Search Results (10,671)

Natural killer (NK) cells are increasingly recognized as a complementary platform to T-cell-based cancer immunotherapies. Their innate, MHC-unrestricted recognition, capacity to mediate antibody-dependent cellular cytotoxicity (ADCC) and comparatively favorable toxicity profile have given rise to a broad therapeutic pipeline that includes cytokine-supported regimens, adoptive NK products, bispecific and trispecific NK engagers, and chimeric antigen receptor (CAR)-engineered NK cells. Clinical data, particularly in hematologic malignancies, show that NK-cell-based strategies can be safe and biologically active, although limited persistence, suboptimal trafficking and immune escape remain key challenges. Nasopharyngeal carcinoma (NPC), an Epstein–Barr virus (EBV)-driven epithelial cancer, illustrates how a tumor microenvironment (TME) can simultaneously impair NK function and create specific vulnerabilities that NK-focused therapies can exploit. This review summarizes NK biology and current therapeutic platforms, analyzes major limitations, highlights the specific context of NK-cell-based strategies in NPC and compares NK- and T-cell-based therapies with an emphasis on clinical translation. Full article

To address the uncertainty of the measurement noise covariance matrix in vehicle state estimation, this paper proposes a symmetry-aware extended Kalman filter optimized by a chaotic-gradient strategy. The symmetry-aware concept is introduced from the approximate mirror symmetry of vehicle lateral dynamics under left and right steering excitations. Under identical road adhesion and vehicle operating conditions, the yaw-rate and sideslip-angle responses should exhibit balanced statistical characteristics for positive and negative lateral motions. However, a fixed measurement noise covariance matrix may break this balance and lead to direction-dependent estimation bias or delayed convergence. To improve the statistical consistency of the estimation process, the proposed method adaptively tunes the measurement noise covariance matrix according to the innovation covariance mismatch. A chaotic search mechanism is first used to enhance global exploration, and a variable-step gradient method is then applied to refine the local optimal solution. Through the iterative combination of chaotic traversal and gradient-based refinement, the proposed observer improves the balance between model prediction and measurement correction under stochastic disturbances. The effectiveness of the proposed method is verified through CarSim and MATLAB/Simulink co-simulation. The results show that, compared with EKF, UKF, and AEKF benchmark observers, the proposed CG_EKF provides more accurate estimation of vehicle yaw rate and sideslip angle. Full article

To address the longitudinal vibration issues in high-speed elevators induced by external excitations, this study constructs a high-precision multi-degree-of-freedom (MDOF) dynamic model to systematically analyze vertical dynamic response characteristics. Utilizing the substructure method, the complex traction system is decomposed into several subsystems, including the traction device, tensioning device, car and car frame, counterweight system, and segmented wire ropes. By integrating Lagrange’s equations with Newton’s second law, the governing differential equations of motion for each component are derived, establishing an adaptable global dynamic model. The forced vibration analysis focuses on the impacts of periodic excitation from traction sheave eccentricity, piecewise reverse braking torque, and vertical impacts from guide rail joints on car vibration response and wire rope dynamic stress. The results indicate that: traction sheave eccentricity leads to periodic fluctuations in car acceleration, with vibration peaks decreasing as the payload increases; reverse braking torque triggers impulsive acceleration overshoots, where the peak value under full-load conditions increases by approximately 15% compared to the no-load condition, accompanied by a longer duration of low-frequency vibrations; guide rail joint impacts produce instantaneous acceleration spikes, which increase by about 18% under high-speed operating conditions; and the wire rope stress exhibits significantly higher sensitivity to load variations within the low-load range of 0–0.2. Full article

Cancer stem cells (CSCs) are a distinct subpopulation within a tumor that play an important role in tumor initiation, metastasis, therapeutic resistance, and cancer relapse. Their persistence is strongly influenced by the tumor microenvironment (TME), which provides a range of biological signals that maintain stemness, promote immune evasion, and resistance to cancer treatment. Therefore, effective targeting of CSCs is essential to improve therapeutic efficacy. In this review, we summarize the key characteristics of CSCs and their niche within the TME, emphasizing their interactions with immune cells, stromal components, and secreted factors. We also discuss the major challenges in targeting CSCs, including immune evasion, metabolic constraints, and intratumoral heterogeneity. We further highlight current and emerging immunotherapeutic strategies targeting CSCs, including immune checkpoint inhibitors, cancer vaccines, monoclonal antibodies, nanobodies, bispecific antibodies, antibody-drug conjugates (ADCs), CAR-T and CAR-NK cell therapies, oncolytic viruses, as well as innovative approaches such as targeted protein degradation. Finally, we emphasize the importance of a combinatorial approach that integrates CSCs targeting with modulation of the TME. Together, these strategies may lead to more durable responses, enhance therapy efficacy and reduce the risk of tumor recurrence. Full article

With the rapid development of Formula Student competitions, higher demands are being placed on the vehicle performance of race cars. To further enhance vehicle performance, this study investigates the optimization of three key indicators: maximum speed, 0–100 km/h acceleration time, and energy consumption under the NEDC driving cycle. First, a vehicle physical model was established on the AVL CRUISE 2019 R2 platform based on the vehicle parameters, and corresponding simulation tasks were configured. Meanwhile, a numerical model was developed in MATLAB R2022a and validated by comparing the predicted maximum speed, acceleration time, and energy consumption with the CRUISE simulation results. On this basis, a genetic algorithm was employed to optimize the battery pack parallel number and the total reduction ratio so as to improve the vehicle performance. The optimized parameters were then re-imported into the CRUISE model for further simulation verification. The results indicate that, compared with the original configuration, the optimized scheme leads to a slight increase in acceleration time, while significantly improving the maximum speed and reducing the energy consumption under the NEDC cycle. Overall, the proposed optimization method effectively enhances the vehicle performance of the Formula Student electric race car. Full article

To enhance braking performance and lateral stability of vehicles equipped with a brake-by-wire system during emergency braking-in-turn maneuver, this paper proposes a Hybrid Optimization Allocation (HOA) strategy for braking force based on a hierarchical architecture. The upper motion control layer utilizes a Two-Degree-of-Freedom vehicle model as its reference and employs the model predictive control algorithm to determine the demand lateral force and yaw moment necessary to maintain vehicle lateral stability. The lower braking force allocation layer, based on the sequential quadratic programming algorithm, divides conventional and limit working conditions and designs differentiated objective functions to realize the hybrid optimal allocation of four-wheel braking forces. Finally, a wheel slip ratio controller is designed to dynamically adjust the target braking force. CarSim/Simulink co-simulation results verify that, compared with the single optimization allocation strategy, the HOA strategy can reduce the braking distance by 1.4~2.56 m while maintaining favorable lateral stability. The Hardware-in-the-Loop test platform is designed to validate the real-time performance and robustness of the HOA strategy. Full article

The Controller Area Network (CAN) protocol used in in-vehicle networks is vulnerable to external attacks because it lacks authentication and encryption mechanisms. Accordingly, CAN Intrusion Detection Systems (IDSs) have been studied. However, existing IDSs remain difficult to deploy in practical vehicles because of their limited real-time capability, complex preprocessing, and high computational cost. To overcome these limitations, this paper proposes an ultra-lightweight Convolutional Neural Network (CNN)-based IDS that significantly reduces parameters and computational complexity while maintaining high detection performance. The proposed IDS improves area efficiency through a streaming pipeline, computation-block reuse, and constrained Processing Element (PE) parallelism. In addition, its lightweighting effect was quantitatively evaluated against an RTL baseline implemented under identical platform and design constraints. When an attack is detected, an Intrusion Prevention System (IPS) integrated with the CAN controller generates an error frame to block it in real time. The proposed IDS achieved over 99.97% detection performance for known frame-level message-injection scenarios on the Car-Hacking Dataset. It also achieved branch-wise real-time feasibility with an 11.46 µs ID-branch precomputation latency and a 5.68 µs DATA-complete-to-decision latency at 50 MHz. In TSMC 28 nm ASIC synthesis, the proposed IDS required 70,592 gates, with an estimated ASIC power of 2.0231 mW and an active inference energy of 34.68 nJ. Full article

Post-infectious bronchiolitis obliterans (PIBO) is a severe chronic airway disease in children following lower respiratory tract infections. Human adenovirus (HAdV) and Mycoplasma pneumoniae (MP) are the major associated pathogens, with geographic variations in their relative importance. This review analytically compares the mechanistic divergence and convergence between HAdV and MP. Both pathogens converge on MyD88/NF-κB/MAPK signaling and neutrophil-driven inflammation, but diverge in initial host engagement (CAR/integrins vs. TLR2/6 and CARDS toxin) and inflammasome activation (TLR9-related vs. NLRP3-related). This review aims to propose an integrative model linking acute immune activation to fibrotic bronchiolar narrowing and to evaluate the risk factors for PIBO. Genetic susceptibility and epigenetic regulation help explain population differences in PIBO risk and geographic distribution. Despite progress, significant knowledge gaps remain, including the lack of single-cell resolution studies, the absence of co-infection animal models, and uncertainty regarding the long-term efficacy of targeted immunomodulatory therapies. Addressing these gaps is essential for improving early diagnosis and clinical outcomes. Full article

The rapid growth of electromobility is increasing pressure on charging infrastructure along major transport corridors. However, existing studies rarely combine corridor-scale microscopic traffic simulation with stochastic charging-demand modeling for both passenger cars and heavy-duty vehicles in order to assess the operational adequacy of charging infrastructure on TEN-T routes. This study presents a simulation-based decision-support framework for the staged development of EV charging infrastructure along the S19 Rzeszów–Barwinek section, a 90 km corridor forming part of the TEN-T and Via Carpathia networks. The methodology combines microscopic traffic simulation in PTV Vissim with probabilistic charging-demand modeling for passenger cars and heavy-duty vehicles. The novelty of the study lies in the integration of corridor-scale microscopic simulation, stochastic charging-demand representation, and planning-oriented infrastructure assessment within a unified framework. Three EV penetration scenarios were analyzed: 10%, 25%, and 45% of the traffic stream. Infrastructure utilization increased from approximately 95–100% in the 10% EV scenario to more than 120% in the 45% scenario, with queue length rising from about 0.2–0.8 to 1–3 vehicles per MOP and unmet charging demand reaching approximately 3–3.5 vehicles per MOP in the most critical periods. Heavy-duty EVs were identified as the dominant operational bottleneck due to longer charging times and limited charger availability. The proposed framework supports adaptive and scenario-based planning of corridor charging infrastructure beyond minimum regulatory requirements. Full article

Background: Immune effector cell-associated hemophagocytic lymphohistiocytosis-like syndrome (IEC-HS) is a rare, life-threatening complication following CAR T-cell therapy. Diagnosis is challenging due to overlap with severe CRS, sepsis and lack of standardized criteria. Clinical data remain limited. Methods: We retrospectively analyzed 301 patients treated with CD19- or BCMA-directed CAR T-cells for hematologic malignancies at a single center from January 2019 to January 2026. IEC-HS was defined according to American Society for Transplantation and Cellular Therapy criteria. Results: Median follow-up was 31 months. IEC-HS was diagnosed in 14 patients (4.7%), median age 67 years. Underlying diseases included diffuse large B-cell lymphoma (n = 4), multiple myeloma (n = 7), mantle cell lymphoma, Burkitt lymphoma and B-lymphoblastic leukemia (n = 1 each). All patients had hyperferritinemia and cytopenias at baseline; most had high tumor burden (9/14) and elevated LDH (10/14). CRS occurred in all patients and ICANS in 6/14. IEC-HS occurred at median 10 days and was characterized by hyperferritinemia (median 15,321 µg/L), neutropenia, thrombocytopenia, hepatic dysfunction and high CAR-T-cell expansion in peripheral blood. Treatment included corticosteroids and anakinra (12/14). Refractory patients received IVIG (5/14), tocilizumab (3/14), siltuximab, ruxolitinib, emapalumab or etoposide (each n = 1). Infections occurred in 11/14; 4/14 had mixed infections. IEC-HS resolved in 7/14 (median 7 days). Mortality was 79% (11/14), mainly due to IEC-HS (7/14). Three patients were alive at last follow-up. One-year OS was lower vs. the whole cohort (31% vs. 69%, p < 0.0001). Conclusions: IEC-HS was associated with severe cytopenias, hyperferritinemia, hepatic dysfunction and high infection risk. Despite intensive immunosuppressive therapy, outcomes remain poor. Early biomarker-driven identification and multicenter studies are needed. Full article

Fine-grained vehicle orientation estimation is widely reported with strong in-domain accuracy, yet performance degrades substantially when models are applied across datasets; the relative contributions of visual domain shift and annotation label incompatibility to this degradation remain poorly understood. A controlled cross-dataset benchmark was conducted using two publicly available datasets—Car Full View (CFV) and Freiburg Static Cars 52 v1.1 (UnsupCar)—under a fixed ConvNeXt-Small predictor with a varied training source, test target, and image preprocessing strategy. All conditions were evaluated with five-fold cross-validation at the vehicle-instance level. Annotation label incompatibility was identified as the dominant source of transfer error: correcting the angular convention mismatch in UnsupCar orientation labels reduced cross-dataset circular mean absolute error (CMAE) by approximately $3.5$–$4.5^{\circ }$. Crop protocol was a similarly large factor—train/test crop mismatch raised CMAE into the 9–${12}^{\circ }$ range. Square cropping with mirrored boundary padding provided the most robust preprocessing across both in-domain and cross-dataset conditions. After label harmonization, a residual transfer gap of approximately $2^{\circ }$ remained, with a consistent directional asymmetry favoring the UnsupCar-to-CFV transfer direction. Joint training on both harmonized datasets achieved the best-balanced performance ($3.{77}^{\circ }$ on CFV; $5.{38}^{\circ }$ on UnsupCar). These results demonstrate that instance-level splitting, explicit label harmonization, and consistent crop definition are necessary preconditions for credible cross-dataset vehicle orientation evaluation. Full article

Due to the curse of dimensionality faced in modern industrial processes, high-dimensional Statistical Process Control (SPC) faces significant challenges in detecting small and sparse process shifts. Traditional multivariate control charts often suffer from noise accumulation and fail at timely identification of anomalies that affect only a small subset of variables. To address this issue, this study proposes an enhanced Multivariate Exponentially Weighted Moving Average (MEWMA) approach with variable selection and adaptive sampling for efficient process monitoring. The proposed smart approach works in two ways: first, it automatically focuses on the variables that are most likely to have changed (variable selection); second, it takes samples more frequently when things look uncertain, and less frequently when everything appears stable (variable sampling interval). This combination allows problems to be detected earlier. A Monte Carlo approach is used to calculate the the Average Time to Signal (ATS) values of the proposed scheme, and comparative results show that the proposed scheme outperforms standard charts like the Fixed Sampling Intervals (FSI) VSME, VSI-$T^2$, and VSI-MEWMA schemes in terms of detection speed for small-to-moderate sparse shifts. Finally, a real example from car body manufacturing is provided as an illustration for the implementation of the proposed scheme. Full article

Great Britain has a temperate climate, but like other countries, its weather patterns have already been profoundly affected by climate change, and the changes are very likely to continue for decades. It also has an older building stock than most other countries, which may mean it is more difficult to adapt the built environment to reduce vulnerability to climate hazards. However, Great Britain has excellent mapping and buildings data. The built environment is better described than most other countries, and the authors’ work on the National Buildings Database for Great Britain, which draws together the most reliable sources of data covering non-domestic buildings in England, Scotland and Wales, provides an unparalleled opportunity to evaluate how many people will be affected by climate hazards. There has been considerable research effort assessing how housing will be affected by climate change, but so far much less systematic assessment of impacts on non-domestic buildings. Here, the authors examine three aspects of climate hazard affecting people in non-domestic buildings in Great Britain: (1) Overheating—How many and what types of non-domestic buildings are vulnerable to overheating risks in a heat wave? What total floor area is affected, and how many people typically occupy these buildings? (2) Flooding—How many and what types of non-domestic buildings are threatened by flooding now and in 2080? How much floorspace is threatened, and how many people typically occupy these buildings? (3) Safe space—How much air-conditioned ‘safe space’ is available where people vulnerable to overheating risks could retreat to in an emergency overheating event (e.g., schools or hospitals)? How many people could be accommodated, and what fraction of the total GB working population does this represent? We propose five new metrics to assess two of the immediate hazards posed by climate change (overheating and flooding) and to begin to assess to what extent Great Britain could find temporary accommodation for people displaced by these hazards. Full article

This study develops a comprehensive carbon footprint assessment model that integrates forward and reverse logistics to evaluate and compare greenhouse gas emissions from online and offline apparel sales channels in China, with a particular focus on high return rates. The model quantifies emissions from transportation, packaging, storage, and operations, incorporating return and exchange logistics. The system boundary is limited to enterprise-controllable sales-phase activities and excludes consumer travel. Three sales models are compared: factory-to-consumer (F2C), traditional business-to-consumer (B2C) e-commerce, and brick-and-mortar retail (BMR). Within this defined boundary, BMR exhibits the lowest carbon footprint (0.296 kg CO₂e/item), followed by F2C (0.408 kg CO₂e/item) and B2C (0.602 kg CO₂e/item). Packaging dominates online emissions (55–57%), whereas store operations are the main contributor to offline emissions (43%). Return rates are identified as a decisive factor, accounting for over 31% of e-commerce emissions and potentially increasing them by 171.3% under extreme scenarios. Sensitivity analysis reveals that trunk line distance (factory to warehouse) has a greater impact on emissions than last-mile return route optimization. Relocating the factory closer to consumers reduces B2C transport emissions by 72.3%, whereas replacing conventional packaging with recycled plastic reduces total B2C emissions by 46.0%. These findings provide channel-specific sustainability strategies: return reduction and packaging innovation for online channels, and energy efficiency improvements for physical stores. These results are conditional on the defined system boundary. If consumer travel by private car were included, the relative advantage of offline channels would diminish or could reverse. Full article