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Calculation and analysis of passive earth pressure under ultimate stress conditions
Reimagining cultural heritage conservation through VR, metaverse, and digital twins: An AI and blockchain-based framework
Recent advances in artificial intelligence (AI), blockchain, virtual reality (VR), and digital twin technologies are transforming approaches to cultural heritage conservation. This study develops an integrated analytical framework that combines AI-driven modeling, interactive functionality, and blockchain/NFT authentication to examine both the direct and mediating effects of these technologies on heritage conservation effectiveness (HCE). Digital twins serve as a core component for simulating and managing heritage environments through dynamic, data-driven representations. An empirical analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) was conducted on 575 valid survey responses. The results indicate that blockchain/NFT authentication indirectly enhances heritage conservation effectiveness by improving digital authenticity. The study theoretically pioneers the integration of multiple digital technologies into a unified framework and empirically demonstrates the mediating roles of user immersive experience and digital authenticity. Practically, the findings offer actionable insights for advancing digital heritage conservation within metaverse environments and intelligent cultural ecosystems.
Neutralizing antibodies against Chikungunya virus and structural elucidation of their mechanism of action
RNA sequencing reveals novel lncRNA modulators in gemini nanocurcumin–treated colorectal cancer cells
Quality of life among hemodialysis patients in a referral center in Kathmandu: A mixed method study
Introduction Chronic kidney disease (CKD) affects about 10% of people worldwide, with rising cases in Nepal. Late diagnosis and limited healthcare access worsen patients’ quality of life (QoL), causing physical symptoms and emotional distress. Financial strain and travel difficulties further burden Nepalese patients on dialysis. While medical treatments address physical issues, mental and social well-being remain overlooked. This mixed-method study explores factors affecting QoL and patients lived experiences, aiming to improve patient-centered care in Nepal. Methods This convergent parallel mixed-method study was conducted at the National Kidney Center in Kathmandu, Nepal, involving adult hemodialysis patients. A quantitative survey of 260 participants was conducted, and 15 purposively selected patients took part in an in-depth qualitative interview until data saturation. Data were collected simultaneously during dialysis sessions using validated Nepali versions of the KDQOL-36 and socio-demographic questionnaires. Quantitative data were analyzed with descriptive and non-parametric tests in SPSS, while qualitative data were transcribed, translated, and thematically coded based on KDQOL domains using a phenomenological approach. Ethical approval was obtained, and informed consent was secured from all participants, ensuring confidentiality and voluntary participation. Results This study examined CKD patients on hemodialysis. Most were male (64.2%), married (76.5%), and in advanced stage of CKD. Treatment burden was reported by 74.2%. Median symptom score was 66.67; physical and mental health median scores were 32.62 and 39.40. Males and those with lower education reported higher burden. Socio-economic status and financial strain significantly affected outcomes. No significant differences were found by marital status, diet, or CKD duration. Patients on hemodialysis experienced significant symptom burden, including fatigue, weight loss, pain, and mobility issues, leading to dependency and emotional distress. Mental health challenges, such as anxiety, depression, and fear of the future, were common, especially among those with financial strain. Family and social support, acceptance, and coping strategies helped some maintain resilience. Socio-economic status was strongly linked to symptom severity and quality of life, emphasizing the need for holistic support in care. Conclusion For hemodialysis patients in Nepal, managing physical symptoms is only part of the struggle; emotional distress, financial pressure, and loss of independence weigh more heavily. These burdens are intensified by limited mental health support, financial security, and access to decentralized care. Addressing these challenges requires a compassionate, patient-centered approach that goes beyond survival; supporting dignity, emotional well-being, and a better quality of daily life.
Enhanced sensitivity for electron affinity measurements of rare elements
Abstract The electron affinity (EA), the energy released when a neutral atom binds an additional electron, is a fundamental property of atoms that is governed by electron-electron correlations and is strongly related to an element’s chemical reactivity. However, conventional techniques for EA determination lack the experimental sensitivity to probe very scarce samples. As a result, the EA for the heaviest elements of the periodic table is entirely uncharted. Here, we present a novel technique to determine EAs through Laser Photodetachment Threshold Spectroscopy, performed in an electrostatic ion beam trap to increase the samples’ exposure to laser photons and, thus, improve the experimental signal sensitivity by three orders of magnitude. Moreover, the additional exposure time allows the use of lower-power continuous-wave narrow-band lasers that reduce uncertainties associated with broadening effects induced by the laser bandwidth. By applying this technique, we measure the EA of 35 Cl to be 3.612720(44) eV, achieving state-of-the-art precision while employing five orders of magnitude fewer anions. The demonstrated sensitivity paves the way for systematic EA measurements across isotopic chains - including isotope shifts and hyperfine splittings - and ultimately for the first direct determination of electron affinities in superheavy elements.
Research on the configuration strategy of active support long-and short-term energy storage devices based on ESD-considered short circuit ratio
Correction: Evaluation of collapsible deformation of foundation under rectangular load based on the improved binary medium model
Reference-based chemical-genetic interaction profiling to elucidate small molecule mechanism of action in Mycobacterium tuberculosis
Abstract We previously reported an antibiotic discovery screening platform that identifies whole-cell active compounds with high sensitivity while simultaneously providing mechanistic insight, necessary for hit prioritization. Named PROSPECT, (PRimary screening Of Strains to Prioritize Expanded Chemistry and Targets), this platform measures chemical-genetic interactions between small molecules and pooled Mycobacterium tuberculosis mutants, each depleted of a different essential protein. Here, we introduce Perturbagen CLass (PCL) analysis, a computational method that infers a compound’s mechanism-of-action (MOA) by comparing its chemical-genetic interaction profile to those of a curated reference set of 437 known molecules. In leave-one-out cross-validation, we correctly predict MOA with 70% sensitivity and 75% precision, and achieve comparable results (69% sensitivity, 87% precision) with a test set of 75 antitubercular compounds with known MOA previously reported by GlaxoSmithKline (GSK). From 98 additional GSK antitubercular compounds with unknown MOA, we predict 60 to act via a reference MOA and functionally validate 29 compounds predicted to target respiration. Finally, from a set of ~5,000 compounds from larger unbiased libraries, we identify a novel QcrB-targeting scaffold that initially lacked wild-type activity, experimentally confirming this prediction while chemically optimizing this scaffold. PCL analysis of PROSPECT data enables rapid MOA assignment and hit prioritization, streamlining antimicrobial discovery.
A lightweight infrared remote sensing architecture for enhanced small target detection using improved DETR with CST modules
Editorial Note: Did place-based industrial policy promote regional economic growth? ---- Evidence from China
Determination of key functional structures of an amorphous VHL-based SMARCA2 PROTAC
Abstract Proteolysis targeting chimeras (PROTACs) enable degradation of disease-related proteins via E3 ligase recruitment. PROTACs often do not easily crystallize, and they are usually formulated in amorphous forms. Determining the key interactions that stabilize the solid drug forms is of high interest. Here, we determine the complete atomic-level structure of an amorphous Von Hippel-Lindau (VHL)-based SMARCA2 PROTAC (PROTAC 2) using nuclear magnetic resonance (NMR) crystallography. We find that PROTAC 2 is more disordered as compared to previously studied amorphous formulations, and that the three functional units of the molecule have distinct structural types. In contrast to smaller drug molecules, where intermolecular hydrogen bonding interactions were found to be the main stabilization mechanism for the amorphous solid form, for PROTAC 2 we postulate that, in analogy to glassy polymers, the main stabilization mechanism is the entropic contribution introduced by the overall flexibility, especially in the linker region of the molecule. We also note that the most populated conformations found in the amorphous form differ from those of bound PROTAC 2 in the ternary protein complex as determined via X-ray crystallography. Our results provide insight into key structural features that stabilize amorphous formulations, specifically for molecules that can target proteins previously considered undruggable.
RETRACTED ARTICLE: Explainable artificial intelligence for predictive modeling of student stress in higher education
New perspectives on stress and negative emotions: Positive effects on adolescent learning, memory, and mental health
Background Negative emotions have long been regarded as detrimental to learning and memory, while the potential benefits of moderate stress remain underexplored. This study investigates how moderate stress influences adolescent learning and memory, advocating for an assessment of emotions to provide novel theoretical insights into adolescent mental health. Methods We recruited 53 middle school students and established a murine stress model. Behavioral tests and physiological indicators were systematically analyzed to evaluate the effects of varying stress intensities on learning, memory, and psychological states. Methods included acute stress tests, short-term memory assessments, and measurements of HPA axis hormone levels. Results The moderate stress group exhibited significantly better memory performance than the control group, whereas the high-stress group showed marked memory decline. Murine experiments revealed that moderate stress enhanced learning efficiency and was associated with normal HPA axis hormone regulation, supporting the complexity of stress effects. Conclusions Moderate stress can enhance adolescent learning and memory, challenging traditional views of stress as purely negative. Theoretically, moderate stress may activate adaptive mechanisms and improve cognitive function, offering a new framework for mental health interventions. These findings underscore the importance of balanced stress management in educational practices and psychological strategies.
Tuning relaxation and nonlinear upconversion of valley-exciton-polaritons in a monolayer semiconductor
Abstract Controlling exciton relaxation and energy conversion pathways via their coupling to photonic modes is a central task in cavity-mediated quantum materials research. In this context, the light-matter hybridization in optical cavities can lead to intriguing effects, such as modified carrier transport, enhancement of optical quantum yield, and control of chemical reaction pathways. Here, we investigate the impact of the strong light-matter coupling regime on energy conversion, both in relaxation and upconversion schemes, by utilizing a strongly charged MoSe 2 monolayer embedded in a spectrally tunable open-access cavity. We find that the charge carrier gas yields a significantly modified photoluminescence response of cavity exciton-polaritons, dominated by an intra-cavity like pump scheme. In addition, upconversion luminescence emerges from a population transfer from fermionic trions to bosonic exciton-polaritons. Due to the availability of multiple optical modes in the tunable open cavity, it seamlessly meets the cavity-enhanced double resonance condition required for an efficient upconversion. The latter can be actively tuned via the cavity length in-situ, displaying nonlinear scaling in intensity and fingerprints of the valley polarization. This suggests mechanisms that include both trion-trion Auger scattering and phonon absorption as its underlying microscopic origin.
Knowledge of fertile windows and associated factors among female high school students in Warra Jarso district, Ethiopia using a mixed-methods approach
Elevating cytosolic NADPH metabolism in endothelial cells ameliorates vascular aging
Scalable hybrid framework for real time and non real time task scheduling in fog computing using federated reinforcement learning and PSO GA
Abstract Fog computing offers a decentralized paradigm to address the low-latency and energy-efficiency requirements of emerging IoT applications. However, the heterogeneity of edge nodes, the dynamic nature of workloads, and the dual need for both real-time and non-real-time scheduling introduce significant challenges in task allocation. This paper presents FRAHTOS, a Federated Reinforcement Learning and Hybrid Optimization Scheduling framework, to address these issues. FRAHTOS integrates Markov Decision Process (MDP) modeling, Federated Reinforcement Learning (FRL) for real-time tasks, and a PSO-GA hybrid optimization algorithm for non-real-time scheduling. Feature preprocessing and dimensionality reduction are performed using Adaptive Variational Autoencoders (VAE), followed by clustering with GMM and DBSCAN, and lightweight labeling using decision trees. The framework further enhances system responsiveness with EDF scheduling and VARIMA-based load forecasting. Simulation results using iFogSim demonstrate 85–95% utility, 86–96% task completion, and 3-5.5 ms latency, outperforming conventional methods. Additionally, the system sustains energy consumption between 50 and 80 mJ, suitable for battery-constrained nodes. FRAHTOS delivers a robust, scalable, and adaptive solution for intelligent IoT task scheduling. Future work includes validation on real-world data and integration with advanced federated simulation platforms.
Powerful one-dimensional scan to detect heterotic quantitative trait loci
Abstract To meet the growing global demand for food, increasing yields through heterosis in agriculture is crucial. A deep understanding of the genetic basis of heterosis has led to the development of a quantitative genetic framework that incorporates both dominance and epistatic effects. However, incorporating all pairwise epistatic interactions is computationally challenging due to the large sequencing depth and population sizes needed to uncover the genes behind complex traits. In this study, we develop hQTL-ODS, a one-dimensional scanning method that directly assesses the net contribution of each quantitative trait locus to heterosis. Simulations show that hQTL-ODS reduces computational time while offering higher power and lower false-positive rate. We apply this method to a population of 5243 wheat hybrids with whole-genome sequenced profile, revealing key epistatic hubs that play a critical role in determining heterosis.