Search Results (32,778)

Background: Burnout is a prevalent issue among mental health nurses. While various interventions have been implemented to address burnout, their effectiveness and sustainability remain unclear in specialised mental health settings. This systematic review aims to clearly evaluate the effectiveness of interventions specifically designed to reduce burnout among mental health nurses, focusing on intervention types, their impact, and the sustainability of results. Methods: A comprehensive search of databases (Embase, CINAHL, Medline, PubMed, Scopus, and Web of Science) identified studies on burnout reduction interventions for mental health nurses. Inclusion criteria focused on mental health nursing populations with pre- and post-intervention burnout measures. Methodological quality was assessed using JBI Critical Appraisal Tools. A narrative synthesis guideline was used to analyse data. Results: Among 2502 studies retrieved, only 4 met the inclusion criteria after a rigorous screening process. These studies explored specific intervention types, including a two-day burnout prevention workshop, an eight-week group-based psychoeducational programme, a twelve-week mindfulness-based psychoeducational intervention, and an eight-week guided self-help mindfulness programme delivered via a digital platform. Significant reductions in burnout were observed across these studies; however, the sustainability of these effects varied. Interventions of greater duration, such as the 12-week mindfulness-based programme and the 8-week group psychoeducational intervention, yielded more enduring improvements. In contrast, shorter interventions, like a two-day workshop, showed transient benefits that diminished over time. Conclusions: This review highlights a critical gap in research on burnout interventions for mental health nurses. While the reviewed interventions showed promise in reducing burnout, the findings underscore the need for sustainable, adaptable interventions and more robust research. Full article

This review explores how multimodal foundation models (MFMs) are transforming biliary tract cancer (BTC) research. BTCs are aggressive malignancies with poor prognosis, presenting unique challenges due to difficult diagnostic methods, molecular complexity, and rarity. Importantly, intrahepatic cholangiocarcinoma (iCCA), perihilar cholangiocarcinoma (pCCA), and distal bile duct cholangiocarcinoma (dCCA) represent fundamentally distinct clinical entities, with iCCA presenting as mass-forming lesions amenable to biopsy and targeted therapies, while pCCA manifests as infiltrative bile duct lesions with challenging diagnosis and primarily palliative management approaches. MFMs offer potential to advance research by integrating radiological images, histopathology, multi-omics profiles, and clinical data into unified computational frameworks, with applications tailored to these distinct BTC subtypes. Key applications include enhanced biomarker discovery that identifies previously unrecognizable cross-modal patterns, potential for improving currently limited diagnostic accuracy—though validation in BTC-specific cohorts remains essential—accelerated drug repurposing, and advanced patient stratification for personalized treatment. Despite promising results, challenges such as data scarcity, high computational demands, and clinical workflow integration remain to be addressed. Future research should focus on standardized data protocols, architectural innovations, and prospective validation studies. The integration of artificial intelligence (AI)-based methodologies offers new solutions for these historically challenging malignancies. However, current evidence for BTC-specific applications remains largely theoretical, with most studies limited to proof-of-concept designs or related cancer types. Comprehensive clinical validation studies and prospective trials demonstrating patient benefit are essential prerequisites for clinical implementation. The timeline for evidence-based clinical adoption likely extends 7–10 years, contingent on successful completion of validation studies addressing current evidence gaps. Full article

This paper introduces the ProVANT Simulator, a comprehensive environment for developing and validating control algorithms for Unmanned Aerial Vehicles (UAVs). Built on the Gazebo physics engine and integrated with the Robot Operating System (ROS), it enables reliable Software-in-the-Loop (SIL) and Hardware-in-the-Loop (HIL) testing. Addressing key challenges such as modeling complex multi-body dynamics, simulating disturbances, and supporting real-time implementation, the framework features a modular architecture, an intuitive graphical interface, and versatile capabilities for modeling, control, and hardware validation. Case studies demonstrate its effectiveness across various UAV configurations, including quadrotors, tilt-rotors, and unmanned aerial manipulators, highlighting its applications in aggressive maneuvers, load transportation, and trajectory tracking under disturbances. Serving both academic research and industrial development, the ProVANT Simulator reduces prototyping costs, development time, and associated risks. Full article

As a consequence of the gradual exhaustion of apatite ore reserves, intensive comminution has been implemented in mineral processing operations to enhance phosphorus liberation. Consequently, improving the flotation efficiency of fine particles has remained a persistent challenge within the phosphate industry. The performance of flotation columns is strongly affected by the interaction between gas (bubble) and particle. The present research was designed to evaluate how certain process variables and chemical dosages influence gas holdup and its correlation with the column flotation performance of fine particles derived from a low-grade apatite ore. Column flotation experiments were conducted employing a factorial experimental approach to evaluate the effects of air flow rate, surfactant concentration, collector dosage, and depressant dosage on gas holdup, P₂O₅ grade, and recovery. The results made it possible to identify the levels of gas holdup that lead to appropriate values of P₂O₅ grade and recovery simultaneously, and their relation with the operating variables and reagent dosage. Gas holdup values higher than 23.5% led to the desired values of P₂O₅ grade (>30%) and recovery (>60%) simultaneously. Statistical models were developed with high correlation coefficients (R² > 0.98) to predict P₂O₅ grade and recovery as functions of the operating variables. This research provides a comprehensive framework of the gas holdup effect on column flotation systems, offering significant potential for improving the economic viability of low-grade phosphate ore processing. Full article

Fog computing is increasingly preferred over cloud computing for processing tasks from Internet of Things (IoT) devices with limited resources. However, placing tasks and allocating resources in distributed and dynamic fog environments remains a major challenge, especially when trying to meet strict Quality of Service (QoS) requirements. Deep reinforcement learning (DRL) has emerged as a promising solution to these challenges, offering adaptive, data-driven decision-making in real-time and uncertain conditions. While several surveys have explored DRL in fog computing, most focus on traditional centralized offloading approaches or emphasize reinforcement learning (RL) with limited integration of deep learning. To address this gap, this paper presents a comprehensive and focused survey on the full-scale application of DRL to the task offloading problem in fog computing environments involving multiple user devices and multiple fog nodes. We systematically analyze and classify the literature based on architecture, resource allocation methods, QoS objectives, offloading topology and control, optimization strategies, DRL techniques used, and application scenarios. We also introduce a taxonomy of DRL-based task offloading models and highlight key challenges, open issues, and future research directions. This survey serves as a valuable resource for researchers by identifying unexplored areas and suggesting new directions for advancing DRL-based solutions in fog computing. For practitioners, it provides insights into selecting suitable DRL techniques and system designs to implement scalable, efficient, and QoS-aware fog computing applications in real-world environments. Full article

Reference crop evapotranspiration (ETo) is a crucial component in calculating crop water requirements, and its accurate prediction is vital for effective agricultural water management and irrigation planning. Generally, the FAO Penman-Monteith 56 equation is recommended as the benchmark’s method for calculating Eto, but it requires extensive meteorological data—posing challenges in regions with sparse monitoring infrastructure. This review addresses a critical gap: the lack of systematic comparative analysis of machine learning (ML) methods for ETo estimation under data-limited conditions. We review 325 studies searched by Web of Science from 2001 to 2024, focusing on applications of machine learning models in ETo modeling and prediction. Then, this review evaluates these models regarding their characteristics, accuracy, and applicability, including artificial neural networks (ANN), support vector machines (SVM), ensemble learning (EL), and deep learning (DL). Crucially, EL models demonstrate superior stability and cost-effectiveness, with typical performance metrics of R² > 0.95 and RMSE ranging from 0.1 to 0.6 mm·d⁻¹. Notably, DL methods achieve the highest accuracy under conditions of data scarcity. Using only temperature data, they attain competitive performance (R² = 0.81, RMSE = 0.56 mm·d⁻¹). Additionally, we further synthesize optimal input variables, performance metrics, and domain-specific implementation guidelines. In summary, this study provides a comprehensive and up-to-date overview of machine learning methods for ETo modeling, thereby offering valuable insights for researchers in the field of evapotranspiration. Full article

Background/Objectives: Swallowing disorder(s), or oropharyngeal dysphagia (OPD), are very common in children with cerebral palsy (CP) and pose a significant risk to their health. Behavioural interventions are frequently recommended when targeting OPD in children with CP; however, their efficacy has yet to be determined. This systematic review aimed to synthesise the current evidence for behavioural interventions in the treatment of OPD in children with CP. Methods: A comprehensive search in six databases in October 2024 sought studies that (1) included participants aged 0–18 years with a diagnosis of CP and OPD; (2) utilised and described a behavioural intervention for OPD; and (3) used a randomised controlled trial (RCT) experimental design. Three reviewers independently extracted the data, and results were tabulated. The Revised Cochrane Risk of Bias (ROB-2) tool was used to determine the methodological quality of eligible articles. Results: From an initial yield of 2083 papers, 99 full-text studies were screened for eligibility. Seven RCTs involving 329 participants aged 9.5 months (SD = 2.03) to 10.6 yrs were included. CP description varied. Most studies used a combination of behavioural interventions to treat OPD (n = 6), and oral sensorimotor treatment was the most frequently utilised treatment (n = 4). Positive outcomes were reported in all (n = 7); however, there was high risk of bias in five studies. Conclusions: The use of behavioural interventions to treat OPD in children with CP continues to be supported by low-level evidence. Rigorously designed RCTs with larger samples of children with CP and OPD are needed to evaluate the true effects of behavioural interventions across the developmental phase of childhood. Importantly, consistency in describing and reporting baseline analysis of swallowing and OPD; together with treatment-component data, is a priority in future research. Full article

Medium-voltage energy storage converter equipment is an important component of the new generation of ship power and power systems. Virtual space vector pulse width modulation, as a modulation optimization method to improve the neutral-point voltage imbalance in medium- and high-voltage multilevel energy storage converters, has become a research hotspot for T-type three-level energy storage inverter modulation methods due to its significant balancing effect and simple implementation. However, the current research method of constructing virtual vectors through redundant small vectors has limitations in regulating the neutral-point potential under full (especially high) modulation ratios. This paper proposes a modulation method that uses hybrid variable virtual small vectors and virtual medium vectors through optimization selection and reconstruction of basic vectors. This method ensures that the neutral-point charge change of the vector is zero and the common-mode voltage is minimized within the switching period under the full modulation ratio, achieving the purpose of controlling the neutral-point voltage balance and suppressing the common-mode voltage. Finally, simulation and experimental results show that the proposed method has good neutral-point voltage regulation and common-mode voltage suppression capabilities within the full modulation ratio range, and the system also has strong robustness and adaptability under different load conditions. Full article

A smart irrigation system based on soil moisture sensors supported by photovoltaic energy is an innovation to address water use efficiency in the agricultural sector, especially in remote areas. This technology utilizes photovoltaic panels as a renewable energy source to operate water pumps, while soil moisture sensors provide real-time data that is used to automatically manage irrigation according to plant needs. This technology not only increases the efficiency of water and energy use but also supports environmental conservation by reducing dependence on fossil fuels. This research was conducted using a Systematic Literature Review (SLR) approach guided by the PRISMA framework to analyze trends, benefits, and challenges in implementing this technology. The analysis results show that this system offers various advantages, including energy efficiency, reduced carbon emissions, and ease of management through the integration of Internet of Things (IoT) technology. Several challenges remain, such as high initial investment costs, limited network access, and obstacles. Technical matters related to installation and maintenance. Various solutions have been proposed, including providing subsidies for small farmers, implementing radiofrequency modules, and using modular designs to simplify implementation. This study contributes to the development of a conceptual framework that can be adapted to various geographic and socio-economic conditions. Potential further developments include the integration of artificial intelligence and additional sensors to increase efficiency and support the sustainability of the agricultural sector globally. Full article

Pipelines play a vital role in material transportation within industrial settings. This review synthesizes detection technologies for early-stage small gas leaks from pipelines in the industrial sector, with a focus on acoustic-based methods, optical gas imaging (OGI), and multimodal fusion approaches. It encompasses detection principles, inherent challenges, mitigation strategies, and the state of the art (SOTA). Small leaks refer to low flow leakage originating from defects with apertures at millimeter or submillimeter scales, posing significant detection difficulties. Acoustic detection leverages the acoustic wave signals generated by gas leaks for non-contact monitoring, offering advantages such as rapid response and broad coverage. However, its susceptibility to environmental noise interference often triggers false alarms. This limitation can be mitigated through time-frequency analysis, multi-sensor fusion, and deep-learning algorithms—effectively enhancing leak signals, suppressing background noise, and thereby improving the system’s detection robustness and accuracy. OGI utilizes infrared imaging technology to visualize leakage gas and is applicable to the detection of various polar gases. Its primary limitations include low image resolution, low contrast, and interference from complex backgrounds. Mitigation techniques involve background subtraction, optical flow estimation, fully convolutional neural networks (FCNNs), and vision transformers (ViTs), which enhance image contrast and extract multi-scale features to boost detection precision. Multimodal fusion technology integrates data from diverse sensors, such as acoustic and optical devices. Key challenges lie in achieving spatiotemporal synchronization across multiple sensors and effectively fusing heterogeneous data streams. Current methodologies primarily utilize decision-level fusion and feature-level fusion techniques. Decision-level fusion offers high flexibility and ease of implementation but lacks inter-feature interaction; it is less effective than feature-level fusion when correlations exist between heterogeneous features. Feature-level fusion amalgamates data from different modalities during the feature extraction phase, generating a unified cross-modal representation that effectively resolves inter-modal heterogeneity. In conclusion, we posit that multimodal fusion holds significant potential for further enhancing detection accuracy beyond the capabilities of existing single-modality technologies and is poised to become a major focus of future research in this domain. Full article

The growing complexity and size of healthcare systems have rendered fraud detection increasingly challenging; however, the current literature lacks a holistic view of the latest machine learning (ML) techniques with practical implementation concerns. The present study addresses this gap by highlighting the importance of machine learning (ML) in preventing and mitigating healthcare fraud, evaluating recent advancements, investigating implementation barriers, and exploring future research dimensions. To further address the limited research on the evaluation of machine learning (ML) and hybrid approaches, this study considers a broad spectrum of ML techniques, including supervised ML, unsupervised ML, deep learning, and hybrid ML approaches such as SMOTE-ENN, explainable AI, federated learning, and ensemble learning. The study also explored their potential use in enhancing fraud detection in imbalanced and multidimensional datasets. A significant finding of the study was the identification of commonly employed datasets, such as Medicare, the List of Excluded Individuals and Entities (LEIE), and Kaggle datasets, which serve as a baseline for evaluating machine learning (ML) models. The study’s findings comprehensively identify the challenges of employing machine learning (ML) in healthcare systems, including data quality, system scalability, regulatory compliance, and resource constraints. The study provides actionable insights, such as model interpretability to enable regulatory compliance and federated learning for confidential data sharing, which is particularly relevant for policymakers, healthcare providers, and insurance companies that intend to deploy a robust, scalable, and secure fraud detection infrastructure. The study presents a comprehensive framework for enhancing real-time healthcare fraud detection through self-learning, interpretable, and safe machine learning (ML) infrastructures, integrating theoretical advancements with practical application needs. Full article

The article presents the results of research on improving construction safety using the eye tracking method. The analysis was carried out during training in the field of construction safety. Eye tracker allows for analysis of the way in which training participants process visual information and elements that attract their attention and the effectiveness of learning the principles of work safety. Eye tracking studies, in the aspect of construction safety, determine the effectiveness of training in this area. Moreover, the main advantage of such studies lies in the possibility of identifying elements of the construction site that are omitted or misunderstood by training participants, and which are important from the point of view of safe implementation of construction works. The study found that employees achieved the highest level of error detection (70%), with a shorter fixation time (240 ms), suggesting the role of experience and cognitive automation. Post-trained students demonstrated the longest fixation time (350 ms) and moderate error detection (35%), suggesting greater cognitive engagement but lower efficiency than experts. Students without training achieved the lowest results (30% detection, 200 ms FT), which is related to a lack of knowledge and experience. ANOVA confirmed statistically significant differences between groups in fixation time (F(3,36) = 244.83; p < 0.0001), with a high confidence level (>99.99%). Tukey’s post hoc test indicated significant differences between untrained and post-trained students and between post-trained students and employees (p < 0.001), underscoring the importance of both training and professional practice. Full article

This study seeks to identify the factors influencing the implementation of Project Management Methodologies (PMM) in Local Government (LG) and identify the concepts, themes and characteristics that make up each of those factors. Semi-structured interviews were employed as the primary technique, engaging practitioners directly involved in local government capital works projects. This approach allowed for flexibility in exploring individual perspectives while maintaining consistency across key thematic areas. The interviews were designed to elicit rich, detailed narratives about organisational practices, procedural challenges, and behavioural attitudes toward PMM. Subsequently, a qualitative thematic analysis was adopted for the study. Through systematically coding, insights emerge regarding the key factors influencing PMM adoption, deployment, and optimisation. The findings suggest that strong leadership commitment, adaptive learning and structured oversight are critical for successful PMM implementation. “Governance”, “Experience and competency” and “Comparison and reflection” appear to be the most influential factors for PMM adoption, deployment and optimisation, respectively. The outcomes of this research will assist LGs in identifying and understanding the factors that influence the implementation of a PMM. Currently, no mandatory national policies standardise project management capabilities within the LG sector in Australia. Therefore, the outcomes of this study will provide a substantial body of knowledge and a platform to identity, analyse and evaluate the factors influencing the implementation of a PMM to the existing management practices within LGs. Full article

Peaches are highly susceptible to rapid deterioration and bacterial infection during postharvest transportation and storage, leading to significant losses. In order to maintain peach fruit postharvest quality and extend its shelf life, it is critical to understand the physiological changes in postharvest fruit and implement effective postharvest technologies. This paper reviews the major postharvest physiological changes in peach fruit, including respiration, ethylene, hormones, texture, sugars, amino acids, phenolics, and volatiles, analyzes the major postharvest peach fruit diseases and their control techniques (covering brown rot, soft rot, and gray mold), and summarizes approaches to extend the storage life of peach fruit and maintain quality through physical, chemical, and biological preservation techniques. This review evaluates the advantages and disadvantages of postharvest peach fruit preservation techniques by analyzing postharvest physiological and nutritional quality, and suggests future research directions aimed at ensuring peach fruit safety and quality assurance. Full article

The evolution of the chilled processing technology has precipitated the emergence of ice-fresh poultry meat as a significant sales channel. The aesthetic appearance of chicken carcasses has become increasingly important in the context of poultry ice-fresh sales, in conjunction with the comprehensive implementation of China’s policies for poultry. Feather follicle development is a significant factor in determining the aesthetic appearance of the carcass. Recent studies have focused on the molecular mechanisms associated with feather follicle development. The WNT, EGF, FGF, SHH, and BMP signalling pathways have been identified as the regulatory mechanisms involved in the development of feather follicles in various segments of poultry skin. However, the BMP signalling pathway, acting as an inhibitor, has been demonstrated to impede the regulatory processes governing feather follicle development via these signalling pathways. This review summarises the structure and overview of feathers and feather follicles, the research progress of signalling pathways that affect the development of poultry feather follicles, the research progress of poultry follicle traits, and the research progress of feather follicle development biotechnology. The present review focuses on summarising the molecular mechanisms that affect feather follicle development, and on providing a summary of the application of biotechnology in this field. It also offers ideas and theoretical references for the molecular mechanism of poultry feather follicle development. Full article