Search Results (6,178)

This paper presents an integrated modelling and decision-support reanalysis of a published 24-run experiment on diamond-spark grinding (electrical-discharge diamond grinding) of two hard alloys—the tungsten-free cermet TN-20 and the WC–TiC–Co alloy HS123—machined jointly with C45 steel; no new experiments are performed. Established components are deliberately combined into one reproducible workflow: quadratic response-surface models fitted by least squares and by minimax (Chebyshev) approximation, validation by prediction-oriented criteria including nested leave-one-out cross-validation of the entire model-selection pipeline, the addressable DEFMOT representation of the 9⁴-factor grid formalized as an ε-constraint procedure, and benchmarking against desirability-function and Pareto analyses. Minimax fitting reduces the maximum absolute residual by 22.5–36.9% at the cost of higher aggregate errors. Nested validation exposes model-selection instability for the TN-20 responses, and a dedicated sensitivity analysis shows that the surrogate-model choice can change the recommended regime: the TN-20 compromise is efficient or one grid step from efficient under all three surrogate families, whereas the preferred HS123 regime shifts qualitatively (including a reversal of the wheel-speed setting) between least-squares and minimax surrogates. A residual-bootstrap analysis propagates data uncertainty through the complete optimization and quantifies how frequently each recommended regime is re-selected. Within the legacy cost basis, point estimates indicate comparable productivity (difference below 9%), an approximately 35% lower specific machining cost for TN-20 and approximately 1.8 times higher diamond consumption; the 95% confidence intervals for the between-material contrasts include zero, so experimental confirmation is required before industrial substitution. The framework quantifies, rather than hides, how surrogate uncertainty propagates into the engineering decision. Full article

This article presents a hybrid methodology for the intelligent control of the reactor-regenerator unit of a residual fluid catalytic cracking (RFCC) plant under conditions of uncertain input information. The main challenge in catalytic cracking process control is the instability of feedstock composition and the delay in laboratory data on product quality, which complicates the operational management of the RFCC process while maintaining the required gasoline quality. The objective of this study is to support effective management of the gasoline production process by maximizing the yield of high-quality gasoline while keeping its density within specified limits. The proposed architecture combines a regression model built on six dominant parameters selected through Pearson’s correlation analysis, a Mamdani-type fuzzy inference system with a database of 26 expert rules, and a machine-learning module (Random Forest and gradient boosting) that corrects the residual error of the combined regression-fuzzy model, allowing the control system to adapt to changing process conditions. Testing the developed models on real data from the Shymkent Oil Refinery shows that the hybrid model reduces the root-mean-square error from 0.1973 (regression model alone) to 0.0161 and the mean absolute percentage error from 0.328% to 0.028%, while the coefficient of determination increases from 0.9895 to 0.9999; this improved accuracy is estimated to correspond to an approximate 3.2% increase in achievable gasoline yield and a 0.4% improvement in density stability. The developed decision support system for catalytic cracking process control, based on the proposed methodology, acts as a “virtual analyzer”, ensuring effective control in real time without the use of expensive in-line quality control devices. Full article

Background: Determinants of non-institutional delivery, particularly in the antenatal period, have been described previously, but less is known about the sequence of events during pregnancy and decision-making processes occurring between labour onset and birth. Aim: We explored reasons for non-institutional births and pathways leading to home and in-transit deliveries in a north Indian setting, with the aim of identifying context-specific, actionable recommendations. Methods: Participants were women from the north Indian site of the Redefining Maternal Anemia in Pregnancy and Postpartum (ReMAPP) cohort who experienced non-institutional delivery. We conducted an exploratory qualitative study using a semi-structured in-depth interview guide. An a priori conceptual framework was developed based on existing continuum-of-care literature and structured around the antenatal, intrapartum, and postnatal phases of care. The framework focused on the “decision-to-delivery pathway” and was subsequently iteratively refined and elaborated based on the thematic findings. Findings: Of the 2000 women enrolled at the north Indian site of the ReMAPP study, 75 (3.8%) experienced non-institutional delivery; among these, 50 participants were interviewed. Among those interviewed, 19 had planned, and 31 had unplanned non-institutional deliveries. Themes identified for planned non-institutional deliveries included perceived mistrust in healthcare, institutional avoidance, reliance on informal maternity care systems, and decision-making uncertainty. For unplanned non-institutional deliveries, themes included intention to institutional delivery, delay in obtaining appropriate maternity care, physical access barriers, reliance on informal birth assistance, and opportunities to avail health system services for post-delivery or newborn care. Conclusions: Our findings suggest that improving institutional delivery coverage requires more than physical access to facilities. Trust, respectful maternity care, timely responsiveness during labour, and birth preparedness appear to strongly influence whether women are able to reach and utilise institutional delivery services. Full article

Background: Relocating logistics infrastructure under wartime conditions requires more than identifying a theoretically suitable location, as available facilities, costs, and emerging risks must also be considered. This study examines the relocation of an enterprise from the frontline Zaporizhzhia region of Ukraine and aims to develop and substantiate a Hybrid Adaptive Model for Relocating Logistics Infrastructure (HAMRLI). Methods: The methodology combines resilience assessment, spatial modelling, digital facility screening, and expert evaluation. The Normalized Resilience Index (NRI) was calculated using EWM and TOPSIS. HAMRLI integrates the Center of Gravity (COG) method, digital verification of available warehouses, and enterprise-specific multi-criteria assessment. Results: COG identified Zhytomyr as a potential relocation area. Seven warehouse offers were identified, and two were shortlisted for expert evaluation. Weighted assessment selected Offer 6. Sensitivity analysis showed that its preference remained stable across the tested scenarios but changed with substantial shifts in criterion weights. The results support the applicability of HAMRLI for relocation decisions under high uncertainty. Conclusions: This study provides an initial empirical application rather than statistical generalization. Further testing of HAMRLI under different enterprise operating conditions is required. Full article

Port State Control (PSC) inspections are essential for maritime safety, but limited inspection resources make efficient vessel pre-screening increasingly important. This study investigates whether external vessel images can provide complementary information on visible ship deficiencies for PSC-oriented decision support. Five convolutional neural networks (ResNet18, GoogLeNet, MobileNetV2, DenseNet201 and SqueezeNet) were evaluated, followed by stacking and a hybrid framework combining ResNet18 deep features with handcrafted descriptors, mRMR feature selection, Principal Component Analysis, boosting classifiers and adaptive threshold optimisation. To make the comparison directly reproducible, duplicate image routes were removed, and all model families were evaluated on fixed target-specific 65/20/15 partitions. On the common independent tests, DenseNet201 provided the strongest balanced result for oxidation (balanced accuracy, 0.672; AUC, 0.742), whereas MobileNetV2 produced the largest point estimates for paint deterioration (balanced accuracy, 0.561; AUC, 0.659) and structural corrosion (balanced accuracy, 0.777; AUC, 0.885). Structural-corrosion recall was 3/4 = 0.750 (95% exact CI, 0.194–0.994) for MobileNetV2 and 2/4 = 0.500 (0.068–0.932) for the strict Hybrid PSC model, illustrating the uncertainty associated with rare positive cases. A controlled ablation further showed that changing only the operating threshold increased recall from 0.10 to 0.85 for oxidation and from 0 to 0.70 for paint deterioration, while increasing the alert burden. The results do not establish universal superiority of the hybrid representation; they show that representation and operating point should be interpreted jointly when visual AI is used as complementary PSC pre-screening support. Full article

Robust control of nonlinear dynamic systems remains challenging because parametric uncertainty and external disturbances can significantly degrade tracking accuracy and transient performance. This study proposes a Quantum-Inspired Photon–Spin Control Framework (QPSCF) that integrates probabilistic state representation and interference-inspired decision-making directly into the online feedback control process while remaining fully executable on classical computing platforms. Unlike quantum-inspired approaches primarily used for offline controller tuning or heuristic optimization, the proposed framework represents multiple candidate operating states probabilistically and adaptively evaluates competing control actions according to current process conditions. The QPSCF was evaluated on a nonlinear benchmark system under parameter variations of up to ±15% and a 10% external disturbance and compared with conventional PID and Mamdani fuzzy controllers under identical simulation conditions. The proposed controller achieved a settling time of 40.70 s, an overshoot of 0.15%, an RMSE of 4.83, an IAE of 121.54, and an ISE of 1652.41. Compared with PID control, QPSCF reduced RMSE, IAE, and ISE by 19.6%, 29.6%, and 41.9%, respectively. It also reduced the maximum disturbance-induced deviation from 1.80 °C to 0.55 °C and the recovery time from 20.30 s to 5.90 s. These results demonstrate that direct probabilistic decision-making within the feedback loop can improve tracking accuracy and disturbance rejection in nonlinear systems under uncertainty. Full article

Nonlinear technological processes are difficult to control because of strong nonlinear dynamics, parameter uncertainties, transport delays, and external disturbances, which can degrade the performance of conventional control strategies. This study proposes a Quantum-Inspired Fuzzy Inference Controller (QIFIC) that combines Mamdani fuzzy reasoning with an online adaptive probabilistic decision mechanism. Unlike conventional fuzzy controllers, which directly aggregate activated rules into a deterministic output, the proposed method preserves multiple candidate control actions and dynamically adjusts their relative probabilities according to the tracking error and its variation. The updated probabilities are combined with fuzzy-rule firing strengths to generate the final control signal, enabling online adaptation without modifying or retraining the original fuzzy knowledge base. A conditional Lyapunov-based analysis is used to establish the uniform ultimate boundedness of the tracking error under bounded modeling uncertainties and time-varying disturbances within the considered operating region. The proposed controller is evaluated using a nonlinear boiler temperature-control benchmark and quantitatively compared with PID and classical fuzzy controllers, while an adaptive fuzzy controller is included as an additional methodological benchmark for positioning the proposed adaptation strategy. Comparative simulations demonstrate improved transient response, disturbance rejection, and control smoothness, confirming the effectiveness of the proposed online probability-weighted inference mechanism. Full article

Wind turbine main bearings are critical components in the drivetrain and are characterised by long service life, low failure rates, and limited failure-interval samples, which increases uncertainty in reliability assessment and maintenance decision-making. To improve the utilisation of limited failure-interval information in small-sample reliability modelling, this study develops a unified three-way expansion Bootstrap strategy combined with a three-parameter Weibull distribution. The principal methodological contribution lies in integrating intra-interval supplementary sampling, left-boundary expansion, and right-boundary expansion within the same sample-generation framework, thereby enabling the main distributional information and boundary information contained in the available failure-interval samples to be utilised jointly. Based on 36 equivalent failure-interval samples obtained from Romax fatigue-life simulations under different operating conditions, the proposed method is compared with traditional Bootstrap and two-way expansion Bootstrap methods. The results of the two-sample K-S test indicated that no statistically significant distributional difference was detected between the expanded samples and the original sample. Using the three-parameter Weibull fitting results obtained from the original 36-sample dataset as the reference, the proposed three-way expansion method yields the smallest relative deviation of the scale parameter η among the three expansion strategies, at 2.69%. The MTBF relative deviations of the traditional Bootstrap, two-way expansion Bootstrap, and three-way expansion Bootstrap methods were 4.81%, 7.36%, and 7.76%, respectively. Repeated simulation results further show that the three-way expansion method provides substantially lower MTBF variability than the traditional Bootstrap method, although the two-way expansion method yielded the smallest MTBF standard deviation. The results demonstrate the methodological potential of the proposed strategy for small-sample MTBF modelling of wind turbine main bearings under the investigated simulation conditions. Full article

Short industrial compressed-air audits can contain thousands of samples while representing only a few dependent operating episodes. Autocorrelation therefore makes nominal sample count an unreliable basis for deciding whether a low-flow regime is sufficiently supported for an energy-efficiency or maintenance action. This paper proposes an audit-to-decision procedure separating three claims: that a low-flow regime exists, that it is measured reliably enough, and that its relative scale justifies action. hv-block selection and minimum-dwell reconstruction are combined with episode repeatability, effective intra-regime size, resampling uncertainty, metrological distinguishability, and relative non-productive consumption in a six-level action scale (D1–D6). The procedure was applied to 29 records from three operating plants. Five PLC-labeled records provided a limited external evaluation of the operational meaning of the identified low-flow state. Nominal BIC and hv-block selection differed in 20 of 24 audit records. Median deviation fell from 32.2–33.9% for nominal-selection baselines to 2.9% when the HMM used the hv-block-selected regime count. Outcomes were 3 × D1, 15 × D2, 7 × D3, and 4 × D4. D-codes were unchanged in 23/24 threshold-tested and 14/16 pressure-augmented records. In the five-record PLC-labeled evaluation, no action unsupported by the labels was triggered. The strongest time-series-only outcome is a mandatory diagnostic work order, not an automatic repair decision. Full article

Sustainable agricultural management faces growing challenges from climate change, water scarcity, energy constraints, soil degradation, and the need to ensure both food production and nutritional security. This study proposes an integrated computational framework combining a Global Agricultural Foundation Representation Model (GAFRM), Self-Evolving Explainable Digital Twin (SEDT), Self-Evolving Evolutionary Foundation Optimizer (SEEFO), and an enhanced Green Regenerative Agriculture Sustainability Index (GRASI). The framework operationalizes the food–water–energy–carbon–nutrition (FWEC-N) nexus by explicitly incorporating crop micronutrient density into the agricultural decision architecture and modeling its relationship with regenerative practices such as cover cropping, biochar application, and zero tillage. Using multi-source global datasets, GAFRM learns transferable agricultural representations, SEDT enables adaptive prediction under climate uncertainty, and SEEFO performs five-objective optimization of agricultural productivity, irrigation water use, energy demand, net carbon balance, and overall sustainability, while nutritional quality is evaluated through the MODI outcome indicator. The enhanced GRASI further evaluates nutrient output, soil restoration, carbon storage, and climate resilience within a unified sustainability framework. The framework was evaluated using a global agricultural dataset covering approximately 60 representative countries across six continents and 15 climate zones over the 2000–2026 period. SEDT achieved an RMSE of 3.18, MAE of 2.29, R² of 0.972, and NSE of 0.968, while SEEFO achieved the highest Hypervolume (0.956) and the lowest GD (0.028), IGD (0.039), and Spread (0.162) among the benchmark optimization algorithms. The observed performance differences were statistically significant according to the Wilcoxon signed-rank and Friedman tests (p < 0.05). The findings indicate that integrating nutritional quality with resource efficiency, carbon balance, soil regeneration, and climate resilience provides a more comprehensive basis for evaluating regenerative agricultural strategies. The architecture establishes a fully transparent, explainable decision-support environment through explainable AI (XAI) feature attributions, bridging the gap between digital precision farming, regenerative ecosystem restoration, and sustainable human nutrition under increasing environmental uncertainty. Full article

Agentic AI can interpret information, plan, make workflow decisions, and use enterprise tools. Yet technical capability does not establish authoritative meaning, legitimate process state, organisational permission, or accountable execution. The challenge is to preserve adaptability while ensuring that consequential actions remain governed. This article develops a domain-independent conceptual framework for governed agentic AI in enterprise workflows based on bounded autonomy, delegated authority, and controlled execution. A consequential action or workflow decision selected by an agent is treated as a proposed action. It may change enterprise state only after independent controls confirm semantic validity, procedural admissibility, policy compliance, and delegated authority. Actions that pass these controls and remain within a task envelope may proceed automatically through controlled enterprise tools. Those exceeding thresholds for consequence, irreversibility, uncertainty, data sensitivity, value, or organisational policy are escalated to an accountable human. Human oversight is therefore risk-proportionate rather than required for every action. The framework integrates enterprise ontologies, governed knowledge graphs, Business Process Model and Notation (BPMN) orchestration, policy and decision services, controlled tool execution, and provenance within explicit responsibility and authority boundaries. A review-informed design-science process synthesises evidence into five connected control gaps and derives ten design requirements, operationalised through task envelopes, capability and authority relations, lifecycle states, exception paths, and conformance criteria. An illustrative online-shopping order exception demonstrates the control logic, while a control-loop walkthrough and ten failure and adversarial conditions trace requirements-to-control mappings, responsibility separation, and defined recovery paths. The framework provides a systematic basis for governing agentic AI as an adaptable enterprise participant. It supports risk-proportionate autonomy, auditability, accountability, and regulatory evidence. The analytical walkthrough supports conceptual coherence and design plausibility but does not establish deployed effectiveness or legal compliance. Full article

Sustainable production planning requires the simultaneous optimization of economic, environmental, and social objectives under conditions where key parameters are not only imprecise but also variably reliable. This study proposes a Z-number-based multi-objective linear programming (Z-MOLP) framework that addresses this dual uncertainty. The framework incorporates eight conflicting objectives-profit maximization alongside minimization of particle board consumption, energy use, carbon emissions, production time, water usage, metal usage, and edge-band consumption-spanning economic, environmental, and social sustainability dimensions. Objective weights are determined through a Z-number-based reciprocal pairwise comparison procedure, and optimal production plans are obtained via weighted aggregation and integer linear programming. The framework is validated using real monthly production data from three furniture manufacturing enterprises in Azerbaijan. A comparative analysis between the full eight-objective model and a reduced six-objective model-excluding carbon emissions and production time-reveals that sustainability-specific objectives consistently constrain production volumes and reduce profit (+17.45%, +4.67%, and +0.57% profit increases when excluded for each of the three enterprises) but that these economic gains are driven entirely by production-scale expansion rather than efficiency improvement, with resource consumption rising in near-exact proportion. These findings confirm the existence of a genuine trade-off between economic performance and sustainability in developing-economy manufacturing contexts and demonstrate that Z-number representations add practical value by propagating data reliability through both the optimization and the output-reporting stages of production planning. Full article

Field crops change continuously across growth stages, exhibit substantial spatial heterogeneity, and must be managed within short operational windows. This study presents a structured narrative review of information sensing and intelligent monitoring from pre-sowing conditions to stand establishment, growth and yield formation, biotic stress, maturity, lodging, and harvest readiness. The literature is organized by growth stage and analyzed through a common chain of agricultural need, observable variable, sensing platform, data processing method, validation design, state interpretation, and management or equipment output. Satellite remote sensing, unmanned aerial vehicle sensing, ground and proximal sensing, field Internet of Things, machinery-mounted sensors, multisource fusion, crop models, and machine learning methods are compared according to spatial support, temporal continuity, scale matching, field robustness, transfer conditions, uncertainty, and operational applicability. The reviewed studies report crop-phenotype retrieval, field-environment characterization, and biotic-stress identification under specified conditions, whereas cross-stage state inheritance, consistent reference measurements, independent validation, and conversion of monitoring results into executable tasks remain insufficiently established. The review therefore develops a lifecycle-oriented information-processing perspective in which multisource observations are quality-marked, interpreted as stage states, linked across time and scale, and checked against management and equipment records. Future work should strengthen cross-crop and cross-region validation, mechanistic and data-driven model coordination, uncertainty reporting, interoperability, and field feedback without presuming universally autonomous decision-making. Full article

In the Interval Job Shop Problem (IJSP), operation processing times are represented by intervals, making machine-sequencing decisions sensitive to temporal uncertainty. We propose Risk-Aware Partial-Order Reinforcement Learning with Timed Automata Completion (RAPO-RL-TAC), which couples Risk-Aware Partial-Order Reinforcement Learning (RAPO-RL) for machine-order construction with Timed Automata Completion (TAC) for execution-time resolution of deferred sequencing decisions. Risk awareness focuses on preserving temporal flexibility in machine-order relations whose preferred ordering is sensitive to interval uncertainty. RAPO-RL selectively commits comparatively determinate machine conflicts while retaining a bounded set of timing-sensitive relations. TAC completes unresolved relations as execution evolves, while statistical model checking characterises completion-time variability across timed executions. On 14 ORB and LA benchmark instances, RAPO-RL-TAC achieves mean midpoint makespans 2.92% and 1.63% lower than population-based neighbourhood search (PNS) and genetic algorithm (GA), respectively. Compared with reproduced Fast Elitist Artificial Bee Colony (fEABC) variants, RAPO-RL-TAC achieves a lower midpoint than at least one variant on 7 of 14 instances. In the controlled ablation study, RAPO-RL-TAC achieves a 13.85% lower mean midpoint makespan than hard enforcement of the learned relations. These results indicate that risk-aware selective commitment preserves temporal flexibility while maintaining competitive nominal schedule quality under interval uncertainty. Full article

Intelligent navigation of mobile robots in unknown environments has become a key enabling technology for service and logistics applications. However, in unstructured scenarios, perception noise and environmental uncertainty can significantly degrade system performance, making it a major challenge to balance safety and efficiency. In this work, we propose a Human–Machine Shared Control method with Model Predictive Control constraints (HMSC). HMSC establishes a confidence-driven human–machine shared control mechanism that maximizes collaborative efficiency by dynamically assessing the reliability of agent decisions to regulate control weights. Simultaneously, the method introduces a composite confidence evaluation model which, by fusing epistemic uncertainty with geometric feasibility from lightweight LiDAR measurements, achieves a robust quantification of policy risk. To ensure safe execution, we develop a multi-trajectory prediction mechanism which, after validating kinematic constraints, minimally intervenes to safely adjust control commands. We conducted Gazebo-based simulation experiments on obstacle avoidance and target navigation using a LiDAR-equipped mobile robot model and validated the rationality of the confidence model. The results demonstrate that the proposed shared control strategy, which combines confidence assessment with deterministic safety boundaries, significantly improves the success rate and robustness of the system in uncertain environments. Full article