Search Results (82)

Regenerative agriculture is increasingly promoted as a pathway towards sustainable food production, climate resilience, and the restoration of soil ecosystem functions. However, evidence on the actual uptake of regenerative practices and the factors influencing their adoption remains limited. This study compared the self-reported prevalence of 17 soil-health-oriented regenerative practices among farmers in Poland, Germany, and Belarus using a questionnaire survey conducted in 2025 (N = 150). The survey also examined farmers’ motivations, perceived barriers, knowledge sources, and definitions of regenerative agriculture. Adoption frequencies were assessed using a five-point Likert scale and analysed using non-parametric statistical methods. Several practices, including crop rotation and soil pH management, were widely implemented across all three countries and showed only slight variation. In contrast, more complex, system-based practices, such as agroforestry, biological soil monitoring, and crop–livestock integration, showed lower and more variable levels of adoption. Additional subgroup analyses were conducted to assess the robustness of the observed cross-country patterns. Although some associations weakened after stratification, many significant differences persisted. Across all countries, improving soil health was the primary motivation for adopting regenerative agriculture, whereas financial constraints and limited equipment access were the main barriers. Digital media served as the primary source of knowledge about regenerative agriculture across the surveyed countries, although in Belarus, peers and neighbours also represented a highly important source of information. Farmers in all three countries expressed a preference for online communication channels for further learning about regenerative agriculture; however, Polish and Belarusian farmers prefer social media, whereas German farmers preferred webinars and dedicated websites. In-person training sessions also attracted considerable interest among Polish and Belarusian farmers, but were the least preferred information source among German respondents. On the basis of these results, targeted investment support and direct financial incentives appear to be key priorities for promoting the further uptake of regenerative agriculture across all surveyed countries. However, communication and knowledge-transfer strategies are likely to require greater adaptation to country-specific preferences, although digital media are likely to represent the most effective primary channel for disseminating information on regenerative agriculture. Full article

This study proposes an integrated self-aligning-torque energy recovery and DC-bus voltage stabilization strategy for a permanent-magnet synchronous motor (PMSM)-driven steer-by-wire system in new energy vehicles. During the front-wheel return-to-center process, self-aligning torque may provide excess mechanical energy to the steering actuator. Instead of dissipating this energy through a braking resistor, the proposed strategy converts part of the self-aligning-torque-induced mechanical energy into electrical energy and feeds it back to the low-voltage DC bus. To avoid ambiguity in the operating-mode description, this paper distinguishes the standard PMSM torque–speed quadrants from the mechanical stages of the steering process. Regenerative operation is defined according to the condition ($T_e{\omega }_m<0$), corresponding to the second or fourth quadrant of the PMSM torque–speed plane, whereas the return-to-center regenerative stage refers to the self-aligning-torque-dominated stage of the steer-by-wire motion. Based on this definition, an electromechanical energy-flow model is established to describe the transfer path from self-aligning torque to the PMSM and then to the DC bus. Considering that regenerative energy injection may cause DC-bus voltage fluctuation or braking-resistor activation, a single-loop bus-voltage stabilization method based on active disturbance rejection control is developed. A third-order linear extended state observer is adopted to estimate the lumped disturbance caused by self-aligning-torque variation, current coupling, load variation, parameter uncertainty, and inverter loss. The observer bandwidth, controller gains, current limitation, and overvoltage protection mechanisms are further discussed to improve the practical implementability of the proposed control strategy. In addition, an energy-accounting method is introduced to distinguish total steering energy consumption, available self-aligning-torque mechanical energy, gross recovered electrical energy, system losses, net recovered energy, and recovery efficiency. Simulation and experimental results show that the proposed strategy can suppress DC-bus voltage rise, reduce braking-resistor energy dissipation, and achieve measurable steering-actuator-level energy recovery during repeated return-to-center maneuvers. The results verify the feasibility of using self-aligning-torque-induced regenerative energy in PMSM-driven steer-by-wire systems, while the actual vehicle-level energy benefit depends on the driving cycle, low-voltage load demand, battery charging acceptance, and converter efficiency. Full article

Energy-efficient operation of railway systems is of great importance for both environmental and economic reasons. Minimizing net energy consumption helps to achieve such energy-efficient operation. In this paper, the train’s speed profile and headway between trains are controlled to achieve lower traction energy consumption and higher train synchronization for better regenerative braking energy utilization. Eventually, the net energy consumption, defined as the difference between the traction energy consumption and the utilization of regenerative braking energy, is minimized. Two optimization problems are defined to solve the problem efficiently. The first main problem is to find the optimal speeds at each segment of the railway track. The second sub-problem’s objective is to find the optimal values of travel time, dwell time, and headway for every suggested solution to the main problem. Both problems are solved using the genetic algorithm. Numerical results are based on the actual operation data of the Beijing Metro Yizhuang Line in China. In the numerical results, the proposed strategy of dividing the problem into two problems and the use of variable headway shows an enhancement in reducing net energy consumption by 7.5% compared to other strategies in the literature. Full article

The increasing energy demand and environmental impact of transportation systems have intensified the need for more efficient railway energy management strategies. Although electric railway systems provide a sustainable alternative, the dynamic nature of traction power systems and the inadequate use of regenerative braking energy still result in significant energy losses. In order to improve energy efficiency and state-of-charge (SOC) stability, this study proposes an optimized fractional-order proportional-integral (FOPI) controller for the control of a wayside energy storage system (ESS) in a DC railway network. The parameters of the FOPI controller are tuned via recent metaheuristic tool of barrel theory-based optimizer (BTO) such that the error between the desired and actual charging/discharging voltages of the ESS is minimized under nonlinear and time-varying operating conditions. The BTO is characterized by strong exploration/exploitation balance that prevents the approach from falling in local optima. Also, the approach has low sensitivity to user-defined parameters. The proposed approach was evaluated using a MATLAB/Simulink (version 2021b) model of a double-track DC railway system incorporating realistic train operations and three distinct traffic scenarios including ideal, perturbed, and stochastic conditions. The BTO was compared to other approaches of particle swarm optimization (PSO) and gray wolf optimizer (GWO). Also, statistical tests using the Friedman, Kruskal–Wallis, ANOVA, and Wilcoxon rank tests were conducted to assess the suggested approach. The obtained results confirm the robustness and competence of the proposed controller compared to either the conventional static control approach or optimized controller via the comparable approaches. As a result, the suggested controller achieved higher total energy savings, improved utilization of regenerative braking energy, and enhanced power demand distribution across substations. While minor increases in SOC deviation were observed in certain scenarios, the overall system performance showed improved robustness and adaptability. These findings highlight the effectiveness of integrating fractional-order PI control designed via the suggested BTO for advanced energy management in railway applications. Full article

As urban rail transit systems evolve within the Industrial Internet of Things (IIoT), the intelligent recovery of regenerative braking energy becomes critical for energy efficiency. However, the existing train operation optimizations primarily focus on time-domain synchronization, frequently neglecting the spatial impedance constraints of the DC traction network. This oversight creates a discrepancy between theoretical energy matching and actual absorption. To address this, this paper proposes a spatiotemporal synergistic optimization framework integrating the analysis of electrical energy transmission factors and train relative motion. First, a dynamic multi-node circuit model based on Kirchhoff’s laws is established to characterize train fleet operations. By evaluating electrical energy transmission factors, the current distribution ratio and line impedance loss are identified as primary determinants of absorption efficiency. This physically quantifies the coupling among instantaneous energy distribution, transmission loss, and source-load relative distance. Second, a time-domain integration-based gradient analysis framework is formulated to deconstruct the energy gradient into amplitude and directional components. By mapping the relative position and speed of interacting trains, their relative motion states are systematically categorized. Subsequently, an adaptive gradient optimization strategy based on these motion states is introduced, which fine-tunes dwell times to precisely guide train trajectories into a low-impedance “optimal window” for energy absorption. Finally, a case study using operational data from Luoyang Metro Line 1 validates the proposed framework. Results demonstrate that the framework achieves dual spatiotemporal matching of braking and traction trains, outperforming the traditional fixed timetable and improving the regenerative braking energy absorption rate by approximately 13%. Full article

Background: Knee osteoarthritis (KOA) is a major cause of disability worldwide, and adipose-derived stromal vascular fraction (SVF) has emerged as a potential regenerative treatment to modify disease progression. Objective: This study aimed to assess the effectiveness of autologous adipose-derived stromal vascular fraction (SVF) through intra-articular injection to treat early-stage knee osteoarthritis (KOA). Materials and Methods: This multicenter observational study (2019–2023) included adults aged 18–65 years with radiographically confirmed knee osteoarthritis. Patients were assigned to one of two groups through a retrospective, non-randomized process based on the actual treatment received during their clinical follow-up. Group T received intra-articular adipose-derived stromal vascular fraction (SVF) injections, while Group C received conservative treatment with non-steroidal anti-inflammatory drugs (NSAIDs) only. SVF was obtained from abdominal adipose tissue using a standardized closed-system device and injected intra-articularly. Pain and functional outcomes were assessed using the Visual Analog Scale (VAS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) at baseline and at 1, 3, 6, 9, and 12 months. Results: Sixty-seven patients (41 SVF, 26 controls) were included, with comparable baseline characteristics (all p > 0.05). Preoperative VAS was lower in the SVF group (7.44 ± 1.44 vs. 8.31 ± 1.09; p = 0.029). At 12 months, VAS significantly decreased to 3.77 ± 1.49 in the SVF group, whereas it increased to 8.85 ± 0.67 in controls (p < 0.001). Similarly, baseline WOMAC scores were lower in the SVF group (62.6 ± 21.7 vs. 76.8 ± 8.76; p = 0.004). At 12 months, WOMAC improved to 29.4 ± 15 in the SVF group but worsened to 87.6 ± 3.21 in controls (p < 0.001). Within-group improvements were significant only in the SVF group (p < 0.001). No procedure-related complications were observed. Conclusions: The autologous adipose-derived stromal vascular fraction is an effective treatment option for early-stage KOA patients. However, a prospective, randomized, controlled study is warranted. Full article

To develop carbon farming practices, decision-makers need detailed spatial data on the soil carbon sequestration (SCS) opportunities that conventional crop and soil management creates. This study exploratorily assessed SCS capacity across agricultural land in the Valencian Community using a simple carbon balance model within a GIS framework. Within this modelling approach, maps of net primary production (NPP), land-use-derived crop harvest indices, current soil organic carbon (SOC) stocks, and NPP and SOC mineralization coefficients were combined. Results show that while NPP across Valencian croplands and grasslands ranges from 0.64 to 6.43 Mg C ha⁻¹ yr⁻¹ (mean 2.42 Mg C ha⁻¹ yr⁻¹), the actual SCS capacity is much lower, ranging from −0.04 to 1.31 Mg C ha⁻¹ yr⁻¹ (mean 0.25 Mg C ha⁻¹ yr⁻¹). Significant variation exists among land uses: rice paddies exhibit the highest SCS capacity, while olive groves present the lowest. Between 2017 and 2021, SCS in Valencian agroecosystems may have offset the sector’s greenhouse gas (GHG) emissions, primarily driven by pasture and citrus because of their large extent and moderate SCS capacity, making agriculture a net-zero emitter. However, helping achieve cross-sectoral mitigation targets will depend in part on the widespread deployment of regenerative soil management (RSM) practices. While this study identifies priority areas for RSM implementation, further research is needed to determine which specific practices are most suitable for each location to maximize SCS. Full article

Operable windows are critical for indoor environmental quality (IEQ) and occupant agency, yet their usability is increasingly compromised by conflicts between regulatory compliance and building performance. This study investigates the gap between geometrically compliant provisions and effectively operable windows through a comparative policy analysis of mandatory codes (Level 1), green rating systems (Level 2), and regenerative frameworks (Level 3). The findings identify a structural discrepancy termed the Geometric Trap: while minimum opening areas are legally required, mechanical ventilation often substitutes for natural access. In the United States, Japan, and Republic of Korea, explicit waivers permit full substitution, while in the United Kingdom, conditional constraints such as environmental noise limit practical operability. Germany, by contrast, maintains operable windows as an independent mandate, restricting substitution to defined environmental conditions. Although emerging green rating systems increasingly recognize resilience and adaptive comfort, operability remains optional. Regenerative standards, however, treat it as a prerequisite for occupant health. This study proposes a shift from static geometric compliance toward an Effective Opening Area framework that evaluates actual accessibility and usability, advancing a performance-based and occupant-centered regulatory perspective. Full article

This paper proposes a collaborative optimization strategy of regenerative braking in heavy-duty electric logistics vehicles under complex driving conditions to improve energy recovery efficiency. Based on the actual operational data of 18-ton electric trucks in the southwestern region of China, three driving scenarios for heavy commercial vehicles are determined via the K-Means clustering algorithm. Key features are extracted using Recursive Feature Elimination and employed to train a Learning Vector Quantization neural network for precise real-time condition recognition. The identified driving condition parameters, including vehicle speed, remaining battery power, and braking force, collectively regulate the intensity of regenerative braking. Simulation results under double-WTVC (World Transient Vehicle Cycle) conditions indicate that the proposed strategy can effectively adapt regenerative braking behavior to diverse road conditions. In comparison with conventional control methods, this approach enhances battery energy recovery efficiency by 5.8% while preventing control discontinuities. Full article

Bee-derived products such as apitoxin, royal jelly, propolis, bee pollen, and honey are increasingly being used as part of cosmetic products because all of them contain a large number of bioactive compounds with antioxidant, anti-inflammatory, antimicrobial, and regenerative properties, which enable them to be used for therapeutic purposes. The aim of this investigation was to assess the performance of an automated growth-based system in order to make a quantitative examination of the total aerobic viable counts in bee-derived personal care products using NF-TVC vials that contained a nutrient-based medium with dextrose as the carbon source. According to USP general chapter <1223>, pivotal validation criteria such as linearity, equivalence of results, operative range, precision, accuracy, ruggedness, limit of quantification, and limit of detection have demonstrated that the automated system can be used for a reliable total aerobic viable count. Moreover, the actual research demonstrated that polysorbates efficiently block the antimicrobiological potential of bioactive compounds, such as phenols, flavonoids, enzymes, peptides, and fatty acids, which naturally occur in apitoxin, royal jelly, propolis, bee pollen, and honey, allowing for efficient microorganism recovery from the bee-made products tested. Therefore, this AGBS could be applied efficiently within the cosmetic industry to assess the total aerobic viable count in bee-derived products such as capillary treatments, toothpaste, and anti-aging cream, affording several benefits associated with faster product release into the market. Full article

Electric bicycles (E-Bikes) are gaining popularity as a sustainable mode of transportation due to their energy efficiency and zero-emission operation. However, challenges such as battery overcharging, overheating, and degradation from improper use can reduce battery lifespan and increase maintenance costs. To address these issues, this paper presents the design and implementation of a Battery Management System (BMS) tailored for E-Bike applications, with a focus on enhancing safety, reliability, and performance. The proposed BMS includes core functionalities such as State of Charge (SOC) estimation, temperature monitoring, and under-voltage and overcharge protection. Different approaches, including open-circuit voltage (OCV), Coulomb counting (CC), and Kalman filter techniques are employed to improve SOC estimation accuracy. The circuit for CC-based BMS was first simulated using Proteus, and system behavior was modeled in MATLAB Simulink is used to validate design assumptions before hardware implementation. An Arduino Uno microcontroller was used to control the system, interfacing with an LM35 temperature sensor, a voltage divider, and an ACS712 current sensor. The BMS controls battery charging based on SOC levels and activates a cooling fan when the battery temperature exceeds 45 °C. It disconnects the charger at 100% SOC and triggers a beep alarm when the SOC falls below 40%. An external charger and regenerative charging from four electrodynamometers on the bicycle chain recharge the battery when the SOC drops below 20%, provided the load is disconnected. Measurement results closely matched simulation data, with the MATLAB model showing 44% SOC after 3 h, compared to the actual real-time 45.85%. The system accurately tracked charging/discharging patterns, validating its effectiveness. This compact and cost-effective BMS design ensures safe operation, improves battery longevity, and supports broader adoption of E-Bikes as an eco-friendly transportation solution. Full article

Regenerative chatter is an unfavorable phenomenon that severely affects machining efficiency and surface finish in milling operations. The prediction of chatter stability is an important way to obtain the stable cutting zone. Based on implicit multistep schemes, this paper presents the third-order and fourth-order implicit exponentially fitted methods (3rd IEM and 4th IEM) for milling stability prediction. To begin with, the delay differential equations (DDEs) with time-periodic coefficients are employed to describe the milling dynamics models, and the principal period of the coefficient matrix is firstly decomposed into two different subintervals according to the cutting state. Subsequently, the fourth-step and fifth-step implicit exponential fitting schemes are applied to more accurately estimate the state term. Two benchmark milling models are utilized to illustrate the effectiveness and advantages of the high-order implicit exponentially fitted methods by making comparisons with the three typical existing methods. Under different radial immersion conditions, the numerical results demonstrate that the 3rd IEM and the 4th IEM exhibit both faster convergence rates and higher prediction accuracy than the other three existing prediction methods, without much loss of computational efficiency. Finally, in order to verify the feasibility of the 3rd IEM and the 4th IEM, a series of experimental verifications are conducted using a computer numerical control machining center. It is clearly visible that the stability boundaries predicted by the 3rd IEM and the 4th IEM are mostly consistent with the cutting test results, which indicates that the proposed high-order exponentially fitted methods achieve significantly better prediction performance for actual milling processes. Full article

Although particulate matter (PM) pollution from vehicles’ exhaust has decreased significantly over the years, the contribution from non-exhaust sources (brakes, tyres) has remained at the same levels. In the European Union (EU), Euro 7 regulation introduced PM limits for vehicles’ brake systems. Regenerative braking, i.e., recuperation of the deceleration kinetic and potential energy to the vehicle battery, is one of the strategies to reduce the brake emission levels and improve vehicle efficiency. According to the regulation, the shares of friction and regenerative braking can be determined with actual testing of the vehicle on a chassis dynamometer. In this study we tested the regenerative capabilities of a plug-in hybrid vehicle, both in the laboratory and on the road, under different protocols (including both smooth and aggressive braking) and covering a wide range of driving conditions (urban, rural, motorway) over 10,000 km of driving. Good agreement was obtained between laboratory and on-road tests, with the use of the friction brakes being on average 7% and 5.3%, respectively. However, at the same time it was demonstrated that the friction braking share can vary over a wide range (up to around 30%), depending on the driver’s behaviour. Full article

This study presents a comprehensive methodology for evaluating electric vehicle (EV) energy consumption by integrating coast down testing with standardized chassis dynamometer protocols under WLTP Class 3b and EPA driving cycles. Coast down tests were conducted to determine road load coefficients—critical for replicating real-world resistance profiles on a dynamometer. Energy usage data were measured using On-Board Diagnostics II (OBD-II) and dynamometer measurements to assess power flow from the battery to the wheels. The results reveal that OBD-II consistently recorded higher cumulative energy usage, particularly under urban driving conditions, highlighting limitations in dynamometer responsiveness to transient loads and regenerative events. Notably, the WLTP low-speed cycle exhibited a significantly lower efficiency of 62.42%, with nearly half of the battery energy consumed by non-propulsion systems. In contrast, the EPA cycle demonstrated consistently higher efficiencies of 84.52% (low-speed) and 93.00% (high-speed). Interestingly, high-speed efficiencies between WLTP and EPA were nearly identical, despite differences in total energy consumption. These findings underscore the importance of aligning test protocols with actual driving conditions and demonstrate the effectiveness of combining coast down data with real-time diagnostics for robust EV performance assessments. Full article

This study presents an advanced method for modeling energy consumption in electric trucks by incorporating regenerative braking probability into conventional modeling equations. Traditional models typically assume uniform regenerative energy recovery, ignoring the variability introduced by differing driving behaviors and braking scenarios. To address this gap, the proposed method explicitly integrates regenerative probability, capturing the dynamic interactions between driving conditions and regenerative braking events. The research involves systematic data preprocessing techniques, including outlier detection and correction, to ensure high data integrity. Moreover, a genetic algorithm is employed to optimize critical features such as aerodynamic drag coefficient, rolling resistance, and regenerative braking efficiency and probability, aiming to minimize discrepancies between predicted and actual energy consumption. The validation results demonstrate that the enhanced model provides a significantly improved accuracy in predicting energy recovery and state-of-charge estimations, supporting more effective and sustainable energy management practices for electric truck operations. Full article