Search Results (7,103)

Bridge decks are continuously subjected to high environmental exposure, traffic loading, and material aging, leading to progressive delamination which can negatively affect structural integrity and public safety. More specifically, subsurface delamination of concrete and corroded steel reinforcement must be repaired to keep the decks operational. Among non-destructive evaluation techniques, Ground-Penetrating Radar (GPR) and Infrared Thermography (IRT) offer complementary capabilities for detecting subsurface and near-surface defects; however, effective GPR-IRT data fusion remains challenging due to fundamental differences in sensing principles, spatial resolution and sensitivity. This study introduces a Physics-Enhanced Multi-Modal Fusion (PE-MMF) framework that integrates GPR and IRT data to improve delamination detection in reinforced concrete bridge decks. The proposed approach leverages transfer learning, cross-modal attention mechanisms, and gated fusion to enable robust learning from heterogeneous sensor inputs. Furthermore, a systematic feature selection protocol is integrated to identify physically meaningful indicators that remain consistent across different bridges, enhancing generalization capability. The framework is trained and validated using the publicly available SDNET2021 dataset, comprising co-registered GPR and IRT measurements from five in-service bridge decks with verified delamination ground truth. Results demonstrate substantial performance improvements, with average F1-score gains of up to 55% over IRT-based methods and 25% over GPR-based methods across all tested bridges. Comparative analysis against state-of-the-art methods confirmed the superior generalization capability of the proposed multi-modal approach over single-modality approaches. The findings highlight the potential of deep learning-based sensor fusion as a scalable and data-efficient decision-support tool to prioritize regions for detailed physical investigation during long-term infrastructure monitoring. Full article

Accurate solar irradiance forecasting is important for efficient planning of solar energy systems, renewable energy integration, and data-driven energy management in smart cities. This becomes more essential in regions with limited measured data availability and varying climatic conditions, where reliable forecasting can support urban energy planning and smart grid operation. Pakistan faces a scarcity of available solar data and has varying climatic conditions, which makes it ideal for such a study. This study utilizes nine geographically diverse stations to develop a benchmark framework for direct one-step-ahead hourly solar irradiance forecasting. The dataset was subjected to data preprocessing, feature engineering, and multi-model evaluation. A staged approach was adopted for feature selection, starting from a base model comprising three input variables: extraterrestrial radiation, solar zenith angle, and relative humidity. Features were added in an incremental order, which resulted in an optimized four-variable input set through the addition of a lagged clearness index to the base model. The forecasting models evaluated in this study, using these input variables, were ANN, NAR, NARX, LSTM, GRU, SARIMA, and Prophet. Deep learning models outperformed the other considered approaches, with LSTM showing the best overall benchmark performance with an average RMSE of 92.93 W/m², MAE of 66.56 W/m², and R-Squared of 0.872. The performance trends were broadly consistent across the evaluated stations, indicating stable behaviour within the adopted dataset and experimental setup. The study shows that a compact and physically interpretable input feature set, used with recurrent deep learning models, provides an effective solution for hourly solar irradiance forecasting, especially in locations with varying climatic conditions. The proposed benchmark can support smart city applications related to distributed solar generation, energy-aware urban planning, and intelligent operation of renewable-rich power systems. Full article

Supplier selection in healthcare is a complex multi-criteria decision-making (MCDM) challenge requiring a balance of sustainability, resilience, and operational efficiency. Traditional methods struggle with scalability and subjectivity when applied to large administrative datasets. This study introduces a transparent hybrid Machine Learning–MCDM (ML–MCDM) framework, validated using a U.S. Medicare dataset of 661 suppliers. The framework integrates eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) for criterion prioritization, the Full Consistency Method (FUCOM) for mathematically consistent weighting, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for final ranking. As the dataset lacks direct sustainability metrics, seven indicators were synthetically generated; thus, the results serve as proof-of-concept demonstration of the framework’s architecture. Specifically, XGBoost–SHAP is trained to predict a synthetically constructed Overall Performance Score (OPS), meaning that the resulting feature importance output constitutes an algorithmic consistency check—confirming that the pipeline correctly recovers importance signals deliberately embedded in the training target. For interpretability, suppliers were segmented into five performance profiles via K-Means: Strategic Partners (17.7%), Green Leaders (18.6%), Reliable Emergency Suppliers (18.2%), Balanced Performers (20.4%), and Developing Suppliers (25.1%). Carbon Footprint Score (0.408) and Emergency Response Capability (0.316) achieved the highest feature importance. FUCOM-derived weights prioritized On-Time Delivery Rate (0.272), Carbon Footprint Score (0.222), and Emergency Response Capability (0.220). The top supplier attained a TOPSIS closeness coefficient of 0.800, showing strong discrimination. Sensitivity analysis across four scenarios confirmed ranking robustness, maintaining Spearman correlations ρ ≥ 0.977. This ML–FUCOM–TOPSIS approach provides an auditable, scalable, and policy-relevant decision-support tool, enabling procurement managers to navigate high-dimensional data while ensuring operational continuity and environmental responsibility in healthcare supply chains. Full article

Thermal overload in internal combustion engines may progressively destabilize lubricant-film integrity and promote severe tribological deterioration within highly stressed contact interfaces. This study investigates the thermo-tribological degradation sequence of a high-mileage gasoline engine subjected to prolonged idle operation under impaired cooling conditions, ultimately resulting in engine seizure. The investigated engine had accumulated 356,724 km, while the lubricant had remained in service for approximately 26,724 km prior to the experiment. The post-failure investigation combined teardown inspection, geometrical camshaft assessment, reverse gravimetric reconstruction, hydraulic tappet surface profiling, XRF surface characterization, laboratory oil analysis, and SEM/EDS evaluation of wear debris. The results demonstrated strongly localized degradation concentrated primarily within the cam–tappet interfaces. Severe non-uniform camshaft wear was accompanied by pronounced hydraulic tappet surface damage and evidence of unstable boundary-lubrication conditions. Laboratory oil analysis revealed elevated wear-metal concentrations, depletion of the alkaline reserve, increased oxidation indicators, and a final Class D oil condition assessment. SEM/EDS characterization identified Fe-bearing wear debris associated with sustained material removal and debris recirculation during the final degradation stage. The combined evidence supports a coupled thermo-tribological degradation mechanism involving lubricant deterioration, boundary-lubrication instability, adhesive wear acceleration, oxidative surface degradation, and debris-assisted surface damage preceding final engine seizure. The present case study provides experimentally documented evidence of lubrication collapse under real-engine thermal runaway conditions and highlights the critical role of lubricant condition in maintaining tribological stability under severe thermal loading. Full article

Emerging economies face mounting pressure to adopt sustainable and cost-efficient methods for delivering products and services in urban areas. This study examines the Electric Vehicle Routing Problem with Time Windows (EVRPTW) within a pragmatic urban context. We concentrate on the short-haul delivery network in Marrakesh, Morocco, whose operational viability is influenced by climatic, infrastructural, and regulatory limitations. We present a simulated annealing (SA) metaheuristic, augmented with repair heuristics and a penalty-based cost function, to concurrently reduce routing costs and lateness fines, subject to time-window and battery capacity restrictions. The technique undergoes evaluation through extensive computer tests utilizing realistic instance sets that replicate local demand patterns and charging infrastructure. The penalty-calibrated model demonstrates delivery completion rates of up to 100%, significantly reducing route costs and the number of unserved clients relative to baseline setups. We thoroughly analyze the tuning parameters among several runs. This study intends to provide a useful tool for real-world decision support by fusing extensive literature synthesis with local context validation and by integrating a simulation module that evaluates time-window settings and charging patterns under realistic traffic. Full article

The biological treatment risk associated with wastewater containing the novel pigment intermediate N,N′-(1,4-phenylene)bis(acetoacetamide) has not been previously characterized. This study systematically evaluated the tolerance and performance of a laboratory-scale anaerobic–anoxic–oxic (AAO) system subjected to progressively increasing loadings of high-concentration (COD > 10,000 mg·L⁻¹) wastewater. During a 39-day trial, the influent proportion was incrementally increased from 0.57% to 52.14% without system collapse. Complete microbial adaptation required approximately seven days. The anaerobic unit exhibited the highest sensitivity to shock loads, followed by the oxic unit, while the anoxic unit remained stable. GC-MS analysis confirmed the degradation of complex organic intermediates throughout the treatment stages, and TEST-based predictions indicated that the effluent exhibited lower predicted toxicity than the influent. Notably, cessation of mother liquor addition resulted in system self-recovery, further demonstrating robust shock resistance. This study provides the first experimental evidence of (i) unit-specific shock sensitivity (anaerobic > oxic > anoxic), (ii) a quantified adaptation period of approximately seven days, (iii) an operational threshold of 52.14% mother liquor without causing system collapse, and (iv) self-recovery following load cessation in an AAO system treating wastewater containing N,N′-(1,4-phenylene)bis(acetoacetamide). These findings extend previous AAO toxicity studies on industrial wastewater and present a practical, cost-effective mitigation strategy for full-scale applications. Full article

Background: Healthcare-associated infections (HAIs) remain a critical patient safety challenge. Hand hygiene is considered the most effective preventive measure, yet traditional monitoring captures only compliance, not technique quality. This prospective before–after study evaluated whether real-time visual feedback via the Semmelweis UV-fluorescence system is associated with improved hand hygiene quality, measured by ATP bioluminescence and microbiological culture. Methods: Three clinical departments (the Intensive Care Unit, Hematology, and Gynecology) at a Romanian tertiary hospital were purposively selected. Seventy-one healthcare workers (HCWs) were enrolled. The 12-week study comprised Phase 1 (baseline, weeks 1–4), Phase 2 (active intervention with Semmelweis feedback, weeks 5–8), a one-week washout (week 9), and Phase 3 (sustainability assessment, weeks 10–12). Paired ATP-CFU samples were collected weekly. Within-group comparisons used Kruskal–Wallis H tests with post hoc Dunn’s tests and Bonferroni correction. Secondary outcomes included Semmelweis global and zone-specific coverage and the correlation between subject-level Semmelweis coverage and ATP bioluminescence (Spearman’s rho). Results: A total of 781 paired ATP-CFU samples and 497 Semmelweis evaluations were analyzed. Mean ATP declined from 195.9 RLU at baseline to 148.2 RLU in Phase 2 (−24.4%) and 154.8 RLU in Phase 3 (−21.0%; Kruskal–Wallis H = 102.73, p < 0.001). CFU/mL declined from 84.8 to 66.2 (−21.9%) and 70.7 (−16.6%; H = 22.48, p < 0.001). Post hoc comparisons confirmed significant Phase 1 versus Phase 2 and Phase 1 versus Phase 3 differences for both markers (all p < 0.01), while Phase 2 versus Phase 3 was non-significant, indicating stabilization at an improved level. Subject-level Semmelweis coverage correlated negatively with ATP (rho = −0.665, 95% CI −0.778 to −0.510, p < 0.001), supporting construct validity at the operator level. Semmelweis global coverage was 93.1% (Phase 2) and 90.6% (Phase 3); interdigital spaces showed the highest inadequacy rate (73.9% protocol-based, 92.5% targeted). Conclusions: Real-time visual feedback via UV-fluorescence imaging was associated with significant and sustained improvements in hand hygiene quality beyond baseline. ATP, CFU, and Semmelweis assessments captured complementary, non-redundant dimensions, supporting multimodal evaluation. Interdigital spaces and fingertips remained persistent failure points requiring targeted educational reinforcement. Full article

This paper addresses a human resource allocation problem with spatial-temporal constraints (HRAP-SC) in the parallel assembly of complex products, such as satellites and aircraft. It involves coordinating a limited pool of multi-skilled workers across geographically distributed workstations, subject to rigorous constraints including team collaboration requirements, operation priorities, technological tail times (e.g., curing), and strict 8 h workdays. Existing exact approaches typically fail to converge due to the combinatorial explosion arising from the strong coupling of shared resources across workstations, while meta-heuristic methods often suffer from performance instability caused by hyper-parameter sensitivity. To overcome these limitations, we propose a pattern-based decomposition algorithm (PDA), a novel parameter-free exact solution framework. By exploiting the inherent symmetry of identical jobs and parallel workstations, PDA defines a set of canonical patterns to drastically reduce the search space. It employs an efficient traversal mechanism reinforced by rigorous mathematical bounds and pruning rules to eliminate unpromising solutions. Computational experiments demonstrate that PDA significantly outperforms state-of-the-art Mixed-Integer Programming (MIP) and Constraint Programming (CP) solvers. Unlike standard solvers, which frequently time out (3600 s), PDA strictly evaluates only a single pattern when proving optimality, and robustly scales to large industrial instances (e.g., six jobs comprising 78 operations) to provide high-quality schedules. By successfully solving complex scheduling problems that remain intractable for monolithic solvers, PDA provides a robust and automated decision-support tool for production management in complex manufacturing systems. Full article

With the continuous advancement of drilling technologies for deep and ultra-deep well operations, drillpipes are subjected to increasingly severe wear and corrosion conditions. To enhance the wear and corrosion resistance of drillpipe surfaces, this study developed a novel Ni60 alloy hardbanding via laser cladding technology. To solve the problem of crack sensitivity, the cracking mechanism of Ni60 coatings directly deposited on 4137H steel substrates was systematically investigated and a crack suppression strategy was proposed. By employing a 316L translation layer between the 4137H substrate and the Ni60 alloy coating, the interfacial thermal stress induced by the mismatch of thermal expansion coefficients between dissimilar materials was relieved. Therefore, crack-free 316L-Ni60 gradient coatings were obtained. The microstructure, phase composition, and mechanical properties of the coatings were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), and microhardness testing. The experimental results demonstrate that the 316L-Ni60 gradient coating exhibits a homogeneous microstructure and forms a dense metallurgical bond with the 4137H steel. The microhardness of the coating is 2.2 times that of the 4137H steel, while its wear rate is reduced by nearly half. Furthermore, the Ni60 coating possesses higher corrosion resistance compared with 4137H steel. This study promotes the potential application of the Ni60 alloy coating as a new type of hardbanding on drillpipes. Full article

Variable-amplitude vibrating screens are widely adopted for screening frozen sea buckthorn berry particles. Investigating their motion characteristics and particle behaviors on the screen surface is essential for optimizing the screening process and improving equipment performance and screening efficiency. In this work, a variable-amplitude vibrating screen is taken as the research subject. Its structural composition and working principle are elaborated, and kinematic simulations are conducted via RecurDyn. The results reveal that the vertical amplitude and velocity of the screen surface increase gradually from the feed end to the discharge end, which facilitates rapid particle penetration. Meanwhile, the horizontal velocity remains stable across all sections of the screen. Specifically, crank length governs the screen amplitude, while crank rotational speed determines the vibration frequency. A dynamic model of particles and the screen surface is established by combining EDEM 2024 and RecurDyn V9R4, and two-way coupling of the discrete element model is realized. Coupled simulation results indicate that the dynamic screening efficiency rises with increasing crank length and rotational speed, reaching the maximum at a crank length of 20 mm and a rotational speed of 208 r/min. Crank parameters exert remarkable effects on the thickness of the particle layer and the quantity of penetrated particles: a thicker particle layer leads to a longer residence time of materials on the screen. Field tests are carried out to verify the model accuracy. It turns out that the simulation results are basically consistent with experimental data. In conclusion, crank length and rotational speed are critical influencing factors for variable-amplitude vibrating screens. Research on the screen’s motion characteristics and particle behaviors can provide a theoretical reference for its efficient operation and optimal design. Full article

A newly isolated red-pigmented yeast, Sporobolomyces reniformis EMCC1691, was evaluated for its biotechnological potential in an integrated case study aimed at developing an efficient microbial cell factory for the valorization of delactosed whey. Fermentation trials in 5 L bioreactors demonstrated robust yeast growth on this dairy by-product, with complete consumption of glucose (21.86 g/L) and galactose (20.36 g/L), leading to the accumulation of approximately 6172 mg/L of lipids and 5634 µg/L of total carotenoids. Fatty acid analysis revealed a final concentration of 3924 mg/L, mainly represented by oleic (2037 mg/L), palmitic (779 mg/L), stearic (403 mg/L), and linoleic (362 mg/L) acids. HPLC analysis showed a pigment profile dominated by torularhodin, torulene, γ-carotene, and β-carotene. To complement downstream processing, the fermented culture was spray-dried into a stable powder and subsequently subjected to a simple, cost-effective, and unconventional mechanical pretreatment using a hydraulic press. This post-drying operation ensured extensive cell-wall disruption without the use of chemical agents or specialized equipment, thereby significantly enhancing the recoverability of intracellular lipids and carotenoids through supercritical CO₂ extraction. Under optimized conditions, SFE-CO₂ with ethanol recovered 92.18 ± 1.61 µg/g of total carotenoids, achieving an extraction efficiency of 84% relative to organic solvent extraction (109.17 ± 2.10 µg/g). Importantly, fermentation also reshaped the fatty acid composition of delactosed whey, shifting it toward a profile enriched in monounsaturated and polyunsaturated fatty acids, thereby further highlighting the metabolic impact and bioconversion potential of S. reniformis EMCC1691. Overall, this work highlights the technological relevance of a recently characterized yeast species and its potential to convert dairy by-products into high-value compounds within a proof-of-concept microbial cell factory framework, paving the way for future scale-up investigations. Full article

Wearable technologies offer practical opportunities for assistive communication and educational support in introductory sign language learning. This paper presents an IoT-enabled smart glove for deterministic static sign language recognition over a bounded vocabulary of 15 isolated static gestures, comprising digits (0–9) and five vowel handshapes (A, E, I, O, U). The system is intended for foundational static gesture and posture practice and is not designed or validated for dynamic gestures, coarticulated signing, continuous sign language recognition, or sentence-level translation. The prototype integrates five 2.2-inch (55.9 mm) resistive flex sensors and an MPU6050 3-axis accelerometer, performs acquisition, exponential moving average filtering, user-specific calibration, normalization, and deterministic classification on a NodeMCU ESP32 board, and transmits selected processed variables to Arduino Cloud through MQTT for remote monitoring. A 10 s calibration routine maps user-specific open-hand and closed-fist responses into normalized flex-sensor ranges, allowing the same deterministic rule structure to operate across participants without model retraining. Experimental evaluation with 10 healthy adult participants aged 20–41 years (mean age: 27 years), all familiar with sign language and all providing written informed consent, produced a balanced dataset of 1500 labeled steady-state sensor vectors. The class-averaged recognition rate was 92.8%, and leave-one-subject-out validation produced a subject-wise accuracy of $92.80\pm 2.03\%$, with individual participant accuracies ranging from 90.00% to 96.00%. The local embedded processing pipeline required less than 2 ms per cycle, the complete path including MQTT visualization produced approximately 150 ms end-to-end latency, and the device operated for up to 14 h using a 3.7 V, 1000 mAh Li-Po battery. The results indicate that calibrated deterministic sensor fusion can provide a low-cost, low-latency, edge-executed solution for bounded static sign-language gesture learning tasks while maintaining stable short-term subject-wise performance under controlled experimental conditions. Full article

In response to the issues of subjective weighting of indicators and insufficient consideration of the temporal dimension of risk factors in current operational safety risk assessments for electric utilities, this paper proposes a method for assessing operational safety risks in electric utilities based on the Accident-Calibrated Cumulative Risk Index (ACCRI). First, in accordance with current standards and operational guidelines, a multi-level indicator system covering four dimensions, namely human resources and personnel behavior, equipment and facilities, environment and conditions, and management and systems, is established to provide a systematic characterization of operational safety risks in electric utilities. On this basis, the ACCRI is defined by weighted accumulation of the average risk exposure values of tertiary indicators within their characteristic periods. Historical accident sample importance is used to calibrate and identify tertiary indicator weights and characteristic periods, thereby reducing the subjectivity of traditional expert-based weighting. Furthermore, considering the differing temporal-scale characteristics of various risk indicators, a characteristic-period identification model is established and solved using an improved sparrow search algorithm to balance the timeliness and accuracy of risk assessment. By incorporating chaotic initialization, Gaussian mutation, and adaptive weighting mechanisms, this algorithm enhances population diversity and balances global search capability with local optimization capability across different solution stages, thereby improving the solution efficiency and accuracy of the model. Finally, the case study preliminarily demonstrates that the proposed method can characterize the temporal-scale differences among risk factors and shows potential for engineering application under the available enterprise data. Full article

Saudi Arabia’s audit market faces three governance challenges that existing frameworks may not fully address. These challenges concern a potential regulatory gap around autonomous AI accountability, a trust dimension that standard technology-adoption models may not fully capture, and limited mechanisms for independently verified ESG assurance under Vision 2030. This study adopts a conceptual design approach within the design science research tradition and proposes the CMA Agentic AI Platform as a practical response to these challenges. The platform comprises two segments. Segment 1 deploys autonomous drone swarms to verify corporate assets across four audit tasks—asset valuation, ESG compliance, anomaly detection and construction progress—using deep learning, thermal imaging and social-media cross-referencing. Segment 2 continuously monitors discretionary accruals and uses objective earnings-management data to inform auditor assignment and rotation decisions. This approach replaces subjective reputational assessments with transparent, quantifiable governance criteria. The platform is governed through the Triadic Agentic Framework, which extends classical agency theory by distributing authority across the Principal, the Human Agent and the AI Agent. The framework also operationalises Trust Expectancy as the primary adoption condition. The evidence base draws on two complementary streams: a PRISMA-guided systematic review and bibliometric analysis of thirty-nine peer-reviewed studies, and a documentary analysis of four national agentic-AI regulatory frameworks (SDAIA, MDDI/IMDA, NIST and ICO). The study contributes the concept of Algorithmic Accountability as a distinct governance domain, the Triadic Agentic Framework as an operational architecture for autonomous regulatory monitoring, and a reframing of the UTAUT trust construct for agentic-AI adoption in mature professional contexts. The platform converts theoretical governance into a regulatory architecture with direct implications for concentrated capital market regulators. Full article

Discussion of consciousness and artificial intelligence has hitherto focused on the question of whether a large language model (LLM) exhibits signs of consciousness or understanding. This paper proposes to redirect attention elsewhere: not to the model itself, but to the orchestrating layer that governs the model—the backend, understood here as the collection of mechanisms (context management, retrieval, evaluation, planning, and tool-use control) that structure the model’s operation. We argue that the backend performs a function functionally analogous to the role of consciousness in the human brain: it stabilizes generative processes, directs attention, maintains context, and mitigates the entropic disintegration of thought. Consciousness fulfills this function through the phenomenal layer—qualia—which creates a persistent subjective “inner canvas”, used here as a metaphor for a more general multimodal phenomenal space. The backend fulfills it only algorithmically, without phenomenal quality. We further show that computation is an informationally conservative process in the sense of Shannon’s Data Processing Inequality (DPI), and therefore cannot increase Shannon information, even though it may yield novel or pragmatically useful recombinations of existing information. We conclude by proposing the hypothesis that consciousness constitutes a phenomenon orthogonal to computation—not an emergent property of complexity, but a qualitative leap into a different dimension. This hypothesis, which builds on the author’s prior work in this Special Issue and in Symmetry, is presented as a conceptual contribution rather than a formal theory, and may have implications for how future artificial intelligence research conceptualizes the limits of computational architectures. Full article