Ensuring consistent quality in friction stir welding (FSW) remains challenging because defect formation and mechanical performance arise from complex thermo-mechanical interactions. This study presents an interpretable convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) framework for patch-level surface-defect detection and weld-level tensile-strength classification in ultrasonic vibration-assisted FSW AA2060-T8E30 joints. Surface images from 54 welds were divided into six ordered patches to preserve contextual information along the welding direction. DenseNet121 and VGG16 were evaluated as standalone CNNs and hybrid CNN-BiLSTM models. DenseNet121-BiLSTM achieved the best defect-detection performance, with 98.15% fixed-test accuracy and 95.54% mean leave-one-weld-out accuracy, compared with 92.59% and 90.23% for VGG16-BiLSTM. The DenseNet121-BiLSTM model was then applied to three-class tensile-strength classification relative to base-material ultimate tensile strength, achieving 88.89% fixed-test accuracy and 77.78% +/- 7.41% under repeated stratified grouped cross-validation. Gradient-weighted Class Activation Mapping (Grad-CAM) visualisations identified surface regions associated with the predictions, while a graphical user interface integrated image input, classification, confidence reporting, and interpretation. The results demonstrate the feasibility of using standard weld-surface images for both visible defect detection and preliminary tensile-strength categorisation to support post-weld decision-making.
Environmental, Social, and Governance (ESG) has become a critical measure of multinational enterprises’ global competitiveness, yet emerging market multinational enterprises (EMNEs) face distinct legitimacy challenges due to weak home-country ESG regulations, known as the liability of home origin. Although prior studies have examined the relationship between internationalization and ESG, they have largely treated the two directions separately, leaving unclear whether they operate as competing explanations or as mutually reinforcing processes. Drawing on legitimacy theory, this study conceptualizes internationalization and ESG performance as exhibiting mutually reinforcing associations over time. Using longitudinal data from 2460 Chinese listed firms between 2009 and 2020, we examine the reciprocal relationship between internationalization and ESG performance, and the moderating role of digitalization. Findings indicate that internationalization is consistently associated with stronger ESG performance, suggesting EMNEs use ESG to mitigate liabilities of origin and build legitimacy abroad. The reverse association is weaker but most robust for foreign-country scope, indicating that ESG performance may support international expansion by signaling alignment with international sustainability expectations. The moderating associations of digitalization are uneven and are concentrated in visible foreign organizational and geographic presence, such as overseas subsidiaries and foreign-country scope, rather than overseas revenues.
Background Studies in post-COVID-19 condition following hospitalisation have focused on maximal exercise, with limited insight into submaximal responses that better reflect daily activities. We compared central haemodynamic, ventilatory, and peripheral muscle oxygen extraction responses during submaximal exercise between individuals with post-COVID-19 condition and healthy age-matched controls. Methods Twelve participants with post-COVID-19 condition, who were assessed 624 (593, 729) days following hospitalisation (median (IQR) length of stay: 7 (4.7, 11.7) days) and seven age-matched healthy controls (59 ± 11 years) completed a 4-min constant-load exercise test at 80% of peak cycling workload. Cardiac output (CO), oxygen uptake (V̇O₂), and ventilatory efficiency (V̇E/V̇CO₂) were assessed throughout exercise. Arteriovenous oxygen content difference was calculated using the Fick equation (C(a–v)O₂ = V̇O₂/CO). Results Post-COVID-19 condition participants exhibited a lower (p = 0.03) mean±SD C(a–v)O₂ compared with healthy controls (135 ± 29 vs. 171 ± 46 mL O₂·L⁻¹ blood). No significant differences were observed between post-COVID-19 condition and controls for V̇O₂ (1.9 ± 0.3 vs. 2.1 ± 0.7 L·min⁻¹), V̇E/V̇CO₂ (26.6 ± 3.0 vs. 27.5 ± 2.4), or CO (12.7 ± 3.0 vs. 11.1 ± 3.6 L·min⁻¹), indicating similar central cardiovascular and ventilatory responses between groups, despite a numerically greater cardiac output in the post-COVID-19 condition group (11 ± 4% higher). In contrast, RER and V̇E/V̇O₂ were significantly higher in healthy controls than in post-COVID-19 condition participants, indicating divergent ventilatory behaviour despite preserved V̇E/V̇CO₂. Conclusion Post-COVID-19 condition following hospitalisation is characterised by reduced peripheral oxygen extraction during submaximal exercise, alongside no significant difference in cardiovascular responses and preserved ventilatory efficiency with respect to carbon dioxide elimination, compared with healthy controls. Although RER and V̇E/V̇O₂ were higher in healthy controls, indicating some divergence in ventilatory behaviour between groups, this did not reflect impaired ventilatory efficiency in post-COVID-19 condition. These findings indicate that differences in exercise responses at workloads representative of daily living are possibly peripheral rather than central, supporting rehabilitation strategies targeting skeletal muscle oxidative function.
Ransomware has evolved into a complex, adaptive, and fast-moving adversary category in which static signatures and monolithic classifiers fail to generalise under concept drift, evasion, and behavioural polymorphism. In this paper we present Agentic SABRE (Semantic-Behavioural Arbitration for Ransomware Evaluation): an uncertainty-aware, neuro-symbolic, multi-agent framework for adaptive ransomware detection. SABRE fuses semantic (representation-based) and behavioural (time-window forensic telemetry) evidence, and employs Monte Carlo Dropout inference to quantify epistemic uncertainty for each agent. We introduce a decision-layer orchestrator that performs risk-and uncertainty-aware triage via two interpretable thresholds: a risk score tau and an uncertainty budget kappa. High-confidence, high-risk samples are automatically contained, while uncertain or borderline cases are escalated to human analysts, establishing a flexible computational contract between autonomous response and analyst oversight. To support auditability and trust, SABRE integrates post-hoc explainability mechanisms including gradient saliency, permutation importance, and counterfactual analysis, enabling both local and global interpretation of agent decisions. Extensive evaluation on RDset and RanSMAP demonstrates that Agentic SABRE preserves perfect discrimination on saturated semantic datasets (AUC = 1.0) while improving robustness under weak behavioural signals, achieving up to a 4.9% relative reduction in false escalations at equal recall and maintaining calibrated predictive uncertainty. Counterfactual analysis further shows that semantic and behavioural decisions can be flipped with bounded perturbation cost, indicating stable and interpretable decision boundaries. Overall, Agentic SABRE is not merely a higher-accuracy detector but an agentic cyber-defence system that combines uncertainty-aware automation, explainable reasoning, and adaptive triage under evolving ransomware threats.
An algebraic structure graph is a graph G(V,E) whose vertex set V has an algebraic structure (group, ring, topological space, vector space, etc.). They offer a link between abstract algebra and discrete mathematics, allowing the study of the algebraic properties of discrete structures by graph-theoretic means and the use of graph invariants in information theory, network design, and cryptography. Two novel classes of algebraic structure graphs, square-difference zero-divisor graph (SDZDG) and primitive-rooted graph, are introduced in this work. The vertex set of the SDZDG consists of all the zero-divisors of Zn; two vertices a and b of the SDZDG are connected by an edge if and only if a2≢b2(modn). The vertex set of the primitive-rooted graph is Zn; two vertices a and b are connected by an edge if and only if ab is a primitive root of n. In the case of n=pq (where p and q are two distinct odd prime numbers), SDZDG has been proved to be regular, and the degree sequence, clique number, independence number, and Zagreb indices have been calculated for n=2pq. In the case of the primitive-rooted graph, connectivity has been characterized, the independence number has been determined, and its components have been described.
