Search Results (144)

Unmanned aerial vehicles (UAVs) have become essential cyber-physical platforms for applications such as surveillance, infrastructure inspection, emergency response, and intelligent transportation. However, their tight coupling among sensing, communication, control, actuation, and swarm coordination also exposes them to sophisticated cyber-physical attacks that are difficult to detect using conventional intrusion detection systems. Existing machine learning, deep learning, graph-based, and federated intrusion detection approaches generally rely on statistical feature representations or temporal patterns, providing limited capability to model causal dependencies among interacting UAV subsystems and to generalize across heterogeneous operating environments. To address these limitations, this paper proposes Fed-CGIDS-UAV, a federated causal graph learning framework for cross-domain intrusion detection in cyber-physical UAV networks. The proposed framework models each telemetry window as a typed causal graph in which nodes represent navigation, sensing, communication, control, actuation, and swarm states, while directed edges capture stable operational dependencies. Intrusions are detected by identifying violations of these learned causal relationships, and the framework provides interpretable node-edge explanations to support root-cause analysis. Furthermore, federated learning enables collaborative model training across distributed UAV clients without sharing raw telemetry, thereby preserving data privacy while improving robustness under heterogeneous operating conditions. The proposed framework was implemented and experimentally evaluated in a controlled simulation environment covering four UAV operating domains and six representative attack classes. All experiments were repeated over five independent runs using different random seeds, and the reported results correspond to the measured average performance. The proposed framework was implemented using Python 3.12 (Python Software Foundation, Wilmington, DE, USA) and PyTorch 2.3 (Meta Platforms, Menlo Park, CA, USA). UAV flight data were generated using Microsoft AirSim 1.9.1 (Microsoft Corporation, Redmond, WA, USA), integrated with PX4 Autopilot v1.14 (Dronecode Foundation, San Francisco, CA, USA) and Gazebo Sim 11 (Open Source Robotics Foundation, Mountain View, CA, USA). Within this simulation-based evaluation, Fed-CGIDS-UAV achieved an accuracy of 0.968, an F1-score of 0.956, and an internal–external stability gap (IESG) of 0.028, outperforming conventional machine learning, deep learning, graph-based, and centralized causal baselines while maintaining competitive computational latency. Although these results demonstrate the effectiveness of the proposed framework under controlled simulation conditions, validation using real-flight UAV telemetry remains an important direction for future research. These results demonstrate that integrating causal graph learning with federated optimization provides an effective and interpretable solution for privacy-preserving intrusion detection in heterogeneous cyber-physical UAV environments. Full article

The implementation of construction 3D printing (3DP) offers a viable approach to enhancing productivity, optimizing material use, and advancing sustainability in infrastructure development, particularly in developing construction markets where economic constraints, labour reliance, and environmental challenges persist. This study proposes and empirically tests a theory-based framework to evaluate the perceived critical success factors (CSFs) shaping readiness for the adoption of 3DP within the Egyptian construction sector. The proposed framework is fundamentally grounded in the Technology–Organization–Environment (TOE) perspective, wherein Resource and Technology Optimization (RTO) and Innovation and Creativity in Design (ICD) constitute the technological dimension, Strategic Project Management (SPM) and Organizational Support (OS) represent the organizational dimension, and Stakeholder and Client Engagement (SCE), Sustainability and Environmental Initiatives (SEI), and Policy and Regulatory Considerations (PRC) define the environmental dimension. A total of 157 valid responses collected from construction professionals were analyzed using the Relative Importance Index (RII) and partial least squares structural equation modelling (PLS-SEM). Because construction 3DP remains at an early, pre-commercial stage of diffusion in Egypt, the survey captures professionals’ perceptions of adoption readiness rather than verified implementation performance. This sample exceeds the requirements of a priori statistical power analysis (n = 103 to detect medium effects with seven predictors at a power of 0.80 and α = 0.05) and the inverse-square-root criterion (n = 137), thereby providing adequate statistical power for the structural estimates. However, generalizability remains bounded by the purposive, single-country sample. The RII findings indicate that inaccurate budgeting and scheduling, limited managerial competence, shortages of skilled labour, cultural resistance, and inadequate awareness constitute the most significant barriers to adoption. The structural model accounted for 81.6% of the variance in 3DP adoption. Furthermore, all hypothesized relationships were positive and statistically significant, with 95% confidence intervals excluding zero; SPM (β = 0.4851), RTO (β = 0.4195), and SCE (β = 0.3343) were identified as the most influential determinants, followed by OS, ICD, SEI, and PRC. Discriminant validity was supported by both the Fornell–Larcker and heterotrait–monotrait (HTMT) criteria, and full collinearity diagnostics indicated no substantial common-method bias. The results reveal that successful 3DP adoption is determined not solely by technological preparedness, but also by effective alignment of managerial capabilities, resource optimization, stakeholder trust, sustainability orientation, and regulatory support. Beyond prior TOE- and SEM-based 3DP studies, which have largely examined technology acceptance in developed markets or tested aggregate constructs, this study advances the field by disaggregating the three TOE contexts into seven empirically tested CSF constructs, quantifying their relative influence, and providing the first empirically tested adoption framework of this kind for the Egyptian construction sector, grounded in perception survey data. The resulting framework can support the prospective implementation of sustainable construction technologies in infrastructure sectors in developing countries. Full article

Federated learning (FL) is a promising approach for training distributed machine learning models while preserving clients’ data privacy. However, in real-world FL systems, data are often not independent and identically distributed (non-IID). This heterogeneity can slow convergence, degrade model performance, and increase client drift. To address these challenges, numerous methods have been proposed to mitigate non-IID data effects by optimizing client selection, local training, and model aggregation strategies. Despite their effectiveness in improving performance and efficiency, these methods rarely consider fairness across clients. Improving global accuracy does not guarantee balanced participation, influence, or outcomes, which may lead to biased model behavior across clients. In this survey, we review existing non-IID mitigation methods in FL from a fairness perspective and provide a systematic analysis of their implicit impact on client participation and influence. Unlike prior surveys that treat fairness as a separate research direction, this work analyzes how these methods designed for non-IID mitigation implicitly shape fairness outcomes across clients. Our taxonomy classifies existing methods into three categories—fairness-aware, semi-fairness-aware, and fairness-unaware—based on their design strategies for client selection and model aggregation. Using this taxonomy, we analyze the advantages, trade-offs, and limitations of each category and highlight that mitigating non-IID data does not guarantee fairness across clients. Finally, we identify open challenges and outline future directions, including system-level FL design that jointly considers non-IID mitigation and fairness and the development of standardized fairness evaluation metrics. Overall, this survey aims to provide a structured perspective on the relationship between non-IID mitigation and fairness and support the development of more balanced and scalable FL systems under non-IID conditions. Full article

Veterinary medicine fundamentally revolves around working with people to positively influence how they care for their animals. Veterinarians have traditionally used directive advice-giving when providing clients with recommendations, which can inadvertently push clients further away from change despite good intentions by evoking resistance and leaving the underlying motivational and contextual barriers to change unaddressed. Motivational interviewing (MI), a collaborative communication approach originally developed for addiction counselling, has been widely adapted across many fields because of its proven effectiveness in strengthening intrinsic motivation to change. MI was first applied in veterinary medicine approximately a decade ago with a small but growing evidence base. This review introduces the theoretical foundations of MI, how it can be applied within different types of clinical consultations, as well as the challenges of developing and sustaining competency in practice and ethical implications specific to the veterinarian–client–animal relationship, including the proxy motivation problem where clients bear the costs of behaviour change for benefits experienced primarily by their animal. This review then critically appraises the veterinary MI literature, which has largely focused on communication training outcomes with limited research on the downstream effects on client behaviour or animal welfare, highlighting important research gaps to promote an increased uptake of MI in clinical practice. Full article

Background/Objectives: Community-based drug rehabilitation is a key component of public health strategies in China, with anti-drug social workers playing a frontline role in relapse prevention, social reintegration, and long-term recovery. However, the sustainability and effectiveness of this workforce remain uncertain due to complex organisational and structural conditions. This study aims to examine the professional roles, work-related challenges, and coping strategies of anti-drug social workers within community-based rehabilitation systems. Methods: A systematic review was conducted in accordance with PRISMA 2020 guidelines and was registered in PROSPERO (Registration ID: 1381833). The literature published between 2009 and 2025 was identified through Google Scholar, PubMed, Web of Science, and the Electronic Library. A total of 35 Chinese and English-language studies met the inclusion criteria and were analysed to synthesise evidence on social work practice in drug rehabilitation contexts. Results: The findings identify three core professional roles: information provider, resource linker, and relationship repairer. These roles highlight the multifaceted contribution of social workers in bridging institutional systems and client needs. However, their effectiveness is constrained by fragmented governance structures, role conflict, professional identity ambiguity, administrative burden, limited training, and sustained emotional labour. These conditions contribute to occupational stress, burnout risk, and workforce instability, which weaken service continuity and client-centred care. Conclusions: Strengthening community-based drug rehabilitation requires addressing workforce and system-level constraints. Clearer role definition, targeted interdisciplinary training, reduced administrative demands, and structured organisational support are essential to enhance professional capacity, improve service delivery, and support long-term recovery outcomes. Full article

Impaction of the mandibular third molar in proximity to the mandibular canal increases the risk of inferior alveolar nerve injury. Panoramic radiography is routinely used to assess this relationship. Automated classification of molar–canal overlap could support clinical triage and reduce unnecessary CBCT referrals, while Federated Learning (FL) enables multi-center collaboration without sharing patient data. We compared Local Learning (LL), FL, and Centralized Learning (CL) for binary overlap/no-overlap classification on cropped panoramic radiographs partitioned across eight independent labelers in a simulated heterogeneous multi-center setting. A pretrained ResNet-34 was trained under each paradigm and evaluated using per-client metrics with locally optimized thresholds and pooled test performance with a global threshold. Performance was assessed using area under the receiver operating characteristic curve (AUC) and threshold-based metrics, alongside training dynamics, Grad-CAM visualizations, and server-side aggregate monitoring signals. On the test set, CL achieved the highest performance (AUC 0.831; accuracy ≈ 0.782), FL showed intermediate performance (AUC 0.757; accuracy ≈ 0.703), and LL generalized poorly across clients (AUC range ≈ 0.619–0.734; mean ≈ 0.672). Training curves suggested overfitting, particularly in LL models, and Grad-CAM indicated more anatomically focused attention in CL and FL. Overall, centralized training provided the strongest performance, while FL offers a privacy-preserving alternative that outperforms LL. Full article

Background: Emerging evidence indicates that epilepsy extends beyond the brain, involving systemic metabolic, immune, and microbiome perturbations that shape neuronal excitability and treatment response. Canine idiopathic epilepsy (CE) offers a naturally occurring model with strong electrophysiological, pharmacological, and clinical homology to human epilepsies. Methods: This scoping review was conducted according to the PRISMA-ScR guidelines. A systematic literature search was performed in Web of Science and MEDLINE (PubMed) to identify original studies reporting metabolic, immunometabolic, or neurochemical alterations in CE compared with healthy controls. Eligible studies included peer-reviewed original research involving client-owned dogs diagnosed with CE according to international consensus criteria (IVETF guidelines). Studies focusing exclusively on genetics or neuroimaging without metabolic outcomes were excluded. Titles, abstracts, and full texts were screened for eligibility, and data were extracted from included studies using a standardized approach. Identified metabolic domains were synthesized narratively and grouped into functional systems, including amino acid and lipid metabolism, micronutrients, neurotransmission, oxidative stress, inflammation and immunology, endocannabinoid signalling, microRNAs, and gut–brain axis-related pathways. In a second step, the identified metabolic domains were evaluated for translational relevance through a targeted, non-systematic narrative synthesis of the human epilepsy literature. This approach aimed to assess cross-species parallels and to provide a conceptual framework to guide future research, rather than to perform a comprehensive systematic review of metabolic alterations in human epilepsy. Results: Across CE studies, consistent alterations were observed in multiple interconnected functional systems, including metabolic, immune, and gut–brain axis pathways, in agreement with findings reported for human epilepsy. These data support a model of epileptogenesis involving systemic dysfunction beyond the central nervous system. Translationally, these findings suggest opportunities for biomarker development, patient stratification, and mechanism-based interventions, including dietary and metabolic approaches (e.g., medium-chain triglyceride supplementation), microbiome modulation, and immunometabolic targeting. The current evidence is limited by small and heterogeneous cohorts, potential confounding effects of antiseizure medications, variability in dietary and fasting conditions, breed-related effects, and a predominance of associative over causal relationships. Conclusions: This review positions CE as a reference framework for future research into epilepsy metabolism, integrating current evidence and its translational relevance to human disease. The findings support a shift toward a systems-level view of epileptogenesis, involving interconnected metabolic, immune, and gut–brain axis pathways beyond the brain. CE represents a valuable translational model to identify shared mechanisms, inform biomarker discovery, and guide the development of mechanism-based therapeutic strategies across veterinary and human epilepsy. Full article

This preliminary qualitative study explores how Cognitive Behavioural Therapy (CBT) practitioners conceptualise resilience and how these conceptualisations influence the selection of intervention strategies aimed at fostering resilience in clients. Three CBT therapists participated in extensive Free Association interviews, with professional experience ranging from three to fourteen years. Data were generated using the Grid Elaboration Method to elicit detailed accounts and were analysed using Thematic Analysis. Three overarching themes were constructed: Self-Concept as a Core Psychological Resource for Resilience; Positive Adaptation as a Contextual and Relational Process; Integrating Traditional and Third-Wave CBT Approaches in Resilience-Building. Our findings provide a nuanced account of how CBT therapists understand resilience and outline how mindfulness-based and CBT-informed interventions are employed to enhance resilience in clinical practice. The study supports existing research highlighting the definitional ambiguity of resilience and its influence on the selection of therapeutic interventions according to the skills prioritised in therapy. In addition, the findings extend current knowledge by demonstrating a relationship between therapists’ held notions of resilience and their lived experiences and underscore the significance of three psychological resources in resilience-focused work: self-esteem, self-compassion, and self-awareness. Distinct contributions of this paper include a social-psychological driven examination of resilience in CBT, foregrounding therapists’ meaning-making processes and demonstrating how theoretical understandings, rather than protocol adherence alone, shape clinical intervention. Full article

Professionalism is central to veterinary practice, shaping not only the quality of care provided to animals but also the wellbeing of practitioners, the satisfaction of clients, and the sustainability of the profession. Prior research has catalogued various attributes of professionalism that are important for career success, but few studies have integrated these multiple perspectives into a cohesive framework. This study synthesizes insights from three key veterinary stakeholder groups—students, clinical practitioners, and clients—using a multi-methods approach including surveys, focus groups, critical incident interviews, and client complaint analyses. Across the datasets, ranking of Likert-scale responses and thematic analysis revealed four recurring themes that were identified as essential for career success: ‘Effective communication’; ‘Accountability, integrity, trustworthiness, and honesty’; ‘Personal wellbeing’; and ‘Quality of service’. These themes were organized into a unifying theoretical model of veterinary professionalism, conceptualized as a ‘navigational compass’, comprising three domains of care: patient-centered care, relationship-centered care, and self-care. By conceptualizing professionalism in terms of a compass, the model illustrates how veterinarians can draw on key professionalism attributes, coupled with consideration of the three domains of veterinary care, to navigate the challenges of practice and sustain long-term career success. The compass provides a reflective framework to guide veterinarians and educators, to support the integration of professionalism into curricula and to guide careers toward excellence in care and lasting personal fulfilment. Full article

The construction industry is central to the socio-economic and infrastructural advancement of developing countries; however, it continues to face persistent performance challenges, most notably recurrent cost overruns. While systematic cost risk management is recognized as a critical approach to improving project outcomes, its adoption across the industry remains limited. This study seeks to identify and rank the critical obstacles that hinder contractors from integrating systematic cost risk management into building construction projects. A comprehensive methodology was employed, including an in-depth literature review and three rounds of Delphi. The Relative Importance Index (RII) was used to evaluate the severity of the identified barriers, and Holm-corrected Spearman’s rank correlation analysis was applied to examine the relationships among them. The findings reveal that the most influential barriers include the absence of structured risk management frameworks within organizations, insufficient top management support, the lack of collaborative risk management mechanisms among stakeholders, limited technical knowledge and skills in risk management, and inadequate client support. The strong positive correlations among these barriers highlight their interdependent nature and underscore the systemic challenges facing contractors. This study contributes to the broader field of civil and structural engineering by providing evidence-based insights that can support the development of targeted strategies to enhance cost risk management practices in developing-country construction environments. Full article

This study models the auditor–client relationship as a strategic game shaped by two-sided information asymmetry and examines how this structure influences key audit outcomes, namely audit delay and audit fees, in Türkiye. Using a game-theoretic framework complemented by empirical analysis, the study analyzes independent audit reports dated 31 December 2024 for 201 Borsa Istanbul firms audited by Big Four auditors. Two ordinary least squares models are estimated: one for audit delay and one for the logarithm of audit fees. The findings indicate that firm size and effort-related cost proxies play a central role in explaining audit fees, reflecting scale-related audit complexity. Financial risk, while not significantly associated with audit fees, is found to be negatively related to audit delay, suggesting that riskier firms may accelerate the reporting process through stronger monitoring, earlier planning, or tighter regulatory scrutiny. Audit opinion, by contrast, does not exhibit a statistically meaningful association with reporting delay, likely due to limited variation within the sample. Overall, the results partially support the risk–effort–cost mechanism proposed by the game-theoretic framework and highlight how institutional features of the Turkish audit market shape the relationship between risk and reporting timeliness. The study contributes to the literature by framing the audit process as a strategic decision environment and by providing updated evidence from an emerging market context. Full article

In the service-oriented modern society, logistics enterprises focusing solely on cost minimization can no longer meet market demands, as customers place greater emphasis on timely delivery and service satisfaction. Therefore, this paper constructs a multi-objective optimization model that simultaneously minimizes distribution costs and hierarchical customer delivery duration. From the perspective of symmetry, the two objectives form a symmetric complementary system, which reflects the mutually restrictive and trade-off relationship between the two objectives, thereby facilitating the achievement of a balance between enterprise benefits and customer satisfaction. An improved multi-objective grey wolf optimizer (IMOGWO) is proposed to solve the model, incorporating a chaotic mapping initialization mechanism, a cosine nonlinear convergence factor, and a learning factor-based hunting mechanism to enhance global optimization capability. The algorithm’s effectiveness is validated through comparisons on benchmark cases. Applied to a Zhengzhou food company, the solution improved distribution efficiency while prioritizing key clients, thereby enhancing service levels and stabilizing important customer relationships, providing a practical reference for logistics enterprises to increase revenue and undergo digital transformation. Full article

Therapeutic horse riding (THR) is a non-traditional intervention that may support mental well-being in individuals with autism spectrum conditions. Despite growing interest, most research has focused on children and has tended to privilege practitioner or caregiver perspectives, leaving autistic adults underrepresented. This qualitative study explores the psychological benefits and systemic barriers associated with THR among Autistic adults, drawing on perspectives from both clients and practitioners. Semi-structured interviews were conducted with six Autistic clients and four practitioners, and the data were analysed using reflexive thematic analysis. Five overarching themes were constructed: Facing the Puissance: barriers to accessing THR, Pathways to Participation, Embodied Engagement, To Understand and To Be Understood, and Beyond the Arena—Impacts That Last. Participants described enjoyment, increased confidence, and a sense of achievement, with effects accumulating over time and often extending beyond the riding arena into daily life. Barriers included cost, accessibility, and limited availability of appropriately trained staff and facilities. These findings add to the limited evidence base on THR for Autistic adults by providing an in-depth, contextually grounded account of participants’ experiences. They suggest that, for verbally fluent Autistic adults who choose to access THR in similar settings, THR can enhance well-being, self-agency, and relationship-building, whilst also revealing structural obstacles that restrict equitable access. Full article

This mixed-methods study investigates ethical practices among 500 social media influencers spanning across 44 countries and eight languages using surveys and in-depth interviews. Contrary to the study’s assumptions of demographic and cultural influence, most structural factors and individual characteristics showed little influence towards sponsorship disclosure practices and attitudes toward problematic digital marketing. Findings show that cultural context (high or low) had no relationship to disclosure practices, while linguistic culture significantly affected disclosure methods, with French-speaking creators being the most likely to directly disclose sponsorships while Chinese creators are the least likely to do so. Follower size was the strongest predictor, with creators with 10 K+ followers being twice as likely to disclose sponsorships directly compared to nano-influencers. Qualitative interviews revealed that contingency factors, such as including personal values alignment with brands, influencer confidence in audience trust, brand type, and client expectations, affect disclosure practices. The study shows the complexity of ethical decisions on sponsorship disclosure practices and the need for an ethical theory-based standard to provide guidance for influencers to optimize disclosure of sponsored product recommendations. Full article

In this age of consumerism, most companies are doing their utmost to convince their customers of their products and to attract new customers. The IT development we see today is a perfect solution for strengthening the relationship between companies and their customers, giving them the opportunity to expand their customer base. The Internet of Things refers to an inter-connected system of smart devices that communicate and exchange data and big data analytics over the internet. As this involves the process of the treating data to unlock hidden information, patterns, and insights, the combination of both tools creates a revolution in customer relations and gives us the opportunity to understand our customers’ needs before they do themselves. This article presents an exploratory literature review of studies analyzing the relationship between IOT and big data in marketing. It provides a deep analysis of various scholars’ works that examine the methodology used by these tools to reinforce customer relations and acquire new ones. This review provides an overview of the most interesting research on this topic and the methods and techniques employed as well as an analysis of the obstacles and challenges involved. The results of this research show that IOT and big data analytics are key factors for an efficient analysis of clients’ needs. Full article