Search Results (42,976)

In modern financial markets, decision-makers increasingly rely on quantitative methods to navigate complex trade-offs among multiple, often conflicting objectives. This paper addresses constrained multi-objective optimization (MOO) with an application to portfolio optimization for minimizing risk and maximizing return. To this end, and to address existing gaps, we propose a novel reinforcement learning (RL)-guided non-dominated sorting genetic algorithm II (NSGA-II) enhanced with gray relational coefficients (GRC), termed RL-NSGA-II-GRC, which combines an RL agent controller and GRC-based selection to improve the convergence and diversity of the Pareto-optimal fronts. The agent adapts key evolutionary parameters online using population-level metrics of hypervolume, feasibility, and diversity, while the GRC-enhanced tournament operator ranks parents via a unified score simultaneously considering dominance rank, crowding distance, and geometric proximity to ideal reference. We evaluate the framework on the Kursawe and CONSTR benchmark problems and on a NASDAQ portfolio optimization application. On the benchmarks, RL-NSGA-II-GRC achieves convergence metric improvements of about 5.8% and 4.4% over the original NSGA-II, while preserving a well-distributed set of non-dominated solutions. In the portfolio application, the method produces a smooth and densely populated efficient frontier that supports the identification of the maximum Sharpe ratio portfolio (with annualized Sharpe ratio = 1.92), as well as utility-optimal portfolios for different risk-aversion levels. The main contributions of this work are three-fold: (1) we propose an RL-NSGA-II-GRC method that integrates an RL agent into the evolutionary framework to adaptively control key parameters using generational feedback; (2) we design a GRC-enhanced binary tournament selection operator that provides a comprehensive performance indicator to efficiently guide the search toward the Pareto-optimal front; (3) we demonstrate, on benchmark MOO problems and a NASDAQ portfolio case study, that the proposed method delivers improved convergence and well-populated efficient frontiers that support actionable investment insights. Full article

Background/Objectives: To develop and validate the DMEK Risk and Outcome Prediction (DROP) Score—a benchmarking model integrating patient, donor, surgical, and center-specific parameters for individualized risk assessment following DMEK. Methods: The DROP Score comprises four subscores, namely the Patient Risk Profile (PRP), Donor Tissue Quality (DTQ), Surgical Complexity Index (SCI), and Center Performance Factor (CPF), with literature-derived weights ($\alpha$ = 0.40, $\beta$ = 0.25, $\gamma$ = 0.20, $\delta$ = 0.15) validated by sensitivity analysis (K = 0.82–0.91). Clinical validation included 76 DMEK eyes and 89 controls (2019–2023). Machine learning models utilized EfficientNetV2B3 transfer learning with Random Forest classifiers and patient-level data partitioning. IVCM imaging comprised 6200 images. Results: The mean DROP Score was 39.35 ± 7.61 (Moderate: 92.1%; High: 7.9%). High-risk patients showed worse 12-month BCVA (0.50 vs. 0.31 logMAR) and higher poor prognosis rates (50.0% vs. 34.3%). The DROP Score showed significant correlations with BCVA (r = 0.305, p = 0.007) and ECD (r = −0.352, p = 0.002). Tissue classification accuracy reached 96.2%. Diabetes mellitus emerged as the strongest prognostic factor (OR: 4.34, p = 0.012), followed by hypertension (OR: 2.65, p = 0.078). Conclusions: The DROP Score provides transparent, individualized DMEK risk assessment. Diabetes mellitus and hypertension emerged as dominant systemic prognostic factors, while rebubbling showed no adverse impact on long-term outcomes. Complete four-domain validation requires ongoing prospective data collection. Full article

Hypoglycemia is associated with cardiovascular events reflected by platelet abnormalities. We hypothesized that sequential endothelial changes may occur during hypoglycemia that may enhance cardiovascular risk. In type 2 diabetes (T2D) (n = 23) and controls (n = 23), blood SOMAscan proteomic analysis of endothelial proteins at baseline, insulin-induced hypoglycemia and post hypoglycemia to 24 h were examined using repeated-measures linear mixed modeling with a prospective parallel study design. Most endothelial proteins that changed over time did not differ between groups. Baseline levels of P-selectin, plasminogen activator inhibitor-1 (PAI-1; serpine-1), E-selectin and angiopoietin-1 (ANGPT1) were significantly higher, whilst cadherin-5 was lower in T2D. Several proteins exhibited changes versus baseline in both T2D and controls. Under hypoglycemia, decreases in cadherin-5 and soluble angiopoietin-1 receptor (sTie-2) were observed, with increased P-selectin, intercellular adhesion molecule-3 (ICAM3), ANGPT1 and PAI-1. Post hypoglycemia, decreased cadherin-5 and ICAM5 were observed at 2 h and PAI-1 at 4 h, as well as increases in P-selectin at 30 min, 1 h and 24 h and ICAM3 at 24 h. Post hypoglycemia, E-selectin, P-selectin and ICAM3 were significantly lower in T2D patients at 2 h, while PAI-1 was significantly lower at 4 h and ICAM3 was significantly lower at 24 h. Baseline endothelial proteins differed between T2D and controls, which may suggest local endothelial inflammatory activation leading to a pro-thrombotic, destabilized vascular phenotype characteristic of diabetic vasculopathy. Hypoglycemia may exacerbate this towards a pro-adhesive and pro-thrombotic phenotype, worsening endothelial dysfunction. Full article

Against the backdrop of rapid urbanization and land development, the degradation of regional ecosystem services and the intensification of ecological risks have become prominent challenges. This study takes the Pinglu Canal Economic Belt—a region characterized by the triple pressures of “large-scale engineering disturbance, karst ecological vulnerability, and port economic agglomeration”—as a case study. Based on remote sensing image data from 2000 to 2020, a landscape ecological risk index was constructed, and regional landscape ecological risk levels were assessed using ArcGIS spatial analysis tools. On this basis, ecological sources were identified by combining the InVEST model with morphological spatial pattern analysis (MSPA),and an ecological resistance surface was constructed by integrating factors such as land use type, elevation, slope, distance to roads, distance to water bodies, and NDVI. Furthermore, the circuit theory method was applied to identify ecological corridors, ecological pinch points, and barrier points, ultimately constructing the ecological security pattern of the Pinglu Canal Economic Belt. The main findings are as follows: (1) Ecological risks were primarily at low to medium levels, with high-risk areas concentrated in the southern coastal region. Over the past two decades, an overall optimization trend was observed, shifting from high risk to lower risk levels. (2) A total of 15 ecological sources (total area 1313.71 km²), 31 ecological corridors (total length 1632.42 km), 39 ecological pinch points, and 15 ecological barrier points were identified, clarifying the key spatial components of the ecological network. (3) Based on spatial analysis results, a zoning governance plan encompassing “ecological protected areas, improvement areas, restoration areas, and critical areas” along with targeted strategies was proposed, providing a scientific basis for ecological risk management and pattern optimization in the Pinglu Canal Economic Belt. Full article

Groundwater is the main water source for irrigated agriculture, accounting for an increasing share of the domestic supply in the hyper-arid district of La Yarada Los Palos (Tacna, Peru); however, at the sector scale, concerns about arsenic, boron and salinity remain poorly quantified. Arsenic and boron were selected as target contaminants because of their naturally elevated concentrations associated with coastal and volcanic hydrogeological settings, and their well-documented implications for human health and irrigation suitability. This study reports a 12-month monitoring program (September 2024–August 2025) in three irrigated sectors, in which wells were sampled monthly and analyzed by inductively coupled plasma–mass spectrometry (ICP-MS) for total arsenic, boron, lithium and sodium, along with electrical conductivity, pH, temperature and total dissolved solids. The sector–month total arsenic means ranged from 0.0089 to 0.0143 mg L⁻¹, with 33 of 36 exceeding the 0.010 mg L⁻¹ drinking water benchmark recommended by the World Health Organization (WHO). Total boron ranged from 1.11 to 2.76 mg L⁻¹, meaning that all observations were above the 0.5 mg L⁻¹ irrigation guideline for agricultural use proposed by the United Nations Food and Agriculture Organization (FAO). A marked salinity gradient was observed from the inland Sector 1-BH (median Na ≈ 77 mg L⁻¹; EC ≈ 1.2 mS cm⁻¹) to the coastal Sector 3-LC (median Na ≈ 251 mg L⁻¹; EC ≈ 3.3 mS cm⁻¹), with Sector 2-FS showing intermediate salinity but the highest median boron and lithium levels. Spearman rank correlations indicate that sodium, electrical conductivity and total dissolved solids define the main salinity axis, whereas arsenic is only moderately associated with boron and lithium and is not a simple function of bulk salinity. Taken together, these results show that groundwater from the monitored wells is not safe for drinking without treatment and is subject to at least moderate boron-related irrigation restrictions. The sector-resolved dataset provides a quantitative baseline for La Yarada Los Palos and a foundation for future work integrating expanded monitoring, health-risk metrics and management scenarios for arsenic, boron and salinity in hyper-arid coastal aquifers. Full article

Background and Clinical Significance: Focal hemorrhagic severity associated with posterior convexity pial brain arteriovenous malformation (AVM) cases can be exacerbated by hemodynamic stress focusing on focal areas of architectural weakness and by superficial venous outflow being restricted by non-redundant superficial venous drainage. This clinical case report exemplifies how bedside neurologic localization and angioarchitectural characteristics can inform the selection of microsurgical approaches for the treatment of ruptured AVMs that are directed at reducing hemorrhage recurrence risk through corridors based on rupture location. Case Presentation: An otherwise healthy young adult male (modified Rankin scale [mRS] pre-morbid = 0) initially presented with a thunderclap headache, emesis, photophobia, decreased level of consciousness (admitted Glasgow Coma Score [GCS] = 11; E3V3M5), and subsequent deficits including left-sided pyramidal weakness, visual field loss, and visuo-spatial neglect. A non-contrast computed tomogram (CT) confirmed an intraparenchymal hemorrhage (ICH) located within the right hemisphere’s posterior lobe. Angiographic evaluation of this AVM with catheter injection and three-dimensional reconstruction revealed a compact right occipital posterior convexity pial AVM (nidus 8 × 3 mm) supplied by distal cortical branches of the right middle cerebral artery (MCA); all blood draining from the nidus was directed to a single cortical vein which then drained into the superior sagittal sinus; there were two additional intranidal saccular aneurysms (approximately 3 × 2 mm and 3 × 3 mm). Because of the acute worsening secondary to ICH and because all venous drainage was superficial-only, a single-stage approach was selected given the urgency: decompressive evacuation of the hematoma via a corridor to the site of the AVM, followed by microsurgical removal of the AVM. The removal of the AVM was accomplished in a feeder-first, vein-last sequence, and en-passage arteries and parasagittal bridging veins were preserved throughout the procedure. Additionally, the two intranidal aneurysms identified as potential weak points during progressive devascularization of the AVM were specifically treated during the removal procedure. Following the successful removal of the AVM, the patient experienced a rapid recovery and returned to a nearly premorbid state of functioning, excepting a persistent small area of quadrantanopia. Conclusions: Rupture of posterior convexity AVMs may result in increased hemorrhagic severity due to localized architectural weaknesses in addition to the overall size of the AVM nidus. By correlating neurological findings, the topography of the hemorrhage, and angioarchitectural features early after rupture, emergency decisions regarding management can be better informed. The application of a hematoma-corridor, feeder-first/vein-last microsurgical approach for the treatment of such AVMs can achieve definitive curative results while minimizing damage to posterior cortical regions. Full article

Paraneoplastic neurological syndromes (PNSs) are immune-mediated disorders caused by an antitumor response that cross-reacts with the nervous system, leading to severe and often irreversible neurological disability. Once considered exceedingly rare, PNSs are now increasingly recognized owing to the identification of novel neural autoantibodies, wider use of commercial testing, and the emergence of immune checkpoint inhibitor (ICI)-related neurotoxicity that phenotypically overlaps with classic PNS. In this narrative review, we performed a structured search of PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar, without date restrictions, to summarize contemporary advances in the epidemiology, pathogenesis, diagnosis, and management of PNS. Population-based data show rising incidence, largely reflecting improved ascertainment and expanding indications for ICIs. Pathogenetically, we distinguish T-cell-mediated syndromes associated with intracellular antigens from antibody-mediated disorders targeting neuronal surface proteins, integrating emerging concepts of molecular mimicry, tumor genetics, and HLA-linked susceptibility. The 2021 PNS-Care criteria are also reviewed, which replace earlier “classical/non-classical” definitions with risk-stratified phenotypes and antibodies, and demonstrate superior diagnostic performance while underscoring that “probable” and “definite” PNS should be managed with equal urgency. Newly described antibodies and methodological innovations such as PhIP-Seq, neurofilament light chain, and liquid biopsy are highlighted, which refine tumor search strategies and longitudinal monitoring. Management principles emphasize early tumor control, prompt immunotherapy, and a growing repertoire of targeted agents, alongside specific considerations for ICI-associated neurological syndromes. Remaining challenges include diagnostic delays, limited high-level evidence, and the paucity of validated biomarkers of disease activity. Future work should prioritize prospective, biomarker-driven trials and multidisciplinary pathways to shorten time to diagnosis and improve long-term outcomes in patients with PNS. Full article

Diabetes develops through a mix of clinical, metabolic, lifestyle, demographic, and environmental factors. Most current classification models focus on traditional biomedical indicators and do not include environmental exposure biomarkers. In this study, we develop and evaluate a supervised machine learning classification framework that integrates heterogeneous demographic, anthropometric, clinical, behavioral, and environmental exposure features to classify physician-diagnosed diabetes using data from the National Health and Nutrition Examination Survey (NHANES). We analyzed NHANES 2017–2018 data for adults aged ≥18 years, addressed missingness using Multiple Imputation by Chained Equations, and corrected class imbalance via the Synthetic Minority Oversampling Technique. Model performance was evaluated using stratified ten-fold cross-validation across eight supervised classifiers: logistic regression, random forest, XGBoost, support vector machine, multilayer perceptron neural network (artificial neural network), k-nearest neighbors, naïve Bayes, and classification tree. Random Forest and XGBoost performed best on the balanced dataset, with ROC AUC values of 0.891 and 0.885, respectively, after imputation and oversampling. Feature importance analysis indicated that age, household income, and waist circumference contributed most strongly to diabetes classification. To assess out-of-sample generalization, we conducted an independent 80/20 hold-out evaluation. XGBoost achieved the highest overall accuracy and F1-score, whereas random forest attained the greatest sensitivity, demonstrating stable performance beyond cross-validation. These results indicate that incorporating environmental exposure biomarkers alongside clinical and metabolic features yields improved classification performance for physician-diagnosed diabetes. The findings support the inclusion of chemical exposure variables in population-level diabetes classification and underscore the value of integrating heterogeneous feature sets in machine learning-based risk stratification. Full article

The evolution mechanism of the bearing layer in the surrounding rock of tunnels with rockburst risk is extremely complex under bolt anchorage in deep strata. In this paper, the stress response, energy evolution, and crack development under different in situ stress levels and rock bolt quantities are systematically investigated. The results found that significant stress concentration and energy accumulation zones tend to form in the surrounding rock under high in situ stress conditions. The rapid unloading of radial stress and the sudden increase in kinetic energy are well-correlated in terms of time, representing important characteristics of dynamic rock failure. A significant decrease occurs in the maximum radial stress, kinetic energy, and strain energy of the surrounding rock as the number of rock bolts increases, while the number and connectivity of cracks notably weaken. This causes the failure process of the surrounding rock to transition from unstable to controlled development. It is indicated that rock bolt support can reduce the potential risk of rockbursts by regulating stress redistribution and energy release paths under high in situ stress. The findings provide a reference for evaluating surrounding rock stability and optimizing support parameters in deep-buried tunnels. Full article

The formation and stability of river ice covers in regulated waterways are critical for uninterrupted hydro-electric operations. This study investigates the modelling of ice cover development in the Beauharnois Canal along the St. Lawrence River with the presence and absence of ice booms. Ice booms are deployed in this canal to promote the rapid formation of a stable ice cover during freezing events, minimizing disruptions to dam operations. Remote sensing data were used to assess the spatial extent and temporal evolution of an ice cover and to calibrate the river ice model RIVICE. The model was applied to simulate ice formation for the 2019–2020 ice season, first for the canal with a series of three ice booms and then rerun under a scenario without booms. Comparative analysis reveals that the presence of ice booms facilitates the development of a relatively thinner and more uniform ice cover. In contrast, the absence of booms leads to thicker ice accumulations and increased risk of ice jamming, which could impact water management and hydroelectric generation operations. Computational efficiencies of the RIVICE model were also sought. RIVICE was originally compiled with a Fortran 77 compiler, which restricted modern optimization techniques. Recompiling with NVFortran significantly improved performance through advanced instruction scheduling, cache management, and automatic loop analysis, even without explicit optimization flags. Enabling optimization further accelerated execution, albeit marginally, reducing redundant operations and memory traffic while preserving numerical integrity. Tests across varying ice cross-sectional spacings confirmed that NVFortran reduced runtimes by roughly an order of magnitude compared to the original model. A test GPU (Graphics Processing Unit) version was able to run the data interpolation routines on the GPU, but frequent data transfers between the CPU (Central Processing Unit) and GPU caused by shared memory blocks and fixed-size arrays made it slower than the original CPU version. Achieving efficient GPU execution would require substantial code restructuring to eliminate global states, adopt persistent data regions, and parallelize at higher level loops, or alternatively, rewriting in a GPU-friendly language to fully exploit modern architectures. Full article

Radiographically visible carotid artery calcifications are typically seen at the level of the C3–C4 cervical vertebrae and can be detected on panoramic dental radiographs. Their early identification is clinically relevant, as they represent a potential marker for increased risk of stroke. In this context, the present study proposes a deep learning method for automatic identification of carotid atheromas using MobileNetV2. From a publicly available dataset, 378 region-of-interest (ROI) images (640 × 320) were prepared and split into train/val/test = 264/57/57 with class counts train 157/107, val 34/23, test 34/23 (negatives/positives). Images underwent standardized preprocessing and on-the-fly augmentation; training used a two-stage scheme (backbone frozen “head” training followed by partial fine-tuning of the top layers), class-weighting, dropout = 0.3, batch normalization (BN) head, early stopping, and partial unfreezing (~70% of the backbone). The decision threshold was selected on validation by Youden’s J. On the independent test set, the model achieved an accuracy (ACC) of 94.7%, sensitivity (SEN) of 95,7%, specificity (SPE) of 0.941, area under the receiver operating characteristic curve (AUC) 0.963, and area under the precision–recall curve (AUPRC) of 0.968. Using a sensitivity-targeted threshold (SEN ≈ 0.80), the model yielded ACC = 91.2%, SEN = 82.6%, and SPE = 97.1%. These results support panoramic radiographs as an opportunistic screening modality for systemic vascular risk and highlight the potential of artificial intelligence (AI)-assisted methods to enable earlier identification within preventive healthcare. Full article

Background/Objectives: Childhood overweight and obesity are influenced by family-level behaviors related to nutrition, physical activity, and daily routines. This study aimed to In contrast to screen time family nutrition and physical activity practices for overweight and obesity among children aged 6–17 years in Türkiye. Methods: A cross-sectional study was conducted with 214 children recruited from a community setting. Sociodemographic data and anthropometric measurements were collected, and family practices were assessed using the Family Nutrition and Physical Activity Scale–Turkish version (FNPA-TR). Binary logistic regression analyses were performed separately for overweight and obesity outcomes. Results: Healthier beverage choices were the only significant predictor of overweight, reducing the odds by 62%. Obesity was predicted by three FNPA domains: family meal frequency, family eating habits, and screen time. Frequent family meals and healthier eating habits were associated with lower obesity risk, whereas higher screen exposure increased the likelihood of obesity. Conclusions: Beverage choices, family meal patterns, eating habits, and screen exposure emerged as key behavioral predictors of unhealthy weight status in children. These findings highlight key family-centered prevention targets for pediatric nursing and public health, including promoting healthy beverage consumption, strengthening structured family eating routines, and reducing screen exposure in children. Full article

Financialisation has an increasing influence on the functioning of non-financial enterprises. It is therefore important to examine whether and to what extent food sector enterprises are subject to the process of financialisation. The research objective was to determine the level of financialisation of food industry enterprises in Poland in relation to the whole industry sector. To achieve this objective, the following research hypothesis was formulated: the process of financialisation of food industry enterprises proceeds similarly to the analogous process undergoing in industrial enterprises but varies across different sectors of the food industry. The research was conducted on the basis of statistical data from Statistics Poland (SP) published in various statistical studies. Financial data from 2010 to 2023 were analysed. For this purpose, research tools used in the paper are referred to in the literature as measures of the level of financialisation, so-called balance sheet indicators. The main limitation of the research is that the results can only be applied to countries with similar economic conditions, especially post-communist countries, and that balance sheet indicators are used to measure financialisation, which, although widely used, are limited in their effectiveness because they focus only on balance sheet data. The results support the research hypothesis. The companies in the analysed industries are characterised by a low level of financialisation. The process of financialisation of food industry companies is similar to the one in industrial companies and is more intense in beverage production than in other food industry sectors. There is room for a sustainable financing policy. The results indicate that there is room for higher financing of food industry enterprises in Poland, but excessive financing may lead to excessive concentration and monopolisation of enterprises and even to speculation on agricultural markets. To maintain financial stability, it will be important to pursue a stable monetary policy, limit the risk of food price volatility, improve communication and coordination in international monetary policy, and increase national food self-sufficiency. This study fills a research gap in understanding the process of financialisation, assessing its degree of advancement and diversity in the main sectors of food processing enterprises. Full article

Early detection of suicidal risk in adolescents requires valid tools adapted to the clinical and educational context. However, there are currently no observational scales developed specifically for use by significant people in the adolescent’s environment. Therefore, the aim of the present study was to design, validate and apply to a pilot sample an observational scale to identify behavioural and emotional signs of suicidal risk in adolescents, from the perspective of adolescents, parents and teachers. Validation study of an Observational Adolescent Suicide Risk Scale (EORSA) based on a theoretical review and expert consensus. Content validity was evaluated through expert judgement by professionals with recognised experience in mental health, psychometrics, and suicide prevention. The scale was subsequently applied to a sample of adolescents, parents and teachers, analysing the mean scores per item in each group. The final scale included 19 items with a high level of agreement among experts (content validity index > 0.80). When applied to the pilot sample, significant differences were observed in the items considered most frequent by each group. The EORSA is a valid and potentially useful tool for identifying signs of suicidal risk in adolescents from an observational perspective. Its design and application allow for a contextualised and multidimensional assessment, favouring preventive interventions adapted to each setting. Full article

The construction sector is central to achieving the objectives of the European Green Deal (EGD). While existing research on transition risks predominantly focuses on project- or firm-level challenges, less is known about the transition risks implied by high-level EU policy documents. This study addresses this gap by systematically analysing 101 EGD-related policy and guidance documents published between 2019 and February 2025. A mixed human–AI content analysis approach was applied, combining human expert manual coding with automated validation using large language models (Kimi K2 and GLM 4.6). The final dataset contains 2752 coded risk references organised into eight main categories and twenty-six subcategories. Results show that transition risks are most frequently associated with environmental, economic, and legislative domains, with Climate Change Impact, Cost of Transition, Pollution, Investment Risks, and Implementation Variability emerging as the most prominent risks across the corpus. Technological and social risks appear less frequently but highlight important systemic and contextual vulnerabilities. Overall, analysis of the EGD policy texts reveals the green transition as being constrained not only by environmental pressures but also by financial feasibility and execution capacity. The study provides a structured, policy-level risk profile of the EGD and demonstrates the value of hybrid human–LLM analysis for large-scale policy content analysis and interpretation. These insights support policymakers and industry stakeholders to anticipate structural uncertainties that may affect the construction sector’s transition toward a low-carbon, circular economy. Full article