Search Results (296)

Sepsis is a life-threatening syndrome caused by a dysregulated host response to infection. It follows a dynamic course in which early hyperinflammation coexists and overlaps with progressive immune suppression, a process best described as immunodynamic disruption. Key mechanisms include extensive lymphocyte death, expansion of regulatory T cells, impaired antigen presentation, and persistent activation of inhibitory checkpoints such as programmed cell death protein 1 (PD-1) and cytotoxic T lymphocyte–associated protein 4 (CTLA-4). These changes reduce immune competence and increase vulnerability to secondary infections. Clinically, reduced expression of Human Leukocyte Antigen–DR (HLA-DR) on monocytes and persistent lymphopenia have emerged as robust biomarkers for patient stratification and timing of immunomodulatory therapies. Beyond the acute phase, many survivors do not achieve full immune recovery but instead develop a Persistent Immune Remnant, defined as long-lasting immune, metabolic, and endothelial dysfunction despite apparent clinical resolution. Recognizing PIR emphasizes the need for long-term monitoring and biomarker-guided interventions to restore immune balance. To integrate these observations, we propose the SIMMP–Sepsis model (Sepsis-Associated Persistent Multiorgan Immunometabolic Syndrome), which links molecular dysfunction to clinical trajectories and provides a framework for developing precision immunotherapies. This perspective reframes sepsis not only as an acute crisis but also as a chronic immunometabolic syndrome, where survival marks the beginning of active immune restoration. Full article

In portfolio optimization, investors often overlook asymmetric preferences for gains and losses. We propose a distributionally robust two-stage portfolio optimization (DR-TSPO) model, which is suitable for scenarios where the loss reference point is adaptively updated based on prior decisions. For analytical convenience, we further reformulate the DR-TSPO model as an equivalent second-order cone programming counterpart. Additionally, we develop a deep learning-based constraint correction algorithm (DL-CCA) trained directly on problem descriptions, which enhances computational efficiency for large-scale non-convex distributionally robust portfolio optimization. Our empirical results obtained using global market data demonstrate that during COVID-19, the DR-TSPO model outperformed traditional two-stage optimization in reducing conservatism and avoiding extreme losses. Full article

We introduce a novel portfolio optimization framework—Distributionally Robust Multivariate Stochastic Cone Order (DR-MSCO)—which integrates partial orders on random vectors with Wasserstein-metric ambiguity sets and adaptive cone structures to model multivariate investor preferences under distributional uncertainty. Grounded in measure theory and convex analysis, DR-MSCO employs data-driven cone selection calibrated to market regimes, along with coherent tail-risk operators that generalize Conditional Value-at-Risk to the multivariate setting. We derive a tractable second-order cone programming reformulation and demonstrate statistical consistency under empirical ambiguity sets. Empirically, we apply DR-MSCO to 23 Borsa Istanbul equities from 2021–2024, using a rolling estimation window and realistic transaction costs. Compared to classical mean–variance and standard distributionally robust benchmarks, DR-MSCO achieves higher overall and crisis-period Sharpe ratios (2.18 vs. 2.09 full sample; 0.95 vs. 0.69 during crises), reduces maximum drawdown by 10%, and yields endogenous diversification without exogenous constraints. Our results underscore the practical benefits of combining multivariate preference modeling with distributional robustness, offering institutional investors a tractable tool for resilient portfolio construction in volatile emerging markets. Full article

The varying characteristics of grid-connected energy resources necessitate a clear and effective approach for managing and scheduling generation units. Without proper control, high levels of renewable integration can pose challenges to optimal dispatch, especially as more generation sources, like wind and solar PV, are introduced. As a result, conventional power sources require an advanced management system, for instance, a virtual power plant (VPP), capable of accurately monitoring power supply and demand. This study thoroughly explores the dispatch of battery energy storage systems (BESSs) and diesel generators (DGs) through a distributionally robust joint chance-constrained optimization (DR-JCCO) framework utilizing the conditional value at risk (CVaR) and heuristic-X (H-X) algorithm, structured as a bilevel optimization problem. Furthermore, Binomial expansion (BE) is employed to linearize the model, enabling the assessment of BESS dispatch through a mathematical program with equilibrium constraints (MPECs). The findings confirm the effectiveness of the DRO-CVaR and H-X methods in dispatching grid network resources and BE under the MPEC framework. Full article

Effective energy management is vital for enhancing reliability, reducing operational costs, and supporting the increasing penetration of electric vehicles (EVs) and renewable energy sources (RESs) in distribution networks. This study presents a dynamic reconfiguration strategy for distribution feeders that integrates EV charging stations (EVCSs), RESs, and capacitors. The goal is to minimize both Energy Not Supplied (ENS) and operational costs, particularly under varying demand conditions caused by EV charging in grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes. To improve optimization accuracy and avoid local optima, an improved Whale Optimization Algorithm (IWOA) is employed, featuring a mutation mechanism based on Lévy flight. The model also incorporates uncertainties in electricity prices and consumer demand, as well as a demand response (DR) program, to enhance practical applicability. Simulation studies on a 95-bus test system show that the proposed approach reduces ENS by 16% and 20% in the absence and presence of distributed generation (DG) and EVCSs, respectively. Additionally, the operational cost is significantly reduced compared to existing methods. Overall, the proposed framework offers a scalable and intelligent solution for smart grid integration and distribution network modernization. Full article

Background/Objectives: Diabetic retinopathy (DR) is a progressive microvascular complication of diabetes mellitus and a leading cause of vision impairment worldwide. Early detection and timely management are critical in preventing vision loss, yet current screening programs face challenges, including limited specialist availability and variability in diagnoses, particularly in underserved areas. This literature review explores the evolving role of artificial intelligence (AI) in enhancing the diagnosis, screening, and management of diabetic retinopathy. It examines AI’s potential to improve diagnostic accuracy, accessibility, and patient outcomes through advanced machine-learning and deep-learning algorithms. Methods: We conducted a non-systematic review of the published literature to explore advancements in the diagnostics of diabetic retinopathy. Relevant articles were identified by searching the PubMed and Google Scholar databases. Studies focusing on the application of artificial intelligence in screening, diagnosis, and improving healthcare accessibility for diabetic retinopathy were included. Key information was extracted and synthesized to provide an overview of recent progress and clinical implications. Conclusions: Artificial intelligence holds transformative potential in diabetic retinopathy care by enabling earlier detection, improving screening coverage, and supporting individualized disease management. Continued research and ethical deployment will be essential to maximize AI’s benefits and address challenges in real-world applications, ultimately improving global vision health outcomes. Full article

The regulation of the active sites of a catalyst is important for its application. Herein, a series of Ag₁Cu_x/SBA-15 catalysts with different molar ratios of Ag to Cu were synthesized via the impregnation method, and the active sites of Ag₁Cu_x were regulated via various pretreatment conditions. These as-prepared Ag₁Cu_x/SBA-15 catalysts were characterized by many technologies, and their catalytic performance was estimated through CO catalytic oxidation. Among these catalysts, Ag₁Cu_0.025/SBA-15, with a Ag/Cu molar ratio of 1:0.025 and pretreated under the condition of 500 °C O₂/Ar for 2 h, followed by 300 °C H₂ for another 2 h, presented optimal CO degradation performance, which could realize the oxidation of 98% CO at 34 °C (T₉₈ = 34 °C). Meanwhile, Ag₁Cu_0.025/SBA-15 also displayed great reusability. Characterization results, such as X-ray diffraction (XRD), ultraviolet–visible diffuse reflectance spectra (UV-vis DRS), temperature-programmed H₂ reduction (H₂-TPR), and physical adsorption, suggested that the optimal catalytic performance of Ag₁Cu_0.025/SBA-15 was ascribed to its high interspersion of Ag nanoparticles, better low-temperature reduction ability, the interaction between Ag and Cu, and its high surface area and large pore volume. This study provides guidance for the regulation of active sites for low-temperature catalytic degradation. Full article

Liver diseases encompass a wide range of etiologies and involve highly heterogeneous cellular environments, yet the specific cellular states through which genetic risk contributes to disease remain incompletely understood. In this study, we integrated genome-wide association study (GWAS) data from six liver diseases and two metabolic traits with transcriptomic profiles of approximately 168,000 human liver cells at single-cell resolution, using the single-cell disease relevance score (scDRS) approach. Our results revealed that disease-associated genetic signals are predominantly localized to non-parenchymal cells—particularly liver sinusoidal endothelial cells (LSECs), cholangiocytes, and specific subsets of lymphocytes. Notably, we identified marked intra-cell-type heterogeneity, with disease associations confined to specific subpopulations exhibiting immune activation or stress-responsive transcriptional programs. For example, autoimmune and viral liver diseases were linked to immunologically active LSECs and cholangiocytes, whereas their metabolically active counterparts showed no enrichment. These findings highlight the necessity of resolving liver cell complexity to uncover the functional basis of genetic risk and suggest that susceptibility to liver disease is driven by specialized cell states within broader cellular categories. Our study provides a refined cellular map of liver disease susceptibility, offering new perspectives for understanding pathogenic mechanisms and informing targeted therapeutic strategies. Full article

Increasing forest destruction from land use and land cover change (LULCC) has altered catchment hydrological processes worldwide. This trend is also endemic to the Belize River Watershed (BRW), a significant source of land and water resources for Belize. This study aims to understand LULCC impacts on BRW hydrological responses from 2000 to 2020 by applying the widely used Soil and Water Assessment Tool (SWAT). This study identified historical trends in LULCC in the BRW and explored an alternative 2020 land cover scenario to elucidate the role of protected forests for hydrological response regulation. A SWAT model for the BRW was developed at the monthly timescale and calibrated on in situ streamflow using SWAT Calibrations and Uncertainty Programs (SWAT-CUP). The results showed that the BRW SWAT model performed satisfactorily for streamflow simulation at the Benque Viejo (BV) gauge station but performed variably at the Double Run (DR) gauge station. Overall, the findings revealed watershed-level increases in monthly average sediment yield (34.40%), surface runoff (24.95%), streamflow (16.86%), water yield (16.02%), baseflow (11.58%), and percolation (3.40%), and decreases in monthly average evapotranspiration (ET) (3.52%). In conclusion, the BRW SWAT model is promising for uncovering the hydrological impacts of LULCCs with opportunities for further model improvement. Full article

Measles remains a major public health issue, particularly among paediatric populations who are unvaccinated or lack of maternal antibody transfer. Although the majority of cases manifest with moderate clinical forms, certain patient categories are at risk for severe disease progression. This study aims to describe the clinical and epidemiological characteristics of paediatric measles cases hospitalized in the Paediatric Departments of the “Prof. Dr. Matei Balș” National Institute of Infectious Diseases, Bucharest, Romania during two distinct epidemic waves: 2017–2019 and 2023–2024. A retrospective analysis evaluated mortality rates, distribution by age and sex, as well as clinical disease patterns. The 2023–2024 measles epidemic was marked by a higher number of paediatric cases (3.114 vs. 1.068), a lower mortality rate (0.32% vs. 3.74%), a shift towards older age groups, and a greater frequency of complications—particularly gastrointestinal, haematological, and ophthalmological—compared to the 2017–2019 wave. The findings underscore the urgent need for strengthened vaccination programs and targeted public health interventions, particularly among vulnerable groups and patients at risk of developing severe forms of the disease. Owing to a sustained decline in measles vaccination coverage among the paediatric population, Romania has experienced two major measles outbreaks within the past decade, interrupted by the COVID-19 pandemic. This study draws attention to the increasing incidence of measles in older children, suggesting a cumulative effect of reduced immunization rates over time. Full article

This study addresses the assembly job shop scheduling problem (AJSSP) with parallel machines. In an assembly job shop, product structures are represented through hierarchical tree diagrams, where components and subassemblies are sequentially assembled to form the final product. A mixed-integer linear programming (MILP) model is formulated to minimize the total completion time. A dominance relations-based variable neighborhood search (DR-VNS) is proposed for solving AJSSP with parallel machines. The proposed approach integrates dominance relations among operations in the initialization phase and employs tailored neighborhood structures to address sequencing and assignment challenges, thereby enhancing the generation of neighboring solutions. Experimental studies conducted on test cases of varying scales and complexities demonstrate the effectiveness of the proposed algorithms in solving the AJSSP with parallel machines. Full article

Background/Objectives: Diabetic retinopathy (DR) is the leading cause of preventable blindness among working-age adults. Early detection through screening programs is essential for managing the condition and preventing visual impairment. In Portugal, the national DR screening program (DR SP) targets diabetic patients, aiming to detect DR at an early stage and refer patients requiring intervention for an ophthalmology appointment. This study aims to assess the effectiveness of the Portuguese DR SP by analyzing patients referred for a hospital appointment following a positive screening result. Methods: An observational retrospective cohort study was conducted at Unidade Local de Saúde de São João (ULS-SJ), including patients referred to a DR SP hospital appointment between January 2020 and December 2023. Data were collected from hospital records upon approval by the Hospital Ethics Committee. Screening and hospital diagnoses were compared for agreement. The Chi-Square test and Cohen’s Kappa were used to assess the association between screening and hospital diagnoses. Results: A total of 1126 patients (2251 retinographies) were analyzed. The median time from screening to hospital consultation was 63 days (Interquartile Range = 39–99), though referral times varied widely within the same classifications (ranging from 8 to 354 days). The most common screening classifications were R2 (pre-proliferative DR, 47.8%) and M1 (maculopathy, 24.6%). In eyes with DR, agreement between screening and hospital diagnoses was highest for R2 (40.1%) and M1 (32.3%), while proliferative DR (R3) showed 30% agreement. The positive predictive value (PPV) of the screening program was 55.9%, with a false positive rate of 44.1%. A statistically significant association between screening and hospital diagnoses was observed (p < 0.001, Chi-Square test), though Cohen’s Kappa values (0.167 Right Eye, 0.157 Left eye) indicated only slight agreement. Conclusions: Our study found that DR SP effectively identifies patients needing ophthalmologic evaluation with moderate diagnostic agreement and a relatively high false positive rate, leading to unnecessary referrals. While this ensures that sight-threatening cases are not missed, improvements in grader training, classification protocols, and Optical Coherence Tomography (OCT) integration could improve results. Strengthening screening adherence and optimizing referral pathways would further improve the program’s impact on early DR detection and management. Full article

The purpose of this paper is to discuss the optimal operation of ice-storage air-conditioning systems by considering thermal comfort and demand response (DR) in order to obtain the maximum benefit. This paper first collects the indoor environment parameters and human body parameters to calculate the Predicted Mean Vote (PMV). By considering the DR strategy, the cooling load requirements, thermal comfort, and the various operation constraints, the dispatch model of the ice-storage air-conditioning systems is formulated to minimize the total bill. This paper takes an office building as a case study to analyze the cooling capacity in ice-melting mode and ice-storage mode. A dynamic programming model is used to solve the dispatch model of ice-storage air-conditioning systems, and analyzes the optimal operation cost of ice-storage air-conditioning systems under a two-section and three-section Time-of-Use (TOU) price. The ice-storage mode and ice-melting mode of the ice-storage air-conditioning system are used as the analysis benchmark, and then the energy-saving strategy, thermal comfort, and the demand response (DR) strategy are added for analysis and comparison. It is shown that the total electricity cost of the two-section TOU and three-section TOU was reduced by 18.67% and 333%, respectively, if the DR is considered in our study. This study analyzes the optimal operation of the ice-storage air-conditioning system from an overall perspective under various conditions such as different seasons, time schedules, ice storage and melting, etc. Through the implementation of this paper, the ability for enterprise operation and management control is improved for the participants to reduce peak demand, save on an electricity bill, and raise the ability of the market’s competition. Full article

The intervention of distributed loads, propelled by the swift advancement of distributed energy sources and the escalating demand for diverse load types encompassing electricity and cooling within virtual power plants (VPPs), has exerted an influence on the symmetry of the grid. Consequently, a quantitative assessment of the annual peak-shaving capability of a VPP is instrumental in mitigating the peak-to-valley difference in the grid, enhancing the operational safety of the grid, and reducing grid asymmetry. This paper presents a peak-shaving optimization method for VPPs, which takes into account renewable energy uncertainty and flexible load demand response. Firstly, wind power (WP), photovoltaic (PV) generation, and demand-side response (DR) are integrated into the VPP framework. Uncertainties related to WP and PV generation are incorporated through the scenario method within deterministic constraints. Secondly, a stochastic programming (SP) model is established for the VPP, with the objective of maximizing the peak-regulation effect and minimizing electricity loss for demand-side users. The case study results indicate that the proposed model effectively tackles peak-regulation optimization across diverse new energy output scenarios and accurately assesses the peak-regulation potential of the power system. Specifically, the proportion of load decrease during peak hours is 18.61%, while the proportion of load increase during off-peak hours is 17.92%. The electricity loss degrees for users are merely 0.209 in summer and 0.167 in winter, respectively. Full article

Communities in the Dominican Republic (DR) face increased natural disasters, poor air quality, food insecurity, and health impacts related to climate change. We evaluated the success of a train-the-trainer program to empower community leaders, women, and at-risk youth with the knowledge and skills to increase individual and community resilience in Cristo Rey, Dominican Republic. Three in-person two-day courses were conducted between July and August 2024 at the Universidad Iberoamericana. Each session included eight lectures and collaborative learning activities on climate change science, adaptation, resilience, and health impacts. Intra-group analyses comparing pre- and post-course surveys assessed participants’ climate change awareness, literacy, and communication and response skills. One hundred and four attendees participated in the survey study. Of the 100 participants with demographic data, 55% (n = 55) were 35 years old or younger, 70% (n = 70) identified as female, and 45% (n = 45) lived in Cristo Rey. The participants reported high baseline climate change awareness. Compared to before the course, the participants reported increased literacy regarding the environmental impacts of climate change relevant to the DR and the specific health impacts (p-value < 0.05) and increased climate change-related communication and response skills (p-value < 0.001). This study suggests competency-based, regional-specific courses deployed in a train-the-trainer model, have the potential to equip community members with knowledge to protect their health. Full article