Search Results (382)

The emerging urbanization and the extensive increase of the transportation sector are responsible for the significant increase in carbon dioxide emissions. Therefore, replacing traditional cars with Electric Vehicles (EVs) is a promising solution, offering a clearer alternative. EVs are becoming more and more well-known and are being quickly used worldwide. However, the exponential rise in EV sales has also raised a number of issues, which are becoming important and demanding. These challenges include the need of driving security, the battery degradation, the inadequate infrastructure for charging EVs, and the uneven energy distribution. In order for EVs to reach their full potential, intelligent systems and innovative technologies need to be introduced in the field of EVs. This is where business intelligence (BI) can be employed, along with artificial intelligence (AI), data analytics, and machine learning. In this paper, we provide a comprehensive survey on the use of BI strategies in the EV transportation sector. We first introduce the EVs and charging station technologies. Then, research works on the application of BI and data analysis techniques in EV technology are reviewed to further understand the challenges and open issues for the research and industry community. Moreover, related works on accident analysis, battery health prediction, charging station analysis, intelligent infrastructure, locating charging stations analysis, and autonomous driving are investigated. This survey systematically reviews 75 peer-reviewed studies published between 2020 and 2025. Finally, we discuss the fundamental limitations and the future open challenges in the aforementioned topics. Full article

To mitigate the frequent occurrence of rear-end collisions on urban expressways under rainy weather conditions, firstly, accident risk levels were classified using traffic conflict indicators. Secondly, three machine learning models were employed to predict the accident severity across different scenarios. Furthermore, key influencing factors of rear-end collisions were identified and analyzed based on SHAP values. Case studies were conducted by simulating vehicle trajectory data under light, moderate, and heavy rain scenarios, using an open urban expressway dataset and car-following parameters for rainy conditions. Next, the Modified Time-to-Collision (MTTC) metric was calculated. Risk thresholds for low-, medium-, and high-risk levels were established for each rainfall category using percentile-based cumulative distribution analysis. Finally, real-time risk prediction under the three rainfall scenarios was conducted using XGBoost, LightGBM, and Random Forest models. The model performances were evaluated in terms of accuracy, recall, precision, and AUC. Overall, the study finds that the LightGBM model achieves the highest predictive capability, with AUC values exceeding 0.78 under all weather conditions. Moreover, the study concludes that factors ranked by SHAP values reveal that the minimum distance has the greatest influence in light rain scenarios. As rainfall intensity increases, the influences of minimum headway time and average vehicle speed are found to grow, highlighting an interaction pattern characterized by “speed-distance-flow” coupling. Full article

Background and Objectives: Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with an increasing global incidence. Gut microbiota dysbiosis is believed to be playing a role in ASD pathogenesis. Fecal microbiota transplantation (FMT) is emerging as a potential therapeutic strategy to alleviate ASD-related and gastrointestinal symptoms, but data in pediatric ASD populations remain limited. Materials and Methods: We conducted a prospective, single-center, open-label controlled study to evaluate the efficacy of colonoscopic FMT in children with ASD. Participants were allocated to two groups: an intervention group that underwent a single FMT procedure and a control group. Gastrointestinal Symptoms Rating Scale (GSRS), Autism Diagnostic Observation Schedule (ADOS), Childhood Autism Rating Scale (CARS), Child Behavior Checklist (CBCL), and Parent Global Impression (PGI-R) scales were assessed for both groups at baseline and at set time points. Results: 30 participants were enrolled, with 15 in each group. At 8 weeks, no significant between-group differences were observed for the prespecified primary endpoint, change in ADOS scores. The intervention group showed significantly greater improvements in CARS (p < 0.001), PGI-R (p < 0.001), CBCL Internalizing Problems (p = 0.001), and GSRS (p = 0.037) compared with controls; CARS and PGI-R improvements persisted at 6 months. Within the intervention group, sustained improvements were noted in CARS, GSRS, and PGI-R up to 18 months. No serious adverse events were observed; three mild, self-limited adverse events were recorded following FMT. Conclusions: Colonoscopic FMT was associated with significant short-term improvements in gastrointestinal and caregiver-reported ASD symptoms (CARS), but not in ADOS scores. Some effects persisted long-term. However, due to a lack of blinding and possible selection bias, these findings should be interpreted as exploratory. Larger randomized controlled trials are needed to confirm efficacy and optimize protocols. Full article

This study investigates whether Battery Electric Vehicles (BEVs) or Fuel Cell Electric Vehicles (FCEVs) represent the superior alternative to conventional vehicles for last-mile delivery, with a particular focus on large enterprises that prioritize both economic feasibility and environmental performance. Life Cycle Assessment and Life Cycle Cost methodologies are applied to evaluate both technologies across the full cradle-to-grave life cycle within a unified framework. The functional unit is defined as one kilometer traveled by a BEV or FCEV in last-mile transportation, and the system boundary includes vehicle manufacturing, operation, maintenance, and end-of-life treatment. The environmental impacts are assessed using the ReCiPe 2016 Midpoint (H) method implemented in OpenLCA 2.0.4, and normalization follows the standards provided by the official ReCiPe 2016 framework. The East China Power Grid serves as the baseline electricity mix for the operational stage. Regarding GHG emissions, FCEVs demonstrate a 12.36% reduction in carbon dioxide (CO₂) emissions compared to BEVs. This reduction is particularly significant during the operational phase, where FCEVs can lower CO₂ emissions by 53.51% per vehicle relative to BEVs, largely due to hydrogen energy’s higher efficiency and durability. In terms of economic costs, BEVs hold a slight advantage over FCEVs, costing approximately 0.8 RMB/km/car less. However, during the manufacturing phase, FCEVs present greater environmental challenges. It is recommended that companies fully consider which environmental issues they wish to make a greater contribution to when selecting vehicle types. This study provides insight and implications for large companies with financial viability concerns about environmental impact regarding selecting the two types of vehicles for last-mile transportation. The conclusions offer guidance for companies assessing which vehicle technology better aligns with their long-term operational and sustainability priorities. It can also help relevant practitioners and researchers to develop solutions to last-mile transportation from the perspective of different enterprise sizes. Full article

This study presents the development and validation of a low-cost, open-source sensor system for cyclists that automatically detects vehicle overtaking events while simultaneously monitoring air quality. The system integrates multiple ultrasonic sensors for autonomous overtaking detection and distance measurement with environmental sensors that record particulate matter, temperature, humidity, and GPS position. By combining these data streams, the system enables the analysis of correlations between traffic interactions and variations in particulate matter exposure under real-world cycling conditions. Test rides conducted in urban environments demonstrated that the system reliably identifies overtaking maneuvers and records corresponding environmental parameters. Elevated concentrations of particulate matter were observed during close vehicle passes and at traffic lights, highlighting moments of increased exposure to exhaust emissions. The automated detection mechanism eliminates the need for manual activation, ensuring complete and unbiased data collection. The modular design and energy-efficient operation of the system allow for flexible deployment in both mobile and stationary configurations. With its ability to objectively capture and relate safety and environmental data, the presented platform provides a foundation for large-scale field studies aimed at improving cyclist safety and understanding pollution exposure in urban traffic. Full article

In today’s fast-paced world, secure and efficient access control is crucial for places like schools, gated communities, and corporate campuses. The system must overcome the issues of manual checking and record maintenance of traditional methods like RFID cards or license plate recognition. Our work introduces a budget-friendly, automated solution. A prototype was developed for a vehicle sticker recognition system to control and monitor gate access at NFC IET University as a case study. The automated system design will replace manual checking by detecting the car stickers issued to each vehicle by the university administration. An optimized lightweight YOLOv8 model is trained to identify three categories: IET stickers (authorized for access), non-IET stickers (unauthorized), and no sticker (denied access). A webcam connected to the Raspberry Pi 4 scans approaching vehicles. Authorized vehicles are allowed when the relevant class is detected, which signals a servo motor to open the gate. Otherwise, access to the gate is denied, and infrared (IR) sensors close the gates. A second set of IR sensors and a servo motor was also added to manage the exit side, preventing unauthorized tailgating. The system’s modular design makes it adaptable for different environments, and its use of affordable hardware and open-source tools keeps costs low, which is ideal for smaller institutions or communities. The prototype model is tested and trained on self-collected datasets comprising 506 images. Full article

A university campus is a composite built environment integrating research, daily life, culture, and ecological green space. Its landscape elements shape environmental perception and overall spatial quality. This study assesses spatial quality by identifying key features and optimizing their joint effects across three perceptions: safety, comfort, and belonging. Using a Chinese campus, we captured street-view images, applied semantic segmentation to quantify elements (grass, trees, buildings, roads, sidewalks), and used explainable machine learning with data augmentation to identify the features most relevant to these perceptions. This study then employed fuzzy-set Qualitative Comparative Analysis (fsQCA) to reveal configuration pathways that enhance spatial quality. Results show that data augmentation mitigates class imbalance and improves prediction accuracy. Key features include sky, river, bridge, people, grass, and sidewalks, and path analysis indicates that greater sky openness and higher densities of people, roads, sidewalks, and grass, together with fewer buildings, cars, and bare earth, enhance safety, comfort, and belonging. This study delivers globally transferable design rules and a replicable, policy-ready workflow that enables evidence-based campus upgrades across diverse regions. Full article

Despite technological progress, glioblastoma (GBM) continues to confer dismal prognoses. Modern neuroimaging methods are assuming an ever greater role in diagnosing and monitoring brain tumors. This review shows current neuroimaging approaches and systemic therapeutic strategies for glioblastoma, with a focus on emerging and innovative treatments. Advances in multiparametric magnetic resonance imaging—MRI (diffusion, perfusion, and spectroscopy) and novel positron emission tomography (PET) tracers, complemented by radiomics and artificial intelligence (AI), now refine tumor delineation, differentiate progression from treatment effects, and may help predict treatment responses. Maximal safe resection followed by chemoradiotherapy with temozolomide remains the standard, with the greatest benefit seen in O⁶-methylguanine DNA methyltransferase (MGMT) promoter-methylated tumors. Bevacizumab and other targeted modalities offer mainly progression-free, not overall survival, gains. Immune checkpoint inhibitors (e.g., nivolumab) have not improved survival in unselected GBM, while early multi-antigen CAR-T (chimeric antigen receptor T-cell) strategies show preliminary bioactivity without established durability. While actionable alterations (NTRK fusions and BRAF V600E) justify selective targeted therapy trials, their definitive benefit in classical GBM is unproven. Future priorities include harmonized imaging molecular integration, AI-driven prognostic modeling, novel PET tracers, and strategies to breach or transiently open the blood–brain barrier to enhance drug delivery. Convergence of these domains may convert diagnostic precision into improved patient outcomes. Full article

The glycan profile of cells comprises a high variety of sugar moieties that are attached to proteins (glycoproteins) and lipids (glycolipids) via a process called ‘glycosylation’. Cancer cells commonly carry aberrant glycans, which may be of interest for cancer diagnosis, prognosis, as well as the development of novel therapeutic strategies. This review focuses on the differential glycosylation patterns on malignant B cells, including both B cell lymphoma and leukemia. Well-known aberrant glycan profiles on malignant B cells include acquired high mannose N-glycans in the B cell receptor (BCR) of follicular lymphoma (FL), and increased expression of the glycosphingolipid Gb3/CD77 on Burkitt’s lymphoma (BL). These structures can be exploited for therapy by using lectins that specifically recognize these patterns with intrinsic cytotoxic activity or in a drug-conjugate format. Furthermore, immunotherapy can be improved by modulating glycans, especially sialic acids. Targeting glycans for immunotherapy is also of interest for chimeric antigen receptor (CAR) T cell therapy, a relatively novel therapy that has been quite effective in various B cell malignancies. Thus, the glycan profile of malignant B cells harbors many opportunities for therapeutic targeting. It is anticipated that further in-depth glycan profiling will open up many more opportunities for the treatment of B cell malignancies. Full article

The trajectory-tracking capability of autonomous mining trucks is critical for accomplishing transportation tasks efficiently. However, due to the diverse road surfaces and rugged terrains in open-pit mines, the existing vehicle dynamics models struggle to accurately capture the complex tire–ground interactions. As a result, conventional trajectory-tracking control methods that rely on linear vehicle dynamics models suffer from degraded tracking performance. To this end, this paper proposes a novel trajectory-tracking control framework that integrates model predictive control (MPC) with model-free adaptive control (MFAC). A warm-start strategy is employed to improve the computational efficiency of MPC, while MFAC is utilized to provide real-time compensation for the control deviations generated by MPC during the trajectory-tracking process. To validate the effectiveness of the proposed trajectory-tracking control method, co-simulations were conducted on the CarSim and MATLAB/Simulink platforms under various road conditions and driving scenarios. Simulation results demonstrate that the proposed method can effectively enhance the trajectory-tracking performance of autonomous mining trucks. For instance, under the S-condition with Class E road elevation, the proposed method achieves improvements of approximately 90.83%, 15.05%, and 71.93% in the mean error, maximum error, and root mean square error (RMSE), respectively, compared with the conventional LQR-based trajectory-tracking control method. In addition, the computation time of MPC is only 2 ms, which significantly improves the overall performance of the trajectory-tracking controller. Full article

CAR-T therapy has transformed the treatment of relapsed or refractory B-cell acute lymphoblastic leukemia (B-ALL), particularly in pediatric and young adult patients. Many studies report one-year overall survival rates of between 60% and 80% following therapy. Event-free survival rates at one year are around 50–70%, with 40–50% of patients in remission after two years. Despite these impressive results, disease relapse remains a problem. Future CAR-T cell platforms should target multiple antigens, and the optimal design of such constructs must be determined. Modern trials should explore the role of CAR-T cell therapy as a consolidation treatment for patients with high-risk ALL, including those with persistent minimal residual disease at the end of induction/consolidation therapy, an IKZF1-positive gene expression profile, or a TP53 mutation or Ph-like gene expression profile. Improving the efficiency of gene-editing methods could lead to higher success rates in creating CAR-T cells, as well as reducing manufacturing time and costs. Producing universal CAR-T cells from healthy donors could significantly reduce production time and costs. These issues underscore the dynamic and evolving nature of B-ALL research. Ongoing studies and clinical trials are addressing many of these challenges in order to improve outcomes for B-ALL patients and expand the applications of CAR-T cell therapy. Full article

Urban green spaces (UGS) provide numerous benefits, but challenges in availability and accessibility often limit their full potential. This study assesses equity and disparities in car-based accessibility to Large Urban Green Spaces (LUGS > 8 ha) in the rapidly growing cities of Sulaimaniyah and Erbil in the Kurdistan Region of Iraq. Road network accessibility was analyzed using OpenRouteService (ORS) and calibrated with real-time Google Maps data to improve accuracy. Google Earth Engine (GEE) was used for NDVI-based vegetation mapping and LUGS quality assessment. Isochrones based on 5, 10, and 15 min from LUGS entrances were generated to measure served areas and population coverage at citywide and zonal levels. The findings reveal notable spatial inequities in both cities, with disparities especially evident at shorter travel times. Accessibility declines from central to outer zones. Azadi Park and Sami Abdulrahman Park emerged as key service hubs. The number of LUGS active entrances, spatial distribution, and population density are among the key determinants of car accessibility to LUGS. The study highlighted the spatial-temporal suggestion for long- and short-term implementation, with opportunities for enhancement. Full article

Urbanization and traffic congestion caused by the surge in car ownership have exacerbated energy consumption and carbon emissions, and dynamic traffic flow prediction and energy-saving route planning have become the key to solving this problem. Dynamic traffic flow prediction accurately captures the spatio-temporal changes of traffic flow through advanced algorithms and models, providing prospective information for traffic management and travel decision-making. Energy-saving route planning optimizes travel routes based on prediction results, reduces the time vehicles spend on congested road sections, thereby reducing fuel consumption and exhaust emissions. However, there are still many shortcomings in the current relevant research, and the existing research is mostly isolated and applies a single model, and there is a lack of systematic comparison of the adaptability, generalization ability and fusion potential of different models in various scenarios, and the advantages of heterogeneous graph neural networks in integrating multi-source heterogeneous data in traffic have not been brought into play. This paper systematically reviews the relevant global studies from 2020 to 2025, focuses on the integration path of dynamic traffic flow prediction methods and energy-saving route planning, and reveals the advantages of LSTM, graph neural network and other models in capturing spatiotemporal features by combing the application of statistical models, machine learning, deep learning and mixed methods in traffic forecasting, and comparing their performance with RMSE, MAPE and other indicators, and points out that the potential of heterogeneous graph neural networks in multi-source heterogeneous data integration has not been fully explored. Aiming at the problem of disconnection between traffic prediction and path planning, an integrated framework is constructed, and the real-time prediction results are integrated into path algorithms such as A* and Dijkstra through multi-objective cost functions to balance distance, time and energy consumption optimization. Finally, the challenges of data quality, algorithm efficiency, and multimodal adaptation are analyzed, and the development direction of standardized evaluation platform and open source toolkit is proposed, providing theoretical support and practical path for the sustainable development of intelligent transportation systems. Full article

This study introduces YOLOv13-Cone-Lite, an enhanced algorithm based on YOLOv13s, designed to meet the stringent accuracy and real-time performance demands for traffic cone detection in autonomous formula racing cars on enclosed tracks. We improved detection accuracy by refining the network architecture. Specifically, the DS-C3k2_UIB module, an advanced iteration of the Universal Inverted Bottleneck (UIB), was integrated into the backbone to boost small object feature extraction. Additionally, the Non-Maximum Suppression (NMS)-free ConeDetect head was engineered to eliminate post-processing delays. To accommodate resource-limited onboard terminals, we minimized superfluous parameters through structural reparameterization pruning and performed 8-bit integer (INT8) quantization using the TensorRT toolkit, resulting in a lightweight model. Experimental findings show that YOLOv13-Cone-Lite achieves a mAP₅₀ of 92.9% (a 4.5% enhancement over the original YOLOv13s), a frame rate of 68 Hz (double the original model’s speed), and a parameter size of 8.7 MB (a 52.5% reduction). The proposed algorithm effectively addresses challenges like intricate lighting and long-range detection of small objects and offers the automotive industry a framework to develop more efficient onboard perception systems, while informing object detection in other closed autonomous environments like factory campuses. Notably, the model is optimized for enclosed tracks, with open traffic generalization needing further validation. Full article

Transportation safety studies identify and analyze different contributing factors affecting the safety of road users using virtual reality (VR) traffic simulations in game engines (e.g., Unity). They often either use simplified VR traffic simulation or develop a more advanced simulation requiring substantial technical expertise and resources. The Simulation of Urban Mobility (SUMO) software is widely employed in the field, offering extensive traffic simulation rules such as car-following models, lane changing models, and right-of-way rules. In this study, we develop an open-source virtual reality traffic co-simulation by integrating two different simulation software, SUMO and Unity, and developing a virtual reality traffic simulation where a VR user in Unity interacts with traffic generated by SUMO. In our methodology, we first explain the process of creating road networks. Next, we programmatically integrate SUMO and Unity. Finally, we measure how well this system works using two indicators: the real-time factor (RTF) and frames per second (FPS). RTF compares SUMO’s simulation time to Unity’s simulation time each second, while FPS counts how many images Unity draws each second. Our results showed that our proposed VR traffic simulation can create a realistic traffic environment generated by SUMO under various traffic densities. This work offers a new platform for driver-behavior research and digital-twin applications. Full article