Search Results (131)

Background/Objectives: Workplace violence remains an important vocational psycho-social risk for nurses employed in the emergency department (ED). We investigated the characteristics of workplace violence against ED nurses, and associations with self-assessed resilience, socio-demographic and vocational parameters, including turnover intention. Methods: ED nurses employed in all public hospitals in the Republic of Cyprus (RC) participated. After obtaining informed consent, data were collected using census sampling (January–June 2024) via the translated 2016 Italian National Survey on Violence towards Emergency Nurses Questionnaire (QuINVIP16) for investigating workplace violence characteristics, and the Connor-Davidson Resilience Scale (CD-RISC-25) for assessing self-perceived resilience. Results: A total of 132 nurses (53.0% response rate) participated. Verbal violence was reported by 70.5% to 92.4% of participants. Long waiting times, overcrowded EDs, and perception of inadequate attention from healthcare professionals were reported as the primary triggers for violence towards participants by patients/visitors. One-third of participants reported that violence-reporting systems were unclear, while 1 out of 4 reported inadequate safety measures against violence. Participants with higher scores of self-perceived resilience were less likely to report turnover intention due to workplace violence (p < 0.001), while those with lower self-perceived resilience reported a significant decrease in work motivation (p = 0.005). Those who experienced decreased work motivation after exposure to a violent episode were more likely to consider a) leaving the profession [OR (95%CI): 79.1(17.7–353.2); p < 0.01], and b) moving to a different work setting [OR (95%CI): 17.0(3.8–76.2); p < 0.01], and actually applying to be transferred to a different work setting [OR (95%CI): 19.6(4.2–91.5); p < 0.01]. Moreover, those who had not attended communication skills training were 4 times more likely to consider leaving the profession following exposure to violence [OR (95%CI): 4.2(1.1–16.2); p = 0.04]. Conclusions: This study is among the few to link workplace violence with both resilience and actual turnover behaviors among emergency nurses, in general and particularly in the post-pandemic era. By showing how personal resilience in the face of violence is shaped by organizational support, such as reporting systems and training, the present findings move beyond individuals-level explanations, and highlight workplace violence as a systematic administrative challenge. This insight represents an important advance in current knowledge, and calls for multifaceted interventions that strengthen both personal and institutional capacity to address violence. Full article

The adoption of artificial intelligence (AI) and smart technologies is reshaping global hospitality. However, in emerging markets, uptake remains limited by financial, organizational, and infrastructural barriers. This study examines the digital readiness of 1821 licensed accommodation providers in Albania, a rapidly expanding tourism economy, using an integrated framework that combines the Technology Acceptance Model (TAM), technology–organization–environment (TOE) framework, and Diffusion of Innovations (DOI). Data were collected via a structured survey and analyzed using descriptive statistics, exploratory factor analysis, cluster analysis, and structural equation modeling. Exploratory factor analysis identified a single robust readiness dimension, covering smart automation, environmental controls, and AI-driven systems. K-means segmentation revealed three adopter profiles: Tech Leaders (17.7%), Selective Adopters (43.5%), and Skeptics (38.8%), with statistically distinct but modest mean differences in readiness, reflecting stronger adoption in central urban and coastal hubs compared to weaker uptake in cultural heritage and non-urban regions. Structural modeling showed that environmental competitive pressure strongly enhanced perceived usefulness, which, in turn, drove behavioral intention, whereas perceived ease of use (operationalized as implementation complexity) had negligible effects. Innovation readiness was consistently associated with broader adoption, although intention was translated into actual use only among Tech Leaders. The findings highlight a fragmented digital ecosystem in which enthusiasm for AI exceeds its feasibility, underscoring the need for differentiated policy support, modular vendor solutions, and targeted capacity building to foster inclusive digital transformation. Full article

Internally displaced people (IDPs) are individuals forced to leave their homes due to conflicts or disasters without crossing international borders. Since 2008, weather-related extreme events—primarily storms and floods—have displaced more than 20 million people annually. With global temperatures rising and extreme weather intensifying, the number of IDPs is projected to increase in the coming decades. In the Philippines, resettlement has emerged as a key climate adaptation strategy, with IDP camps established to reduce risks in highly vulnerable areas. Yet, it remains unclear whether these camps are actually located in regions of lower climate vulnerability. This study aims to examine the climate vulnerability of 17 IDP camps by considering physical and infrastructural dimensions to assess whether they are located in safer areas, and to suggest the development of urban forms that can improve community resilience and the living conditions of their populations. Results show significant variation in climate vulnerability, with Villa Diana scoring the lowest and Villa Sofia the highest. Using emergency response facilities as a proxy for social capital, we identified drivers of vulnerability: Villa Sofia faces heightened risks due to population density, flood exposure, and limited emergency facilities, while Villa Diana benefits from greater emergency capacity and abundant vegetation that reduces risk. Our findings provide a systematic framework for assessing climate vulnerability among IDPs and highlight the critical role of social capital in mitigating climate impacts for displaced populations in the Global South, where climate risk mapping and reliable data remain limited. Full article

Urban livability and sustainable development remain major global challenges, yet the interplay between urban planning layouts and actual human activities has not been sufficiently examined. This study investigates this relationship in Beijing by integrating multi-source spatiotemporal data, including point of interest (POI), Land Use Cover Change (LUCC), remote sensing data, and the railway network. Defining urban functional units as “street + railway network”, we analyze the spatial–temporal evolution within the 6th Ring Road over the past four decades and propose targeted strategies for the urban functional layout. The results reveal the following: (1) The evolution of Beijing’s urban functions can be divided into four stages (1980–1990, 1990–2005, 2005–2015, and 2015–2020), with continuous population growth (+142%) driving the over-concentration of functions in central districts. (2) Between 2010 and 2020, the POI densities of medical services (+133.6%) and transport services (+130.48%) increased most rapidly, subsequently stimulating the expansion of other urban functions. (3) High-density functional facilities and construction land (+179.10%) have expanded significantly within the 6th Ring Road, while green space (cropland, forestland and grassland) has decreased by 86.97%, resulting in a severe imbalance among land use types. To address these issues, we recommend the following: redistributing high-intensity functions to sub-centers such as Tongzhou and Xiongan New Area to alleviate population pressure, expanding high-capacity rail transit to reinforce 30–50 km commuting links between the core and periphery, and establishing ecological corridors to connect green wedges, thereby enhancing carbon sequestration and environmental quality. This integrated framework offers transferable insights for other megacities, providing guidance for sustainable functional planning that aligns human activity patterns with urban spatial structures. Full article

Rapid urbanization in developing countries has widened the gap between urban and rural development, due to inefficient land markets and weak institutional systems in rural areas. China’s innovative “Urban–rural Construction Land Linkage” policy was designed to address this imbalance by encouraging rural land consolidation and creating a transferable development rights mechanism. While this approach has shown potential in improving the utilization efficiency of existing construction land and continuously supplying urban development space, concerns remain about its actual benefits to villagers and rural development, with some arguing it disrupts traditional livelihoods and favors government interests over rural needs. To respond to this debate, this study investigates two core questions: first, does China’s transferable land development rights (TDR) program genuinely improve rural welfare as intended; second, why does the theoretically preferred self-organized governance model sometimes fail in practice? To address these research questions, this paper develops a new analytical framework combining the IAD framework of Ostrom with the hierarchical institutional framework of Williamson to examine three implementation approaches in China’s TDR implementation: government-dominated, market-invested, and self-organized models. Based on case studies, surveys, and interviews across multiple regions, this study reveals distinct strengths and weaknesses in each approach in improving villagers’ lives. Government-dominated projects demonstrate strong resource mobilization but limited community participation. Market-based models show efficiency gains but often compromise equity. While self-organized initiatives promise greater local empowerment, they frequently face practical challenges including limited management capacity and institutional barriers. Furthermore, this study identifies the preconditional institutional environment necessary for successful self-organized implementation, including clear land property rights, financial support, and technical assistance. These findings advance global understanding of how to combine efficiency with fair outcomes for all stakeholders in land governance, which is particularly relevant for developing countries seeking to manage urban expansion while protecting rural interests. Full article

Accurate prediction of a robot’s dynamic parameters, including mass and moment of inertia, is essential for adequate motion planning and control in autonomous systems. Traditional methods often depend on manual computation or physics-based modelling, which can be time-consuming and approximate for intricate, real-world environments. Recent advances in machine learning, primarily through artificial neural networks (ANNs), offer profitable alternatives. However, the potential of quantum-inspired models in this context remains largely uncharted. The current research assesses the predictive performance of a classical artificial neural network (CANN) and a quantum-inspired artificial neural network (QANN) in estimating a car-like mobile robot’s mass and moment of inertia. The predictive accurateness of the models was considered by minimizing a cost function, which was characterized as the RMSE between the predicted and actual values. The outcomes indicate that while both models demonstrated commendable performance, QANN consistently surpassed CANN. On average, QANN achieved a 9.7% reduction in training RMSE, decreasing from 0.0031 to 0.0028, and an 84.4% reduction in validation RMSE, dropping from 0.125 to 0.0195 compared to CANN. These enhancements highlight QANN’s singular predictive accuracy and greater capacity for generalization to unseen data. In contrast, CANN displayed overfitting tendencies, especially during the training phase. These findings emphasize the significance of quantum-inspired neural networks in enhancing prediction precision for involved regression tasks. The QANN framework has the potential for wider applications in robotics, including autonomous vehicles, uncrewed aerial vehicles, and intelligent automation systems, where accurate dynamic modelling is necessary. Full article

Background: Patients with early-stage (I–IIIA) resectable non-small cell lung cancer (NSCLC) often experience reduced physical activity (PA) after surgery. PA telecoaching may support a more active lifestyle, but evidence in this population is limited. Objective: To evaluate acceptability, feasibility, safety, and actual usage of an automated and manual PA telecoaching program following surgery for NSCLC. Methods: In this multicenter, single-blind study, patients received either an eight-week automated coaching program (ACP) with a customized smartphone app or a manual coaching program (MCP) with weekly phone calls from a coach. Both groups used an activity tracker, linked to their smartphone, to monitor steps and receive feedback. Primary outcomes included acceptability, feasibility, safety and usage, assessed via questionnaires and interviews. Secondary outcomes included objectively measured PA (accelerometry), functional exercise capacity (six-minute walk distance) and symptoms (dyspnea, fatigue) and quality of life, evaluated via questionnaires. Results: Nineteen patients (12 males; 68 ± 6 years; baseline daily steps 7820 ± 2799) were included. The majority (18/19) found the intervention enjoyable, and a minority (6/19) reported minor smartphone issues. All patients wore the activity tracker consistently. No adverse events occurred. The ACP required significantly less coach contact time compared to the MCP (25 ± 14 vs. 54 ± 15 min, p = 0.0003). No other differences in primary outcomes were observed between groups. Changes in secondary outcomes were limited in both groups. Conclusion: PA telecoaching is feasible, well accepted, and safe in patients with NSCLC post-surgery, with excellent activity tracker adherence. The ACP required less coach involvement. However, increasing PA remains challenging, and no conclusions can be made about the effectiveness of telecoaching. Future research should explore longer interventions in larger populations to assess efficacy and long-term outcomes. Full article

Although wind-assisted ship propulsion (WASP) is an effective technique for reducing the emissions of merchant ships, the best fuel type for complementing WASP remains an open question. This study presents a new original life cycle assessment method for ship fuels that uses a validated ship performance prediction model and actual operation conditions for a WASP ship. As a case study, the method is used to evaluate the fuel consumption and environmental impact of different fuels for a WASP ship operating in the Baltic Sea. Using a novel in-house-developed platform for predicting ship performance under actual operation conditions using hindcast data, the engine and fuel tank were sized while accounting for fluctuating weather conditions over a year. The results showed significant variation in the required fuel tank capacity across fuel types, with liquid hydrogen requiring the largest volume, followed by LNG and ammonia. Additionally, a well-to-wake life cycle assessment revealed that dual-fuel engines using green ammonia and hydrogen exhibit the lowest global warming potential (GWP), while grey ammonia and blue hydrogen have substantially higher GWP levels. Notably, NOx, SOx, and particulate matter emissions were consistently lower for dual-fuel and liquid natural gas scenarios than for single-fuel marine diesel oil engines. These results underscore the importance of selecting both an appropriate fuel type and production method to optimize environmental performance. This study advocates for transitioning to greener fuel options derived from sustainable pathways for WASP ships to mitigate the environmental impact of maritime operations and support global climate change efforts. Full article

Cross-plate joints between panels are commonly used in diaphragm wall construction to ensure structural integrity. However, research on the mechanical behaviour of these joints remains limited, and they are often disregarded in numerical modelling due to their complexity. This paper fabricated two types of specimens with cross-plate joints, which were subsequently employed in bending and shear tests, respectively. The load–displacement curves and the joint openings were experimentally measured. It was found that the load–displacement curves exhibited approximately four linear stages in the bending tests and two in the shear tests. Based on the test results, a multi-linear spring model was proposed to simplify the mechanical behaviour of the joints, and the stiffness of each linear stage was determined through back-analysis of the tested data. The calculated load–displacement curves ultimately agreed well with those obtained from the tests, with average errors of 3.6% in the bending test and 2.6% in the shear test. The proposed model was then applied to a devised case study, thereby demonstrating its capacity to capture joint opening phenomena and revealing the spatial variability of joint opening within the excavation depth. Compared with conventional 2D and 3D models, the proposed model yields displacement results that better reflect the actual deformation of the diaphragm wall. Furthermore, the precise modelling calculation for joints, which is time-consuming, is also avoided, and the calculation time of the proposed model is only 1.52 times that of the conventional 3D model. Full article

Accurate State of Charge (SOC) estimation for retired power batteries remains a critical challenge due to their degraded electrochemical properties and heterogeneous aging mechanisms. Traditional methods relying solely on electrical parameters (e.g., voltage and current) exhibit significant errors, as aged batteries experience altered internal resistance, capacity fade, and uneven heat generation, which distort the relationship between electrical signals and actual SOC. To address these limitations, this study proposes a surface temperature-assisted SOC estimation method, leveraging the distinct thermal characteristics of retired batteries. By employing infrared thermal imaging, key temperature feature regions—the positive/negative tabs and central area—are identified, which exhibit strong correlations with SOC dynamics under varying operational conditions. A Gated Recurrent Unit (GRU) neural network is developed to integrate multi-region temperature data with electrical parameters, capturing spatial–temporal thermal–electrical interactions unique to retired batteries. The model is trained and validated using experimental data collected under constant current discharge conditions, demonstrating superior accuracy compared to conventional methods. Specifically, our method achieves 64.3–68.1% lower RMSE than traditional electrical-parameter-only approaches (V-I inputs) across 0.5 C–2 C discharge rates. Results show that the proposed method reduces SOC estimation errors compared to traditional voltage-based models, achieving RMSE values below 1.04 across all tested rates. This improvement stems from the model’s ability to decode localized heating patterns and their hysteresis effects, which are particularly pronounced in aged batteries. The method’s robustness under high-rate operations highlights its potential for enhancing the reliability of retired battery management systems in secondary applications such as energy storage. Full article

RC piers in mountainous coastal or saline areas face the dual threats of rockfall impacts and chloride-induced steel corrosion, but their combined effects on dynamic response and residual bearing capacity remain unquantified. This study aims to investigate these combined effects over a 90-year service time and propose a damage assessment formula. A validated numerical model (relative error ≤14.7%) of corroded RC columns under impact is developed using ABAQUS, based on which the dynamic response and residual bearing capacity of an actual RC pier subjected to rockfall impacts during the service time of 90 years incorporating corrosion initiation (via Life-365 software 2.2) and propagation are analyzed, with the consideration of various impact energies (1–5 t mass, 5–15 m/s velocity). Results show that (1) increasing impact mass/velocity expands damage and increases displacement (e.g., the velocity of increases peak displacement by 33.41 mm in comparison to 5 m/s); (2) a 90-year service time leads to >50% severe surface damage and 47.1% residual capacity loss; and (3) the proposed and validated damage formula assessment formula for the residual bearing capacity enables lifecycle maintenance guidance. This work provides a validated framework for assessing combined corrosion-rockfall effects, aiding design and maintenance of structures. Full article

Free-space optical (FSO) communication is a promising high-capacity solution for future wireless networks, particularly for backhaul and fronthaul links in 5G and emerging 6G systems. However, it remains highly vulnerable to environmental impairment, especially in arid regions prone to dust storms. While prior studies have addressed atmospheric effects such as fog and turbulence, the specific impact of dust on signal performance remains insufficiently explored. This work presents a probabilistic modeling framework for evaluating the performance of an intensity modulation/direct detection (IM/DD) FSO system under dust storm conditions. Using a controlled laboratory environment, we conducted measurements of the optical signal under dust-induced channel conditions using real-world dust samples collected from an actual dust storm. We identified the Beta distribution as the most accurate model for the measured signal fluctuations. Closed-form expressions were derived for average bit error rate (BER), outage probability, and channel capacity. The close agreement between the analytical, approximate, and simulated results validates the proposed model as a reliable tool for evaluating FSO system performance. The results show that the forward error correction (FEC) BER threshold of ${10}^{-3}$ is achieved at approximately 10.5 dB, and the outage probability drops below ${10}^{-3}$ at 10 dB average SNR. Full article

This study investigates the dynamic characteristics of three-dimensional (3D) printed acrylonitrile butadiene styrene (ABS) cantilever beams using Experimental Modal Analysis (EMA). The effects of Fused Deposition Modelling (FDM) process parameters—specifically infill pattern, infill density, nozzle size, and raster angle—on the natural frequency, mode shapes, and damping ratio were examined. Although numerous studies have addressed the static mechanical behaviour of FDM parts, there remains a significant gap in understanding how internal structural features and porosity influence their vibrational response. To address this, a total of seventy-two specimens were fabricated with varying parameter combinations, and their dynamic responses were evaluated through frequency response functions (FRFs) obtained via the impact hammer test. Damping characteristics were extracted using the peak-picking (half power) method. Additionally, the influence of internal porosity on damping behaviour was assessed by comparing the actual and theoretical masses of the specimens. The findings indicate that both natural frequencies and damping ratios are strongly influenced by the internal structure of the printed components. In particular, gyroid and cubic infill patterns increased structural stiffness and resulted in higher resonant frequencies, while low infill densities and triangle patterns contributed to enhanced damping capacity. Response Surface Methodology (RSM) was employed to develop mathematical models describing the parameter effects, providing predictive tools for applications sensitive to vibration. The high R² values obtained in the RSM models based on the input variables show that these variables explain the effects of these variables on both natural frequency and damping ratio with high accuracy. The models developed (with R² values up to 0.98) enable the prediction of modal behaviour, providing a valuable design tool for engineers optimizing vibration-sensitive components in fields such as aerospace, automotive, and electronics. Full article

The traditional carrying capacity assessment method fails to effectively quantify the difference in spatial distribution of rooftop photovoltaic (PV) resources and ignores the temporal fluctuation of PV output and load demand, as well as the temporal and spatial matching characteristics of sources and loads. This leads to problems such as a disconnect between the assessment and the actual grid acceptance capacity and insufficient dynamic adaptability. In response to the above issues, this paper proposes a dynamic assessment model for distributed photovoltaic carrying capacity based on the combination of improved DeepLabv3+ and game-theoretic weighted assignment. First, the DeepLabv3+ model was improved by integrating the Efficient Channel Attention (ECA) mechanism and the strip pooling (SP) module to enhance roof recognition accuracy. Ablation experiments showed that the mIoU increased to 77.53%, 6.29% higher than the original model. The simulation results in the summer scenario demonstrated that, with the optimal coordination of STMF and scene scoring, the comprehensive carrying coefficient reached 0.73. Next, a photovoltaic carrying capacity evaluation system was established, considering the source, grid, and load perspectives, with dynamic evaluation using a game-theory-based weighting method. Finally, a comprehensive carrying coefficient was introduced, accounting for the spatiotemporal match between photovoltaic output and load, leading to the development of a distributed photovoltaic carrying capacity model. The case study results show that, in summer, due to the optimal coordination of STMF and scene scoring, the comprehensive carrying coefficient reaches 0.73. With a total PV access capacity of 6.48 MW, all node voltages remain within limits, verifying the model’s effectiveness in grid adaptability. Full article

The state of health (SoH) of lithium-ion batteries is critical for diagnosing the actual capacity of the battery. Data-driven methods have achieved impressive accuracy, but their sensitivity to sensor noise, missing samples, and outliers remains a limitation for their deployment. This paper proposes a robust, purely data-driven SoH estimation methodology that addresses these challenges. Our method uses a proposed non-iterative closed-form signal reconstruction derived from a modified Tikhonov regularization. Five new features were extracted from reconstructed voltage and temperature discharge profiles. Finally, a Huber regression model is trained using these features for SoH estimation. Six ageing scenarios built from the public NASA and Sandia National Laboratories datasets, under severe Gaussian noise conditions (10 dB SNR), were employed to validate our proposed approach. In noisy environments and with limited training data, our proposed approach maintains a competitive accuracy across all scenarios, achieving low error metrics, with an RMSE on the order of ${10}^{-4}$, an MAE on the order of ${10}^{-2}$, and a MAPE below 1%. It outperforms state-of-the-art deep neural networks, direct-feature Huber models, and hybrid physics/data-driven models. In this work, we demonstrate that robustness in SoH estimation for lithium-ion batteries is influenced by the choice of machine learning architecture, loss function, feature selection, and signal reconstruction technique. In addition, we found that tracking the time to minimum discharge voltage and the time to maximum discharge temperature can be used as effective features to estimate SoH in data-driven models, as they are directly correlated with capacity loss and a decrease in power output. Full article