LSM6DSOX + LIS3MDL FeatherWing), a rotary encoder, a VL530X time-of-flight sensor, and two wearable BioMech Health IMUs to capture upper-limb motion. CuffLink is designed to facilitate controlled, home-based exercise while enabling clinicians to remotely monitor joint function. Concurrent validity and test–retest reliability were used to assess device accuracy and repeatability. The results showed moderate to good validity for shoulder rotation (ICC = 0.81), device rotation (ICC = 0.94), and linear tracking (from zero: ICC = 0.75 and RMSE = 2.41; from start: ICC = 0.88 and RMSE = 2.02) and good reliability (e.g., RMSEs as low as 1.66 cm), with greater consistency in linear tracking compared to angular measures. Shoulder rotation and abduction exhibited higher variability in both validity and reliability measures. Future improvements will focus on manufacturability, signal stability, and force sensing. CuffLink supports accessible, data-driven rehabilitation and holds promise for advancing digital health in orthopedic recovery. Full article

The increasingly observable financial vulnerability of households in emerging market countries makes it imperative to investigate the factors influencing it. Considering that China stands as a representative of emerging market economies, analyzing the factors influencing household financial vulnerability in China presents great reference significance for the sustainable development of households in emerging market countries. Using data from the China Household Finance Survey (CHFS) household samples, this paper presents the regional distribution of households with financial vulnerability in China. Utilizing machine learning (ML), this research examines the factors that influence household financial vulnerability in China and determines the most significant ones. The results reveal that households with financial vulnerability in China takes up a proportion of more than 63%, and household financial vulnerability is lower in economically developed coastal regions than in medium and small-sized cities in the central and western parts of China. The analysis results of the SHAP method show that the debt leverage ratio of a household is the most significant feature variable in predicting financial vulnerability. The ALE plots demonstrate that, in a household, the debt leverage ratio, the age of household head, health condition, economic development and literacy level are significantly nonlinearly related to financial vulnerability. Heterogeneity analysis reveals that, except for household debt leverage and insurance participation, the key characteristic variables exerting the most pronounced effect on financial fragility differ between urban and rural households: household head age for urban families and physical health status for rural families. Furthermore, digital financial inclusion and social security exert distinct impacts on financial vulnerability, showing significantly stronger effects in high per capita GDP regions and low per capita GDP regions, respectively. These findings offer valuable insights for policymakers in emerging economies to formulate targeted financial risk mitigation strategies—such as developing household debt relief and prevention mechanisms and strengthening rural health security systems—and optimize policies for household financial health. Full article

Cattle face recognition is a feasible way for identification of cattle in information management of large farms or identity verification in commercial insurance for farms. Recent cattle face recognition approaches, based on supervised learning, heavily depend on annotation which is both labor-intensive and time-consuming. Unsupervised learning for cattle face recognition aims at learning discriminative representations for cattle retrieval from unlabeled data. However, the inherent noise in pseudo-labels significantly hinders the performance. Thus, we propose an unsupervised learning framework with part-attention-based pseudo-label refinement reciprocal compact loss (USL-PARC) to enhance the reliability of the pseudo-label by the fine-grained local context derived via attention mechanism, while obtaining separable and discriminative features by contrastive learning with the compact loss. Firstly, we propose a part-attention-based pseudo-label refinement framework to refine the pseudo-labels of global features by dynamically supplementing local fine-grained information, thereby mitigating the effects of pseudo-label noise. Secondly, ResNet-Sim network, augmented with the SimAM attention mechanism, is constructed to strengthen the ability of capturing more informative localized supplementary information. Finally, we raise compact loss to increase the tightness of the clustering of feature points from the same identity in the feature space. It is encouraging to find that USL-PARC achieves 97.4% accuracy, outperforming the state-of-the-art unsupervised learning models on our CattleFace2025 dataset. These results demonstrate the effectiveness of our proposed USL-PARC on mitigating the impact of pseudo-label noise and enhancing the learning ability of separable and discriminative features. Full article

The Black–Scholes model is a fundamental concept in modern financial theory. It is designed to estimate the theoretical value of derivatives, particularly option prices, by considering time and risk factors. In the context of agricultural insurance, this model can be applied to premium determination due to the similar characteristics shared with the option pricing mechanism. The primary challenge in its implementation is determining a fair premium by considering the potential financial losses due to crop failure. Therefore, this study aimed to analyze the determination of rainfall index-based agricultural insurance premiums using the standard and fractional Black–Scholes models. The results showed that a solution to the fractional model could be obtained through the Daftardar-Gejji Laplace method. The premium was subsequently calculated using the Black–Scholes model applied throughout the growing season and paid at the beginning of the season. Meanwhile, the fractional Black–Scholes model incorporated the fractional order parameter to provide greater flexibility in the premium payment mechanism. The novelty of this study was in the application of the fractional Black–Scholes model for agricultural insurance premium determination, with due consideration for the long-term effects to ensure more dynamism and flexibility. The results could serve as a reference for governments, agricultural departments, and insurance companies in designing agricultural insurance programs to mitigate risks caused by rainfall fluctuations. Full article

Aim: New disease-specific and mechanism-based treatments for migraine that share good evidence of efficacy have recently been introduced. However, due to reimbursement problems with insurance companies and high costs, classical anti-migraine drugs continue to be used. The objective of this study was to assess the clinical efficacy and tolerability of flunarizine for the preventive treatment of episodic migraine without aura in a Turkish cohort, concentrating on alterations in headache frequency, pain intensity, and migraine-related disability as measured by MIDAS scores within a practical clinical environment. Methods: Clinical and demographic data of 243 patients with episodic migraine without aura (175 females, 68 males; mean age 33.9 years) were evaluated. Headache frequency, side effects of flunarizine, pain intensity, and MIDAS scores were recorded during initial and 3-month follow-up periods. Results: After three months of flunarizine treatment, significant improvements were observed in headache parameters. The mean Numeric Pain Rating Scale (NPRS) score, the mean MIDAS score, and the monthly migraine attack frequency declined significantly (all p values < 0.001). Adverse events were reported in 21.8% of patients, most commonly weight gain and tiredness, followed by mood changes, gastrointestinal symptoms, and numbness or tingling. Patients experiencing side effects were significantly older (p = 0.023), though side effects did not impact treatment efficacy. Regression analysis identified no significant predictors of disability improvement. Conclusion: Our results demonstrated that flunarizine had considerable short-term efficacy in decreasing the frequency of migraine attacks, alleviating headache severity, and reducing migraine-related disability among patients experiencing episodic migraine without aura. Although mild to moderate side effects were fairly prevalent, especially in older individuals, they did not compromise the effectiveness of the treatment. Notably, early adverse events occurring within the first two weeks resulted in treatment discontinuation for some patients, highlighting the necessity for vigilant monitoring during the initial phase of treatment. Full article

This study investigated the dynamic factors influencing food security in Saudi Arabia, a critical concern for the nation’s stability and development. The purpose of this research was to analyze the impact of several key determinants on the Food Security Index and to distinguish between their short-term and long-term effects, thereby providing evidence-based policy recommendations. Using annual time-series data spanning 1990 to 2023, the research employs the Autoregressive Distributed Lag (ARDL) and Vector Error Correction Model (VECM) methods. We specifically examined the roles of agricultural GDP contribution, agricultural insurance coverage, food price stability, government policies related to agriculture, climate change impacts, agricultural productivity, and technology adoption. Short-run estimates reveal that agricultural GDP contribution, government policies, and agricultural productivity express a significant positive influence on food security. Importantly, climate change showed a counterintuitive positive association in the short term, potentially indicating immediate adaptive responses. Conversely, food price stability exhibited an unexpected negative association, which may indicate that the index captures high price levels rather than just volatility. The long-run analysis highlights the crucial importance of sustained factors for food security. Agricultural GDP contribution, agricultural insurance coverage, and agricultural productivity are identified as having significant positive impacts over the long term. In contrast, climate change demonstrates a significant negative long-run impact, underscoring its detrimental effect over time. Government policies, while impactful in the short term, become statistically insignificant in the long run, suggesting that sustained structural factors become dominant. Granger causality tests indicate short-term causal relationships flowing from climate change (positively), agricultural GDP contribution, government policies, and agricultural productivity towards food security. The significant error correction term confirms the existence of a stable long-run equilibrium relationship among the variables. On the basis of these findings, the study concludes that strengthening food security in Saudi Arabia requires a multifaceted approach. Short-term efforts should focus on enhancing agricultural productivity and implementing targeted measures to mitigate immediate climate impacts and refine food price stabilization strategies. For long-term resilience, priorities must include expanding agricultural insurance coverage, investing in sustainable agricultural practices, and continuing to boost agricultural productivity. The study contributes to the literature by providing a comprehensive dynamic analysis of food security determinants in Saudi Arabia using robust time-series methods, offering specific insights into the varying influences of economic, policy, environmental, and agricultural factors across different time horizons. Further research is recommended to explore the specific mechanisms behind the observed short-term relationship with climate change and optimize food price policies. Full article

The damage to shipping containers during port handling operations continues to pose a significant challenge that adversely affects operational efficiency, equipment integrity, and supply chain accountability. This study utilises real-world measurement data gathered through accelerometers to examine the occurrence and dynamics of physical impacts, particularly side and rear collisions, during the handling of containers at Klaipėda City Port. The research prioritises two critical scenarios: side impacts during stacking operations with reach stackers and rear impacts during trailer loading procedures. Impact events are meticulously recorded and analysed to ascertain the magnitudes of acceleration across multiple axes. This reveals that side impacts produce significantly greater forces, particularly in the lateral direction, than rear impacts. This study employs sensor-based monitoring, advanced data visualisation techniques, and structured scenario analysis to delineate the variability and intensity of mechanical interactions during these operations. The findings emphasise the structural stress that containers experience and underscore the importance of embedded monitoring technologies for real-time event detection and damage prevention. The results contribute to the expanding body of knowledge that supports the digital transformation of container terminals and furnish actionable insights for enhancing handling protocols, informing insurance assessments, and improving safety measures within both automated and conventional port environments. Full article

Background/Objectives: Parkinson’s disease (PD) and benign paroxysmal positional vertigo (BPPV) are both prevalent in the geriatric population. While dizziness is a common non-motor symptom in PD, the relationship between PD and incident BPPV remains unclear. Limited data suggest potential shared mechanisms, including mitochondrial dysfunction and oxidative stress, but large-scale epidemiological evidence is lacking. This investigation focused on assessing the incidence of BPPV in patients with PD compared to matched controls using a nationwide cohort. Methods: Data from the Korean National Health Insurance Service–Health Screening Cohort were used to perform a retrospective cohort analysis. We identified 8232 newly diagnosed PD patients and matched them 1:4 with 32,928 controls based on age, sex, income, and residential region. Stratified Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident BPPV. Subgroup and Kaplan–Meier analyses were also performed. Results: Over 220,151 person-years of follow-up revealed a lower incidence of BPPV in the PD group relative to the control group (4.98 vs. 5.95 per 1000 person-years); the corresponding adjusted HR was 0.77 (95% CI: 0.66–0.90; p = 0.001), indicating a 23% reduced risk. The inverse association remained consistent across most subgroups, including older adults and rural residents. Kaplan–Meier analysis further illustrated a significant decline in the cumulative incidence of BPPV in PD patients (p = 0.007). Conclusions: PD may contribute to a lower incidence of BPPV, which could be explained by reduced mobility, altered vestibular function, or diagnostic challenges. Clinicians should consider BPPV in PD patients presenting with dizziness. Full article

Due to increased competition in the marketplace, companies in all industries are facing the problem of customer attrition. In order to expand their market share and increase profits, companies have shifted from the concept of ‘acquiring new customers’ to ‘retaining old customers’. In this study, we design a deep learning model based on multi-network feature extraction and an attention mechanism, convolutional neural network–bidirectional long and short-term memory network–fully connected layer–coordinate attention (CNN-BiLSTM-FC-CoAttention), and apply it to customer churn risk assessment. In the data preprocessing stage, the imbalanced dataset was processed using the SMOTE-ENN hybrid sampling method. In the feature extraction stage, a sequence-based CNN and time-based BiLSTM are combined to extract the local and time series features of the customer data. In the feature transformation stage, high-level features are extracted using a fully connected layer of 64 Relu neurons and the sequence features are reshaped into matrix features. In the attention enhancement stage, the extracted feature information is refined using a coordinate attention learning module to fully learn the channel and spatial location information of the feature map. To evaluate the performance of the proposed model, we include public datasets from telecom, bank and insurance industries for ten-fold cross-validation experiments, and the results show that the CNN-BiLSTM-FC-CoAttention model outperforms the comparison models in all metrics. Our proposed model improves the accuracy and generalisation of the model prediction by combining multiple algorithms, enabling it to be widely used in multiple industries. As a result, the model gives enterprises a better and more general decision-making reference for the timely identification of potential churn customers. Full article