Search Results (213)

Background: Intimate partner violence (IPV) represents a major public health problem in Europe, with significant physical, psychological, and social consequences. Nurses are often the first professionals capable of detecting early signs of IPV, yet they lack validated instruments to assess their clinical competency in detection, evaluation, documentation, and intervention. This study aimed to develop and validate the Intimate Partner Violence Nursing Competency Scale (IPVNCS), aligned with the Nursing Intervention Classification (NIC 6403). Methods: A cross-sectional psychometric study was conducted among registered nurses in the Community of Madrid. A 30-item Likert-type self-administered instrument (1–5 scale) was developed based on NANDA, NIC 6403, and NOC frameworks. A total of 202 nurses participated. Reliability was assessed through Cronbach’s alpha. Construct validity was examined using exploratory factor analysis (EFA) with Promax rotation and confirmatory factor analysis (CFA) using AMOS 26. Ethical approval was obtained (CEU San Pablo, code 843/24/104). Results: After item refinement, 26 items remained across four dimensions: (1) Intervention and Referral, (2) Detection and Assessment, (3) Documentation and Recording-keeping, (4) Psychosocial Support. The instrument showed excellent reliability ($\alpha$ = 0.97). KMO was 0.947 and Bartlett’s test was significant (p < 0.001). CFA demonstrated satisfactory fit: ${\chi }^2$/df = 2.066, RMSEA = 0.073, CFI = 0.92, TLI = 0.91, NFI = 0.86. The final model adequately represented the latent structure. After debugging, its psychometric properties were significantly improved. Four redundant items were eliminated, achieving internal consistency ($\alpha$ = 0.97), a KMO value of 0.947 and a significant Bartlett’s test of sphericity. It showed a better fit, according to ${\chi }^2$/df = (2.066); Parsimony = (720.736); RMR (0.0529; RMSEA (0.073); NFI (0.860); TLI (0.910) and CFI (0.920). The final model provides an adequate representation of the latent structure of the data. This study provides initial evidence of construct validity and internal consistency reliability of the IPVNCS. Conclusions: The IPVNCS is a valid and reliable tool to assess nursing competencies for clinical management of IPV. It supports structured evaluation across four core nursing domains, enabling improved educational planning, clinical decision-making, and quality of care for victims. The scale fills a gap in clinical nursing assessment tools and can support protocol development in emergency, primary care, and hospital settings. Full article

Edge detection is a key building block in real-time image-processing applications such as drone-based infrastructure inspection, autonomous navigation, and remote sensing. However, its computational cost remains a challenge for resource-constrained embedded systems. This work presents a hardware-accelerated edge detection architecture based on a homogeneous 2D-mesh Network-on-Chip (NoC) integrating systolic arrays to efficiently perform the convolution operations required by the Sobel filter. The proposed architecture was first developed and validated as a 3 × 3 mesh prototype on FPGA (Xilinx Zynq-7000, Zynq-7010, XC7Z010-CLG400A, Zybo board, utilizing 26,112 LUTs, 24,851 flip-flops, and 162 DSP blocks), achieving a throughput of 8.8 Gb/s with a power consumption of 0.79 W at 100 MHz. Building upon this validated prototype, a reduced 2 × 2 node cluster with 14-bit word width was subsequently synthesized at the physical level as a proof-of-concept using the OpenLane RTL-to-GDSII open-source flow targeting the SkyWater 130 nm PDK (sky130A). Post-layout analysis confirms the manufacturability of the design, with a total power consumption of 378 mW and compliance with timing constraints, demonstrating the feasibility of mapping the proposed architecture to silicon and its suitability for drone-based infrastructure monitoring applications. Full article

Neuromorphic computing has emerged as a promising paradigm for edge AI systems owing to its event-driven operation and high energy efficiency. However, conventional spiking neural network (SNN) architectures often suffer from redundant computation and inefficient power control, particularly during on-chip learning. This paper proposes a network-on-chip (NoC) architecture featuring a unified refractory-enabled neuron (UREN)-based router that globally coordinates spike-driven computation across multiple neuron cores. The router applies a unified refractory time to all neurons following a winner spike event, effectively enabling clock gating and suppressing redundant activity. The proposed design adopts a star-routing topology with multicasting support and integrates nearest-neighbor spike-timing-dependent plasticity (STDP) for local online learning. FPGA-based experiments demonstrate a 30% reduction in computation and 86.1% online classification accuracy on the MNIST dataset compared with baseline SNN implementations. These results confirm that the UREN-based router provides a scalable and power-efficient neuromorphic processor architecture, well suited for energy-constrained edge AI applications. Full article

Background/Objective: Post-intensive care syndrome (PICS) is a multifactorial, multidimensional condition common among patients who survive critical illness with protracted intensive care unit (ICU) length of stay. Survivors often present physical, cognitive, and psychological impairments that can arise during ICU hospitalization. Virtual reality has emerged as a promising tool in the healthcare field, as it offers innovative solutions to the challenges faced by critically ill survivors. We think that VR might help support people recovering from PICS at home, and this study aims to explore the benefits across the spectrum of PICS to describe the technological characteristics that could support and augment these interventions and present clinical recommendations. Methods: This systematic review searched PubMed, the Cochrane library, Science Direct, and Scopus databases up to July 2025. In this study we included randomized controlled trials (RCTs) examining the impact of VR on PICS. The methodological quality was assessed with the PEDro scale for RCTs and with NOC for non-RCTs. Results: A total of five studies met the inclusion criteria and were included. Three were RCTs, one non-RCT, and one series of cases. The studies presented good methodological quality. Virtual reality was found to be safe for critically ill survivors. All aspects of PICS were examined, with positive results in recovery of psychological disorders, such as anxiety and PTSD, and muscle strength as assessed by hand grip and cognition. The main limitation could be the limited number of RCT studies due to the novelty of the intervention. Conclusions: Virtual reality technology could be safely implemented in the field of post-ICU recovery and effectively assist the rehabilitation of physical, cognitive, and mental disorders of ICU patients. The protocol was registered in the Open Science Framework registry. Full article

This paper proposes a novel fault-tolerant routing method without creating faulty regions for 3D mesh Network-on-Chips (NoCs). Most conventional methods create faulty regions containing faulty nodes and route packets around them to reach the destinations. However, the creation of faulty regions results in low communication performance and low node utilization. To overcome the two problems, the proposed method does not create faulty regions based on the idea of predefining paths in the absence of shortest paths while allowing the passage of faulty nodes. Simulation results show that, compared with conventional methods, the proposed method reduces average latency by about 44.5% and improves node utilization rate by about 41.2% for 3D mesh NoCs of $5\times 5\times 5$ nodes. Full article

The rabies virus remains a significant public health threat, particularly in regions with limited access to vaccination. This study shows that the BelloStage™-3000 bioreactor, operating on the “Tide Motion” principle, in combination with BioNOC^® II macrocarriers, ensures highly efficient rabies virus cultivation in BSR and Vero cells grown in serum-free OptiPRO™ SFM medium. This system supports effective cell attachment, formation of a dense and metabolically active cell layer, and reduces microbial contamination risks associated with serum-containing media. For comparison, rabies virus cultivation was also performed on Cytodex 1 and Cytodex 3 microcarriers in spinner flasks. The use of the BelloStage™-3000 bioreactor system with BelloCell™ 500A disposable vials and BioNOC II^® macrocarriers resulted in significantly higher virus titers compared to traditional Cytodex 1 and Cytodex 3 microcarrier culture systems. Thus, in the BSR cell culture, the maximum virus titer reached 5.6 × 10⁸ FFU/mL by day 4 of cultivation, which exceeded the titers obtained on Cytodex 1 and Cytodex 3 microcarriers by about 19.3-fold and 15.3-fold, respectively. A similar trend was observed for the Vero cell line: the peak titer was 2.0 × 10⁸ FFU/mL by day 5 of culturing, which was higher than the values obtained on Cytodex 1 and Cytodex 3 by about 14.0-fold and 9.6-fold, respectively. These findings demonstrate that the integrated use of BioNOC^® II macrocarriers, the BelloStage™-3000 bioreactor, and a serum-free medium provides a scalable, reproducible, and biosafe platform for rabies virus production, offering substantial advantages over traditional microcarrier-based systems. Full article

Brucellosis remains one of the most significant zoonotic diseases, posing a serious threat to both human health and livestock. This issue is particularly relevant for Kazakhstan, which is among the countries endemic for brucellosis with a high incidence rate. Such circumstances highlight the urgent need for the development and implementation of effective preventive measures, including modern vaccine platforms capable of providing reliable protection for the population and reducing the economic impact on the agricultural sector. Recombinant capripoxviruses are considered promising vector platforms for vaccine development, as they ensure high expression of target antigens, elicit strong immune responses, and are safe for humans. In this study, the replication of recombinant capripoxviruses expressing Brucella antigens (SPPV (TK-) OMP19/SODC and SPPV (TK-) OMP25) was evaluated in Vero cells using the BelloStage™-3000 bioreactor system in combination with BioNOC II^® macrocarriers. Application of the bioreactor resulted in nearly a 100-fold increase in Vero cell density compared with static cultures and provided optimal conditions for cell adhesion, growth, and metabolic activity. Consequently, a significant increase in viral titers was observed: for SPPV (TK-) OMP19/SODC, mean titers reached 7.50 log₁₀ TCID₅₀/mL versus 4.50 in static culture (p < 0.0001), while SPPV (TK-) OMP25 achieved 7.08 log₁₀ TCID₅₀/mL versus 4.33 (p < 0.001). These findings confirm the reliability, reproducibility, and scalability of this bioreactor-based approach, demonstrating clear advantages over conventional cultivation methods. Overall, the study highlights the high potential of the BelloStage™-3000 system with BioNOC II^® macrocarriers for the industrial production of recombinant capripoxvirus-based vaccines against brucellosis and for the broader development of other recombinant viral vaccines. Full article

This article explores the concept of low-level modeling of networks-on-chip (NoCs). A method for reducing the low-level NoC model by replacing the real IP blocks with a data packet generator module is proposed. This method is implemented in the low-level NoC modeling ECAD tool HDLNoCGen. This makes it possible to significantly increase the maximum number of nodes in the simulated NoC, as well as speed up the modeling and investigate the resource costs for network synthesis. A universal interface that can be used to connect new components to the network is also described. This interface has two main benefits: it reduces connection resource costs by eliminating the need to modify the connected component and shortens the time required to configure the connection interface itself. The proposed methodology of low-level NoC modeling is shown to be effective in analyzing the operation of routing algorithms of the NoC communication subsystem based on various topologies. Full article

Context: Modern software systems have become increasingly complex, making it difficult to interpret raw requirements and effectively utilize traditional tools for software design and analysis. Unified Modeling Language (UML) class diagrams are widely used to visualize and understand system architecture, but analyzing them manually, especially for large-scale systems, poses significant challenges. Objectives: This study aims to automate the analysis of UML class diagrams by assessing their complexity using a machine learning approach. The goal is to support software developers in identifying potential design issues early in the development process and to improve overall software quality. Methodology: To achieve this, this research introduces a Visual Question Answering (VQA)-based framework that integrates both computer vision and natural language processing. Vision Transformers (ViTs) are employed to extract global visual features from UML class diagrams, while the BERT language model processes natural language queries. By combining these two models, the system can accurately respond to questions related to software complexity, such as class coupling and inheritance depth. Results: The proposed method demonstrated strong performance in experimental trials. The ViT model achieved an accuracy of 0.8800, with both the F1 score and recall reaching 0.8985. These metrics highlight the effectiveness of the approach in automatically evaluating UML class diagrams. Conclusions: The findings confirm that advanced machine learning techniques can be successfully applied to automate software design analysis. This approach can help developers detect design flaws early and enhance software maintainability. Future work will explore advanced fusion strategies, novel data augmentation techniques, and lightweight model adaptations suitable for environments with limited computational resources. Full article

The Network-on-Chip applications’ performance and efficiency depend on task allocation and message routing, which are complex problems. The existing solutions assign priorities to messages in order to regulate their transmission. Unfortunately, this message classification can lead to routings that block the best global solution. In this work, we propose to use the Hungarian algorithm to dynamically route messages with the minimal cost, i.e., minimizing the communication times while consuming the least energy possible. To meet the real-time constraints coming from requiring results at each flit transmission, we also suggest a hardware version of it, which reduces the processing time by an average of 42.5% with respect to its software implementation. Full article

In Network-on-Chip (NoC) research, the task mapping problem has attracted considerable attention as a core issue influencing system performance. As an NP-hard problem, it remains challenging, and existing algorithms exhibit limitations in both mapping quality and computational efficiency. To address this, a method named ET (Enhanced Coati Optimization Algorithm) is proposed, which leverages the nature-inspired Coati Optimization Algorithm (COA) for task mapping. An incremental hill-climbing strategy is integrated to improve local search capabilities, and a dynamic mechanism for adjusting the exploration–exploitation ratio is designed to better balance global and local searches. Additionally, an initial mapping strategy based on spectral clustering is introduced, which utilizes inter-task communication strength to cluster tasks, thereby improving the quality of the initial population. To evaluate the effectiveness of the proposed algorithm, the performance of the ET algorithm is compared and analyzed against various existing algorithms in terms of communication cost, energy consumption, and latency, using both real benchmark task maps and randomly generated task maps. Experimental results demonstrate that the ET algorithm consistently outperforms the compared algorithms across all performance metrics, thereby confirming its superiority in addressing the NoC task mapping problem. Full article

In modern multi-core processors, memory request latency critically constrains overall performance. Prefetching, a promising technique, mitigates memory access latency by pre-loading data into faster cache structures. However, existing core-side prefetchers lack visibility to the DRAM state and may issue suboptimal requests, while conventional memory-side prefetchers often default to simple next-line policies that miss complex access patterns. We propose a comprehensive memory-side prefetch optimization scheme, which includes a prefetcher that utilizes advanced prefetching algorithms and an optimization module. Our prefetcher is capable of detecting more complex memory access patterns, thereby improving both prefetch accuracy and coverage. Additionally, considering the characteristics of DRAM memory access, the optimization module minimizes the negative impact of prefetch requests on DRAM by enhancing coordination with memory operations. Additionally, our prefetcher works in synergy with core-side prefetchers to deliver superior overall performance. Simulation results using Gem5 and SPEC CPU2017 workloads show that our approach delivers an average performance improvement of 10.5% and reduces memory access latency by 61%. Our prefetcher also operates in conjunction with core-side prefetchers to form a multi-level prefetching hierarchy, enabling further performance gains through coordinated and complementary prefetching strategies. Full article

Background: It is generally well-known that narration of care is critically important to high-quality nursing care. Narration of care is loosely defined as a nurse’s ability to describe to patients and families the clinical purpose behind nursing practice, what is hoped to be achieved, and the “why” (or clinical rationale) behind nursing activities. Despite the importance of narration of care, there is little practical guidance given to nurses about how to narrate care—what makes for effective or ineffective narration of care. Objective: Our aim was to develop a framework for teaching nurses and patient care assistants (PCAs) on how to effectively narrate care. In this article, we provide a practical framework for teaching nurses and PCAs how to narrate care. We describe the process of developing the framework as part of quality improvement efforts and implementing a course for eight hospitals based on the framework. Methods: Consistent with a Plan-Do-Study Act (PDSA) quality improvement approach, we developed the framework by first conducting a data and literature review, then convening a taskforce, discussing with patients on our existing committees, and finally formulating a framework. We then drafted supplementary cases and course material and implemented a course to teach nurses and PCAs how to narrate care. Results: The narration of care framework (NOC) that we developed and implemented consisted of the following five principles, which can be called RECAP as an acronym: 1. The “R” in RECAP stands for removing uncertainty. 2. The “E” in RECAP stands for explaining the environment. 3. The “C” in RECAP stands for being calm and sincere. 4. The “A” in RECAP stands for assume nothing. 5. The “P” in RECAP stands for personal connection. As for the course developed based on the RECAP principles, there was a total of 276 course offerings conducted by 30 facilitators, and 7341 nurses and PCAs completed the course. The evaluations reflected that 99% of learners believed their learning was improved by the course. Discussion: There are several multifaceted benefits to NOC: nurses’ and PCAs’ capability to narrate care well shows empathy and compassion to patients; it strengthens patient understanding and education that can lead to improved patient outcomes; and it helps allay patients’ uncertainties and anxieties. In essence, narrating care in an effective manner cultivates a strong nurse–patient therapeutic relationship. Yet, in the absence of any practical guidance, nurses and PCAs are left to develop narration skills on their own, learning by trial and error, and, in doing so, perhaps failing to meet patients’ needs and failing to fully derive the many benefits that the NOC is designed to achieve. Our hope is that, if hospital systems adopt our work, nurses and PCAs can comfortably and confidently enter the profession knowing the purpose or narrating care, its many benefits, and how to practically conduct sufficient narration, and what would constitute insufficient narration. Hospitals, in turn, can specify and clearly articulate their expectations for nurses and PCAs narrating with patients—what would make for a strong, compassionate process and what would be inadequate. For more experienced nurses, they can use the RECAP framework to reflect on their own practices and perhaps strengthen or refreshen existing skills. Conclusions: NOC is acknowledged, somewhat implicitly, as being critical to nursing and PCA practice, yet practical instruction and specified principles are lacking. We aimed to fill this gap by developing, implementing, and teaching a practical framework, armed with many tools nurses can use. Full article

Background/Objectives: Nursing diagnosis is a complex process that requires clinical judgment, time, and resources and whose implementation is hindered by factors such as workload, lack of time, and resistance to computerized systems. This study aimed to compare the quality and efficiency of care plans generated by nursing professionals versus those produced by an artificial intelligence (AI) model, using the NANDA, NOC, and NIC taxonomies as criteria. Methods: An observational study was carried out with three simulated clinical cases. Thirty experts, fifty-four nursing professionals, and the ChatGPT model (GPT-4) were included. The experts established the referral plans using the Delphi technique. Responses were evaluated with a validated rubric (EADE-2) and analyzed using nonparametric tests. Professionals’ perceptions on the use of computer systems were also collected. Results: ChatGPT scored significantly higher on several dimensions (p < 0.001) and resolved all three cases in 35 s, compared to an average of 30 min for practitioners. Professionals expressed dissatisfaction with current diagnostic documentation systems. Conclusions: AI demonstrates high potential in optimizing the diagnostic process in nursing, although for its implementation human supervision, ethical aspects and improvements in current systems must be considered to achieve effective integration. Full article

This study establishes a fractional-order model (FOM) to describe the rumor spreading process. Members of society in this FOM are classified into three categories that change with time—the population that is ignorant of the rumors and does not know them, the population that is aware of the truth of the rumors but does not believe them, and the spreaders of rumors—taking into consideration awareness programs (APs) through media reports as a subcategory that changes over time where paying attention to these APs makes ignorant individuals avoid believing rumors and become better-informed individuals. We prove the positivity and boundedness of the FOM solutions. The feasible equilibrium points (EPs) and their local asymptotical stability (LAS) are analyzed based on the control reproduction number (CRN). Then, we examine the influence of model parameters that emerge with the CRN through a sensitivity analysis.A fractional optimal control problem (FOCP) is formulated by considering three time-dependent control measures in the suggested FOM to capture the spread of rumors; $u_1$, $u_2$, and $u_3$ represent the contact control between rumor spreaders and ignorant people, control media reports, and control rumor spreaders, respectively. We derive the necessary optimality conditions (NOCs) by applying Pontryagin’s maximum principle (PMP). Different optimal control strategies are proposed to reduce the negative effects of rumor spreading and achieve the maximum social benefit. Numerical simulation is implemented using a forward–backward sweep (FBS) approach based on the predictor–corrector method (PCM) to clarify the efficiency of the proposed strategies in order to decrease the number of rumor spreaders and increase the number of aware populations. Full article