Search Results (870)

Older primary schools in Croatia are frequently located in densely built older settlement cores. Micro-locations surrounding school entrances are often not the result of prior urban or traffic planning; instead, they are retroactively managed through infrastructure and signalling interventions. Pupils participate in traffic as pedestrians, cyclists, e-scooter users, or passengers in cars, school buses, or public buses. The proposed integrated research approach includes: an online survey of pupils’ travel behaviour, systematic safety assessments of entrance micro-locations using the iRAP methodology, as well as field measurements and in-depth analysis of vehicle speeds, traffic flow and structure. For classes organised in two shifts, an online survey of parents (for classroom-based education) and pupils (for subject-based education) covered 56% of the pupil population. Because pupils’ travel mode is the factor most susceptible to influence through infrastructure improvements, statistical analysis was conducted using the χ²-test for the purpose of investigating relationships with the other three traffic-relevant determinants: school age group, pupils’ sex, and distance from school. Approximately three-quarters of pupils live less than 2 km from the typical school. If peak vehicle traffic does not coincide with the peak of pupil arrivals and departures during the overlap of two school shifts, part of the traffic on the school-access street may be unrelated to direct school-access activities. Vehicle-type restrictions and one-way traffic operation should be considered as measures to improve pupils’ safety. The proposed groups of measures for improving pupils’ safety include: (i) educational workshops for pupils, parents and teachers; (ii) reconstruction of school entrance micro-locations; (iii) targeted interventions in the traffic environment within a 2 km perimeter. Full article

Foamed concrete has been used to construct lightweight embankments as a substitute for conventional fills, aiming to promote its engineering application in soft-soil regions. However, the dynamic response and safety mechanism of foamed concrete embankments during vehicle collision are not yet fully understood. In this paper, the safety performance of lightweight foamed concrete embankments under vehicle–guardrail collision and the optimal design of the guardrail foundation are investigated from the perspectives of lateral displacement and stress distribution. Through static uniaxial compression tests, the stress–strain curves, compressive strength, elastic modulus, and statistical variability of foamed concrete with six different mix proportions were obtained. On this basis, a coupled finite element model of the vehicle–guardrail–lightweight embankment system was established (the guardrail and its foundation were modeled using a linear elastic constitutive model, the embankment using a crushable foam model, and the vehicle using a 1.5 t passenger car model validated by full-scale crash tests). According to the passenger car impact conditions specified in current Chinese regulations (velocity 100 km/h, angle 20°), the peak lateral displacement and peak principal stress of the lightweight embankment were analyzed for four foundation base slab lengths (L₀, 1.1 L₀, 1.2 L₀, 1.3 L₀). The results show that increasing the base slab length effectively reduces lateral displacement and stress concentration. Increasing the length by 10–20% reduces the peak lateral displacement by up to 68%, and the peak principal stress remains far below the material strength. From the perspectives of structural stability and cost-effectiveness, a 10–20% increase in the base slab length is recommended. The ratio of the peak principal stress to the material strength can serve as a criterion for evaluating the safety margin and assessing the rationality of the foundation design. This study provides quantitative evidence for optimizing the guardrail foundation base slab length to enhance the collision safety of lightweight foamed concrete embankments, and the proposed design range offers a cost-effective reference for practical engineering applications in soft-soil regions. Full article

Background: Chimeric antigen receptor (CAR) T-cell therapy has transformed haematological-malignancy care, but its dermatologic safety profile remains incompletely characterised across products and indications. Methods: We analysed 8,431,841 deduplicated FDA Adverse Event Reporting System reports from 2016 Q3 to 2026 Q1. Seven approved CAR T-cell products were identified. The primary outcome was any dermatologic adverse event, defined using the MedDRA Skin and subcutaneous tissue disorders system organ class. Secondary outcomes included broad severe cutaneous adverse reactions, narrow Stevens-Johnson syndrome/toxic epidermal necrolysis, and 14 phenotype-specific categories. Multivariable models adjusted for demographics, polypharmacy, cancer, immune checkpoint inhibitor exposure, lymphodepleting chemotherapy and cytokine release syndrome. Additional sensitivity analyses evaluated HSCT/GVHD co-reporting proxies, infection and cytopenia/bleeding proxies, severe-event clinical characteristics, comparator robustness and multiplicity correction. Results: Dermatologic adverse events were identified in 996,654 reports, including 425 CAR-T-associated cases. CAR T-cell exposure was associated with reduced adjusted reporting odds for the primary outcome (adjusted odds ratio 0.13, 95% confidence interval 0.09–0.20) and broad severe cutaneous adverse reactions (0.35, 0.23–0.52). The primary SKIN_ANY reduced-reporting pattern was consistent across all-FAERS, haematological-malignancy and active haematology-oncology comparators. HSCT/GVHD proxy co-reporting was uncommon and did not materially alter estimates. Severe dermatologic reports frequently co-mentioned CRS and serious outcomes. The tisagenlecleucel vascular cutaneous signal was nominally significant but attenuated after excluding infection-attributable and cytopenia/bleeding-proxy reports. Conclusions: Within spontaneous reporting systems, CAR T-cell therapy showed reduced relative reporting of dermatologic adverse events across broad, phenotype-specific and product-level analyses. These results should be interpreted as differences in reporting behaviour, not as evidence of reduced true clinical incidence or lower patient-level risk. Early severe cutaneous reports frequently overlapped with cytokine release syndrome, while infection, cytopenia/bleeding proxies and supportive-care drugs were important alternative explanations for selected cutaneous signals. Full article

CD19 chimeric antigen receptor (CAR) T cells have demonstrated promising outcomes in B-cell malignancies. However, using pretreated autologous T cells currently faces limitations, including compromised T-cell fitness and the challenge of manufacturing sufficient cell numbers for treatment. Consequently, natural killer (NK) cells have emerged as an alternative due to their natural ability to mediate cytotoxicity and their favorable safety profile. This study aims to generate patient autologous hematopoietic stem cell-derived NK (HSC-NK) cells and assess their therapeutic potential compared to peripheral blood NK (PB-NK) cells. We successfully cultivated HSC-NK under a 28-day, two-step differentiation and expansion protocol, achieving a cumulative 290-fold expansion using optimized memory-like cytokines and feeder cell stimulation. The expanded HSC-NK cells demonstrated a distinct phenotype (CD56⁺CD16^low), representing an immature differentiation state, characterized by a lower expression of inhibitory receptors (NKG2A, KIR2DL, and CD94) and the exhaustion markers (LAG3, PD-1, TIM-3, and CTLA-4) compared to PB-NK cells. Prominent expression of CD62L, alongside sustained expression of CD69 and CD107a, was observed, translating into NK cell proliferation, activation, and cytotoxicity against cancer cells comparable to PB-NK cells. In conclusion, generating HSC-NKs is feasible while preserving essential NK cell phenotypes and activities. Our findings emphasize the potential of HSCs as an alternative NK cell source for cancer immunotherapy. Full article

Modern connected vehicles rely on the controller area network (CAN) to disseminate safety-critical in-vehicle information, including sensor-related and vehicle-state signals such as engine revolutions per minute (RPM) and gear state, among electronic control units (ECUs). Because CANs lack built-in authentication and encryption, malicious message injection and spoofing can compromise the integrity and availability of vehicular sensing and control functions. Existing deep-learning-based intrusion-detection systems (IDSs) show a clear trade-off: supervised methods perform well on known attacks but rely on costly labels, whereas unsupervised methods can identify unseen attacks but often suffer from high false-positive rates. To address these limitations, this paper proposes a semi-supervised generative adversarial network (SGAN) framework for CAN bus intrusion detection that combines image-based CAN representation with adversarial learning. Consecutive CAN messages are converted into $64\times 9$ grayscale images, and the proposed framework is trained in three phases. First, the discriminator establishes an initial decision boundary using a small labeled subset. It then refines this boundary through distribution-level likelihood objectives and generated samples. Finally, the generator is trained to produce realistic samples capable of deceiving the discriminator. The proposed method was evaluated on the Hacking and Countermeasure Research Lab (HCRL) car-hacking dataset using leave-one-class-out experiments to simulate unknown attacks and achieved an average accuracy of 99.73% and an average F1-score of 99.63% on unknown attacks. Moreover, with only 0.21 M parameters and 3.25 M floating-point operations (FLOPs), the model is well suited for resource-constrained in-vehicle platforms. These results indicate that the proposed framework can serve as a practical cybersecurity component for protecting CAN-carried data in vehicular sensing applications. Full article

Chimeric antigen receptor (CAR) T-cell therapy is a new standard of care for patients with diffuse large B-cell lymphoma (DLBCL); however, studies including healthcare resource utilization (HRU) during routine care are lacking. Accordingly, a population-based study was conducted using linked administrative databases from Ontario, Canada. Patients with DLBCL that failed ≥2 lines of systemic therapy were included. Cox proportional hazard models estimated associations between covariates and overall survival (OS). Logistic, binomial and Poisson regression explored associations between covariates with toxicity and HRU. We identified 308 patients enrolled to receive CAR T-cell therapy of which 255 patients received CAR T-cells (mean age 59 years; 39% female). From the date of CAR T-cell infusion, the median OS was 25.0 months (95% CI, 21.6–28.1 months). Cytokine release syndrome and immune effector cell-associated neurotoxicity syndrome data were available for 155 patients and were reported in 135 (87.1%) and 42 (27.1%) patients, respectively. Of those that received CAR-T cells, 172 patients (67%) were hospitalized with a median length of stay of 5 days (IQR, 0–20) and 243 (95%) had an emergency department visit without hospitalization. Our prospective population-based study demonstrates comparable efficacy and safety of CAR T-cell therapy in the real-world to the pivotal trials and highlights this as an efficacious and relatively safe treatment option for patients with DLBCL in routine clinical care. Full article

This paper presents a novel approach to design an attitude motion control strategy of a vehicle to mitigate lateral or longitudinal inertial forces acting on the passenger during cornering, braking, and acceleration maneuvers. The collaboration of active suspension system and active airfoil substantially enhances the attitude motion of a vehicle. By incorporating integral control action for both the desired body attitude roll or pitch angle and zero dynamic tire deflection within the performance index, the optimal controller maintains the ideal roll or pitch angle while preserving the road holding capability. The computer simulations were conducted to evaluate the dynamic performance of the proposed system in comparison with various other suspension systems based on a 4-degree-of-freedom half-car model. Four scenarios for rolling and pitching motions were simulated as follows: the first case examines the rolling response to a one-sided bump input applied to a lateral half-car model during straight-line driving. The second case investigates the rolling performance during a cornering maneuver. The third and fourth cases analyze the pitching responses to braking and acceleration using a longitudinal half-car model. The simulation results demonstrate that the proposed system maintains the ideal body attitude, attenuates the effect of the lateral or longitudinal inertial forces and keeps an ideal road holding capability. As a result, the proposed control system substantially improves ride comfort while enhancing the dynamic safety of the vehicle. Full article

The intensifying climate problem requires substantial decarbonisation in the energy, industry, and transportation sectors, with hydrogen recognised as a crucial energy carrier. The increase in global energy consumption, driven by population growth and industrialisation, challenges the constraints of fossil fuel resources and their detrimental impact on CO₂ levels. Hydrogen, noted for its high energy density and versatility in generating power from both fossil and renewable sources, acts as a crucial supplement to direct electrification. Currently, worldwide hydrogen production exceeds 100 million tonnes per year, predominantly in the form of “grey hydrogen,” which significantly contributes to CO₂ emissions without the use of carbon capture systems. This analysis comprehensively assesses hydrogen’s contribution to decarbonisation, encompassing the entire value chain: production methods, storage options (compressed gas, liquid hydrogen, and complex hydrides), transportation techniques (pipelines, cars, rail, and ammonia carriers), and various uses. Key performance parameters indicate trade-offs concerning energy density, storage, production expenses, and transportation alternatives. Notwithstanding advancements in hydrogen technologies, obstacles persist, encompassing energy penalties, infrastructural requirements, and safety issues. This evaluation highlights the need for coordinated policies and investment to enhance hydrogen’s adaptability, ensuring alignment with direct electrification policies to achieve net-zero emissions by 2050. Full article

Background: For almost two decades now, chimeric antigen receptor-natural killer cells (CAR-NK) have been investigated in pre-clinical breast cancer models, yet clinical evidence on efficacy remains scarce. This meta-analysis provides pooled evidence of pre-clinical CAR-NK effectiveness and safety in breast cancer and an overview of current clinical trials to support clinical translation. Methods: Following PRISMA guidelines and a registered protocol (PROSPERO, CRD420251131530), PubMed, Web of Science, and Scopus were searched up to 30 June 2025 for pre-clinical CAR-NK studies in breast cancer. Clinical studies were retrieved from clinicaltrials.gov and the International Clinical Trial Registry Platform (ICTRP) up to 1 March 2026. Pre-clinical studies without in vivo data or non-human CAR-NK cells were excluded. Primary outcomes were tumor burden (ratio of means, ROMs) and survival (median survival ratio, MSR). Data were analyzed in JASP™ and risk of bias (RoB) was assessed using SYRCLE’s tool. Results: Fourteen pre-clinical studies (38 CAR-NK treatment groups targeting EGFR, HER2, tissue factor, CD70, mesothelin, or folate receptor, with peripheral blood as the primary NK source, and a 5–10 million cell dose) and 11 early-phase clinical studies (targeting HER2, TROP2, PD-L1, MUC1, or NKG2D ligands under ongoing investigation) were included. In pooled pre-clinical analysis, CAR-NK significantly reduced tumor burden against untreated and unmodified/mock controls (ROM 0.311 [0.22–0.44] and 0.42 [0.33–0.53], p < 0.001, respectively). Survival was also prolonged significantly (MSR 1.47 [1.15–1.87], p = 0.010 vs. untreated; 1.30 [1.09–1.60], p = 0.007 vs. unmodified/mock NK cells). Subgroup analyses indicated improved efficacy with peripheral blood source and 5–10 M dosing. No treatment-related toxicities were reported. CAR-NK persistence was generally higher than unmodified/mock NK cells. Discussion and Conclusions: Significant heterogeneity was observed in ROM analysis which the multi-level meta-analysis configured as intra-study interventional variability. There was moderate RoB in pre-clinical studies. Published results from clinical trials remain limited, highlighting early stages of investigation. Overall, CAR-NK therapy demonstrated consistent pre-clinical efficacy and safety, supporting further translational and clinical evaluation in breast cancer. Full article

In urban areas, where car congestion is increasing daily, improving traffic-signal control is a key area of study that directly affects people’s quality of life. It is expected that such improvement will reduce environmental traffic load and increase mobility. However, the inability to determine traffic load deterministically complicates the problem. Multi-agent reinforcement-learning approaches provide a solution thanks to their adaptive learning capabilities from instantaneous data. This study evaluates the Multi-Agent Deep Q-Network (MADQN) algorithm and the QMIX value decomposition method (QMIX) in an urban traffic network with 16 signalized intersections, comparing them with Fixed-Time and Max-Pressure. Experiments were conducted under three vehicle-density levels within the same network geometry and phase-matching rules. Performance was evaluated using waiting time, travel time, speed, efficiency, carbon dioxide (CO₂) emissions, time to collision below 1 s (TTC < 1 s), and post-encroachment time below 1 s (PET < 1 s).. In all demand scenarios, both reinforcement-learning controllers achieved successful results. MADQN consistently provided lower average waiting times, whereas QMIX consistently achieved higher efficiency and, in some settings, lower CO₂ and lower cross-seed variation. No statistical superiority between MADQN and QMIX was established. Overall, the results support the value of adaptive control in this test environment and indicate trade-offs among efficiency, emissions, and proxy safety. Full article

The rapid expansion of the new energy vehicle (NEV) market underscores a critical gap in the absence of a scientific health evaluation method for official inspections and annual checks. To address this, our study develops a comprehensive and quantitative health calibration system tailored for four specific application scenarios: annual inspection, battery health assessment, maintenance, and used car evaluation. Utilizing the Delphi method and Fuzzy Analytic Hierarchy Process (FAHP), we propose a construction method for a hierarchical and quantitative evaluation system. For each scenario, an independent quantitative evaluation table is established, identifying key indicators through a combination of specific operational contexts and expert opinions. The FAHP is then applied to determine the precise weights of these selected indicators, yielding a clear weighting structure for health metrics across different scenarios. This work culminates in a quantitative evaluation methodology for the health degree of in-use NEVs. By extending vehicle service life, reducing premature battery degradation, and enhancing safety, the proposed system directly supports the sustainable development of the NEV industry. It contributes to resource conservation, lower environmental impact, and greater consumer trust in green transportation. The proposed system is significant for fostering the healthy development of the NEV industry, enhancing vehicle safety and reliability, promoting technological progress, and strengthening consumer purchase confidence. Full article

Angelica sinensis (Oliv.) Diels (AS) is a medicinal and food plant that has long faced a persistent challenge: its quality and price are often influenced by environmental conditions and geographical origins. To achieve substantial profits, items that are not produced in primary regions, along with counterfeit products, are frequently misbranded as originating from main production areas; this leads to fraud regarding geographic origin and product tampering. Rapid, effective and feasible methods for distinguishing the geographic origin of AS are important for ensuring consumer safety and protecting their interests. This study establishes the authenticity and geographical origins of AS. Meanwhile, diverse machine learning strategies are used to identify the optimal combination by incorporating spectral pre-processing techniques, feature wavenumber selection methods and classification algorithms. The findings reveal that the backpropagation neural network (BPNN), convolutional neural network (CNN) and radial basis function neural network (RBF) excel in determining the authenticity of AS. To distinguish among different growing environments of AS, three models obtained 98.94% classification accuracy on the test set: (1) multiplicative scatter correction (MSC) pre-processing with an RBF classifier, (2) standard normalised variate (SNV) pre-processing with an RBF classifier and (3) Savitzky–Golay (SG) smoothing pre-processing, competitive adaptive reweighted sampling (CARS) for selecting features and a BPNN for classification. This study validates the feasibility of ensemble learning combined with MIR for discriminating AS from authenticity and different geographical sources. Full article

In recent years, the feasibility of immunotherapy targeting activated fibroblasts in pulmonary fibrosis has received further support. Recent studies have shown that transient FAP-targeted immunotherapy can alleviate pulmonary fibrosis by eliminating excessively activated fibroblasts, improving the aberrant extracellular matrix environment, and promoting alveolar cell lineage remodeling, suggesting that FAP-associated pathological stromal cells are amenable to therapeutic intervention. Based on this, research on FAP-centered engineered cell therapies is being gradually extended from settings such as myocardial fibrosis to pulmonary fibrosis. In this context, primary human NK cells represent a promising effector cell platform, as they are generally associated with a lower risk of severe treatment-related toxicities and relatively limited in vivo persistence, which may confer a more controllable therapeutic window. This feature is particularly important in fibrotic diseases, because long-term and continuous depletion of fibroblast populations may disrupt tissue homeostasis and injury repair. In addition, current studies of FAP-targeted CAR-NK therapy have mainly relied on NK cell lines such as NK-92, but these systems may not fully reflect the functional characteristics, receptor signaling, or clinical potential of primary human NK cells. Based on these considerations, it is necessary to develop a FAP-targeted cell therapy platform with greater clinical relevance for pulmonary fibrosis. In this study, we established a primary human FAP-CAR-NK-cell platform and conducted a proof-of-concept evaluation in pulmonary fibrosis-related models, including in vitro systems, a human pulmonary fibrosis-like organoid model, and an acute in vivo observation model. The main novelty of this study lies in the use of primary human NK cells for FAP-targeted intervention in pulmonary fibrosis-related models. We focused on whether these engineered cells could selectively target and eliminate FAP-positive activated fibroblasts, retain effector function in a fibrotic microenvironment, and show short-term feasibility after adoptive transfer. The study was not intended to assess long-term therapeutic efficacy or systemic safety, but rather to examine the feasibility of FAP-directed fibroblast targeting by primary human CAR-NK cells in pulmonary fibrosis and to provide a basis for further preclinical investigation. Full article

Cycling is widely recognized as a sustainable urban mobility solution, and many municipalities focus on cycling infrastructure expansion to promote improved environmental sustainability. However, the current literature on cycling has predominantly focused on safety and health benefits, while the environmental benefits including GHG mitigation benefits remain less explored. To summarize findings from the current literature that explore the GHG emissions-related benefits (or costs) of cycling infrastructure, we conducted a literature review using five major scientific databases, following the PRISMA guidelines. Out of 824 screened records, 17 studies met the inclusion criteria. Most studies were published in the last decade, reflecting a limited but growing interest in this topic. The current analytical approaches include mode shift analysis, life cycle assessment, and scenario modelling. Among these, mode shift analysis (i.e., assessing the potential benefits related to replacement of car trips with cycling) remains a commonly used method. We found that cycling offers significant operational benefits by reducing GHG emissions, especially in the context of large-scale expansions of cycling infrastructure. Existing research indicates that even when embodied emissions are considered, bicycle is a more sustainable mode of transportation compared to cars or even public transit. However, emissions associated with installation and maintenance of cycling infrastructure may sometimes negate the GHG benefits associated with additional cycling. We discussed gaps in the current literature and directions for future research. Full article

The braking system is a critical safety mechanism for elevators, making precise performance prediction essential for robust design. However, high-speed elevator braking is a complex nonlinear dynamic process featuring real-time stick-slip transitions at the rope–sheave interface. Conventional models often overlook these transient characteristics or assume constant friction, which compromises predictive accuracy. To address these limitations, this study proposes a 16-scenario adaptive dynamic model for elevator braking. By incorporating the Stribeck friction formulation and explicit numerical criteria, the state-space framework autonomously solves multi-condition, time-varying slip dynamics without manual intervention. The proposed model was experimentally validated using a test elevator with a rated capacity of 1000 kg. Empirical results showed high accuracy in predicting macroscopic braking distances (relative errors under 10%) and transient responses, particularly the temporal evolution of velocity and deceleration. Leveraging this validated model, the influence of key parameters—including car load, time-varying friction, and traction capacity—on dynamic braking behavior was further investigated. Ultimately, this 16-scenario framework provides a robust theoretical foundation for predicting transient braking stability and optimizing elevator mechanical design. Full article