Search Results (304)

In-storage food loss is a persistent yet under-addressed source of economic, environmental, and social waste in small and medium-sized enterprise (SME) cold chains, where continuous monitoring and lean workflows are typically absent. This study asks how such loss can be reduced under SME constraints and what benefits an intervention designed for that setting could yield. Following a design science approach in a Thai chilled warehouse case, it develops an integrated intervention coupling an Internet of Things (IoT) early-warning platform—ESP-32 and DHT22 sensing with commodity-specific alerting through the LINE Messaging API—with value stream mapping (VSM) of the depositing and withdrawing workflows. Monitoring showed that 7.4% of quality-controlled readings exceeded the 6 °C control threshold. Value stream analysis established that elapsed time is governed by information latency rather than physical work and that produce spends 290 min per handling cycle outside controlled conditions. The redesigned workflows project lead-time reductions of 47.5% and 69.4% and remove 125 min of that exposure. An ex ante Triple Bottom Line assessment estimates approximately 19,700 kg of avoided produce loss, 6500 kg CO₂e, and 590,000 THB retained annually. This study contributes a complementarity account of digital monitoring and process improvement, advancing SDG Target 12.3. Full article

Background: Elite soccer performance emerges from the interplay of cognitive, emotional, psychophysiological, and tactical processes that operate in real time during matches. Advances in wearable sensors and artificial intelligence (AI) now allow continuous monitoring of physiological and psychological states. They also allow modeling of how these states relate to tactical and physical performance. Existing reviews have examined machine learning in soccer, heart rate variability (HRV) monitoring, and psychological determinants of performance separately. No scoping review has mapped the intersection of AI analytics, wearable psychophysiological monitoring, and psychological performance constructs as one integrated decision-support framework in elite soccer. Aim: The aim of this study was to map the available evidence on the integration of AI and machine learning with psychophysiological monitoring for performance modeling in elite soccer, to identify the psychological constructs already used as model inputs, to describe the wearable technologies and AI methods applied, and to set out the translational challenges and evidence gaps that need priority attention. Methods: The review followed the PRISMA extension for Scoping Reviews (PRISMA-ScR) and the updated Joanna Briggs Institute (JBI) methodology. The protocol was registered on the Open Science Framework (OSF). Six databases (PubMed/MEDLINE, Scopus, Web of Science, SPORTDiscus, IEEE Xplore, and PsycINFO) were searched from January 2000 to March 2026 using the Population–Concept–Context (PCC) framework. Two reviewers independently screened titles, abstracts, and full texts (Cohen’s kappa = 0.82). Results: Thirty-six sources met the eligibility criteria after screening of 3104 records. AI and machine learning have been applied widely to predict physical and tactical performance in soccer, yet they rarely include psychological constructs. Reported models (decision trees, gradient boosting, and artificial neural networks) reach high accuracy for physical outcomes in internal validation, for example, above 66% for injury risk. Multi-modal models that add physiological and psychological inputs report stronger prediction. These figures come mostly from internal validation, and external validation and overfitting controls are seldom reported, so they should be read as optimistic upper bounds. Psychological and psychophysiological inputs remain under-represented. Explainable AI (XAI) methods, in particular Shapley Addictive exPlanations (SHAP) values, are appearing, but validation with domain experts is scarce. HRV has been reviewed as a psychophysiological marker in soccer, yet its use within AI decision-support tools for real-time psychological readiness has not been mapped. Three translational challenges stand out: the ecological validity gap between laboratory cognitive tests and match-embedded psychophysiology; the interpretability problem of opaque AI in high-stakes decisions; and the data fragmentation problem created by disconnected physical, tactical, and psychological data streams. Conclusions: Integrating AI with wearable psychophysiological monitoring offers a credible route toward integrated performance modeling in elite soccer. Closing this gap calls for multi-modal frameworks that combine psychological constructs, physiological markers, and tactical data within explainable AI. Research priorities include ecologically valid psychophysiological assessment protocols, position-specific psychological profiling, and practitioner-validated tools that turn AI outputs into usable coaching recommendations. Full article

Enggal Jaya MSME is a small-scale enterprise specializing in metal casting and the production of fire rings, with a monthly capacity exceeding 15,000 units. Currently, its production process exhibits significant inefficiencies across the seven wastes of lean manufacturing: overproduction, defects, inventory, motion, transportation, overprocessing, and waiting. This study aims to map the information and production workflows for fire ring components, analyze the types and root causes of waste, and propose actionable solutions to improve shop-floor efficiency. Value Stream Mapping (VSM) and the Waste Assessment Model (WAM) were utilized as the primary methodologies. The current-state VSM revealed a Process Cycle Efficiency (PCE) of 69%, whereas the proposed future-state VSM demonstrates an improved PCE of 73%. Furthermore, Waste Assessment Questionnaire (WAQ) calculations identified five major types of critical waste: overproduction (19.2%), defects (18.0%), inventory (16.1%), motion (15.2%), and transportation (13.3%). Proposed countermeasures include systematically recording production activities, developing Standard Operating Procedures (SOPs), integrating appropriate material handling tools, and implementing rigorous production forecasting and scheduling. Full article

Entity-aware quality assessment may reduce false interpretations of electronic health record (EHR) data, but evidence from small, rule-aligned benchmarks cannot establish operational effectiveness. We revised H-StreamQ as a proof-of-concept framework and evaluated its laboratory component using the complete MIMIC-IV v3.1 labevents file (158,374,764 events; 313,442 patients). Ten thousand patients were sampled across laboratory-activity quintiles and split at patient level into training (6000), threshold-calibration (2000), and test (2000) groups. The independent test set contained 918,651 numeric laboratory events. Without excluding naturally alerted records, 54,788 mutually exclusive defects were introduced using subtle value shifts, unit/scale errors, mapping errors, delayed records, and patient-clustered correlated defects. Rules, a context-aware Isolation Forest, their union (Hybrid), a context-free Isolation Forest, Local Outlier Factor (LOF), and linear and radial-basis-function (RBF) One-Class support vector machines (OCSVMs) were compared at a threshold fixed by a 2.5% calibration alert budget. Patient-cluster bootstrap intervals and event-micro and patient-macro results were reported. Rules alone achieved the highest event-micro F1-score (0.637; 95% confidence interval [CI] 0.547–0.722), followed by Hybrid (0.576; 0.484–0.668) and RBF One-Class SVM (0.559; 0.433–0.670). Hybrid increased recall over rules by only 0.004 (95% CI 0.003–0.006) while reducing F1 by 0.061 and increasing the background-alert rate by 0.015. Context conditioning did not improve aggregate Isolation Forest performance. In six batch-level drift simulations, an exponentially weighted moving average (EWMA) and a fixed-window monitor detected 97–100% and 98–100% of changes, respectively, whereas a custom Hoeffding adaptive-window detector was more conservative and often missed smaller or recurrent changes. These results support H-StreamQ as an explainable research framework, not as a validated clinical or production system. Patient-macro F1, which weights every patient equally, was substantially lower than event-micro F1 for every method (rules 0.395 versus 0.637; Hybrid 0.320 versus 0.576), indicating that event-level performance is weighted towards high-activity patients. Precision and F1 are computed relative to injected synthetic labels and are not clinically adjudicated estimates. The entity-aware architecture spans patients, admissions, diagnoses, transfers, and dictionaries, but the quantitative detection benchmark evaluates the numeric laboratory component only; other entities are used for linkage and contextual attachment and are audited descriptively rather than evaluated against labels. Full article

Traditional traffic safety analysis has long suffered from a reliance on retroactive, sparse crash logs, which mathematically struggle to capture the highly stochastic and non-linear dynamics of real-time traffic streams, rendering proactive safety prevention difficult. To bridge this gap, this study introduces an innovative, data-driven Traffic Operational Risk (TOR) assessment framework that integrates unsupervised deep learning with extreme-value statistics to achieve continuous, proactive risk monitoring. By mapping macroscopic traffic flow parameters and microscopic aggressive driving behaviors (ADBs) onto a unified spatiotemporal grid, we deploy a Variational Autoencoder (VAE) to learn the continuous latent “safe traffic manifold”. On this basis, Extreme Value Theory (EVT) is introduced to mathematically calibrate a dynamic, robust safety frontier on a unified scale (0–100), effectively suppressing sensor noise. The evaluation results demonstrate that the VAE-EVT framework robustly quantifies dynamic operational risks, effectively overcoming the linear limitations of traditional surrogate models. Furthermore, spatial frequency mapping reveals that elevated operational risks inherently cluster at geometric bottlenecks, such as merge/diverge zones and sharp curves. This spatial aggregation elucidates a typical “High Risk, Low Crash” phenomenon primarily driven by driver compensatory behaviors. Crucially, the integration of a novel multidimensional risk decoupling mechanism successfully isolates micro-behavioral volatility from macro-flow degradation. By tracing this causal progression, the framework captures the mechanistic evolution of traffic breakdowns, securing a critical 10 to 15 min proactive pre-warning window before systemic crashes or congestion materialize. Ultimately, this methodology liberates risk assessment from retroactive crash logs, providing a mathematically rigorous paradigm for precision-guided highway safety management. Full article

Non-contact body weight (BW) estimation provides a low-stress and low-cost approach for precision beef cattle management, but single-view RGB images may not fully capture body-shape information. This study proposed a practical dual-view RGB framework for cattle BW estimation. A total of 3210 paired top-view and side-view RGB images were collected from 107 Simmental beef cattle with BW ranging from 169 to 980 kg. An EMA-enhanced YOLO11n-seg model was adopted to improve cattle foreground extraction, and a two-stream CBAM-ResNet50-SE network was constructed to learn dorsal and lateral morphological features for BW regression. The EMA-YOLO11n-seg model achieved mAP@0.5 values of 99.18% and 98.35% for top-view and side-view images, respectively. On the test set, the proposed BW estimation model achieved an MAE of 14.96 kg, an RMSE of 17.86 kg, and an R² of 0.85. The model also showed stable performance across different growth stages and posture conditions. Adaptation experiments using a Sanhe cattle dataset further demonstrated the adaptability of the proposed framework. These results suggest that the practical dual-view RGB framework developed in this study provides an effective solution for non-contact beef cattle BW estimation under fixed image-acquisition conditions. Full article

Forest fires are a major environmental concern in Northern Thailand, contributing to ecosystem degradation, biodiversity loss, and seasonal air pollution. This study identified the environmental factors influencing forest fire occurrence and developed a forest fire susceptibility map using an integrated Geographic Information System (GIS) and Logistic Regression (LR) framework in Chom Thong District, Chiang Mai Province, Thailand. Fire occurrence data were derived from Visible Infrared Imaging Radiometer Suite (VIIRS) active fire hotspots detected by the Suomi National Polar-orbiting Partnership satellite (Suomi-NPP satellite) during 2023–2025. A total of 1674 hotspots were identified (616 in 2023, 889 in 2024, and 169 in 2025). Ten environmental variables, including elevation, slope, aspect, Topographic Wetness Index (TWI), stream density, rainfall, Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Land Surface Temperature (LST), and land-use, were analyzed. The LR model was trained using 2293 training samples (70%) and validated using 983 samples (30%). The results revealed that slope, rainfall, stream density, and LST were significant predictors of forest fire occurrence, with deciduous and evergreen forests exhibiting the highest susceptibility among land-use classes. The resulting forest fire susceptibility map classified 235.12 km² (21.16%) and 204.16 km² (18.38%) of the district as very high and high susceptibility, respectively, primarily in mountainous forest areas. The model achieved an overall accuracy of 77.5% and an Area Under the Curve (AUC) value of 0.852, indicating good predictive performance. Furthermore, the proposed Geographic Information System-Logistic Regression (GIS-LR) framework provides an interpretable and transferable approach for forest fire susceptibility assessment and generates spatial information that can support forest fire prevention, resource allocation, and environmental management in Northern Thailand and other fire-prone regions. Full article

Smart Factory programs increasingly connect shop-floor, quality, asset and planning data, but integrated data infrastructures do not necessarily align operational decisions. This paper reviews how smart manufacturing literature explains the transition from industrial data integration to closed-loop operations synchronization and value capture in high-throughput manufacturing contexts. Using the Systematic Search Flow method, 1949 records were screened and reduced to a final portfolio of 73 studies. The papers were coded by thematic cluster, dominant technology, research method, primary theme, value-stream coverage and operations-synchronization relevance. The coding shows that roadmaps, interoperability architectures, analytics applications and digital-twin models dominate the portfolio. Explicit operations-synchronization mechanisms are addressed in 16 of the 73 studies, mainly through planning-execution coupling and digital-twin-based decision support. Coverage across value streams is uneven, with stronger evidence for Strategy, Make and Plan than for Quality and Assets. Based on this evidence map, the paper proposes a Data-Driven Operations Synchronization Stack that links operational data capture, semantic and IT/OT interoperability, analytics-supported decision-making, closed-loop synchronization and operational or financial value capture. Full article

Generative artificial intelligence (AI) has diffused rapidly, yet adoption has not reliably progressed to AI transformation (AX). Firms grant tool access but fail to redesign workflows, clarify accountability, govern risks, or measure value. The gap is a socio-technical systems problem, not a productivity problem: AI tools are inserted into existing routines without redesigning task interdependencies, decision rights, oversight loops, or performance feedback. This paper develops AX-5R, a socio-technical systems architecture that converts fragmented AI use into accountable, governable, and measurable work systems. Synthesizing seven literature streams, it maps failure modes to five interdependent design functions: readiness, redesign, role, risk, and return. AX-5R treats transformation as joint optimization of technical and social subsystems requiring configurational alignment. A supplementary ablation probe generated 252 artifacts across three workflows and seven prompt arms from two language models, scored by blinded cross-provider judges; the full frame outscored a sham five-part control and its four artifact-relevant ablations, significant under two-sided Holm-corrected testing, with an independent human-expert-rating check. With a failure-mode derivation matrix, implementation artifacts, and six testable propositions, the framework specifies a minimum architecture in which readiness sets boundaries, redesign restructures tasks, role assigns accountability, risk establishes control, and return supplies learning feedback. Full article

This paper proposes a structured methodology for constructing a digital twin (DT) of a planned flexible manufacturing system (FMS) for gear wheel production, integrating value stream mapping (VSM), discrete-event simulation (DES), production scheduling, and CAD/CAM modeling into a hierarchical, iterative design framework. The methodology was validated on an industrial case study involving a Polish manufacturer of gear transmissions undergoing modernization from conventional machining to a fully automated system incorporating CNC machining centers, industrial robots, automated guided vehicles (AGVs), and an automated storage and retrieval system (ASRS). Production scheduling was performed using eight algorithms including a random-search method and an ant colony optimization (ACO) algorithm, applied to a representative of 1072 parts across 10 gear wheel types. Simulation models of both conventional and automated systems were developed in FlexSim 2024. The best random instance achieved a makespan of 72,317 s in the automated model, compared to 75,634 s for the list rule—a 4.4% improvement that corresponds to approximately 55 min of production time per batch. AGV fleet sizing experiments identified four vehicles as the optimal configuration, beyond which marginal gains fall below 2%. The proposed digital twin framework enables virtual commissioning, continuous production planning, and what-if analysis prior to and during physical system implementation. Full article

Chromium contamination in aquatic systems represents a significant environmental concern due to its toxicity and complex geochemical behaviour. This study analyzes the spatial distribution of total chromium (Cr) in sediments from streams and surface waters of the Iron Quadrangle (IQ), one of the world’s most important mining provinces, with the aim of establishing regional reference values and identifying anomalous concentrations. A total of 487 samples were collected, corresponding to an average sampling density of one sample per 14.37 km². Statistical approaches, including the Upper Inner Fence (UIF) method, were applied to distinguish background levels, elevated concentrations, and anomalies, while contamination factor and enrichment factor indices were used to assess contamination and enrichment patterns. The results indicate a wide range of Cr concentrations, with sediments reaching up to 2581 mg·kg⁻¹ and surface waters up to 384.7 μg·L⁻¹. Although most samples reflect natural background conditions, significant anomalies were identified, particularly in areas associated with mafic–ultramafic lithologies and mining activities. Approximately 73.3% and 42.3% of sediment samples exceeded TEL and PEL thresholds, respectively, while 35.3% of surface water samples surpassed drinking water limits. The results highlight the need for continuous environmental monitoring and reinforce the importance of integrating geochemical mapping with risk assessment approaches to better understand contamination dynamics in complex mining regions. Full article

Poly(lactic acid) (PLA) is widely recognized as a biodegradable bioplastic, yet reliance on industrial composting alone can forfeit embedded material and energy value when recovery is technically feasible. Recycling can retain this value, but PLA performance is affected by service-life aging, hydrolytic cleavage, thermal and shear history, and contamination. Whereas previous literature often treats end-of-life routes separately, this review integrates mechanical reprocessing, reactive upgrading, chemical and hydrothermal depolymerization, and life-cycle assessment within a feedstock–process–structure–performance–safety–circularity framework. We examine how molar mass, rheology, crystallinity, and mechanical performance evolve during recycling, and compare upgrading strategies, including chain extenders, plasticizers, blends, and fillers, in terms of property restoration, recyclability, migration, and ecotoxicity trade-offs. Chemical and hydrothermal routes are evaluated according to monomer yield, stereochemical purity, additive tolerance, repolymerization potential, and process severity. Life-cycle evidence shows that circularity cannot be defined solely by climate impact or biodegradability, as burden shifting may occur in terms of toxicity, energy demand, land use, and resource consumption. Accordingly, we propose a decision map linking feedstock quality with suitable routes and target applications. Overall, clean, dry, and traceable PLA should be prioritized for mechanical recycling, whereas degraded or contaminated streams require evidence-based upgrading or depolymerization instead of default disposal or composting practices. Full article

Smart-home sensor networks enable unobtrusive monitoring of daily activity and are increasingly used to support independent living among older adults. However, many anomaly detection methods produce scalar anomaly scores or binary alerts without explaining how the detected behavior differs from a resident’s normal routine. This paper proposes a two-stage framework for interpretable routine-deviation assessment using smart-home motion and door-contact sensors. In Stage 1, raw sensor streams are aligned on a two-second master calendar, aggregated into hourly event counts, mapped into functional household activity zones, and converted into daily routine profiles. A Gated Recurrent Unit (GRU) routine prediction model is trained using a three-day lookback window to predict expected daily zone-level activity. Candidate routine-deviation days are automatically identified from daily prediction errors. In Stage 2, the recent monitoring period is plotted as 24-h radar profiles against the learned routine model, allowing a human expert to visually assess deviations in timing, location, and severity. The workflow was evaluated using 28 days of smart-home data collected from multiple independent residents. The proposed GRU framework achieved RMSE values ranging from 0.136 to 0.180 and MAE values ranging from 0.126 to 0.138 across the four participants, consistently outperforming the Previous-Day Baseline and generally providing lower prediction errors than the Seasonal Naïve Baseline. These findings demonstrate the effectiveness of participant-specific routine modeling for personalized routine-deviation detection in smart-home environments. The results indicate that deviation-sensitive target zones differed across the four participants, suggesting the importance of participant-specific routine modeling. The proposed approach successfully links automated candidate routine-deviation identification with radar-based visual analytics, providing a proof-of-concept, personalized, and interpretable decision support workflow for ambient assisted living research. Full article

Robotic table tennis perception typically produces a continuous three-dimensional (3D) ball-state stream, whereas rule understanding, score attribution, and training review need discrete, ordered, and auditable events. We cast this gap as hybrid event reasoning and propose a layer that maps continuous state samples, finite rally modes, geometric guard predicates, event-bus updates, and structured event outputs into one auditable framework. Given a 3D state stream and a calibrated table, the layer emits BounceIn, NetCross, RuleBounceOut, LandingValidity, and RuleScore events with dual timestamps, geometric margins, and detection sources. A central design choice decouples observable table contact from the rule-level fact of an out-of-table landing, using a prediction-error radius to separate definite out-of-table, edge-ambiguous, and unknown cases. Under bounded state error, sampling gaps, and Lipschitz guard margins, we derive sufficient conditions for finite-window event observability, finite-delay confirmation, and event-sequence stability. On real rally clips, triggered-event review over 18 clips (787.42 s) yields validity (PPV) values of 99.13% and 99.41% for BounceIn and NetCross; a full-timeline dense audit reports F1 scores of 92.46%, 97.72%, 71.43%, and 85.71% for the four event types; an offline microbenchmark measures 1.062 μs per-frame overhead. Together, these results support the layer as a lightweight and auditable intermediate representation for local rally understanding in real-time robotic table tennis, rather than as a complete competition-level refereeing system. Full article

Engineer-to-Order (ETO) manufacturing environments are characterized by high product variety, low repetitiveness, and unstable demand, which pose significant challenges to the application of Lean Manufacturing (LM). This study investigates the application and adaptation of LM principles and tools in an ETO production line using an action research approach integrated with Value Stream Mapping (VSM). The research was conducted at a manufacturer of highly customized electrical equipment. An adapted method for calculating representative cycle times based on weighted production volumes was developed to support line sizing and workload balancing. The proposed future-state design incorporates multifunctional operators, FIFO lanes, daily scheduling, and pitch-based control. The results show a 9.5% reduction in labor requirements, a 61.7% decrease in manufacturing lead time, and a 75.0% reduction in overtime hours. Statistical validation using daily PPC records confirmed significant improvements in actual output, schedule adherence, overtime, and lead time after implementation. In addition to operational improvements, this study offers methodological contributions by proposing practical adaptations of LM tools suitable for high-variability ETO environments, thereby contributing to both theory and industrial practice. Full article