Search Results (87,689)

Industrial Internet of Things (IIoT) systems generate high-dimensional, non-stationary sensor streams under strict memory and computational constraints, limiting the applicability of classical batch dimensionality reduction methods. While incremental PCA (IPCA) enables online updates, it produces dense components and lacks mechanisms for drift adaptation and interpretability. Existing sparse PCA methods, in contrast, are predominantly batch-oriented and unsuitable for streaming deployment. This paper presents incremental sparse adaptive PCA (ISAPCA), a unified streaming framework that integrates exponential forgetting for concept drift adaptation, mini-batch Oja–Sanger subspace tracking for online variance maximization, and proximal ${\ell }_1$ soft thresholding with QR re-orthonormalization for stable sparse component learning. The contribution lies in the coordinated implementation of these established mechanisms within a constant-memory architecture tailored to industrial edge and TinyML settings. We evaluate ISAPCA on three industrial datasets (SmartBuilding, Tennessee Eastman Process, and GasSensor) and compare it against streaming IPCA and offline upper-bound methods (randomized PCA, sparse PCA, and dictionary learning). ISAPCA retains approximately 93% and 96% of IPCA’s explained variance on SmartBuilding and Tennessee Eastman streams, respectively, while achieving improved explained variance on GasSensor (0.862 vs. 0.822 for IPCA, respectively). Across datasets, ISAPCA enforces sparse loadings without severe degradation in reconstruction fidelity. Ablation analysis confirms the necessity of both forgetting and sparsity components for stable performance under drift. Runtime measurements show sub-millisecond batch updates (0.234–0.606 ms for 256-sample mini-batches), demonstrating suitability for real-time deployment. These results indicate that ISAPCA provides a practical and interpretable solution for streaming dimensionality reduction in non-stationary industrial IoT environments, balancing variance retention, sparsity, and computational efficiency. Full article

Removing subsoil compaction caused by agricultural traffic is energy-demanding and therefore expensive. Experimental work was undertaken on a Typic Argiudoll to quantify the energy required to remove subsoil compaction and determine the associated effects on yield and profitability. The following treatments were compared: (T1) soil under no-tillage for 20 years, which was used as a control; (T2) deep tillage performed with a paratill on soil that had had no-tillage in the 20 years prior to this study; and (T3) deep tillage performed with a chisel plow on soil that had had no-tillage in the 20 years prior to this study. The paratill and chisel plow were operated at depths of 400 and 250 mm, respectively, and the energy required to perform both (deep tillage) operations was determined. Soil cone index and maize yield were measured over three growing seasons and compared with T1. Results showed that the effect of deep tillage lasted for two years, after which the soil reconsolidated reaching soil strength values comparable to their pre-treatment condition. The reconsolidation of tilled soil over this period was due to both natural settlement and post-treatment (random) machinery traffic. The paratill treatment significantly increased maize yield compared with no-tillage, which therefore improved crop gross margins across all three seasons. The chisel plow treatment increased crop yields compared with no-tillage, but yield differences were small and therefore the average crop gross margins were not significantly different. Deep tillage with paratill costed US$76 per ha and generated an average gross income of US$1134 per ha, whereas deep tillage with chisel plow costed US$29 per ha and generated an average gross income of US$1027 per ha. These results compared with an average gross income of US$1001 per ha obtained under no-tillage. If (strategic) deep tillage needs to be performed on long-term no-tillage soil to remediate compaction, paratill may be preferred to chisel plow, but care should be exercised not to re-compact the soil after the operation has been performed. One effective way to do this is by implementing controlled traffic. Full article

The sustainable valorization of marine biowaste, particularly shrimp residues, has emerged as a promising strategy to develop eco-friendly agricultural inputs that enhance crop productivity and reduce environmental impacts. This study investigated the effects of a biotechnologically processed fermented shrimp-waste (Parapenaeus longirostris) formulation as a biostimulant on the growth, physiological performance, and development of a local mays variety (Zea mays L., DKC 744) under controlled pot conditions. The experiment evaluated root, foliar, and combined applications of the biostimulant at three concentrations (5%, 10%, and 15%) over a 90-day vegetative cycle. Morphological parameters, including stem height, leaf number, leaf mass, and root biomass, were measured at regular intervals, while chlorophyll a and b contents were assessed to evaluate photosynthetic efficiency. The results indicated that all biostimulant treatments significantly enhanced mays growth. Root-applied biostimulants primarily stimulated root biomass by up to 764.0 ± 66.8 g at the 10% concentration, whereas foliar applications improved above-ground traits, including stem elongation and leaf formation, reaching maximum heights of 200.0 ± 1.9 cm and 17.0 ± 0.4 leaves under intermediate concentrations. Combined root and foliar applications produced the highest stem height (240.0 ± 5.6 cm), leaf number (19.0 ± 0.0), leaf mass (1034.0 ± 11.1 g), and chlorophyll content (2.44 ± 0.9 for chlorophyll a) at 10–15% concentrations. The results also revealed that moderate concentrations generally provided the most balanced stimulation, suggesting the presence of an optimal dose threshold. This study demonstrated the comparative effectiveness of root, foliar, and combined applications of a fermented shrimp-waste biostimulant and identified an optimal concentration. However, its limitations lie in the use of controlled pot conditions and a single crop variety, which restrict the extrapolation of results to field-scale applications and diverse agroecological environments. Therefore, more research is needed to explore the action mechanisms of the studied biostimulant and elicitors, mainly the interaction between biocompounds and the treated plant. Full article

Mobile robot navigation remains challenging when fast convergence, collision avoidance and deployability must be satisfied simultaneously. The original Q-learning with Artificial Potential Field (QAPF) paradigm is extended in this paper with three coordinated mechanisms that together yield a reported-horizon convergence reduction of approximately four orders of magnitude (from $\sim 3\times {10}^6$ episodes to $\sim 200$ to 230 episodes under the present protocol) and an internal-ablation collision-rate reduction of approximately one order of magnitude ($6.2\%$ to $0.3\%$), and that open a new capability frontier covering dynamic obstacles, multi-robot coordination, energy-aware velocity modulation and embedded-deployable inference timing. The first mechanism is a potential-based reward-shaping schedule whose unclipped fixed-weight form follows the policy-invariant shaping theorem, while the implemented clipped and time-varying form is used as an empirically stable approximation. Under the present experimental protocol, the reported convergence horizon is reduced from the $\sim 3\times {10}^6$ episodes reported for the original QAPF formulation to approximately 200 to 230 episodes; this comparison is protocol-dependent and is not claimed as a controlled one-to-one runtime speedup. The second mechanism is a discrete Control Barrier Function (CBF)-inspired action filter (thediscrete filter described in this paper is inspired by the continuous-time CBF literature, but does not carry a forward-invariance proof; it is used as an empirical safety mechanism rather than as a formal Control Barrier Function in the formal continuous-time sense) with per episode visit memory by which the held-out collision rate is reduced from $6.2\%$ for QAPF alone to $0.3\%$ while $93.8\%$ task completion is maintained, where this collision-rate comparison is internal to the QAPF ablation because the prior QAPF reference does not report a comparable held-out collision metric. The third mechanism is a set of extensions to dynamic obstacles, two-robot cooperative navigation under a centralized scheme (with an explicit $\mathcal{O}(N^2)$ scaling-cost analysis and three decentralization strategies for fleets beyond the small-N regime), curriculum learning and energy-aware velocity modulation. Disturbance robustness tests, empirical timeout/stagnation detection for unreachable-goal cases, i7 reference inference timing with projected embedded-device latencies, multi-axis generalization over obstacle density and grid size, scalability analysis for centralized multi-robot coordination and a scope comparison against A* and RRT* are added by the revised evaluation. Across 30 independent seeds on held-out static maps, $94.5\pm 2.1\%$ success is achieved by adaptive QAPF while $93.8\pm 2.3\%$ success with $0.3\pm 0.4\%$ collisions is achieved by QAPF+CBF. Under a separate finite robustness suite, $85.0\pm 4.1\%$ success is retained by QAPF+CBF in the combined disturbance regime. The timing study indicates that the 20 Hz real-time threshold is comfortably exceeded by all methods on the measured i7 reference platform and by all projected embedded-device equivalents. The results show that a lightweight and safety-oriented navigation policy for grid-based mobile-robot settings can be provided by APF-guided tabular reinforcement learning when it is paired with a discrete safety filter and a clarified energy and robustness analysis. Full article

This study investigates people management efficiency as a multidimensional organizational capability, contributing to the broader discussion on how organizational culture and internal processes are associated with management effectiveness as a socially embedded organizational outcome beyond formal institutional arrangements. A sequential exploratory mixed-methods design was employed, comprising three interrelated studies. Study 1 involved semi-structured interviews with 15 auditors to identify the key dimensions of people management. Study 2 used cognitive interviews with 28 professionals to refine and validate the measurement items. Study 3 consisted of a survey administered to 286 employees, aiming to validate the People Management Efficiency Scale (PMES) and to test a parallel mediation model. Exploratory and confirmatory factor analyses supported a stable five-dimensional structure. The results show that organizational culture is positively associated with people management efficiency, both directly and indirectly, with work organization and continuous improvement operating as statistically significant mediating variables within the tested model. No statistically significant differences were identified between certified and non-certified organizations in terms of people management efficiency, work organization, and transparency. However, statistically significant differences were observed for organizational culture and continuous improvement. Overall, the findings suggest that people management efficiency is closely associated with the alignment between cultural values and internal organizational practices as socially embedded processes. These results highlight the relevance of internal organizational mechanisms in shaping people management outcomes. Given the simplified operationalization of certification in the present study, the findings should be interpreted with caution and do not support definitive comparative conclusions between culture-driven processes and formal certification mechanisms. Full article

The rapid deployment of 5G networks and the proliferation of Internet of Things (IoT) devices have significantly increased the complexity of urban electromagnetic radiation (EMR) environments. Conventional ground-based monitoring systems are spatially limited and unable to provide three-dimensional field characterization. This paper proposes an integrated IoT–UAV framework for high-resolution EMR monitoring, spatial reconstruction, and intelligent source classification. A four-layer architecture combining distributed sensing, edge computing, cloud analytics, and visualization is developed. A formal electromagnetic propagation model is introduced to ensure consistency between broadband exposure measurements and frequency-selective spectral analysis. A CNN–LSTM architecture is implemented for spectral–temporal source classification, achieving 95% validation accuracy across five EMR categories. Simulation-based validation demonstrates up to an eightfold improvement in spatial coverage compared to fixed ground networks while maintaining a practical anomaly detection threshold of −55 dBm in the spectrum-analysis RF chain. The proposed framework establishes a mathematically consistent and practically deployable solution for next-generation EMR monitoring systems. Full article

The exhaust gas temperature (EGT) of the gas generator is a critical indicator for the health management system of a marine gas turbine engine. Therefore, EGT prediction can not only support predictive maintenance decision-making but also serves as a reliable virtual sensor for EGT measurement. However, the engine EGT exhibits strongly nonlinear coupling relationships with other gas path variables, which causes challenges for data-driven prediction. Graph neural networks (GNNs) are particularly effective in capturing the coupling relationships among gas path sensor variables. However, conventional static graph structures fail to characterize the varying coupling strengths under different operating conditions. In this study, a thermodynamic knowledge-driven graph attention network (TKD-GAT) method is proposed for accurate and robust EGT prediction. First, a physics-guided graph topology is constructed based on the gas turbine thermodynamic equations. Subsequently, a multi-head attention mechanism is introduced to generate edge weights that capture the varying thermodynamic coupling strengths under different operation conditions. The proposed model is evaluated on a real-world LM2500 gas turbine, which is widely used in modern propulsion systems of commercial and military ships. The ablation study confirms that the thermodynamic knowledge-driven graph topology and the attention mechanism-based edge weights are both necessary to enhance the EGT prediction performance. The TKD-GAT model shows the best performance with an RMSE of 0.446% and an R² of 0.971 compared with state-of-the-art models. The paired t-test and effect size measurement (Cohen’s d) statistically confirm the significance of performance improvements. The statistical results from multiple independent experiments prove the stability of the TKD-GAT model. Additionally, the model achieves a competitive computational cost despite the integration of a physics-guided graph topology and attention mechanisms. Crucially, an interpretability analysis confirms that the learned attention weights adhere to thermodynamic principles under different operation conditions. The proposed TKD-GAT model provides an effective solution for EGT prediction in health management systems. Full article

This study assessed the impact of cocrystal essential oil (CEO) inclusion on growth performance, nutrient digestibility, intestinal morphology, and antioxidant status in weaned piglets. Ninety-six weaned piglets were assigned to four groups (n = 8, three piglets per pen). The piglets in the four groups were fed basal diets added with 0, 120, 180, or 240 mg/kg of CEO, respectively, over a 28-day trial period. Results showed that during weeks 1–2, piglets in the 240 mg/kg CEO group exhibited a lower diarrhea rate and diarrhea index compared to the control group (p < 0.05). In weeks 3–4, the 180 and 240 mg/kg CEO groups demonstrated a reduced diarrhea rate and diarrhea index compared to the control group (p < 0.05). Relative to the control group, the apparent total tract digestibility (ATTD) of dry matter was elevated in piglets fed diets added with 120, 180, or 240 mg/kg CEO at both day 14 and day 28 (p < 0.05). Additionally, the ATTD of crude protein was elevated in the 120 mg/kg CEO group at day 14 and in the 180 mg/kg CEO group at day 28 (p < 0.05). Regarding intestinal morphology, supplementation with 180 or 240 mg/kg CEO increased jejunal villus height (VH) and the villus height to crypt depth (VH/CD) ratio compared with the control group (p < 0.05). Furthermore, 240 mg/kg CEO supplementation augmented the ileal VH/CD ratio relative to the control group (p < 0.05). For antioxidant status, 180 mg/kg CEO supplementation elevated serum glutathione peroxidase (GSH-Px) activity in piglets relative to the control group (p < 0.05). Importantly, no differences were found between the 180 mg/kg and 240 mg/kg CEO groups across all measured parameters (p > 0.05). In conclusion, dietary inclusion with 180 mg/kg CEO is recommended for weaned piglets, given its comprehensive benefits in alleviating diarrhea, improving nutrient digestibility, enhancing intestinal morphology, and bolstering antioxidant status. Full article

With the rapid and ongoing expansion of urban areas, the need for accurate, reliable, and regularly updated topographic maps has become increasingly critical for planning and sustainable development. While traditional aerial photogrammetry—whether analog or digital—has long been the standard for such tasks, it remains costly, time-consuming, and logistically demanding, particularly when large or inaccessible regions are involved. This study proposes an alternative approach based on very-high-resolution satellite imagery, focusing specifically on data acquired from Morocco’s Mohammed VI A and B satellites. The research evaluates the capacity of this satellite imagery to support large-scale topographic mapping, both in terms of geometric accuracy and the ability to identify essential urban features. To validate the results, we conducted a comparative analysis of satellite data with conventional photogrammetric imagery from analog cameras (RMK TOP) and digital sensors (ADS, DMC), using ground control points (GCPs) and differential GPS (DGPS) measurements for calibration and accuracy assessment. The outcomes demonstrate that planimetric accuracy from satellite imagery meets the required standards for mapping at 1:10,000 and 1:5000 scales. However, altimetric accuracy is closer to the upper permissible limits, especially in applications requiring finer detail. While major urban elements such as roads, buildings, and vegetation are well identified, smaller infrastructure components, such as power lines, remain challenging to detect. Despite these limitations, the study highlights the growing potential of satellite imagery as a cost-effective and operationally efficient alternative to traditional methods, particularly in rapidly evolving urban environments where frequent map updates are essential. Integration with GeoAI workflows is identified as a key direction for future research and is not part of the current methodology. Full article

In response to the increasing operational complexity of inland waterway systems, this study develops a multi-objective optimization framework for ship lock scheduling under energy-consumption and resource constraints. The model evaluates five operational dimensions, namely average waiting time, lock utilization, total energy consumption, arrival rescheduling rate, and berth-overcapacity penalty. Based on historical lockage records from the Da Teng Gorge Ship Lock Hub, four representative multi-objective algorithms—NSGA-II, NSGA-III, MOEA/D, and SPEA-II—are comparatively examined. The revised analysis emphasizes trade-off performance rather than unsupported absolute dominance claims: NSGA-III shows the most balanced overall behavior on the preserved empirical instance, MOEA/D remains competitive in time-sensitive scenarios, and SPEA-II performs well in some overcapacity-control settings. To improve methodological transparency, the paper clarifies the physical meaning and source of major parameters, distinguishes measured quantities from scenario settings, and reports carbon impact as a derived indicator linked to energy consumption. These revisions provide a more transparent and practically interpretable basis for intelligent ship lock scheduling under congestion, energy, and resource constraints. Full article

This paper presents the design and field evaluation of a low-cost automatic steering system for agricultural tractors. The system employs a PID controller whose gains are tuned using a metaheuristic optimization method. Core hardware includes an ESP32 microcontroller, an MPU9250 inertial measurement unit, a GPS module, and a servo motor for closed-loop yaw angle control, with a complementary filter fusing gyroscope and magnetometer data for robust heading estimation. Nine optimization algorithms were systematically compared: Grid Search, Random Search, Bayesian Optimization, Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Moth-Flame Optimization (MFO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), and Salp Swarm Algorithm (SSA). A cost function combining overshoot and settling time was used. Step response analysis showed that WOA achieved the best performance, with an integral absolute error of 6.31°·s, a settling time of 2.15 s, and a minimal overshoot of 0.08°. In field tests on asphalt and farmland, the WOA-tuned system reduced lateral deviation by 69% (from 12.4 cm to 3.8 cm) and 67% (from 18.7 cm to 6.2 cm), respectively, compared to manual steering. Repeated-measures ANOVA and paired t-tests confirmed statistically significant improvements (p < 0.001) with large effect sizes (Cohen’s d > 2.7). The core components cost under $150 USD. The study offers a reproducible pipeline for comparative metaheuristic evaluation in agricultural vehicle guidance. Full article

Classification of vehicle engines using the chemical composition of the exhaust from these engines can be used to identify the engine’s design and verify compliance with environmental regulations through the vehicle’s emissions. This paper describes a method to identify the type of vehicles using machine learning (ML), where low-cost MQ series sensors measure the gases and particle emissions from a vehicle exhaust system, while simultaneously collecting and measuring the vehicle’s temperature and humidity levels. A custom-designed multi-sensor exhaust sensing module is employed to capture real-time exhaust emissions prior to entering the atmosphere. Exhaust samples are collected from vehicles representing three major engine categories: petrol, diesel, and compressed natural gas (CNG). In addition, fresh air samples are collected as a baseline environmental reference for comparison. All exhaust measurements are collected under controlled and consistent engine operating conditions to ensure comparable emission profiling across vehicle classes. To ensure consistent combustion-based emission profiling, this study focuses on conventional fuel-powered vehicles. MQ-series gas sensors are sensitive to combustion by-products emitted during engine operation, such as carbon monoxide (CO), hydrocarbons (HC), while also exhibiting cross-sensitivity to other gaseous components present in exhaust mixtures. Nevertheless, the proposed system performs pattern-based classification using relative sensor response signatures. Standardization of data is achieved through z-score normalization. The best models developed (based on three separate experimental designs) are trained and validated using six supervised machine learning algorithms such as Logistic Regression, Support Vector Machine (RBF), k-Nearest Neighbors, Random Forest, Gradient Boosting Decision Tree, and XGBoost and are compared against one another. Evaluation of the tested algorithms using various evaluation metrics demonstrated that ensemble models outperformed all other algorithms, achieving the highest accuracy of 99.5%. Furthermore, noise analysis confirms that the proposed solution maintains high classification accuracy (more than 89%) even under substantial sensor perturbations mimicking the real-world deployment. The solution proposed below illustrates how using gas sensors and advanced algorithms can provide accurate exhaust identification and identify engines in real-time. Full article

Efficient crop management requires intelligent control strategies capable of handling uncertainty, nonlinear environmental interactions and dynamic crop growth conditions. This study presents a multisensor data fusion-based intelligent crop management framework for Capsicum cultivation using both a Mamdani fuzzy inference system (MFIS) and an adaptive Mamdani fuzzy inference system (AMFIS). The Capsicum dataset from the SmartFasal platform includes temperature, humidity and soil moisture at three depths, recorded over a four-month period (March–June 2020) with a total of 7188 samples. The proposed MFIS and AMFIS models are implemented and evaluated in the simulation environment. A Capsicum yield of 60–63 t/ha (3.6–3.8 kg/plant) is predicted via a regression model built on raw sensor inputs under conventional environmental management. An expert-rule MFIS with triangular memberships improves the regulation of agricultural parameters, increasing yield to 70–73 t/ha (4.2–4.4 kg/plant), a 15–18% increase. To improve adaptability, the AMFIS model incorporates fuzzy C-means (FCM) clustering for the automatic tuning of Gaussian membership functions and enables the controller to adjust dynamically to sensor data distributions. The adaptive system achieves a predicted productivity range of 82–87 t/ha (4.9–5.2 kg/plant), a 30–35% increase over the baseline. The regression model validation metrics R² = 0.86, RMSE = 2.1 t/ha, and MAE = 1.7 t/ha confirm the reliability of the yield estimation within the simulation framework rather than experimentally measuring crop performance. A correlation analysis, histograms, scatter plots, and Bland–Altman assessments reveal that compared with the MFIS, the AMFIS results in smoother control transitions, lower variability, and higher resource-use efficiency. This study represents a data-driven simulation framework, and future work will focus on real-time implementation and experimental validation under actual greenhouse conditions. Full article

Three-dimensional (3D) printing is increasingly used for the fabrication of definitive crowns; however, whether specific post-curing hardware is mandatory for clinical success remains a practical concern. This study provided a practical comparison evaluating the effect of two post-curing units on the biaxial flexural strength (BFS), Weibull modulus (m), Martens hardness (HM), indentation modulus (EIT), water sorption (WSP), and water solubility (WSL) of 3D-printed resins for permanent crowns, compared with a conventional resin composite. A total of 200 specimens were fabricated from two 3D-printed resins (Permanent Crown™ and CrownTec™) and a conventional resin composite (Filtek Universal Restorative™) used as a control. The 3D-printed specimens were post-cured using either a Formcure or an Otoflash G171 unit. WSP and WSL were measured after 90 days of water ageing, while BFS, HM, and EIT were evaluated after 24 h of storage using standardised methods. All materials exhibited WSP and WSL values within ISO limits, with the control group showing significantly higher values and superior mechanical properties. Among the 3D-printed resins, post-curing significantly affected only HM and EIT for Permanent Crown™ resin, with no significant differences in BFS. Overall, the tested 3D-printed resins demonstrated high processing stability across different curing protocols, suggesting that clinical performance remains consistent regardless of the post-curing unit used. Full article

This study presents an exploratory reinforcement learning (RL)-based simulation framework for examining antimicrobial resistance (AMR) dynamics under repeated exposure to a non-antibiotic stressor, using copper as a simplified model compound. The objective is not to provide mechanistic or predictive insight into microbial evolution, but to evaluate how alternative sequential decision-making strategies perform within a constrained and transparent simulation environment. Three agent strategies were compared: random action selection, a rule-based heuristic, and a tabular Q-learning agent. Simulations were conducted over fixed 40-cycle episodes in which agents adjusted copper exposure in response to evolving resistance-related state variables. Across experimental runs, the Q-learning agent exhibited lower cumulative resistance burden, measured by area under the curve (AUC) of minimum inhibitory concentration (MIC) trajectories for chloramphenicol and polymyxin B, while maintaining lower cumulative copper exposure relative to baseline strategies. The rule-based agent demonstrated intermediate performance, whereas the random agent showed greater variability and less stable trajectories. These findings reflect differences in simulated control behavior within a simplified stochastic system. Overall, this work introduces an interpretable reinforcement learning simulation tool intended to support comparative evaluation of adaptive versus static strategies in antimicrobial pressure management under limited observability. Full article