Search Results (5,928)

The rapid development of intelligent systems has introduced increasingly sophisticated optimization problems across diverse domains. While contemporary metaheuristic algorithms, including the recent Artificial Lemming Algorithm (ALA), have shown considerable promise, they frequently encounter difficulties such as premature convergence, inadequate local refinement, and diminished performance in high-dimensional multimodal environments. To overcome these issues, this study presents HALA, a new hybrid dual-subpopulation optimizer that effectively integrates an enhanced ALA with the SHADE algorithm. HALA employs two interacting subpopulations: one leverages an improved ALA with hybrid t-distribution and Levy flight perturbations to promote persistent long-range exploration and diversity preservation; the other applies SHADE’s success-history adaptation and external archive for accurate local exploitation. Periodic bidirectional elite migration facilitates knowledge transfer between the subpopulations, reducing early stagnation in the enhanced ALA and strengthening SHADE’s global search capability. HALA is thoroughly benchmarked against 17 advanced metaheuristics, including ALA, LSHADE, LSHADE-SPACMA, AOOA, BAEO, BPBO, CCO, CEO, CQALA, DFL, DMOA, DHOA, FGO, KLA, PGA, SO, and SOO, using the IEEE CEC2017 suite in 10, 30, 50, and 100 dimensions and the IEEE CEC2022 suite in 10 dimensions. Comprehensive analyses involving qualitative visualization, convergence curves, boxplots, and statistical tests indicate that HALA achieves competitive or superior solution quality, comparable or faster convergence, and robust stability on a substantial proportion of the test instances. In particular, HALA obtains the most favorable Friedman average ranking values among the compared algorithms, which are 2.55, 2.38, 2.34, and 2.55 for the 10-, 30-, 50-, and 100-dimensional CEC2017 functions, respectively, and 2.58 for the 12 10-dimensional CEC2022 functions. Moreover, HALA is successfully applied to five well-known constrained engineering design problems—pressure vessel, rolling element bearing, tension/compression spring, cantilever beam, and gear train—where it reliably achieves optimal or near-optimal results that match or surpass the compared methods. These findings underscore HALA’s competitive strength and broad potential for practical engineering optimization. Full article

The adoption of Large Language Models (LLMs) in highly regulated, domain-specific sectors is constrained by high computational costs, cloud dependency, and strict data privacy regulations. Furthermore, specific-domain knowledge is usually locked in static, unstructured document formats, preventing automated reasoning. To address these challenges, this study proposes a generalizable end-to-end pipeline for developing domain-specialized Small Language Models (SLMs) optimized for resource-constrained environments starting from unstructured data. To validate the proposed pipeline, Malaysian Clinical Practice Guidelines (CPGs) in PDF format were used as a test case. The methodology systematically digitizes these unstructured data into a NoSQL database and employs an isomorphic teacher model to generate a strictly grounded synthetic instruction-tuning dataset. Through Quantized Low-Rank Adaptation (QLoRA) and 4-bit Post-Training Quantization (PTQ), a general-purpose model is transformed into a highly compressed, domain-specialized SLM, named SpecioSLM. Systematic workstation benchmarking across four candidate architectures identified the Microsoft Phi-3-Mini (3.8B) variant as the optimal model. The model achieved a throughput of 91.59 tokens per second (TPS), a Time to First Token (TTFT) of 0.17 s, and a semantic fidelity BERTScore of 90.27. A preliminary ARM64-based simulation is further conducted targeting a specific edge device to validate architectural and memory footprint viability. Full article

Lean manufacturing has historically focused on eliminating waste from physical production processes; however, increasing digitalization has shifted a substantial portion of operational effort toward information processing and decision making. Existing Lean frameworks lack formal mechanisms to model and quantify inefficiencies arising within these cognitive processes. This paper introduces Cognitive Waste Theory, a mathematical extension of Lean manufacturing that defines cognitive inefficiency as a distinct form of operational waste. Cognitive waste is conceptualized as non-value-adding mental effort generated by misaligned information flow, task structure, and organizational learning dynamics. The framework decomposes cognitive waste into five analytically separable categories: Information Overload, Context Switching, Knowledge Fragmentation, Cognitive Load, and Learning Lag, each expressed through formal mathematical representations grounded in cognitive and operations theory. To enable quantitative assessment, the study proposes normalized waste functions and develops two composite indices: the Cognitive Efficiency Index (CEI), capturing the ratio of effective decision output to cognitive load, and Information Flow Efficiency (IFE), structured analogously to Overall Equipment Effectiveness. Furthermore, classical Lean instruments are reformulated for analytical application in the cognitive domain through Information Value Stream Mapping and Cognitive 5S. By embedding cognitive constructs within a measurable Lean framework, this work provides an attempt to establish a rigorous foundation for analyzing, comparing, and improving cognitive performance in digitally intensive manufacturing systems. Full article

Background/Objectives: Improving the prevention and management of pressure injuries requires adequate nursing knowledge. In this context, understanding the current knowledge levels of nurses is important for informing educational and organizational strategies. This study aimed to assess the pressure injury knowledge of nurses at the national level and examine differences according to their educational, professional, and informational characteristics. Methods: A national cross-sectional quantitative study was conducted in public secondary-level hospitals in the Republic of Croatia. A total of 1139 participants from 19 hospitals across all four geographical regions of Croatia participated. The PZ-PUNKT instrument was used, and an analysis was conducted using descriptive and bivariate inferential statistics. Results: Participants with bachelor’s and master’s degrees achieved higher PZ-PUKT scores than those with secondary education (p = 0.026 to < 0.001), although the effect sizes were very small (ε² = 0.008–0.017). Significant differences were observed across clinical departments (p < 0.001; ε² = 0.03–0.04), whereas no statistically significant differences were found according to the frequency of working with patients with pressure injuries (p > 0.05). Participants who reported recently attending educational activities, consulting the professional literature, or searching for information achieved higher knowledge scores across all domains (p < 0.001); however, the effect sizes remained small (ε² = 0.034–0.060; rpb = 0.133–0.214). Conclusions: Although the observed effect sizes were generally small, higher knowledge scores were observed among nurses who reported recent engagement with educational activities, the professional literature, and information-seeking behaviors. No significant differences were identified according to the frequency of working with patients with pressure injuries. These findings provide a national overview of pressure injury knowledge among Croatian nurses and may inform future educational initiatives and research. Full article

Road traffic crashes remain a major global public health and economic challenge, with heavy vehicle drivers disproportionately involved in severe incidents, particularly in low- and middle-income countries. In Ethiopia, limited access to continuous professional training constrains efforts to improve drivers’ safety-related knowledge and awareness. This study explored the impact potential and user acceptance of gamified e-learning modules designed to enhance heavy vehicle drivers’ knowledge and awareness of fatigue management, speed-related behavior, and eco-driving practices. A randomized pretest–post-test control-group design was employed, in which professional drivers were assigned to either an intervention group that completed three gamified e-learning modules or a control group that received no training. Data were analyzed using mixed repeated-measures analysis of variance. The results revealed significant time × group interaction effects across all domains (p < 0.001), with substantially greater improvements in the intervention group and large effect sizes. Participants also reported high perceived usefulness, behavioral intention, and trust in the system. These findings provide preliminary evidence that gamified e-learning may be a feasible and promising approach for improving short-term safety-related knowledge among professional heavy vehicle drivers. Further research is needed to determine whether these improvements are sustained over time and translate into behavioral change and measurable road safety outcomes before broader implementation can be recommended. Full article

Vertically linked commodity markets—global futures, regional spot, and farm-gate prices—transmit information through directed cross-market channels whose strength varies with latent volatility regimes. Standard deep learning forecasters absorb both the directed cross-market dependence and the regime dependence of intrinsic-mode-aligned latent components into shared model weights, with no explicit architectural mechanism that exposes either as an inspectable structure. This paper proposes HVB-RA, a modular framework that combines two such mechanisms with a per-tier Variational Mode Decomposition and bidirectional LSTM backbone: (i) a directed cross-market attention layer in which the upstream-to-downstream topology is supplied from domain knowledge and the time-varying upstream-source attention intensities at the farm-gate tier (the regional-spot tier, with a single upstream key, reduces algebraically to a fixed residual upstream fusion) are extracted from data, and (ii) a regime-informed modal-weighting layer that mixes two trainable softmax weight profiles over IMF-aligned latent components through a filtered Markov-switching state probability fitted in a separate stage. An auxiliary post hoc projection enforces an exact linear constraint defined by long-run sample-mean ratios across tiers; the paper does not claim that these descriptive ratios are cointegrating relations or equilibrium coefficients. The framework is evaluated on three tiers of daily natural-rubber prices spanning 2038 trading days, against three external benchmarks (random walk, ARIMA(2,0,2), and an exogenous-only LSTM) and a contemporary neural hierarchical-interpolation forecaster (NHITS). Root mean squared error is reported per tier-horizon cell; a decision-aware income-smoothing metric quantifies the operational value of $h=5$ farm-gate forecasts under a 5-day selling rule; and a within-method comparison evaluates the marginal contribution of the auxiliary constraint projection. On the present single-regime test window, HVB-RA attains a lower point error than the contemporary NHITS baseline at every tier-horizon cell, while no method—including HVB-RA—improves on the random-walk floor at most cells; the regime-conditional components of the architecture are not identifiable because every calibration and test origin is classified as a high-volatility regime by the trained Markov-switching model. The paper contributes to machine learning and knowledge extraction by demonstrating how time-varying upstream-source attention intensities at the farm-gate tier and regime-dependent latent-component-weight profiles—two forms of latent structure typically absorbed into model weights—can be exposed as explicit, inspectable, and individually testable components of a multi-tier forecasting architecture, and by providing a reproducibility package documenting the conditions under which each component is expected to be identifiable. Full article

Deception detection is a multifaceted challenge that has gained attention in domains such as forensics, security, and human–computer interaction. However, most EEG-based studies focus on binary classification between truthful and deceptive responses, overlooking the complexity of cognitive processes underlying different deceptive strategies. To address this limitation, we present a multi-class EEG dataset designed to investigate distinct behavioral roles in deception, including honest, bluffer, liar, and deceiver, collected from 51 participants using a controlled mock-crime scenario. In this setup, subjects were assigned predefined roles and interrogated under a standardized protocol with carefully designed questions and responses. EEG signals were recorded using a 16-channel Biosemi ActiveTwo system at a sampling rate of 2048 Hz, with event markers enabling precise temporal segmentation of experimental phases. The dataset captures neural activity associated with varying cognitive load and decision-making across deception types. To the best of our knowledge, this is the first EEG dataset that explicitly incorporates and differentiates four distinct deception-related behavioral roles within a unified experimental framework. Full article

In the fuzzy front end of innovation, firms often lack sufficient citation, market, and performance data, which limits the usefulness of outcome-based approaches to screening early-stage product innovation opportunities. To address this problem, this study develops a text co-occurrence network-based measurement system for assessing early-stage product innovation opportunities in new product development. We first preprocess idea texts through concept extraction and semantic cleaning, and then construct an integrated semantic network by combining market-related texts with ideation data. The Leiden algorithm is applied to detect latent knowledge communities in the network. Building on this structure, we assess early-stage product innovation opportunities along two complementary dimensions: cross-domain knowledge recombination, capturing the extent to which an idea draws on concept communities that are otherwise weakly connected, and network structural perturbation, capturing the degree to which an idea reconfigures existing semantic boundaries and connection patterns. Based on community entropy and modularity change, we construct a composite indicator for the ex ante assessment of early-stage ideas with stronger product innovation potential. Compared with traditional approaches relying on patent citations, market outcomes, or expert judgments, the proposed method enables earlier screening of ideas that deviate from dominant semantic trajectories and may warrant further development attention. The framework is explicitly positioned as an ex ante screening and attention-allocation tool for early-stage product innovation opportunities, not as a deterministic predictor of later market success. Full article

The expansion of China’s modernized eco-environmental monitoring networks has generated vast amounts of data. Consequently, traditional, expertise-reliant analysis is increasingly ill-suited for agile regulatory decision-making. Although large language models (LLMs) present a promising alternative, their practical deployment remains limited by domain-specific knowledge gaps, hallucinations and an inherent difficulty in managing multi-faceted ecological tasks. This study introduces EnvSentry, a novel multi-engine collaborative LLM framework designed for intelligent eco-environmental monitoring and governance. EnvSentry coordinates reasoning, instruction, and multimodal engines, supported by a dynamic, vector-indexed knowledge base and retrieval-augmented generation (RAG) to ensure factual veracity. By transitioning operational workflows from fragmented, latent batch processing to integrated, real-time intelligent agent chains, the system achieves a closed-loop capability of intent recognition, data retrieval, and quality control. The model was evaluated across distinct environmental contexts, specifically water quality anomaly detection and air quality forecasting. Results show that EnvSentry yields higher analytical precision and attribution rates than baseline methods, while compressing decision-making latency from hours to seconds. Relative to baseline models, EnvSentry achieves a 25% improvement in water quality attribution accuracy (50% to 75%), a 90% reduction in decision making latency for anomaly detection, and a 10% absolute gain in data anomaly detection accuracy. In air quality forecasting, it reduces expert judgment time from 60 to 20 min and attains >85% agreement with expert forecasts when used by non-specialist personnel. These improvements suggest a practical shift in eco-environmental monitoring—moving from fragmented, reactive measures toward an integrated and proactive system. Consequently, this approach offers a viable path toward data-driven autonomous ecological management. Full article

Aligning academic transcripts with occupational competency requirements remains challenging because course labels and job-skill terms are semantically ambiguous, role-specific, and difficult to explain. This paper proposes the Ontology Framework for Multi-level Competency Mapping (O4CM), a semi-automated framework integrating a Large Language Model (LLM) ensemble, Human-in-the-Loop (HITL) verification, Sentence-BERT (SBERT) semantic representation, the Path Consistency Index (PCI), and Total Accumulated Competency Score/Normalised Total Accumulated Competency Score (TACS/NTACS) ranking. O4CM was evaluated on a historical job-posting corpus and anonymised transcripts from five university programmes through ablation, sensitivity analysis, baseline comparison, and expert-labelled validation. The LLM ensemble reached high consensus for 21 of 22 Occupational Information Network (O*NET) knowledge-domain mappings (95.45%), each of which was subsequently expert-verified. In a computing-only expert-aligned analysis, the full framework most closely matched expert rankings across three data-domain roles. Within this dataset, ontology-path evidence can support more transparent competency ranking for educational advising and exploratory recruitment screening. Full article

This work presents a comprehensive review of axion physics through the generative lens of a novel theoretical framework: Real-Now-Front (RNF) cosmology. Moving beyond the standard treatment of the axion as a fundamental particle in a pre-existing spacetime, we systematically reinterpret it as a specific collective excitation, a “twist” mode, arising from the alignment dynamics of the more fundamental Chronon field, from which spacetime itself emerges. Within this paradigm, the axion’s mass, its couplings to photons and matter, and the symmetry-breaking scale $f_a$ are not independent parameters but are derived from the microscopic stiffness and correlation length of the Chronon field, governed by the Temporal Coherence Principle. We re-examine the entire axion landscape, including benchmark models (KSVZ, DFSZ, ALPs) and the full spectrum of experimental constraints from terrestrial haloscopes, helioscopes, and astrophysical environments, translating them into probes of Chronon alignment dynamics. Furthermore,this generative framework yields unique, testable predictions, such as emergent bimetric effects and primordial black hole seeds from closed domain walls, providing independent avenues for falsification. By synthesizing established knowledge with this foundational new perspective, the review aims to establish a unified basis for the next generation of axion searches, positioning them as direct tests of the microscopic architecture of emergent spacetime, leveraginga multi-decade, multi-messenger observational campaign. Full article

Background: Large language models (LLMs) are increasingly integrated into real-world medical practice as chatbots for answering clinical queries. However, the perceptions of this technology among its end-users remain understudied. Existing research on physicians’ attitudes toward LLMs relies on non-validated questionnaires, raising concerns about the accuracy and reliability of the findings. The aim of this study is to develop and validate a questionnaire to assess physicians’ attitudes toward LLM-based chatbots used as a reference tool for answering queries. Methods: The instrument was based on the previously developed and validated ATRAI-14 questionnaire assessing radiologists’ attitudes toward artificial intelligence. Items for the new questionnaire were formulated and refined through focus group testing. Validation involved 562 physicians of various specialties working in medical institutions within the Moscow healthcare system. Some respondents had prior experience working with medical LLMs. We assessed face, content, construct, and criterion validity. Criterion validity was evaluated through correlation between respondents’ self-assessed attitudes toward LLMs measured by visual analogue scale (VAS), and construct validity through confirmatory factor analysis. Results: The resulting ATRAI-LLM questionnaire comprised 19 items (8 in the background part and 11 in the main part). The questionnaire demonstrated acceptable internal consistency (Cronbach’s α = 0.770, McDonald’s ω_t = 0.830). It encompasses three domains: “Willingness to Use”, “Implementation Perspective”, and “Hopes and Fears.” Confirmatory factor analysis supported the three-factor structure, with satisfactory fit indices achieved (RMSEA = 0.05, CFI = 0.97, TLI = 0.96, SRMR = 0.03). Criterion validity was confirmed as acceptable with moderate correlation between the final score and VAS scores (Spearman’s rho 0.68, p < 0.001). Conclusions: ATRAI-LLM is a validated instrument for assessing physicians’ attitudes toward LLMs as a knowledge base. Full article

Magnetic graphene composites have emerged as a frontier material platform, offering designable properties and multifunctional integration across environmental, biomedical, electromagnetic, and energy applications. Despite extensive research, a coherent knowledge framework that systematically connects synthesis, structure, property, and application remains lacking. This review addresses this gap by establishing an integrated “synthesis–structure–property–application” design paradigm. We first propose a four-tier evolutionary framework for synthesis strategies, tracing the progression from modular in-situ assembly, substrate-guided single-component in-situ formation, and synchronous in-situ formation to molecular-scale precursor co-conversion. This framework reveals the causative relationships between synthesis pathways and microstructures, and culminates in an application-oriented synthesis decision-making tool that enables rational strategy selection. Building on this synthesis foundation, we systematically analyze three core structural regulation strategies—interface engineering, defect and doping engineering, and hierarchical structure construction—demonstrating how they function as synergistic “control knobs” for tailoring composite properties. Through detailed case studies across four application domains, we quantitatively show how targeted structural design drives performance breakthroughs: enabling high-capacity and selective pollutant removal in environmental remediation; constructing intelligent theranostic platforms in biomedicine; reconciling the “thin, lightweight, broadband, and strong” paradox in electromagnetic interference (EMI) shielding; and ensuring long-cycle stability of high-capacity electrodes in energy storage. Finally, we summarize the paradigm shift from “functional combination” to “performance synergy” and outline future directions, including dynamic intelligent systems, sustainable manufacturing, and data-driven design. This review provides a systematic theoretical framework and practical roadmap for the rational design and on-demand fabrication of MGCs, marking the field’s transition from empirical exploration toward predictive, design-driven science. Full article

This paper studies a bi-objective distributed flexible job shop scheduling problem considering machine on–off decisions. A mathematical model is formulated to minimize the makespan and total energy consumption while distinguishing processing energy, idle energy, and on–off energy. To address the coupled effects among job-to-factory assignment, machine selection, operation sequencing, and machine on–off states, a domain-knowledge-driven memetic algorithm (DKMA) is proposed. The algorithm represents each schedule with a three-layer encoding scheme and integrates hybrid initialization, knowledge-driven neighborhood search, and energy-saving reconstruction to improve solution-set quality and the use of on–off-eligible idle intervals. The proposed model and algorithm are evaluated through Taguchi parameter tuning, small-scale mixed-integer linear programming (MILP) validation, component ablation experiments, and multi-algorithm comparisons. The results show that DKMA improves solution-set coverage, Pareto-front approximation, and energy control on the tested instances, which supports its applicability to distributed green scheduling with machine on–off decisions. Full article

Recent advancements in sequence generation models have transformed the development of conversational chatbots, enabling more dynamic and emotionally aware interactions. While English-language chatbots have achieved notable progress through large language models (LLMs), Arabic-language systems continue to face significant challenges, particularly in handling dialectal variation, morphological complexity, and generating emotionally aligned responses. This paper introduces two innovative approaches to enhance empathetic response generation in Arabic conversational AI. The first, Emotion-Driven Response Generation (EDRG), employs a two-stage pipeline: it first classifies user emotions using marBERT and then routes inputs to the most suitable Arabic LLM (AraBERT, AraELECTRA, AraGPT-2, or MT5) for contextually appropriate response generation. The second, EmoLlama, is a Retrieval-Augmented Generation (RAG)-based framework that integrates a curated knowledge base with the LLaMA model to retrieve relevant conversational contexts before generating semantically rich and empathetic responses. To support these approaches, a large-scale open-domain Arabic dataset was curated, containing over 600,000 dialogue entries spanning empathetic and neutral responses across seven Ekman-based emotion categories. Experimental evaluations using BLEU, Perplexity (PPL), and Cosine Similarity metrics validated the effectiveness of our models. EDRG achieved strong BLEU scores across multiple emotions, reflecting high lexical alignment, while also attaining a Cosine Similarity of 0.51. In contrast, EmoLlama significantly outperformed in semantic similarity, achieving a Cosine Similarity of 0.91, demonstrating its superior ability to generate contextually and semantically rich responses. These results highlight the complementarity of lexical and semantic metrics in evaluating emotionally intelligent Arabic dialogue systems. Full article