Accurate diffraction between non-parallel planes is essential for holographic displays employing tilted spatial light modulators (SLMs). However, applying the standard angular spectrum method (ASM) directly to a tilted plane leads to geometric distortion and spectral aliasing. We present a two-step propagation method and its validation by experiments. The proposed method combines the standard ASM with a spatial-domain rotational transformation. The target image is first back-propagated to a parallel intermediate plane, then geometrically remapped onto the tilted SLM with phase correction. Implemented on an off-axis holographic display with $$30^\circ$$ and $$45^\circ$$ tilted SLM which leads to a large steering angle between the incident and reflected beam, the method is evaluated with multi-depth and multi-angle reconstructions. Results show improved image fidelity, stable depth performance, and robust compensation of off-axis holographic displays.

Extracellular vesicle (EV)-based liquid biopsy is increasingly recognized as a promising strategy for cancer diagnosis and prognosis, as EVs carry abundant, stable biomolecular cargo. N6-methyladenosine (m6A), the most prevalent modification in eukaryotic intracellular RNA, plays a critical role in regulating diverse cellular processes and has been implicated in tumor initiation and progression. However, the potential of EV-associated m6A-modified RNA (EV-m6A RNA) as a clinically useful biomarker for cancer detection remains unclear.

EV-RNA was isolated from tumor tissues and matched serum samples collected from 76 colorectal cancer (CRC) patients. Serum samples from 30 healthy donors were included as controls. Serum EVs were prepared using the ExoQuick™ precipitation reagent. EV-RNA was extracted using Trizol reagent, and EV-m6A RNA levels were quantified using an enzyme-linked immunosorbent assay (ELISA).

Precipitation-enriched serum particles (PESPs) isolated from CRC patients and healthy donors were identified as CD9(+)/CD63(+)/ALIX(+) small particles, with a mean particle diameter of 60–70 nm. The ApoB expression was detectable in PESPs. The mean PESP concentration and PESP-RNA yield were significantly higher in CRC patients than in healthy controls (PESP concentration: 11 ± 8 × 10¹²/mL vs. 5 ± 2 × 10¹²/mL, p < 0.001; PESP-RNA yield: 57 ± 52 ng/µL vs. 22 ± 15 ng/µL, p < 0.001). Through m6A modification-specific ELISA quantification, receiver operating characteristic (ROC) curve analysis demonstrated good discriminatory performance of normalized PESP-m6A RNA levels for CRC detection (AUC, 0.8346; 95% CI, 0.7583–0.9110; p < 0.0001). PESP-m6A RNA levels were noted to be higher in late-stage (III/IV) CRC patients than in those with early-stage (I/II) disease (0.015 ± 0.001% vs. 0.009 ± 0.005%, p < 0.001) with no significant correlation with intratumoral METTL3 expression, a m6A writer. Increased PESP-m6A RNA abundance further predicted a worse overall survival (OS) in CRC patients (p = 0.0107; HR, 3.938), while m6A RNA levels in tumor tissues showed no significant prognostic value (p = 0.7765; HR, 1.153).

These findings support circulating PESP-m6A RNA levels as a feasible and noninvasive biomarker for CRC diagnosis, with additional potential for liquid biopsy-based prognostication and longitudinal disease monitoring.

An efficient course recommendation system plays a critical role in improving individual learning efficiency and advancing educational equity. However, existing course recommendation systems often overlook the presence of noise in learning sequences and fail to fully capture the inter-course dependencies. To address these limitations, we propose a novel course recommendation model that integrates large language models (LLMs) and course dependency structures to enhance sequence modeling. Specifically, we first employ prompt-engineered LLMs for learning courses denoising. Subsequently, we construct a course dependency graph from large-scale learning behavior data, and apply graph convolutional networks (GCNs) to learn course semantics. These representations are then fed into a Transformer to capture learners’ dynamic learning interests. Furthermore, we leverage LLMs to infer learners’ long-term goals and introduce a contrastive learning strategy to align these goals with sequential learning interests, thereby further improving recommendation accuracy. Extensive experiments on two real datasets demonstrate that the proposed model outperforms other baselines. The simulated noise experiment also highlights the superior performance of the model against noisy interactions.

Prostate cancer is a highly prevalent malignancy, and PET imaging targeting prostate-specific membrane antigen (PSMA) has become essential for assessing recurrence, metastatic spread and eligibility for radioligand therapy, with a 38% increase in PSMA PET activity reported in France between 2022 and 2023. [⁶⁸Ga]Ga-PSMA-11 prepared from single-vial cold kits such as Locametz^® is now widely used, but kit cost, generator activity decay, and preparation procedures restricted to the marketing authorization dossier can limit the number of patients treated per day. This multicenter study investigated, under GMP conditions, an off-label strategy combining aliquoting of Locametz^® vials with single or dual elutions from GalliaPharm^® and GalliAd^®Ge/⁶⁸Ga generators and compared manual versus automated eluate fractionation. Four main approaches were evaluated: single or double elution for a full kit vial and single or double elution for two aliquots prepared 4–6 h apart, with full quality control according to the European Pharmacopoeia and the Locametz^® summary of product characteristics (SmPC), as well as pharmaco-economic and dosimetric evaluations.

Radiolabeling was effective under all conditions that remained within the kit’s buffer capacity. Single-generator labeling of complete vials or aliquots reliably yielded radiochemical purity (RCP) > 98% and stable complexes for 4 h. Aliquoting allowed for two separate preparations from a single Locametz^® kit without compromising RCP. Elutions with dual generators proved efficient when the overall HCl 0.1 M volume was < 2.4 mL, resulting in activities reaching approximately 1.7 GBq with RCP ≥ 98%. High acidity or uneven distribution of aliquots caused pH variations and decreased RCP, causing a restricted number of failures (7%). Radiochemical yields achieved approximately 89% in refined standard procedures. Automated eluate fractionation greatly decreased radiation exposure to hands in comparison to manual handling. Pharmaco-economic evaluation showed significant cost savings per patient, with routine daily capacity ranging from 8 to 16 patients per kit, depending on generator age.

Aliquoting Locametz^® vial and optimizing generator use significantly increase [⁶⁸Ga]Ga-PSMA-11 production capacity. These strategies expand patient throughput and reduce costs of PSMA imaging. They provide a practical solution for centers facing rising PSMA PET demand and limited access to ¹⁸F-PSMA tracers and can be potentially applied to other ⁶⁸Ga cold-kit radiopharmaceuticals.

Detection of discordances among the stakeholders is an important activity of stakeholders’ analysis process which needs to be completed prior to starting of the software requirements elicitation process. Various fuzzy based methods have been developed for the detection of discordances among the stakeholders in which vague or uncertain statements of stakeholders’ perceptions on software requirements are expressed by linguistic terms. Based on our review, we found that these methods focus on subjective justifications and lacks objectivity, and as a result it may affect the stakeholders’ analysis process and subsequently may lead to inappropriate decision-making during the information system development process. Therefore, to address this issue, in this paper we developed a method for the detection of discordances among stakeholders of an information system using rough-set theory. The implementation of the proposed method is discussed by considering the stakeholders and requirements of library information system.

This paper examines how manufacturers’ fairness concerns and government subsidies affect carbon emission mitigation and operational performance in a closed-loop supply chain. To analyze the effect of power asymmetry, we develop game-theoretic models under three typical supply chain power structures: Manufacturer-led Stackelberg (MS), Retailer-led Stackelberg (RS) and Vertical Nash (VN). Several key findings emerge from the equilibrium analysis. First, optimal emission reduction is achieved in the VN mode under low fairness concerns and in the RS mode under high subsidies. Second, the power structure critically determines the optimal subsidy: higher subsidies are warranted in RS mode, as retailer dominance dampens the manufacturer’s voluntary investment, whereas lower subsidies suffice in MS mode, where manufacturer leadership incentivizes greater investment. Finally, contrary to conventional expectations, a manufacturer’s fairness concerns do not invariably improve its profits. Under MS mode, strong fairness concerns can reduce profits by distorting pricing and investment decisions, as the manufacturer may prioritize equitable distribution over economic optimality. This study offers insights into the interplay of power structures, behavioral preferences, and environmental policies in sustainable closed-loop supply chains.

Prospective Payment Systems (PPS) have been widely adopted over the past two decades as instruments for cost containment and efficiency improvement in health systems. However, much of the existing literature focuses on individual payment arrangements or single-country experiences, with limited attention to broader temporal, structural, and regional patterns reported across PPS-related reforms. This study examines patterns reported in the PPS-related literature published between 2000 and 2024, with particular emphasis on temporal trends, payment architecture, and regional variation in reform approaches.

This study constitutes a secondary narrative analysis of 168 studies included in a prior systematic review. No additional searches were conducted. Using an interpretive analytical framework, the included studies were synthesized across three dimensions: temporal trends reported in the literature, structural characteristics of payment architecture, and regional or country-level variation in implementation and reform patterns.

From 8,615 identified records, 168 studies met the inclusion criteria. Diagnosis-Related Groups (DRG)-based systems (32.1%) and pay-for-performance arrangements (26.2%) were the most frequently examined configurations, followed by global budget and capitation approaches (17.9%). A substantial increase in publications was observed after 2016, with 63.7% of studies published between 2016 and 2024. Across the reviewed literature, three broad periods of policy and research emphasis were identified: an earlier period focused primarily on cost standardization and inpatient expenditure control (2000–2007); a subsequent period characterized by increasing attention to hybrid arrangements and quality-linked components (2008–2015); and a more recent period emphasizing bundled payments, context-adapted designs, and integration with budgetary constraints (2016–2024). Regional variation revealed heterogeneous implementation and reform patterns across North America, Europe, and Asia.

The findings of this review suggest that PPS-related reforms are more appropriately understood as configurations of payment architecture embedded within broader institutional and governance contexts rather than as isolated payment models. Reported outcomes appear to depend less on model labels and more on design configuration and implementation capacity. Future research and policy analysis may benefit from more systematic reporting of payment architecture and its institutional prerequisites.

This study aims to investigate the dynamic characteristics of a variable lead screw weft-guiding mechanism considering screw pair clearance. Modeling the clearance collision of screw pairs is inherently challenging, let alone when the screw lead is variable. The motion law of the follower is predetermined, and the variable lead screw is designed using a planar analysis method. A hybrid contact force model together with the improved Gonthier model is adopted to characterize the collision force within the clearance. The Newton-Euler equations are employed to establish the dynamic model of the variable lead screw weft-guiding mechanism with screw pair clearance, which is accomplished for the first time. The effects of clearance value, rotational speed, and spiral working profile on the dynamic responses of the mechanism are systematically investigated. Results indicate that screw pair clearance significantly affects the stability and accuracy of the rapier. Moreover, the dynamic responses of the mechanism vary under different clearance values, rotational speeds, and spiral working profiles. These findings provide a theoretical basis for the optimal design of weft-guiding mechanisms.

Tracking research evolution is necessary for keeping up with the rapid progress of research in all fields. Analyzing the global overview of scientific topics in various fields is a crucial step within the scientific literature, which is necessary for researchers to go on with trending evolution. Many methods have been developed for detecting the topic in social networks, but the limited word frequency, more computational time and sparse nature are the common problems. To eradicate this issue in trending scientific topic detection, a Stock exchange trading light spectrum optimization (SETLSO) enabled Random Multi-Model Deep Learning (RMDL) to be developed. SETLSO is formed by the integration of Stock exchange trading optimization (SETO) and Light Spectrum Optimization (LSO). Here, Bidirectional Encoder Representations from Transformers (BERT) tokenization helps to break sentences into tokens for the input text data. Next to this, the tokenized outcomes are allowed for Aspect Term Extraction (ATE) for extracting aspect terms, followed by the formation of feature vectors. Moreover, the trending scientific topic detection is done employing RMDL, which is optimized by SETLSO. Finally, the performance of SETLSO_RMDL is analyzed by considering various evaluation metrics, such as precision, recall, and F1-score, with superior values of 0.920, 0.924, and 0.914, wherein Mean Square Error (MSE) is least of 0.087 with best-attained value.

The treatment of acute myeloid leukemia (AML) is a major clinical challenge, with patients often having poor prognoses, especially in subtypes with high-risk genetic profiles. PGAM5 plays a critical role in the progression of various malignancies; however, its biological function and underlying molecular mechanisms in AML remain unclear. The study aimed to systematically investigate the role and potential mechanisms of PGAM5 in AML. Bioinformatics analysis revealed that PGAM5 expression was significantly upregulated in AML patients compared with healthy controls, and high PGAM5 expression was closely associated with unfavorable prognosis. Further analysis suggested that PGAM5 may be related to the PI3K/AKT/mTOR signaling pathway. By establishing AML cell models with PGAM5 knockdown, functional experiments demonstrated that suppressing PGAM5 expression significantly arrested cell cycle progression, inhibited proliferation, and induced apoptosis. Mechanistic studies indicated that PGAM5 is closely associated with glycolytic metabolism in AML and enhances glycolytic flux through the transcription factor HIF-1α. Upon PGAM5 knockdown, AML cells exhibited significant reductions in glucose consumption, ATP production, and lactate output. Furthermore, treatment with the PI3K activator 740Y-P in PGAM5-knockdown cells provided additional evidence that PGAM5 plays a critical role in supporting AML cell proliferation and metabolic reprogramming, suggesting that PGAM5 may represent a candidate molecule for further functional investigation in AML. In summary, this study elucidates the key role of PGAM5 in metabolic remodeling in AML, suggesting its potential as a novel therapeutic target for AML treatment.

Female reproductive aging is a fundamental biological process characterized by a progressive decline in ovarian function, oocyte quality, and endocrine homeostasis, ultimately leading to reduced fertility and increased susceptibility to age-related diseases. Accumulating evidence indicates that reproductive aging is not merely a passive consequence of time but rather a tightly regulated process governed by complex genetic, epigenetic, and metabolic mechanisms. However, mechanistic dissection and translational exploration of female reproductive aging remain constrained by the limited availability of suitable animal models that faithfully recapitulate the human reproductive trajectory. In this review, we synthesize the current advances in understanding the molecular regulatory networks underlying female reproductive aging, with particular emphasis on key signaling pathways, cellular senescence, epigenetic regulation, hormonal control, and mitochondrial dysfunction coupled with oxidative stress. We highlight how the dysregulation of these interconnected mechanisms contributes to ovarian reserve depletion, follicular atresia, and declining oocyte competence across species. We propose that laying hens are a powerful and underutilized model for studying female reproductive aging. Laying hens exhibit a well-defined and highly reproducible reproductive lifespan characterized by distinct phases of peak and declining reproductive output, closely paralleling the age-related fertility decline in women. At the molecular level, hens share conserved regulatory features with humans, including hormonal signaling via the hypothalamic–pituitary–ovarian axis, age-associated oxidative stress, mitochondrial dysfunction, and epigenetic modulation of reproductive tissues. The daily ovulation cycle, measurable reproductive output, and responsiveness to metabolic and environmental interventions in hens further facilitate high-resolution and high-throughput investigations into aging-related mechanisms. By integrating evidence from human studies, mammalian models, and avian systems, this review highlights the translational value of laying hens in elucidating conserved genetic and epigenetic drivers of female reproductive aging. We discuss the current limitations and future perspectives for cross-species validation and multi-omics integration, aiming to facilitate the identification of actionable targets for delaying reproductive aging and improving female reproductive health.

Medical research is essential for advancing healthcare systems and improving patient outcomes. However, evidence regarding healthcare providers’ engagement in research in Sudan remains limited. Understanding their knowledge, attitudes, practices, and perceived barriers is important for strengthening national research capacity.

To assess healthcare providers’ engagement with medical research in Sudan and identify factors influencing their participation.

A national cross-sectional study was conducted between July 1 and October 5, 2025, using an online questionnaire adapted from a previously published instrument. Data were analyzed using descriptive statistics and multivariable logistic regression to identify factors associated with good knowledge, positive attitudes, and good research practice.

Among the 3,238 respondents, 65% were female and 53% were aged 26–30 years. General practitioners (36%) and house officers (15%) constituted the largest professional groups. Knowledge of research concepts was variable; while many participants answered correctly, only 46% identified stratified random sampling as appropriate for nationwide studies, and 44% correctly interpreted a p-value < 0.05. Attitudes toward research were highly positive, with 90% agreeing that healthcare providers should participate more in research and 90% supporting increased research training. Research practice was moderate: 74% had participated in writing research, but only 26% had published a paper. The main barriers were lack of financial support (66%), time constraints (51%), limited training opportunities (49%), and inadequate skills (48%). Major motivators included improving skills and knowledge (75% each) and addressing Sudan’s health problems (58%). Regression analysis indicated that participation in research methodology workshops and prior research experience predicted better knowledge and practice, while higher qualifications were associated with more positive attitudes.

Healthcare providers in Sudan show strong interest in medical research but face structural and educational barriers that limit active participation. Strengthening training programs, mentorship, and institutional research support may help enhance research capacity and productivity.

This study addresses a critical gap in the innovation and entrepreneurship literature by proposing and empirically validating a novel, theory-grounded framework—“The Composite Effects Model of FinTech Adoption on Startup Performance”—to explain how digital transformation drives entrepreneurial success in emerging economies. Grounded in an integrated theoretical lens comprising the Resource-Based View, Diffusion of Innovations Theory, and Institutional Theory, the model conceptualizes FinTech not as a standalone technological tool but as a strategic, VRIN resource whose performance-enhancing effects are contingent upon enabling digital infrastructure and supportive institutional frameworks. Using a balanced panel dataset of 150 Saudi startups (2018–2023), the study employs a multi-stage analytical approach: (1) composite indices for FinTech Adoption, Digital Infrastructure, Government Support, Banking Inclusion, and Startup Performance were constructed via Principal Component Analysis (PCA), with factor score coefficients used to compute weighted index values ensuring methodological transparency and replicability; (2) multiple regression analysis estimated direct effects; and (3) path modeling via SmartPLS 4.0 (with 1,000 bootstrap replications) decomposed total effects into direct and indirect pathways, explicitly testing the mediated sequence Digital Infrastructure → FinTech Adoption → Startup Performance. Results confirm statistically significant positive relationships: FinTech adoption is associated with 24% higher annual revenue growth and 19% greater employment generation among adopters versus non-adopters (β = 0.49, p < 0.001), while digital infrastructure (β = 0.34, p < 0.01) and proactive regulatory support function as critical antecedents. Importantly, the analysis reveals that gaps in digital financial literacy constrain diffusion—a finding corroborated by cross-sectoral evidence from Saudi digital health transformation, suggesting that the logic of infrastructure-enabled, institutionally-mediated technology adoption transcends sectoral boundaries. Theoretically, this research extends the Resource-Based View to digital entrepreneurial ecosystems by demonstrating how state-market synergies transform FinTech into a sustainable competitive advantage in emerging economies. Methodologically, it advances replicable practices for composite index construction and path-analytic modeling in secondary panel data contexts. Practically, it offers evidence-based policy recommendations aligned with national diversification agendas, emphasizing inclusive infrastructure investment, institutionalized regulatory sandboxes, and targeted digital literacy programs. By explicitly modeling the causal mechanisms linking infrastructure, institutions, and entrepreneurial outcomes, this study contributes directly to scholarly discourse on innovation-driven ecosystems in transitional economies, while offering transferable insights for interdisciplinary research on digital transformation across finance, health, and public service sectors.

Exchange-Traded Funds (ETFs) provide diversified and liquid exposure to broad markets and sectors, yet retail ETF timing decisions still depend heavily on rule-based indicators and often underuse unstructured sentiment information. This paper proposes ETFAgents, a neuro-symbolic, role-specialized multi-agent framework for ETF decision support. The system adapts institutional-style separation of duties into five agent teams covering analysis, research, risk management, trading, and managerial arbitration. Unlike purely LLM-driven workflows, ETFAgents combines probabilistic language-model reasoning with deterministic risk governance, including liquidity checks, no-leverage retail constraints, NAV premium or discount adjustment, and a Hard Veto mechanism that can override unsafe candidate actions generated by LLM-assisted agents. For ETF sentiment analysis, we fine-tune Llama 2-7B using LoRA to build ETFlora, which achieves a weighted F1 score of 87.43%. Backtests on the 510210 ETF and 159901 ETF cover January–March 2024 and the full year of 2024. To isolate the effect of the sentiment backbone, we further conduct a 2025 controlled replacement test in which only the Analyst Team Agent’s sentiment model is changed while the downstream agent workflow, risk rules, execution model, transaction cost, slippage, and liquidity constraints remain fixed. The 2025 stress test also includes a Hard Veto conflict case that connects a risk override to investor review. Interpretability is evaluated in the 2026 Q1 audit window through Agent-SHAP, global Role-SHAP summaries, monthly stability diagnostics, and role neutralization. The results suggest that the proposed architecture is associated with improved risk-adjusted ETF decision metrics in the evaluated settings. They should not be interpreted as evidence of universal investment outperformance.

Recent advances in generative artificial intelligence have enabled significant progress in reconstructing visual content from functional magnetic resonance imaging (fMRI) data. Early approaches were based on standalone generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), which demonstrated the feasibility of neural decoding but were limited in capturing both structural fidelity and semantic richness. More recent developments have shifted toward hybrid, multi-stage reconstruction pipelines, in which neural signals are first mapped into structured latent representations (e.g., CLIP or VDVAE embeddings) and subsequently used to condition diffusion-based generative models for high-fidelity image synthesis. A structured narrative review of generative AI approaches for fMRI-based visual reconstruction is provided, analyzing the evolution from standalone generative models to representation-driven and diffusion-based architectures. A comparative analysis is conducted across major benchmarks, particularly the Natural Scenes Dataset (NSD) and the Generic Object Decoding (GOD) dataset, highlighting differences in model behavior, evaluation strategies, and reconstruction performance. In addition, a framework for scalable representation learning and dimensionality reduction is introduced to address key challenges associated with high-dimensional neural data and computational complexity. Critical limitations in current evaluation practices are also identified, including the lack of standardized metrics and the inherent trade-off between low-level visual fidelity and high-level semantic accuracy. Finally, emerging research directions are discussed, including domain-informed diffusion models, cross-subject generalization, multimodal integration, and large-scale foundation models, positioning generative AI-based neural decoding within a broader big data and computational neuroscience context.

Accurate assessment of the distance between dental implants and the mandibular canal is essential for preventing nerve injury. Although cone-beam computed tomography (CBCT) is widely used for implant planning, its accuracy in resolving submillimeter distances remains uncertain. This study evaluated the measurement accuracy of CBCT for assessing distances below 1.0 mm.

A custom phantom enabling 0.0–1.0 mm implant–canal distances in 0.1-mm increments was developed. CBCT images were acquired at varying distances, positional shifts (X, Y, Z), and tube voltages. Five dentists involved in implant treatment measured the implant–canal distances using medical-grade and general-purpose monitors. Measurement error and contributing imaging factors were statistically analyzed.

CBCT did not reliably distinguish distances ≤ 0.4 mm, with the greatest instability observed at 0.3 mm (interquartile range = 0.121). Although a linear trend was observed from 0.1 to 0.4 mm, variability exceeded clinically acceptable limits. For distances ≥ 0.5 mm, reproducibility was high, but CBCT consistently underestimated the true gap by 0.2–0.3 mm. The central field of view produced the most stable measurements, whereas accuracy decreased with off-center positioning. Tube voltage and monitor type had minimal influence on measurement accuracy.

CBCT cannot accurately identify implant–canal distances ≤ 0.4 mm, which may directly affect clinical risk assessment. Even for distances ≥ 0.5 mm, CBCT underestimates the true distance by 0.2–0.3 mm. These findings provide practical guidance for setting safe margins in implant planning and postoperative evaluation.

Early-life stress constitutes a major risk factor for the development of neuropsychiatric and substance use disorders, exerting enduring effects on stress responsivity and emotional behavior. Maternal deprivation (MD) is a well-established model of early adversity that induces persistent neurobiological alterations. During adolescence, alcohol exposure presents an additional challenge that may interact with early-life stress, potentially influencing long-term adaptations in a sex-dependent manner. Among the systems involved, lipid signaling pathways, including the endocannabinoid system (ECS) and lysophosphatidic acid (LPA) signaling, have emerged as key regulators of stress adaptation.

Male and female Wistar rats were subjected to a single 24-h MD episode on postnatal day 9 (PND9). During adolescence (PND31–55), animals received intermittent alcohol exposure (3 g/kg) or saline. Behavioral assessments included the forced swim test and elevated plus maze. Plasma levels of corticosterone, monoacylglycerols, N-acylethanolamines, LPA, and autotaxin were measured. Concurrently, mRNA expression of genes encoding ECS- and LPA-related receptors, as well as enzymes involved in the metabolism of these lipid signaling pathways, was analyzed in the medial prefrontal cortex (mPFC). Data were evaluated using three-way ANOVA with sex, MD, and adolescent alcohol exposure as factors.

MD induced persistent metabolic alterations and revealed sex-specific effects on alcohol pharmacokinetics, with increased blood alcohol concentrations in MD females. Behavioral analyses demonstrated sex-dependent effects, with MD increasing active coping in males and decreasing it in females, while enhancing open-arm exploration independently of adolescent alcohol exposure. Adolescent alcohol exposure increased plasma corticosterone levels. MD and sex significantly altered circulating lipid mediators, showing opposite patterns in males and females. Additionally, MD and adolescent alcohol exposure differentially modulated the expression of ECS- and LPA-related genes in the mPFC, indicating sex-dependent molecular adaptations.

Early-life stress establishes long-lasting, sex-specific alterations in behavioral and lipid-mediated stress responses. Adolescent alcohol exposure acts as a secondary challenge, unmasking or amplifying these adaptations. The integration of peripheral lipid profiles with mPFC molecular changes highlights lipid signaling pathways as key mediators linking early adversity and adolescent experiences with long-term vulnerability to stress-related psychopathology.

Employing comprehensive multidecade panel data of the International Energy Agency (IEA) member countries, the study examines the effects of Energy Technology RD&D Budgets for Energy Efficiency across uncertainty episodes. The study shows the favorable value of technology innovation for green growth through a constant predicted decrease in energy intensity levels. Global economic and climate policy uncertainties induce a decreased marginal joint effect of technological innovation finance on energy efficiency. However, the long-term green value of energy efficiency innovation finance remains inevitable across uncertainty scenarios. Environmental policy stringency and the global climate transition are the two important drivers of the green value of energy efficiency innovation finance. Evolving stringent environmental policies and innovation finance commitment benefit the IEA-member countries with constantly enhanced energy efficiency levels for economic activities, by their critical contributions to green growth. The study validates their empirical predictions through a battery of panel data estimators, difference-in-differences (DID) regressions with a matched sample by propensity scores matching (PSM). The findings remain robust when we test for sub-samples excluding the COVID-19 pandemic, as well as a rich set of policy uncertainty measures and sovereign social, environmental, and governance (ESG) factors included in our fitted models.

The prediction of wind-induced buffeting response in long-span bridges is a fundamental scientific challenge in bridge wind engineering. Conventional buffeting analysis methods are constrained by simplifying theoretical assumptions, high experimental costs, and inherent model errors, making accurate full-scale prediction difficult. This study focuses on the Xihoumen Bridge, a representative long-span suspension bridge, and utilizes five-year multi-source heterogeneous monitoring data acquired from its structural health monitoring (SHM) system. By integrating deep learning techniques with classical buffeting theory, a frequency-domain, data-driven buffeting response prediction framework is proposed. Guided by the Davenport–Scanlan buffeting theory, the physical mapping relationship between wind field features and structural acceleration power spectral density (PSD) is first established, which motivates the design of an Attention-embedded Frequency Convolutional Network (Att-FCN) and a Residual Bidirectional Gated Recurrent Unit (Res-BiGRU) module. The prediction process is then physically decomposed into two sequential mapping stages—wind field to buffeting force spectrum and buffeting force spectrum to structural response spectrum—yielding a Cross-Attention Buffeting Network (CA-Buffeting Net). Experimental results demonstrate that CA-Buffeting Net reduces the mean squared error (MSE) by 29.7%, the mean absolute error (MAE) by 20.2%, and improves the cosine similarity (CS) by 1.2% points relative to the baseline model. Visualization of the channel and frequency attention coefficients further validates the physical interpretability of the proposed model, offering an effective technical tool for wind-resistance analysis and structural safety assessment of long-span bridges.

The carbon cycle of terrestrial ecosystems acts a fundamental role in regulating the global carbon balance, making it an essential component in the study of both global and regional climate dynamics and the impacts of human activities. As an extremely important characteristic of ecosystems, net ecosystem productivity (NEP) characterizes the carbon accumulation rate of ecosystems. Clarifying its spatiotemporal dynamics and underlying driving mechanisms is critical for supporting effective ecosystem protection and restoration. This study estimates the NEP based on remote sensing data and analyzes its spatiotemporal variation characteristics from 2001 to 2023 in the Yangtze River Economic Belt (YEB) by using trend analysis methods. Subsequently, explore the driving mechanisms between the influencing factors and NEP by utilizing the Geodetector. The following results were obtained: (1) In terms of time, the NEP in the YEB exhibited a fluctuating upward trend from 2001 to 2023, from 174gC·(m²·a)^–1 to 209gC·(m²·a)^–1. (2) In terms of space, NEP shows a distribution feature of "higher values in the southwest and lower values in the northeast" in the YEB. (3) The factor detection results indicate that temperature, fraction vegetation coverage and elevation are the main influencing factors of NEP spatial differentiation in the YEB. The explanatory power of each factor is more significant after interaction with others than when it acts as an individual factor, among interaction between elevation and vegetation coverage is the greatest.