Search Results (5,468)

The development of plant-based functional beverages is often limited by inherent matrix defects, particularly undesirable off-flavors, astringency, and colloidal instability. Water kefir (WK), a highly resilient multispecies symbiotic consortium, offers a robust biorefining platform to address these challenges. This review systematically elucidates the underlying micro-ecological logic and biochemical mechanisms of WK-mediated plant matrix remodeling. We first detail how spatial niche differentiation and cross-feeding networks among lactic acid bacteria, yeasts, and acetic acid bacteria drive ecological homeostasis. Next, we highlight core molecular events that elevate sensory quality: protein unfolding for off-flavor elimination, enzymatic depolymerization of phenolics to mitigate astringency, and exopolysaccharide synthesis for rheological and flavor diffusion control. Finally, to overcome industrial scale-up challenges, we outline a precision fermentation framework, integrating systems multi-omics, real-time biomimetic monitoring, and sensory topological modeling. Ultimately, this synthesis provides theoretical guidance for the reverse flavor engineering and targeted nutritional design of novel plant-based beverages. Full article

LiDAR-based 3D object detection for autonomous driving must balance localization accuracy with real-time inference, while sparse point measurements make small-scale objects such as pedestrians and cyclists particularly challenging to represent at long range. This paper presents PES-PointPillars, an enhanced PointPillars detector with three coordinated design changes. First, pinwheel-shaped convolution (PConv) replaces selected backbone convolutions to expand horizontal and vertical receptive fields for sparse structural patterns. Second, an Improved Inter-Layer Feature Correlation (I-EFC) module uses soft gating and adaptive thresholding to fuse multi-level features through continuous, input-dependent weights. Third, a Smooth L1-NWD (SNWD) loss applies normalized Wasserstein distance to planar position and scale while retaining Smooth L1 regression for vertical position, height, and orientation. Using the parameter settings and configuration of the original PointPillars implementation, the locally executed PES-PointPillars experiment achieves Moderate 3D average precision values of 77.1% for cars, 46.7% for pedestrians, and 62.9% for cyclists at 68.3 FPS on the KITTI validation split. Relative to the source-reported PointPillars reference, the corresponding numerical differences are 2.1, 3.2, and 3.8 percentage points. The reported component-wise and staged ablations show category-dependent gains, with the complete model providing the strongest aggregate performance among the evaluated configurations. Full article

Triboelectric nanogenerators (TENGs) are promising for harvesting low-frequency mechanical energy, but rotating TENGs (R-TENGs) driven by low-speed water flow remain constrained by limited driving torque, sliding-contact losses, and rotating-system stability. Here, a three-dimensional (3D) riveted complementary-structure rotating triboelectric nanogenerator (RCSR-TENG) is proposed for low-speed water-flow energy harvesting. A semi-analytical formulation incorporating a force-dependent real-contact fraction is developed to describe the coupled relationships among output voltage, transferred charge, rotation angle, and contact force. Because the contact parameters were not independently calibrated, the formulation is used for sensitivity and trend analysis rather than as a quantitatively validated predictive model. For the single prototype tested for each configuration, at 1000 rpm under the fixed effective measurement load of 9 MΩ, the RCSR-TENG produced a peak output power of 544 μW, compared with 304 μW for the flat R-TENG, representing an increase of approximately 79%. The same RCSR-TENG prototype maintained a stable voltage amplitude of over 150,000 rotation cycles. When coupled to a fully passive flapping-foil collector in a 0.55 m s⁻¹ water flow, the system generated periodic electrical output with a peak area-normalized power exceeding 5000 μW m⁻². These results demonstrate the structural-performance advantage of the riveted complementary design and its proof-of-concept applicability to low-speed water-flow energy harvesting. Full article

Global labour shortages in the agricultural sector, combined with diminishing arable land and a growing population, are driving investment in autonomous agricultural machinery. Autonomous systems that can navigate crop environments and perform planting, treatment, and harvesting alongside humans are required, but the gap between published research and commercially deployed systems remains wide across most operational scenarios. Why are agricultural robots still not widely deployed in real farms despite decades of research in autonomous navigation and path planning, and what is preventing full farm autonomy? This paper reviews the principal enabling technologies for autonomous agricultural integration, with a specific focus on path planning as the differentiator between research-stage and deployed systems. Current research in human–robot integration, open-field navigation, row identification and following, crop sensing, and power efficiency is synthesised and evaluated against a deployability criterion. A Deployability Assessment Framework is introduced, comprising structured tables that assign Technology Readiness Levels to twelve path-planning families and benchmark eleven commercial and research platforms against field-validated accuracy data. The analysis shows that point-to-point GNSS navigation has reached TRL 9 with over one million commercial units deployed, vision-based crop row following is at TRL 5–7 depending on crop and season, and whole-farm autonomy with dynamic re-planning is at TRL 3–5. The primary barriers are the absence of standardised evaluation benchmarks, the failure of perception models to generalise across seasons and crop types, and the decoupling of terrain and slip feedback from global path planners. Our review reveals that open-field GNSS navigation is commercially mature, but true whole-farm agricultural autonomy remains unsolved because current systems are not robust enough across seasons, terrain, sensing conditions, and operational transitions. Full article

This work reviews the strategic valorization of industrial by-products as sustainable sources of biopolymers and bioactive compounds, promoting a circular economy through the efficient use of renewable resources and reducing waste generation. These strategies contribute to lowering the carbon footprint of conventional packaging and plasticulture while supporting more resilient and diverse agriculture systems. Special emphasis is placed on processing roots and tubers as renewable raw materials for the production of biodegradable films for agronomic applications as eco-friendly alternatives to petroleum-based plastics and contributing to soil and ecosystem protection. Additionally, the incorporation of by-products from yerba mate (Ilex paraguariensis) demonstrate significant potential as both matrix-forming and filler materials in biodegradable composites while also providing antioxidant activity and pH-sensing capacity. This sustainable framework is further expanded through the utilization of non-traditional species like rosehip (Rosa rubiginosa), Aloe vera (Aloe barbadensis), and topinambur (Helianthus tuberosus), which provide versatile functional matrices and bioactive compounds. Finally, the development of active and intelligent food packaging is addressed. Extracting natural pH-sensitive pigments from red cabbage and topinambur flowers enables the formulation of eco-friendly inks for real-time freshness monitoring. Ultimately, integrating these waste streams drives technological disruption, scaling sustainable, tailored solutions for global industry needs. Full article

Autonomous vehicles increasingly require capabilities that extend beyond perception towards contextual understanding, semantic reasoning, and explainable decision-making. Knowledge graphs (KGs) have emerged as a promising solution by integrating heterogeneous sensor data, traffic regulations, domain knowledge, and contextual information into unified semantic frameworks. This review systematically examines knowledge graph-based intelligent reasoning in autonomous driving through a PRISMA 2020-guided analysis of 47 peer-reviewed studies identified from the literature published from 1 January 2018 to 31 January 2026. The findings reveal that semantic scene understanding and ontology-based representations currently dominate the field, with 66.0% of studies integrating knowledge graphs with deep learning approaches. Neuro-symbolic methods and explainable AI components were identified in 38.3% and 34.0% of publications, respectively, indicating increasing research interest in hybrid and transparent AI architectures. The analysis further demonstrates that 80.9% of studies remain limited to benchmark datasets and simulation environments, whereas only 19.1% provide real-world validation, suggesting relatively low technological maturity and limited industrial readiness. Although KG-enabled approaches substantially improve contextual awareness, hidden hazard anticipation, and explainability compared with conventional perception-centric architectures, major challenges remain regarding scalability, ontology interoperability, semantic error propagation, real-time reasoning, and certification requirements. The review identifies the convergence of knowledge graphs, large language models, and neuro-symbolic AI as a promising direction for next-generation autonomous driving systems. Future research should therefore focus on uncertainty-aware reasoning, adaptive explainability, standardised evaluation methodologies, and certification-oriented real-world deployment strategies. Full article

Twin Rotor Multi-Input Multi-Output (TRMS) platforms combine strong aerodynamic cross-coupling, gravitational loading on the vertical axis, friction-dominated horizontal dynamics, and systematic mismatch between idealised models and laboratory hardware. The platform provides only two optical encoders, so the angular rates and the rotor states are unavailable for feedback. This paper presents an observer-based output-feedback architecture that addresses both difficulties. A high-gain observer built on the fully coupled six-state model, including the gyroscopic terms that the control design cannot retain, reconstructs the four unmeasured states from the two encoder angles. The reconstructed states drive a Hybrid Backstepping–Super-Twisting (B-STA) controller in which a second-order continuous sliding mode is embedded at the final recursive step through a composite surface. Because backstepping requires strict-feedback structure, which the centralised coupled model does not possess, the controller is synthesised on a decentralised design model and the residual coupling is rejected as a matched perturbation of the sliding variable. Closed-loop behaviour is analysed as a three-stage cascade covering observer error, sliding variable, and tracking error, yielding practical stability under bounded residuals with an explicit input-to-state gain. The residual bounds are evaluated numerically from the actuator saturation limit and the identified coefficients rather than assumed, and the resulting figures are shown to predict the marked difference in sliding-variable behaviour observed between the two axes. A second architecture applies a windowed Grey Wolf Optimiser (B-GWO) to the backstepping gains, in which each candidate is applied to the plant for a fixed test window, scored on its own accumulated integral of time-weighted absolute error, and followed by a settle window at the incumbent best. We prove that this windowing is a requirement rather than a convenience: a fitness evaluated at a single sample is common to all candidates, cancels from the population ranking, and reduces the search to the minimiser of its own regularisation term. Both schemes are implemented on a physical TRMS through a Simulink Desktop Real-Time interface at a control period of $10\mathrm{ms}$. On a $100\mathrm{s}$ experimental run, B-STA attains a pitch tracking error of $0.0318\mathrm{rad}$ RMS, $7.94\%$ of the reference amplitude, and the lowest control energy on both axes among the strategies compared, reducing pitch control energy by $72.9\%$ relative to a first-order Backstepping–Sliding Mode baseline recorded on the same interface. Numerical disturbance rejection tests on the fully coupled model confirm the mechanism: under a matched actuator step the super-twisting integrator state migrates to a new steady level that cancels the disturbance, driving the residual pitch error to $2\times {10}^{-4}\mathrm{rad}$, whereas the same recursive law without the second-order injection retains a permanent offset of $0.28\mathrm{rad}$. Full article

Distributed-drive electric vehicles (DDEVs) enable independent wheel-torque control, providing flexibility to improve energy efficiency and vehicle stability. However, tire–road adhesion, motor and battery capabilities, and actuator availability constrain these objectives, which may conflict under low-adhesion conditions, high-power acceleration, emergency braking, and combined longitudinal–lateral maneuvers. This paper provides a structured review of coordinated torque-allocation strategies for balancing energy efficiency and stability in DDEVs. Existing research is examined in terms of regenerative braking, tire-slip energy-loss reduction, and stability control under longitudinal, yaw, and combined conditions. Control approaches are classified as rule-based, stability-region-based, mode-switching, multi-objective optimization and predictive control, state-adaptive dynamic-priority coordination, and learning-based safety-hybrid methods. These approaches differ in real-time performance, constraint handling, adaptability, interpretability, and engineering maturity. A hierarchical hybrid architecture integrating rule-based supervision, state assessment, constraint-aware optimization, and learning-based enhancement appears more suitable for practical deployment than a single algorithm or fixed-weighting scheme. Key challenges include dynamic stability-boundary estimation, safety-assured coordination, multi-actuator fault tolerance, real-time implementation, and standardized vehicle-level validation. This review provides guidance for coordinated control-system development and future research on DDEVs. Full article

Road surface hazards remain a persistent concern for vehicle safety, passenger comfort, and the operational continuity of transport infrastructure. Among these hazards, potholes are particularly significant because they can cause tire damage, suspension wear, wheel misalignment, and sudden vehicle instability. In addition to direct mechanical damage, potholes may reduce driving comfort, increase maintenance costs, and degrade traffic efficiency in urban environments where roads are heavily used and rapidly deteriorate. For these reasons, the timely detection of potholes is an important requirement for road safety and infrastructure management. This work presents a tailor-made dataset for road pothole detection in urban environments, referred to as the Bridgestone Dataset (BS Dataset). The dataset was designed to support object detection from vehicle-mounted imagery collected from a test vehicle under realistic road conditions, thereby aligning the training data more closely with the target deployment scenario. The resulting dataset is intended to support real-time monitoring systems for road hazard detection and maintenance planning. The dataset was also designed as a multimodal resource. In addition to pothole bounding-box annotations, it provides accelerometer and GPS signals to characterize the vehicle dynamics during operation which might help identifying hazard severity and the potential risk to the vehicle. To collect the dataset, the authors developed a smartphone application, which supports the acquisition of both images and vehicle telemetry by leveraging the device’s internal sensors. Full article

Level 3 automated driving necessitates rapid driver reengagement following a takeover request, making human–machine interface design critical for safety. As a sensor-based in-vehicle interface, the augmented reality head-up display (AR-HUD) integrates real-time environmental sensing with visual information delivery, yet how different information presentation strategies influence driver visual attention during takeover remains underexplored. We examined the effects of presentation form (static/dynamic) and presentation timing (concurrent/progressive) using a 2 × 2 within-subject design. In the experiment, twenty-one licensed drivers viewed prerecorded automated driving takeover scenarios. Eye tracking measured mean fixation duration, mean saccade amplitude, time to first fixation, and fixation count, while ratings assessed usability, acceptance, and intention comprehension. Progressive presentation significantly reduced all eye-tracking measures and improved perceived usability ratings compared with concurrent presentation. This pattern indicates more concentrated and orderly gaze allocation and is consistent with lower visual-search and information-integration demands. However, the lower fixation count may partly reflect reduced information exposure. Static presentation reduced mean fixation duration but showed no consistent overall advantage on the remaining measures. Significant form-by-timing interactions showed that progressive presentation reduced saccade amplitude and time to first fixation and improved intention comprehension under static, but not dynamic, presentation. Among the four combinations, static-progressive presentation yielded the most favorable overall pattern. Overall, presentation timing affected more measured outcomes than presentation form. These findings provide preliminary evidence that presentation form and presentation timing shape gaze behavior and subjective evaluations of AR-HUD takeover displays. Full article

To comply with stringent emission regulations, the deployment of hybridized powertrains is continuously expanding. However, architectures such as plug-in and parallel hybrids intrinsically reduce the overall runtime of the internal combustion engine (ICE). Because the battery state-of-charge (SOC) dictates intermittent engine activation, this operational strategy inevitably induces frequent cold-start events. This study investigates the thermal dynamics of commercial catalyst geometries (300–1200 cpsi, 2–8 mil) via 1D numerical simulations under real-world driving conditions. Without active heating, high-thermal-mass substrates unexpectedly outperform ultra-thin-wall variants by buffering against convective quenching during prolonged idling. However, integrating start–stop functionality halts cold exhaust flow, elevating mean temperatures and marginalizing geometric disparities. Evaluating electrically heated catalysts (EHCs) reveals that discrete preheating is highly inefficient due to rapid heat dissipation. Conversely, continuous closed-loop heating coupled with start–stop functionality sustains operational temperatures for over 90% of the cycle. Under continuous heating, substrate geometry ceases to dictate thermal performance; instead, it governs electrical efficiency. Low-thermal-mass monoliths minimize cumulative energy demand to 213 kJ (versus 277 kJ for high-mass variants), incurring a negligible CO₂ penalty. Consequently, future hybrid architectures must integrate lightweight EHCs to ensure sustainable emission control. Full article

Owing to the stringent reliability requirements of silicon carbide (SiC) MOSFETs, conventional gate-driving methods with fixed parameters are no longer sufficient to ensure reliable operation under varying operating conditions. To address this issue, this study proposes an adaptive gate-driving method based on model prediction dealing with operating conditions. First, the influence of operating conditions on crosstalk is comprehensively analyzed, starting with an explanation of the crosstalk mechanism. Then, a two-variable behavioral model is established to estimate the amplitude of the crosstalk voltage under the specified operating condition. Based on this prediction model, a lightweight optimization algorithm is developed to select the negative gate turn-off voltage according to the operating conditions. Finally, a double-pulse test platform is built to validate the proposed dynamic gate-voltage selection strategy. The experimental results show that, compared with the fixed −3 V turn-off voltage scheme, the proposed method selects the optimal turn-off voltage of −2 V under specific operating conditions, which can reduce unnecessary negative gate-voltage stress while maintaining the gate-source voltage within the allowable range. Additionally, the measured turn-off loss of the proposed method is 172.7 μJ, lower than the 222.3 μJ loss of the parallel gate-source capacitance method, indicating a better trade-off between crosstalk suppression and switching loss. The proposed behavioral model requires only 69.5 microseconds to predict positive and negative crosstalk peaks, whereas the analytical model requires 46.6 milliseconds. The low computational burden makes the proposed method suitable for potential real-time implementation on resource-constrained microcontrollers. Full article

Background/Objectives: Isocitrate Dehydrogenase (IDH)1/2-mutant diffuse gliomas represent a biologically distinct subgroup of adult brain tumors in which eary metabolic reprogramming and accumulation of the oncometabolite D-2-hydroxyglutarate (D-2HG) drive epigenetic, immunologic, and clinical characteristics, including a high burden of glioma-associated epilepsy. This review summarizes the molecular and metabolic consequences of IDH mutations, their role in glioma-associated epilepsy, and the evolving impact of mutant IDH-targeted therapies in contemporary neuro-oncology. Methods: We conducted a narrative review of key molecular, translational, imaging, and clinical studies on IDH-mutant diffuse gliomas. The literature included the 2021 (World Health Organization) WHO Classification of Tumours of the Central Nervous System, studies investigating D-2HG biology and glioma-associated epilepsy, and prospective clinical trials and real-world evidence evaluating IDH-targeted therapies and contemporary antiseizure management. Particular emphasis was placed on vorasidenib, advanced metabolic imaging, and emerging liquid biopsy approaches. Results: IDH mutations are early driver events that promote D-2HG accumulation, resulting in widespread epigenetic reprogramming, metabolic dysregulation, and an immunosuppressive tumor microenvironment. D-2HG has also been implicated in the development of glioma-associated epilepsy, although the underlying mechanisms remain incompletely understood. Advances in integrated histomolecular diagnostics, magnetic resonance spectroscopy, amino acid positron emission tomography, and cerebrospinal fluid liquid biopsy have improved disease classification and treatment monitoring. Mutant IDH inhibitors, particularly vorasidenib, prolong progression-free survival, delay the need for subsequent treatment, and reduce intratumoral D-2HG concentrations, and have shown encouraging early signals of improved seizure control and preserved health-related quality of life in patients with grade 2 IDH-mutant gliomas, although this evidence remains preliminary and requires confirmation in larger prospective studies. Conclusions: IDH-mutant diffuse gliomas exemplify precision neuro-oncology, in which a single metabolic alteration informs diagnosis, disease monitoring, and targeted therapeutic approach. Additionally, ongoing studies are expected to further define the role of IDH inhibition across different disease stages and in combination with immunotherapy and standard treatments. Lastly, future clinical trials should systematically incorporate seizure outcomes, neurocognitive function, patient-reported outcomes, and immunologic endpoints to optimize both tumor control and quality of life. Full article

Accurate actuator modeling is critical for robust design validation and sim-to-real control transfer in humanoid robotics. Yet, in practice, developers rely on simplified actuator models built from sparse datasheets or offline system identification, which often omit internal control logic, saturation, sensor dynamics, and electromechanical actuator dynamics. This limits model fidelity under changing conditions and contributes to sim-to-real failures. We propose actuator Digital Twins (DTs) as a scalable solution for predictive simulation. In this work, predictive simulation is defined as the forward computation of joint position and actuator torque from prescribed reference trajectories, controller parameters, mechanical configuration, and initial conditions, with prediction accuracy evaluated against measurements from the physical actuator. We validate a DT of the Pulsar PULSE115 quasi-direct-drive actuator that reproduces the actuator electromechanical dynamics, physical operating limits, sensing characteristics, and embedded cascaded controller executed at 10 kHz on a 1-DOF pendulum testbed, comparing real-world experiments with simulations using both the DT and a simplified model. Across varying trajectories and configurations, the DT maintains low error-from-real, while the simplified model degrades outside its tuned regime, particularly under changes in trajectory dynamics, mechanical load, and controller gains. We further embed the DT in a 4-DOF humanoid arm simulation and show that it runs significantly faster than the real-time version, achieving a simulation speedup factor of approximately 6.3× on a standard laptop. These results demonstrate that actuator-specific electromechanical and embedded control modeling improves the forward prediction of physical actuator behavior while remaining computationally practical for multi-joint robotic simulation. Full article

Artificial Intelligence (AI) and Machine Learning (ML) are increasingly driving the digital transformation of manufacturing systems, enabling the transition from conventional process operation toward intelligent, adaptive, and data-centric production environments. This work presents the development of AI-enabled advisory systems for casting processes, integrating singular value decomposition (SVD)-based reduced-order models with a Variational Autoencoder with Arbitrary Conditioning (AC-VAE) and hybrid simulation frameworks to support real-time process prediction and optimization. The proposed approach leverages manufacturing data to establish predictive models capable of rapidly evaluating process conditions, optimizing operating parameters, and enhancing product quality while reducing material waste, energy consumption, and production costs. By combining physics-based understanding with AI-driven analytics, the framework facilitates real-time decision support, adaptive process control, and continuous performance improvement within modern manufacturing ecosystems. These capabilities contribute to the broader objectives of Industry 4.0 and emerging Industry 5.0 paradigms, including automation, connectivity, operational resilience, sustainability, and human-centered manufacturing. A representative Horizontal Direct Chill (HDC) continuous casting case study is presented to demonstrate the practical implementation of the framework, encompassing database generation, model training, validation, and deployment of predictive advisory tools for real-time manufacturing applications. Full article