Search Results (250)

This paper presents the design, development, and evaluation of a Reinforcement Learning (RL)–based torque-split controller for the regenerative braking system (RBS) in battery electric vehicles (BEVs). The controller employs a Deep Deterministic Policy Gradient (DDPG) agent to distribute the braking demand between regenerative and frictional braking systems with the aim of maximizing energy recovery while adhering to the physical and operational constraints. To capture the charging limitation of the battery, a State-of-Power (SoP) calculation mechanism is incorporated, providing a time-varying bound on the regenerative charge power. The agent is trained in a MATLAB/Simulink environment representing the digital twin of a BEV drivetrain, and considers a mix of different braking scenarios, i.e., light braking, medium braking, hard braking, and emergency braking. The RL’s reward shaping promotes efficient utilization of the SoP-limited regenerative capability while discouraging constraint violations and aggressive control behavior. Across a range of State-of-Charge (SoC) conditions and driving cycles, including the Worldwide Harmonized Light–Vehicle Test Procedure (WLTP) and synthetic random-rich driving cycle, the RL controller consistently delivers promising performance, yielding energy recovery of up to ~98% of the total braking energy available on WLTP type 3 driving cycle while being able to operate closely to the battery SoP limit. The results demonstrate the proposed controller’s capability for adaptive, constraint-aware energy management in BEVs and underline its potential for future intelligent braking strategies. Full article

Workspace digitalisation presents a transformative shift from traditional, physically bounded offices to virtual, technology-enabled environments. Digital technologies like cloud computing, artificial intelligence, and the Internet of Things enable remote collaboration, data accessibility, and operational efficiency, thereby accelerating this transformation. Digital workspaces transcend geographical limitations, enabling a more flexible, inclusive, and adaptive work culture. They offer better work–life balance, with flexible options, reduced commuting time, and increased personal autonomy and control over commitments, compared to traditional workspaces. Despite these benefits, digitalisation creates cybersecurity, data privacy, and digital divide issues, where unequal access to digital tools and skills can exacerbate social and economic inequalities. The lack of physical interaction affects team cohesion and company culture. Hence, this paper explores these phenomena to uncover their implications and consider possible strategies to optimise workspace digitalisation, providing a comprehensive systematic review of extant literature within the study context, offering pragmatic insights and recommendations for workspaces. This study has found workspace digitalisation to be a complex, multifaceted phenomenon that provides flexibility, efficiency, and innovation, but also poses challenges that must be carefully managed. It postulates that as technology and work progress, a hybrid model that blends digital and traditional workspaces would be suited to each organisation’s needs and goals. Full article

This work contributes to advancing sustainable energy and waste management strategies by investigating the thermochemical conversion of food-based biomass through pyrolysis, highlighting the role of artificial intelligence (AI) in enhancing process modelling accuracy and optimization efficiency. The main objective is to explore the potential of underutilized biomass resources like spent coffee grounds (SCGs) and DSs (date seeds) for sustainable hydrogen production. Specifically, it aims to optimize the pyrolysis process while evaluating the performance of these resources both individually and as blends. Proximate, ultimate, fibre, TGA/DTG, kinetic, thermodynamic, and Py-Micro-GC analyses were conducted for pure DS, SCG, and blends (75% DS-25% SCG, 50%DS-50%SCG, 25%DS–75%SCG). Blend 3 offered superior hydrogen yield potential but had the highest activation energy (Ea: 313.24 kJ/mol), while Blend 1 exhibited the best activation energy value (Ea: 161.75 kJ/mol). The kinetic modelling based on isoconversional methods (KAS, FWO, and Friedman) identified KAS as the most accurate. These approaches work together to provide a detailed understanding of the pyrolysis process with a particular emphasis on the integration of artificial intelligence (AI). An LSTM model trained with lignocellulosic data predicted TGA curves with exceptional accuracy (R²: 0.9996–0.9998). Full article

Background/Objectives: The rise of artificial intelligence is injecting intelligence into the healthcare sector, including surgery. Vision-based intelligent systems that assist surgical procedures can significantly increase productivity, safety, and effectiveness during surgery. Surgical instruments are central components of any surgical intervention, yet detecting and locating them during live surgeries remains challenging due to adverse imaging conditions such as blood occlusion, smoke, blur, glare, low-contrast, instrument scale variation, and other artifacts. Methods: To address these challenges, we developed an advanced segmentation architecture termed the frozen-filters-based morphology-aware segmentation network (FFMS-Net). Accurate surgical instrument segmentation strongly depends on edge and morphology information; however, in conventional neural networks, this spatial information is progressively degraded during spatial processing. FFMS-Net introduces a frozen and learnable feature pipeline (FLFP) that simultaneously exploits frozen edge representations and learnable features. Within FLFP, Sobel and Laplacian filters are frozen to preserve edge and orientation information, which is subsequently fused with learnable initial spatial features. Moreover, a tri-atrous blending (TAB) block is employed at the end of the encoder to fuse multi-receptive-field-based contextual information, preserving instrument morphology and improving robustness under challenging conditions such as blur, blood occlusion, and smoke. Datasets focused on surgical instruments often suffer from severe class imbalance and poor instrument visibility. To mitigate these issues, FFMS-Net incorporates a progressively structure-preserving decoder (PSPD) that aggregates dilated and standard spatial information after each upsampling stage to maintain class structure. Multi-scale spatial features from different encoder levels are further fused using light skip paths (LSPs) to project channels with task-relevant patterns. Results/Conclusions: FFMS-Net is extensively evaluated on three challenging datasets: UW-Sinus-surgery-live, UW-Sinus-cadaveric, and CholecSeg8k. The proposed method demonstrates promising performance compared with state-of-the-art approaches while requiring only 1.5 million trainable parameters. In addition, an open-source large language model is integrated for non-clinical summarization of the surgical scene based on the predicted mask and deterministic descriptors derived from it. Full article

Background and Objectives: Diabetes mellitus represents one of the most prevalent chronic metabolic disorders worldwide, necessitating precise insulin dose management to prevent both acute and long-term complications. The optimization of insulin dosing remains a significant clinical challenge, as inappropriate dosing can lead to hypoglycemia or hyperglycemia, each carrying substantial morbidity risks. Machine learning approaches have emerged as promising tools for developing clinical decision support systems; however, their practical implementation requires both high predictive accuracy and model interpretability. This study aimed to develop and evaluate an explainable machine learning framework for predicting insulin dose adjustments in diabetic patients. We sought to compare multiple ensemble learning approaches and identify the optimal model configuration that balances predictive performance with clinical interpretability through comprehensive SHAP and LIME analyses. Materials and Methods: A comprehensive dataset comprising 10,000 patient records with 12 clinical and demographic features was utilized. We implemented and compared nine machine learning models, including gradient boosting variants (XGBoost, LightGBM, CatBoost, GradientBoosting), AdaBoost, and four ensemble strategies (Voting, Stacking, Blending, and Meta-Learning). Model interpretability was achieved through SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) analyses. Performance was evaluated using accuracy, weighted F1-score, area under the receiver operating characteristic curve (AUC-ROC), precision-recall AUC (PR-AUC), sensitivity, specificity, and cross-entropy loss. Results: The Meta-Learning Ensemble achieved superior performance across all evaluation metrics, attaining an accuracy of 81.35%, weighted F1-score of 0.8121, macro-averaged AUC-ROC of 0.9637, and PR-AUC of 0.9317. The model demonstrated exceptional sensitivity (86.61%) and specificity (91.79%), with particularly high performance in detecting dose reduction requirements (100% sensitivity for the ‘down’ class). SHAP analysis revealed insulin sensitivity, previous medications, sleep hours, weight, and body mass index as the most influential predictors across different insulin adjustment categories. The meta-model feature importance analysis indicated that LightGBM probability estimates contributed most significantly to the ensemble predictions. Conclusions: The proposed explainable Meta-Learning Ensemble framework demonstrates robust predictive capability for insulin dose adjustment recommendations while maintaining clinical interpretability. The integration of SHAP-based explanations facilitates clinician understanding of model predictions, supporting transparent and informed decision-making in diabetes management. This approach represents a significant advancement toward the clinical implementation of artificial intelligence in personalized insulin therapy. Full article

The integration of artificial intelligence (AI) into early childhood education presents both opportunities and challenges to longstanding Froebelian pedagogies, particularly regarding child agency and nature-based play. This mixed-methods study explores this tension within the Chinese context. It examines how Chinese Froebelian practitioners perceive the alignment between AI tools and core principles and investigates the strategies they employ to navigate the integration of technology with humanistic educational values. The survey results, from 50 practitioners, revealed that AI can support autonomous and holistic learning, yet significant concerns persisted regarding the displacement of sensory and nature-based experiences. Follow-up interviews uncovered a practitioner-led “dual-track integration” approach, which strategically blends physical manipulation and nature engagement with AI-enabled personalization. Through an iterative dialogue between theory and data, this study develops and refines the “dual-track integration” framework as an empirically grounded, sensitizing model. This framework offers principled strategies for hybrid learning that uphold the developmental primacy of play. Situated within the discourse on Sustainable Development Goal 4 (quality education) and Goal 10 (reduced inequalities), the analysis highlights AI’s dual potential to advance or hinder equity. By examining China’s hybrid position, which combines advanced digital infrastructure with persistent equity gaps, this research highlights the critical role of educator agency and pedagogical design in leveraging AI to advance equitable, high-quality early childhood education. Full article

Accurate energy forecasting is critical for the financial and environmental sustainability of the hospitality sector, particularly in energy-intensive subtropical climates. This research addresses a significant gap by conducting a comprehensive, comparative analysis of six machine learning algorithms—Artificial Neural Networks (ANN), Random Forest (RF), XGBoost, Radial Basis Function (RBF), Autoencoder, and Decision Trees—to predict the hourly energy consumption of a hotel in Cuba. We significantly enhance predictive performance through a novel hybrid ensemble scheme, integrating voting, stacking, and blending techniques. Furthermore, this study pioneers a long-term forecasting methodology by utilizing a Long Short-Term Memory (LSTM) model to project the hotel’s energy demand over a 50-year horizon, providing the strategic insight necessary for evaluating major retrofits. Our results demonstrate that ensemble methods, particularly blending, achieve superior accuracy and stability, with correlation coefficients up to 0.975 and the lowest error metrics. The subsequent high-fidelity predictions, including an analysis revealing a minimal specific CO₂ emission of 0.025 kg from natural gas use, provide a quantitative foundation for formulating sustainable energy policies, incentivizing investment in efficient technologies, and ensuring that long-term emission reduction targets are both financially viable and technically robust. Full article

Biopolymers from renewable sources are increasingly explored to reduce the carbon footprint of materials and mitigate plastic pollution. This review synthesizes the last five years of progress across the biopolymer value chain, comparing plant, microbial/fermentation, fungal, and marine/algal resources and critically assessing greener extraction and fractionation routes (ultrasound and microwave intensification, subcritical water, supercritical CO₂ with co-solvents, ionic liquids, deep eutectic solvents including natural deep eutectic solvents, and enzymatic or bio-mediated processes). We emphasize yield-selectivity trade-offs, scalability, energy demand, and solvent recovery. Downstream, we summarize purification and performance tuning via crosslinking, derivatization, blending/plasticization, and nanocomposites, and we map advanced characterization to targeted functional properties to bridge processing choices with end-use performance. Applications are organized across food and agriculture, biomedical and pharmaceutical technologies, packaging, and cosmetics, with cross-cutting attention to safety and regulatory compliance, quality-by-design, techno-economics, and life-cycle assessment. Key bottlenecks are feedstock variability, viscosity and recyclability limitations of designer solvents, and persistent gaps in barrier and thermal properties versus petrochemical benchmarks, compounded by uneven composting and recycling infrastructure. Promising directions include low-viscosity or switchable solvents, data- and artificial intelligence (AI)-guided process optimization, engineered biopolymers, and circular end-of-life strategies that align material design with realistic recovery routes. Full article

Background/Objectives: 40 Hz sensory stimulation is being explored for cognitive health applications, but sustained use may be constrained by the listenability of simple 40 Hz auditory stimuli. We examined user-perceived acceptability and implementation considerations for 40 Hz auditory stimulation delivered by embedding a pure 40 Hz sine wave within nature-based soundscapes. Methods: Eleven adults aged ≥ 40 years in Seoul, Republic of Korea were assigned to waves or forest soundscapes (between-participants) and completed a within-session exposure to two conditions within the assigned set: 40 Hz–OFF (soundscape-only) and 40 Hz–ON (soundscape plus an additively layered 40 Hz sine wave). Each condition comprised seven cycles of 50 s playback and 10 s silence (~7 min) with a 10 min washout. After completing both listening blocks, participants provided brief comparative session-end ratings to aid recall and then completed a semi-structured interview focused on detectability and comparative impressions while blinded to condition identity. Following debriefing about the 40 Hz manipulation, participants completed a session-end 7-point Likert appraisal of the intended intervention stimulus (40 Hz–ON). Interview transcripts were analyzed using thematic analysis and interpreted using the Theoretical Framework of Acceptability and Proctor et al.’s implementation outcomes as sensitizing frameworks. Results: Session-end appraisals suggested that the 40 Hz-integrated soundscape (40 Hz–ON) was generally listenable, with mid-to-high comfort and immersion (medians = 5) and low unpleasantness (median = 2), while perceived artificiality spanned the full scale (range 1–7) and overall preference was moderate (median = 4). Interviews indicated that acceptability was governed by perceptual integration: natural blending supported “backgroundable” listening, whereas salient low-frequency rumble or a mechanical/artificial timbre contributed to negative reactions. Implementation-relevant themes highlighted context fit (bedtime vs. morning routines), low-friction automation (timers/scheduling), and conservative acoustic safeguards (gentle onset and default levels). Conclusions: In a single-session evaluation among adults aged ≥ 40 years, embedding a 40 Hz sine wave within nature-based soundscapes was generally acceptable, with acceptability sensitive to perceptual integration and usage context. This qualitative study does not assess clinical or cognitive efficacy. These findings inform implementation considerations for cognitive health-oriented delivery, including space-oriented playback options, simplified automation, conservative acoustic safeguards, and coherence-supportive user guidance without overclaiming. Full article

Conventional production methods of aragonite production utilize chemical inducers to promote the evolution of the calcite crystalline phase to the aragonite phase of calcium carbonate. The chemical inducers used require a considerable amount of magnesium chloride (MgCl₂) to induce crystallization, which is a major operational cost. Application of such materials in large amounts can be a deterrent to achieving a sustainable and economically feasible end-product derived from carbon dioxide (CO₂) molecules. A number of previous research works focused mainly on optimizing the usage of MgCl₂ or introducing alternative chemical inducers for aragonite production. In this work, we are proposing the usage of natural seawater as it is a naturally available and abundant resource to induce the synthesis and continuous production of aragonite compounds. Due to inconsistent quality and salinity of the natural seawater sampled, harvested, and dried, Red Sea Salt is utilized, blended at 33 g/L throughout the laboratory experiments for better statistical control, and is referred to as blended or artificial seawater. A methodology of utilizing seawater, which has a considerable concentration of MgCl₂ compound, can be utilized as a sustainable, natural, and economically feasible natural inducer to synthesize aragonite has been developed by utilizing artificial seawater for laboratory proof of concept. The main effects identified for the optimization of aragonite synthesis are lime (CaO) feedstock concentration in seawater, reaction temperature, and reaction duration. The experiment results indicated that only by increasing temperature and reaction duration, or both, can the aragonite yield be increased. It is suggested that the range of operation to obtain > 80% aragonite purity has been identified with the reaction temperature at 90 °C, reaction duration of 10 min, and CaO concentration in seawater at 1 g/L. The quality of the aragonite synthesized via seawater is characterized using XRD, ICP, FESEM, and TGA, and compared with aragonite particles synthesized using MgCl₂ inducers. In comparison, seawater aragonite has lower residual alkalinity compared to both calcite and aragonite via MgCl₂ and has a mixture of predominantly needle-shaped crystalline structure and remnants of cubic-shaped particles, presumably calcite, suitable for application in food, beverages, and pharmaceuticals (calcium antacids, nutritional supplements, chewable, lozenges). Full article

Background: Salmonella enterica remains a leading cause of foodborne illness worldwide despite decades of advances in surveillance and control. Traditional interventions have targeted specific points in the food chain, yet recurrent outbreaks show that Salmonella exploits system-wide gaps and inconsistencies. Methods: This review synthesises recent evidence from epidemiology, experimental microbiology, and regulatory practice to evaluate how management decisions, from farm through processing, influence Salmonella risk in livestock-derived foods. Results: Poultry, pig, and cattle farms employ targeted measures, including rodent control, litter management, batch rearing, and secure feed storage, to reduce contamination. The greatest reductions in Salmonella prevalence occur when these measures are embedded in coherent farm-to-fork programmes. Future gains are likely to come less from novel interventions and more from rigorous implementation, integration, and the validation of existing tools, supported by high-resolution surveillance (including whole-genome sequencing) and prevention-focused management systems. Artificial intelligence can enhance control through real-time surveillance, predictive risk modelling, and targeted interventions informed by diverse farm data. Conclusions: Sustained progress in Salmonella control will depend on rigorously applying existing interventions, supported by high-resolution surveillance and prevention-focused management. Carefully governed AI can enhance real-time monitoring and risk prediction, but its value hinges on addressing data, cost, and regulatory challenges. Full article

This research examines the feasibility of using waste cooking oil (WCO) as a substitute for traditional diesel fuel in internal combustion engines, with a focus on biodiesel production. The aim of this research is to evaluate the effects of WCO–diesel blends on engine performance, with particular emphasis on critical metrics including brake specific fuel consumption (BSFC) and brake thermal efficiency (BTE). The study utilizes artificial neural networks (ANNs) to model and forecast the performance and emission characteristics of engines operating with different fuel combinations. The study employs a methodology that involves conducting experiments to evaluate the mixtures of waste cooking oil (WCO) and diesel fuel in diesel engines. Furthermore, artificial neural networks (ANNs) are employed to develop models for predicting engine performance. The analysis focuses on critical metrics, including BSFC and BTE, under various operating conditions. This research aims to improve sustainable energy solutions by demonstrating the benefits of alternative fuels and advanced artificial intelligence (AI) prediction models in automotive applications. Full article

Artificial intelligence transforms lubricant research by linking molecular modeling, diagnostics, and industrial operations into predictive systems. In this regard, machine learning methods such as Bayesian optimization and neural-based Quantitative Structure–Property/Tribological Relationship (QSPR/QSTR) modeling help to accelerate additive design and formulation development. Moreover, deep learning and hybrid physics–AI frameworks are now capable to predict key lubricant properties such as viscosity, oxidation stability, and wear resistance directly from molecular or spectral data, reducing the need for long-duration field trials like fleet or engine endurance tests. With respect to condition monitoring, convolutional neural networks automate wear debris classification, multimodal sensor fusion enables real-time oil health tracking, and digital twins provide predictive maintenance by forecasting lubricant degradation and optimizing drain intervals. AI-assisted blending and process control platforms extend these advantages into manufacturing, reducing waste and improving reproducibility. This article sheds light on recent progress in AI-driven formulation, monitoring, and maintenance, thus identifying major barriers to adoption such as fragmented datasets, limited model transferability, and low explainability. Moreover, it discusses how standardized data infrastructures, physics-informed learning, and secure federated approaches can advance the industry toward adaptive, sustainable lubricant development under the principles of Industry 5.0. Full article

The emergence of artificial intelligence (AI) coaching challenges the professional roles and identities of human coaches, yet empirical research on this transformation remains scarce. This qualitative field study investigates how professional coaches navigate their roles following the organizational adoption of AI coaching. Drawing on the automation-augmentation paradox, occupational role identity, and role ambiguity theories, we analyzed 15 semi-structured interviews with 12 professional coaches in an Asian coaching firm, contextualized by pre- and post-interviews with the company CEO and the AI provider. Findings reveal that top-down AI implementation triggered significant role ambiguity, catalyzing both protective and expansive identity work. Coaches defended their unique human value (e.g., empathy), while simultaneously experimenting with AI, shifting their perception from threat to collaborative tool. This adaptive process enabled the emergence of distinct AI functions and new “blended” human–AI coaching models. Our resulting conceptual framework demonstrates that resolving the automation-augmentation paradox in relational professions is fundamentally an identity-driven process rather than a technical task reallocation. Furthermore, our findings demonstrate that organizationally induced role ambiguity can serve as a catalyst for professional renewal and vocational adaptation, particularly when supported by participatory leadership, thereby advancing theory and contributing new insights to the literature on technological and vocational transformation in organizational contexts. Full article

Existing 3D point cloud enhancement methods typically rely on artificially designed geometric transformations or local blending strategies, which are prone to introducing illogical deformations, struggle to preserve global structure, and exhibit insufficient adaptability to diverse degradation patterns. To address these limitations, this paper proposes SFD-ADNet—an adaptive deformation framework based on a dual spatial–frequency domain. It achieves 3D point cloud augmentation by explicitly learning deformation parameters rather than applying predefined perturbations. By jointly modeling spatial structural dependencies and spectral features, SFD-ADNet generates augmented samples that are both structurally aware and task-relevant. In the spatial domain, a hierarchical sequence encoder coupled with a bidirectional Mamba-based deformation predictor captures long-range geometric dependencies and local structural variations, enabling adaptive position-aware deformation control. In the frequency domain, a multi-scale dual-channel mechanism based on adaptive Chebyshev polynomials separates low-frequency structural components from high-frequency details, allowing the model to suppress noise-sensitive distortions while preserving the global geometric skeleton. The two deformation predictions dynamically fuse to balance structural fidelity and sample diversity. Extensive experiments conducted on ModelNet40-C and ScanObjectNN-C involved synthetic CAD models and real-world scanned point clouds under diverse perturbation conditions. SFD-ADNet, as a universal augmentation module, reduces the mCE metrics of PointNet++ and different backbone networks by over 20%. Experiments demonstrate that SFD-ADNet achieves state-of-the-art robustness while preserving critical geometric structures. Furthermore, models enhanced by SFD-ADNet demonstrate consistently improved robustness against diverse point cloud attacks, validating the efficacy of adaptive space-frequency deformation in robust point cloud learning. Full article