Search Results (685)

In many developing African countries, milk safety is often managed through traditional methods such as fermentation or boiling over firewood. While these approaches reduce some microbial risks, they present critical limitations. Firewood dependency contributes to deforestation, depletion of agricultural residues, and loss of soil fertility, which, in turn, compromise environmental health and food security. Solar pasteurization provides a reliable and sustainable method for thermally inactivating pathogenic microorganisms in milk and other perishable foods at sub-boiling temperatures, preserving its nutritional quality. This study aimed to evaluate the thermal and microbial performance of a low-cost solar milk pasteurization system, hypothesized to effectively reduce microbial contaminants and retain milk quality under natural sunlight. The system was constructed using locally available materials and tailored to the climatic conditions of the Savanna ecological zone in West Africa. A flat-plate glass solar collector was integrated with a 0.15 cm thick stainless steel cylindrical milk vat, featuring a 2.2 cm hot water jacket and 0.5 cm thick aluminum foil insulation. The system was tested in Navrongo, Ghana, under ambient temperatures ranging from 30 °C to 43 °C. The pasteurizer successfully processed up to 8 L of milk per batch, achieving a maximum milk temperature of 74 °C by 14:00 GMT. Microbial analysis revealed a significant reduction in bacterial load, from 6.6 × 10⁶ CFU/mL to 1.0 × 10² CFU/mL, with complete elimination of coliforms. These results confirmed the device’s effectiveness in achieving safe pasteurization levels. The findings demonstrate that this locally built solar pasteurization system is a viable and cost-effective solution for improving milk safety in arid, electricity-limited regions. Its potential scalability also opens avenues for rural entrepreneurship in solar-powered food and water treatment technologies. Full article

Recently, a number of natural biologically active substances have been proven to be attractive alternatives to conventional anticancer medicine or as adjuvants in contemporary combination therapies. Although lignin-based materials were previously accepted as waste materials with limited usefulness, recent studies increasingly report the possibility of their use for novel applications in various industrial branches, including biomedicine. In this regard, the safety, efficiency, advantages and limitations of lignin compounds for in vitro/in vivo applications remain poorly studied and described. This study was carried out to investigate the possibility of using newly synthesized, alkali lignin-based micro-/nano-biopolymer formulations (Lignin@Formulations/L@F) as carriers for substances with antioxidant and/or anticancer effectiveness. Moreover, we tried to assess the opportunity for using an electro-assisted approach for achieving improved intracellular internalization. An investigation was conducted on an in vitro panel of breast cell lines, namely two breast cancer lines with different metastatic potentials and one non-tumorigenic line as a control. The characterization of all tested formulations was performed via DLS (dynamic light scattering) analysis. We developed an improved separation procedure via size/charge unification for all types of Lignin@Formulations. Moreover, in vitro applications were investigated. The results demonstrate that compared to healthy breast cells, both tested cancer lines exhibited slight sensitivity after treatment with different formulations (empty or loaded with antioxidant substances). This effect was also enhanced after applying electric pulses. L@F loaded with Quercetin was also explored only on the highly metastatic cancer cell line as a model for the breast cancer type most aggressive and non-responsive to traditional treatments. All obtained data suggest that the tested formulations have potential as carriers for the electro-assisted delivery of natural antioxidants such as Quercetin. Full article

The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper presents the design and development of an innovative self-balancing microvehicle under the H2020 LEONARDO project, which aims to address these challenges through advanced engineering and user-centric design. The vehicle combines features of monowheels and e-scooters, integrating cutting-edge technologies to enhance safety, stability, and usability. The design adheres to European regulations, including Germany’s eKFV standards, and incorporates user preferences identified through representative online surveys of 1500 PLEV users. These preferences include improved handling on uneven surfaces, enhanced signaling capabilities, and reduced instability during maneuvers. The prototype features a lightweight composite structure reinforced with carbon fibers, a high-torque motorized front wheel, and multiple speed modes tailored to different conditions, such as travel in pedestrian areas, use by novice riders, and advanced users. Braking tests demonstrate deceleration values of up to 3.5 m/s², comparable to PLEV market standards and exceeding regulatory minimums, while smooth acceleration ramps ensure rider stability and safety. Additional features, such as identification plates and weight-dependent motor control, enhance compliance with local traffic rules and prevent misuse. The vehicle’s design also addresses common safety concerns, such as curb navigation and signaling, by incorporating large-diameter wheels, increased ground clearance, and electrically operated direction indicators. Future upgrades include the addition of a second rear wheel for enhanced stability, skateboard-like rear axle modifications for improved maneuverability, and hybrid supercapacitors to minimize fire risks and extend battery life. With its focus on safety, regulatory compliance, and rider-friendly innovations, this microvehicle represents a significant advancement in promoting safe and sustainable urban mobility. Full article

This study investigates the surface modification of Ti-6Al-4V alloy through the electrical discharge machining (EDM) process to improve its suitability for orthopedic and dental implant applications. The analysis focused on evaluating the morphological, wettability, roughness, hardness, and biocompatibility properties of the modified surfaces. Samples were subjected to different dielectric fluids and polarities during EDM. Subsequently, optical microscopy, roughness measurements, Vickers microhardness, contact angle tests, and in vitro cytotoxicity assays were performed. The results demonstrated that EDM processing led to the formation of distinct layers on the sample surfaces, with surface roughness increasing under negative polarity by up to ~304% in Ra and 305% in Rz. Additionally, wettability measurements indicated that the modified surfaces presented a lower water contact angle, which suggests enhanced hydrophilicity. Moreover, the modified samples showed a significant increase in Vickers microhardness, with the highest value reaching 1520 HV in the recast layer, indicating improvements in the mechanical properties. According to ISO 10993-5, all treated samples were classified as non-cytotoxic, presenting RGR values above 75%, similar to the untreated Ti-6Al-4V alloy. Therefore, it is concluded that surface modification through the EDM process has the potential to enhance the properties and safety of biomedical implants made with this alloy. Full article

Electrochemical biosensors are revolutionizing food testing by addressing critical limitations of conventional strategies that suffer from cost, complexity, and field-deployment challenges. Emerging fluorescence and Raman techniques, while promising, face intrinsic drawbacks like photobleaching and matrix interference in opaque or heterogeneous samples. In contrast, electrochemical biosensors leverage electrical signals to bypass optical constraints, enabling rapid, cost-effective, and pretreatment-free analysis of turbid food matrices. This review highlights their operational mechanisms, emphasizing nano-enhanced signal amplification (e.g., Au nanoparticles and graphene) and biorecognition elements (antibodies, aptamers, and molecularly imprinted polymers) for ultrasensitive assay of contaminants, additives, and adulterants. By integrating portability, scalability, and real-time capabilities, electrochemical biosensors align with global food safety regulations and sustainability goals. Challenges in standardization, multiplexed analysis, and long-term stability are discussed, alongside future directions toward AI-driven analytics, biodegradable sensors, and blockchain-enabled traceability, ultimately fostering precision-driven, next-generation food safety and quality testing. Full article

The design and optimization of drive distribution strategies are critical for enhancing the performance of Formula Student electric racing cars, which face demanding operational conditions such as rapid acceleration, tight cornering, and variable track surfaces. Given the increasing complexity of racing environments and the need for adaptive control solutions, a multi-mode adaptive drive distribution strategy for four-wheel-drive Formula Student electric racing cars is proposed in this study to meet specialized operational demands. Based on the dynamic characteristics of standardized test scenarios (e.g., straight-line acceleration and figure-eight loop), two control modes are designed: slip-ratio-based anti-slip control for longitudinal dynamics and direct yaw moment control for lateral stability. A CarSim–Simulink co-simulation platform is established, with test scenarios conforming to competition standards, including variable road adhesion coefficients (μ is 0.3–0.9) and composite curves. Simulation results indicate that, compared to conventional PID control, the proposed strategy reduces the peak slip ratio to the optimal range of 18% during acceleration and enhances lateral stability in the figure-eight loop, maintaining the sideslip angle around −0.3°. These findings demonstrate the potential for significant improvements in both performance and safety, offering a scalable framework for future developments in racing vehicle control systems. Full article

The transition to electric mobility has significantly increased the demand for lithium-ion batteries, raising concerns about their end-of-life management. Therefore, this study presents the design, development and first implementation steps of a stationary energy storage system utilizing second-life electric vehicle (EV) batteries. These batteries, no longer suitable for traction applications due to a reduced state of health (SoH) below 80%, retain sufficient capacity for less demanding stationary applications. The proposed system is designed to be flexible and scalable, serving both research and commercial purposes. Key challenges include heterogeneous battery characteristics, safety considerations due to increased internal resistance and battery aging, and the need for flexible power electronics. An optimized dual active bridge (DAB) converter topology is introduced to connect several batteries in parallel and to ensure efficient bidirectional power flow over a wide voltage range. A first prototype, rated at 50 $\mathrm{k}$$\mathrm{W}$, has been built and tested in the laboratory. This study contributes to sustainable energy storage solutions by extending battery life cycles, reducing waste, and promoting economic viability for industrial partners. Full article

This research introduces an innovative passive vibration control methodology employing acoustic black hole (ABH) structures to mitigate vibration transmission in electric automotive steering machines—a prevalent issue adversely affecting driving comfort and vehicle safety. Leveraging the inherent bending wave manipulation properties of ABH configurations, we conceive an integrated vibration suppression framework synergizing advanced computational modeling with intelligent optimization algorithms. A high-fidelity finite element (FEM) model integrating ABH-attached steering machine system was developed and subjected to experimental validation via rigorous modal testing. To address computational challenges in design optimization, a hybrid modeling strategy integrating parametric design (using Latin Hypercube Sampling, LHS) with Kriging surrogate modeling is proposed. Systematic parameterization of ABH geometry and damping layer dimensions generated 40 training datasets and 12 validation datasets. Surrogate model verification confirms the model’s precise mapping of vibration characteristics across the design space. Subsequent multi-objective genetic algorithm optimization targeting RMS velocity suppression achieved substantial vibration attenuation (29.2%) compared to baseline parameters. The developed methodology provides automotive researchers and engineers with an efficient suitable design tool for vibration-sensitive automotive component design. Full article

The accurate estimation and prediction of internal states in lithium-ion (Li-Ion) batteries, such as State of Charge (SoC) and Remaining Useful Life (RUL), are vital for optimizing battery performance, safety, and longevity in electric vehicles and other applications. This paper presents a unified, modular, and extensible machine learning (ML) framework designed to address the heterogeneity and complexity of battery state prediction tasks. The proposed framework supports flexible configurations across multiple dimensions, including feature engineering, model selection, and training/testing strategies. It integrates standardized data processing pipelines with a diverse set of ML models, such as a long short-term memory neural network (LSTM), a convolutional neural network (CNN), a feedforward neural network (FFNN), automated machine learning (AutoML), and classical regressors, while accommodating heterogeneous datasets. The framework’s applicability is demonstrated through five distinct use cases involving SoC estimation and RUL prediction using real-world and benchmark datasets. Experimental results highlight the framework’s adaptability, methodological transparency, and robust predictive performance across various battery chemistries, usage profiles, and degradation conditions. This work contributes to a standardized approach that facilitates the reproducibility, comparability, and practical deployment of ML-based battery analytics. Full article

Given the rising importance of cost-effective solutions in battery research, this study employs an accessible testing approach using low-cost, sensor-equipped platforms that enable broader research and educational applications. It presents a comparative evaluation of lithium-ion battery degradation under two charging strategies: static charging (constant current at 1.2 A) and dynamic charging (stepped current from 400 mA to 800 mA) over 200 charge–discharge cycles. A custom-built, low-cost test platform based on an ESP32 microcontroller was developed to provide real-time monitoring of voltage, current, temperature, and internal resistance, with automated control and cloud-based data logging. The results indicate that static charging provides greater voltage stability and a lower increase in internal resistance (9.3%) compared to dynamic charging (30.17%), suggesting reduced electrochemical stress. Discharge time decreased for both strategies, by 6.25% under static charging and 18.46% under dynamic charging, highlighting capacity fade and aging effects. Internal resistance emerged as a reliable indicator of degradation, closely correlating with reduced runtime. These findings underscore the importance of selecting charging profiles based on specific application needs, as dynamic charging, while offering potential thermal benefits, may accelerate battery aging. Furthermore, the low-cost testing platform proved effective for long-term evaluation and degradation analysis, offering an accessible alternative to commercial battery cyclers. The insights gained contribute to the development of adaptive battery management systems that optimize performance, lifespan, and safety in electric vehicle applications. Full article

Blind and visually impaired (BVI) people face significant challenges in perception, navigation, and safety during travel. Existing infrastructure (e.g., blind lanes) and traditional aids (e.g., walking sticks, basic audio feedback) provide limited flexibility and interactivity for complex environments. To solve this problem, we propose a real-time travel assistance system based on deep learning. The hardware comprises an NVIDIA Jetson Nano controller, an Intel D435i depth camera for environmental sensing, and SG90 servo motors for feedback. To address embedded device computational constraints, we developed a lightweight object detection and segmentation algorithm. Key innovations include a multi-scale attention feature extraction backbone, a dual-stream fusion module incorporating the Mamba architecture, and adaptive context-aware detection/segmentation heads. This design ensures high computational efficiency and real-time performance. The system workflow is as follows: (1) the D435i captures real-time environmental data; (2) the processor analyzes this data, converting obstacle distances and path deviations into electrical signals; (3) servo motors deliver vibratory feedback for guidance and alerts. Preliminary tests confirm that the system can effectively detect obstacles and correct path deviations in real time, suggesting its potential to assist BVI users. However, as this is a work in progress, comprehensive field trials with BVI participants are required to fully validate its efficacy. Full article

Silicon carbide (SiC) half-bridge power modules are widely utilized in new energy power generation, electric vehicles, and industrial power supplies. To address the research gap in collaborative validation between electro-thermal coupling models and process reliability, this paper proposes a closed-loop methodology of “design-simulation-process-validation”. This approach integrates in-depth electro-thermal simulation (LTspice XVII/COMSOL Multiphysics 6.3) with micro/nano-packaging processes (sintering/bonding). Firstly, a multifunctional double-pulse test board was designed for the dynamic characterization of SiC devices. LTspice simulations revealed the switching characteristics under an 800 V operating condition. Subsequently, a thermal simulation model was constructed in COMSOL to quantify the module junction temperature gradient (25 °C → 80 °C). Key process parameters affecting reliability were then quantified, including conductive adhesive sintering (S820-F680, 39.3 W/m·K), high-temperature baking at 175 °C, and aluminum wire bonding (15 mil wire diameter and 500 mW ultrasonic power/500 g bonding force). Finally, a double-pulse dynamic test platform was established to capture switching transient characteristics. Experimental results demonstrated the following: (1) The packaged module successfully passed the 800 V high-voltage validation. Measured drain current (4.62 A) exhibited an error of <0.65% compared to the simulated value (4.65 A). (2) The simulated junction temperature (80 °C) was significantly below the safety threshold (175 °C). (3) Microscopic examination using a Leica IVesta 3 microscope (55× magnification) confirmed the absence of voids at the sintering and bonding interfaces. (4) Frequency-dependent dynamic characterization revealed a 6 nH parasitic inductance via Ansys Q3D 2025 R1 simulation, with experimental validation at 8.3 nH through double-pulse testing. Thermal evaluations up to 200 kHz indicated 109 °C peak temperature (below 175 °C datasheet limit) and low switching losses. This work provides a critical process benchmark for the micro/nano-manufacturing of high-density SiC modules. Full article

Remaining useful life (RUL) serves as a pivotal metric for quantifying lithium-ion batteries’ state of health (SOH) in electric vehicles and plays a crucial role in ensuring their safety and reliability. In order to achieve accurate and reliable RUL prediction, a novel RUL prediction method which employs a back propagation (BP) neural network based on the Harris Hawks optimization (HHO) algorithm is proposed. This method optimizes the BP parameters using the improved HHO algorithm. At first, the circle chaotic mapping method is utilized to solve the problem of the initial value. Considering the problem of local convergence, Gaussian mutation is introduced to improve the search ability of the algorithm. Subsequently, two key health factors are selected as input features for the model, including the constant-current charging isovoltage rise time and constant-current discharging isovoltage drop time. The model is validated using aging data from commercial lithium iron phosphate (LiFePO₄) batteries. Finally, the model is thoroughly verified under an aging test. Experimental validation using training sets comprising 50%, 60%, and 70% of the cycle data demonstrates superior predictive performance, with mean absolute error (MAE) values below 0.012, root mean square error (RMSE) values below 0.017 and mean absolute percentage error (MAPE) within 0.95%. The results indicate that the model significantly improves prediction accuracy, robustness and searchability. Full article

Barro Alto processes nickel laterite ore using rotary kilns and six-in-line rectangular electric arc furnaces. This study evaluated the briquetting of ferronickel ore to reduce kiln fines, improve furnace charge permeability, and enhance process safety. Binderless briquettes were produced from screened ore at two size fractions (−6.3 mm and −12.5 mm), with moisture contents of 16% and 24%, cured under closed and open conditions. The physical and metallurgical properties of the briquettes were assessed using ISO standard tests. The results confirmed successful agglomeration of the ore into binderless briquettes. Screening the run-of-mine (ROM) ore improved the feed quality, increasing the NiO grade from 2.0% to 2.2% in the −6.3 mm fraction. The briquettes from the −6.3 mm ore at 16% moisture exhibited the highest green strength (559 N). Higher moisture content reduced the briquette strength and increased both the reduction disintegration and decrepitation indices. The decrepitation index increased from 0.33% to 0.61% for the −6.3 mm briquettes when the moisture increased from 16% to 24%. The reduction levels were 33.4% and 39.2% for −6.3 mm and −12.5 mm briquettes with 16% moisture, respectively. This study concludes that optimal performance was achieved using −6.3 mm ore, 16% moisture, and open curing, thereby balancing reduction efficiency and mechanical stability. Full article

While electric unmanned aerial vehicles (UAVs) offer advantages in noise reduction, safety, and operational efficiency, their endurance is limited by current battery technology. Extending flight autonomy without compromising performance is a critical challenge in UAV system development. Previous studies introduced hybrid micro-turbogenerator architectures, but limitations in control stability and output power constrained their practical implementation. This study aimed to finalize the design and experimental validation of an optimized hybrid power system featuring a micro-turboprop engine mechanically coupled to an upgraded electric generator. A fuzzy logic-based control algorithm was implemented on a single-board computer to enable autonomous voltage regulation. The test bench architecture was reinforced and instrumented to allow stable multi-stage testing across increasing power levels. Results demonstrated stable voltage control at 48 VDC and electrical power outputs up to 3 kW, with an estimated maximum of 3.5 kW at full throttle. Efficiency was calculated at approximately 67%, and analysis of the generator’s KV constant revealed that using a lower KV variant (KV80) could reduce required rotational speed (RPM) and improve performance. These findings underscore the value of adaptive hybridization in UAVs and suggest that tuning generator electromechanical parameters can significantly enhance overall energy efficiency and platform autonomy. Full article