Search Results (1,630)

This paper introduces a robust and efficient Localized Spline-based Path-Planning (LSPP) algorithm designed to enhance autonomous vehicle navigation on highways. The LSPP approach prioritizes smooth maneuvering, obstacle avoidance, passenger comfort, and adherence to road constraints, including lane boundaries, through optimized trajectory generation using quintic spline functions and a dynamic speed profile. Leveraging real-time data from the vehicle’s sensor fusion module, the LSPP algorithm accurately interprets the positions of surrounding vehicles and obstacles, creating a safe, dynamically feasible path that is relayed to the Model Predictive Control (MPC) track-following module for precise execution. The theoretical distinction of LSPP lies in its modular integration of: (1) a finite state machine (FSM)-based decision-making layer that selects maneuver-specific goal states (e.g., keep lane, change lane left/right); (2) quintic spline optimization to generate smooth, jerk-minimized, and kinematically consistent trajectories; (3) a multi-objective cost evaluation framework that ranks competing paths according to safety, comfort, and efficiency; and (4) a closed-loop MPC controller to ensure real-time trajectory execution with robustness. Extensive simulations conducted in diverse highway scenarios and traffic conditions demonstrate LSPP’s effectiveness in delivering smooth, safe, and computationally efficient trajectories. Results show consistent improvements in lane-keeping accuracy, collision avoidance, enhanced materials wear performance, and planning responsiveness compared to traditional path-planning methods. These findings confirm LSPP’s potential as a practical and high-performance solution for autonomous highway driving. 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

Sepsis is a life-threatening condition caused by a dysregulated immune response to infection, characterized by an initial hyperinflammatory phase frequently followed by compensatory immunosuppression (CARS). Regulatory T cells (Tregs) play a critical, biphasic role: inadequate suppression during early hyperinflammation fails to control cytokine storms, while excessive/persistent activity in late-phase immunosuppression drives immune paralysis and secondary infection susceptibility. This review explores advances in targeting Treg immunoregulation across bacterial, viral, and fungal sepsis, where pathogenic type critically influenced the types of immunoresponses, shaping Treg heterogeneity in terms of phenotype, survival, and function. Understanding this multifaceted Treg biology offers novel therapeutic avenues, highlighting the need to decipher functional heterogeneity and develop precisely timed, pathogen-tailored immunomodulation to safely harness beneficial Treg roles while mitigating detrimental immunosuppression. Full article

This article presents a comparative study of the storage of energy produced by photovoltaic panels by means of two types of batteries: Lead–Acid and Lithium-Ion batteries. The work involved the construction of a model in MATLAB-Simulink for controlling the loading/unloading of storage batteries with energy produced by photovoltaic panels through a buck-type DC-DC convertor, controlled by means of the MPPT algorithm implemented through the method of incremental conductance based on a MATLAB function. The program for the MATLAB function was developed by the author in the C++ programming environment. The MPPT algorithm provides maximum energy transfer from the photovoltaic panels to the battery. The electric power taken over at a certain moment by Lithium-Ion batteries in photovoltaic panels is higher than the electric power taken over by Lead–Acid batteries. Two types of batteries were successively used in this model: Lead–Acid and Lithium-Ion batteries. Based on the results being obtained and presented in this work it may be affirmed that the storage battery Lithium-Ion is more performant than the Lead-Acid storage battery. At the Laboratory of Electrical Machinery and Drives of the Engineering Faculty of Bacau, an experimental stand was built for a storing system for electric energy produced by photovoltaic panels. For controlling DC-DC buck-type convertors, a program was developed in the programming environment Arduino IDE for implementing the MPPT algorithm for incremental conductance. The simulation part of this program is similar to that of the program developed in C++. Through conducting experiments, it was observed that, during battery charging, along with an increase in the charging voltage, an increase in the filling factor of the PWM signal controlling the buck DC-DC convertor also occurred. The findings of this study may be applicable to the storage of battery-generated electrical energy used for supplying electrical motors in electric cars. Full article

The automotive suspension must perform competently to support comfort and safety when driving. Traditionally, car suspension control tuning is performed through trial and error or with classical techniques that cannot guarantee optimal performance under varying road conditions. The study aims at designing a Linear Quadratic Regulator-based Bacterial Memetic Algorithm (LQR-BMA) for suspension systems of automobiles. BMA combines the bacterial foraging optimization algorithm (BFOA) and the memetic algorithm (MA) to enhance the effectiveness of its search process. An LQR control system adjusts the suspension’s behavior by determining the optimal feedback gains using BMA. The control objective is to significantly reduce the random vibration and oscillation of both the vehicle and the suspension system while driving, thereby making the ride smoother and enhancing road handling. The BMA adopts control parameters that support biological attraction, reproduction, and elimination-dispersal processes to accelerate the search and enhance the program’s stability. By using an algorithm, it explores several parts of space and improves its value to determine the optimal setting for the control gains. MATLAB 2024b software is used to run simulations with a randomly generated road profile that has a power spectral density (PSD) value obtained using the Fast Fourier Transform (FFT) method. The results of the LQR-BMA are compared with those of the optimized LQR based on the genetic algorithm (LQR-GA) and the Virus Evolutionary Genetic Algorithm (LQR-VEGA) to substantiate the potency of the proposed model. The outcomes reveal that the LQR-BMA effectuates efficient and highly stable control system performance compared to the LQR-GA and LQR-VEGA methods. From the results, the BMA-optimized model achieves reductions of 77.78%, 60.96%, 70.37%, and 73.81% in the sprung mass displacement, unsprung mass displacement, sprung mass velocity, and unsprung mass velocity responses, respectively, compared to the GA-optimized model. Moreover, the BMA-optimized model achieved a −59.57%, 38.76%, 94.67%, and 95.49% reduction in the sprung mass displacement, unsprung mass displacement, sprung mass velocity, and unsprung mass velocity responses, respectively, compared to the VEGA-optimized model. 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

Car interior design should evoke emotions, offer comfort, convey safety and at the same time project the brand identity of the car manufacturer. Lighting is used to address these functions. Modules required for automotive interior lighting often feature injection-moulded (IM) light guides, whereas woven fabrics with polymer optical fibres (POFs) offer certain technological advantages and show first-series applications in cars. In the future, car interior illumination will become even more important in the wake of megatrends such as autonomous driving. Since the increase in deployment of these technologies facilitates a need for an economical comparison, this paper aims to deliver a cost-driven approach to fulfil the aforementioned objective. Therefore, the cost structures of the supply chains for an IM-based and a POF-based illumination module are analysed. The employed research methodologies include an activity-based costing approach for which the data is collected via document analysis and guideline-based expert interviews. To account for data uncertainty, Monte Carlo simulations are conducted. POF-based lighting modules have lower initial costs due to continuous fibre production and weaving processes, but are associated with higher unit costs. This is caused by the discontinuous assembly of the rolled woven fabric which allows postponement strategies. The development costs of the mould generate high initial costs for IM light guides, which makes them beneficial only for high quantities of produced light guides. For the selected scenario, the POF-based module’s self-costs are 11.05 EUR/unit whereas the IM module’s self-costs are 14,19 EUR/unit. While the cost structures are relatively independent from the selected scenario, the actual self-costs are highly dependent on boundary conditions such as production volume. Full article

With the development of electrification and intelligent technologies in vehicles, ride comfort issues represented by motion sickness have become a key constraint on the performance of autonomous driving. The occurrence of motion sickness is influenced by the comprehensive movement of the vehicle in the longitudinal, lateral, and vertical directions, involving ACC, LKA, active suspension, etc. Existing motion sickness control method focuses on optimizing the longitudinal, lateral, and vertical directions separately, or coordinating the optimization control of the longitudinal and lateral directions, while there is relatively little research on the coupling effect and coupled optimization of the longitudinal and vertical directions. This study proposes a coupled framework of ACC and active suspension control system based on MPC. By adding pitch angle changes caused by longitudinal acceleration to the suspension model, a coupled state equation of half-car vertical dynamics and ACC longitudinal dynamics is constructed to achieve integrated optimization of ACC and suspension for motion suppression. The suspension active forces and vehicle acceleration are regulated coordinately to optimize vehicle vertical, longitudinal, and pitch dynamics simultaneously. Simulation experiments show that compared to decoupled control of ACC and suspension, the integrated control framework can be more effective. The research results confirm that the dynamic coordination between the suspension and ACC system can effectively suppress the motion sickness, providing a new idea for solving the comfort conflict in the human vehicle environment coupling system. Full article

Microgreens, having short growth cycles and efficient nutrient uptake, are ideal candidates for biofortification. This study investigated the effects of selenium (Se) and zinc (Zn) on photosynthetic performance in four hydroponically grown Brassica microgreens (broccoli, pak choi, kohlrabi, and kale), using direct and modulated chlorophyll a fluorescence and chlorophyll-to-carotenoid ratios (Chl/Car). The plants were treated with Na₂SeO₄ at 0 (control), 2, 5, and 10 mg/L or ZnSO₄ × 7H₂O at 0 (control), 5, 10, and 20 mg/L. The results showed species-specific responses with Se or Zn uptake. Selenium enhanced photosynthetic efficiency in a dose-dependent manner for most species (8–26% on average compared to controls). It increased the plant performance index (PI_tot), particularly in pak choi (+62%), by improving both primary photochemistry and inter-photosystem energy transfer. Kale and kohlrabi exhibited high PSII-PSI connectivity for efficient energy distribution, with increased cyclic electron flow around PSI and reduced Chl/Car up to 8.5%, while broccoli was the least responsive. Zinc induced variable responses, reducing PI_tot at lower doses (19–23% average decline), with partial recovery at 20 mg/L (9% average reduction). Broccoli exhibited higher susceptibility, with inhibited Q_A re-oxidation, low electron turnover due to donor-side restrictions, and increased pigment ratio (+3.6%). Kohlrabi and pak choi tolerated moderate Zn levels by redirecting electron flow, but higher Zn levels impaired PSII and PSI function. Kale showed the highest tolerance, maintaining stable photochemical parameters and total electron flow, with increased pigment ratio (+4.5%) indicating better acclimation. These results highlight the beneficial stimulant role of Se and the dual essential/toxic nature of Zn, thus emphasizing genotype and dose-specific optimizations for effective biofortification. Full article

Image segmentation is a foundation for autonomous driving frameworks that empower vehicles to explore and navigate their surrounding environment. It gives a fundamental setting to the dynamic cycles by dividing the image into significant parts like streets, vehicles, walkers, and traffic signs. Precise segmentation ensures safe navigation and the avoidance of collisions, while following the rules of traffic is very critical for seamless operation in self-driving cars. The most recent deep learning-based image segmentation models have demonstrated impressive performance in structured environments, yet they often fall short when applied to the complex and unpredictable conditions encountered in autonomous driving. This study proposes an Adaptive Ensemble Attention (AEA) mechanism within a Generative Adversarial Network architecture to deal with dynamic and complex driving conditions. The AEA integrates the features of self, spatial, and channel attention adaptively and powerfully changes the amount of each contribution as per input and context-oriented relevance. It does this by allowing the discriminator network in GAN to evaluate the segmentation mask created by the generator. This explains the difference between real and fake masks by considering a concatenated pair of an original image and its mask. The adversarial training will prompt the generator, via the discriminator, to mask out the image in such a way that the output aligns with the expected ground truth and is also very realistic. The exchange of information between the generator and discriminator improves the quality of the segmentation. In order to check the accuracy of the proposed method, the three widely used datasets BDD100K, Cityscapes, and KITTI were selected to calculate average IoU, where the value obtained was 89.46%, 89.02%, and 88.13% respectively. These outcomes emphasize the model’s effectiveness and consistency. Overall, it achieved a remarkable accuracy of 98.94% and AUC of 98.4%, indicating strong enhancements compared to the State-of-the-art (SOTA) models. Full article

This study contributes to the broader sustainability discourse by evaluating public knowledge, awareness, and practices regarding air pollution among residents of Hanoi, Vietnam, focusing on its causes, health impacts, and mitigation strategies. A cross-sectional survey was conducted with 521 individuals in suburbs around Hanoi. A multistage sampling technique, combining cluster and simple random sampling, was used for participant recruitment. Three central and three suburban districts of Hanoi were randomly selected as clusters. One individual from each household was invited to participate and answer a structured survey, which assessed perceptions of air pollution, its human-induced causes, recognised health impacts, and individual and community-level mitigation behaviours. Nearly all participants (98.3%) were aware of air pollution, with 65.3% attributing it to human activities and 61.2% recognising specific air pollutants as primary contributors. The majority (93.9%) acknowledged health impacts, citing respiratory infections (55.1%) and sinus issues (51.2%) as prevalent concerns. Vulnerable groups, such as children under 5 (82.3%) and adults over 65 years old (77.4%), were identified as disproportionately affected. Social media (68.9%) and television (58.3%) were the dominant sources of information. Despite a recognition of air pollution’s importance (98.5%), there was limited engagement in systemic sustainability actions, such as supporting renewable energy initiatives. Most participants (84.3%) reported personal mitigation efforts, including energy-saving practices (35.5%) and walking instead of driving a car or bike (35.3%). While awareness of air pollution and its health impacts is high among Hanoi residents, proactive engagement in systemic solutions remains limited. Policymakers should prioritise community-based programs, public–private partnerships, sustainability education, and culturally tailored policy interventions to bridge gaps between awareness and action. Tailored interventions addressing demographic and cultural factors are essential to fostering socio-environmental sustainability in rapidly urbanising contexts. Full article

There has been a great need for replacing combustion-powered vehicles with electric vehicles (EV), and fully electric cars are meant to replace combustion engine cars. This has led to considerable research into improving the performance of EVs, especially via electric motor voltage control. A wide range of optimization algorithms have been used as traditional approaches, but the dynamic parameters of electric motors, impacted by temperature and different driving cycles, continue to be a problem. This study introduces an improved version of the Parrot Optimizer (IPO) aimed at enhancing voltage regulation in EVs. The algorithm can intelligently adjust certain motor parameters for adaptive management to maintain performance based on different situations. To ensure a stable and sustainable power supply for the powertrain of the EV, a photovoltaic (PV) system is used with energy storage batteries. Such an arrangement seeks to deliver permanent electric energy, a solution to traditional grid electricity reliance. This demonstrates the effectiveness of IPO, with the resultant motor performance remaining optimal despite parameter changes. It is also illustrated that energy production, by integrating PV systems, prevents excessive voltage line drops and thus voltage imbalances. The proposed intelligent controller is verified based on multiple simulations, demonstrating and ensuring significant improvements in EV efficiency and reliability. Full article

Previous studies have described vehicle acceleration profiles in non-car-following scenarios; however, the underlying mechanisms governing these profiles remain incompletely understood. This study aims to enhance the understanding of these mechanisms by proposing an improved model based on an optimal control problem with two bounded conditions (OCP2B), segmenting vehicle acceleration curves into three distinct phases. Specifically, the proposed model imposes constraints on acceleration through maximum jerk and maximum acceleration functions, thereby capturing essential dynamics previously unexplained by conventional models. Our key contributions include establishing a comprehensive analytical framework for accurately describing vehicle acceleration profiles and elucidating critical characteristics overlooked in the prior literature. Our findings demonstrate that incorporating human-centric considerations, such as driving comfort, significantly enhances the model’s practical applicability. Moreover, the proposed approach provides crucial insights for designing autonomous vehicle (CAV) trajectories consistent with human driving behaviors and effectively predicts the movements of human-driven vehicles (HVs), thus facilitating smoother interactions and potentially reducing conflicts between CAVs and HVs. Full article

B-cell maturation antigen (BCMA)-targeted therapies including both chimeric antigen receptor (CAR) T-cell therapies and bispecific antibodies (BsAbs), have revolutionized the treatment landscape for relapsed/refractory multiple myeloma (MM), offering both deep and durable responses, even in heavily pretreated patients. Despite these advances, most patients ultimately experience relapse. This is likely related to the development of resistance mechanisms that limit the long-term efficacy and durability of BCMA-targeted approaches. In this review, we examine the current landscape of BCMA-directed therapies, including Idecabtagene Vileucel, Ciltacabtagene Autoleucel, Teclistamab, and Elranatamab and explore the multifactorial mechanisms driving resistance. These mechanisms include tumor-intrinsic factors, host-related and tumor-extrinsic factors, and factors related to the tumor-microenvironment itself. We outline emerging strategies to overcome resistance, such as dual-targeting therapies, γ-secretase inhibitors, immune-checkpoint blockade, armored CAR T constructs, and novel combination regimens. Additionally, we discuss the role of therapy sequencing, emphasizing how prior exposure to BsAbs or CAR T-cell therapies may influence the efficacy of subsequent treatments. A deeper understanding of resistance biology, supported by integrated immune and genomic profiling, is essential to optimizing therapeutic durability and ultimately improve patient outcomes for patients with MM. Full article

In recent decades, environmental requirements for reducing the toxic components emitted from vehicle exhausts have decreased drastically. Technologies for after-treatment of diesel vehicle emissions are being improved continuously in order to meet increasingly stringent regulations. Passenger cars are a significant source of air pollution, especially in urban areas. The EU has decided to phase out internal combustion engines. Stricter Real Driving Emissions (RDE) testing procedures have also been introduced, aiming to assess the emissions of nitrogen oxides (NO_x) and particle number (PN). The present work investigates the interaction between performance and smoke emissions of a diesel vehicle on a pre-established route in an urban environment with an everyday (normal) driving style. The results showed that when the vehicle is technically sound and meets its technical specifications, smoke emissions are within normal limits. Full article