Search Results (4,272)

Bike sharing is an established component of urban mobility infrastructure, offering a low-emission alternative to motorized transport for short trips in cities worldwide. Accurate demand forecasting is essential for efficient system operation: it enables better bike redistribution, reduces user wait times, and lowers the operational costs associated with rebalancing. This study evaluated multiple ensemble strategies for hourly bike-sharing demand prediction, comparing bagging methods (Random Forest, Extra Trees), boosting methods (AdaBoost, Gradient Boosting Regressor, Histogram-based Gradient Boosting Regressor), and a Voting ensemble, while systematically investigating the impact of hyperparameter optimization. A repeated hold-out protocol was used, in which the dataset was randomly divided into 80% training and 20% test subsets across 10 random splits; 5-fold cross-validation was applied within each training fold exclusively for hyperparameter tuning, ensuring the test set remained unseen during model selection. Random Search and Bayesian Optimization were compared under identical budgets of 60 configurations per model. Results show that optimization substantially improves all models, with the most pronounced gains for AdaBoost (58% RMSE reduction) and Gradient Boosting Regressor (45% RMSE reduction). A Voting ensemble combining a Random Search-tuned Gradient Boosting Regressor and a Bayesian-optimized Histogram-based Gradient Boosting Regressor achieves the best overall performance (RMSE of 38.48, R² of 0.955) with the lowest variance among all repeated splits. Feature importance analysis confirms that hour of day and temperature are the dominant demand drivers, consistent with the operational patterns of urban bike-sharing systems. The performance difference between Random Search and Bayesian Optimization is negligible for most models, suggesting that well-designed search spaces allow simpler strategies to achieve competitive results. A controlled comparison conducted under identical experimental conditions shows that the Voting ensemble is statistically equivalent to XGBoost and nominally better than LightGBM, while CatBoost achieves a statistically significant advantage, highlighting it as a strong individual alternative. Full article

This paper introduces a laser interferometric elastography (LIE) system that uses a narrow linewidth laser and a single photodetector to measure mechanical displacements induced by surface acoustic waves (SAWs) generated by an electrically driven piezoelectric transducer. The method relies on phase delay analysis of the resulting interference signal to determine displacement within the medium, thereby eliminating the need for complex interferometers and broadband light sources. By substantially reducing optical hardware requirements, the system provides a compact and cost-effective platform for elasticity mapping in biological samples. Quantitative assessment of mechanical properties is achieved through controlled mechanical excitation and phase-resolved signal collection, demonstrating the practicality of simplified LIE for real-world applications. Full article

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

In recent decades, the Ucayali region, the main territory of the Asheninka communities, has experienced increasing socio-environmental pressures associated with climate change, educational inequality, and territorial vulnerability in rural and indigenous contexts. In response, this research proposes the design of a sustainable agricultural school for the Asheninka community, conceived as an educational building that integrates biophilic strategies to enhance environmental performance and spatial quality. The methodological approach comprises a literature review, site-specific environmental analysis based on hydrometeorological data, and the development of an architectural proposal focused on sustainable building design. Digital tools such as Revit and SketchUp were employed alongside official climatic data sources to support design decision-making. The proposal includes twelve biophilic agricultural classrooms incorporating passive design strategies, rainwater harvesting systems with a capacity of 22.5 m³ per day per classroom, and photovoltaic-powered public lighting systems. Results indicate that the integration of natural ventilation, green infrastructure, and locally sourced materials contributes to significant improvements in thermal comfort, humidity control, and energy autonomy within the educational facilities. The architectural complex is complemented by green corridors and collective open spaces that reinforce environmental performance at the site scale. This study demonstrates that sustainable educational buildings adapted to local ecosystems and climatic conditions can function as effective infrastructures for environmental mitigation and resilient rural development, contributing to more sustainable forms of urban and rural living. Full article

To address the challenges of unstable target localization and poor multi-module coordination in automated green pepper harvesting—caused by occlusions from branches and leaves, as well as varying lighting conditions—this paper presents the design and implementation of a modular robotic picking system. At the perception level, the system integrates a YOLOv8 detector with a RealSense D435i camera to identify and locate the calyx–ectocarp junctions of green peppers. An integrated multi-target tracking and filtering framework is proposed, which fuses multi-feature association, trajectory smoothing and coordinate denoising strategies to suppress depth noise and trajectory jitter, thereby enhancing the stability and accuracy of 3D localization. At the control and execution level, a depth-first picking sequence strategy with ID freeze-state management is implemented within a multithreaded software–hardware co-design architecture. This approach avoids task conflicts and duplicate operations while supporting continuous multi-fruit harvesting. Field experiments under natural outdoor lighting and varying occlusion levels demonstrate that the proposed system achieves recognition rates of 91.57% and 80.29% and harvesting success rates of 82.85% and 77.68% for non-occluded and lightly occluded fruits, respectively. The average picking cycle per pepper fruit is 9.8 s. This system provides an effective technical solution for addressing stability control challenges in the automated harvesting process of green peppers. Full article

State-of-charge (SOC) estimation for lithium-ion batteries in smartphones is complicated by nonlinear load variation, electro-thermal coupling, aging effects, and heterogeneous user behaviors. This study proposes a multi-physics coupled SOC estimation framework, termed the Multi-Physics Thermo-Electrochemical Coupled SOC Model (MTEC-SOC), to characterize battery behavior under representative user-load conditions within controlled ambient thermal boundaries. The model combines system-level power profiling, thermal evolution, voltage dynamics, and aging-related capacity correction within a unified framework. To support model development and validation, a dual-source dataset is established using laboratory battery characterization data and real-world smartphone behavioral data, from which users are classified into light, heavy, and mixed usage patterns. Comparative results against four benchmark models (M1–M4) show that MTEC-SOC achieves the highest overall accuracy, with average MAE, RMSE, and TTE error values of 0.0091, 0.0118, and 0.08 h, respectively. The results suggest distinct degradation tendencies across user types: calendar aging dominates under prolonged high-voltage dwell in light-use scenarios, whereas, within the tested thermal range, heavy-use scenarios exhibit stronger voltage sag, relative temperature rise, and polarization-related stress; mixed-use scenarios are characterized by transient responses induced by abrupt load switching. Sensitivity analysis further indicates that the predictive behavior of the model is strongly scenario-dependent, with higher-load operation within the calibrated range amplifying parameter perturbations. Overall, the proposed MTEC-SOC framework provides accurate SOC estimation and physically interpretable insight within the evaluated dataset and operating conditions, offering potential guidance for battery management and energy optimization in intelligent mobile terminals. Full article

For irregularly shaped 820CrMnTi carburizing and nitriding parts, the challenges of high reflectivity-induced overexposure, low surface contrast, and interference from minute burrs in industrial online inspection are addressed in this paper. An innovative precision detection method integrating adaptive imaging and a dual-drive heterogeneous coupling model (RGFCN) is proposed. Such parts, due to surface photovoltaic characteristic changes caused by carburizing and nitriding heat treatment and the complex on-site lighting environment, are prone to local overexposure and “false out-of-tolerance” measurements caused by outlier sensitivity in traditional inspections. First, an innovative programmatic adaptive exposure control algorithm based on grayscale histogram feedback is introduced, which dynamically adjusts imaging parameters in real time to effectively suppress high-brightness overexposure under specific working conditions. Second, a novel adaptive main-axis scanning strategy is designed to construct a dynamic follow-up coordinate system, eliminating projection errors introduced by random positioning from a geometric perspective. Additionally, Gaussian gradient energy fields are combined with the Huber M-estimation robust fitting mechanism to suppress thermal noise while automatically reducing the weight of burrs and oil stains, achieving “immunity” to non-functional defects. Meanwhile, a data-driven innovative compensation approach is introduced. Based on sample training, gradient boosting decision trees (GBDTs) are integrated to explore the nonlinear mapping relationship between multidimensional feature spaces and system residuals, achieving implicit calibration of lens distortion and environmental coupling errors. By simulating factory conditions with drastic 24 h day–night lighting fluctuations and strong oil stain interference, statistical analysis of over 1000 mass-produced parts shows that this method exhibits excellent robustness in complex environments. It reduces the false out-of-tolerance rate caused by burrs by over 90%, and the standard deviation of repeated measurements converges to the micrometer level. This effectively addresses the visual inspection challenges of irregular, highly reflective parts on dynamic production lines. Full article

Extra-virgin olive oil (EVOO) is a chemically complex lipid matrix whose minor constituents—especially phenolic secoiridoids—drive sensory quality, oxidative stability, and health benefits. However, these bioactives are vulnerable to heat, light, oxygen, and pro-oxidant metals during processing and distribution, while the high cost of EVOO often makes it a target for adulteration and mislabeling. This review critically assesses nano-enabled, food-grade strategies that (i) preserve phenolics and aroma compounds through nanoencapsulation, inclusion complexes, Pickering stabilization, and structured lipid systems; (ii) control their release and bioaccessibility during digestion; and (iii) enhance authenticity verification via sensor-ready packaging, spectroscopy/chemometrics, and digital traceability systems (IoT, machine learning, blockchain). We align these innovations with the “product identity constraints” of the EVOO category and with official quality standards used in routine control (IOC/EU). Finally, we explore circular valorization of olive-mill by-products within food-centered biorefineries, outlining pathways to convert biomass into ingredients, materials, and energy, thus reducing environmental impacts. Research priorities are proposed to develop scalable, regulation-compliant nanotechnologies that extend shelf life and increase consumer trust without compromising EVOO category standards. Full article

This study presents a nanosensor network system for autonomous microclimate optimization in precision horticulture, leveraging a field-programmable gate array (FPGA)-based control architecture that is integrated with an edge-level machine learning inference. Unlike the conventional greenhouse automation systems, which exhibit thermal and hygroscopic hysteresis often exceeding 32 °C and 78% relative humidity, the proposed framework embeds a random forest regression (RFR) model directly within the Altera DE2-115 FPGA fabric to enable predictive environmental regulation. The model achieved an R² of 0.985 and root mean square error (RMSE) of 0.28 °C, allowing proactive compensation for the thermodynamic disturbances from the high-intensity light-emitting diode (LED) lighting with a 120 s predictive horizon. The real-time monitoring and remote supervision were supported via a NodeMCU-based IoT gateway, achieving a 140 ms mean communication latency and a 99.8% packet delivery reliability. The preliminary validation using lettuce (Lactuca sativa) optimized the environmental parameters, while the subsequent experiments with pepper (Capsicum annuum), a commercially important and environmentally sensitive crop, demonstrated system performance under real-world conditions. The control system maintained a temperature and humidity within ±0.3 °C and ±1.2% of the setpoints, respectively, and outperformed the baseline rule-based control with a 28% increase in fresh biomass, a 22% improvement in dry matter accumulation, a 25% reduction in actuator duty-cycle switching, and an 18% decrease in overall energy consumption. These results highlight the efficacy of FPGA-integrated edge intelligence combined with low-latency IoT telemetry as a scalable, energy-efficient, and high-fidelity solution for sub-degree environmental control in next-generation, controlled-environment, and vertical farming systems. Full article

Cancer remains a major global health challenge, and the limitations of conventional therapies have intensified interest in treatment strategies that combine improved selectivity with reduced systemic toxicity. Photothermal therapy and photodynamic therapy have emerged as minimally invasive approaches capable of achieving spatiotemporally controlled tumour ablation. In this context, molybdenum disulfide (MoS₂), a transition metal dichalcogenide with strong near-infrared absorption, high photothermal conversion efficiency, and versatile surface chemistry, has gained increasing attention as a multifunctional platform for drug delivery and light-triggered cancer therapy. This review examines recent advances in engineered MoS₂ nanoplatforms for drug-enhanced cancer phototherapy, with emphasis on how surface design and therapeutic cargoes mechanistically amplify light-triggered tumour killing. Approaches such as polymer coatings, biomimetic membranes, targeting ligands, chemotherapeutic agents, nucleic acids, and photosensitisers have been explored to improve colloidal stability, tumour targeting, immune evasion, and stimulus-responsive drug release, while also adding complementary cytotoxic pathways such as chemotherapy, ROS generation, or gene silencing. Available in vitro and in vivo studies indicate that these systems generally exhibit favourable short-term biocompatibility under the tested conditions and can produce significant antitumour effects following irradiation. The review also discusses key biological barriers and translational challenges, including biodistribution, long-term safety, reproducibility, and regulatory considerations, highlighting opportunities for the development of clinically viable MoS₂-based phototherapeutic platforms. Full article

Sol–gel-based optical functional sensor coatings were developed for real-time monitoring of multiphase saponification reactions in microreactors. Various pH-sensitive indicator mixtures, including bromocresol green and bromocresol purple (BCG and BCP) and methyl red–methyl orange, were incorporated into sol–gel coatings and evaluated on test plates across pH range of 2–12. Coatings with BCG and BCP 1:3 demonstrated the most pronounced color change at high pH (11–12), with distinct hue (H) transitions providing a reliable measure of local pH. These optimized coatings were integrated into microreactor channels to track the passage of oil and NaOH slugs under varying flow rates. Hue analysis produced reproducible plateaus corresponding to NaOH-rich (H = 50°) and oil-rich (H = 41°) phases, enabling droplet-level resolution of slug flow and detection of flow-regime transitions. The sensor response was fully reversible, highlighting the robustness and reusability of the coatings. Unlike previous high-resolution fluorescence-based systems, this approach relies on simple visible-light imaging and low-cost data extraction, leaving the reaction chemistry unaltered. The results demonstrate that sol–gel coatings coupled with hue-based analysis provide a practical, noninvasive, and real-time monitoring strategy for multiphase reactions in microreactors, with potential for implementation in industrial or IoT-enabled process control systems. Full article

Background: Rabies is a highly fatal zoonotic disease that causes approximately 59,000 human deaths worldwide each year. Current inactivated rabies vaccines require multiple doses and are associated with high costs. The full-length rabies virus glycoprotein (RVG), a membrane protein, exhibits substantial instability in its trimeric structure during recombinant expression. This instability makes it difficult to obtain high-purity, correctly folded antigens. Objectives: This study focuses on the preparation of a full-length recombinant RVG subunit vaccine candidate expressed in a glycoengineered Pichia pastoris system with mammalian-like glycosylation. Methods: The full-length RVG gene (including the transmembrane domain and cytoplasmic tail) from the Challenge Virus Standard-11 (CVS-11) strain was codon-optimized and inserted into the pPICZαA vector to construct the recombinant expression plasmid pPICZαA-RVG. The plasmid was transformed into glycoengineered Pichia pastoris X33-7 (low-mannose type) by electroporation for inducible expression. The target protein was purified by nickel affinity chromatography, anion-exchange chromatography, and Superdex-200 size-exclusion chromatography. The structural characteristics of the purified protein were analyzed by dynamic light scattering (DLS) and transmission electron microscopy (TEM). The purified antigen was formulated with the adjuvants AS03 or MF59. BALB/c mice (n = 5 per group) were immunized intramuscularly following a four-dose schedule (days 0, 7, 14, and 28). Antigen-specific IgG antibody titers were measured by ELISA, and neutralizing antibody titers were determined using the rapid fluorescent focus inhibition test (RFFIT). Results: Glycoengineered Pichia pastoris yeast strains expressing wild-type RVG (RVG-WT) or a mutant variant (RVG-M6: R84S, R199S, H270P, R279S, K300S, and R463S) were successfully constructed. The purified RVG antigen formed nanoparticles with an average particle size of approximately 75 nm. Immunized mice generated robust RVG-specific IgG responses, with titers reaching approximately 6.31 × 10⁵ for RVG-WT after the fourth immunization, compared to 3.16 × 10³ for RVG-M6 and 5.62 × 10³ for the RVG-WT-PEG control. Two weeks after the fourth immunization, RVG-WT formulated with AS03 or MF59 induced significant neutralizing antibody responses compared with the control group (p < 0.0001 and p < 0.01, respectively). The neutralizing antibody titers reached 1:79.43 in the AS03 group and 1:33.11 in the MF59 group, whereas the WT-PEG + AS03 control group showed a low titer of 1:3.72. In contrast, RVG-M6 formulated with MF59 failed to induce detectable neutralizing antibodies (1:3.02). Furthermore, RVG-WT + AS03 induced significantly higher neutralizing antibody responses than the WT-PEG + AS03 control group (p < 0.0001), and a significant difference was also observed between the RVG-WT + MF59 and RVG-M6 + MF59 groups (p < 0.01). Conclusions: The glycoengineered Pichia pastoris expression system successfully produced uniform full-length rabies virus glycoprotein nanoparticles with high purity. When formulated with the AS03 adjuvant, RVG-WT induced high-titer neutralizing antibodies in mice, suggesting a promising strategy for the development of recombinant subunit vaccines against rabies. However, this study is limited by the absence of challenge studies and validation in target animal species, which will be further investigated in future work. Full article

Indoor microgreen production systems are becoming increasingly popular because they can achieve high yields and quality, especially in unfavorable climates and urban settings. Light is a critical environmental factor that influences plant development; however, limited information exists on the effects of photoperiod (PP) on the growth of chia and arugula microgreens and on the associated electricity costs. The objective of this study was to evaluate the impact of different blue LED light (Light-Emitting-Diode) PPs, 24:0, 18:6, 12:12, and 6:18 h of light:dark compared with natural light, on the growth and biomass production of Salvia hispanica (chia) and Eruca sativa (arugula) grown indoors under controlled conditions (25 °C and 189.4 μmol·m⁻²·s⁻¹). In chia, shoot length increased (p ≤ 0.05) with shorter PP, particularly under the 6:18 and 12:12 h·d⁻¹ photoperiods, while arugula showed no significant response. Root length and total plant length were unaffected by photoperiod in either species. Leaf area was the most responsive growth parameter, with larger leaves produced under PP of 18 h or more per day. Total chlorophyll content was highest at 12:12 and 18:6 h light:dark. Fresh biomass reached its maximum at 18:6, with 637.6 g m⁻² in chia and 883.7 g m⁻² in arugula. TOPSIS was used as a multi-criteria decision-making tool for comprehensive treatment evaluation, showing that the 6:18 treatment achieved the highest overall ranking, whereas the 18:6 treatment resulted in the greatest biomass production. Full article

Biobased hybrid semiconducting composites are attracting significant attention as sustainable alternatives to traditional inorganic photocatalysts for environmental remediation and energy-related applications. Recent research progress in biobased hybrid photocatalytic systems is critically reviewed to outline their design strategies, photocatalytic mechanisms, and environmental applications. These composites integrate bioderived polymers with metal oxide semiconductors, forming hybrid architectures that improve interfacial contact at the molecular level, enhance charge transfer efficiency, and impart higher structural flexibility. The polymer matrix not only provides mechanical adaptability and functional surface groups, but also serves as an environmentally friendly support that can modulate surface electronic states and influence the photoinduced electron–hole dynamics in the inorganic phase. By controlling the molecular interactions between the polymer chains and metal oxide surfaces, these hybrids can mitigate key limitations of conventional metal oxides, such as rapid electron–hole recombination and restricted visible-light absorption. This review first summarizes the fundamental electronic and structural properties of widely employed metal oxide semiconductors and highlights their intrinsic limitations in photocatalytic processes. It then examines the role of biopolymers from the perspective of molecular structure, charge transport pathways, and interfacial interaction mechanisms with the inorganic component. Various synthesis strategies—including sol–gel, hydrothermal, in situ nanoparticle generation, green synthesis, and surface functionalization—are discussed, with emphasis on their ability to tune the nanoscale morphology and interfacial chemistry of the hybrids. Applications of these biohybrid systems in dye degradation, pharmaceutical pollutant removal, heavy metal reduction, and antimicrobial photocatalysis are analyzed alongside mechanistic insights into charge separation efficiency and band alignment at the molecular interface. Furthermore, challenges related to long-term stability, reproducibility, scalability, and performance in real wastewater matrices are also addressed. Overall, this review provides a thorough discussion on the design principles, photocatalytic mechanism, and environmental applications of biobased hybrid semiconductors, while emphasizing future opportunities for the development of efficient and sustainable photocatalytic systems. Full article

The series resonant converter (SRC) is widely used in power conversion systems that require high efficiency and high-power density. However, under light-load conditions, the resonant current decreases, and a higher switching frequency is often required to regulate the output voltage, which leads to efficiency degradation. To mitigate this issue, phase-shift control can be applied to the SRC, and an appropriate small-signal model is essential for accurate dynamic analysis and controller design. Conventional extended describing function (EDF)-based small-signal models provide high accuracy, but their complex equivalent circuits make analytical derivation of the transfer functions difficult and limit intuitive physical interpretation. To overcome this limitation, this paper proposes a non-coupled third-order equivalent-circuit model for the phase-shift SRC. The proposed model reduces the complexity of the conventional EDF-based fifth-order model while preserving the essential low-frequency dynamic characteristics. By employing approximations based on the relationship between the modulation frequency and the switching frequency, together with the superposition principle and equivalent transformations, the model removes the coupling among state variables and enables analytical derivation of the transfer functions. The proposed model is verified through comparisons of the low-frequency small-signal frequency responses with the conventional fifth-order model, PLECS simulations, and experimental measurements. Full article