Search Results (162)

Diabetes self-management education (DSME) programs are evidence-based interventions that improve glycemic control and self-care behaviors, yet their effectiveness may be limited by unaddressed health-related social needs (HRSN) (e.g., food insecurity, housing or utility instability, transportation barriers). This qualitative multiple case study examined the feasibility of integrating HRSN assessments into DSME delivery within three community-based organizations (CBOs) across urban and rural U.S. settings. Guided by the Consolidated Framework for Implementation Research, semi-structured interviews were conducted with 15 DSME facilitators and program leadership to identify contextual factors influencing implementation. Findings revealed that while DSME’s structured, manualized design promotes fidelity and client autonomy, it constrains responsiveness to the client’s HRSN. Facilitators expressed openness to integrating HRSN screening, particularly during intake, yet cited limited infrastructure, role clarity, and training as key barriers. CBOs were recognized as trusted, accessible spaces for holistic care, but growing expectations to address HRSN without adequate resources for referral created sustainability concerns. Participants recommended a parallel support model involving navigators or community health workers to manage HRSN screening and referrals alongside DSME sessions. Integrating HRSN assessment processes into DSME may enhance engagement, reduce attrition, and extend the reach of diabetes education to populations most affected by HRSN. However, successful implementation requires dedicated funding, workforce development, and cross-sector coordination. Findings underscore the importance of supporting CBOs as critical partners in bridging diabetes education and social care to advance whole-person, chronic disease management. Full article

Although DNS has been continuously extended to improve usability and security, its centralized, server-based architecture leaves fundamental limitations unresolved, including single points of failure (SPOF), susceptibility to censorship, and exposure to DDoS. This study examines blockchain-based DNS (BDNS) as an alternative proposed to mitigate these structural issues. We first synthesize prior research and systems on BDNS, and then conduct a comparative analysis using practical deployability as the primary criterion. Specifically, we selected seven representative BDNS projects, including Namecoin, Handshake, and Ethereum Name Service (ENS), and evaluated them under a common set of criteria: (i) the record model, finality, and TTL semantics; (ii) friction along real resolution paths involving resolvers, browsers, and gateways; and (iii) interoperability with the legacy DNS, including DNSSEC and DNS over TLS(DoT)/DNS over HTTPS(DoH), together with migration scenarios. The analysis indicates that many systems rely on gateways and client-side extensions, limiting native resolution paths. It further finds that finality latency, dependence on off-chain indexing and availability, and the interplay of key management and tokenomics design increase operational complexity and raise barriers to adoption. Building on these findings, the paper derives operational requirements and proposes a coexistence-first, five-layer migration framework that incrementally integrates BDNS while retaining the legacy DNS. This provides an incremental path toward a more resilient, inclusive, and secure global naming infrastructure. Full article

With the development of decentralized artificial intelligence (AI) networks, federated learning (FL) has received extensive attention for its ability to enable collaborative modeling without sharing raw data. However, existing methods are prone to convergence instability under non-independent and identically distributed (non-IID) conditions, lack robustness in adversarial settings, and have not yet sufficiently addressed fairness and incentive issues in multi-source heterogeneous environments. This paper proposes a Symmetry-Preserving Federated Learning (SPFL) framework that integrates blockchain auditing and fairness-aware incentive mechanisms. At the optimization layer, the framework employs group-theoretic regularization to maintain parameter symmetry and mitigate gradient conflicts; at the system layer, it leverages blockchain ledgers and smart contracts to verify and trace client updates; and at the incentive layer, it allocates rewards based on approximate Shapley values to ensure that the contributions of weaker clients are recognized. Experiments conducted on four datasets, MIMIC-IV ECG, AG News-Large, FEMNIST + Sketch, and IoT-SensorStream, show that SPFL improves average accuracy by about 7.7% compared to FedAvg, increases Jain’s Fairness Index by 0.05–0.06 compared to FairFed, and still maintains around 80% performance in the presence of 30% Byzantine clients. Convergence experiments further demonstrate that SPFL reduces the number of required rounds by about 30% compared to FedProx and exhibits lower performance degradation under high-noise conditions. These results confirm SPFL’s improvements in fairness and robustness, highlighting its application value in multi-source heterogeneous scenarios such as medical diagnosis, financial risk management, and IoT sensing. Full article

With the rapid deployment of intelligent transportation systems (ITS), the Internet of Vehicles (IoV) has become an increasingly vital component in the development of smart cities. However, the openness of IoV also gives rise to critical issues such as message security and identity privacy. Consequently, addressing message authentication in the IoV environment is a fundamental requirement for ensuring its sustainable and stable evolution. Firstly, this paper proposes an adaptive traffic authentication strategy (ATAS) By integrating traffic flow dynamics evaluation, traffic status scoring, time sensitivity assessment, and comprehensive strategy decision-making, the scheme achieves an effective balance between authentication efficiency and security in IoV scenarios. Secondly, to tackle the high overhead and security issues caused by multiple message transmissions in large-scale IoV application scenarios, this paper proposes a secure message transmission and authentication method based on the cloud-edge-client collaborative architecture. Leveraging aggregate message authentication code (AMAC) technology, this method validates both the authenticity and integrity of messages, effectively reducing communication overhead while maintaining reliable authenticated transmission. Finally, this paper builds an IoV co-simulation experimental environment using the SUMO 1.19.0, OMNeT++ 6.0.3, and Veins 5.0.0 software platforms. It simulates the interactive authentication process among vehicles, Road Side Units (RSUs), and the cloud platform, as well as the effects of traffic response strategies under different scenarios. The results demonstrate the potential of IoV authentication technology in improving traffic management efficiency, optimizing road resource utilization, and enhancing traffic safety, providing strong support for the secure communication and efficient management of IoV. Full article

The construction industry is struggling to resolve the issue of the enormous quantity of waste produced during construction processes, which impacts the performance and sustainability of projects. Causes of waste generation have been studied by researchers to formulate waste minimization strategies for these projects. The research on waste in highway infrastructure projects and waste causes specific to the roles and competencies of project team members is inadequate. This quantitative study addresses this gap by evaluating the influence of project managers (PMs) in minimizing CW through a structured questionnaire survey administered to 300 professionals, yielding 129 valid responses (43% response rate). The results indicate that 8.5% of construction materials are wasted in highway projects. Among four key project stakeholders (PM, quantity surveyor, designer, and client), PMs were rated as having the most significant impact on waste minimization (mean Likert score: 4.5/5). Using the Relative Importance Index (RII), the study identified the top five waste causes linked to PM competencies: faulty work requiring the work to be carried out again (RII = 0.742), wrong construction methods (0.734), lack of awareness (0.731), poor supervision (0.721), and poor material planning (0.706). A waste minimization framework is proposed, linking each of these causes to specific PM competencies and actionable strategies. These findings provide empirical support for targeting PM training and resource planning to reduce material waste in highway construction projects. Full article

Purpose: The construction industry is now experiencing a thorough transformation through digital technologies, especially with building information modeling (BIM). Despite significant BIM advantages, most construction projects suffer from low BIM performance due to the fragmented BIM use mode. To facilitate lifecycle-integrated BIM implementation, this study demonstrates that introducing BIM lifecycle service providers (BLSPs) is feasible and offers significant improvements in terms of BIM benefits. Hence, this study proposes a customized framework to select BLSPs. Approach: This study utilized both qualitative and quantitative methods. It first adopted semi-structured interviews as part of the qualitative method to deduce the initial criteria for BLSPs’ selection. 30 interviews were conducted iteratively with managers proficient and experienced in selecting BLSPs, through which 25 initial criteria were identified. Then, as the basis of the applied quantitative method, a questionnaire survey was used to evaluate these criteria by determining the critical ones, identifying the latent factor groupings, and assigning criteria weights. Subsequently, an assessment framework was established. Finally, the study was in favor of eight construction projects, highlighting the practicality and validity of the framework. Findings: The results depicted that project BIM service capability is a primary factor for BLSPs’ selection. Within this factor, several specialized criteria need to be considered, such as “boundary spanning competence of the BIM manager” and “BIM service plans with lifecycle cognition.” Meanwhile, “past innovative BIM service practices” and “BIM research and development (R&D)” that originate in corporate innovation capacity were emphasized when selecting BLSPs. Furthermore, for holistic assessment and recognizing the peculiarities of digital BIM service, the study found that criteria like “Privacy and security” and “Backup system” are required, which demonstrate BIM service reliability. Originality/value: This study expands on the conventional partner selection frameworks in the construction sector and thus defines and validates a tailored one for BLSPs’ selection. Moreover, drawing such a reference solution from the framework, the study enables the selection of appropriate BLSPs for clients. Full article

The successful integration of Terrestrial and Non-Terrestrial Networks (T/NTNs) in 6G is poised to revolutionize demanding domains like Earth Observation (EO) and Intelligent Transportation Systems (ITSs). Still, it requires Distributed Machine Learning (DML) frameworks that are scalable, private, and efficient. Existing methods, such as Federated Learning (FL) and Split Learning (SL), face critical limitations in terms of client computation burden and latency. To address these challenges, this paper proposes a novel hierarchical DML paradigm. We first introduce Federated Split Transfer Learning (FSTL), a foundational framework that synergizes FL, SL, and Transfer Learning (TL) to enable efficient, privacy-preserving learning within a single client group. We then extend this concept to the Generalized FSTL (GFSTL) framework, a scalable, multi-group architecture designed for complex and large-scale networks. GFSTL orchestrates parallel training across multiple client groups managed by intermediate servers (RSUs/HAPs) and aggregates them at a higher-level central server, significantly enhancing performance. We apply this framework to a unified T/NTN architecture that seamlessly integrates vehicular, aerial, and satellite assets, enabling advanced applications in 6G ITS and EO. Comprehensive simulations using the YOLOv5 model on the Cityscapes dataset validate our approach. The results show that GFSTL not only achieves faster convergence and higher detection accuracy but also substantially reduces communication overhead compared to baseline FL, and critically, both detection accuracy and end-to-end latency remain essentially invariant as the number of participating users grows, making GFSTL especially well suited for large-scale heterogeneous 6G ITS deployments. We also provide a formal latency decomposition and analysis that explains this scaling behavior. This work establishes GFSTL as a robust and practical solution for enabling the intelligent, connected, and resilient ecosystems required for next-generation transportation and environmental monitoring. Full article

Norepinephrine (NE) and dopamine (DA) are vasopressors used to treat vasodilatory shock for decades, and norepinephrine is considered the preferred first-line vasopressor in human patients. However, there is a dearth of evidence to support specific treatment recommendations for the management of hypotensive, non-anesthetized, fluid-replete dogs. The objective of this study was to compare the effects of NE and DA on systolic blood pressure (SBP), heart rate, and shock index (SI) when used as first-line vasopressors for the treatment of vasodilatory shock in dogs. Twenty-four client-owned canine patients of similar age, sex, and weight with hypotension necessitating vasopressor therapy were randomized to receive NE or DA; attending clinicians were blinded. Twenty-two dogs were included in the final analysis (10 in the NE group and 12 in the DA group). Seventy-seven percent of all dogs achieved normotension. In both groups, SBP increased significantly compared to baseline (p = 0.0004 in the NE group and p = 0.006 in the DA group). The SI also decreased in both groups compared to baseline values (p = 0.01 in the NE group and p = 0.01 in the DA group). The heart rate in the NE group was higher than in the DA group at timepoints 6–10 (p = 0.023). Both NE and DA cause an increase in blood pressure and a decrease in SI in dogs with vasodilatory hypotension. Further investigation is warranted to determine if there are differences between NE and DA or the requirement for a second vasopressor, occurrence of arrhythmias, length of stay, and survival. Full article

This research focuses on developing neural network-based models for predicting time and cost overruns in construction projects during the construction phase, incorporating sustainability considerations. Previous studies have identified seven key performance areas that affect the final outcome: productivity, quality, time, cost, safety, team satisfaction, and client satisfaction. Although the interconnections among these performance areas are recognized, their exact relationships and impacts are not fully understood. Hence, the utilization of a neural networks proves to be highly beneficial in predicting the outcome of future construction projects, as it can learn from data and identify patterns, without requiring a complete understanding of these mutual influences. The neural network was trained and tested on the data collected on five completed construction projects, each analyzed at three distinct stages of execution. A total of 182 experiments were conducted to explore different neural network architectures. The most effective configurations for predicting time and cost overruns were identified and evaluated, demonstrating the potential of neural network-based approaches to support more sustainable and proactive project management. The time overrun prediction model demonstrated high accuracy, achieving a MAPE of 10.93%, RMSE of 0.128, and correlation of 0.979. While the cost overrun model showed a lower predictive accuracy, its MAPE (166.76%), RMSE (0.4179), and correlation (0.936) values indicate potential for further refinement. These findings highlight the applicability of neural network-based approaches in construction project management and their potential to support more proactive and informed decision-making. Full article

Digital transformation in healthcare has introduced data privacy challenges, as hospitals struggle to protect patient information while adopting digital technologies such as AI, IoT, and cloud more rapidly than ever before. The adoption of powerful third-party Machine Learning as a Service (MLaaS) solutions for disease prediction has become a common practice. However, these solutions offer significant privacy risks when sensitive healthcare data are shared externally to a third-party server. This raises compliance concerns under regulations like HIPAA, GDPR, and Australia’s Privacy Act. To address these challenges, this paper explores a decentralized, privacy-preserving approach to train the models among multiple healthcare stakeholders, integrating Federated Learning (FL) with Homomorphic Encryption (HE), ensuring model parameters remain protected throughout the learning process. This paper proposes a novel Homomorphic Encryption-based Adaptive Tuning for Federated Learning (HEAT-FL) framework to select encryption parameters based on model layer sensitivity. The proposed framework leverages the CKKS scheme to encrypt model parameters on the client side before sharing. This enables secure aggregation at the central server without requiring decryption, providing an additional layer of security through model-layer-wise parameter management. The proposed adaptive encryption approach significantly improves runtime efficiency while maintaining a balanced level of security. Compared to the existing frameworks (non-adaptive) using 256-bit security settings, the proposed framework offers a 56.5% reduction in encryption time for 10 clients and 54.6% for four clients per epoch. Full article

Thoracic epidural anesthesia (TEA) is widely used in human medicine to provide effective perioperative analgesia, yet its application in veterinary species—particularly cats—remains underexplored. This retrospective case study describes the use of TEA in nine client-owned cats undergoing major surgeries. All cats received a single epidural injection of 0.2 mL/kg of 0.5% ropivacaine combined with 0.1 mg/kg morphine at the T12–T13 interspace using a 25 G × 25 mm Quincke needle. Intraoperative physiological parameters were continuously monitored, and postoperative analgesia was assessed using a validated pain scale. Only one cat exhibited inadequate analgesic coverage, likely due to TEA failure. Of the nine cats, seven required minimal to no intraoperative rescue analgesia, while five received postoperative opioids on the day following surgery. Hemodynamic stability was observed in most cases, with no significant complications reported. These findings suggest that TEA is a feasible and effective technique for perioperative pain management in cats undergoing major surgery. Further prospective studies are warranted to confirm these initial findings and investigate the safety of the technique in a larger population. Full article

The impact of cultural identity on music performance anxiety (MPA) is under-researched. This retrospective case study explores the treatment of a professional musician in her 30s who presented with MPA associated with performing music related to her estranged father’s cultural background. The case formulation identified attachment ruptures and negative cognitions associated with her mixed cultural heritage that contributed to an experience of imposterism—referred to in lay literature as ‘racial imposter syndrome’ (RIS). It was hypothesized that RIS served to perpetuate her MPA. An attachment-based approach and Acceptance and Commitment Therapy framework was adopted, drawing on evidence-based treatment for MPA and mixed heritage individuals. The Depression Anxiety Stress Scale-21 (DASS-21), Outcome Rating Scale (ORS) and Session Rating Scale (SRS) were used as outcome measures. These measures fluctuated throughout the therapy. While improvements were observed in depression scores midway through treatment, elevated stress and depression scores at the conclusion of treatment were understood to reflect situational factors related to financial and housing precarity. Nonetheless, at the conclusion of treatment, the client showed improvement in managing MPA, evidenced by her progress in recording an album and reengagement with public performances. This case study adds to the limited research on treating MPA in racially minoritized and mixed-race individuals, Further research is required across larger and more diverse samples to better understand the relationship between MPA and RIS and to develop effective interventions. Full article

Heat shock protein 90 (HSP90) is a molecular chaperone that plays a pivotal role in the stabilization and functional activation of numerous oncoproteins and signaling molecules essential for cancer cell survival and proliferation. Despite the extensive development and clinical evaluation of HSP90 inhibitors, their therapeutic potential as monotherapies has been limited by suboptimal efficacy, dose-limiting toxicity, and the emergence of drug resistance. Recent studies have demonstrated that combination therapies involving HSP90 inhibitors and other anticancer agents such as chemotherapeutics, targeted therapies, and immune checkpoint inhibitors can enhance anticancer activity, overcome resistance mechanisms, and modulate the tumor microenvironment. These synergistic effects are mediated by the concurrent degradation of client proteins, the disruption of signaling pathways, and the enhancement of antitumor immunity. However, the successful clinical implementation of such combination strategies requires the careful optimization of dosage, administration schedules, toxicity management, and patient selection based on predictive biomarkers. In this review, we provide a comprehensive overview of the mechanistic rationale, preclinical and clinical evidence, and therapeutic challenges associated with HSP90 inhibitor-based combination therapies. We also discuss future directions leveraging emerging technologies including multi-omics profiling, artificial intelligence, and nanoparticle-mediated delivery for the development of personalized and effective combination regimens in oncology. Full article

An electronic nose is a device capable of characterizing samples of substances and products by their aroma. The development of such devices relies on a series of non-specific sensors that react to gases and generate different signals, which can be used for compound identification and sample classification. The deployment of such devices often requires the possibility of having remote access over the Internet to manage their operation and to collect the sampled data. In this context, the application of web technologies to the monitoring and supervision of these systems connected to the Internet, which can be considered as an Internet of Things (IoT) device, offers the advantage of not requiring the development of client-side applications. Users can employ a browser to connect to the IoT device and monitor or control its operation. Moreover, web design enables the development of cross-platform web monitoring systems. In addition, the inclusion of the MQTT protocol and the utilization of a virtual private network (VPN) enable a secure transmission and collection of the sampled data. In this work, all these technologies have been applied in the development of a system to manage and collect data to monitor rot in lemons treated with sodium benzoate before harvest. Full article

Non-Intrusive Load Monitoring (NILM), a technique that extracts appliance-level energy consumption information through analysis of aggregated electrical measurements, has become essential for smart grids and energy management applications. Given the increasing diversification of electrical appliances, real-time NILM systems require continuous integration of knowledge from new client-side appliance data to maintain monitoring effectiveness. However, current methods face challenges with inter-client knowledge conflicts and catastrophic forgetting in distributed multi-client continual learning scenarios. This study addresses these challenges by proposing a confidence-based collaborative distributed continual learning framework for NILM. A lightweight layer-wise dual-supervised autoencoder (LWDSAE) model is initially designed for smart meter deployment, supporting both load identification and confidence-based collaboration tasks. Clients with learning capabilities generate new models through one-time fine-tuning, facilitating collaboration among client models and enhancing individual client load identification performance via a confidence judgment method based on signal reconstruction deviations. Furthermore, an anomaly sample detection-driven model portfolios update method is developed to assist each client in maintaining optimal local performance under model quantity constraints. Comprehensive evaluations on two public datasets and real-world applications demonstrate that the framework achieves sustained performance improvements in distributed continual learning scenarios, consistently outperforming state-of-the-art methods. Full article