Browse Articles

The effect of punishment on cooperation in a multilevel public goods game: compositional data analysis

Abstract When addressing collective challenges, cooperation is essential both within and across subgroups. In multilevel social dilemmas, cooperation can occur at two levels: local cooperation (within one’s subgroup) and global cooperation (across subgroups). While punishment has been shown to foster local cooperation in standard, non-multilevel public goods games, its role in promoting global cooperation remains unclear. To examine cooperative behavior at both levels, the present study involves 120 participants and employs a multilevel public goods game with punishment. Participants decided how to allocate endowments among self, local, and global accounts. We tested (i) whether punishment promotes local and/or global cooperation, (ii) whether individuals punish in-group or out-group members more, and (iii) what behaviors are punished. Using compositional data analysis suitable for examining allocation ratios among multiple targets, we found that punishment promoted both local and global cooperation by primarily targeting non-cooperators. Importantly, punishment behavior toward in-group and out-group members did not differ. These findings suggest that punishment can facilitate cooperation beyond subgroup boundaries and provide a potential mechanism for sustaining collective action across groups.

Ethane Chlorination Toward Vinyl Chloride Synthesis: Mechanistic and Catalytic Perspectives

ABSTRACT Ethane chlorination has emerged as a promising alternative to conventional ethylene‐ and acetylene‐based routes for the production of vinyl chloride monomer (VCM). Unlike conventional catalytic processes, this approach relies on chlorine radical‐mediated activation to convert ethane into 1,2‐dichloroethane, followed by thermal cracking to VCM. However, this route remains in its early stages, hindered by the complexity of gas‐phase radical chemistry and catalyst deactivation under chlorination conditions. This review provides a critical assessment of the mechanistic foundations of ethane chlorination, highlighting the interplay between radical‐mediated and surface‐catalyzed pathways. Particular attention is given to advances in rare‐earth oxychloride catalysts, which have shown the ability to stabilize key intermediates. We also discuss major deactivation mechanisms, including phase transformation and surface hydroxylation, that limit catalyst lifetime. Furthermore, we highlight the feasibility of ethane chlorination as a low‐carbon VCM production route under future decarbonized energy scenarios. Finally, key directions in catalyst design, mechanistic understanding, and process integration are outlined to advance ethane chlorination from laboratory‐scale innovation to industrial reality.

The stress hyperglycemia ratio as a novel risk marker for postoperative delirium after cardiac valve surgery

Abstract The stress hyperglycemia ratio (SHR) has been demonstrated to be associated with numerous adverse outcomes; however, its relationship with postoperative delirium (POD) in patients undergoing cardiac valve surgery remains unclear. This study aimed to investigate the association between SHR and POD in this surgical population. This retrospective study analyzed the data from 1830 adult patients who underwent cardiac valve surgery from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database. Primary outcome was the incidence of POD within 7 postoperative days. Secondary outcomes included lengths of stay in the intensive care unit (ICU) and hospital, as well as 28-day and 90-day mortality. Multivariable logistic regression identified SHR as an independent risk factor for POD (odds ratio [OR] 1.47, 95% confidence interval [CI] 1.03–2.11, P  = 0.034). Using the optimal SHR cutoff, patients were stratified into high-SHR (≥ 1.164) and low-SHR (< 1.164) groups. Those with high SHR had a significantly elevated risk of POD compared to the low-SHR group (OR 1.55, 95% CI 1.18–2.03, P  = 0.002). Sensitivity analyses confirmed the robustness of these findings. After 1:1 propensity score matching based on key confounders, the high-SHR group also exhibited prolonged ICU stay and higher 28-day and 90-day mortality. No significant interactions were detected in any of the predefined subgroup analyses. These findings suggest that SHR is an important risk factor for POD following cardiac valve surgery and demonstrates considerable potential as a novel risk marker for POD in this surgical population.

Multi-objective optimization design of oil spray cooling system for hairpin motor based on particle swarm optimization-backpropagation-non-dominated sorting genetic algorithm III

Validation and application of a standardized quantitative PCR assay for the assessment of antimicrobial resistance genes in surface water

Abstract Antimicrobial resistance can be an indicator of anthropogenic contamination in surface waters and is a potential public health threat. Methodological standardization for characterization of antimicrobial resistance in the environment is lacking. Quantitative PCR (qPCR) is used for rapid assessment of antibiotic resistance genes (ARGs) from environmental sources, including surface water. Here we describe the validation and application of a qPCR assay for 47 bacterial gene targets intended for surface water samples. The qPCR assay displayed excellent sensitivity (97.66%) and specificity (98.71%) for detecting ARGs when compared to whole genome sequencing of bacterial isolates. The qPCR assay was able to detect up to 6/8 (75.0%) of ARGs spiked into sterile water at varying concentrations and four sample ultrafiltration volumes. Nineteen different ARGs were detected across six samples sites at three national parks in Alaska using ultrafiltered surface water samples. The number of unique ARGs detected was higher at sites within parks with greater visitation. The relative abundance of ARGs/16S from Exit Creek in Kenai Fjords National Park, downstream from a visitor center was greater than all other sampled sites. We have demonstrated a robust qPCR assay for monitoring ARGs in surface waters, including those that are minimally human impacted.

Characterization of sooty blotch and flyspeck fungi on mango (Mangifera indica L.) in Peninsular Malaysia

Propofol and dexmedetomidine sedation share the similar functional activity but distinct functional synchronization

Engineering properties and microscopic mechanism of phosphogypsum-rubber composite cemented soil

Thermal analysis of flat plate solar air heater system with radiation reflectors and W-shaped roughness: artificial neural network & machine learning approach

Abstract The lower thermal behavior of solar-based thermal systems limits the contribution of solar systems to meet current energy demand of industries. The Flat Plate Solar Air Heater (FPSAH) is extensively utilized in many applications requiring reasonable heat but struggles from inherent limitation in convective heat release and mediocre efficiency. In this study, the challenges are addressed with a novel means of dual mode augmented technique. This mode integrated absorber surface of the FPSAH system by introducing two radiation reflectors on either edge of the rectangular channel. Primarily these reflectors forward back the solar irradiance over the absorber plate, thus increasing the actual solar flux. Simultaneously, a W-shaped artificial rib roughness pattern is merged on the underneath (air-side) of the absorber plate. This coarseness is intended to persuade measured flow disorder inside the channel that disrupt the boundary layer development and may consequently augment convective heat transfer. Experimental testing is conducted with different combinations of roughened absorber surface and radiation reflectors. The performance enhancement is evaluated in terms of Nusselt number ( Nu ) and thermal efficiency of the FPSAH system. The maximum Nu achieved is 1.63 times higher using a set of radiation reflectors along with W-shaped roughness on the absorber surface compared to the plain configuration without radiation reflector. Finally, artificial neural network (ANN) and machine learning (ML) algorithms were used to predict Reynolds number in each set of experiments. A very good curve fitting was achieved by the Robust Regression algorithm with $$R^2 = 0.99$$ for the testing dataset and $$R^2 = 0.94$$ by the Random Forest Regression algorithm for ML.

Qualitative analysis of chemical components in Berberis kaschgarica Rupr. and study on the in vitro anti-inflammatory effects of its alkaloids

Retraction Note: Magnetic and pH sensitive nanocomposite microspheres for controlled temozolomide delivery in glioblastoma cells

Retraction Note: An assessment of physiological and health responses in Catla catla fingerlings after polystyrene microplastic exposure

An integrated framework for proactive deepfake mitigation via attention-driven watermarking and blockchain-based authenticity verification

Coordinate transformation method for beam grazing angle calculation of space-based early warning radar

Abstract With the rapid development of space technology and the increasing demand for global security early warning, space-based early warning Radar (SBR) is playing an increasingly important role in defending against potential threats. Based on the perturbed motion model of low-earth orbit satellites, this paper proposes a method for calculating the beam grazing angle of SBR using coordinate transformation techniques. The method is characterized by its clear process and simple calculation. The effectiveness of this method has been verified through simulation experiments, providing a practical method for the real-time calculation of the beam grazing angle of SBR.

Optimized flood scene segmentation with swin transformer-based architecture

The impact of fly ash and slag on the microscopic interface of recycled concrete and its destruction evolution

Evaluation of a sensitive real-time PCR assay for Group B Streptococcus detection in vaginal-rectal swab

Multistep loss of catalytic and ligand binding abilities of hexameric purine nucleoside phosphorylase

Abstract It is commonly believed that enzymatic catalysis is such a complex process, that even a small change in any physicochemical property of the enzyme results in a complete loss of the catalytic and ligand-binding capacity of the molecule. Therefore, when the enzyme sample is not fully active, but is electrophoretically pure, the inactive fraction is often considered equal to the non-binding fraction, and constants characterizing ligand binding by such an enzyme and ligand-induced inhibition are determined under this assumption. Here, we present an enzyme, hexameric purine nucleoside phosphorylase, whose gradual loss of a catalytic activity towards natural substrates does not correlate with the loss of the ability to bind ligands, substrates, and inhibitors. The values of dissociation constants characterizing ligand binding depend on the specific activity of the enzyme used in the experiment. Furthermore, there is a stable state of the enzyme, in which it is no longer able to catalyse reaction with natural substrates, but can still catalyse the same reaction if a substrate resembling the transition state is used. The active site conformations of individual subunits in the X-ray structure of this hexameric molecule reflect the presence of intermediate states observed in the enzyme activity decline profiles.

BERT-spaCy hybrid NLP and blockchain-enhanced adaptive CTI for IOC extraction and threat prediction

Abstract Cyber-attacks pose a significant risk to digital infrastructure, resulting in losses at both individual and organizational levels, underscoring the need for proactive and intelligent defense mechanisms. This study proposes a hybrid Cyber Threat Intelligence (CTI) system integrating an immutable blockchain ledger, adaptive machine-learning models, and natural-language processing algorithms for timely detection, classification, and secure sharing of threat data. The system forecasts future attacks by analyzing aggregated data and recommending mitigation strategies. A BERT-based model, combined with spaCy and regular expressions for extracting Indicators of Compromise (IOCs) from unstructured data, achieved 95% accuracy and a 95.7% F1-score, with a 55% latency reduction (from 120ms to 54ms for 200 reports). Validation used 10-fold cross-validation with paired t-tests across 10,000 Monte Carlo simulations (t = 3.45, p  < 0.001, Cohen’s d ranging 0.76–1.12 from heatmaps) on CIC-IDS2017 and UNSW-NB15 datasets. The Cross-Dataset Robustness Index (CRI) confirmed strong generalization, with BERT at 0.999, slightly outperforming LSTM (0.998), SVM (0.95), and Naïve Bayes (0.92). The system excels in high-volume data processing, event correlation, and threat detection/response rates. This scalable solution suits Security Operations Centers (SOCs), IoT environments, and financial cybersecurity, providing robust unstructured data handling and adaptability to evolving threats.

Blockchain-enabled traceability evaluation framework for mineral resource development and utilization: a fuzzy comprehensive assessment approach

Abstract Effective traceability management in mineral resource development faces persistent challenges including information asymmetry, data falsification, and verification difficulties across complex value chains. This paper proposes a comprehensive blockchain-enabled traceability evaluation framework integrating distributed ledger technology with systematic assessment methodologies. A four-layer architecture encompassing data acquisition, blockchain storage, analysis processing, and evaluation application is designed to ensure data integrity throughout the mineral lifecycle. A hierarchical indicator system spanning five dimensions—traceability breadth, depth, precision, timeliness, and data credibility—is constructed, with the Analytic Hierarchy Process employed for weight determination and fuzzy comprehensive evaluation applied for performance assessment. Empirical validation through case study analysis of Huaxin Mining Group demonstrates the framework’s practical applicability, yielding a comprehensive traceability score of 81.2 (Good grade). Comparative analysis reveals that blockchain-based systems achieve 96.8% data accuracy versus 82.4% for traditional approaches, with trace-back efficiency improving from 127.3 min to 4.7 min. The blockchain technology contribution ratio reaches 47.3% toward maximum traceability improvement. These findings provide theoretical foundations and practical guidance for advancing transparent and accountable mineral resource governance.