Search Results (30)

Background/Objectives: Burnout syndrome among nurses can significantly contribute to the nursing shortage, leading to high turnover and negative impacts on both nurses and patient care. The primary objective of this project was to evaluate the feasibility, acceptability, and preliminary effect of a mindfulness-based mobile application (MBMA) on burnout and well-being in emergency department (ED) nurses over four weeks. Methods: An EBPQI with a descriptive approach was taken to evaluate ED nurses’ burnout and well-being, which was measured with the Mini-Z Single Item (MZSI) and Nurses’ Well-Being Index (NWBI). We also asked three open-ended questions about their experience using the once-daily MBMA over the four-week period. Twelve participants from a mid-western hospital were recruited, and six completed both the pre-test and post-test surveys. Results: Results found no statistically significant improvement in burnout (p = 1.00) or well-being (p = 0.783). However, upon a secondary analysis using imputed data, a statistically significant improvement in burnout was found (p = 0.012). Among the six participants who completed the post-intervention, a significant and positive correlation between burnout and well-being was identified (r = 0.81, p = 0.048). Themes from qualitative responses included perceived helpfulness of MBMA tools, perceived usefulness, and lack of time for daily participation. Although statistical improvements were not observed, individual comments indicated that the tool was helpful; however, setting aside time to engage with it remained difficult. Conclusions: Findings from this project support the need for further research exploring the impact of individualized interventions specifically targeting ED nurses as well as organizational strategies aimed at those already experiencing burnout or impaired well-being. Full article

Aircraft maintenance is a complex, multifaceted process that greatly benefits from IT systems designed to improve supervision, record keeping, and task management. This study focuses on the role of a dedicated mobile application, integrated into the broader IT Aircraft Maintenance Support System, which supports maintenance operations for the M-346 BIELIK training aircraft. Aircraft maintenance is a highly intricate and multifaceted process that significantly benefits from advanced IT systems designed to enhance supervision, streamline record keeping, and optimize task management. This study explores the pivotal role of a dedicated mobile application integrated into the broader IT Aircraft Maintenance Support System, specifically tailored to support the maintenance operations of the M-346 BIELIK training aircraft. By focusing on the analysis of Intelligent Transportation Systems (ITSs), the research highlights how the application contributes to maintenance reliability and operational efficiency, with sustainability considerations in mind. The ITS-based approach assesses maintenance scheduling, tracking, and resource optimization, thereby enhancing the reliability of aircraft operations while reducing unnecessary resource consumption. This alignment with sustainable practices not only improves reliability characteristics and exploitation rates but also positively impacts the efficiency and effectiveness of aviation training. By accurately estimating the time requirements of specific maintenance tasks during periodic inspections, the application aids in identifying and addressing organizational bottlenecks, ultimately supporting both operational sustainability and improved task reliability across maintenance activities. Full article

Health risk behaviors continue to disproportionately affect Hispanic youth. Despite the existence of successful family and school-based interventions, there is a need for developing and testing individually-based preventive interventions that are easily accessed and widely disseminated. Therefore, this study aimed to develop a prototype (proof of concept) for an individual-level mobile application (app), informed by Hispanic parents and adolescents, to prevent/reduce drug use and sexual risk behaviors among Hispanic youth. An iterative user-centered approach was used to inform the development of the app prototype via focus groups with 66 participants (n = 46 adolescents, n = 20 parents). A coding team analyzed data from the focus groups and identified major themes. The coding team summarized interview data into sub-categories that yielded five intervention modules for Hispanic adolescents, three more than originally proposed (i.e., drug use and sexual risk behaviors): (1) effective communication, (2) depression, (3) sexual health, (4) drug use, and (5) mindfulness. A mobile application for health risk behaviors can be used as an additional preventive tool to decrease the existing behavioral health disparities among Hispanic youth. Incorporating a user-centered approach to inform development is important for including the needs and voices of this population. Full article

Background: The issue of renewable energy (RE) source intermittency, such as wind and solar, along with the geographically uneven distribution of the global RE potential, makes it imperative to establish an energy transport medium to balance the energy demand and supply areas. A promising energy vector to address this situation is hydrogen, which is considered a clean energy carrier for various mobile and portable applications. Unfortunately, at standard pressure and temperature, its energy content per volume is very low (0.01 kJ/L). This necessitates alternative storage technologies to achieve reasonable capacities and enable economically viable long-distance transportation. Among the hydrogen storage technologies using chemical methods, liquid organic hydrogen carrier (LOHC) systems are considered a promising solution. They can be easily managed under ambient conditions, the H₂ storage/release processes are carbon-free, and the carrier liquid is reusable. However, the evolution of the proposals from the carrier liquid type and catalyst elemental composition point of view is scarcely studied, considering that both are critical in the performance of the system (operational parameters, kinetic of the reactions, gravimetric hydrogen content, and others) and impact in the final cost of the technology deployed. The latter is due to the use of the Pt group elements (PGEs) in the catalyst that, for example, have a high demand in the hydrogen production sector, particularly for polymer electrolyte membrane (PEM) water electrolysis. With that in mind, our objective was to examine the evolution and the focus of the research in recent years related to proposals of LOHCs and catalysts for hydrogenation and dehydrogenation reactions in LOHC systems which can be useful in defining routes/strategies for new participants interested in becoming involved in the development of this technology. Data sources: For this systematic review, we searched the SCOPUS database and forward and backward citations for studies published in the database between January 2011 and December 2022. Eligibility criteria: The criteria include articles which assessed or studied the effect of the type of catalyst, type of organic liquid, reactor design(s)/configuration(s), and modification of the reactor operational parameters, among others, over the performance of the LOHC system (de/hydrogenation reaction(s)). Data extraction and analysis: The relevant data from each reviewed study were collected and organized into a pre-designed table on an Excel spreadsheet, categorized by reference, year, carrier organic liquid, reaction (hydrogenation and/or dehydrogenation), investigated catalyst, and primary catalyst element. For processing the data obtained from the selected scientific publications, the data analysis software Orbit Intellixir was employed. Results: For the study, 233 studies were included. For the liquid carrier side, benzyltoluene and carbazole dominate the research strategies. Meanwhile, platinum (Pt) and palladium (Pd) are the most employed catalysts for dehydrogenation reactions, while ruthenium (Ru) is preferred for hydrogenation reactions. Conclusions: From the investigated liquid carrier, those based on benzyltoluene and carbazole together account for over 50% of the total scientific publications. Proposals based on indole, biphenyl, cyclohexane, and cyclohexyl could be considered to be emerging within the time considered in this review, and, therefore, should be monitored for their evolution. A great activity was detected in the development of catalysts oriented toward the dehydrogenation reaction, because this reaction requires high temperatures and presents slow H₂ release kinetics, conditioning the success of the implementation of the technology. Finally, from the perspective of the catalyst composition (monometallic and/or bimetallic), it was identified that, for the dehydrogenation reaction, the most used elements are platinum (Pt) and palladium (Pd), while, for the hydrogenation reaction, ruthenium (Ru) widely leads its use in the different catalyst designs. Therefore, the near-term initiatives driving progress in this field are expected to focus on the development of new or improved catalysts for the dehydrogenation reaction of organic liquids based on benzyltoluene and carbazole. Full article

The Industrial Metaverse paradigm can be broadly described as a virtual environment that integrates various technologies such as augmented reality and mixed reality to enhance business operations and processes. It aims to streamline workflows, reduce error rates, improve efficiency, and provide a more engaging experience for employees. The promise of the Industrial Metaverse to drive sustainability and resource efficiency is compelling. Using advanced technologies such as the Industrial Metaverse is vital in an endeavor to have a competitive edge in a rapidly evolving business environment. However, the environmental impact of the technologies underpinning the Industrial Metaverse, like data centers and network infrastructure, should not be overlooked. The ecological footprint of these technologies must be considered in the sustainability equation. Researchers have warned that, by 2025, without sustainable artificial intelligence (AI) practices, AI will consume more energy than the human workforce, significantly offsetting zero carbon gains. As the Metaverse persists in evolving and gaining momentum, it will be necessary for companies to prioritize sustainability and explore new ways to balance technological advancements with environmental stewardship. However, recent studies have conjectured that the Metaverse holds the potential to reduce carbon emissions, as digital replacements for physical goods become more prevalent and physical activities like mobility and construction are reduced. Moreover, the specific extent to which this substitution can alleviate environmental concerns remains an open issue, presenting a knowledge gap in understanding the real-world impact of digital replacements. Thus, the objective of this paper is to provide a comprehensive review of the Industrial Metaverse, as well as explore the environmental impact of the Industrial Metaverse. The integrative literature review design and methodological approach involved multiple sources from the Web of Science and databases such as the ACM library, IEEE Library, and Google Scholar, which were analyzed to provide a comprehensive understanding of the developments in the Industrial Metaverse. Firstly, by considering the Industrial Metaverse’s architecture, we elucidate the Industrial Metaverse concept and the associated enabling technologies. Secondly, we performed an exploration through a discussion of the prevalent use cases and the deployment of the emerging Industrial Metaverse. Thirdly, we explored the impact of the Industrial Metaverse on the environment. Lastly, we address novel security and privacy risks, as well as upcoming research challenges, keeping in mind that the Industrial Metaverse is based on a strong data fabric. The results point to the Industrial Metaverse as having both positive and negative environmental effects in terms of energy consumption, e-waste, and pollution. Research, however, indicates that most Industrial Metaverse applications have a positive environmental impact and subsequently trend toward sustainability. Finally, for sustainability in the Industrial Metaverse, enterprises may consider utilizing renewable energy sources and cloud services. Furthermore, we examined the effects of products on the environment, as well as in the creation of a circular economy. Full article

Context: YOLO (You Look Only Once) is an algorithm based on deep neural networks with real-time object detection capabilities. This state-of-the-art technology is widely available, mainly due to its speed and precision. Since its conception, YOLO has been applied to detect and recognize traffic signs, pedestrians, traffic lights, vehicles, and so on. Objective: The goal of this research is to systematically analyze the YOLO object detection algorithm, applied to traffic sign detection and recognition systems, from five relevant aspects of this technology: applications, datasets, metrics, hardware, and challenges. Method: This study performs a systematic literature review (SLR) of studies on traffic sign detection and recognition using YOLO published in the years 2016–2022. Results: The search found 115 primary studies relevant to the goal of this research. After analyzing these investigations, the following relevant results were obtained. The most common applications of YOLO in this field are vehicular security and intelligent and autonomous vehicles. The majority of the sign datasets used to train, test, and validate YOLO-based systems are publicly available, with an emphasis on datasets from Germany and China. It has also been discovered that most works present sophisticated detection, classification, and processing speed metrics for traffic sign detection and recognition systems by using the different versions of YOLO. In addition, the most popular desktop data processing hardwares are Nvidia RTX 2080 and Titan Tesla V100 and, in the case of embedded or mobile GPU platforms, Jetson Xavier NX. Finally, seven relevant challenges that these systems face when operating in real road conditions have been identified. With this in mind, research has been reclassified to address these challenges in each case. Conclusions: This SLR is the most relevant and current work in the field of technology development applied to the detection and recognition of traffic signs using YOLO. In addition, insights are provided about future work that could be conducted to improve the field. Full article

Technological advances in eye-tracking have resulted in lightweight, portable solutions that are capable of capturing eye movements beyond laboratory settings. Eye-tracking devices have typically relied on heavier, video-based systems to detect pupil and corneal reflections. Advances in mobile eye-tracking technology could facilitate research and its application in ecological settings; more traditional laboratory research methods are able to be modified and transferred to real-world scenarios. One recent technology, the AdHawk MindLink, introduced a novel camera-free system embedded in typical eyeglass frames. This paper evaluates the AdHawk MindLink by comparing the eye-tracking recordings with a research “gold standard”, the EyeLink II. By concurrently capturing data from both eyes, we compare the capability of each eye tracker to quantify metrics from fixation, saccade, and smooth pursuit tasks—typical elements in eye movement research—across a sample of 13 adults. The MindLink system was capable of capturing fixation stability within a radius of less than 0.5${}^{\circ }$, estimating horizontal saccade amplitudes with an accuracy of 0.04${}^{\circ }$± 2.3${}^{\circ }$, vertical saccade amplitudes with an accuracy of 0.32${}^{\circ }$± 2.3${}^{\circ }$, and smooth pursuit speeds with an accuracy of 0.5 to 3$\frac{{}^{\circ }}{\mathrm{s}}$, depending on the pursuit speed. While the performance of the MindLink system in measuring fixation stability, saccade amplitude, and smooth pursuit eye movements were slightly inferior to the video-based system, MindLink provides sufficient gaze-tracking capabilities for dynamic settings and experiments. Full article

Spinal fusion surgery leads to the restriction of mobility in the vertebral segments postoperatively, thereby causing stress to rise at the adjacent levels, resulting in early degeneration and a high risk of adjacent vertebral fractures. Thus, to address this issue, non-fusion surgery applies some pedicle screw-based dynamic stabilisation systems to provide stability and micromotion, thereby reducing stress in the fusion segments. Among these systems, the hybrid performance stabilisation system (HPSS) combines a rigid rod, transfer screw, and coupler design to offer a semi-rigid fixation method that preserves some mobility near the fusion site and reduces the adjacent segment compensatory effects. However, further research and confirmation are needed regarding the biomechanical effects of the dynamic coupler stiffness of the HPSS on the intrinsic degenerated adjacent segment. Therefore, this study utilised the finite element method to investigate the impact of the coupler stiffness of the HPSS on the mobility of the lumbar vertebral segments and the stress distribution in the intervertebral discs under flexion, extension, and lateral bending, as well as the clinical applicability of the HPSS on the discs with intrinsic moderate and severe degeneration at the adjacent level. The analytical results indicated that, regardless of the degree of disc degeneration, the use of a dynamic coupler stiffness of 57 N/mm in the HPSS may reduce the stress concentrations at the adjacent levels. However, for severely degenerated discs, the postoperative stress on the adjacent segments with the HPSS was still higher compared with that of the discs with moderate degeneration. We conclude that, when the discs had moderate degeneration, increasing the coupler stiffness led to a decrease in disc mobility. In the case of severe disc degeneration, the effect on disc mobility by coupler stiffness was less pronounced. Increasing the coupler stiffness ked to higher stress on intervertebral discs with moderate degeneration, while its effect on stress was less pronounced for discs with severe degeneration. It is recommended that patients with severe degeneration who undergo spinal dynamic stabilisation should remain mindful of the risk of accelerated adjacent segment degeneration. Full article

This paper provides a comprehensive review of the literature concerning the utilization of Natural Language Processing (NLP) techniques, with a particular focus on transformer-based large language models (LLMs) trained using Big Code, within the domain of AI-assisted programming tasks. LLMs, augmented with software naturalness, have played a crucial role in facilitating AI-assisted programming applications, including code generation, code completion, code translation, code refinement, code summarization, defect detection, and clone detection. Notable examples of such applications include the GitHub Copilot powered by OpenAI’s Codex and DeepMind AlphaCode. This paper presents an overview of the major LLMs and their applications in downstream tasks related to AI-assisted programming. Furthermore, it explores the challenges and opportunities associated with incorporating NLP techniques with software naturalness in these applications, with a discussion on extending AI-assisted programming capabilities to Apple’s Xcode for mobile software development. This paper also presents the challenges of and opportunities for incorporating NLP techniques with software naturalness, empowering developers with advanced coding assistance and streamlining the software development process. Full article

A critical part of Automated Material Handling Systems (AMHS) is the task allocation and dispatching strategy employed. In order to better understand and investigate this component, we here present an extensive experimental evaluation of three different approaches with randomly generated, as well as custom designed, environment configurations. While previous studies typically focused on use cases based on highly constrained navigation capabilities (e.g., overhead hoist transport systems), our evaluation is built around highly mobile, free-ranging vehicles, i.e., Autonomous Mobile Robots (AMR) that are gaining popularity in a broad range of applications. Consequently, our experiments are conducted using a microscopic agent-based simulation, instead of the more common discrete-event simulation model. Dispatching methods often are built around the assumption of the asynchronous evaluation of an event-based model, i.e., vehicles trigger a cascade of individual dispatching decisions, e.g., when reaching intersections. We find that this does not translate very well to a fleet of highly mobile systems that can change direction at any time. With this in mind, we present formulations of well known dispatching approaches that are better suited for a synchronous evaluation of the dispatching decisions. The formulations are based on the Stable Marriage Problem (SMP) and the Linear Sum Assignment Problem (LSAP). We use matching and assignment algorithms to compute the actual dispatching decisions. The selected algorithms are evaluated in a multi-agent simulation environment. To integrate a centralised fleet management, a digital twin concept is proposed and implemented. By this approach, the fleet management is independent of the implementation of the specific agents, allowing to quickly adapt to other simulation-based or real application scenarios. For the experimental evaluation, two new performance measures related to the efficiency of a material handling system are proposed, Travel Efficiency and Throughput Effort. The experimental evaluation indicates that reassignment mechanisms in the dispatching method can help to increase the overall efficiency of the fleet. We did not find significant differences in absolute performance in terms of throughput rate. Additionally, the difference in performance between SMP- and LSAP-based dispatching with reassignment seems negligible. We conclude with a discussion, where we consider potential confounding factors and relate the findings to previously reported results found in the literature. Full article

This paper examines mindfulness as a costless cognitive asset in reducing stress and improving subjective well-being and psychological well-being among Malaysian bottom-forty-percent and middle-forty-percent income earners, known as B40 and M40, respectively. The participants recruited for this experimental study were divided into intervention and control groups and completed pre- and post-assessment questionnaires. The leveraging on digital technologies during pandemic times from May to June 2021 enabled participants in the intervention group (n = 95) to undergo four weekly online mindfulness intervention sessions through Google Meet and completed daily home mindfulness practices using the mobile application for mindfulness: the MindFi version 3.8.0 mobile app. Based on the Wilcoxon signed-rank test, the intervention group’s mindfulness and well-being levels increased significantly after four weeks. This outcome contrasted to those in the control group (n = 31), who exhibited lower mindfulness and well-being levels. The PLS-SEM structural model consists of mindfulness as an independent variable, subjective and psychological well-being as dependent variables, and perceived stress and financial desire discrepancies as the mediators. This model has a goodness-of-fit of 0.076, proving that it is a fit and strong model. There is a positive relationship between mindfulness and subjective well-being (β = 0.162, p-value < 0.01). This model supports the mediation effect of perceived stress between mindfulness and subjective well-being variables (β = 0.152, p-value < 0.05). The overall structural model implies that the effectiveness of mindfulness intervention training not only enhanced bottom- and middle-income earners’ well-being but also lowered the perceived stress level that, henceforth, brought the mind and body together in the present moment. Full article

The emergence of fog-based Internet of Things (IoT) systems have played a significant role in enhancing the applicability of the IoT paradigm. In such systems, fog-nodes are proficient enough to retain, process and transmit the data coming from IoT devices. Nevertheless, as an extension of cloud computing, inheriting the security and privacy concerns of cloud computing is also inevitable in fog-based IoT systems. To deal with such challenges, a diverse range of security solutions are reported in the literature. However, most of them have several limitations (i.e., vulnerability to known security attacks and high computation overhead) that curtail their practical implementation applicability. Keeping these limitations in mind, this paper propose a privacy-preserving hash-based authenticated key agreement protocol using XOR and concatenation operations for fog-driven IoT systems. Using healthcare as a case study, the security of the novel protocol is evaluated by using informal and formal security analysis. In order to obtain the experimental results, the key cryptographic operations used at the user, fog node and cloud server-side are implemented on a mobile device, Arduino and cloud server, respectively. Findings from the performance evaluation results show that the proposed protocol has the least computation cost compared to several related competing protocols. Full article

Background and aims: Breast and ovarian cancers affect the lives of many women worldwide. Female cancer survivors often experience hot flashes, a subjective sensation of heat associated with objective signs of cutaneous vasodilatation and a subsequent drop in core temperature. Breast and Ovarian cancer patients also suffer from sleep difficulties and mental health issues. The present study aimed to assess the effectiveness of Bubble, a novel artificial intelligence–virtual reality (AI–VR) intervention for the treatment of hot flashes in female breast or ovarian cancer patients. Methods: Forty-two women with breast and/or ovarian cancer participated in the study. The mean age was 47 years (range: 25–60 years). Patients suffered from hot flashes at different frequencies. They used Bubble, a virtual reality (VR) mobile psychological intervention based on elements from both cognitive behavioral therapy and mindfulness-based stress reduction. The intervention took place in a VR environment, in a winter wonderland setting called Frosty. Patients were instructed to use Bubble at home twice a day (morning and evening) and when experiencing a hot flash. Participants were asked to use the application for 24 consecutive days. Before and after this 24-day period, patients completed self-report questionnaires assessing hot flashes, general psychiatric distress, perceived stress, illness perception, sleep quality, and quality of life. Results: Between pre- and post-intervention, participants reported a significant reduction in the daily frequency of hot flashes, stress, general psychiatric distress, several domains of QOL, and sleep difficulties, as well as an improvement in illness perception. In addition, they reported very high satisfaction with Bubble. Importantly, both age and baseline levels of psychopathology moderated the effect of Bubble on sleep difficulties. Discussion: This study showed preliminary evidence for the potential of VR interventions in alleviating hot flashes and accompanying mental distress among those coping with breast and ovarian cancer. VR is a powerful therapeutic tool, able to address mind–body aspects in a direct, vivid way. More studies are needed in order to fully understand the potential of this unique intervention. Full article

Small-to-medium sized systems able to perform multiple operations are a promising option for use in agricultural robotics. With this in mind, we present the conceptualization and implementation of a versatile and modular unmanned ground vehicle prototype, which is designed on top of a commercial wheeled mobile platform, in order to test and assess new devices, and motion planning and control algorithms for different Precision Agriculture applications. Considering monitoring, harvesting and spraying as target applications, the developed system utilizes different hardware modules, which are added on top of a mobile platform. Software modularity is realized using the Robot Operating System ($ROS$). Self- and ambient-awareness, including obstacle detection, are implemented at different levels. A novel extended Boundary Node Method is used for path planning and a modified Lookahead-based Line of Sight guidance algorithm is used for path following. A first experimental assessment of the system’s capabilities in an emulated orchard scenario is presented here. The results demonstrate good path-planning and path-following capabilities, including cases in which unknown obstacles are present. Full article

Gesture recognition is a topic in computer science and language technology that aims to interpret human gestures with computer programs and many different algorithms. It can be seen as the way computers can understand human body language. Today, the main interaction tools between computers and humans are still the keyboard and mouse. Gesture recognition can be used as a tool for communication with the machine and interaction without any mechanical device such as a keyboard or mouse. In this paper, we present the results of a comparison of eight different machine learning (ML) classifiers in the task of human hand gesture recognition and classification to explore how to efficiently implement one or more tested ML algorithms on an 8-bit AVR microcontroller for on-line human gesture recognition with the intention to gesturally control the mobile robot. The 8-bit AVR microcontrollers are still widely used in the industry, but due to their lack of computational power and limited memory, it is a challenging task to efficiently implement ML algorithms on them for on-line classification. Gestures were recorded by using inertial sensors, gyroscopes, and accelerometers placed at the wrist and index finger. One thousand and eight hundred (1800) hand gestures were recorded and labelled. Six important features were defined for the identification of nine different hand gestures using eight different machine learning classifiers: Decision Tree (DT), Random Forests (RF), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) with linear kernel, Naïve Bayes classifier (NB), K-Nearest Neighbours (KNN), and Stochastic Gradient Descent (SGD). All tested algorithms were ranged according to Precision, Recall, and F1-score (abb.: P-R-F1). The best algorithms were SVM (P-R-F1: 0.9865, 0.9861, and 0.0863), and RF (P-R-F1: 0.9863, 0.9861, and 0.0862), but their main disadvantage is their unusability for on-line implementations in 8-bit AVR microcontrollers, as proven in the paper. The next best algorithms have had only slightly poorer performance than SVM and RF: KNN (P-R-F1: 0.9835, 0.9833, and 0.9834) and LR (P-R-F1: 0.9810, 0.9810, and 0.9810). Regarding the implementation on 8-bit microcontrollers, KNN has proven to be inadequate, like SVM and RF. However, the analysis for LR has proved that this classifier could be efficiently implemented on targeted microcontrollers. Having in mind its high F1-score (comparable to SVM, RF, and KNN), this leads to the conclusion that the LR is the most suitable classifier among tested for on-line applications in resource-constrained environments, such as embedded devices based on 8-bit AVR microcontrollers, due to its lower computational complexity in comparison with other tested algorithms. Full article