Search Results (4,243)

Background: Sleep quality is crucial for patient recovery and well-being, yet hospitalized patients often suffer from poor sleep due to environmental disruptions, clinical routines, and psychosocial stressors. While these challenges are well-documented, qualitative insights into nurses’ perspectives—essential frontline providers shaping the sleep environment—are scarce, especially within rapidly evolving healthcare systems like Saudi Arabia’s. This study explores nurses’ perceptions of factors influencing patient sleep quality in a private hospital in Al Ahsa, Saudi Arabia, and identifies strategies for improvement. Methods: We conducted a qualitative, cross-sectional study using semi-structured interviews with 14 registered nurses from diverse nationalities, specialties (Obstetrics/Gynecology, Medical-Surgical, Pediatrics, Intensive Care, Orthopedics, Bariatrics), and experience levels. Interviews were conducted in Arabic or English, audio-recorded, transcribed, and thematically analyzed using ATLAS.ti software. Roy’s Adaptation Model guided the examination of environmental, patient-specific, and systemic factors affecting sleep. Findings: Four primary themes emerged: (1). Environmental Factors: noise from alarms, equipment, family presence, and late-night activities, along with abrupt lighting changes, consistently disrupted sleep. (2). Patient-Specific Factors: pain, emotional distress, cultural expectations, and family involvement influenced sleep experiences. (3). Systemic and Contextual Factors: language barriers, infrastructural disparities between private and governmental hospitals, and limited resources can impeded effective sleep-promoting strategies. (4). Role of Health Technology: nurses recognized the potential of innovations like smart lighting and wearable monitors to enhance sleep quality but faced challenges in implementation due to knowledge gaps and limited familiarity. Roy’s Adaptation Model highlighted how effective adaptation through physiological and cognitive–emotional pathways, as observed by nurses, was facilitated or hindered by these factors. Conclusions: Enhancing in-hospital sleep quality requires a holistic, culturally sensitive approach that integrates environmental modifications, patient-centered care, and systemic improvements. Strategic investments in staff communication training, infrastructural upgrades, language support services, and the adoption of health technologies can promote adaptive responses and optimize patient rest. By leveraging theory-driven insights and context-specific strategies, healthcare systems—particularly those undergoing rapid development—can better support nurses in fostering restorative sleep environments as a fundamental component of patient-centered care, thereby enhancing patient recovery, satisfaction, and overall well-being. Full article

The field of additive manufacturing increasingly demands innovative solutions to optimize material processing, improve equipment efficiency, and address maintenance challenges in high-utilization environments. This study investigates the operation and management of an FFF 3D printing production line comprising eight remotely controlled printers. The system supports custom manufacturing and educational activities, focusing on processing a range of thermoplastics and composite materials. A key contribution of this work lies in addressing the impact of frequent hardware servicing caused by shared use among users. Augmented reality (AR)-guided assembly and disassembly workflows were developed to ensure uninterrupted operations. These workflows are accessible via smart devices and provide step-by-step guidance tailored to specific material and equipment requirements. The research evaluates the effectiveness of AR-enhanced maintenance in minimizing downtime, extending equipment lifespans, and ensuring consistent material performance during manufacturing processes. Furthermore, it explores the role of AR in maintaining the mechanical, thermal, and chemical properties of processed materials, ensuring high-quality outputs across diverse applications. This paper highlights the integration of advanced material processing methodologies with emerging technologies like AR, aligning with the focus on enhancing manufacturing schemes. The findings contribute to improving process efficiency and adaptability in additive manufacturing, offering insights into scalable solutions for remote-controlled and multi-user production systems. Full article

Indonesia’s pursuit of the Indonesia Emas 2045 vision emphasizes sustainable economic growth and equitable prosperity, with strategic initiatives such as establishing a new national capital and enhancing infrastructure to bolster economic competitiveness. This study examines the relationship between Strategic Orientation and Sustainable Firm Performance, with Firm Competitive Advantage as a mediator and Dynamic Environment as a moderator. Using data collected from 474 private companies affiliated with the Indonesian Chamber of Commerce and Industry between September and December 2024, and analyzed via Structural Equation Modeling with SmartPLS4, the findings demonstrate that Strategic Orientation significantly enhances Firm Competitive Advantage, which partially mediates its impact on Sustainable Firm Performance. Moreover, Dynamic Environment amplifies the Strategic Orientation–Firm Competitive Advantage relationship. The study contributes to the Resource-Based View and Dynamic Capability Theory, providing actionable insights for private firms to enhance strategic adaptability and for policymakers to foster private sector sustainability amidst market dynamics. Full article

This study aims to describe and assess the impact of critical infrastructure (CI) cybersecurity issues on the sustainable development of smart cities. This study highlights the integration of PayTech systems into the broader CI landscape, highlighting their impact on maintaining economic stability and ensuring the smooth operation of city services. Key companies within smart regions, particularly those operating in the payment industries, are essential to maintaining the functionality of critical services. These companies facilitate the processing of services provided to citizens, enabling access to vital municipal services. As key players in the PayTech and online e-commerce sectors, they form a crucial part of modern critical infrastructure, operating within an ever-evolving digital environment. This study examines the recovery processes employed after cyberattacks, focusing on the differing perspectives of internal and external professionals. It identifies significant differences in the perceptions of recovery strategies among internal stakeholders, such as investor relations (IR) teams, reputation management (RM) experts, and Chief Information Security Officers (CISOs), who represent critical infrastructure companies. Additionally, it explores the roles of external auditors, who provide impartial emergency support and perform specialized recovery tasks. Importantly, this study underscores the current attitudes toward future information security strategies and their influence on the financial recovery and reputation of reliable companies following cyber incidents. This research contributes to the existing knowledge by shedding light on the perspectives of both a company’s internal and external specialists involved in the recovery process and cyber resilience strategies in critical infrastructure sectors. Full article

With the rapid development of the Internet of Vehicles (IoV) and autonomous driving technologies, robust and accurate visual pose perception has become critical for enabling smart connected vehicles. Traditional deep learning-based localization methods face persistent challenges in real-world vehicular environments, including occlusion, lighting variations, and the prohibitive cost of collecting diverse real-world datasets. To address these limitations, this study introduces a novel approach by combining Vision-LSTM (ViL) with synthetic image data generated from high-fidelity 3D models. Unlike traditional methods reliant on costly and labor-intensive real-world data, synthetic datasets enable controlled, scalable, and efficient training under diverse environmental conditions. Vision-LSTM enhances feature extraction and classification performance through its matrix-based mLSTM modules and advanced feature aggregation strategy, effectively capturing both global and local information. Experimental evaluations in independent target scenes with distinct features and structured indoor environments demonstrate significant performance gains, achieving matching accuracies of 91.25% and 95.87%, respectively, and outperforming state-of-the-art models. These findings underscore the innovative advantages of integrating Vision-LSTM with synthetic data, highlighting its potential to overcome real-world limitations, reduce costs, and enhance accuracy and reliability for connected vehicle applications such as autonomous navigation and environmental perception. Full article

Ultrasonic flowmeters are essential sensor devices widely used in remote metering systems, smart grids, and monitoring systems. In these environments, a low-power design is critical to maximize energy efficiency. Real-time data collection and remote consumption monitoring through remote metering significantly enhance network flexibility and efficiency. This paper proposes a low-complexity structure that ensures an accurate time-of-flight (ToF) estimation within an acceptable error range while reducing computational complexity. The proposed system utilizes Hilbert envelope detection and a differentiator-based parallel peak detector. It transmits and collects data through ultrasonic transmitter and receiver transducers and is designed for seamless integration as a node into wireless sensor networks (WSNs). The system can be involved in various IoT and industrial applications through high energy efficiency and real-time data transmission capabilities. The proposed structure was validated using the MATLAB software, with an LPG gas flowmeter as the medium. The results demonstrated a mean relative deviation of 5.07% across a flow velocity range of 0.1–1.7 m/s while reducing hardware complexity by 78.9% compared to the conventional FFT-based cross-correlation methods. This study presents a novel design integrating energy-efficient ultrasonic flowmeters into remote metering systems, smart grids, and industrial monitoring applications. Full article

The application of smart packaging technology in fruit and vegetable preservation has shown significant potential with the ongoing advancement of science and technology. Smart packaging leverages advanced sensors, smart materials, and Internet of Things (IoT) technologies to monitor and regulate the storage environment of fruits and vegetables in real time. This approach effectively extends shelf life, enhances food safety, and reduces food waste. The principle behind smart packaging involves real-time monitoring of environmental factors, such as temperature, humidity, and gas concentrations, with precise adjustments based on data analysis to ensure optimal storage conditions for fruits and vegetables. Smart packaging technologies encompass various functions, including antibacterial action, humidity regulation, and gas control. These functions enable the packaging to automatically adjust its internal environment according to the specific requirements of different fruits and vegetables, thereby slowing the growth of bacteria and mold, prolonging freshness, and retaining nutritional content. Despite its advantages, the widespread adoption of smart packaging technology faces several challenges, including high costs, limited material diversity and reliability, lack of standardization, and consumer acceptance. However, as technology matures, costs decrease, and degradable smart packaging materials are developed, smart packaging is expected to play a more prominent role in fruit and vegetable preservation. Future developments are likely to focus on material innovation, deeper integration of IoT and big data, and the promotion of environmentally sustainable packaging solutions, all of which will drive the fruit and vegetable preservation industry toward greater efficiency, intelligence, and sustainability. Full article

Mobility hubs are meeting points for shared and active mobility within the existing public transport system. Despite an extensive public transportation network, private vehicle dependency remains high, indicating a critical need to enhance alternative transport modes. The aim of this study was to investigate public acceptance of smart and green mobility hubs, a crucial parameter for the successful transition from passenger cars to public transport. To achieve this objective, a stated preference survey was developed and distributed to a sample of 152 participants at two stations, namely Voula and Irakleio, that correspond to different public transport modes, tram and metro. Results indicated a generally positive attitude towards the development of green and smart mobility hubs. The survey revealed variations in commuter preferences between the two locations, with green spaces and smart charging benches being highly valued. Ultimately, this research illustrated that well-designed mobility hubs are instrumental in creating efficient, sustainable and livable urban environments, setting a new standard for future urban planning and development. The proposed interventions are expected to substantially contribute to the promotion of sustainable urban mobility in the respective areas and the broader city, enhancing quality of life and reducing the environmental footprint. Full article

The information interaction characteristics of connected vehicles are distinct from those of non-connected vehicles, thereby exerting an influence on the conventional traffic flow model. The original lane-changing model for non-connected vehicles is no longer applicable in the context of the new traffic flow environment. The modelling of the new hybrid traffic flow, comprising both connected and ordinary vehicles, is set to be a pivotal research topic in the coming years. The objective of this paper is to present a methodology for optimal mixed traffic flow dynamic modelling and cooperative control in intelligent and connected environments (ICE). The study utilizes the real-time perception and information interaction of connected vehicles for traffic information, taking into account the access characteristics of both connected and non-connected vehicles. The satisfaction-based free lane-changing and mandatory lane-changing models of connected vehicles are designed. Secondly, a mixed traffic flow lane-changing model based on influence characteristics is constructed for the influence area of connected vehicles. This model takes into account the degree of influence that connected vehicles have on non-connected vehicles, with different distances being considered respectively. Subsequently, a vehicle guidance strategy for mixed traffic flows comprising grid-connected and conventional vehicles is proposed. A variety of speed guidance scenarios are considered, with an in-depth analysis of the speed optimization of connected vehicles and the movement law of non-connected vehicles. This comprehensive analysis forms the foundation for the development of a vehicle guidance strategy for mixed traffic flows, with the overarching objective being to minimize the average delay of vehicles. In order to evaluate the effectiveness of the proposed method, the intersection of Gaota Road and Fangshui North Street in Yanqing District, Beijing, has been selected for analysis. The results of the study demonstrate that by modifying the density of the mixed traffic flow, the overall average speed of the mixed traffic flow declines as the density of vehicles increases. The findings reported in this study reflect the role of connected vehicles in enhancing road capacity, maximizing intersection capacity and mitigating the occurrence of queuing phenomena, and improving travel speed through the mixed traffic flow lane-changing model based on impact characteristics. This study also provides some guidance for future control of the mixed traffic flow formed by emergency vehicles and social vehicles and for realizing a smart city. Full article

Background: This study examined the role of total quality management, just-in-time, and green supply chain management practices to improve environmental performance. Methods: Data from 207 manufacturing industry respondents from a developing economy were tested through a quantitative method using PLS-SEM with the help of SmartPLS to validate the measurement model. Results: The results show that just-in-time significantly impacts total quality management and green supply chain management practices. Similarly, total quality management significantly affects environmental performance. However, just-in-time insignificantly affects the environment. Likewise, total quality management is insignificant and negatively affects green supply chain practices. Conclusion: This research provides practical insight to practitioners for understanding and implementing practices in their supply chain networks. These findings support the strategic use of just-in-time and total quality management to promote green supply chain practices as a core skill to improve environmental performance. The findings are also helpful for supply chain practitioners, policymakers, and industrialists. This research enriches the literature in the supply chain. Full article

Since the introduction of robot-assisted laparoscopic surgery, efforts have been made to incorporate force sensing technologies to monitor critical components and to provide force feedback. The advanced laparoscopic robotic system (AdLap RS) is a robotic platform that aims to make robot technology more sustainable through the use of the fully reusable shaft-actuated tip-articulating (SATA) instruments. The SATA instrument driver features electronics and sensors exposed to the sterile environment, which complicate the sterilisation process. The aim of this study was to develop and validate smart sensing in stepper motors using the back electromotive force in a newly developed Smart SATA Driver (SSD), eliminating the need for sensors in the sterile environment. Methods: The stepper drivers were equipped with TMC2209 ICs featuring StallGuard technology to measure back EMF. The tip was actuated up until a set StallGuard threshold value was reached, at which the resulting tip force was measured. This cycle was repeated ten times for a range of threshold levels. A regression analysis with a power series model was used to determine the quality of the fit. Results: The SSD is capable of exerting tip forces between 2.4 and 8.2 N. The back EMF force test demonstrated a strong correlation between obtained StallGuard values and measured tip forces. The regression analysis showed an R-squared of 0.95 and a root Mean squared error of 0.4 N. Discussion: The back EMF force test shows promise for force feedback, but its accuracy limits real-time use due to back EMF fluctuations. Future improvements in motor stability and refining the back EMF model are needed to enable real-time feedback. Conclusion: The strong correlation during the back EMF force test shows its potential as a low-budget method for detecting motor stalls and estimating tool–tissue forces without the need for sensors in laparoscopic instruments. Full article

Insecticide use in agriculture has significantly increased over the past decades, reaching 774 thousand metric tons in 2022. This widespread reliance on chemical insecticides has substantial economic, environmental, and human health consequences, highlighting the urgent need for sustainable pest management strategies. Early detection, insect monitoring, and population forecasting through Artificial Intelligence (AI)-based methods, can enable swift responsiveness, allowing for reduced but more effective insecticide use, mitigating traditional labor-intensive and error prone solutions. The main challenge is creating AI models that perform with speed and accuracy, enabling immediate farmer action. This study highlights the innovating potential of such an approach, focusing on the detection and prediction of black aphids under state-of-the-art Deep Learning (DL) models. A dataset of 220 sticky paper images was captured. The detection system employs a YOLOv10 DL model that achieved an accuracy of 89.1% (mAP₅₀). For insect population prediction, random forests, gradient boosting, LSTM, and the ARIMA, ARIMAX, and SARIMAX models were evaluated. The ARIMAX model performed best with a Mean Square Error (MSE) of 75.61, corresponding to an average deviation of 8.61 insects per day between predicted and actual insect counts. For the visualization of the detection results, the DL model was embedded to a mobile application. This holistic approach supports early intervention strategies and sustainable pest management while offering a scalable solution for smart-agriculture environments. Full article

Multirotor drones play an increasingly significant role in smart cities and are among the most widely discussed emerging technologies. They are expected to support various applications such as package delivery, data collection, traffic policing, surveillance, and medicine. As part of their services, future drones should be able to solve the last-mile challenge and land safely in urban areas. This paper addresses the path planning task for an autonomous drone searching for a landing place in an urban environment. Our algorithm uses a novel multi-resolution probabilistic approach in which visual information is collected by the drone at decreasing altitudes. As part of the exploration task, we present the Global Path Planning (GPP) problem, which uses probabilistic information and the camera’s field of view to plan safe trajectories that will maximize the search success by covering areas with high potential for proper landing while avoiding no-fly zones and complying with time constraints. The GPP problem is formulated as a minimization problem and then is shown to be NP-hard. As a baseline, we develop an approximation algorithm based on an exhaustive search, and then we devise a more complex yet efficient heuristic algorithm to solve the problem. Finally, we evaluate the algorithms’ performance using simulation experiments. Simulation results obtained from various scenarios show that the proposed heuristic algorithm significantly reduces computation time while keeping coverage performance close to the baseline. To the best of our knowledge, this is the first work referring to a multi-resolution approach to such search missions; further, in particular, the GPP problem has not been addressed previously. Full article

Path reasoning in knowledge graphs is a pivotal task for uncovering complex relational patterns and facilitating advanced inference processes. It also holds significant potential in domains such as power electronics, where real-time reasoning over dynamic, evolving data is essential for advancing topology design and application systems. Despite its importance, traditional approaches often encounter substantial limitations when applied to dynamic, time-sensitive scenarios. These models typically fail to adequately capture intricate logical dependencies and demonstrate suboptimal performance in data-constrained environments. To address these challenges, we introduce Path-Reasoning Logic (PRlogic), an innovative framework that seamlessly integrates rule-based logical reasoning with cutting-edge neural network methodologies. PRlogic enhances path inference by leveraging a context-aware logical association network adept at handling temporal and event-driven attributes, enabling improved reasoning for dynamic systems such as IoT-based power electronics and smart grids. This adaptability allows the framework to better accommodate evolving knowledge structures, significantly improving reasoning accuracy under resource-scarce conditions. Furthermore, PRlogic employs a multi-stage refinement strategy, harmonizing logic-based rules with learned contextual representations to achieve heightened robustness and scalability. Comprehensive experiments on widely-recognized benchmark datasets validate the superiority of PRlogic, demonstrating its consistent outperformance of existing models in path reasoning tasks. These results underscore the efficacy of incorporating logic-driven mechanisms into knowledge graph reasoning and highlight PRlogic’s potential as a powerful solution for applications in dynamic data environments. Full article

This study investigates the unique interplay between thermal comfort and antimicrobial properties in base fabrics, shaping the foundation for the development of “Smart Socks” as advanced personal protective equipment (PPE). By delving into the inherent qualities of fibers such as cotton, polyester, bamboo, and wool and exploring fabric structures like single jersey, terry, rib, and mesh, the research captures the dynamic relationship between material composition and performance. Terry fabrics emerge as insulators, wrapping the user in warmth ideal for cold climates, while mesh structures breathe effortlessly, enhancing air circulation and moisture wicking for hot environments. Cotton mesh, with its natural affinity for moisture, showcases exceptional moisture management. Antimicrobial testing, focused on fabrics’ interactions with Staphylococcus aureus, highlights the dormant potential of bamboo’s bio-agents while revealing the necessity for advanced antimicrobial treatments. This study unveils a vision for combining innovative fabric structures and fibers to craft smart socks that balance thermal comfort, hygiene, and functionality. Future directions emphasize sensor integration for real-time physiological monitoring, opening pathways to revolutionary wearable PPE. Full article