Search Results (12,154)

Traditional Case-Based Reasoning (CBR) methods face significant methodological challenges, including limited information resources in case databases, methodologically inadequate similarity calculation approaches, and a lack of standardized case revision mechanisms. These limitations lead to suboptimal case matching and insufficient solution adaptation, highlighting critical gaps in the development of CBR methodologies. This paper proposes a novel CBR framework enhanced by generative AI, aiming to improve and innovate existing methods in three key stages of traditional CBR, thereby enhancing the accuracy of retrieval and the scientific nature of corrections. First, we develop an ontology model for comprehensive case representation, systematically capturing scenario characteristics, risk typologies, and strategy frameworks through structured knowledge representation. Second, we introduce an advanced similarity calculation method grounded in triangle theory, incorporating three computational dimensions: attribute similarity measurement, requirement similarity assessment, and capability similarity evaluation. This multi-dimensional approach provides more accurate and robust similarity quantification compared to existing methods. Third, we design a generative AI-based case revision mechanism that systematically adjusts solution strategies based on case differences, considering interdependence relationships and mutual influence patterns among risk factors to generate optimized solutions. The methodological framework addresses fundamental limitations in existing CBR approaches through systematic improvements in case representation, similarity computation, and solution adaptation processes. Experimental validation using actual case data demonstrates the effectiveness and scientific validity of the proposed methodological framework, with applications in risk assessment and emergency response scenarios. The results show significant improvements in case-matching accuracy and solution quality compared to traditional CBR approaches. This method provides a robust methodological foundation for CBR-based decision-making systems and offers practical value for risk management applications. Full article

This study presents an analytical model to reduce the impact of wave-induced forces on a vertical seawall by introducing a floating elastic plate (EP) located at a specific distance from two bottom-standing porous structures (BSPs). The hydrodynamic interaction with the EP is described using thin plate theory, while the fluid flow through the porous medium is described by the model developed by Sollit and Cross. The resulting boundary value problem is addressed through linear potential theory combined with the eigenfunction expansion method (EEM), and model validation is achieved through consistency checks with recognized results from the literature. A comprehensive parametric analysis is performed to evaluate the influence of key system parameters such as the porosity and frictional coefficient of the BSPs, their height and width, the flexural rigidity of the EP, and the spacing between the EP and BSPs on vital hydrodynamic coefficients, including the wave force on the seawall, free surface elevation, wave reflection coefficient, and energy dissipation coefficient. The results indicate that higher frictional coefficients and higher BSP heights significantly enhance wave energy dissipation and reduce reflection, in accordance with the principle of energy conservation. Oscillatory trends observed with respect to wavenumbers in the reflection and dissipation coefficients highlight resonant interactions between the structures. Moreover, compared with a single BSP, the double BSP arrangement is more effective in minimizing the wave force on the seawall and free surface elevation in the region between the EP and the wall, even when the total volume of porous material remains unchanged. The inter-structural gap is found to play a crucial role in optimizing resonance conditions and supporting the formation of a tranquility zone. Overall, the proposed configuration demonstrates significant potential for coastal protection, offering a practical and effective solution for reducing wave loads on marine infrastructure. Full article

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

Background: Pain is a multifaceted and debilitating symptom in patients with gastrointestinal cancer, especially those undergoing surgical resection followed by chemotherapy. The interplay between inflammatory, neuropathic, and psychosocial components often renders conventional analgesia insufficient. Psychobiotics—probiotic strains with neuroactive properties—have recently emerged as potential modulators of pain perception through neuroimmune and gut–brain axis pathways. Methods: This post hoc analysis is based on the ProDeCa randomized, placebo-controlled trial, which originally aimed to assess the psychotropic effects of a four-strain psychobiotic formulation in postoperative gastrointestinal cancer patients receiving chemotherapy. In the current analysis, we evaluated changes in pain perception among non-depressed and depressed participants, who received either psychobiotics or placebo, along with standard analgesic regimes. Pain was assessed at baseline, after a month of treatment, and at follow-up, 2 months thereafter, using the Short-Form McGill Pain Questionnaire (SF-MPQ), capturing both sensory and affective components, as well as with the Present Pain Intensity and the VAS scores. Results: Psychobiotic-treated participants—particularly the non-depressed ones—exhibited a significant reduction in both quantitative and qualitative pain indices over time compared with placebo-treated ones. Improvements were noted in total pain rating index scores, sensory and affective subscales, and present pain intensity. These effects were sustained up to 2 months after intervention. In contrast, placebo groups demonstrated worsening in pain scores, probably influenced by ongoing chemotherapy and disease progression. The analgesic effect was less pronounced but still observable in the subgroup with symptoms of depression. Conclusions: Adjunctive psychobiotic therapy appears to beneficially modulate pain perception in gastrointestinal oncology patients receiving chemotherapy, with the most pronounced effects being in non-depressed individuals. These findings suggest psychobiotics as a promising non-opioid add-on for comprehensive cancer pain management and support further investigation in larger pain-targeted trials. Full article

Background: Chewing gum is a simple, accessible tool with high user compliance, traditionally associated with oral health benefits. Although its potential effects on different aspects of health and well-being, beyond its oral applications, have been explored, the area remains relatively under-researched. This mapping review and evidence gap map (EGM) aimed to evaluate the existing literature on the non-oral health applications of chewing gum and to identify gaps in the literature. Methods: A comprehensive search was conducted across five databases (Scopus, Embase, PubMed, PsycINFO, and CINAHL) using tailored search strategies. Abstracts were screened against predefined eligibility criteria using EPPI-Reviewer version 6, with full texts reviewed only when relevant information could not be drawn. The included studies were coded by gum type, outcome, and study design, and the EGM was constructed using EPPI-Mapper version 2.4.5. Results: Of the 2614 identified records, 1326 were screened after duplicate removal, and 260 studies were included in the final analysis. Three main areas of application emerged: for enhancing well-being and performance, as a medical aid and as a surgical/procedural aid. The EGM indicated that the most frequently studied uses of chewing gum were in sports performance, smoking cessation, and post-operative recovery. However, notable research gaps were found, particularly in paediatric and geriatric contexts. Conclusions: Chewing gum has been extensively studied as a surgical or procedural aid, particularly for post-operative gastrointestinal recovery, but its broader applications for well-being, performance, and its use in paediatric and elderly populations remain underexplored. Further high-quality research using standardised methodologies is needed to address these gaps. 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: Cardiac sarcoidosis (CS) is an inflammatory cardiomyopathy with a strong clinical impact on patients affected by the disease and a challenging diagnosis. Methods: This comprehensive narrative review evaluates the role of [18F]fluorodesoxyglucose ([18F]FDG) positron emission tomography (PET)-based radiomics and machine learning (ML) analyses in the assessment of CS. Results: The value of [18F]FDG PET-based radiomics and ML has been investigated for the clinical settings of diagnosis and prognosis of patients affected by CS. Even though different radiomics features and ML models have proved their clinical role in these settings in different cohorts, the clear superiority and added value of one of them across different studies has not been demonstrated. In particular, textural analysis and ML showed high diagnostic value for the diagnosis of CS in some papers, but had controversial results in other works, and may potentially provide prognostic information and predict adverse clinical events. When comparing these analyses with the classic semiquantitative evaluation, a conclusion about which method best suits the final objective cannot be drawn with the available references. Different methodological issues are present when comparing different papers, such as image segmentation and feature extraction differences that are more evident. Furthermore, the intrinsic limitations of radiomics analysis and ML need to be overcome with future research developed in multicentric settings with protocol harmonization. Conclusions: [18F]FDG PET-based radiomics and ML show preliminary promising results for CS evaluation, but remain investigational tools since the current evidence is insufficient for clinical adoption due to methodological heterogeneity, small sample sizes, and lack of standardization. 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

This study focuses on a comprehensive analysis of the common methods for road infrastructure monitoring, as well as the perspective of various fiber-optic sensor (FOS) realization solutions in road monitoring applications. Fiber-optic sensors are a topical technology that ensures multiple advantages such as passive nature, immunity to electromagnetic interference, multiplexing capabilities, high sensitivity, and spatial resolution, as well as remote operation and multiple physical parameter monitoring, hence offering embedment potential within the road pavement structure for needed smart road solutions. The main key factors that affect FOS-based road monitoring scenarios and configurations are analyzed within this review. One such factor is technology used for optical sensing—fiber Bragg grating (FBG), Brillouin, Rayleigh, or Raman-based sensing. A descriptive comparison is made comparing typical sensitivity, spatial resolution, measurement distance, and applications. Technological approaches for monitoring physical parameters, such as strain, temperature, vibration, humidity, and pressure, as a means of assessing road infrastructure integrity and smart application integration, are also evaluated. Another critical aspect concerns spatial positioning, focusing on the point, quasi-distributed, and distributed methodologies. Lastly, the main topical FOS-based application areas are discussed, analyzed, and evaluated. Full article

This study investigates the certification outcomes of intermediate-level ICs in The National Fire Service Academy in South Korea through a comprehensive quantitative analysis of their evaluated competencies. Using assessment data from 141 candidates collected from 2022 to 2024, we examine how scores on six higher-order competencies—comprising 35 sub-competencies—influence pass or fail results. Descriptive statistics, correlation analysis, logistic regression (a statistical model for binary outcomes), random forest modeling (an ensemble decision-tree machine-learning method), and principal component analysis (PCA; a dimensionality reduction technique) were applied to identify significant predictors of certification success. Visualization techniques, including heatmaps, box plots, and importance bar charts, were used to illustrate performance gaps between successful and unsuccessful candidates. Results indicate that competencies related to decision-making under pressure and crisis leadership most strongly correlate with positive outcomes. Furthermore, unsupervised clustering analysis (a data-driven grouping method) revealed distinctive performance patterns among candidates. These findings suggest that current evaluation frameworks effectively differentiate command readiness but also highlight specific skill domains that may require enhanced instructional focus. The study offers practical implications for fire training academies, policymakers, and certification bodies, particularly in refining curriculum design, competency benchmarks, and evaluation criteria to improve fireground leadership training and assessment standards. Full article

Background: Creatine supplementation is widely used to enhance exercise performance, mainly resistance training adaptations, yet its differential effects on upper- and lower-body strength and muscular power remain unclear across populations. Objective: This systematic review and meta-analysis aimed to quantify the effects of creatine supplementation in studies that included different exercise modalities or no exercise on upper- and lower-body muscular strength and power in adults. Methods: A comprehensive search of PubMed, Scopus, and Web of Science was conducted through 21 September 2024 to identify randomized controlled trials evaluating the effects of creatine supplementation on strength (bench/chest press, leg press, and handgrip) and power (upper and lower body). Weighted mean differences (WMDs) and 95% confidence intervals (CIs) were calculated using random-effects modeling. Subgroup analyses examined the influence of age, sex, training status, dose, duration, and training frequency. Results: A total of 69 studies with 1937 participants were included for analysis. Creatine plus resistance training produced small but statistically significant improvements in bench and chest press strength [WMD = 1.43 kg, p = 0.002], squat strength [WMD = 5.64 kg, p = 0.001], vertical jump [WMD = 1.48 cm, p = 0.01], and Wingate peak power [WMD = 47.81 Watts, p = 0.004] when compared to the placebo. Additionally, creatine supplementation combined with exercise training revealed no significant differences in handgrip strength [WMD = 4.26 kg, p = 0.10] and leg press strength [WMD = 3.129 kg, p = 0.11], when compared with the placebo. Furthermore, subgroup analysis based on age revealed significant increases in bench and chest press [WMD = 1.81 kg, p = 0.002], leg press [WMD = 8.30 kg, p = 0.004], and squat strength [WMD = 6.46 kg, p = 0.001] for younger adults but not for older adults. Subgroup analyses by sex revealed significant increases in leg press strength [WMD = 9.79 kg, p = 0.001], squat strength [WMD = 6.43 kg, p = 0.001], vertical jump [WMD = 1.52 cm, p = 0.04], and Wingate peak power [WMD = 55.31 Watts, p = 0.001] in males, but this was not observed in females. Conclusions: This meta-analysis indicates that creatine supplementation, especially when combined with resistance training, significantly improves strength in key compound lifts such as the bench or chest press and squat, as well as muscular power, but effects are not uniform across all measures. Benefits were most consistent in younger adults and males, while older adults and females showed smaller or non-significant changes in several outcomes. No overall improvement was observed for handgrip strength or leg press strength, suggesting that the ergogenic effects may be more pronounced in certain multi-joint compound exercises like the squat and bench press. Although the leg press is also a multi-joint exercise, results for this measure were mixed in our analysis, which may reflect differences in study design, participant characteristics, or variability in testing protocols. The sensitivity of strength tests to detect changes appears to vary, with smaller or more isolated measures showing less responsiveness. More well-powered trials in underrepresented groups, particularly women and older adults, are needed to clarify population-specific responses. Full article

China’s major function-oriented zoning (MFOZ) serves as a crucial policy instrument for functional regulation of land use, playing a significant role in the latest territorial spatial planning. Studies on the implementation of MFOZ have been conducted since its release in 2012, but there is a lack of comprehensive methods to assess the effectiveness of its implementation. In China, the newly initiated City Examination provides novel technical support for verifying MFOZ planning, addressing the gap in comprehensive evaluation methodologies and channels. This study proposes a comprehensive urban assessment framework and a major function classification approach based on City Examination data, enabling the identification of implementation deviations in MFOZ planning based on the current urban conditions reflected by City Examination. The methodology incorporates dimensionality reduction, multi-indicator clustering, entropy-weighted overlays, and natural break classification techniques and examines the degree of strategic deviation in China’s MFOZ through a comprehensive and systematic assessment. Due to the timeliness and long-term nature City Examination data, the method allows for the long-time dynamic tracking and evaluation of the real-time progress in MFOZ. Empirical analysis of Hubei Province revealed that 77.9% of its urban development aligns with the 2011 MFOZ scheme while demonstrating discernible deviation types and hierarchical discrepancies, with geographically clustered patterns observed among cities exhibiting such deviations. Full article

(1) Background: Integrating assessment with training helps to enhance precision prosthetic rehabilitation of trans-radial amputees. This study aimed to validate a self-developed closed-loop rehabilitation platform combining accurate measurement in comprehensive assessment and immediate interaction in virtual reality (VR) training in refining patient-centered myoelectric prosthesis rehabilitation. (2) Methods: The platform consisted of two modules, a multimodal assessment module and an sEMG-driven VR game training module. The former included clinical scales (OPUS, DASH), task performance metrics (modified Box and Block Test), kinematics analysis (inertial sensors), and surface electromyography (sEMG) recording, verified on six trans-radial amputees and four healthy subjects. The latter aimed for muscle coordination training driven by four-channel sEMG, tested on three amputees. Post 1-week training, task performance and sEMG metrics (wrist flexion/extension activation) were re-evaluated. (3) Results: The sEMG in the residual limb of the amputees upgraded by 4.8%, either the subjects’ number of gold coins or game scores after 1-week training. Subjects uniformly agreed or strongly agreed with all the items on the user questionnaire. In reassessment after training, the average completion time (CT) of all three amputees in both tasks decreased. CTs of the A1 and A3 in the placing tasks were reduced by 49.52% and 50.61%, respectively, and the CTs for the submitting task were reduced by 19.67% and 55.44%, respectively. Average CT of all three amputees in the ADL task after training was 9.97 s, significantly lower than the pre-training time of 15.17 s. (4) Conclusions: The closed-loop platform promotes patients’ prosthesis motor-control tasks through accurate measurement and immediate interaction according to the sensorimotor recalibration principle, demonstrating a potential tool for precision rehabilitation. Full article

Understanding the intricate relationship between sensory perception and physicochemical properties of cacao-based products is crucial for advancing quality control and driving product innovation. However, effectively integrating these heterogeneous data sources poses a significant challenge, particularly when sensory evaluations are derived from low-quality, subjective, and often inconsistent annotations provided by multiple experts. We propose a comprehensive framework that leverages a correlated chained Gaussian processes model for learning from crowds, termed MAR-CCGP, specifically designed for a customized Casa Luker database that integrates sensory and physicochemical data on cacao-based products. By formulating sensory evaluations as regression tasks, our approach enables the estimation of continuous perceptual scores from physicochemical inputs, while concurrently inferring the latent, input-dependent reliability of each annotator. To address the inherent noise, subjectivity, and non-stationarity in expert-generated sensory data, we introduce a three-stage methodology: (i) construction of an integrated database that unifies physicochemical parameters with corresponding sensory descriptors; (ii) application of a MAR-CCGP model to infer the underlying ground truth from noisy, crowd-sourced, and non-stationary sensory annotations; and (iii) development of a novel localized expert trustworthiness approach, also based on MAR-CCGP, which dynamically adjusts for variations in annotator consistency across the input space. Our approach provides a robust, interpretable, and scalable solution for learning from heterogeneous and noisy sensory data, establishing a principled foundation for advancing data-driven sensory analysis and product optimization in the food science domain. We validate the effectiveness of our method through a series of experiments on both semi-synthetic data and a novel real-world dataset developed in collaboration with Casa Luker, which integrates sensory evaluations with detailed physicochemical profiles of cacao-based products. Compared to state-of-the-art learning-from-crowds baselines, our framework consistently achieves superior predictive performance and more precise annotator reliability estimation, demonstrating its efficacy in multi-annotator regression settings. Of note, our unique combination of a novel database, robust noisy-data regression, and input-dependent trust scoring sets MAR-CCGP apart from existing approaches. Full article

This study introduces the Human-AI Symbiotic Theory (HAIST), designed to guide authentic collaboration between human researchers and artificial intelligence in academic contexts, while pioneering a novel AI-assisted approach to theory validation that transforms educational research methodology. Addressing critical gaps in educational theory and advancing validation practices, this research employed a sequential three-phase mixed-methods approach: (1) systematic theoretical synthesis integrating five paradigmatic perspectives across learning theory, cognition, information processing, ethics, and AI domains; (2) development of an innovative validation framework combining three established theory-building approaches with groundbreaking AI-assisted content assessment protocols; and (3) comprehensive theory validation through both traditional multi-framework evaluation and novel AI-based content analysis demonstrating unprecedented convergent validity. This research contributes both a theoretically grounded framework for human-AI research collaboration and a transformative methodological innovation demonstrating how AI tools can systematically augment traditional expert-driven theory validation. HAIST provides the first comprehensive theoretical foundation designed explicitly for human-AI partnerships in scholarly research with applicability across disciplines, while the AI-assisted validation methodology offers a scalable, reliable model for theory development. Future research directions include empirical testing of HAIST principles in live research settings and broader application of the AI-assisted validation methodology to accelerate theory development across educational research and related disciplines. Full article