Search Results (271)

Universities play a critical role in knowledge creation and technological innovation, serving as key drivers of regional development. However, existing research has paid limited attention to the mechanisms through which university innovation inputs translate into firm-level performance, particularly in the context of science and technology corridors in emerging economies. This study investigates how university innovation resources affect enterprise performance in the G60 Science and Technology Corridor within China’s Yangtze River Delta, one of the country’s most dynamic innovation regions. Using a panel dataset of 55 universities across nine cities from 2008 to 2017, we employ spatial analysis and fixed-effects panel regression models to examine the relationship between university innovation inputs and firm performance and further explore the mediating roles of local human capital and firm R&D investment. The results show that university innovation inputs significantly enhance enterprise performance, although excessive human resource inputs exhibit a negative effect on both short-term and long-term outcomes. Local human capital and firm R&D investment serve as key mediating mechanisms, with input and output resources influencing enterprise performance through distinct pathways. Heterogeneity analysis reveals that non-state-owned enterprises and small- and medium-sized enterprises derive greater long-term benefits from university resources. These findings contribute to the literature by clarifying the conceptual distinction between university innovation inputs and outputs, and by demonstrating the micro-level mechanisms—R&D investment and human capital—through which university-generated knowledge affects firm performance. The results also provide empirical evidence from an emerging economic context, extending the applicability of knowledge spillover and absorptive capacity theories. Policy implications include optimizing university human resource allocation, strengthening university–enterprise collaboration, and providing targeted support for non-state-owned enterprises and SMEs. Future research may extend the analysis to include institutional factors and university heterogeneity. Full article

Transmission towers, as critical infrastructure in power systems, are frequently threatened by multiple hazards such as strong winds and flood scour. Traditional structural health monitoring methods face limitations in data feedback timeliness and mechanical interpretation, making real-time condition awareness and early warning under disaster scenarios challenging. To address these issues, this paper proposes a digital twin framework for transmission tower structures, integrating Building Information Modeling (BIM), Internet of Things (IoT) technology, and the Finite Element Method (FEM) for structural health monitoring and visual warning under wind loads and flood scour effects. The framework achieves cross-platform collaboration through the FEM Open Application Programming Interface (OAPI) and Python scripts. In the physical domain, fluctuating wind loads are simulated based on the Davenport spectrum, flood scour depth is modeled using the HEC-18 formulation, and foundation constraint degradation is represented through nonlinear spring stiffness reduction. In the FEM domain, dynamic time-history analyses are conducted to obtain structural responses. In the BIM domain, a three-level warning mechanism based on stress change rate (ΔR) is established to achieve intuitive rendering and dynamic feedback of structural damage. A 44.4 m high latticed angle steel tower is employed as the case study for validation. Results demonstrate that the simulated wind spectrum closely matches the theoretical target spectrum, confirming the validity of the load input. A critical scour evolution threshold of 40% is identified, beyond which the first two natural frequencies exhibit nonlinear decay with a maximum reduction of 80.9%. Non-uniform scour induces significant load transfer, with axial forces at leeside nodes increasing from 27 kN to 54 kN. During the 0–60 s wind loading process, BIM visualization accurately captures the full stress evolution from the tower base to the upper structure, showing excellent agreement with FEM results. The proposed framework establishes a closed-loop interaction mechanism of “physical sensing–digital simulation–visual warning”, effectively enhancing the timeliness and interpretability of structural health monitoring for transmission towers under multiple hazards, providing an innovative approach for intelligent disaster prevention in power infrastructure. Full article

With the widespread adoption of various types of Unmanned Aerial Vehicles (UAVs), their non-compliant operations pose a severe challenge to public safety, necessitating the urgent identification and detection of UAV targets. However, in complex backgrounds, UAV targets exhibit small-scale dimensions and low contrast, coupled with extremely low signal-to-noise ratios. This forces conventional target detection methods to confront issues such as feature convergence, missed detections, and false alarms. To address these challenges, we propose a Multi-View Feature Fusion Network (MVFF) that achieves precise identification of small, low-contrast UAV targets by leveraging complementary multi-view information. First, we design a collaborative view alignment fusion module. This module employs a cross-map feature fusion attention mechanism to establish pixel-level mapping relationships and perform deep fusion, effectively resolving geometric distortion and semantic overlap caused by imaging angle differences. Furthermore, we introduce a view feature smoothing module that employs displacement operators to construct a lightweight long-range modeling mechanism. This overcomes the limitations of traditional convolutional local receptive fields, effectively eliminating ghosting artifacts and response discontinuities arising from multi-view fusion. Additionally, we developed a small object binary cross-entropy loss function. By incorporating scale-adaptive gain factors and confidence-aware weights, this function enhances the learning capability of edge features in small objects, significantly reducing prediction uncertainty caused by background noise. Comparative experiments conducted on a multi-perspective UAV dataset demonstrate that our approach consistently outperforms existing state-of-the-art methods across multiple performance metrics. Specifically, it achieves a Structure-measure of 91.50% and an F-measure of 85.14%, validating the effectiveness and superiority of the proposed method. Full article

Background/Objectives: Telerehabilitation expands access to specialized neuropediatric physiotherapy for families facing barriers related to geography, work, or caregiving. This systematic review aimed to summarize the evidence regarding the effects of telerehabilitation on gross motor function (GMF) in children with cerebral palsy (CP). Methods: An electronic search was conducted in the following databases: PubMed, Web of Science, Embase, and the Cochrane Library; Google Scholar was consulted for additional literature. The search targeted randomized and non-randomized intervention studies evaluating the effects of telerehabilitation on GMF in children with CP at various levels of the Gross Motor Function Classification System (GMFCS), as well as related functional outcomes. The risk of bias in the included studies was assessed using the original Cochrane Collaboration risk of bias tool. The certainty of evidence was graded according to the GRADE framework. Results: Five studies involving 152 children were included, with CP aged 2.5 to 17 years. Telerehabilitation programs varied in duration, frequency, and type of intervention, as well as in caregiver involvement, comparator conditions, and outcome measures. The included studies suggested potential benefits in GMF and related functional outcomes; however, findings were heterogeneous, and superiority over comparison conditions was not consistently demonstrated. Conclusions: Although the reviewed studies suggest that telerehabilitation may be a feasible and potentially beneficial approach for children with CP, the limited number of studies and variability of interventions highlight the need for caution in interpreting these findings. Further high-quality studies with standardized outcome reporting are needed to clarify its contribution to GMF. Full article

Background: Mental health needs are rising globally, while workforce shortages constrain access to timely care. Israel launched formal training for Psychiatric–Mental Health Nurse Practitioners (PMHNPs) in 2023 as part of broader efforts to strengthen the public mental health system. This narrative review provides a focused synthesis of international and Israeli literature on PMHNP roles, models of practice, outcomes, and implementation considerations relevant to the Israeli context. Methods: We conducted a narrative, non-systematic literature review of international and Israeli literature on Psychiatric–Mental Health Nurse Practitioners (PMHNPs). Searches were conducted in PubMed/MEDLINE, CINAHL, PsycINFO, and Scopus (January 2000–December 2024), alongside targeted policy and regulatory documents. Eligible sources addressed NP/PMHNP roles, scope of practice, clinical and service outcomes, implementation processes, workforce implications, or policy considerations in high-income health systems. Findings were synthesized thematically. Results: Across the reviewed literature, particularly in primary care and community-based settings, PMHNP/NP-delivered care was generally associated with comparable outcomes on selected quality and safety indicators, alongside improved accessibility, continuity, and high patient satisfaction. Successful implementation depended on regulatory clarity, organizational readiness, interprofessional collaboration, and the development of a clear professional identity. In Israel, the role is emerging within a cautious regulatory framework and may face early barriers related to role ambiguity, variable organizational support, and limited stakeholder awareness. Conclusions: PMHNP implementation may offer an important strategy for strengthening mental health service capacity in Israel. However, the extent of its contribution will depend on regulatory clarity, organizational support, implementation quality, and future empirical evaluation in the Israeli context. Full article

Most camera view planning algorithms are employed in exploration tasks that maximise information gain, but few address the specific challenge of observing targeted surface areas with optimal image quality. This paper presents a novel camera view planning algorithm designed for dermoscopic mole mapping, which is crucial for early melanoma detection. Traditional full-body scanners, though beneficial, suffer from fixed camera positions that can compromise image quality due to varying body contours and patient sizes. Our algorithm addresses this limitation by dynamically optimizing the camera position on a set of collaborative robot (cobot) arms to enhance image resolution, safety, and viewing angles during skin examinations. The proposed method formulates the problem as a non-linear least-squares optimisation that ensures no camera occlusion and a safe distance from the end effector encapsulating the camera to the patient while adjusting the pose of the camera based on the topography of the body. This approach not only maintains optimal imaging conditions by considering resolution and angle of incidence but also prioritises patient safety by preventing physical contact between the camera and the patient. Extensive testing demonstrates that our algorithm adapts effectively to different body shapes and sizes, ensuring high-resolution images across various patient demographics. Moreover, the integration of our camera view planning algorithm into an intelligent dermoscopy system has shown promising results in improving the efficiency and geometric quality of dermoscopic image acquisition, which could lead to more reliable and faster diagnoses. This technology holds significant potential to transform melanoma screening and diagnosis, providing a scalable, safer, and more precise approach to dermatological imaging. Full article

To address the inherent limitations in the robustness of fully autonomous unmanned aerial vehicle (UAV) visual perception and the high cognitive workload associated with manual control, this paper proposes a human-in-the-loop brain–computer interface (BCI) control framework. The system integrates steady-state visual evoked potential (SSVEP) with deep learning techniques to create a spatio-temporally dynamic interaction paradigm, enabling real-time alignment between visual targets and frequency stimuli. At the perception level, an enhanced YOLOv11 network incorporating partial convolution (PConv) and shape intersection over union (Shape-IoU) loss is developed and coupled with the DeepSort multi-object tracking algorithm. This configuration ensures high-speed execution on edge computing platforms while maintaining stable stimulus coverage over dynamic targets, thus providing a robust visual induction environment for EEG decoding. At the neural decoding level, an enhanced task-discriminant component analysis (TDCA-V) algorithm is introduced to improve signal detection stability within non-stationary flight conditions. Experimental results demonstrate that within the predefined fixation task window, the system achieves 100% success in maintaining target identity (ID). The BCI system achieved an average command recognition accuracy of 91.48% within a 1.0 s time window, with the TDCA-V algorithm significantly outperforming traditional spatial filtering methods in dynamic scenarios. These findings demonstrate the system’s effectiveness in decoupling human cognitive intent from machine execution, providing a robust solution for human–machine collaborative control. Full article

Credit default prediction from firm-level accounting statements is central to risk management, yet the underlying financial data are highly sensitive and often siloed across banks, auditors, and platforms. Federated learning (FL) offers a practical route to collaborative modeling without centralizing raw records, but standard FL optimization can converge slowly under severe client heterogeneity, heavy-tailed accounting features, and label imbalance typical of default events. This paper proposes FedQuAD, a novel fast-converging FL algorithm that couples (i) quasi-Newton curvature aggregation on the server with a lightweight limited-memory update to accelerate global progress, (ii) a proximal variance-reduced local solver that stabilizes client drift under non-IID accounting distributions, and (iii) federated robust standardization of tabular financial ratios via secure aggregated quantile statistics to mitigate scale instability and outliers. FedQuAD is communication-efficient by design: It transmits compact gradient and curvature sketches and adapts local computation to each client’s stochasticity and drift. We provide convergence guarantees for strongly convex default-risk objectives (logistic and calibrated GLM losses) under bounded heterogeneity, and extend the analysis to nonconvex deep tabular models via expected stationarity bounds. Experiments on public credit-risk benchmarks with simulated cross-silo (institutional) partitions demonstrate that FedQuAD reaches target AUC and calibration error with substantially fewer communication rounds than representative baselines while maintaining privacy constraints compatible with secure aggregation and optional client-level differential privacy accounting. Full article

Introduction: Oncological therapies have significantly improved patient outcomes but are increasingly associated with renal toxicity, which can markedly influence therapeutic decisions. Integrating early identification of kidney injury into clinical workflows is essential for personalized medicine, allowing treatment tailoring based on individual risk profiles. Aim: To evaluate the incidence of acute kidney injury (AKI) and chronic kidney Disease (CKD); assess indices of renal function recovery in patients who developed AKI; and investigate the incidence of renal immune-related adverse events (irAEs) in patients receiving immunotherapy. Materials: Renal function, serum electrolytes, inflammatory markers, blood gas analysis, and urinalysis were evaluated at baseline before oncological therapy (T0), after approximately 2 weeks (T1), and after 3 months (T2). Results: Seventy patients were analyzed (median age 71.5 years). AKI occurred in 43 patients (61.4%) and CKD in 18 (25.7%). Patients receiving immunotherapy displayed significantly higher blood urea nitrogen (p < 0.01) and creatinine (p < 0.01) levels compared to those undergoing traditional therapies (targeted therapy and chemotherapy). Treatment discontinuation was required in 14 (56%) immunotherapy patients versus 7 (19.4%) receiving traditional therapy (anti-VEGF and cisplatin) (p < 0.01). Among 25 immunotherapy-treated patients, 13 (52%) developed immune-related adverse events (irAEs). Patients with irAEs predominantly experienced AKI (92.3%), whereas those without irAEs showed both AKI and CKD (44.4%) (p < 0.01). Treatment discontinuation occurred in 84.6% of patients with irAEs compared to 11.1% without irAEs (p < 0.001). Conclusions: We showed a high incidence of AKI and CKD among cancer patients; in particular, the majority of patients receiving immunotherapy presented irAEs. CKD also occurs in association with comorbidities, such as previous use of NSAIDs, investigations with contrast agents and episodes of AKI on CKD determined by drugs. It seems necessary for there to be multidisciplinary collaboration between oncologists and nephrologists to individualize treatment plans; thus allowing the non-suspension of therapy, which positively influences the prognosis of patients. Full article

Background/Objectives: Nurse–mother communication is central to maternal participation in Neonatal Intensive Care Units (NICUs), yet high acuity and workflow rhythms can compress dialogue and weaken shared understanding. This study used Communication Accommodation Theory and the Transactional Model of Stress and Coping to explain multilevel drivers of communication barriers and to co-produce feasible improvement strategies. Methods: A dyadic qualitative design was conducted across four Level III NICUs. Data were triangulated from 37 semi-structured interviews (18 mothers and 19 nurses, recruited through purposive maximum-variation sampling), approximately 40 h of non-participant observation, and 12-unit documents. A team-based codebook thematic analysis was applied, integrating observational logs with interview and document data to refine patterns and mechanisms. Results: A context-produced pattern of interactional compression was identified. Mothers contributed 2 or fewer speaking turns in 21/30 logged bedside encounters and were present in 13/16 observed round episodes, speaking in 5/13 of those episodes. Interpretability and language access gaps were common: unexplained technical terms occurred in 24/46 logged interactions; teach-back prompts occurred in 7/18 education encounters; professional interpreters were present in 3/9 language-discordant events. Three participation configurations described coping-linked engagement: threat–compression (n = 8), convergence-to-coping (n = 6), and resource-scaffolded participation (n = 4). In co-production, stakeholders co-produced (i.e., collaboratively identified and prioritized) three mechanism-targeted changes: protected post-round question-and-answer time incorporating teach-back, standardized visual “mini-packs,” and 24/7 interpreter access. Conclusions: Nurse–mother communication in NICUs can be structurally compressed by workload rhythms and uneven interpretability supports. Co-produced organizational scaffolds may expand opportunities for accommodation, comprehension verification, and equitable maternal participation. Full article

The introduction of modern anticancer therapies, including targeted therapies (TTs), immune checkpoint inhibitors (ICIs), and antibody–drug conjugates (ADCs), has significantly improved survival across a wide range of malignancies. At the same time, these agents have expanded the spectrum of treatment-related adverse events, with ocular toxicities emerging as a clinically relevant and increasingly recognized complication. Ocular adverse events may affect multiple anatomical structures, including the ocular surface, cornea, anterior and posterior segments, and optic nerve, often reflecting drug class-specific biological mechanisms. The pathogenesis of ocular toxicity is multifactorial and includes on-target inhibition of signaling pathways expressed in ocular tissues, off-target effects on rapidly renewing epithelia, non-specific uptake of cytotoxic payloads in ADCs, immune-mediated inflammation associated with ICIs, and microvascular dysregulation observed with selected targeted agents, such as mitogen-activated protein kinase (MEK) inhibitors. Because ocular adverse events are inconsistently reported in clinical trials and frequently described through case reports or pharmacovigilance data, their true incidence is likely underestimated and management strategies remain heterogeneous. This narrative review provides an overview of the epidemiology, biological mechanisms, and clinical manifestations of ocular toxicities associated with contemporary anticancer therapies. In addition, it offers practical, mechanism-based recommendations for prevention, monitoring, and stepwise management, emphasizing the importance of multidisciplinary collaboration to preserve visual function while maintaining effective oncologic treatment. Full article

Current research lacks systematic understanding of cross-scale correlations between micro-texture geometry and macro-lubrication behavior. This study presents a multi-scale collaborative optimization methodology for gear Micro-Textured Meshing Interface (MTMI). An objective function targeting macroscopic interfacial performance is formulated, and a topology optimization strategy is employed to achieve optimal MET configuration. The homogenization analysis captures the modulating effects of MET on local flow and stress fields, while topology optimization transcends conventional parametric geometric constraints, enabling the generation of non-regular MET topological patterns tailored to complex operating conditions, thereby ensuring optimal macroscopic ASLBC. The proposed scheme is validated through numerical simulations of two representative problems capturing distinct lubrication regimes: (1) IEL, characterizing transient load-bearing dynamics governed by temporally evolving MET configurations; and (2) ASLBC, elucidating steady-state load-bearing capacity modulation via spatially heterogeneous MET distributions. A Taylor expansion-based surrogate model is developed to efficiently explore the MET configuration design space, significantly enhancing computational efficiency and solution accuracy for multi-scale optimization. While the gradient-based algorithm cannot guarantee global optimality, extensive numerical simulations and cross-validation studies demonstrate consistent convergence toward high-performance MET configurations, with sensitivity analyses of design parameters further confirming the engineering applicability of the optimized solutions. Full article

Background: Pediatric oral mucosal lesions are common and may indicate local or systemic disease, yet their recognition in primary healthcare often depends on non-dental professionals. Aim: To assess the preparedness of dentists, pediatricians, and family/general practitioners for pediatric oral mucosal conditions based on self-assessed diagnostic confidence, clinical management, and referral behavior. Methods: An online cross-sectional survey was conducted among 632 primary healthcare professionals (dentists: n = 262; family/general practitioners: n = 278; pediatricians: n = 92). The questionnaire assessed clinical exposure, self-assessed knowledge, diagnostic confidence, management practices, and referral patterns. Data were analyzed using chi-square or Fisher’s exact test and the Kruskal–Wallis test (p < 0.05). Results: Dentists reported significantly higher self-assessed knowledge and diagnostic confidence than pediatricians and family/general practitioners (p < 0.001). Good self-assessed knowledge of pediatric oral health was reported by 26.3% of dentists, compared with 7.9% of family/general practitioners and 6.5% of pediatricians. While most pediatricians (80.4%) and family/general practitioners (77.0%) reported routinely examining the oral cavity in children, independent treatment of oral mucosal lesions was more frequently reported by dentists (75.2%) than by pediatricians (52.2%) or family/general practitioners (70.9%) (p < 0.001). Referral patterns differed between groups, and willingness to attend future pediatric oral health education was high across all professionals (75.0–84.2%). Conclusions: Dentists demonstrated higher diagnostic confidence in pediatric oral mucosal lesions than pediatricians and family/general practitioners, who more often relied on referral. These findings support the value of targeted education and strengthened interdisciplinary collaboration in primary pediatric healthcare. Full article

Tracking highly maneuvering, non-cooperative UAVs poses significant challenges due to rapid and unpredictable changes in target dynamics. Under such conditions, traditional single-model filters often fail to maintain reliable state estimates, resulting in degraded tracking performance. Multiple-Model Kalman Filter (MMKF) approaches, including the Generalized Pseudo Bayesian (GPB1) and Interacting Multiple-Model (IMM) algorithms, improve robustness by simultaneously considering multiple candidate motion models and weighting them according to the observed target behavior. Adaptive strategies, such as ${\chi }^2$-test-based or t-test-based methods, further enhance performance by dynamically responding to changes in maneuvering patterns. This paper presents a multi-criteriacomparative assessment of four MMKF formulations—GPB1, IMM, ${\chi }^2$-test-based, and t-test-based filters—under a consistent modeling and simulation framework. Particular emphasis is placed on systematically analyzing the role of the transition probability matrix (TPM), investigating how fixed, adaptive, and TPM-free strategies affect estimation accuracy, robustness to noise, and mode-identification performance. Beyond conventional Root Mean Square Error (RMSE) metrics, the filters’ comparison is carried out through confusion matrices and dwell time analysis to highlight performance nuances and trade-offs. This allows to establish which filter formulation is preferable in different operational conditions. Full article

Background/Objectives: Blood transfusions save lives and improve health, but there has been a global blood shortage and a significant disparity in blood donation worldwide in recent years. As future healthcare professionals who educate and promote blood donation, the knowledge and attitude of nursing students are paramount to improving future motivation and engagement. In this study, we aimed to investigate the knowledge and motivations of nursing students regarding blood donation following the COVID-19 pandemic in Hong Kong. Methods: With the cross-sectional descriptive design of this study, we used a well-validated Blood Donor Identity Survey (Chinese version), as well as convenience sampling of university students aged ≥18 enrolled in full-time nursing programs at a large university. Ethical approval was sought prior to data collection in November 2023. A total of 711 of the ~2200 target participants returned the questionnaires, with 650 completing them (29.5%). Results: Of these 650, 465 (71.5%) were non-blood donors and 185 (28.5%) were blood donors, with blood donors (9.77 ± 1.28) demonstrating a significantly higher total knowledge score than non-blood donors (9.46 ± 1.33), with p = 0.006. Prior experience of receiving blood (OR = 5.81, 95% CI: 2.92–11.54, p < 0.001), older age (OR = 1.22, 95% CI: 1.12–1.33, p < 0.001), knowing someone who had donated blood (OR = 2.63, 95% CI: 1.29–5.35, p = 0.008), and having a religious affiliation (OR = 1.81, 95% CI: 1.07–3.06, p = 0.028) were found to be significant factors associated with a greater willingness to donate blood. Conversely, taking medication was found to be a significant factor associated with a lower likelihood of being a blood donor (OR = 0.24, 95% CI: 0.09–0.63, p = 0.004). Within the ratings of donor identity, higher Amotivation scores reduced the odds of blood donation (OR = 0.83, 95% CI: 0.78–0.89, p < 0.001), while an increased score on Identified Regulation was significantly and positively related to donor status (OR = 1.15, 95% CI: 1.09–1.22, p < 0.001). Conclusions: Blood donation rates among local nursing students were low after the pandemic, despite moderate overall knowledge. Collaboration among the Hong Kong government, healthcare organizations, and university nursing faculties is vital to promote blood donation among future nursing professionals and the public. Full article