Search Results (171)

With rapid urbanization and population growth, efficient transportation systems are increasingly crucial, particularly in sectors like healthcare and pharmaceutical logistics, which face unique challenges. In Morocco, there is a lack of studies on pharmaceutical transport, especially regarding costs and delivery conditions, creating a need for a specialized model. This research presents the development and validation of a predictive model for optimizing urban transport in Morocco. Tested across key sectors—pharmaceuticals, agri-food, electronics, and manufactured goods—the model demonstrated strong performance, though variations emerged based on product complexity. Notably, the agri-food sector presented greater logistical challenges, while the manufacturing and electronics sectors yielded higher prediction accuracy. By integrating statistical process control (SPC) and Lean Six Sigma principles, the model ensures ongoing performance monitoring and continuous improvement. It supports cost reduction, time optimization, and lower environmental impact through enhanced route planning and delivery efficiency. The pharmaceutical sector was selected as a case study due to its critical logistical constraints, such as cold chain requirements and the need for high reliability. Python was used for model development, enabling rapid iteration and collaborative validation. The results confirm the model’s adaptability and generalizability to similar urban environments across North and Sub-Saharan Africa. The study offers a robust and scalable framework for improving transport efficiency while aligning with sustainability and smart mobility goals. Full article

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by impairments in social interaction and communication, along with atypical behavioral patterns. Affected individuals often seem isolated in their inner world and exhibit particular sensory reactions. The World Health Organization has indicated a persistent increase in the global prevalence of autism, with approximately 1 in 127 persons affected worldwide. This study contributes to the growing research effort by presenting a comprehensive analysis of functional connectivity patterns for ASD prediction using rs-fMRI datasets. A novel approach was used for ASD identification using the ABIDE II dataset, based on functional networks derived from BOLD signals. The sparse functional brain connectome (Lean-NET) model is employed to construct subject-specific connectomes, from which local graph metrics are extracted to quantify regional network properties. Statistically significant features are selected using Welch’s t-test, then subjected to False Discovery Rate (FDR) correction and classified using a Support Vector Machine (SVM). Our experimental results demonstrate that locally derived graph metrics effectively discriminate ASD from typically developing (TD) subjects and achieve accuracy ranging from 70% up to 91%, highlighting the potential of graph learning approaches for functional connectivity analysis and ASD characterization. Full article

Reliable estimation of oil palm carbon stock is essential for climate mitigation, concession management, and sustainability certification. While satellite-based approaches offer scalable solutions, redundancy among spectral indices and inter-sensor variability complicate model development. This study evaluates machine learning regressors for predicting oil palm carbon stock at tree (CO_tree, kg C tree⁻¹) and hectare (CO_ha, Mg C ha⁻¹) scales using spectral indices derived from Landsat-8, Landsat-9, and Sentinel-2. Fourteen vegetation indices were screened for multicollinearity, resulting in a lean feature set dominated by NDMI, EVI, MSI, NDWI, and sensor-specific indices such as NBR2 and ARVI. Ten regression algorithms were benchmarked through cross-validation. Ensemble models, particularly Random Forest, Gradient Boosting, and XGBoost, outperformed linear and kernel methods, achieving R² values of 0.86–0.88 and RMSE of 59–64 kg tree⁻¹ or 8–9 Mg ha⁻¹. Feature importance analysis consistently identified NDMI as the strongest predictor of standing carbon. Spatial predictions showed stable carbon patterns across sensors, with CO_tree ranging from 200–500 kg C tree⁻¹ and CO_ha from 20–70 Mg C ha⁻¹, consistent with published values for mature plantations. The study demonstrates that ensemble learning with sensor-specific index sets provides accurate, dual-scale carbon monitoring for oil palm. Limitations include geographic scope, dependence on allometric equations, and omission of belowground carbon. Future work should integrate age dynamics, multi-year composites, and deep learning approaches for operational carbon accounting. Full article

End-of-life wind turbine blade accumulation is a growing global materials management problem and current industrial recycling routes for glass fiber-reinforced polymer composites remain limited in material recovery value. There is limited understanding on how to recover clean glass fibers while keeping thermal exposure and energy input low, and existing studies have not quantified whether very short isothermal thermal residence can still result in complete matrix removal. The hypothesis of this study is that a mild two-step thermochemical sequence can recover clean glass fibers at lower temperature and near zero isothermal dwell if pyrolysis and oxidation are separated. We used wind-blade epoxy-based GFRP in a step-batch reactor and combined TGA-based thermodynamic mapping, short pyrolysis at 425 °C, and mild oxidation at 475 °C with controlled dwell from zero to thirty minutes. We applied model-free kinetics and machine learning methods to quantify activation energy trends as a function of conversion. The thermal treatment of 425 °C for zero minutes in nitrogen, followed by 475 °C for fifteen minutes in air, resulted in mechanically sound, visually clean white fibers. These fibers retained 76% of the original tensile strength and 88% of the Young’s modulus, which indicates the potential for energy-efficient GFRP recycling. The activation energy was found to be approximately 120 to 180 kJ mol⁻¹. These findings demonstrate energy lean recycling potential for GFRP and can inform future industrial scale thermochemical designs. Full article

This study presents the experimental and modelling investigation of the performance of an SI methanol marine engine operating under lean conditions. The effects of spark timing and excess air ratio on combustion characteristics, engine performance, and emissions are explored. Multiple machine learning models, including Support Vector Machines (SVM), Artificial Neural Network (ANN), LightGBM, and Random Forest (RF), are employed to predict the engine performance and emission characteristics. Experimental results show that as spark timing advances, the combustion phase advances, with the burn duration being extended. When the excess air ratio is less than 1.35, there exists an optimal spark timing, corresponding to a maximum brake thermal efficiency. The optimal spark timing exhibits an advancing tendency along with increasing excess air ratio. HC emission is primarily determined by the excess air ratio and shows no significant variation under the different spark timings. NO_x emission is initially increased and then decreased with advancing spark timing. Compared with ANN, LightGBM, and RF, SVM demonstrates a superior predictive accuracy, with R² values for engine performance exceeding 0.98 and R² values for emissions above 0.92. Full article

Given the increasing volume and sophistication of cyber-attacks, there has always been a need for improved and adaptive real-time intrusion detection systems. Machine learning algorithms have presented a promising approach for enhancing their capabilities. This research has focused on investigating the impact of different dimensionality reduction approaches on performance, and we have chosen to work with both Batch PCA and Incremental PCA alongside Logistic Regression, SVM, and Decision Tree classifiers. We started this work by applying machine learning algorithms directly on pre-processed data, then applied the same algorithms on the reduced data. Our results have yielded an accuracy of 98.61% and an F1-score of 98.64% with a prediction time of only 0.09 s using Incremental PCA with Decision Tree. We also have obtained an accuracy of 98.44% and an F1-score of 98.47% with a prediction time of 0.04 s from Batch PCA with SVM, and an accuracy of 98.47% and an F1-score of 98.51% with a prediction time of 0.05 s from Incremental PCA with Logistic Regression. The findings demonstrate that Incremental PCA offers near real-time IDS deployment in large networks. Full article

The global demand for high-quality rice necessitates advancements in milling technologies and quality assessment techniques that are rapid, accurate, and scalable. Traditional methods of rice evaluation are time-consuming and subjective, and are increasingly being replaced by artificial intelligence driven solutions that offer non-destructive, real-time monitoring capabilities. This review presents a comprehensive synthesis of current AI applications including machine vision, deep learning, spectroscopy, thermal imaging, and hyperspectral imaging for the assessment and classification of rice quality across various stages of processing. Major emphasis is put on the recent advances in convolutional neural networks (CNNs), YOLO architectures, and Mask R-CNN models, and their integration into industrial rice milling systems is discussed. Additionally, the review highlights next steps, notably designing lean AI architectures suitable for edge computing, hybrid imaging systems, and the creation of open-access datasets. Across recent rice-focused studies, classification accuracies for grading and varietal identification are typically ≥$90\%$ using machine vision and CNNs, while NIR–ANN models for physicochemical properties (e.g., moisture/protein proxies) commonly report strong fits ($R^2\approx 0.90\text{–}0.99$). End-to-end detectors/segmenters (e.g., YOLO/YO-LACTS) achieve high precision suitable for near real-time inspection. These results indicate that AI-based approaches can substantially outperform conventional evaluation in both accuracy and throughput. Full article

This study implemented a Lean Six Sigma (LSS) methodology to enhance the productivity of the mechanical and industrial engineering technology workshops of an international higher education institution. The efficiency and effectiveness of the engineering workshops were often compromised by poor housekeeping and operational practices, which resulted in incomplete tasks, long operational and activity times, disorganized tools, cluttered workspaces, and a lack of systematic processes for managing materials. These issues led to waste in the form of lost time, unnecessary movement, and safety risks. This eventually affected the overall productivity of the workshops. Following the combination of the Define, Measure, Analyze, Improve, and Control (DMAIC) methodology of Six Sigma with Lean manufacturing, the investigation was conducted in two parts. The first part of this research mainly consisted of measuring the existing state of the three workshops to map the process and frame issues and origins of variations. During the second part of this study, the focus shifted towards Lean thinking while applying the chosen Lean Six Sigma (LSS) tools. Implementation revealed several benefits in the workshops during each phase of DMAIC. A Plan–Do–Check–Act (PDCA) continuous improvement board was installed in the main workshop to promote continuous improvement and sustainability. The process capability increased for the main workshop and welding laboratory, which shows an increase in service and performance standards after LSS implementation. For the main workshop, the process capability ‘Cp’ increased from 0.33 to 1.24 and the process capability index (Cpk) increased from 0.26 to 0.99. The process capability index (Cpk) for the main workshop increased; however, it did not reach the value of 1.33 due to the computer workstation installation not being completed during the study. The welding laboratory showed an increased ‘Cp’ from 0.67 to 2.13, and the process capability index (Cpk) increased from 0.18 to 1.34. The layout of the workshop office was improved to support efficient workflow by providing easy access to frequently used resources while keeping movement paths clear, thereby minimizing interruptions and promoting productivity. As a result, machines and tools were used more productively and operation times decreased. The mechanical workshops can continue increasing their process capability by following the outcomes and findings of the current study, leading to sustainable quality, efficiency, and operational reliability improvements. Full article

Background/Objectives: Subjective well-being (SWB) in children is a key indicator of healthy development, influenced by physical activity and sports, with physical literacy (PL) as a potential mediator. Traditional linear models overlook non-linear and heterogeneous effects in diverse populations. This study uses causal machine learning (ML) to examine PL’s mediating role between sports participation and SWB in a multinational cohort. Methods: Data from the International Survey of Children’s Well-Being (ISCWeB) (n = 128,184 children aged 6–14, 35 countries) were analyzed. SWB was a composite (six items, α = 0.85); PL was a proxy (three items excluding sports frequency, α = 0.70); sports participation was continuous (0–5). Confounders were age, gender, parental listening, and school satisfaction. CausalForestDML estimated the effects; GroupKFold and bootstrap were used for robustness; SHAP/PDP was used for interpretability. Results: Total ATE = 0.083 (95% CI [0.073, 0.094]); indirect via PL = 0.055 (CI [0.049, 0.061]); direct = 0.028 (CI [0.020, 0.038]); mediation proportion = 0.660. Sensitivity with lean PL (2 items) was as follows: indirect = 0.045 (CI [0.040, 0.050]). For SHAP, school satisfaction was (+0.28), and parents were (+0.20) top. For PDP, there was a non-linear rise at PL 4–6 (+1.2 units) and a plateau ~9.2. The cross-cultural mean ATE = 0.083 ± 0.01 (from within-country meta-analysis); this was stronger in older children (CATE 0.30 for 12–14). For Rho sensitivity at 0.1, it was indirect −0.129; at Rho sensitivity of 0.2, it was −0.314 (robust to low confounding). Conclusions: The findings, grounded in SDT/PYD, support interventions targeting PL through sports to enhance SWB, addressing inactivity. Limitations are its cross-sectional nature and proxy measures; we recommend longitudinal studies. Full article

Rapid decarbonization relies on knowing which structural and energy factors affect national carbon dioxide emissions. Much of the literature leans on linear and additive assumptions, which may gloss over curvature and interactions in this energy–emissions link. Unlike previous studies, we take a different approach. Using a panel of 80 high- and upper-middle-income countries from 2011 to 2020, we model emissions as a function of fossil fuel energy consumption, methane, the food production index, renewable electricity output, gross domestic product (GDP), and trade measured as trade over GDP. Our contribution is twofold. First, we evaluate how different modeling strategies, from a traditional Generalized Linear Model to more flexible approaches such as Support Vector Machine regression and Random Forest (RF), influence the identification of emission drivers. Second, we use Double Machine Learning (DML) to estimate the incremental effect of fossil fuel consumption while controlling for other variables, offering a more careful interpretation of its likely causal role. Across models, a clear pattern emerges: GDP dominates; fossil fuel energy consumption and methane follow. Renewable electricity output and trade contribute, but to a moderate degree. The food production index adds little in this aggregate, cross-country setting. To probe the mechanism rather than the prediction, we estimate the incremental role of fossil fuel energy consumption using DML with RF nuisance functions. The partial effect remains positive after conditioning on the other covariates. Taken together, the results suggest that economic scale and the fuel mix are the primary levers for near-term emissions profiles, while renewables and trade matter, just less than is often assumed and in ways that may depend on context. Full article

Hydrogen propulsion technologies are emerging as a key enabler for decarbonizing the aviation sector, especially for regional commercial aircraft. The evolution of aircraft propulsion technologies in recent years raises the question of the feasibility of a hydrogen propulsion system for beyond regional aircraft. This paper presents a comprehensive review of hydrogen propulsion technologies, highlighting key advancements in component-level performance metrics. It further explores the technological transitions necessary to enable hydrogen-powered aircraft beyond the regional category. The feasibility assessment is based on key performance parameters, including power density, efficiency, emissions, and integration challenges, aligned with the targets set for 2035 and 2050. The adoption of hydrogen-electric powertrains for the efficient transition from KW to MW powertrains depends on transitions in fuel cell type, thermal management systems (TMS), lightweight electric machines and power electronics, and integrated cryogenic cooling architectures. While hydrogen combustion can leverage existing gas turbine architectures with relatively fewer integration challenges, it presents its technical hurdles, especially related to combustion dynamics, NOx emissions, and contrail formation. Advanced combustor designs, such as micromix, staged, and lean premixed systems, are being explored to mitigate these challenges. Finally, the integration of waste heat recovery technologies in the hydrogen propulsion system is discussed, demonstrating the potential to improve specific fuel consumption by up to 13%. Full article

The search for higher efficiency in agribusiness supports the adoption of digital tools and Lean Production principles in agricultural spraying, a crucial operation for crops. Spraying is essential to ensure yield, quality, cost efficiency, and environmental protection. This study analyzed operational data from self-propelled sprayers in soybean and corn fields, classifying hours, calculating efficiencies, and applying statistical process control. Efficiencies were investigated by combining Lean Production principles with CAN-based digital monitoring, which enabled the identification of non-value-adding activities and supported the real-time management of spraying operations. The results showed that productive time accounted for 41.2% of total recorded hours, corresponding to effective operation and auxiliary tasks directly associated with the execution of spraying activities. A high proportion of unrecorded hours (21.2%) was also observed, reflecting discrepancies between administrative work schedules and machine-logged data. Additionally, coefficients of variation for operational speed and fuel consumption were 12.1% and 24.0%, respectively. Correcting special causes increased work capacity (4.9%) and reduced fuel consumption (0.9%). Economic simulations, based on efficiencies, operating parameters of the sprayer, and cost indicators, indicated that increasing scale reduces costs when installed capacity is carefully managed. Integrating telemetry with Lean Production principles enables real-time resource optimization and waste reduction. Full article

Against the global biodiversity crisis, arid and semi-arid regions are sensitive indicators of terrestrial ecosystems. However, research on their habitat quality (HQ) evolution mechanism faces dual challenges: insufficient multi-scale dynamic simulation and fragmented driving mechanism analysis. To address these gaps, this study takes northern China’s arid and semi-arid regions as the object, innovatively constructing a “pat-tern-process-mechanism” multi-dimensional integration framework. Breaking through single-model/discrete-method limitations in existing studies, it realizes full-process integrated research on regional HQ spatiotemporal dynamics. Based on 1990–2020 Land Use and Land Cover Change (LUCC) data, the framework integrates the InVEST and PLUS models, solving poor continuity between historical assessment and future projection in traditional research. It also pioneers combining the XGBoost-SHAP model and Geographically and Temporally Weighted Regression (GTWR): XGBoost-SHAP quantifies nonlinear interactive effects of natural, socioeconomic, and landscape drivers, while GTWR explores spatiotemporal heterogeneous mechanisms of landscape pattern evolution on HQ, effectively addressing the dual challenges. Results show the following: (1) In 1990–2020, cultivated and construction land expanded, with grassland declining most notably; (2) Overall HQ decreased by 0.82%, with high-value areas stable in the west and northeast, low-value areas concentrated in the central region, and 2030 HQ optimal under the Ecological Protection (EP) scenario; (3) Natural factors contribute most to HQ change, followed by socioeconomic factors, with landscape indices being least impactful; (4) Under future scenarios, landscape Patch Density (PD) has the most prominent negative effect—its increase intensifies fragmentation and reduces connectivity. This study’s method integration breakthrough provides a quantitative basis for landscape pattern optimization and ecosystem management in arid and semi-arid regions, with important scientific value for promoting integration of landscape ecology theory and sustainable development practice. Full article

Background Chronic low-back pain (LBP) is the largest contributor to disability worldwide, yet many assessments still reduce a complex, spatially distributed condition to a single 0–10 score. Body-map drawings capture location and extent of pain, but manual digitization is too slow and inconsistent for large studies or real-time telehealth. Methods Paper pain drawings from 332 adults in the multicenter COMEBACK study (four University of California sites, March 2021–June 2023) were scanned to PDFs. A Python pipeline automatically (i) rasterized PDF pages with pdf2image v1.17.0; (ii) resized each scan and delineated anterior/posterior regions of interest; (iii) registered patient silhouettes to a canonical high-resolution template using ORB key-points, Brute-Force Hamming matching, RANSAC inlier selection, and 3 × 3 projective homography implemented in OpenCV; (iv) removed template outlines via adaptive Gaussian thresholding, Canny edge detection, and 3 × 3 dilation, leaving only patient-drawn strokes; (v) produced binary masks for pain, numbness, and pins-and-needles, then stacked these across subjects to create pixel-frequency matrices; and (vi) normalized matrices with min–max scaling and rendered heat maps. RGB composites assigned distinct channels to each sensation, enabling intuitive visualization of overlapping symptom distributions and for future data analyses. Results Cohort-level maps replicated classic low-back pain hotspots over lumbar paraspinals, gluteal fold, and posterior thighs, while exposing less-recognized clusters along the lateral hip and lower abdomen. Neuropathic-leaning drawings displayed broader leg involvement than purely nociceptive patterns. Conclusions Our automated workflow converts pen-on-paper pain drawings into machine-readable digitized images and heat maps at the population scale, laying practical groundwork for spatially informed, precision management of chronic LBP. Full article

Trust in robots is often analyzed with scales built for either humans or automation, making cross-species comparisons imprecise. Addressing that gap, this paper distils decades of trust scholarship, from clinical vs. actuarial judgement to modern human–robot teaming, into a lean two-factor framework: Mortal vs. Machine (MvM). We first surveyed classic technology-acceptance and automation-reliance research and then integrated empirical findings in human–robot interaction to identify diagnostic cues that can be instantiated by both human and machine agents. The model includes (i) ability—perceived task competence and reliability—and (ii) value congruence—alignment of decision weights and trade-off priorities. Benevolence, oft-included in trust studies, was excluded because current robots cannot manifest genuine goodwill and existing items elicit high dropout. The resulting scale travels across contexts, allowing for researchers to benchmark a robot against a human co-worker on identical terms and enabling practitioners to pinpoint whether performance deficits or priority clashes drive acceptance. By reconciling anthropocentric and technocentric trust literature in a deployable diagnostic, MvM offers a field-ready tool and a conceptual bridge for future studies of AI-empowered robotics. Full article