Search Results (1,496)

This study evaluates the extent and quality of tax transparency reporting among the Top 40 firms listed on the Johannesburg Stock Exchange (JSE), distinguishing between mandatory tax disclosures and voluntary transparency practices. A qualitative, disclosure-based research design was employed, involving content analysis of publicly available annual reports, integrated reports, and sustainability reports. A structured tax transparency framework grounded in stakeholder theory and legitimacy theory, and adapted from prior empirical studies was applied to systematically assess tax-related disclosures. Findings indicate high compliance with mandatory tax disclosure requirements, reflecting strong adherence to accounting standards and regulatory obligations. In contrast, voluntary tax transparency shows considerable variation: firms predominantly provide narrative, policy-oriented, and governance-related information, while detailed, forward-looking, and jurisdiction-specific disclosures remain limited. The discussion highlights that voluntary transparency is shaped by stakeholder expectations, legitimacy concerns, and perceived reputational and commercial risks, leading to selective disclosure. Regulatory compliance emerges as the primary driver of tax reporting, whereas voluntary practices are influenced by firm-specific and contextual factors. The results hold relevance for investors, regulators, and policymakers seeking greater corporate accountability, and for standard-setters aiming to enhance the consistency and depth of tax transparency reporting. Overall, the study enriches the limited literature on corporate tax transparency in emerging markets by offering contemporary empirical evidence from South Africa and identifying key areas requiring improvement in voluntary tax disclosures. Full article

Consumer wellbeing systems are characterized by conceptual fragmentation, heterogeneous data sources, and multilevel interactions across economic, psychological, social, and environmental domains. Existing monitoring approaches remain largely unidimensional and lack integrative system architectures capable of supporting real-time, adaptive analysis. This paper proposes a Federated Digital Twin (FDT) framework for Consumer Wellbeing Systems, designed to integrate decentralized, multimodal data while preserving autonomy and privacy. The proposed architecture builds on a five-dimensional digital twin model and extends it through federated interoperability, data fusion, adaptive learning, simulation capabilities, and human-in-the-loop mechanisms. The framework enables the synchronization of observed, self-reported, contextual, and synthetic data across distributed environments, supporting system-level modeling, prediction, and optimization. As an illustrative application, the paper examines Shopping Wellbeing and Shopping–Life Balance as sub-systems within broader wellbeing ecosystems, demonstrating how federated digital twins can unify fragmented theoretical constructs into a coherent, dynamic monitoring structure. The study contributes a system-oriented conceptual architecture for modeling complex human-centric wellbeing ecosystems and outlines implications for systems design, governance, and future interdisciplinary research. Full article

Citrus trees exhibit severe alternate bearing, resulting in significant annual yield fluctuations and posing substantial challenges to orchard management planning. Accurate citrus bud counting provides an effective solution by supplying essential data for tree-level and orchard-level yield prediction. However, citrus buds are extremely small (5–10 mm in diameter) and are frequently occluded by leaves during the flowering stage, which makes precise detection highly challenging in complex orchard environments. To address these challenges, this paper proposes a Contextual and Frequency-Aware Detector (CFADet) for robust citrus bud detection. Specifically, an Enhanced Feature Fusion (EFF) module is introduced in the neck to refine multi-scale feature aggregation and strengthen information flow for small targets. A Contextual Boundary Enhancement Module (CBEM) is designed to capture surrounding contextual cues and enhance boundary representation through dimensional interaction and max-pooling operations. To suppress background interference, a Frequency-Aware Module (FAM) is developed to adaptively recalibrate frequency components in the amplitude spectrum, thereby enhancing target features while reducing background noise. In addition, Spatial-to-Depth Convolution (SPDConv) is employed to reconstruct the backbone to preserve fine-grained bud features while reducing model parameters. Experimental results show that CFADet achieves 81.1% precision, 80.9% recall, 81.0% F1-score, and 87.8% mAP, with stable real-time performance on mobile devices in practical orchard scenarios. This study presents a preliminary investigation into robust citrus bud detection in real-world orchard environments and provides a promising technical foundation for intelligent orchard monitoring and early yield estimation, while further validation on larger and more diverse datasets is still required. Full article

Background: Dementia is one of the most common diseases of the elderly worldwide. Sharing experiences of caring for patients with dementia with other carers is essential to improve the quality of care, promote better outcomes, and learn from others. Aim: to explore nurses’ experiences of working with patients with dementia in Lithuanian supportive treatment and nursing hospitals. Methods: A qualitative descriptive design was employed in this study, with data collected through semi-structured interviews. Nurses with direct experience caring for patients with dementia in supportive treatment and nursing hospitals were recruited through purposive sampling. This sampling strategy was chosen to ensure that participants could provide rich, contextual, and experience-based insights into the phenomenon under investigation. Open-ended questions were divided into three themes: 1. Identifying nursing needs. 2. Care for people with dementia. 3. Patient behavior management and situation management. To ensure methodological rigor and transparency, the Consolidated Criteria for Reporting Qualitative Research (COREQ) were applied throughout the study’s planning, data collection, and analysis processes. Results: Nine nurses working in three different Lithuanian hospitals participated in the study. Theme 1: respondents reported that the needs of patients with dementia depend on their previous lifestyle and hobbies, as well as on essential physiological needs such as eating and drinking, bathing and personal hygiene, and the absence of pain. Theme 2: All participants emphasized that ensuring a safe environment is crucial for people with dementia. Theme 3: When faced with inappropriate patient behaviour, nurses attempt to calm the patient, speak gently, provide distraction, or, when necessary, temporarily separate the patient from others. Additional actions include administering medication and stabilizing the patient. Overall, these findings illustrate that dementia care requires continuous emotional presence, situational judgment, and adaptation to each patient’s individual needs. Conclusions: Patients with dementia require highly individualized care focused on nutrition, hygiene, pain control, and communication. Nurses’ daily activities centered on essential bodily care, medication management, and mobility support to maintain safety and prevent complications. Full article

Glioblastoma (GBM) remains among the most treatment-refractory human malignancies. It is characterized by profound radioresistance and a highly immunosuppressive tumor microenvironment, limiting the durable efficacy of radiotherapy. Beyond direct cytotoxicity, ionizing radiation can induce immunogenic cell death and the release of damage-associated molecular patterns (DAMPs), including surface-exposed calreticulin, HMGB1, extracellular ATP/adenosine, and tumor-derived DNA. These signals engage pattern-recognition receptors and cGAS–STING–type I interferon pathways, transiently promoting antigen presentation and immune activation. In GBM, however, DAMP signaling frequently evolves toward chronic inflammation and immune suppression, characterized by myeloid cell recruitment, adenosine accumulation, and immune checkpoint upregulation, thereby contributing to tumor regrowth and radioresistance. This dual immune-regulatory role of DAMPs highlights the importance of temporal and contextual interpretation of radiation-induced immune responses. In this review, we summarize current mechanistic and translational evidence on DAMP-mediated immunomodulation in GBM radiotherapy; discuss modality-dependent considerations across photon, proton, and high-LET irradiation; and evaluate the emerging potential of DAMPs as dynamic biomarkers of treatment response. We further outline how integration of DAMP profiling with liquid biopsy, imaging, and nanotheranostic platforms may support biologically informed and adaptive radiotherapy strategies for glioblastoma. Full article

Cross-topic stance detection degrades when encoders entangle stance signals with topic-specific vocabulary, causing representations that fail to transfer to unseen targets. Existing methods commit to either topic-invariant or topic-aware representations and apply the same strategy uniformly to every input, sacrificing complementary information. We propose ReDyGait, a three-stage framework that disentangles these two types of signals through dedicated contrastive pre-training and recombines them adaptively at inference time. Stage 1 trains a topic-invariant encoder with supervised contrastive loss over cross-topic positives. Stage 2 trains a topic-contextual encoder with bidirectional pair contrastive loss over within-topic positives; both stages employ topic-aware hard negative mining to prevent shortcut learning. Stage 3 freezes the two contrastive encoders and learns a gating network that produces per-instance weights over invariant, contextual, and base-encoder pathways. On VAST, ReDyGait achieves a macro-averaged F1 of 0.782 in the zero-shot setting and 0.752 in the few-shot setting, improving over the strongest baseline by 1.1 points in both; on SEM16t6 in a leave-one-target-out setup, ReDyGait reaches an average F1 of 0.612. Analysis of the learned gate weights shows that the model shifts toward the invariant pathway for unfamiliar topics and toward the contextual pathway when topic-specific patterns are available, confirming that the disentanglement operates as intended. Full article

The rapid expansion of the tourism industry underscores the necessity for sustainable hotel recommendation systems that guide user choices while safeguarding the long-term viability of the tourism ecosystem. However, existing methods often struggle to reconcile individual user preferences with sustainable consumption objectives, frequently encountering the “information cocoon” effect and lacking interpretability in their decision-making processes. To address these issues, this study proposes a multi-objective, context-aware hotel recommendation framework that integrates text mining, sequential behavior modeling, and reinforcement learning. The framework begins by employing unsupervised learning to extract multidimensional hotel features from online reviews, with an explicit emphasis on comprehensive sustainability metrics. It subsequently applies a dynamic state representation approach that merges long-term and short-term interests with real-time contextual information to accurately reflect evolving consumer needs. Furthermore, a dynamic feature weighting module is incorporated to enhance interpretability and enable context-adaptive evaluation of both commercial and sustainable attributes. The recommendation process is structured as a Markov Decision Process, leveraging a composite reward function comprising diversity penalties and sustainability incentives. Empirical analysis using real-world data validates the framework, demonstrating its contribution to sustainable tourism and achieving recommendation accuracy that surpasses existing benchmark models. Full article

NetworkGuard is a modular edge-based virtual network sensing framework designed for residential smart home security. The system interprets network telemetry—such as DNS queries, firewall events, VPN latency, and connection establishment delay—as structured sensing signals for gateway-level monitoring. Implemented on a Raspberry Pi 4 and managed via an Android interface, NetworkGuard integrates DNS filtering (Pi-hole), firewall enforcement (UFW), encrypted VPN tunneling (WireGuard), and an AI-assisted advisory layer for contextual log interpretation. During a six-week residential deployment, DNS blocking efficiency improved from 81.2% to 97.0% following blocklist refinement, while VPN connection establishment time decreased from approximately 3012 ms to 2410 ms after configuration tuning. ICMP-based measurements indicated a stable tunnel latency under moderate traffic conditions. Controlled validation scenarios—including DNS manipulation attempts, port scanning, and VPN interruption testing—confirmed consistent firewall enforcement and tunnel containment. The results demonstrate that layered security principles can be adapted into a lightweight, reproducible edge architecture suitable for small-scale residential IoT environments without a reliance on enterprise infrastructure. Full article

Zero Trust Network Access (ZTNA) has emerged as a fundamental paradigm for securing cloud-native and distributed computing environments. However, existing ZTNA implementations remain largely limited by static policy enforcement and opaque machine-learning-based anomaly detection mechanisms, which often lack contextual adaptability, policy awareness, and interpretable decision-making capabilities. These limitations create significant challenges in dynamic multi-cloud environments where access behavior continuously evolves and security decisions must be both accurate and explainable. To address these challenges, this study proposes Cognitive ZTNA framework, a unified neuro-symbolic trust enforcement framework that integrates transformer-based behavioral trust modeling with ontology-guided symbolic reasoning. The proposed architecture enables continuous trust evaluation by combining behavioral access patterns with explicit policy semantics through a hybrid trust fusion mechanism. This design allows the system to capture long-range behavioral dependencies while maintaining policy-compliant and interpretable access control decisions. The framework is evaluated using the CloudZT-Bench-2025 dataset, comprising 4.2 million cross-platform access events derived from enterprise security telemetry, AWS CloudTrail logs, and simulated adversarial scenarios. Experimental results demonstrate that Cognitive ZTNA achieves Precision = 0.96, Recall = 0.93, and F1-score = 0.95, significantly outperforming rule-based and machine-learning baselines while reducing the false positive rate to 0.03. In addition, the system maintains real-time feasibility with an average decision latency of 24 ms and explanation latency below 5 ms, while achieving 92% analyst-rated explanation sufficiency. These findings demonstrate that integrating behavioral intelligence with symbolic policy reasoning enables adaptive, interpretable, and policy-aware Zero Trust enforcement. The proposed framework therefore provides a practical foundation for next-generation ZTNA systems capable of supporting secure, transparent, and context-aware access control in modern cloud environments. Full article

Detecting unnatural pauses is a critical component of automated quality assessment (AQA) in interpreter training, as pause patterns directly reflect an interpreter’s cognitive load and fluency. Traditional pause detection methods rely on static temporal thresholds (e.g., 1.0 s), which often fail to account for segment-specific speech rate variability and individual speaking styles. This study proposes a context-adaptive pause detection framework that integrates unsupervised anomaly detection using Isolation Forest (iForest) with a sliding window technique. To enhance pedagogical validity, we specifically focused on intra-sentential pauses by delineating sentence boundaries using a specialized segmentation model. The proposed model was evaluated against ground-truth labels annotated by professional interpreting experts. Our results demonstrate that the sliding window–based contextual anomaly detection model significantly outperforms the conventional static baseline, particularly in terms of recall and Cohen’s kappa. Furthermore, by applying a weighted F3-score and the “Recognition-over-Recall” principle, we confirmed that the proposed model substantially reduces the instructor’s total operational burden by shifting the workload from de novo annotation creation to more efficient corrective pruning. These findings suggest that speech-adaptive modeling provides a more reliable and labor-saving framework for automated interpreting assessment and feedback. Specifically, this study makes three main contributions: (1) the proposal of a context-adaptive pause detection framework using anomaly detection, (2) the integration of sliding window–based local contextual modeling for speech-rate–aware analysis, and (3) the introduction of an evaluation strategy based on the Recognition-over-Recall principle to reduce instructor workload in interpreter training. Full article

Underwater object detection plays a crucial role in the domain of marine engineering. Due to blur, uneven illumination and noise in underwater images, generic object detectors often fail to accurately detect underwater targets. Existing underwater object detection methods generally neglect the enhancement and refinement of multi-scale features, limiting further improvements in detection accuracy. In response to these challenges, we propose the Feature Enhancement and Alignment Pyramid Network (FEAPN), a novel underwater object detection framework. FEAPN consists of two key innovations. First, the Adaptive Feature Refinement Module (AFRM) is developed to adaptively enhance contextual features from complex backgrounds. Second, the Dual-path Feature Alignment Module (DFAM) is designed to align multi-scale features, utilizing cross-layer information to optimize feature representation. Extensive experiments demonstrate that FEAPN achieves state-of-the-art performance. Specifically, FEAPN achieves a 2.4% mAP improvement over the baseline and outperforms the current leading underwater detector by 1.2% mAP. Furthermore, the effectiveness of each component is validated through ablation studies. Full article

Large-scale infrastructure development disrupts not only the material foundations of agrarian livelihoods but also the social and ecological systems through which households manage uncertainty. This study argues that the livelihood shifts observed among households affected by the construction of the Bili-Bili Reservoir in Lanna Sub-district, Gowa Regency, Indonesia, are best understood as products of contextual rationality operating at the individual level and enacted through household-level strategies. Using a qualitative phenomenological approach with 15 purposively selected informants, each representing a distinct household directly affected by the reservoir’s construction functioned as a structural shock that exceeded the adaptive capacity of the existing agrarian system, triggering differentiated household responses—including reservoir fisheries, small-scale trade, home-based enterprise, and labor migration—whose variation reflects systematic differences in individual skills, asset endowments, and social capital access rather than arbitrary or purely compelled choice. Theoretically, this study advances the sustainable livelihoods framework by specifying the mechanism linking individual rationality to household adaptive outcomes, and by showing how social capital—in its bonding, bridging, and linking dimensions—shapes the option set within which rational calculations are made. These findings suggest that post-displacement livelihood recovery is more effectively supported by policies that strengthen social network structures alongside physical and financial provision. Full article

Background/Objectives: Understanding how different fatigue contexts influence muscle architecture is essential for optimizing training and recovery strategies in endurance athletes. Ultramarathon running involves prolonged mechanical load and high eccentric demands, which may elicit different acute responses compared to controlled laboratory protocols. This study aimed to examine the effects of time, condition (laboratory vs. race), and muscle on ultrasound-derived muscle architecture in ultratrail runners. Methods: A repeated-measures within-subject design was employed. Forty ultratrail runners completed two fatigue conditions: (1) a standardized laboratory downhill running protocol and (2) an ultramarathon race (CSP 2025; 106 km, +5600 m elevation gain). Muscle thickness and pennation angle of the rectus femoris, vastus lateralis, and medial gastrocnemius were assessed using ultrasound before and after each condition. Linear mixed models were used to evaluate the effects of time, condition, muscle, and their interactions. Results: Forty participants were recruited; 29 completed all assessments. No significant effects of time or condition were observed for muscle thickness, and no interaction effects were detected, indicating that muscle size remained stable across conditions and time points. A significant main effect of muscle was identified (p < 0.001), reflecting inherent morphological differences, with greater thickness in the vastus lateralis compared to the rectus femoris and medial gastrocnemius. In contrast, pennation angle showed a significant main effect of condition (p = 0.031) and a significant condition × muscle interaction (p = 0.005), indicating muscle-specific differences between laboratory and race contexts. No significant effect of time was observed for pennation angle. Conclusions: Muscle thickness appears to remain stable following acute fatigue, regardless of the assessment context. In contrast, pennation angle may be more sensitive to condition-specific and muscle-dependent factors. These findings suggest that ultrasound-derived architectural changes observed immediately after exercise likely reflect acute physiological responses rather than true structural adaptations. Therefore, the interpretation of muscle architecture should consider both contextual factors and methodological constraints. Full article

Physical inactivity is a major preventable cause of non-communicable disease and premature mortality. Mobile health interventions can promote physical activity, but their effectiveness depends on the ability to adapt to user’s context and motivation. Reinforcement learning (RL), particularly contextual bandits (CBs), offers a promising framework for such adaptive personalization. However, in practice, RL-based models face the cold start problem (CSP), due to the lack of initial training data. This study examines whether theory-driven simulated data can mitigate the CSP in training RL systems for personalized physical activity recommendations. A scoping review of 18 empirical studies on the Integrated Behavioral Change Model (IBC) provided population parameters for key constructs, used to simulate 2000 virtual users via multivariate modeling and structural equation calibration. A CB algorithm with an ε-greedy policy was trained with this dataset and compared with data from real world pilot using the Apptivate mHealth web-app (n = 588). Results showed close alignment between simulated and real behaviors. Our findings demonstrate that behaviorally informed synthetic data can effectively be used to train RL algorithms, offering an interpretable, sustainable, scalable, and privacy-safe solution to the CSP in personalized digital health interventions. Full article

Public spaces (PSs) are vital to urban life, enhancing environmental sustainability, fostering social connections, and boosting public health. Cities have suffered from pandemics, which are becoming more frequent. Recent health crises have revealed significant shortcomings in public spaces’ capacity to preserve social support, safety, and functionality during pandemics. Despite urban resilience having developed as a critical concept for tackling such difficulties, its implementation in PS design remains limited, particularly in low- and middle-income contexts. The study’s main aim is to ascertain how ideas of urban resilience (UR) can be turned into measurable metrics to create and evaluate resilient public spaces during health crises. Several methods, including spatial analysis and questionnaire-based evaluations (n = 145), were analyzed using the Relative Importance Index (RII). This approach highlights key resilience factors, such as flexibility, accessibility, inclusion, and adaptation, that influence the effectiveness of PSs. The results indicate that “Safety & Well-being” emerged as the highest-ranked resilience dimension (RII = 0.636), suggesting that focusing on resilience in design can improve the long-term effectiveness of PSs by supporting social well-being, multifunctionality during crises, and compliance with health guidelines. By (1) translating urban resilience theory into clear PS indicators; (2) empirically assessing and ranking these indicators based on expert and user perspectives; and (3) developing a contextual framework specific to Egyptian cities and similar urban environments, this study advances existing research. These findings offer practical guidance for architects, urban planners, and policymakers to build flexible, inclusive, and resilient PSs capable of facing current and future challenges. Full article