Browse Articles

Alignment in yrast structure and negative-parity sideband in the proton-rich nucleus $$^{134}$$Sm

Correction: Physical activity and cohabitation status moderate the link between diabetes mellitus and cognitive performance in a community-dwelling elderly population in Germany

Opto- and magneto-tunable exceptional degeneracies in non-Hermitian ferromagnet/p-wave magnet junctions

Purpose in life and coping strategies: Main associations and moderation by concurrent distress

Individuals with more purpose in life tend to report less subjective stress. This association may be due, in part, to the use of more adaptive coping strategies. This research examines how purpose is associated with common coping strategies and whether the relations differ by either sociodemographic factors or current psychological distress. Participants ( N  = 1,998) completed a survey that included purpose in life, coping strategies, current symptoms of depression and anxiety, and current feelings of stress. Purpose in life was related to more use of active (β = .36, p  < .001) and support (β = .32, p  < .001) strategies, and less use of disengaged strategies (β = −.08, p  < .001). The associations between purpose and coping were similar across sociodemographic factors (age, sex, race, education). The association with disengaged coping differed by current distress (β interaction  = .23, .15, .16, respectively, for depression, anxiety, and stress, all p s < .001): The expected negative association was apparent for participants not in distress, whereas there was a positive association among participants experiencing depression, anxiety, or stress. Purpose in life is associated with coping strategies that can help regulate stress, which may contribute to the lower stress and better psychosocial and health outcomes among people with high purpose.

A transparent wheel-based platform for locomotion-on-demand and multi-view body and facial kinematics in head-fixed mice

Hypergammaglobulinemia in treated and untreated people with HIV

Background B cell abnormalities are an early feature of human immunodeficiency virus-1 (HIV) infection, leading to hypergammaglobulinemia. Objective(s) The aim of this study was to evaluate serum levels of immunoglobulin G (IgG) as well as the inflammatory biomarkers neopterin, β2-microglobulin and albumin in a large cohort of untreated as well as virologically suppressed people with HIV (PWH) on antiretroviral therapy (ART). Methods We conducted a cross-sectional analysis of blood samples from untreated PWH and virologically suppressed participants on ART. Of 750 collected blood samples, 267 were from ART-suppressed participants and 483 from untreated participants. Levels of IgG, HIV-RNA, CD4 + T cells, β2-microglobulin, neopterin and albumin were analyzed with regard to age, sex and stage of HIV infection. Results Hypergammaglobulinemia was present in all subgroups of untreated PWH except in those with primary HIV infection. Participants with a CD4 + T cell count between 50 and 349 cells/µL exhibited the highest IgG concentrations. Elevated IgG concentrations prevailed in 24% of participants on ART. Serum neopterin and β2-microglobulin levels were significantly elevated in untreated PWH. They decreased in participants on ART, but remained abnormally elevated in approximately half of those. Conclusion Although IgG levels were normalized in a majority of participants on ART, hypergammaglobulinemia prevailed in about a quarter of cases, indicating a remaining B cell hyperactivity despite viral suppression. The finding of elevated levels of inflammatory biomarkers despite ART suppression is suggestive of residual inflammatory activity and parallels the finding of persisting hypergammaglobulinemia.

Physiological and morphological drivers of early-spring vigor in warm-climate crops under suboptimal temperatures

Molecular and structural reprogramming of gastric cancer revealed by systems-level transcriptomic analysis

Gastric cancer (GC) is a major cause of cancer mortality and remains difficult to diagnose early and treat effectively. Although transcriptomic profiling has defined extensive molecular heterogeneity, many studies are not anchored to clinicopathological variables or interpreted in the context of tissue-level pathobiology. We applied an integrative transcriptomic and network-based framework to identify molecular signatures that reflect structural and biochemical reprogramming of gastric tissue during malignant transformation. RNA-seq expression profiles and clinical annotations were analyzed using non-parametric differential expression filtering, functional enrichment, and protein–protein interaction network modeling. Expression patterns were further evaluated across clinicopathological strata (stage, grade, nodal status, and metastasis) and by network-informed clustering. Across clinical strata, GC showed a consistent signature of developmental reactivation and loss of gastric epithelial identity. Developmental regulators, most prominently HOX-cluster genes and the histone variant HIST1H3J, were upregulated, consistent with epigenetic/chromatin reprogramming. In parallel, gastric differentiation and secretory lineage markers (ATP4A, KCNE2, PTF1A, VSTM2A) were persistently downregulated, reflecting suppression of parietal/ductal programs and altered metabolic/secretory function. ADIPOQ demonstrated stage-dependent repression and was associated with poorer survival, supporting a context-dependent prognostic role. Enrichment and network analyses also highlighted FGFR-centered signaling as a dominant oncogenic axis linked to proliferation and invasion. This study identifies a molecular pathology framework in which GC progression involves coordinated chromatin-level developmental reprogramming and sustained loss of gastric differentiation programs, accompanied by FGFR-driven oncogenic signaling and stage-dependent metabolic disruption. These signatures provide candidate diagnostic and prognostic biomarkers and support prioritization of therapeutically actionable pathways in GC.

Climate Finance and Youth NEET in Africa: Cross-Country Evidence from 46 Nations

Abstract This paper examines whether climate finance contributes to reducing youth inactivity, measured by the share of young people not in education, employment, or training (NEET), across 46 African countries from 2011 to 2021. Drawing on Human Capital Theory, the study conceptualizes climate finance as an external investment that can expand capabilities, enhance skills, and strengthen labor-market inclusion when effectively absorbed by domestic institutions. Using panel data and the Driscoll–Kraay corrections as well as the Instrumental Variables Generalized Method of Moments (IV–GMM) techniques, the analysis finds a consistent and statistically significant negative association between total climate‑finance inflows and youth NEET rates. This relationship remains robust across multiple model specifications, extended socio‑economic controls, and heterogeneity tests. The findings suggest that climate finance functions not only as an environmental mechanism but also as a developmental tool, promoting youth engagement through human capital formation and skill development. Policy‑wise, aligning climate‑finance frameworks with national education, training, and employment systems can potentially transform external funds into effective catalysts for reducing NEET rates.

Effects of massive transfusion (10-20 litres) versus ultramassive transfusion (≥20 litres) on mortality in adult liver transplant recipients: A propensity-score matched study

Background Ultramassive perioperative fluid transfusion during orthotopic liver transplantation (OLT) identifies a high-risk recipient phenotype and is associated with substantially increased mortality compared with conventional massive transfusion. OLT frequently necessitates high-volume fluid resuscitation, yet the prognostic implications of ultramassive perioperative transfusion in this setting remain uncertain. We evaluated whether ultramassive transfusion (≥20 L total perioperative fluid) is independently associated with worse survival than massive transfusion (10– < 20 L) in adult OLT recipients. Methods In this single-centre retrospective cohort (2009–2023), we included adults undergoing primary OLT who received ≥10 L of total perioperative fluid (crystalloids, colloids, blood, and blood products administered intraoperatively and within 24 hours postoperatively). Propensity score matching was used to compare recipients receiving ultramassive (≥20 L) versus massive (10– < 20 L) transfusion, balancing recipient, donor, and intraoperative characteristics. Primary outcomes were 90-day, 3-year, and overall all-cause mortality; secondary outcomes included graft failure, graft and patient survival time, primary non-function, early allograft dysfunction, thrombotic complications, acute kidney injury, and hospital length of stay. Results Of 993 OLT recipients, 306 (30.8%) received ≥10 L perioperative fluid and comprised the unmatched cohort. In the propensity-matched cohort (n = 188), ultramassive transfusion was associated with significantly higher 90-day, 3-year and overall mortality compared with massive transfusion, with consistent effect estimates across multiple matching strategies and sensitivity analyses. In contrast, ultramassive transfusion showed no robust or consistent association with early allograft dysfunction, thrombotic complications, graft loss, or other secondary endpoints, likely reflecting low event rates and limited power. Conclusions Among adult OLT recipients exposed to high-volume perioperative resuscitation, ultramassive transfusion delineates a distinct high-risk phenotype characterised by substantially increased short- and long-term mortality. Although causal inference is precluded by the observational design, these findings suggest that reaching an ultramassive transfusion threshold may mark a particularly vulnerable subgroup that warrants intensified intraoperative optimisation strategies and tailored long-term surveillance.

Incorporating environmental costs into long-term open-pit mine planning for sustainable resource optimization

Abstract Long-term production planning in open-pit mines should be conducted in a manner that satisfies current societal demand for mineral resources while maintaining an appropriate balance with environmental protection. Achieving such a balance requires that environmental considerations be systematically incorporated into mine design and production planning processes. Given the absence of a comprehensive framework for quantifying environmental costs in production planning, this study identifies the various categories of these costs and proposes practical methods for their quantification. Accordingly, a Mixed-Integer Linear Programming (MILP) model is developed in which all environmental costs are directly and consistently integrated into the objective function based on ore and waste rock types. The proposed model enables the coherent integration of all environmental costs associated with open-pit mining into the production planning process and can be fully implemented within a three-dimensional block model framework. The results show that although the ultimate pit limit and production schedule derived from the proposed model lead to a lower apparent NPV, they offer a more realistic representation of actual mining conditions. A comparison of the evaluated scenarios indicates that directly incorporating environmental costs into the objective function of the proposed MILP model increases the total mine NPV in the case study by approximately 14.87% compared with a scenario that block model is designed as in conventional models in which environmental costs are excluded from the objective function and the total mine NPV is calculated including the related environmental costs. This result highlights that integrating environmental costs at the optimization stage supports more realistic and sustainability-oriented decision-making. In addition, the cost distribution analysis demonstrates that acid mine drainage (AMD) control costs and greenhouse gas (GHG) emission costs are the most influential environmental cost components affecting the project’s economic performance.

Retraction: SARS-CoV2 mRNA vaccine intravenous administration induces myocarditis in chronic inflammation

Sustainable char production from pyrolysis of refuse-derived fuel for nitrate removal from water

Abstract This study explores a sustainable approach to simultaneously valorising municipal solid waste and remediating nitrate-contaminated water by generating an engineered adsorbent from refuse-derived fuel (RDF) via a thermochemical pathway. RDF feedstock was pyrolysed at 500 °C in a pilot-scale rotary kiln to produce RDF500 char, subsequently modified using zinc chloride in 2:1 wt.% ratio (RDF500-2) to enhance surface functionality and adsorption performance. Modification with ZnCl 2 has been extensively studied, and when handled properly, it does not pose greater environmental risks, thereby supporting sustainability aspects. Furthermore, characterisation through FTIR, BET, and SEM confirmed the introduction of oxygenated groups (–OH, –COOH, –C=O), increased surface heterogeneity, and the development of a micro-mesoporous structure conducive to nitrate adsorption. Fixed-bed column experiments were performed under varying operational conditions, including bed height (5–10 cm), influent flow rate (3–7 mL min -1 ), and initial nitrate concentration (67–185 mg L -1 ) utilising RDF500-2, to determine breakthrough behaviour and adsorption kinetics. The maximum adsorption capacity was 21.32 mg g -1 , corresponding to 89% nitrate removal efficiency under optimal conditions. The kinetic modelling using Thomas, Yoon–Nelson, Adams–Bohart, Wolborska, and Dose–Response models exhibited an excellent correlation (R 2  > 0.98), with the Dose–Response and Thomas models showing the best predictive accuracy for column optimisation. A scale-up analysis for a 100 L min -1 continuous flow system predicted 80% removal efficiency, confirming the practical applicability of modified RDF char (RDF500-2).

Observer-based prescribed-time lag bipartite consensus of nonlinear multi-agent systems under event-triggered mechanism

Considering the complexity of lag time and the convergence time, as well as the influence of the observer and the event-triggered mechanism, the prescribed-time lag bipartite consensus (PTLBC) control problem for nonlinear multi-agent systems (MASs) is researched in the paper. First of all, it designs the prescribed-time dynamic observer (PTDO) for followers to get the states of the leader within an arbitrarily prescribed time. Furthermore, to significantly decrease the communication consumption, the innovative event-triggered mechanism is studied for followers. To realize the lag bipartite consensus (LBC) of nonlinear MASs within an arbitrarily prescribed time, a PTLBC control scheme is presented via the aforementioned PTDO and event-triggered mechanism. With Lyapunov stability analysis method, sufficient conditions are obtained and detailed stability analysis is studied, which indicates that the nonlinear MASs can realize PTLBC. Furthermore, the analysis proves that the proposed event-triggered mechanism excludes Zeno behaviour. The theoretical analysis is validated by means of a simulation example.

Integrated RBF–EKF observer and MPC for simultaneous trajectory tracking and stability control of 4WID-EVs

Abstract Accurate trajectory tracking and stable motion control under extreme operating conditions remain significant challenges for Four-Wheel-Independent-Drive Electric Vehicles (4WID-EVs). To address these issues, this paper focuses on state estimation and trajectory tracking control, proposes novel algorithms and conducts comprehensive simulations and experiments. Firstly, a high-fidelity 7-Degree-of-Freedom (7-DOF) vehicle dynamics model is established and validated against a commercial CarSim model, providing a reliable platform for controller design. Secondly, to tackle the difficulty in directly measuring key states like the sideslip angle, a hybrid observer that fuses a Radial Basis Function Neural Network (RBFNN) with an Extended Kalman Filter (EKF) is proposed. The RBFNN, optimized via the control variates method, generates a "pseudo-sideslip angle," which is then fed as an observation into the EKF, significantly enhancing estimation accuracy. Thirdly, a Linear Time-Varying Model Predictive Control (LTV-MPC) based trajectory tracking controller is designed. The nonlinear vehicle model is linearized at each sampling point, transforming the optimal control problem into a Quadratic Programming (QP) problem. Crucially, explicit mathematical relationships between control variables and stability indices (sideslip angle, tire slip angle) are derived and embedded as stability constraints within the optimization framework, effectively coordinating tracking accuracy with vehicle stability. Finally, co-simulations (CarSim/Simulink) and Hardware-in-the-Loop (HIL) tests on a dSPACE platform are performed. Results demonstrate that the proposed RBF–EKF observer reduces the Root-Mean-Square Error (RMSE) of sideslip angle estimation by up to 30.43% compared to the standard EKF. Furthermore, the proposed Model Predictive Control (MPC) controller outperforms the traditional Preview Driver Model (PDM), reducing the maximum lateral tracking error and yaw angle error by 0.30 m and 3.11°, respectively, in high-speed double-lane-change scenarios, while ensuring all states remain within stability boundaries.

Retraction: Food insecurity and mental health of women during COVID-19: Evidence from a developing country

Ice core reveals longest-ever continuous record of Earth’s climate

Machine learning evaluation of TyG-based metrics for arteriosclerosis progression

Against the grain: Leveraging machine learning to analyze mudbrick structures

Mudbricks have been a fundamental building material since the Neolithic, yet their compositional variability and technological flexibility can be challenging for systematic and reproducible fabric characterization. Morphometric parameters such as grain size and shape influence the physical properties of mudbricks, providing insights into raw material selection, preparation practices, and construction techniques. While petrographic point counting remains the standard approach for fabric assessment, it is time-consuming, subjective, and difficult to standardize across studies. This study evaluates the application of automated image analysis for quantitative grain morphometry in petrographic thin sections of archaeological mudbricks. We developed a computational workflow based on K-means clustering to extract grain size, sorting, and shape descriptors from 45 cross-polarized light images from two geographically and chronologically distinct sites: the Urartian and Hellenistic city of Artaxata (Armenia) and the Early Iron Age hillfort of Los Villares de la Encarnación (Spain), both previously characterized through detailed petrographic analysis. We assessed the inter-site grain morphometric differences using non-parametric statistical testing, multivariate ordination, and supervised classification, and the automated results were compared with existing fabric descriptors. Our results indicate statistically significant differences in grain size, sorting, and shape between the two sites, with clear separation in multivariate space and good classification accuracy (>84%). Automated morphometric patterns show strong correspondence with the previously defined petrographic fabrics, indicating that automated grain morphometry captures assemblage-level technological variability. The proposed workflow establishes a methodological foundation for quantitative and comparative studies in earthen building architecture and supports the integration of computational tools into archaeological thin-section petrography.

Hierarchical attentive transformer with label guided fusion for multimodal movie scene segmentation

Abstract Existing movie scene segmentation methods struggle with long-range temporal dependencies, attention degradation, and rigid multimodal fusion, often ignoring the inherent hierarchical structure of movies. To address these limitations, this paper proposes a Hierarchical Attentive Transformer (HATrans). First, to capture multi-granularity semantic consistency and discriminative scene boundaries, we adopt a shot-to-segment hierarchical encoding pipeline that explicitly models structural priors. Second, to mitigate attention degradation when processing extremely long shot sequences, we introduce a hierarchical masked attention mechanism combined with a temporal position-aware bias, which restricts irrelevant connections and enhances structural sensitivity. Third, to overcome the inflexibility of conventional multimodal integration, we propose a label-guided attention fusion module that leverages semantic category priors to dynamically weight visual, audio, and subtitle features based on varying semantic contexts. Experimental results on MovieNet-42 K suggest that HATrans achieves competitive performance, outperforming several baselines including CMTS.