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Correction: E2SVM: Electricity-Efficient SLA-aware Virtual Machine Consolidation approach in cloud data centers

Developing a partner selection framework for digital agricultural science and technology innovation alliances

Abstract Selecting appropriate collaborators represents a critical factor in ensuring optimal innovation outcomes for Digital Agricultural Science and Technology (DAST) alliances. The partner identification process for DAST innovation coalitions constitutes a multi-criteria decision analysis (MCDA) challenge characterized by temporal dynamics and information ambiguity. This study proposes a time-weighted orthogonal projection approach within dynamic intuitionistic fuzzy environments to address this challenge. This approach fully considers decision-making rules and resource complementarity: First, it allocates weights by integrating the time dimension with multi-objective optimization model construction, fully absorbing decision information at different stages through time weight solutions to reduce uncertainty in multi-stage information collection; Second, it employs fuzzy set theory and orthogonal projection method to evaluate members and candidate partners. Based on this theoretical foundation, this paper proposes a field model incorporating resource complementarity for selecting optimal partners. This novel field model can assist agricultural science and technology innovation alliances in implementing collaborative innovation practices, while simultaneously providing decision-making guidance for optimizing dynamic partner selection models to build long-term stable partnership relationships. Practical validation through China’s 5G Agricultural Digitalization Alliance case study demonstrates the methodology’s practicality and operational efficacy.

Selective Electrosynthesis of Ethanol from CO ₂ Enabled by High Cu ^I Content and Enhanced H ₂ O Activation of Molecularly Modified Cu-Based Catalyst

Efficient bidirectional quantum frequency conversion between telecom and visible bands using adaptively phase-matched III-V nanophotonic waveguides

Protocol: A multi-factorial, multi-centre study, for biomarker identification in healthy controls for comparison to babies with moderate-severe NESHIE

Neonatal encephalopathy suspected to be hypoxic-ischaemic encephalopathy (NESHIE) remains a leading cause of neonatal mortality and long-term neurodevelopmental impairment, particularly in low- and middle-income countries. While therapeutic hypothermia reduces mortality in moderate to severe cases, a significant proportion of affected infants continue to experience adverse neurological outcomes. This multi-centre observational study aims to elucidate the clinical and biological mechanisms underlying NESHIE by conducting a comprehensive comparative analysis of neonates with moderate to severe NESHIE and healthy term controls. Participants with NESHIE were previously recruited under an existing approved protocol (University of Pretoria ethics reference: 481/2017), and healthy neonates will be newly enrolled. The study will integrate clinical and molecular data to: (1) identify clinical risk factors associated with NESHIE; (2) perform whole genome sequencing to detect relevant genetic variants; (3) analyse DNA methylation patterns via bisulfite sequencing; (4) assess gene expression using bulk and single-cell RNA sequencing (RNA-seq); (5) characterise proteomic and metabolomic profiles through liquid chromatography–mass spectrometry of dried blood spot samples; (6) examine the placental microbiome; and (7) evaluate placental histopathological differences between groups. By offering a multi-dimensional view of the molecular and microbial landscape of NESHIE in a South African cohort, this study aims to enhance understanding of the disease pathogenesis. Ultimately, the findings may support the development of biomarkers for early diagnosis, improve risk stratification, and guide novel therapeutic strategies for affected neonates. The study has received National Health Research Database (NHRD) registration under GP_202411_053 (Gauteng) and WC_202411_026 (Western Cape), with ethics approvals granted by the University of Pretoria (184/2024), University of the Witwatersrand (250406B), and Stellenbosch University (N24/12/154_RECIP_UP184/2024) as well as their respective tertiary academic hospitals.

Simulation based new method for population variance using auxiliary information

Room-Temperature Conversion of Methane with Ozone over MOR Lattice-Confined Pd Sites

Differentiating interfacial water structures via alkali metal cation promotor for H2O2 electrosynthesis in acid

Abstract Electrocatalytic oxygen reduction reaction (ORR) for H 2 O 2 production represents a sustainable alternative route to the energy-intensive anthraquinone process. Nevertheless, under industrially-relevant acidic conditions, excessive protons at the reaction interface exacerbate low H 2 O 2 selectivity and severe H 2 O 2 reduction. Herein, we propose a universal alkali metal cation (AMC: Li + , Na + , K + , or Cs + ) dosing strategy to markedly boost the acidic H 2 O 2 electrosynthesis. Upon Cs + addition, 2e − ORR selectivity increases from 20% to 80%, concurrently suppressing an H 2 O 2 reduction current by 50% and achieving an H 2 O 2 production rate of 9.2 mol g −1 h −1 at 500 mA cm −2 . Microelectrode hydrogen evolution measurements witness impeded proton diffusion in AMC-dosed acidic electrolytes, directly restricting proton supply to catalytic active sites. In situ spectroscopic analysis combined with molecular dynamics simulation demonstrate AMCs help reconfigure interfacial water networks via cation hydration shells, thereby disrupting proton-hopping pathways. The efficacy trend (Li + <Na + <K + <Cs + ) originates from distinct cation-specific interfacial water restructure, delivering mechanistic insights into cation-promoted selective H 2 O 2 electrosynthesis in acidic media.

A transfer-aware, deployment-oriented evaluation framework for NetFlow-based intrusion detection systems (TAN-IDS)

Machine learning-based Intrusion Detection Systems (IDS) often report high detection accuracy under controlled, single-dataset evaluation, yet experience severe performance degradation when deployed in unseen network environments due to domain shift. To bridge this gap between laboratory benchmarking and real-world deployment, this paper presents TAN-IDS, a transfer-aware and deployment-oriented evaluation framework for NetFlow-based intrusion detection. Rather than proposing a new detection model, TAN-IDS contributes a methodological evaluation framework that unifies heterogeneous traffic datasets under a compact 8-dimensional NetFlow feature interface. This constrained representation supports interoperable and deployment-realistic evaluation across datasets collected in different network settings, enabling performance degradation to be more reliably attributed to domain shift rather than feature-space incompatibilities. Within this unified interface, TAN-IDS formalizes key deployment conditions as explicit evaluation scenarios, including in-dataset evaluation, direct cross-dataset transfer, mixed-domain training, and lightweight target-domain fine-tuning. Extensive experiments conducted within the proposed evaluation framework, using representative machine learning models and neural architectures, including a lightweight Transformer-based control model, show that strong in-dataset performance does not translate to cross-dataset robustness and that increased model complexity alone is insufficient to mitigate domain shift. In contrast, domain-aware training strategies are effective: mixed-domain training improves generalization, while fine-tuning with only 5% labeled target-domain data substantially recovers attack-class recall and F1-macro, exceeding 95% in several scenarios. Overall, TAN-IDS provides a reproducible, deployment-centric evaluation framework that reveals robustness limitations overlooked by benchmark-centric IDS evaluation.

Synergistic enhancement of electrochemical performance in AA2024 aluminum alloy processed through severe plastic deformation with nanostructured architecture for innovative applications

Programming Charge Dynamics in Photocatalytic Covalent Organic Frameworks through Heterometallic Symmetry Breaking

Association of marital and parental status with stress, support, adherence, and quality of life among breast cancer women

Backgrounds Breast cancer presents multifaceted challenges that extend beyond physical illness, profoundly influencing patients’ psychological well-being, treatment adherence, and social relationships. Marital and parental status further affect these experiences by influencing coping capacity, emotional stability, and perceived support systems. This study aimed to assess how marital and parental status influence the lives of women with breast cancer, particularly their psychological well-being, support, treatment adherence, and overall quality of life (QoL). Methods This cross-sectional study of 503 patients utilized validated research instruments for data collection: The Depression, Anxiety, and Stress Scale (DASS-21), the Multidimensional Scale of Perceived Social Support (MSPSS), the Medication Adherence Report Scale (MARS-5), and the WHOQOL-BREF. Descriptive statistics, Kruskal-Wallis tests, and Spearman’s correlation (95% CI) were used as an exploratory data analysis. Multiple linear regression was utilized to measure the effect of different variables on the domains of QoL. The p < 0.05 was considered significant. Results Across marital status, psychological distress, notably depression (median = 16.5, Q1-Q3 = 15–22.8, p > 0.001 ), was predominantly observed in divorced/widowed women, whereas single women exhibited better social support (median = 72, Q1-Q3 = 60–81.2, p > 0.001 ) and overall WHOQOL (median = 86, Q1-Q3 = 79–93, p > 0.001 ). Relating to parental status, mothers, particularly those with multiple children, experienced greater psychological distress (median = 16, Q1-Q3 = 12–18, p > 0.001 ). At the same time, childless women exhibited better social support (median = 72, Q1-Q3 = 60–76, p > 0.001 ) and QoL (median = 86, Q1-Q3 = 79–93, p > 0.001 ). A negative relationship of medication adherence with both social support and QoL was observed, while it showed a positive correlation with psychological distress in breast cancer patients, with a significant value ( p > 0.001 ). Conclusions In breast cancer patients, depression was highest among divorced/widowed women and mothers with multiple children, while single and childless women reported greater social support and QoL. Medication adherence showed a positive association with psychological distress and a negative correlation with both social support and QoL.

Design and implementation of Cs/GO/TiO2 nanocomposite for controlling sulfamethoxazole

Abstract The persistent presence of sulfamethoxazole (SMX), a widely used antibiotic, in aquatic environments poses significant ecological and health risks. This issue stems largely from its incomplete removal by conventional wastewater treatment processes. In this study, density functional theory (DFT) calculations were employed to investigate the adsorption behavior of hydrated SMX on chitosan/graphene oxide/titanium dioxide (Cs/GO/TiO 2 ) composites. Two adsorption configurations were examined: interaction via the amine (–NH 2 ) group of chitosan and coordination between the isoxazole nitrogen of SMX and TiO 2 . Electronic and topological properties were analyzed using total dipole moment (TDM), HOMO–LUMO energy gap (ΔE), molecular electrostatic potential (MESP), global reactivity descriptors (GRDs), density of states (TDOS/PDOS/OPDOS), quantum theory of atoms in molecules (QTAIM), and non-covalent interaction (NCI) analyses. The results reveal increased polarity, reduced energy gaps, and notable charge redistribution upon adsorption. The calculated adsorption energies (–2.31 eV and − 3.69 eV) indicate energetically favorable interactions, with relative stability depending on the adsorption site. These findings provide atomic-level insight into SMX–composite interactions and highlight the potential role of Cs/GO/TiO₂ composites in adsorption-based removal of antibiotic contaminants.

Metal-Free Ferromagnetism in Triangulene Two-Dimensional Frameworks

Comparative analysis of text readability and writing styles in AI-generated vs. Human-written academic abstracts

Research article abstracts are vital in scientific publications for readers to assess a study’s significance. The increasing use of AI tools, such as Kimi, ChatGPT and DeepSeek, to generate abstracts raises concerns about their readability and writing styles compared to human-written ones. The study aims to compare the differences in text readability and writing styles between human-written against AI-generated abstracts. A total of 150 abstracts of high-impact journal articles in the field of linguistics and computer science, 75 from each discipline, and another 150 AI-generated abstracts from the same corpus of articles served as the source texts for analysis. The Readability Scoring System, a computational tool, yielded readability and writing style metrics, while expert evaluation was performed to assess the quality of AI-generated academic abstracts. The quantitative data generated were analysed using SPSS 27 with non-parametric statistical methods. Key findings revealed: (1) AI-generated abstracts exhibited significantly lower readability across eight metrics, indicating greater complexity and lower readability; (2) Discipline-specific analysis showed five differing metrics in linguistics and eight in computer science; (3) Interdisciplinary comparisons revealed non-significant differences across nine readability metrics, highlighting AI’s potential to mimic natural writing. However, it still faces challenges in generating lexically diverse content. These results underscored the current limitations of AI in generating readable and human-like abstracts, especially in technical fields.

A hybrid spatial–frequency attention-based algorithm using efficientnet for robust and interpretable deepfake detection

Pyrrole-Bearing Porous Network Polymers Synthesized via Halogen-Bond-Assisted Radical Solid-Phase Polymerization for Highly Efficient and Selective Adsorption of Lithium Ions from Model Seawater Reverse Osmosis (SWRO) Brine

Animal abuse by falsification–Recognition amongst the veterinary profession in The Netherlands

Presently, Animal Abuse by (condition/ illness) Falsification (AAF), has received little scientific attention. Feigning illness in children for attention purposes has been studied, indicating that the involved children can suffer serious consequences. Although to date little is known about AAF, perplexing presentation has been mentioned. We aim to add to scientific information on veterinary awareness of AAF. Our exploratory survey-based research addressed veterinarians and vet technicians/ assistants, asking questions on their awareness of AAF and recognition of possible AAF signs. We found that only 12% of our 88 participants, who mainly treated companion animals, had previously received education on AAF. Despite this, 83% reported familiarity with the phenomenon of AAF. Half of the participants (51%), indicated to likely see AAF cases in their veterinary clinic, 5% indicated to see them with certainty (unsure: 32%; not to see such cases: 12%). Most often dogs and cats were indicated as a proxy (other animals: rabbits, rodent, horse). When asked how likely a participant would regard a sign/ symptom indicative of AAF, participants scored higher likeliness for those signs/ symptoms regarding client behaviours than animal/ medical aspects. Reporting of AAF in this sample was low: 92% indicated to have never reported AAF as animal abuse. Barriers to reporting AAF included a lack of knowledge on AAF and how to identify clients suffering from the condition. Our study engaged a limited number of participants in only one country, but indicates that knowledge on AAF may facilitate the recognition of this form of animal abuse.

Big data application and firm markups: evidence from China

Abstract This study investigates the relationship between big data applications and firms’ price markups. By constructing a heterogeneous firm model with variable markups, we analyze the mechanisms through which big data applications influence firms’ price markups and conduct empirical tests using micro-level firm data. The results indicate that big data applications significantly enhance firms’ price markups. Mechanism analysis reveals that promoting product innovation and improving production efficiency are two key channels through which big data applications contribute to higher markups. Furthermore, the positive effect of big data applications on firms’ markups exhibits heterogeneity across organizational, technological, and environmental dimensions. These findings suggest that while big data applications positively influence firms’ markups, the realization of this effect depends on the synergistic support of various complementary resources. The research uncovers the intrinsic mechanisms through which big data applications shape firms’ competitive advantages and market power, providing valuable insights for policy formulation.

Developing and validating a measure of L2-specific emotion regulation strategies

Background Despite recent scholarly interest in the regulation of academic emotions in second language (L2) learning, L2 studies have primarily relied on domain-general instruments to measure L2 emotion regulation (ER) strategies. In response, this study created and validated a domain-specific instrument, the L2 Emotion Regulation Strategies Questionnaire (L2ERSQ). Methods Two waves of data were collected from 811 Chinese tertiary EFL learners. The factorial structure, reliability, and validity of the L2ERSQ were assessed through a series of analyses such as exploratory factor analysis and confirmatory factor analysis. Results Exploratory factor analysis and confirmatory factor analysis have confirmed a 7-factor structure of the questionnaire, which possessed satisfactory construct reliability and validity. ER strategies significantly correlated with L2 enjoyment and boredom within both waves of data, demonstrating the concurrent criterion validity of the L2ERSQ. Model comparisons indicate that the L2ERSQ possesses longitudinal measurement invariance, showing its potential for longitudinal studies of emotion regulation. Conclusion This study demonstrates the reliability and validity of a questionnaire assessing L2-specific emotion regulation strategies. The newly developed instrument enriches the existing taxonomy of emotion regulation strategies by distinguishing between value upgrading and value downgrading within the broader category of value appraisal. The findings indicate that some emotion regulation strategies (e.g., value upgrading) are adaptive, while others (e.g., value downgrading) are ambivalent. Implications for research on L2 emotion regulation and the development of interventions to enhance emotion regulation are discussed.