Search Results (94)

Demand for mental health support has exerted unprecedented pressure on statutory services. Innovative solutions such as Green or Nature-Based Social Prescribing (NBSP) programmes may help address unmet need, improve access to personalised treatment, and support the sustainable delivery of primary services within a prevention model of population health. We piloted an innovative health economics-informed Social Return on Investment (SROI) analysis and forecast of a ‘Making Well’ therapeutic craft and horticulture programme for mental health between October 2021 and March 2022. Quantitative and qualitative outcome data were collected from participants with mild-to-moderate mental health conditions at baseline and nine-weeks follow-up using a range of validated measures, including the Short Warwick–Edinburgh Mental Well-being Scale, ICEpop CAPability measure for Adults (ICECAP-A), General Self-Efficacy Scale (GSES), and a bespoke Client Service Receipt Inventory (CSRI). The acceptability and feasibility of these measures were explored. Results indicate that the Making Well programme generated well-being-related social value in the range of British Pound Sterling (GBP) GBP 3.30 to GBP 4.70 for every GBP 1 invested. Our initial pilot forecast suggests that the programme has the potential to generate GBP 5.40 to GBP 7.70 for every GBP 1 invested as the programme is developed and delivered over a 12-month period. Despite the small sample size and lack of a control group, our results contribute to the evidence-base for the effectiveness and social return on investment of NBSP as a therapeutic intervention for improving health and well-being and provides an example of the use of health economic well-being outcome measures such as ICECAP-A and CSRIs in social value analysis. Combining SROI evaluation and forecast methodologies with validated quantitative outcome measures used in the field of health economics can provide valuable social cost–benefit evidence to decision-makers. Full article

Automatic Speaker Verification (ASV) systems are increasingly employed to secure access to services and facilities. However, recent advances in speech deepfake generation pose serious threats to their reliability. Modern speech synthesis models can convincingly imitate a target speaker’s voice and generate realistic synthetic audio, potentially enabling unauthorized access through ASV systems. To counter these threats, forensic detectors have been developed to distinguish between real and fake speech. Although these models achieve strong performance, their deep learning nature makes them susceptible to adversarial attacks, i.e., carefully crafted, imperceptible perturbations in the audio signal that make the model unable to classify correctly. In this paper, we explore adversarial attacks targeting speech deepfake detectors. Specifically, we analyze the effectiveness of Basic Iterative Method (BIM) attacks applied in both time and frequency domains under white- and black-box conditions. Additionally, we propose an ensemble-based attack strategy designed to simultaneously target multiple detection models. This approach generates adversarial examples with balanced effectiveness across the ensemble, enhancing transferability to unseen models. Our experimental results show that, although crafting universally transferable attacks remains challenging, it is possible to fool state-of-the-art detectors using minimal, imperceptible perturbations, highlighting the need for more robust defenses in speech deepfake detection. Full article

The relationship between ecosystem services (ESs) and human well-being (HWB) is a central issue of sustainable development. However, current research often relies on qualitative frameworks or indicator-based assessments, limiting a comprehensive understanding of the relationship between natural environment and human acquisition, which still needs to be strengthened. As an element transferred in the natural–society coupling system, carbon can assist in characterizing the dynamic interactions within coupled human–natural systems. Carbon, as a fundamental element transferred across ecological and social spheres, offers a powerful lens to characterize these linkages. This study develops and applies a novel analytical framework that integrates carbon flow as a unifying metric to quantitatively assess the spatiotemporal dynamics of the land use and land cover change (LUCC)–ESs–HWB nexus in Guizhou Province, China, from 2000 to 2020. The results show that: (1) Ecosystem services in Guizhou showed distinct trends from 2000 to 2020: supporting and regulating services declined and then recovered, and provisioning services steadily increased, while cultural services remained stable but varied across cities. (2) Human well-being generally improved over time, with health remaining stable and the HSI rising across most cities, although security levels fluctuated and remained low in some areas. (3) The contribution of ecosystem services to human well-being peaked in 2010–2015, followed by declines in central and northern regions, while southern and western areas maintained or improved their levels. (4) Supporting and regulating services were positively correlated with HWB security, while cultural services showed mixed effects, with strong synergies between culture and health in cities like Liupanshui and Qiandongnan. Overall, this study quantified the coupled dynamics between ecosystem services and human well-being through a carbon flow framework, which not only offers a unified metric for cross-dimensional analysis but also reduces subjective bias in evaluation. This integrated approach provides critical insights for crafting spatially explicit land management policies in Guizhou and offers a replicable methodology for exploring sustainable development pathways in other ecologically fragile karst regions worldwide. Compared with conventional ecosystem service frameworks, the carbon flow approach provides a process-based, dynamic mediator that quantifies biogeochemical linkages in LUCC–ESs–HWB systems, which is particularly important in fragile karst regions. However, we acknowledge that further empirical comparison with traditional ESs metrics could strengthen the framework’s generalizability. Full article

Because neural networks pervade many industrial domains and are increasingly complex and accurate, the trained models themselves have become valuable intellectual properties. Developing highly accurate models demands increasingly higher investments of time, capital, and expertise. Many of these models are commonly deployed in cloud services and on resource-constrained edge devices. Consequently, safeguarding them is critically important. Neural network watermarking offers a practical solution to address this need by embedding a unique signature, either as a hidden bit-string or as a distinctive response to specially crafted “trigger” inputs. This allows owners to subsequently prove model ownership even if an adversary attempts to remove the watermark through attacks. In this manuscript, we adapt three state-of-the-art watermarking methods to “tiny” neural networks deployed on edge platforms by exploiting symmetry-related properties that ensure robustness and efficiency. In the context of machine learning, “tiny” is broadly used as a term referring to artificial intelligence techniques deployed in low-energy systems in the mW range and below, e.g., sensors and microcontrollers. We evaluate the robustness of the selected techniques by simulating attacks aimed at erasing the watermark while preserving the model’s original performances. The results before and after attacks demonstrate the effectiveness of these watermarking schemes in protecting neural network intellectual property without degrading the original accuracy. Full article

The widespread deployment of service robots in industries such as hospitality has significantly transformed service delivery, influencing not only customers but also employees. This study examines the multi-dimensional impact of service robots on hotel employees, focusing on their attitudes, emotional responses, and willingness to collaborate, as shaped by perceived benefits (service reliability, process efficiency, and job crafting) and risks (inefficiency, insufficient intelligence, and privacy concerns). Data were collected from 471 hotel employees in South Korea with experience working alongside service robots, and Hayes’ Process Macro Model 4 was employed for hypothesis testing. The findings reveal that perceived benefits positively influence employees’ attitudes, emotions, and willingness to collaborate, while perceived risks exert a negative impact. Furthermore, attitudes and emotional responses mediate these relationships. These findings provide theoretical and practical insights for managers, policymakers, and service robot manufacturers to address employee concerns, improve human–robot collaboration, and promote sustainable technological integration within the service industry. Full article

Despite the proliferation of biomedical and psychological treatments, the global burden of chronic intractable (long-term) pain remains high—a treatment-prevalence paradox. The biopsychosocial model, introduced in the 1970s, is central to strategies for managing pain, but has been criticised for being decontextualised and fragmented, compromising the effectiveness of healthcare pain support services and patient care. The aim of this study was to apply a simplified version of Ken Wilber’s All Quadrant All Levels (AQAL) framework to pain in a healthcare context to advance a biopsychosocial understanding. Utilising domain knowledge, the author mapped features of pain and coping to intrasubjective, intraobjective, intersubjective, and interobjective quadrants (perspectives), as well as levels of psychological development. Narratives were crafted to synthesize the findings of mapping with literature from diverse disciplines within the contexts of salutogenesis and a social model of health. The findings showed that AQAL-mapping enhanced contextual biopsychosocial coherence and exposed the conceptual error of reifying pain. Its utility lay in highlighting upstream influences of the painogenic environment, supporting the reconfiguration of pain within a social model of health, as exemplified by the UK’s Rethinking Pain Service. In conclusion, a simple version of the AQAL framework served as a heuristic device to develop an integral vision of pain, opening opportunities for health promotion solutions within a salutogenic context. Full article

Symmetry in IT system design is essential for improving efficiency, consistency, and manageability in data center operations. Symmetry guarantees that all system elements—be it hardware, software, or network configurations—are crafted to be consistent, thereby minimizing variability and streamlining operations. This principle is especially pertinent in cluster computing, where uniform server configurations facilitate efficient maintenance and consistent system performance. Symmetric designs reduce variations among nodes, alleviating performance discrepancies and resource imbalances commonly encountered in heterogeneous environments. This paper examines the advantages of symmetric configurations via an experimental analysis of the lifecycle management process. The findings indicate that clusters constructed with a symmetric server architecture enhance operational efficiency. From a lifecycle management standpoint, symmetry streamlines hardware provisioning and maintenance, diminishing complexities related to Day-1 and Day-2 operations. Furthermore, by guaranteeing consistent performance across all servers, symmetric designs facilitate a more predictable quality of service (QoS), reducing bottlenecks and improving overall system stability. Experimental results indicate that, when properly configured, symmetric clusters surpass asymmetric configurations in sustaining QoS, especially during peak loads or hardware failures, owing to their enhanced resource allocation and failover mechanisms. This research highlights the significance of symmetry as a fundamental principle in cluster-based data center architecture. Full article

This study examines the impact of ESG strategies on sustainable performance in multinational enterprises (MNEs), with a particular focus on the mediating role of internal market-oriented culture (IMOC) and the moderating effect of job crafting. To validate these key mechanisms, the study employs a stratified random sampling method to collect data from 614 employees across manufacturing, service, and technology-intensive industries. The data were analyzed using structural equation modeling (SEM) to test the hypothesized relationships. The empirical results demonstrate that ESG strategies significantly enhance sustainable organizational performance, with IMOC serving as a critical mediator linking ESG strategies to improved outcomes. Furthermore, job crafting strengthens the positive relationship between IMOC and sustainable performance, indicating that when employees proactively reshape their tasks and roles to align with ESG objectives, organizational sustainability is further enhanced. The influence of cultural context plays a crucial role in this process. The findings reveal that the effects of IMOC and job crafting are more pronounced in collectivist cultures (e.g., China and Japan), while in individualist cultures (e.g., the United States and Germany), performance improvements rely more heavily on individual incentives and feedback mechanisms. These insights provide practical guidance for MNE managers on how to tailor ESG strategies, IMOC, and job crafting approaches to specific regional and cultural settings. This study contributes to the literature by demonstrating how ESG strategies interact with internal cultural and behavioral mechanisms to drive sustainable performance in MNEs. It highlights the role of IMOC and job crafting as internal drivers that bridge ESG initiatives with tangible organizational outcomes. From a practical perspective, the findings offer actionable guidance for MNEs on how to design adaptive ESG strategies and employee engagement practices tailored to culturally diverse environments. These insights provide managers with a clear pathway to enhance employee-driven change and improve sustainable performance across global operations. Full article

This study examines the relationship between personal values embedded in brand-owned social media (OSM) messages and customer brand advocacy (CBA) using text mining data of tweets from Fortune 500 companies. We employ an economics approach to examine how personal values influence CBA across service and manufacturing brands. Our findings reveal that self-enhancement and conservation values significantly boost CBA, while self-transcendence and openness to change are less impactful. Furthermore, this study highlights that conservation values are more effective for manufacturing brands, whereas self-enhancement, self-transcendence, and openness to change drive higher CBA in service brands. This study contributes to the literature on social media marketing by providing actionable insights for brand managers, demonstrating that aligning OSM content with personal values can enhance CBA. This study offers a nuanced understanding of how personal values and industry characteristics intersect to influence consumer behavior, helping brands craft more effective and resonant social media strategies. Full article

Vending machines have evolved into a critical element of the intelligent healthcare service system. To enhance the precision of pill box detection counting and cater to the lightweight requirements of its internal embedded controller for deep learning frameworks, an enhanced lightweight YOLOv8n model is introduced. A dataset comprising 4080 images is initially compiled for model training and assessment purposes. The refined YOLOv8n-ShuffleNetV2 model is crafted, featuring the integration of ShuffleNetv2 as the new backbone network, the incorporation of the VoVGSCSP module to bolster feature extraction capabilities, and the utilization of the Wise-IoU v3 loss function for bounding box regression enhancement. Moreover, a model pruning strategy based on structured pruning (SFP) and layer-wise adaptive magnitude pruning (LAMP) is implemented. Comparative experimental findings demonstrate that the enhanced and pruned model has elevated the mean Average Precision (mAP) rate from 94.5% to 95.1%. Furthermore, the model size has been reduced from 11.1 MB to 6.0 MB, and the inference time has been notably decreased from 1.97 s to 0.34 s. The model’s accuracy and efficacy are validated through experiments conducted on the Raspberry Pi 4B platform. The outcomes of the experiments underscore how the refined model significantly amplifies the deployment efficiency of the deep learning model on resource-limited devices, thus greatly supporting the advancement of intelligent medicine management and medical vending machine applications. Full article

The science of gastronomy is a context-sensitive and qualitative application of human knowledge. While qualitative content analysis is a widely recognized research method, it is time-consuming, and its results heavily depend on the analyst’s expertise. This study addresses these challenges by employing automated content analysis (ACA), including tools like VOSViewer and AntConc, alongside qualitative methods from systemic functional linguistics (SFL). By analyzing 25 interviews with various professionals in gastronomy, the research aims to reduce subjectivity in interpreting data and uncover the essence of being a gastronomic chef. The findings highlight the complexity of a chef’s role, emphasizing creativity, collaboration, and innovation as key factors in delivering exceptional service and crafting the ultimate dining experience. Understanding a chef’s knowledge is crucial for enhancing service quality and comprehending consumer behavior, which is vital for service development. Full article

This study investigated the impact of mobile financial services on the shadow economy in Southern Africa countries and explored how regulatory quality moderates this relationship. Utilising panel data from 1993 to 2022, this study employed dynamic common-correlated effect (DCCE) and dynamic seemingly unrelated regression (DSUR) methods to assess long-run effects. The findings reveal that increased mobile financial services adoption markedly diminishes the scale of the underground economy by enhancing transaction transparency and accessibility, thus drawing more participants into the formal economy. The results are consistent across DCCE and DSUR estimations. Additionally, improvements in regulatory quality further diminish the shadow economy by bolstering trust and compliance within the financial system, suggesting that well-crafted regulations enhance the effectiveness of mobile financial services. Economic and financial sector developments also contribute to a reduced shadow economy, indicating that broader economic growth and advanced financial systems facilitate formal sector participation. Conversely, larger public sector expenditures appear to expand the shadow economy enterprises, likely due to inefficient resource allocation and increased fiscal burdens that push economic activities underground. Policy recommendations include the need to expand mobile financial services infrastructure, enhance financial literacy, and optimise financial regulatory frameworks to balance oversight with innovation encouragement. Full article

With the recent growth of the sharing economy, businesses offering shared-kitchen services are expanding rapidly. Due to the communal nature of these kitchens, there is a heightened need for systematic food safety management. However, existing research on blockchain applications has largely overlooked shared kitchens, a complex setting with numerous stakeholders and sensitivity to real-time kitchen conditions. This study addresses this gap by proposing a blockchain-based food safety management system for shared kitchens. The system’s functional requirements were meticulously outlined based on guidelines from South Korea’s Ministry of Food and Drug Safety. Key participants were identified as system users, and use cases were crafted in alignment with their responsibilities and roles to ensure effective safety management. Additionally, the blockchain system’s mechanisms for enhancing safety in shared kitchens were substantiated through specific use cases and detailed data structures, addressing issues related to forgery, alteration, and management challenges. This study also offers practical insights that can facilitate more structured safety management in shared-kitchen environments. Full article

Unmanned aerial vehicles (UAVs) have emerged as pivotal tools in emergency communication scenarios. In the aftermath of disasters, UAVs can be communication nodes to provide communication services for users in the area. In this paper, we establish a meticulously crafted virtual simulation environment and leverage advanced deep reinforcement learning algorithms to train UAVs agents. Notwithstanding, the development of reinforcement learning algorithms is beset with challenges such as sparse rewards and protracted training durations. To mitigate these issues, we devise an enhanced reward function aimed at bolstering training efficiency. Initially, we delineate a specific mountainous emergency communication scenario and integrate it with the particularized application of UAVs to undertake virtual simulations, constructing a realistic virtual environment. Furthermore, we introduce a supplementary shaped reward function tailored to alleviate the problem of sparse rewards. By refining the DQN algorithm and devising a reward structure grounded on potential functions, we observe marked improvements in the final evaluation metrics, substantiating the efficacy of our approach. The experimental outcomes underscore the prowess of our methodology in effectively curtailing training time while augmenting convergence rates. In summary, our work underscores the potential of leveraging sophisticated virtual environments and refined reinforcement learning techniques to optimize UAVs deployment in emergency communication contexts. Full article

Community-based medical rehabilitation encompasses diverse programs that cater to patients outside of inpatient settings, such as home rehabilitation, day rehabilitation centers, and ambulatory clinics. While inpatient rehabilitation principles are widely standardized, outpatient programs display significant variability influenced by healthcare models, local environments, economic constraints, and available resources. This narrative review aims to explore and synthesize the various models of non-inpatient rehabilitation services, evaluating their effectiveness, cost-efficiency, and patient satisfaction. The review also seeks to identify optimal practices and strategies to enhance community-based rehabilitation, alleviate the burden on inpatient facilities, and improve patient outcomes through multidisciplinary and patient-centered approaches. Additionally, the study examines the critical role of a professional program coordinator and the importance of effective clinical communication in outpatient rehabilitation. A comprehensive search of peer-reviewed literature was conducted across multiple databases, focusing on studies that examined community-based rehabilitation models. The findings suggest that community-based rehabilitation programs are generally more cost-effective than inpatient programs, with their success being heavily dependent on the intensity and timing of interventions. Multidisciplinary approaches and high-intensity rehabilitation have shown promise in improving patient quality of life, though their effectiveness varies by condition. Despite limited research, the involvement of a Physical and Rehabilitation Medicine (PRM) physician as a program coordinator appears vital for ensuring continuity of care. Moreover, effective clinical communication is essential, impacting all aspects of patient care and interprofessional collaboration, with continuous adaptation required to meet the evolving needs of diverse patient populations. Full article