Search Results (269)

Humans tend to rely on heuristic strategies for intuitive judgment during decision-making. Existing research proposes an availability heuristic, suggesting that individuals are inclined to use highly available information as a basis for judgment. To explore the behavioral and electrophysiological characteristics of the availability heuristic in information visualization, 24 right-handed participants were recruited for the experiment. Using behavioral and event-related potentials (ERPs) analysis techniques, within-subject behavioral and electroencephalogram (EEG) experiments were conducted under four conditions: polar coordinate system with higher number, polar coordinate system with lower number, Cartesian coordinate system with higher number, and Cartesian coordinate system with lower number. The behavioral results revealed that in the angle estimation task, the polar coordinate condition induced a more significant availability heuristic effect compared to the Cartesian coordinate condition, exhibiting smaller numerical estimation deviations. This indicates that the degree of semantic relevance between the available information and the target task is a critical factor determining the facilitative effect of such information on judgment. The ERPs results showed that the polar coordinate condition elicited smaller N2 and P2 amplitudes than the Cartesian coordinate condition during angle judgment, suggesting reduced semantic conflict and lower attentional demand in task processing under the polar coordinate condition. By providing behavioral and electrophysiological evidence of intuitive decision-making processes, this study lays a theoretical foundation for the rational application of intuitive effects in information visualization design. Furthermore, the findings imply that using available information semantically aligned with the target task can significantly enhance the effectiveness of the availability heuristic, thereby mitigating availability bias. Full article

Real-world decision-making often involves uncertainty, incomplete data, and the need to evaluate alternatives based on both quantitative and qualitative criteria. To address these challenges, this study presents FAS-XAI, a unified methodological framework that integrates fuzzy clustering and explainable artificial intelligence (XAI). FAS-XAI supports interpretable, data-driven decision-making by combining three key components: fuzzy clustering to uncover latent behavioral profiles under ambiguity, supervised prediction models to estimate decision outcomes, and expert-guided interpretation to contextualize results and enhance transparency. The framework ensures both global and local interpretability through SHAP, LIME, and ELI5, placing human reasoning and transparency at the center of intelligent decision systems. To demonstrate its applicability, FAS-XAI is applied to a real-world B2B customer service dataset from a global ERP software distributor. Customer engagement is modeled using the RFID approach (Recency, Frequency, Importance, Duration), with Fuzzy C-Means employed to identify overlapping customer profiles and XGBoost models predicting attrition risk with explainable outputs. This case study illustrates the coherence, interpretability, and operational value of the FAS-XAI methodology in managing customer relationships and supporting strategic decision-making. Finally, the study reflects additional applications across education, physics, and industry, positioning FAS-XAI as a general-purpose, human-centered framework for transparent, explainable, and adaptive decision-making across domains. Full article

Background/Objectives: In the healthcare sector, burnout has become a critical concern due to high job demands and emotional strain. The main objective of the study is to examine the predictive role of psychosocial work-related risks in the development of burnout. Methods: A cross-sectional study was conducted, using a snowball recruitment method, from May to September 2025, among 154 healthcare workers. Data were collected using the psychosocial risk factors scale (INSAT_ERPS) and the Burnout Assessment Tool (BAT) and analyzed with descriptive and inferential statistics to analyze the predictive role of the psychosocial risk on burnout dimensions. Results: Psychosocial risk factors are consistently linked to the development of burnout symptoms. For exhaustion, the predictors included Working Hours (β = 0.312, p < 0.001), Social Work Relations (β = 0.196, p = 0.026), and Emotional Demands (β = 0.295, p = 0.002). For mental distance, the predictors included Work Intensity (β = −0.193, p = 0.049), Emotional Demands (β = 0.294, p = 0.004), and Work Values (β = 0.348, p = 0.003). For cognitive impairment, Work Values (β = 0.240, p = 0.042) and for emotional impairment, Employment Relations (β = 0.182, p = 0.038) emerged only one significant positive predictor. Conclusions: Findings underscore a crucial understanding: the development of burnout is not solely determined by the workload intensity, or the number of hours worked, the quality of working life and the dynamics within the workplace play pivotal roles in predicting burnout. A multidomain evaluation aligns with a holistic well-being approach to well-being by emphasizing that enhancing healthcare workers’ health demands systemic interventions addressing psychosocial work environment. Full article

Accessing operational information across industrial systems (ERP, MES, SCADA, PLC) in concrete plants requires 15–30 min and specialized knowledge. This work addresses this accessibility gap by developing a conversational AI system that democratizes industrial information access through natural language. A five-layer cognitive architecture was implemented integrating the Mistral-7B model quantized in GGUF Q4_0 format (3.82 GB) on a Raspberry Pi 5, Spanish speech recognition/synthesis, and heterogeneous industrial protocols (OPC UA, MQTT, REST API) across all automation pyramid levels. Experimental validation at Frumecar S.L. (Murcia, Spain) characterized performance, thermal stability, and reliability. Results show response times of 14.19 s (simple queries, SD = 7.56 s), 16.45 s (moderate, SD = 6.40 s), and 23.24 s (complex multilevel, SD = 6.59 s), representing 26–77× improvement over manual methods. The system maintained average temperature of 69.3 °C (peak 79.6 °C), preserving 5.4 °C margin below throttling threshold. Communication latencies averaged 8.93 ms across 10,163 readings (<1% of total latency). During 30 min of autonomous operation, 100% reliability was achieved with 39 successful queries. These findings demonstrate the viability of deploying quantized LLMs on low-cost edge hardware, enabling cognitive democratization of industrial information while ensuring data privacy and cloud independence. Full article

Background: Opioid use disorder (OUD) and associated overdose deaths have reached epidemic proportions worldwide over the past two decades, with death rates for men consistently reported at twice the rate for women. We have recently reported sex-specific differences in electrocortical activity in persons with OUD in a visual object recognition task. The mesolimbic dopamine (DA) system is implicated in OUD but also plays a critical role in some disorders of visual attention and a modulatory role in the processing of visual stimuli in the blue cone pathway of the retina. We hypothesized that electrocortical responses to color stimuli would be affected differentially in men and women with OUD. Methods: Using a controlled, cross-sectional, age-matched (18–56 years) design, we evaluated color processing in male and female subjects recruited from a community-based, high-intensity residential substance abuse and detoxification treatment program. We evaluated electroencephalogram (EEG) event-related potentials (ERPs) and reaction time (RT), in male and female participants with OUD (n = 38) vs. sex- and age-matched non-OUD control participants (n = 37) in a simple color recognition Go/No-Go task, as well as perceptual and behavioral responses in physiological and neuropsychological tests. Results: N200, P300, and late potential (LP) Relevant stimulus-induced ERPs were evoked by the task and were well-differentiated from Irrelevant distractor stimuli. P300 amplitudes were significantly greater and N200 and LP latencies were significantly shorter in male vs. female non-OUD controls in this task. There were significant sex differences in N200, P300, and LP amplitudes and latencies between male vs. female non-OUD subjects and OUD differences with blue color as the Relevant stimulus. In the Binocular Rivalry Test, there were shorter dwell times for perceiving a blue stimulus in male OUD subjects and there were significant sex and OUD differences in neuropsychological tests including Finger Tapping, Trails A/B, and Symbol Digit Modalities Test. Conclusions: These findings suggest that there are significant sex-related physiological, perceptual, and cognitive differences in color processing that may result from deficits in DA production in the retina that mirror deficits in mesolimbic DA transmission correlating with OUD, suggesting that blue color processing has the potential to be an effective biomarker for brain DA and for diagnosis and monitoring of treatment efficacy in substance use disorders. Full article

This study presents an integrated predictive maintenance framework for industrial machinery, designed through the combined use of digital twin technology, enterprise resource planning (ERP) systems, and machine learning algorithms. The proposed system focuses on enhancing machine reliability and operational automation by connecting physical assets with their virtual counterparts and management systems. The digital twin acts as a real-time virtual model of critical equipment—such as aeration motors, mixers, and reactors—enabling continuous monitoring, dynamic simulation, and predictive fault detection. Meanwhile, the ERP system provides an integrated environment for maintenance scheduling, data management, and resource allocation, ensuring that maintenance decisions are data-driven and synchronized with operational workflows. Machine learning algorithms, implemented using hybrid physical–data models, predict equipment degradation trends and optimize maintenance interventions. The proposed framework was validated in an industrial-scale composting facility, where results demonstrated a 40% increase in mean time to failure (MTTF), a 35% reduction in repair time, and a 30% decrease in maintenance costs, resulting in a return on investment of 42.5% within the first year. The system’s modular architecture and high adaptability to different machinery types confirm its potential applicability to broader machine design and automation contexts, supporting the transition toward intelligent, self-maintaining industrial systems. Full article

With the gradual implementation of the EU Battery Regulation and the DBP (Digital battery passport), it has become critical to determine the carbon footprint of lithium-ion battery products that contain recycled lithium resources. However, the diversity of recycling pathways substantially increases the complexity of carbon footprint accounting and DBP construction for recycled lithium batteries. This paper proposes a carbon activity based granular allocation and integration mechanism. Built on organizational operational data in EIS (Enterprise information systems) (ERP (Enterprise resource planning)/MES (Manufacturing execution system)/SCADA (Supervisory control and data acquisition), etc.) and using carbon activities as the linkage for mapping, the mechanism supports the acquisition and sound allocation of product carbon data, thereby improving the availability of carbon data and the rationality of allocation throughout the accounting process, and enabling more robust product carbon footprint calculations. Across different recycling routes, the carbon footprint results for recycled lithium resources can differ by more than 65%. When considering spodumene as the lithium source, mixing primary and recycled lithium carbonate in varying proportions can lead to up to a tenfold difference in the carbon footprint of products containing recycled lithium. Therefore, precisely tracing the carbon emission activities associated with different lithium sources is crucial for enhancing the accuracy of carbon footprint accounting, promoting the sustainable development of lithium resources, and meeting the requirements of the new Battery Regulation and the DBP. Full article

Event-Related Potentials (ERPs) are low-amplitude neural responses elicited by sensory or cognitive stimuli, widely exploited as biomarkers in the early diagnosis of neurodevelopmental and neurodegenerative disorders such as autism spectrum disorder and Alzheimer’s disease, and as control signals in brain–computer interface (BCI) systems for severely disabled individuals. However, their extremely low signal-to-noise ratio (SNR) necessitates robust denoising, especially in real-time BCI applications where low latency, minimal computational overhead, and single-channel operation are critical constraints. While advanced offline methods like Independent Component Analysis (ICA) and wavelet-based thresholding offer effective denoising in multichannel settings, they are ill-suited for embedded, causal, and resource-constrained environments. To address this gap, we propose a Partial-Update Variable Step-Size LMS (PU-VSS-LMS) algorithm that complementarily combines dynamic step-size adaptation with a magnitude-driven partial-update strategy. Evaluated on synthetic ERP-like signals embedded in realistic EEG noise (SNR = 6 dB and 0 dB), PU-VSS-LMS achieves lower mean squared error (MSE: 0.0780 vs. 0.0850 at 6 dB) and higher output SNR (8.10 dB vs. 7.80 dB) than standard VSS-LMS, while outperforming ICA in waveform preservation and noise suppression. Importantly, it reduces computational load by 75% (updating only 4 of 16 coefficients), enabling faster execution without sacrificing accuracy. These results establish PU-VSS-LMS as a highly efficient and effective solution for real-time ERP denoising in embedded, single-channel biomedical systems. Full article

This study examines the role of internal auditing in urban development within the Egyptian public sector, emphasising its contribution to governance and accountability in state-led projects. The research introduces a state-centric participatory audit approach tailored for urban development governance, diverging from traditional corporate-focused models by integrating institutional alignment and public sector accountability mechanisms. Unlike existing participatory audit frameworks, this model emphasises cross-agency coordination and sustainability governance within the Egyptian public sector, addressing gaps in oversight and collaborative planning. Findings reveal that internal auditing serves as a critical mechanism for aligning institutional objectives, enhancing transparency, and fostering participatory governance in urban development initiatives. Furthermore, the study advances institutional alignment through enterprise resource planning (ERP)-enabled participatory auditing, offering a governance-oriented framework for sustainability oversight in the public sector. Practically, the findings provide actionable guidance for public sector managers on embedding sustainability key performance indicators (KPIs) into audit processes and leveraging ERP systems for real-time monitoring and assurance reporting. From a policy perspective, the study informs regulatory reforms and governance strategies aimed at institutionalising accountability and participatory oversight in large-scale urban development projects. These insights offer practical implications for policymakers and practitioners seeking to strengthen accountability and sustainability in public sector development programs. Full article

This study examines how gender dynamics shape greenhouse gas (GHG) accounting and carbon accountability in a Mediterranean maritime agency. It adopts an interpretive single-case study design with ethnographic elements, combining interviews, document analysis, and direct observations derived from insider access. The results reveal that digitalization strengthens the technical capacity for carbon accounting, particularly for Scopes 1 and 2, by making data more traceable and auditable through ERP and principal-mandated systems. Empirically, the study finds that women perform most of the carbon data work, compiling, reconciling, and uploading approximately 80% of emissions-related information, yet hold limited decision rights over strategic boundary setting and KPI definition. This imbalance highlights how operational reliability depends on gendered divisions of labor, while strategic accountability remains constrained by hierarchical decision structures. The study reframes carbon accountability as a gendered organizational practice, advancing debates on Sustainable Development Goal (SDG) 5 (Gender Equality) and SDG 13 (Climate Action) in shipping. It also proposes a gender-inclusive accountability framework, including a Responsible–Accountable–Consulted–Informed (RACI) matrix with gender overlays, contractual/Application Programming Interface (API) exchanges for Scope 3, and participatory system design, and discusses implications for principals and port authorities. The findings contribute to critical and interpretive accounting by distinguishing operational from strategic accountability and demonstrating how the distribution of voice and authority conditions decarbonization credibility and effectiveness. Full article

This paper presents a concept for a Learning Factory (LF) designed for interdisciplinary engineering education. Learning factories are experiential learning environments that bridge the gap between theory and practice while supporting the demands of digital transformation. The proposed LF concept was developed using an integrated approach that assessed stakeholder needs and reviewed institutional infrastructure and capacity. These inputs were triangulated into a concept consisting of five core thematic components: Lean processes as an educational anchor, Enterprise Resource Planning (ERP) systems, Internet of Things (IoT)-based integration, simulation, and physical prototyping. Validation workshops with Small- and Medium-sized Enterprise (SME) managers, academic experts, and students confirmed the perceived relevance of this structure and its potential. The resulting concept focuses on practice-orientated, team-based learning methods that are in line with the principles of Education 4.0. The design sets goals in four key dimensions: educational integration, technological readiness, industrial relevance with SME orientation and flexibility and scalability. These design principles and practical insights can be utilized for future academic implementations of learning factories. Full article

The importance of sustainability continues to grow, and various standards now combine to form an important mechanism that underpins the entire sustainability management system. These standards originate from five main international organisations and standard-setting bodies: (1) The Climate Disclosure Project, (2) The Climate Disclosure Standards Board, (3) The Global Reporting Initiative, (4) The International Integrated Reporting Council, and (5) The Sustainability Accounting Standards Board. In addition to these specific bodies, the European Union issues The European Sustainability Reporting Standards. Digitisation is a key tool to improve the measurement and monitoring of sustainability. In Lithuanian financial institutions, ERP, Clarity AI, and artificial intelligence are critical tools alongside external ESG rating providers such as MSCI ESG, Sustainalytics, Refinitiv, and Bloomberg. Existing research often focuses on large multinational institutions or EU-level policy, with limited attention paid to how financial companies address the practical challenges of sustainability—particularly in Lithuania. This article addresses this gap in the research, consulting seven experts to explore the performance of financial companies, their use of sustainability standards, and the key challenges encountered during implementation. To achieve these aims, a structured survey analysing the issues posed by sustainability management is presented, with a particular focus on using standards to discuss problems in this area through exploratory analysis. The interviews produce insights that can help shape the future of sustainability management from the perspectives of both stakeholders and policymakers, as well as providing promising directions for future research. Full article

Manufacturers increasingly face the challenge of delivering high product variety while managing the internal complexity and costs this creates across the value chain. Modular product architectures are often promoted as a solution, yet adoption remains limited due to the absence of robust, quantitative tools for evaluating their systemic effects. This study develops and applies a data-driven framework that explicitly links product variety and complexity to overhead activities across the value chain. The framework integrates principles from time-driven activity-based costing (TDABC), complexity management, and hierarchical product decomposition, and is operationalized through a structured methodology that combines semi-structured interviews, enterprise resource planning (ERP) data analysis, and model-based simulations. This enables the allocation of previously untraceable cost pools such as engineering, procurement, production preparation, and sales hours to the product structure. Application in an engineer-to-order (ETO) equipment manufacturer demonstrates how the framework can identify high-impact subsystems, quantify potential reductions in engineering and procurement hours, and support scenario testing of alternative product architectures. The results indicate that even approximate estimates provide valuable, directional insights into customization-driven cost distributions. The study concludes that the framework constitutes a scalable and flexible decision-support tool for bridging the gap between theoretical modularization benefits and their quantification in industrial practice. Full article

Background: Grasping actions, owing to their manipulated nature, play a central role in research on embodied action language. However, their foundational contribution to the cognition of using actions remains debated. This study examined the relationship between grasping and using actions from the perspective of subthreshold semantic processing. Methods: Participants engaged with objects affording both action types while behavioral responses and event-related potentials (ERPs) were recorded. Semantic congruency between subliminally presented grasping verbs and the actions of target objects was systematically manipulated. Results: Subthreshold processing of grasping verbs facilitated the recognition of using actions, as reflected in faster response times and modulations of ERP components. Spatiotemporal analyses revealed a processing pathway from occipital to parietal and frontal regions, with the posterior parietal cortex serving as a critical hub for integrating object function semantics with action information. Conclusions: These findings provide novel evidence that grasping action semantics support the recognition of using actions even below conscious awareness, elucidating the neural dynamics of embodied cognition and refining the temporal characterization of manipulative action processing pathways proposed by the two-action system theory. Full article

This study aimed to identify electrophysiological markers (event-related potentials, ERPs) of intentional, need-related mental activity under controlled gaze fixation, with potential applications in brain–computer interface (BCI) development for individuals with severe motor impairments. Methods: Using stimuli from the PAIN Pictionary—a pictogram database for non-verbal communication in locked-in syndrome (LIS) contexts—neural responses were recorded via high-density EEG in 30 neurologically healthy adults (25 included after artifact-based exclusion). Participants viewed randomized sequences of pictograms representing ten fundamental need categories (e.g., “I am cold”, “I’m in pain”), with one category designated as the target per sequence. Each pictogram was followed by a visual cue prompting a button press: during training, participants executed the press; during the main task, they performed right-hand motor imagery while maintaining central fixation. Results: ERP analyses revealed a robust P300 response (450–650 ms; p < 0.0002) over centro-parietal regions for target cues, reflecting enhanced attentional allocation and stimulus choice. An early Contingent Negative Variation (CNV, 450–750 ms; p = 0.008) over fronto-lateral sites indicated anticipatory attention and motor preparation, while a left-lateralized late CNV (2250–2750 ms; p = 0.035) appeared to embody the preparation of a finalized motor plan for the forthcoming right-hand imagined response. A centro-parietal P600 component (600–800 ms; p = 0.044) emerged during response monitoring, reflecting evaluative and decisional processes. SwLORETA source analyses localized activity within a distributed network spanning prefrontal, premotor, motor, parietal, and limbic areas. Conclusions: These findings demonstrate that motor imagery alone can modulate pattern-onset ERP components without overt movement or gaze shifts, supporting the translational potential of decoding need-related intentions for thought-driven communication systems in individuals with profound motor impairments. Full article