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Background: Peer-assisted learning (PAL) is used in medical education. Its impact in basic medical sciences remains unexplored, namely considering the perspectives of all the populations involved: students, undergraduate teaching assistants (UTAs), and faculty members. We evaluated the educational impact of a PAL programme in Histology and Embryology in a medical course at the Faculty of Medicine of the University of Porto (FMUP). Methods: A cross-sectional convergent mixed-methods study was conducted in the Histology and Embryology course. Tailored online questionnaires comprising Likert-type items and open-ended questions were replied to by students attending theoretical–practical classes with UTAs, the UTAs, and the professors. Internal consistency was assessed using Cronbach’s alpha. Students’ theoretical–practical examination scores were compared between classes with and without UTAs using the Mann–Whitney U test, and effect size (r) was calculated to estimate the magnitude of differences observed. Results: Students (n = 190) reported highly positive perceptions regarding the creation of a more approachable learning environment, clarification of doubts, and identification of histological structures and strongly recommended the extension of UTAs to other courses of the medical school. UTAs (n = 17) described gains in disciplinary understanding, broader perspectives on the medical curriculum, communication and public-speaking skills, teamwork, leadership, self-confidence, and interest in academic careers. Professors (n = 6) valued PAL for improving individual support, facilitating time management, and contributing to UTA training, while highlighting the need for structured pedagogical preparation. Students attending classes with UTAs achieved significantly higher theoretical–practical examination scores (p = 0.04; effect size r = 0.12). Conclusions: PAL was perceived as highly beneficial by all groups involved in the project, enhancing the learning environment, supporting knowledge consolidation, and developing pedagogical and interpersonal skills. A grade analysis indicated that PAL was associated with improved academic performance. These findings reinforce the value of integrating PAL initiatives into preclinical medical education while highlighting the importance of sustained tutor preparation and supervision. Full article

The objective of this study is to develop a methodological approach to the integral assessment of the investment attractiveness of metallurgical enterprises to improve the efficiency of investment activities and the implementation of projects and ensure sustainable development. The metallurgy industry faces the challenge of balancing efficiency goals and sustainable objectives (ESG) and risks. Our approach takes into account the relationship between investment potential, realized opportunities, and the level of risk. Based on a systematic analysis of theoretical approaches, an integral investment attractiveness index is proposed that aggregates investment potential (consisting of seven sub-potentials), an assessment of the results of project implementation, and an aggregated risk index. Assessing investment attractiveness is important for ensuring the sustainable implementation of effective projects and determining their priority. A panel dataset was constructed using data from two metallurgy companies. The relationship between investment attractiveness and classical indicators (ROIC, EVA, MVA, Tobin’s Q, and P/BV) is examined through panel regression with fixed effects, cross-correlation analysis of the temporal structure of relationships, a CUSUM test for model stability, and decomposition of investment attractiveness changes. Decomposition of investment attractiveness changes makes it possible to quantify the contribution of potential, opportunities, and risk to the dynamics of investment attractiveness across various periods, including crisis and post-crisis ones describing the specifics of the metallurgic industry. The presented methodology is relevant for increasing the efficiency of project implementation within the framework of an integral company policy and contributes to the acceleration of industrial implementation of sustainable projects in the metallurgy sector. Full article

The Standard Model accurately describes particle phenomena through continuous gauge fields, color, chirality, generations, and Yukawa couplings, but it does not derive these labels from a deeper structural principle. This paper proposes a carrier-resolution interpretation in which particle species are carrier-readable manifestations of a common loop-detectable codimension-two archetype defect. The carrier supplies Lorentzian propagation and globally available $U\left(1\right)$ phase closure, while particle labels arise through holonomy, embedding, closure, and read-out conditions. The first persistent asymmetric resolution contains a lepton-like $Z_2$-Lorentz branch and a hadron-supporting branch with confined $Z_3$ closure. The $Z_2$ branch accounts for spinorial and chiral read-out through twofold holonomy and Lorentz embedding, while the three observed fermion generations are interpreted as the three leading saturated projective embedding layers of the common $Z_2$-Lorentz branch, not as consequences of the $Z_3$ color-like layer. In this framework, $Z_3$ supplies hadronic sectorality, and higher $Z_n$ refinements provide suppressed mass and response corrections rather than additional ordinary generations. The usual $SU{\left(3\right)}_C$ QCD description is retained as the effective after-read-out continuum gauge theory of color dynamics revealed by high-energy probes. The proposal does not replace QCD; instead, it interprets confined $Z_3$ closure as a pre-read-out structural condition whose incomplete sectors are not carrier-readable as isolated hadrons. As a quantitative test, the neutron–proton magnetic-moment ratio is derived from an ideal $Z_3$-complete baseline, a rule-generated closure-interface sequence, and a neutral-parent magnetic completion. The same-branch sequence reaches a sub-ppm residual and then saturates, so the remaining discrepancy is assigned to a neutral magnetic-completion seam rather than to deeper $Z_n$ terms. The resulting prediction is $-0.684979364944$, differing from the CODATA value of $-0.68497935\left(16\right)$ by about $0.022$ ppm, or $0.093$ standard deviations. No coefficient is adjusted to fit the observed value. The result is presented as a sharp no-fit test of carrier-resolution and neutral-parent closure, not as a replacement for QCD or a complete theory of all baryon magnetic moments. Full article

Developing effective adaptation and mitigation strategies depends on climate predictions capable of representing future conditions across multiple temporal scales. Decadal climate predictions bridge seasonal forecasting and long-term climate projections, providing near-term climate information for decision-making and adaptation planning at multi-year timescales. This study assesses the predictive skill of CMIP6 decadal precipitation forecasts from the Decadal Climate Prediction Project for three strategic catchments in state of Ceará, in the Brazilian semi-arid region. Forecast skill was assessed using deterministic and probabilistic metrics for three averaging horizons corresponding to years 1, 1–5, and 1–10 after initialization. Systematic biases were assessed and corrected. The results indicate that predictive skill varies across forecast systems, averaging horizons, and catchments. While skill was generally lower for the 1–5-year averaging horizon, several forecast systems showed positive skill relative to climatology for the 1-year and the 1–10-year averaging horizons, especially for below-normal and above-normal precipitation categories. Although bias correction reduced effectively systematic errors, it did not consistently improve forecast skill. These findings suggest potentially useful predictive skill at decadal timescales and highlight the potential of decadal climate information to provide complementary information for near-term water resources planning and drought preparedness in the Brazilian semi-arid region. Full article

Under low-temperature and high-humidity conditions, stable ice layers readily form on asphalt pavements in cold regions, and the enhanced ice–asphalt interfacial adhesion significantly increases deicing difficulty and traffic safety risks. To clarify the factors governing ice–asphalt interfacial adhesion strength, this study combines macroscopic pull-off tests and molecular dynamics simulations to systematically investigate the effects of interfacial contact area, temperature, pull-off rate, and molecular characteristics of representative asphalt components. The pull-off results show that adhesion strength increases markedly with decreasing temperature, rising from approximately 163 kPa at −2 °C to 242 kPa at −10 °C. In contrast, the nominal adhesion strength decreases with increasing ice specimen size, suggesting that size-related interfacial heterogeneity and nonuniform stress transfer may contribute to the pull-off response. The adhesion strength also generally decreases as the pull-off rate increases. Molecular dynamics simulations show that smaller asphalt–ice interfacial models exhibit higher molecular-scale nominal adhesion responses, while temperature-dependent simulations provide short-range asphalt–ice interaction descriptors for interpreting the experimental temperature trend. The calculated short-range asphalt–ice interaction energy becomes less negative from −531.4 to −352.5 kJ mol⁻¹ with increasing temperature, supporting the experimentally observed strengthening of adhesion at lower temperatures. Single-molecule pull-off simulations of 12 representative asphalt molecules reveal pronounced molecular differences, with molecular-scale nominal adhesion strengths ranging from 303.7 to 734.6 MPa. Asphaltene and polar aromatic molecules generally show stronger adhesion, which is associated with larger projected contact area, flatter molecular configurations, and heteroatom-induced polar sites. The molecular polarity index shows a moderate positive association with molecular-scale nominal adhesion strength. These results establish a scale-aware mechanistic correspondence between macroscopic pull-off behavior and molecular interaction descriptors at the ice–asphalt interface, providing insights for interfacial adhesion regulation and anti-icing design of asphalt pavement materials in cold regions. Full article

The middle reaches of the West Liaohe River Basin, a typical semi-arid to semi-humid transition and agro-pastoral ecotone in northern China, exhibit high ecological sensitivity, low resilience, and pronounced fragility. Despite growing concerns, existing studies in this region lack a comprehensive assessment paradigm that effectively couples inherent ecological attributes with nonlinear predictive modeling. To fill this gap, we developed an integrative framework that innovatively combined the SRP conceptual model with a stacking ensemble learning technique. This coupling is methodologically novel because it moves beyond linear assumptions, enables the detection of complex nonlinear response surfaces, and establishes a seamless analytical chain from historical evaluation to future projection. By selecting 13 indicators, including topography, climate, soil, vegetation, and socio-economic factors, the weight was determined by the comprehensive application of the analytic hierarchy process and entropy weight method, and the ecological fragility of the middle reaches of the West Liaohe River Basin from 2000 to 2020 was evaluated at multiple scales. The spatial differentiation driving factors were analyzed using a geographic detector. Therefore, an Ensemble Learning Regression model was used to simulate and predict the ecological fragility pattern in 2030. The results show that from 2000 to 2020, the ecological fragility of the study area showed a decreasing trend overall, with the Ecological Vulnerability Synthetical Index (EVSI) decreasing from 3.48 to 2.68, and the spatial pattern gradually shifting from “high in the northwest, low in the southeast” to “overall stability, local optimization.” The spatial agglomeration of ecological fragility gradually weakened, indicating that high-fragility areas tend to disperse and low-fragility areas expand in contiguous areas, and the ecosystem structure tends to develop towards equilibrium. The driving mechanism shows an evolution characteristic from “soil erosion dominated” to “biological abundance dominated,” with the impact of climate factors first increasing and then stabilizing, and the direct pressure from human activities continuously weakening. Under the assumption that historical trends continue, the ensemble learning model projects that by 2030, the ecological vulnerability pattern will be dominated by Mild and Moderate levels, with the area of extremely vulnerable regions significantly reduced to 0.36%. This study verified the applicability of the SRP model in transitional river basins, and the constructed “evaluation-driving mechanism-prediction” framework can provide a scientific basis for the ecological protection and adaptive management of the West Liaohe River Basin and provide a methodological reference for ecological fragility research in similar areas. However, limitations persist: the indicator system and weight assignment are subject to inherent subjectivity, and the 2030 scenario projection based on the Stacking ensemble learning model relies on the BAU (Business-As-Usual) assumption, which fails to account for abrupt climate extremes or major policy shifts. Future studies should incorporate multi-scenario constraints to reduce predictive uncertainty. Full article

To control the quality of mineral-medicinal waters and ensure their therapeutic benefits, spas often rely on periodic discrete sampling to analyze the physico-chemical properties of their pools. The AQUAPRED project aims to digitize this process by deploying IoT systems within the spa facilities, enabling real-time data acquisition via calibrated multi-parameter probes. Using data collected by these pilot systems, we develop and validate a predictive machine learning model capable of forecasting the short-term evolution of the thermal water properties. Historical data from each facility allow the model to learn the specifics dynamics of each spa. As a practical application, we propose an anomaly detection module based on residual analysis from predicted and observed values. Significant discrepancies signal events of interest and emergent trends, such as anomalous readings, contamination or sensor drift. The methodology is evaluated using real data from six spas associated with the AQUAPRED project. The results demonstrate the model’s effectiveness and support its feasibility for deployment in other thermal establishments. Full article

Adaptive and adverse brain states are often assumed to lie on a shared molecular continuum, but this assumption has rarely been evaluated against explicit transcriptomic alternatives. This study aimed to compare two representations of cross-context brain transcriptomic organization: a transcriptome-wide global-axis model and a low-dimensional reciprocal model. We benchmarked these models across a curated cross-study brain cohort spanning exercise, alcohol-related adversity-like contexts, stress, aging, and neurodegeneration, using prespecified intervention-like and adversity-like directional contrast labels rather than assuming homogeneous biological states. We assessed the competing representations using signed-effect correlations, permutation analyses, non-linear fitting, and held-out reconstruction, and we then examined the resulting structure through region-specific human bulk evaluation and exploratory cellular, single-nucleus, spatial, and chromatin projection analyses. These downstream analyses were used to examine localization and biological interpretability and were not treated as independent evaluation of the module 1/module 2 (M1/M2) partition. The combined signed-effect statistics were interpreted as representation-level directional summaries rather than estimates of a homogeneous cross-study biological effect. The global-axis model received limited support: intervention-like and adversity-like signed-effect summaries were only weakly correlated, were not stronger than permutation null expectations, and were not improved by non-linear fitting. Within the selected reciprocal-gene space, a rank-1 latent profile reconstructed held-out genes more accurately than the hard M1/M2 partition, whereas the M1/M2 discretization provided a more interpretable but selection-conditioned directional summary. Human analyses yielded an asymmetric pattern: a significant M1 association was observed only in the hippocampal dataset, whereas M2, the reciprocal index, and the other examined brain regions showed no consistent corresponding effects; leave-one-stratum-out analyses indicated poor cross-stratum reproducibility of the exact gene-level partition. These findings motivate a low-dimensional reciprocal representation as an exploratory framework while emphasizing context dependence, cohort dependence, and heterogeneity. Full article

Large language models produce semantically rich embeddings, yet direct employment of generic LLM embeddings fails to satisfy news recommendation demands due to inherent semantic mismatches with task targets. Existing fine-tuning methods including LoRA can narrow such gaps but bring prohibitive computational overhead, restricting real-world deployment. This work proposes lightweight task-adaptive modules (TAMs). It keeps LLM parameters fixed and transforms offline embeddings into task-specialized representations without full-model backpropagation, drastically cutting training costs. Evaluated on MIND benchmarks across ten mainstream recommendation architectures, TAMs achieves comparable accuracy to LoRA, with computational cost reduced to 1/10 of LoRA’s level. TAMs outperform GloVe-based models by 1.3–12.4% in AUC and scale effectively to MINDlarge. Ablation experiments confirm that the nonlinear projection is pivotal to performance improvement, and statistical validation across three random seeds confirms result robustness. This paradigm provides an efficient low-cost solution for LLM-based news recommendation under resource constraints. Full article

To address the safety assessment challenges associated with repeated and continuous excavation-induced vibrations in high-rise residential projects founded on hard rock, this study proposes a novel multi-scale dual-control evaluation framework bridging macroscopic ground peak particle velocity (PPV) with microscopic structural principal tensile stress. A geotechnical–structural decoupling approach was adopted. First, a two-dimensional dynamic finite element model of the ground was developed to simulate the propagation and attenuation characteristics of elastic waves under continuous excavation impact loading. Subsequently, the acceleration time histories extracted from the soil–structure interface were applied as base excitations in a three-dimensional structural finite element model to systematically investigate the dynamic response characteristics. The scientific novelty of this research lies in quantitatively decoupling the dynamic sensitivities between primary load-bearing shear walls and secondary non-load-bearing walls, thereby overcoming the inherent blind spots of conventional PPV-only macroscopic assessments. The results indicate that in hard-rock foundations, vibration energy is dominated by vertical attenuation characteristics. Compared with primary load-bearing structures mainly governed by static self-weight effects, secondary non-load-bearing components with lower stiffness exhibit significantly higher dynamic sensitivity and localized vulnerability to continuous impact loading. Parametric analysis further reveals that while the number of operating equipment shows a linear positive correlation with vibration amplitude, the impact frequency dictates the system’s dynamic response. Notably, when the excitation frequency approaches the dynamically sensitive frequency range of the foundation system, a pronounced divergence in response characteristics is observed—manifested as a sharp increase in local structural stress despite a reduction in ground macroscopic vibration intensity. These findings provide a rigorous theoretical foundation and a refined methodology for vibration impact assessment in urban hard-rock foundation construction. Full article

Pinching-antenna systems (PASSs) have emerged as a promising waveguide-based architecture for high-frequency wireless communications. Recent directional PASS studies have shown that practical pinching antennas (PAs) exhibit directional, pencil-like radiation rather than idealized omnidirectional radiation. However, most existing designs focus on PA placement or instantaneous sum-rate maximization and neglect the reconfiguration time required for PA movement and rotation. This paper investigates reconfiguration-aware joint optimization for a multi-user downlink directional PASS with a finite frame duration. To evaluate transmission performance, an effective spectral efficiency metric is defined to account for both reconfiguration-induced rate gain and effective transmission-time reduction. Based on this metric, a joint optimization problem is formulated for user scheduling, PA positions, PA orientations, and digital beamforming. To tackle this mixed discrete–continuous non-convex problem, a reconfiguration-aware alternating optimization algorithm is developed by combining weighted minimum mean-square error (WMMSE)-based beamforming, local orientation search, projected position update, and restricted user scheduling. Simulation results show that the proposed scheme achieves higher effective spectral efficiency than representative fixed-configuration, sum-rate-oriented, and joint-search schemes. These results indicate that reconfiguration overhead should be considered, since instantaneous-rate-oriented designs may cause excessive PA movement or rotation and degrade effective spectral efficiency. Full article

Docker simplifies application deployment, yet improperly written Dockerfiles often lead to suboptimal images with security and efficiency issues, termed “Docker smell”. Existing approaches for the identification and repair of Docker smells predominantly rely on expert-defined static rules, which exhibit notable limitations when addressing structurally complex or infrequent smells. This paper proposes a novel Detect–Guide–Repair (DGR) framework, which integrates rule-based smell detection with a context-aware repair mechanism driven by large language models (LLMs), enabling a more flexible and intelligent automated repair process. We systematically evaluated DGR on 417 real Dockerfiles from prominent GitHub projects. Experimental results show that DGR reduces the number of smells to 44.68% of the original while maintaining a build success rate of 89.20%, demonstrating significant improvements in both repair effectiveness and usability. Furthermore, we present three practical enhancement pathways: (1) a hybrid strategy combining rules and DGR to improve repair effectiveness further; (2) an automated error-correction mechanism to restore buildability; and (3) task-specific model fine-tuning to enable efficient deployment of smaller models. Collectively, these approaches provide a promising empirical foundation for automated Docker smell repair. Full article

The classical monetary trilemma implies a binding trade-off among exchange rate stability, capital mobility, and monetary autonomy. Yet, emerging market economies increasingly operate hybrid policy configurations that depart systematically from the trilemma’s corner solutions. This paper proposes a continuous, time-varying measure of such departures—the Hybridity of Regime Index (HRI)—extracted via a dynamic factor model from sub-indices capturing exchange rate hybridity, capital account segmentation, and effective monetary autonomy for a balanced panel of thirty emerging markets over the period 2005–2024. The analysis yields four principal findings. First, a secular increase in average regime hybridity is observed, with a marked acceleration following the financial fragmentation shocks of 2022. Second, moderate hybridity is associated with attenuated output and inflation volatility, and local projections show that high-HRI economies experience milder output contractions in the immediate aftermath of global financial shocks. Third, panel threshold regressions identify an endogenous HRI level beyond which the stabilizing effect reverses: further hybridity amplifies macroeconomic volatility and erodes reserve adequacy. Fourth, the post-2022 geopolitical fragmentation of the international monetary system has amplified the pre-existing trend toward hybridity, with sanction-affected economies exhibiting discontinuous jumps in HRI that push them into the high-vulnerability regime. This paper characterizes this non-linear pattern as a resilience–vulnerability nexus and discusses its implications for early warning indicators and for the assessment of policy responses to the fragmentation of the international monetary system. Full article

To achieve rapid and nondestructive evaluation of subgrade compaction quality, this study proposes a subgrade compaction quality evaluation method based on the dynamic modulus obtained from a portable falling weight deflectometer (PFWD), and field experiments were conducted relying on the Gaoyi North Connection Line Project of the Hengxi Expressway in Hebei Province. PFWD tests were carried out on the roadbed under different compaction passes, combined with compaction degree tests using the cutting ring method (CRM) and falling weight deflectometer (FWD) tests, to systematically analyze the evolution law and spatial uniformity of dynamic modulus during the subgrade compaction process. The results indicate that the PFWD dynamic modulus (E_PFWD) exhibits a staged variation characteristic of “rapid increase—slow increase—tending to stability” with increasing compaction passes, and becomes basically stable after the eighth compaction pass. The overall longitudinal compaction quality of the subgrade is relatively uniform, while certain discreteness still exists among different lanes and local areas. The E_PFWD shows good linear correlations with both compaction degree and the equivalent Benkelman Beam deflection values derived from FWD measurements, among which the correlation with FWD results is stronger. The study demonstrates that PFWD can effectively characterize the overall structural stiffness and compaction uniformity of subgrade, providing a reliable basis for rapid detection and uniformity evaluation of subgrade construction quality. Full article

Surface deformation of a phased array antenna (PAA) induced by external loads can degrade its radiation performance. To restore the beam of a deformed PAA, this paper proposes a new strain–excitation mapping model (SEMM) capable of rapidly calculating excitation adjustments based on measured structural strains. In the derivation of the SEMM, an analytical formula establishing the relationship between antenna excitations and the element positions and orientations for a PAA with an arbitrary surface shape is derived using the projection principle. Subsequently, the positions and orientations of the elements are expressed as functions of a limited number of strain measurements from the deformed antenna structure. An X-band PAA experimental system, equipped with a deformable mechanism and strain measurement capabilities, was developed. Two typical deformations were taken as examples to validate the proposed SEMM. Experimental results demonstrate that the SEMM can effectively recover the distorted pattern across the observation region. Compared with existing models, the proposed model achieves better sidelobe recovery. The rapid computation capability and analytical formulation of the SEMM make it highly suitable for developing an adaptive PAA that can autonomously preserve radiation beam quality under in-service deformations. Full article