Search Results (107,753)

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

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

Establishing precise welding parameters is essential to achieving the desired bead geometry and ensuring consistent quality in manufacturing processes. However, determining the optimal configuration of parameters remains a challenge, particularly when relying on limited experimental data. This study proposes the use of artificial neural networks (ANNs), with their architecture optimized via differential evolution (DE), to predict key MAG welding parameters based on target bead geometry. To address data limitations, cross-validation and data augmentation techniques were employed to enhance model generalization. In addition to the ANN model, machine learning algorithms commonly recommended for small datasets, such as K-nearest neighbors (KNNs) and support vector machines (SVMs), were implemented for comparative evaluation. The results demonstrate that all models achieved good predictive performance, with SVM showing the highest accuracy among the techniques tested, reinforcing the value of integrating traditional ML models for benchmarking purposes in low-data scenarios. Full article

Orthogonal Frequency Division Multiplexing (OFDM) modulation currently dominates the physical layer design in modern transmission systems. Its primary advantage is the simple reconstruction of channel frequency response (CFR). However, the Least Squares (LS) algorithm commonly used here is prone to significant estimation errors due to noise interference. A promising and relatively simple alternative is a DFT-based strategy that uses a pre-computed refinement/correction matrix to improve estimation performance. This paper investigates two implementation approaches for CFR reconstruction with pre-computed matrices. Focusing on multiplication operations, a threshold number of active subcarriers was identified at which these two implementations exhibit comparable numerical complexity. A numerical performance factor was defined and a detailed performance analysis was carried out for different guard interval (GI) lengths and the number of active subcarriers in the OFDM signal. Additionally, to maintain channel estimation quality irrespective of GI length, a channel impulse response (CIR) energy detection procedure was introduced. This procedure refines the results of the symbol synchronization process and, by using the circular shift property, preserves constant values of the precomputed matrix coefficients without system performance loss, as measured by Bit Error Rate (BER) and Mean Square Error (MSE) metrics. Full article

Monocrystalline silicon is an excellent semiconductor material for integrated circuits. Its surface quality has an enormous effect on its service life. The surfaces are formed by ultra-precision machining using nano-grinding, one of the technologies that can achieve surface roughness at the nano- or sub-nano-scale. Therefore, subsurface damage of monocrystalline silicon in nano-grinding was studied by establishing a molecular dynamics simulation model, and the impact of machining parameters on the force–thermal behavior was analyzed. The results reveal that the mechanism of subsurface damage is mainly structural phase transformation and amorphization. In nano-grinding of monocrystalline silicon, the tangential grinding force has a relatively major role in material removal. With increasing grinding depth and grinding speed, the grinding heat rises, and a certain degree of high temperature strengthens the toughness of the material, improving the subsurface quality of monocrystalline silicon. Therefore, subsurface damage in monocrystalline silicon can be controlled by reducing the grinding depth and increasing the grinding speed. Full article

The floral choices of honey bees (Apis mellifera) were studied using artificial flower patches to understand how foragers manage changing floral landscapes. Bees were observed under conditions where reward quality changed over time in blue and white flowers. We evaluated initial learning and reversal learning, varying the magnitude of reward quality-difference and color distinctness in the honey bee’s color vision space (being either similar or more distinct). Flower color fidelity was higher when flower colors were more distinct, but it also made it more difficult for bees to abandon the flower color in the reversal learning phase. Smaller differences in reward quality reduced flower color fidelity and promoted reversal learning. When reward difference between flower colors was created (initial learning), a decrease in one of the flower color rewards elicited a stronger behavioral response from foragers than an increase in reward. Our work highlights that bees used and integrated information from different axes of information: distinctiveness of color cues, magnitude of reward difference, and directionality (being stronger for losses than gains). Thus, flower distinctiveness, opportunity cost, and loss aversion drive honey bee foraging decisions. Higher accuracy at initial learning has stronger costs in behavioral adaptations at changing floral landscapes. Full article

(1) Background: In today’s digital society, challenges like cyberbullying, harmful social media use, and unhealthy digital habits demand innovative and inclusive educational responses. This study investigates the potential of service-learning (SL) and electronic service-learning (e-SL) as experiential approaches to enhance digital well-being among youth. By actively engaging students, educators, and community stakeholders in co-designed projects, SL/e-SL promotes critical awareness, digital citizenship, and prosocial values while addressing digital risks. (2) Methods: This review offers a literature-based analysis of existing programs and good practices that apply experiential education to encourage responsible digital engagement. It explores SL and e-SL experiences across various educational settings. (3) Results: The findings show that SL and e-SL can be effective educational tools, creating meaningful opportunities for youth to participate in tackling digital issues and building inclusive spaces where students, faculty, and communities collaborate to foster digital literacy and well-being. The analysis also led to the development of quality standards for SL and e-SL practices that promote digital well-being. (4) Conclusions: This study highlights key implications for teaching, underscoring the value of integrative pedagogies that connect experiential learning to digital challenges, promoting a more inclusive and responsible digital culture. Full article

Background/Objective: Sacubitril/Valsartan are a combination drug approved for heart failure treatment, known to enhance natriuretic peptide activity and inhibit the renin–angiotensin–aldosterone system (RAAS). While its clinical efficacy is well-established, its broader impact on human metabolism remains insufficiently characterized. This study aimed to explore the time-resolved metabolic changes induced by Sacubitril/Valsartan in healthy individuals using an untargeted metabolomics approach. Methods: Fourteen healthy male volunteers received a single oral dose of Sacubitril/Valsartan (200 mg; 97.2 mg Sacubitril and 102.8 mg Valsartan) across two phases separated by a two-week washout period. Plasma samples were collected at eight individualized time points based on pharmacokinetic profiles. Metabolites were extracted and analyzed using high-resolution liquid chromatography–mass spectrometry (LC-QToF HRMS). Data processing included peak alignment, annotation via HMDB and METLIN, and statistical modeling through multivariate (PLS-DA, OPLS-DA) and univariate (ANOVA with FDR correction) analyses. Results: Out of 20,472 detected features, 13,840 were retained after quality filtering. A total of 315 metabolites were significantly dysregulated (FDR p < 0.05), of which 31 were confidently annotated as endogenous human metabolites. Among these, key changes were observed in the pyrimidine metabolism pathway, particularly elevated levels of uridine triphosphate (UTP) associated with cellular proliferation and metabolic remodeling. OPLS-DA models demonstrated clear separation between pre-dose and Cmax samples (R²Y = 0.993, Q² = 0.768), supporting the robustness of the time-dependent effects. Conclusions: This is the first study to characterize the dynamic metabolomic signature of Sacubitril/Valsartan in healthy humans. The findings reveal a distinctive perturbation in pyrimidine metabolism, suggesting possible links to drug mechanisms relevant to cardiac cell cycle regulation. These results underscore the utility of untargeted pharmacometabolomics in uncovering systemic drug effects and highlight potential biomarkers for monitoring therapeutic response or guiding precision treatment strategies in heart failure. 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

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

Improving sowing quality is crucial for ensuring wheat emergence and healthy growth. To address issues of poor wheat sowing quality, such as uneven sowing depth and inadequate soil coverage, in the Yangtze River Delta region of China, this study systematically analyzed the effects of the implement’s structural and operational parameters on sowing quality. Based on this analysis, a double-shaft rotary tillage and double-press seeder was designed. Protrusions on the grooving press roller are used to form seed furrows, rotary tiller blades cover the seeds with soil, and the rear press roller compacts the soil. DEM-MBD (discrete element method–multibody dynamics) coupled simulations, combined with single-factor and central composite design (CCD) experiments, were conducted with seeding depth as the evaluation index and four experimental factors: the protrusion height on the press grooving roller, forward speed, seed mass in the seed box, and straw mulching amount. The optimal protrusion height was 29 mm. The effects of rotary tiller blade working depth, rotational speed, and forward speed on soil-covering mass and its coefficient of variation were evaluated through discrete element method (DEM) simulations. The optimal working depth and rotational speed were found to be 55 mm and 350 r·min⁻¹, respectively, based on single-factor and Box–Behnken Design experiments. Field experiments based on optimized parameters showed results consistent with the simulations. The qualified rate of seeding depth decreased as forward speed increased. The optimal forward speed was 4.5 km·h⁻¹, at which the average seeding depth was 25.7 mm, the qualified seeding depth rate was 90%, the soil-covering mass within a 50 cm² area was 143.2 g, and the coefficient of variation was 13.21%, meeting the requirements for wheat sowing operations. Full article

The Capacitated Vehicle Routing Problem (CVRP) is a classic combinatorial optimization problem in logistics and distribution, with significant theoretical and practical importance. To address the limitations of traditional evolutionary algorithms—particularly their use of fixed operator selection and simplistic search strategies—this paper proposes a Q-learning-based evolutionary algorithm (QEA). By incorporating a reinforcement learning mechanism, the QEA adaptively selects among multiple neighborhood search operators, effectively balancing global exploration and local exploitation. In addition, a novel insertion-based crossover operator and a set of diverse neighborhood search strategies are designed to further enhance solution quality and search efficiency. Experimental results on a variety of standard CVRP benchmark instances show that the QEA demonstrates a superior performance and strong robustness, significantly outperforming several representative state-of-the-art algorithms for solving the CVRP. These results confirm the effectiveness and practical value of the proposed method. Full article

Soy protein isolate (SPI) microgels were produced via heat-set gelation (4, 6, 8, and 10% by mass) followed by ultrasonication (400 W, 70% amplitude, 3 or 6 min) and used as stabilizers of oil–water emulsions (10% oil phase). The SPI concentration and ultrasonication time affected microgel size (236–356 nm) and polydispersity (0.253–0.550). The physical stability of the emulsions stabilized with 6 and 8% SPI microgels (6 min of ultrasonication) was evaluated for 14 d, influencing on the average size, creaming index and instability index of the emulsions, where those with 6% SPI microgels resulted in a major stability. The emulsions produced with these microgels encapsulated beta-carotene and were incorporated into whole yogurt at three concentrations: 5 (YE5), 10 (YE10), and 15% (YE15). The addition of the emulsions did not affect the physicochemical or microbiological quality of the yogurt. Rheological tests revealed that the yogurt behaved as a non-Newtonian and pseudoplastic fluid, with yogurts with more emulsions being less viscous. Sensory evaluation revealed consumer acceptance regarding color and texture; however, the perception of residual flavor was proportional to the amount of emulsion added. SPI microgels are effective stabilizers for β-carotene-loaded emulsions and a promising strategy for this compound delivery in yogurt. Full article

This article identifies different forms of poetic transport—understood in the sense of metaphor, transition, transfer, crossing and translation—in Hölderlin’s poem “Patmos”. There are several motifs scattered throughout the poem that semantically express a transition using highly metaphorical language: motifs reflecting on the mediation between the divine and the human, signalling the hybridization of Greek and Christian religion, and indicating transfer from ancient to modern thought. Initially, this article examines the metaphorical quality of language in contrast to its analytical capacity and proposes that the former—by seeking forms of transitions—enables mediation between the associative-affective reading of the text and the critical-analytic method of the scientific view. Hölderlin reflects on this fundamental issue as a result of his spatial transition to Regensburg. The article will further show that various forms of transfer sustain the entire poem: motifs ranging from an epochal transfer to the transition from a topographical space into the text, the superimposition of different figures and the transformation of the biblical narrative, as well as the crossing between the different layers of the draft and the poet’s task of a creative translation of various forms of encountering the world, all describe issues central to Patmos. Full article

A systematic literature review is fundamental to building a robust research foundation, informing experimental methodology, and ensuring the quality of future scientific output. However, manual extraction of targeted information from scientific publications is often laborious and prone to error, especially when researchers require rapid access to relevant findings without specialized hardware. This paper introduces an automated workflow for information extraction from scientific publications in the engineering domain. The proposed workflow consists of two primary stages: data preparation and information extraction. During data preparation, PDF files are converted to plain text and segmented into logical sections using a rule-based block detection and classification algorithm for keeping semantics. Information extraction is then performed by applying regular expressions both on keys and values in the same sentence to identify and extract relevant process and material data from the segmented text. The approach was evaluated on a dataset of 18 open-access scientific publications from various journals and conference proceedings in the AM domain. The results of the automated extraction were compared with manual extraction and with a modern large language model (LLM)-based approach. The findings demonstrate that the proposed workflow can accurately and efficiently extract relevant process and material data, achieving competitive performance relative to the LLM-based method. The workflow offers a significant reduction in time and potential errors associated with manual extraction, with automated processing averaging 15 seconds per document compared to one hour for manual extraction, and achieving a 76% match rate. This efficiency enables researchers to rapidly and effectively extract data. The methodology is readily transferable to other scientific fields where systematic literature reviews and structured data extraction are required. Full article