Search Results (9,088)

We document a network-level vulnerability in pension-fund systems: lifecycle allocation regulation, designed to protect individual participants, compresses cross-fund return dynamics to the point where provider choice offers little diversification. Using daily net asset value data from second-pillar pension funds in Lithuania over 2019–2025, we found that a single common factor explains more than 72% of total return variance even in the calmest observed periods. We develop an unsupervised regime-detection framework that combines a PCA-based absorption ratio, DTW-based hierarchical clustering, and a Gaussian hidden Markov model with a data-driven crisis threshold. The HMM specification is supported by a dual empirical calibration of the stickiness prior, and its emission estimates agree closely with a fully Bayesian sticky-HMM specification. The framework identifies three latent regimes in which elevated systemic co-movement is the structural norm rather than an exceptional state and shows that funds separate first by lifecycle segment (conservative versus growth cohorts) and only secondarily by provider, with no single label axis reproducing the structure on its own. The absorption ratio has no significant relationship with global equity benchmarks in either calm or High-Concentration regimes, indicating that the detected regimes are not explained by the external benchmarks considered, including global equity indices and a euro-area rate/bond proxy. Cluster-level mean-absolute-active-return amplification of $1.06\times$ to $1.33\times$ during High-Concentration episodes confirms that even conservative funds serving retirement-age participants are not insulated. Full article

Load aggregators play a pivotal role in demand-side regulation by coordinating flexible resources between electricity retailers and end users. However, existing studies have rarely considered their dual-role attribute, namely acting as followers of electricity retailers while serving as leaders of end users. Moreover, most studies assume fully rational user behavior, which may not accurately reflect practical decision-making processes under heterogeneous preferences. To address these gaps, this paper proposes a coordination strategy for EV and air-conditioning loads based on a Stackelberg–evolutionary game framework. A three-layer Stackelberg–evolutionary game model is first constructed, with the electricity retailer serving as the leader and the load aggregator acting both as a follower and a leader, thereby revealing the interest interactions among multiple stakeholders. Subsequently, an evolutionary game based on the Logit protocol is introduced to establish a dynamic evolution equation for users’ collective strategy choices, which captures users’ heterogeneous trade-offs between electricity costs and thermal comfort, as well as their strategic interactions. Next, a genetic algorithm was used to solve the problem. Finally, case study results demonstrate that, compared with the pure Stackelberg game, the proposed strategy increases the aggregator’s profit by 57.9% while reducing users’ electricity costs by 41.2%, thereby validating its effectiveness. Full article

Resource-constrained dynamic heterogeneous redundancy (DHR) systems use executor diversity and runtime reconfiguration to reduce stable attack surfaces. However, effective scheduling cannot rely only on heterogeneity or movement frequency, because repeated exposure, shared vulnerability sources, service disturbance, and switching overhead jointly shape executor-subset selection. This paper proposes RACS, a risk-aware cost-constrained scheduling method for resource-constrained DHR systems. RACS evaluates candidate subsets by jointly considering heterogeneity, historical confidence, readiness, common-vulnerability risk, exposure memory, and switching cost. We evaluate RACS using a controlled simulation protocol covering multiple scheduling principles and attacker behaviors, including common-vulnerability pressure, burst-adaptive exploitation, and adaptive target selection based on observed scheduling patterns. The results show that RACS does not optimize a single metric in isolation, but maintains a consistent security–cost trade-off. It reduces common-vulnerability risk and switching cost in common-vulnerability settings, reduces burst-triggering high-risk states under adaptive pressure, and maintains competitive risk–cost behavior when attackers adapt to historical scheduling behavior. Robustness and scalability analyses clarify the effects of vulnerability-family estimation errors, parameter choices, and executor-pool size. These findings provide controlled simulation evidence for joint risk–cost modeling in DHR executor-subset scheduling, while testbed validation remains future work. Full article

Cash crop expansion has contributed to income growth among smallholder farmers in developing countries, but its long-term implications for soil fertility remain insufficiently understood. In particular, few studies have incorporated farmers’ perceptions of crop profitability and soil impacts into dynamic simulation models. This study examines the long-term effects of crop-choice behavior on soil fertility in Shan State, Myanmar, using a perception-based dynamic simulation model. Profit indices and soil-load indices for major cash crops were constructed from interviews with local farmers and applied to three crop-choice scenarios: profit maximization, continuous cropping restriction, and crop rotation. Soil fertility was simulated over a 50-year period under each scenario. Under the interview-derived parameter settings, the profit maximization scenario, corresponding to continuous cultivation of perilla, achieved both the highest cumulative profit and the highest level of soil fertility. In contrast, the continuous cropping restriction scenario resulted in the greatest decline in soil fertility because the replacement crop, peanut, was also perceived as imposing a relatively high soil load. Incorporating sweet potato into a four-year crop rotation substantially improved soil fertility compared with the continuous cropping restriction scenario, demonstrating that the effectiveness of crop rotation depends on the combination of crops included in the rotation. These findings suggest that farmers’ perceptions of crop profitability and soil impacts can strongly influence long-term land-use outcomes and highlight the importance of considering local farming knowledge when designing sustainable agricultural management strategies. Full article

Background: Actinium-225 (²²⁵Ac) is receiving major attention as the radionuclide of choice for targeted alpha therapy (TAT) due to its outstanding physical properties such as a long physical half-life of 9.9 days and a short range of alpha (α)-particles which are responsible for the destruction of malignant tumors, whilst sparing normal surrounding tissues. Although the physical properties of ²²⁵Ac make it a desirable radionuclide for TAT, its application is challenging due to the lack of chelators available to stabilize its daughter radionuclides, resulting in the recoil effect. This occurs when there is a breakdown between the radionuclide and the chelator, therefore minimizing the therapeutic effects of the radiopharmaceutical. Nanodrug delivery systems (NDDSs) may minimize the challenge of ²²⁵Ac’s recoiling daughters and increase tumor penetration. Aim: This study aimed at using poly(lactic-co-glycolic)acid (PLGA) and chitosan (CS) nanoparticles as a delivery vehicle for targeted alpha therapy of prostate cancer in order to increase the therapeutic effect of ²²⁵Ac PSMA617-TFA. Methods and Results: PLGA nanoparticles were prepared using a nanoprecipitation method, after which they were functionalized with chitosan and folic acid. Following synthesis of ²²⁵Ac PSMA617-TFA, the radiopharmaceutical was loaded onto the nanoparticles. SEM analysis and FTIR were performed for characterization of the nanoparticles, and in-vitro drug release of ²²⁵Ac PSMA617-TFA at pH = 6.5 and pH = 7.4, respectively, was measured. The nanoparticles prepared had an average size of 200 nm and had a positive charge. This was further confirmed using a zetasizer and with scanning electron microscope (SEM) analysis. The PLGA-CS nanoparticles indicated a high encapsulation efficiency after 24 h. The results also showed a controlled release of ²²⁵Ac PSMA617-TFA over 72 h. The results of this study indicate that PLGA-CS nanoparticles are suitable for retaining ²²⁵Ac and its recoiling daughters (²²¹Fr and ²¹³Bi) at the tumor site, potentially providing a platform for future therapeutic evaluation. Conclusions: The results of this study indicate that PLGA-CS nanoparticles demonstrate feasibility as a drug delivery vehicle for ²²⁵Ac PSMA617-TFA, with effective retention of ²²⁵Ac and its decay daughters. However, biological validation through in vitro cellular studies and in vivo preclinical models is required before therapeutic effectiveness can be established. Full article

Artificial intelligence (AI) technology has become the core force driving industrial transformation in today’s world. In-depth exploration of the spillover effects between artificial intelligence and new energy, as well as high-carbon-emission industries is of great significance for optimizing the industrial structure, preventing systemic risks in the industrial system, and achieving high-quality development. Based on the DY and BK spillover index model under the TVP-VAR framework, this paper analyzes the spillover effects between artificial intelligence and new energy, as well as high-carbon-emission industries from a time–frequency perspective, and constructs a spillover network to analyze the risk spillover transmission path. Finally, it explores the optimal investment portfolio weights and investment hedging strategies in the financial market. The results show that there is a significant static spillover effect between artificial intelligence and new energy, as well as high-carbon-emission industries. The intensity of this effect follows the pattern of “short-term > medium-term > long-term”. Moreover, new energy and some high-carbon-emission industries (such as the non-ferrous metals industry, the petrochemical industry, and the chemical industry) are the net spillover sources, while artificial intelligence and some high-carbon-emission industries (such as the power industry, the building materials industry, and the aerospace industry) are the net receiving parties. The dynamic spillover effect exhibits significant time-varying characteristics, being significantly impacted by major events such as environmental protection policies, the COVID-19 pandemic, and technological innovations. The chemical industry is the largest spillover outputter in all frequency domains, while the building materials industry is the largest receiver. From the perspective of the spillover network, the artificial intelligence industry, as a key node of the spillover network, plays a crucial role in the transmission of risk spillover. From the perspective of investment practice, the minimum connectedness portfolio (MCoP) performs well in terms of risk hedging effectiveness and return performance and may be the best choice for investors to balance risk and return. Full article

In the era of intelligent assessment, psychometric tests are becoming increasingly important for personnel selection, career development, and mental health assessment. Forced-choice tests are common in personality assessments because they require participants to select from closely related options, lowering the risk of response distortion. However, traditional latent trait models for forced-choice tests suffer from severe computational bottlenecks in high-dimensional settings. Furthermore, existing deep learning-based cognitive diagnosis models are primarily designed for independent items in educational scenarios (predicated on absolute scoring), making them structurally maladapted to the ipsative data (relative preference comparisons) generated by forced-choice tests. To address these challenges, this study presents a deep learning-based Forced-Choice Neural Latent Trait (FCNLT) Model that overcomes the limitations of traditional models and is applicable to the three most common item block types found in forced-choice tests. To account for the unidimensionality of items, participants’ latent trait levels and item characteristics are represented as interpretable latent embeddings. FCNLT mines these features through nonlinear mapping and introduces a weighted BPR-based ranking loss to natively align with the relative-scoring nature of forced-choice data. Additionally, the monotonicity assumption is utilized to improve the interpretability of the trait estimates. The FCNLT’s effectiveness is validated by experiments on real-world and simulated datasets that show its accuracy, interpretability, and robustness. Full article

Confronted with the constraints of global carbon reduction mandates and the widespread implementation of cap-and-trade (CAT) policy, competing manufacturers face critical choices in carbon quota trading, such as engaging in external markets or internal agreements. We develop a duopolistic game model comprising a low-carbon manufacturer (M_G) and a traditional manufacturer (M_T) under a CAT framework. In a perfect carbon quota trading market, manufacturers simultaneously cooperate and compete, facing a strategic choice between external trading through the open carbon market and internal trading agreements. We investigate how the low-carbon development level, carbon quota surplus, and internal carbon price affect their choices of carbon quota trading modes. Analytical results indicate that in the scenario where M_G’s quota surplus is insufficient to fully meet M_T’s demand, both manufacturers can achieve Pareto improvement in their respective profits within a certain range of internal carbon prices. Otherwise, the internal trading agreements may only guarantee an increase in their aggregate profits. A numerical analysis based on the actual situation of China’s steel industry verifies the theoretical conclusions. Full article

Regression models are widely used for continuous function approximation in applied research, yet selecting an appropriate model remains challenging for applied users who must balance predictive accuracy, interpretability, robustness, computational cost, and preprocessing requirements. This methodological review provides a decision-oriented synthesis of regression model families, preprocessing strategies, and evaluation criteria for transparent and reproducible model selection. The reviewed methods are organized by modeling principle, including linear and regularized models, robust and distribution-aware estimators, online learning methods, tree-based ensembles, kernel-based and probabilistic approaches, instance-based regressors, neural networks, and symbolic regression. The main contribution is a practical framework that connects data characteristics, including linearity, dimensionality, feature scale, target distribution, noise, outliers, and sample size, with suitable model families, preprocessing choices, and performance metrics. The review distinguishes theoretical guarantees, empirical tendencies, and implementation-dependent behavior because properties such as robustness, interpretability, scalability, and approximation capacity cannot be reduced to universal binary categories. The resulting comparative tables and decision criteria provide a compact reference for applied researchers designing regression workflows that are theoretically grounded, practically feasible, and aligned with research objectives. Full article

Archaeological feature detection at complex, mixed-environment sites requires accurate, efficient methods for identifying subtle morphological signatures. This study presents a semi-automated remote sensing pipeline for the detection and delineation of archaeological earthworks at Cahokia Mounds (Illinois, USA), a major Mississippian urban center and UNESCO World Heritage Site. Three LiDAR datasets, two collected via UAV-mounted sensors and one from a piloted aircraft survey, were processed into Digital Terrain Models and transformed into Local Relief Models (LRM). K-means clustering was applied to segment the LRMs into feature classes, followed by contour bounding using the OpenCV library to outline mounds and borrow pits. Additionally, SAR-derived Local Incidence Angle (LIA) rasters from PALSAR-3 and Sentinel-1 were processed through angular deviation mapping to identify slope anomalies associated with archaeological features. Results across all five datasets demonstrate the complementary strengths of LiDAR and SAR: LiDAR excels at resolving elevation-defined features such as mound footprints, while LIA captures directional slope behavior that highlights mound edges, borrow pit rims, and linear features such as causeways. Comparative analysis of LiDAR acquisition frequencies reveals minimal differences in archaeological feature recovery between pulse settings, suggesting that sensor platform choice matters more than power-density tradeoffs for this application. Despite the need for human review to filter modern disturbances and natural false positives, the integrated workflow meaningfully accelerates prospection and reduces interpretive subjectivity. The methods are scalable, site-invariant, and work with open-access data, making them applicable to archaeological landscapes worldwide. Full article

Road traffic injuries remain a central challenge for sustainable transport, public health, and mobility governance. The task of monitoring these injuries requires indicators that jointly capture harm severity, road and environmental context, and patterns of vehicle involvement at scale. Using harmonised English-language Romanian police crash exports (2023–2025), we build 92,790 records with 36 variables and estimate two complementary k-means typologies: a severity partition based on the fatality, injury, and vehicle-count fields (a register proxy for involvement, not vehicle-type attributes) and a context partition based on the road, environment, mechanism, and cause fields with one-hot encoding and TruncatedSVD. Reported tables and figures reproduce the archived MiniBatch pipeline for replication; for context, full-batch k-means clustering on the same embedding is the recommended default when cross-year prevalence stability is required (train–test TVD 0.039 versus 0.569 under MiniBatch). We report silhouette-guided choices ($k=6$ severity, $k=4$ context), cross-seed stability, feature ablations, and a 2023–2024 versus 2025 prevalence comparison. A Pearson ${\chi }^2$ test on severity × context labels reveals strong statistical significance, yet Cramér’s V remains small—statistical association with limited practical coupling, consistent with complementary rather than redundant partitions. Limitations include police-reported injury counts; a coarse vehicle proxy; weak context geometry; and large MiniBatch context drift, which binds inference to within-year descriptive profiling unless analysts refit the model, add version labels, or adopt full-batch context clustering. The contribution is an integrated, reproducible profiling and governance workflow for dashboards and follow-on modelling—not a fixed multi-year cluster taxonomy. Full article

Public YOLO model releases typically provide high-level architectural descriptions and headline benchmark results but offer limited empirical attribution of performance to individual blocks under controlled training conditions. This paper presents a modular, block-level analysis of YOLO26’s object detection architecture, detailing the design, function, and contribution of each component. We systematically examine YOLO26’s convolutional modules, bottleneck-based refinement blocks, spatial pyramid pooling, and position-sensitive attention mechanisms. Each block is analyzed in terms of objective and internal flow. In parallel, we conduct targeted ablation studies to quantify the effect of key design choices on accuracy (mAP@0.50:0.95) and inference latency under a fixed seed-0, COCO-only, fully specified training and benchmarking protocol. Experiments use the MS COCOdataset with the standard train2017 split (≈118 k images) for training and the full val2017 split (5 k images) for evaluation. The result is a self-contained empirical architectural-attribution reference that supports interpretability, reproducibility, and evidence-based architectural decision-making for real-time detection models. Beyond isolated ablations, we further synthesize the best-performing design choices into combined YOLO26n configurations and compare them against the default baseline. The best combined configuration improves mAP@0.50:0.95 from 0.3933 to 0.3969, while introducing only a marginal latency increase from 0.99 ms to 1.00 ms under TensorRT FP16 benchmarking. This analysis identifies an improved accuracy–latency trade-off and provides an incremental architectural configuration contribution supported by controlled experiments. The study is, therefore, framed as a controlled empirical analysis and configuration-refinement study of YOLO26, rather than as the proposal of a new detector family or a claim of universal detector superiority. Full article

The electrification of the industrial sector is an important pathway to decarbonizing the industry and achieving a sustainable society. High-temperature heat pumps (HTHPs) are critical devices for providing industrial heat and consuming green electricity. The goal of the present work is the theoretical thermodynamic analysis of a reverse Brayton HTHP that operates with novel working fluids. Specifically, the idea of using mixtures of working fluids with CO₂ is studied for the first time with the aim of suggesting new candidates to increase the performance of industrial HTHPs. A model of an HTHP with an internal heat exchanger is developed and verified in the MATLAB programming language. Nine different mixtures are studied: CO₂/R152a, CO₂/R1234ze(E), CO₂/Propane, CO₂/Butane, CO₂/Isobutane, CO₂/Pentane, CO₂/Isopentane, CO₂/Hexane and CO₂/Heptane. The examined industrial heat production temperatures are 150 °C, 200 °C and 250 °C, while the waste heat stream temperatures that drive the heat pump are considered to be 80 °C and 100 °C. The results prove that the application of the mixtures can enhance the COP, especially in the case of lower temperature lifts. CO₂/R152a seems to be a promising choice compared to pure CO₂, presenting performance enhancements ranging from 4.12% to 64.02% among the studied scenarios. Full article

The marine capture fisheries sector of the Philippines employs approximately 2.3 million Filipinos, yet recent declines (including a 15.3% drop in Q1 2026 production relative to Q1 2025) underscore the need for forecasting systems resolved at the regional and sectoral level. Existing Philippine approaches rely on univariate classical time-series methods and seldom integrate multivariate oceanographic predictors. This study addresses three questions: (RQ1) How do nine candidate machine learning algorithms compare in forecasting regional fish production from environmental predictors? (RQ2) Which environmental predictors most strongly drive model output, as quantified by explainable AI (XAI) SHAP-based feature attribution? (RQ3) To what extent do model performance and predictor importance vary across regions? Across 32 region–sector panels spanning 2002–2025, kernel and neural network models were selected as the best-performing architecture in 26 of 32 panels (81.3%), achieving a mean composite score 12.7% higher than tree-based ensembles, a gap attributable to extrapolation along trending physical predictors. Feature attribution identified the partial pressure of CO₂ as the leading driver in both sectors, exceeding the second-ranked variable by factors of 2.5 (commercial) and 3.4 (marine municipal). Regional heterogeneity in retained predictors, winning algorithms, and SHAP attribution rankings supports region-specific forecasting as a necessary design choice. Mean absolute percentage error of 22–25% and directional accuracy of 0.62–0.66 indicate operational utility for early-warning applications, establishing a basis for evidence-driven priority-setting in Philippine fisheries governance. Full article

Large Language Models (LLMs) achieve strong performance on standardized examinations, yet the language-mediated mechanisms through which they assess answer options in Multiple-Choice Questions (MCQs) and diverge from human difficulty judgments remain poorly understood. We argue that predicting the difficulty of MCQs provides a lens for studying how LLMs represent plausibility, uncertainty, and error across competing response options. reviewer1comments1mlAlthough recent deep machine learning approaches achieve competitive accuracy through large feature sets and complex architectures, their limited interpretability reduces their value for understanding model behavior. We propose an interpretable framework that decomposes item difficulty into LLM-based plausibility estimates over response options. These estimates are elicited through direct prompting and pairwise contrastive comparisons, and then integrated into a rational model that expresses item difficulty as a ratio between the plausibility of distractors and the plausibility of the correct option. We evaluated this approach on two high-stakes datasets. reviewer1comments1usmleUsing the United States Medical Licensing Examination (USMLE) dataset, reviewer1comments1rmsethe model achieved a Root Mean Squared Error (RMSE) of $0.277$, comparable to previous approaches, while reducing the representation of the underlying elements from hundreds of features to only three parameters. Under Spearman rank correlation, the model reached $\rho =0.427$ on USMLE, representing a $70.8\%$ relative improvement over previously reported results, and $\rho =0.488$ on ICFES, a new dataset. A complementary ranking analysis further reveals a systematic inversion between LLM-based difficulty judgments and those of experts, exposing divergences between model-internal assessments and human response patterns. These findings position option plausibility based on divide-and-conquer prompting as a principled framework for probing LLM decision processes, their rank-order misalignment with human response patterns, and their challenges in educational settings. Full article