Search Results (2,016)

Urban traffic congestion remains a persistent global challenge, contributing to significant economic inefficiencies, elevated greenhouse gas emissions, and diminished quality of life. This paper presents a real-world video-based traffic monitoring study combined with a proposed adaptive signal control framework. In the monitoring component, YOLOv11 object detection was applied directly to footage recorded from an overhead bridge position on a 40 km/h road. The model successfully detected and tracked multiple road-user categories, including cars, trucks, buses, motorcycles, cyclists, and pedestrians, yielding 1041 vehicle detections across 25 unique tracked objects. Vehicle speeds were estimated from inter-frame centroid displacement, and a Region of Interest (ROI) occupancy model was used to classify congestion states as High, Medium, or Free Flow using thresholds grounded in Highway Capacity Manual (HCM) level-of-service criteria. The system detected 11 high-congestion frames (3.8%), 184 medium-congestion frames (63.9%), and 93 free-flow frames (32.3%), consistent with moderate congestion observed during the recording period. In the proposed control component, a Proximal Policy Optimisation (PPO)-based reinforcement learning signal controller is designed around the YOLOv11 detection outputs as its state representation. Based on comparable adaptive traffic signal control studies in the literature, the proposed framework is projected to achieve approximately 25% higher peak-hour throughput, 35% shorter queue lengths, and 32% lower average waiting times relative to a fixed-time signal baseline. The detection accuracy (mAP@0.5 = 93.2%) and inference speed (32 FPS) cited are published YOLOv11 benchmarks used as indicative performance references. This work bridges real-world perception and proposed intelligent control, providing a transparent and reproducible methodology for next-generation smart city traffic management. Full article

Cities preparing Sustainable Urban Mobility Plans (SUMPs) increasingly require practical tools capable of merging diverse mobility datasets and transforming them into planning-relevant indicators. This article introduces PRIORITY (Platform for the tRansition to sustaInable zerO-caRbon mobilITY), a prototype platform designed to support mobility analysis and decision-making in urban contexts. The platform integrates Floating Car Data, GTFS feeds describing public transport supply, and detailed land-use and zoning information. By relying on these heterogeneous data streams, PRIORITY generates indicators such as travel and stop times, trip distances, trip volumes, energy consumption, pollutant emissions, external costs, and electric-vehicle charging behavior. The platform is organized into two main components: a back end and a front end. The back end, which constitutes the operational core, manages all collected data and ensures their structured storage in a shared database capable of handling large volumes of information on urban form, individual mobility patterns, public transport services, and modeling outcomes. The front end provides an intuitive and versatile interface that dynamically presents the outputs generated by the platform’s analytical and modeling processes. A case application for the Metropolitan City of Rome (Italy) illustrates the operational use of the prototype and shows how PRIORITY can support transparent and reproducible evaluations during the preparation and monitoring of SUMPs. The demonstrated workflow highlights the prototype’s value for public authorities and planners seeking data-informed approaches to urban mobility assessment and decarbonization strategies. Full article

Infrared small-UAV detection remains difficult because the target often appears as a weak thermal point rather than a clear object. This problem is clear in the SIDD dataset, where most test targets are smaller than 32 × 32 pixels. To address this case, this paper proposes S-Drone-YOLO, a compact YOLO-based detector that maintains a high-resolution P2 prediction path and leverages it carefully during classification. The model starts from a lightweight YOLOv5-style detector. It adds a stride-4 P2 path and replaces the C3 neck blocks with C2fAttn to improve feature reuse before prediction. Two components are then added to the Architecture II design. The Coordinate-Aware Residual C2f Block, CAR-C2f, strengthens the P2 branch using coordinate attention and residual scaling. The P2-Guided Quality-Aware Detection Head (P2-QADH) combines local P2 details with nearby P3 context. It produces a quality map that adjusts the classification logits. The regression branch, output tensor format, and training loss interface remain unchanged. On the SIDD infrared drone dataset, S-Drone-YOLO reaches 0.988 precision, 0.939 recall, 0.699 mAP50-95, and 0.962 F1-score. It uses 6.45 M parameters and 31.3 GFLOPs. Compared with the Architecture I model, recall increases by 0.8 percentage points and mAP50-95 increases by 0.4 percentage points. At the same time, the parameter count decreases by 20.3%, and GFLOPs decrease by 43.7%. Fine-tuning on five RGB UAV datasets and a second thermal dataset (ThermalUAV2UAV) yields F1 scores ranging from 0.941 to 0.999, with an mAP50-95 of 0.843 on the thermal dataset. The background analysis also shows stable F1-scores across sky, sea, city, and mountain scenes. These results suggest that controlled P2 guidance can improve infrared small-UAV detection while keeping the model size practical. Full article

Multi-object tracking and segmentation (MOTS) aims to jointly perform pixel-level instance segmentation and temporal identity association for multiple objects in video sequences. Existing online decoupled MOTS methods face several challenges in complex scenarios, including limited front-end mask quality, corruption of memory representations under prolonged occlusion, and unstable data association and trajectory recovery. To address these limitations, we propose TrackRefine, a plug-and-play decoupled enhancement framework. TrackRefine enhances overall performance through back-end refinement without modifying the architecture of the front-end instance segmenter or relying on additional end-to-end joint training. Specifically, we introduce a lightweight Fast GrabCut-based mask refinement module to optimize mask boundaries, a multimodal long-short-term memory bank that integrates appearance, semantic, and shape cues for identity modeling, and a progressive three-stage association strategy for stable matching and long-term trajectory recovery. Experimental results on MOTS20 show that TrackRefine achieves 69.4 sMOTSA, 82.7 MOTSA, and 478 Frag. Experimental results on KITTI MOTS show that it achieves 62.4/73.7 sMOTSA and 78.0/85.4 MOTSA for pedestrians and cars, respectively. Extensive experiments with different front-end instance segmenters verify its plug-and-play flexibility and decoupled design, while ablation studies confirm the effectiveness of each core module. These results show that TrackRefine provides an efficient and practical solution for online MOTS in complex scenarios. Full article

This study quantified total biomass stocks in Carrasco (CAR, n = 12), a dense tropical deciduous vegetation type from the Brazilian semi-arid region for which biomass information remains scarce. We also evaluated differences in floristic composition, diversity, structure, and biomass allocation patterns relative to Cerrado sensu stricto (CSS, n = 40). Forest inventories were conducted in southeastern Brazil. Woody biomass was estimated using a regional allometric equation. Roots were sampled in a position adjacent to the plots, and litter was collected at the center of each plot using a frame. Necromass was assessed along a linear transect corresponding to the length of each plot using the line-intersect method. Biomass differences between vegetation types were assessed using generalized linear and mixed-effects models (GLMs and GLMMs). Total biomass reached 45.24 Mg ha⁻¹ in CSS and 59.01 Mg ha⁻¹ in CAR. In CSS, woody biomass predominated (20.47 Mg ha⁻¹; 45%), followed by roots (18.47 Mg ha⁻¹; 41%), litter (5.49 Mg ha⁻¹; 12%), and necromass (0.81 Mg ha⁻¹; 2%). In CAR, roots were the dominant component (32.37 Mg ha⁻¹; 55%), followed by woody biomass (16.57 Mg ha⁻¹; 28%), litter (8.39 Mg ha⁻¹; 14%), and necromass (1.68 Mg ha⁻¹; 3%). CSS and CAR shared only 10% of their species and showed significant differences in total biomass (TB) and belowground biomass (BGB), while aboveground biomass (AGB), aboveground woody biomass (AGWB), litter, and necromass did not differ significantly (α = 0.05). The BGB/AGWB ratio was <1 in CSS and >1 in CAR, resembling global patterns of savanna/shrubland and grassland formations, respectively. Considering the sampling design adopted, despite the higher stem density in CAR, larger individuals in CSS compensated for structural differences, resulting in similar aboveground biomass stocks. Our findings reinforce the floristic and structural distinctiveness of Carrasco and reveal contrasting biomass allocation strategies, with a strong dominance of belowground biomass in CAR. These results demonstrate that aboveground-based assessments can substantially underestimate total biomass in semi-arid transitional vegetation and highlight the need to incorporate non-forest ecosystems into biomass inventories, conservation planning, and climate change mitigation strategies. 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

Chimeric antigen receptor T (CAR-T) cell therapy has transformed the treatment of hematologic malignancies, yet its broader application, particularly in solid tumors, remains constrained by high cost, labor-intensive manufacturing, limited production capacity, and variable clinical performance, as well as barriers such as poor trafficking, antigen heterogeneity, and an immunosuppressive tumor microenvironment. In vivo CAR-T cell engineering, in which CAR-T cells are generated directly within the patient, offers a paradigm shift by eliminating the need for ex vivo cell processing and complex logistical infrastructure. Among emerging approaches, messenger RNA (mRNA)-loaded lipid nanoparticles (LNPs) have emerged as a promising and clinically tractable platform for in vivo CAR-T cell generation, enabling direct reprogramming of T lymphocytes within the patient and thereby circumventing the need for leukapheresis, viral vector production, and prolonged ex vivo culture, effectively transforming the patient into their own cell therapy factory. This review synthesizes advances in mRNA-LNP-mediated in vivo CAR-T cell generation, encompassing ionizable lipid chemistry and emerging T cell-targeted delivery strategies, including surface functionalization approaches. We discuss the implications of transient CAR expression for immune activation, safety, and therapeutic durability, alongside CAR design optimization through co-stimulatory domains and safety switches. Preclinical evidence from murine tumor models and non-human primates is integrated with current regulatory considerations, and key barriers to clinical translation are highlighted. Collectively, progress in nucleic acid delivery, synthetic immunology, and precision medicine positions in vivo mRNA-CAR-T therapy as a promising modality for oncology and beyond. Full article

Hematological malignancies encompass a broad spectrum of relatively rare cancers with diverse biological and clinical characteristics that are capable of affecting individuals across all age groups, though certain subtypes show a predilection for specific age ranges. Advances in next-generation sequencing have greatly enhanced our understanding of the molecular and genetic basis of these diseases, while epigenetic, transcriptional, and proteomic analyses have further clarified their pathogenesis. These developments have shaped the classification and treatment of lymphoma. Updated classification frameworks which include the identification of clinically relevant molecular targets have opened the door to a number of targeted agents, each designed to exploit specific vulnerabilities within malignant cells, while stem cell transplantation continues to offer curative potential for eligible patients, with improving safety profiles over time. CAR-T-cell therapy has been extended to multiple blood cancer indications, achieving lasting remissions in patients with previously exhausted treatment options. Bispecific antibodies have further broadened the immunotherapy landscape by redirecting the body’s own T cells against tumor cells, offering a readily available alternative that overcomes many of the practical limitations associated with CAR-T-cell production. The ability to combine these strategies has fundamentally changed what is achievable in blood cancer treatment, with long-term remission now a realistic goal for many patients. This review seeks to outline the core molecular mechanisms underlying lymphoma and leukemia, evaluate currently approved treatment options, discuss significant ongoing clinical trials with practice-changing potential, and explore the prospect of chemotherapy-free approaches in carefully selected patient groups. Full article

Modern vehicles rely on in-vehicle network protocols such as Controller Area Network (CAN) protocol, but these protocols were designed without encryption or authentication. Therefore, the vehicles are exposed to cyber attacks. Motion-based Intrusion Detection Systems (MIDSs) exploit correlation between physically related signals to detect attacks. However, we show that MIDSs are vulnerable, because correlation coefficient is invariant to positive linear scaling. Hence, an adversary may manipulate a signal while keeping its correlation high. In this paper, we propose a Correlation Scaling Attack (CSA) that forges wheel speed signals by scaling their original value while keeping the temporal trend consistent with the other signal. We analyze that correlation coefficient remains unchanged when the signal is forged. Consequently, the CSA evades conventional MIDSs. To mitigate this limitation of MIDS, we exploit covariance between two signals as a complementary indicator, since covariance provides magnitude information. We evaluate the proposed attack and defense mechanism using CAN log data collected from a real vehicle. Experimental results verify the effectiveness of CSA, and we demonstrate that CSA can be detected by observing covariance between two signals. Our research not only indicates that the CSA is a significant threat to cars, but provides a feasible mitigation exploiting the covariance. Full article

Chang Moi Subdistrict is in Chiang Mai Province, Thailand. It is a subdistrict characterized by cultural heritage and everyday community life. The study pursued three objectives: (1) to explore the tourism context of Chang Moi together with tourist attitudes and behaviors; (2) to develop creative tourism routes and evaluate their carbon implications; and (3) to propose appropriate routes and activities for low-carbon creative tourism development. A mixed-method design was employed, comprising qualitative interviews with key stakeholders, a quantitative tourist survey (n = 408), route development, an LCA-informed greenhouse gas assessment, route testing, and synthesis of findings. Three representative route programs were developed: a one-day walking route for international tourists, a one-day private-car route for Thai tourists, and a two-day mixed route. The carbon-footprint results showed that the one-day routes generated substantially lower greenhouse gas emissions (Program 1 = 10.58 kg CO₂ eq; Program 2 = 10.82 kg CO₂ eq) than the two-day overnight route (Program 3 = 31.52 kg CO₂ eq). Waste management was the largest contributor in the one-day routes, whereas Program 3 showed a more distributed emission profile across waste management, creative activities, food and beverage services, and accommodation. Among the assessed activities, flower arranging generated the highest carbon footprint. Overall, the findings indicate that low-carbon creative tourism development in Chang Moi should emphasize compact and walkable route structures, lower-impact creative activities, sustainability-oriented interpretation, and community-based implementation. The study provides an evidence-based basis for tourism planning in Chang Moi and offers implications for other compact creative districts pursuing low-carbon tourism transition. Full article

Vehicle damage detection is an important task in intelligent transportation systems and insurance assessment, yet it remains challenging due to the subtle appearance, irregular shapes, and spatial dispersion of damage regions in complex environments. We propose a specialized structural synergy that organically integrates a GSConv-based Slim-Neck, a dynamic Bi-Level Routing Attention mechanism, and an orientation-aware SIoU loss. Rather than a superficial architectural combination, this cooperative design introduces a novel methodological framework engineered specifically to resolve the fundamental conflict between edge-deployment efficiency and fine-grained feature preservation in vehicle inspection. The method is evaluated on the publicly available Car Damage Detection dataset and compared with representative two-stage and one-stage detectors, including DETR, Faster R-CNN, YOLOv5n, YOLOv8n, and YOLO11n. Experimental results show that the proposed approach achieves a mAP50 of 67.9% and mAP50–95 of 53.8%, outperforming the baseline YOLO11n and other lightweight YOLO variants with only a moderate increase in computational cost. These results indicate that the proposed framework offers a favorable trade-off between detection accuracy and efficiency, showing potential for vehicle damage inspection under resource-constrained conditions. Full article

Background/Objectives: In-hospital cardiac arrest (IHCA) remains a devastating event associated with high morbidity and mortality among general ward patients. While Rapid Response Systems (RRS) can help identify deteriorating patients, maintaining these systems in secondary hospitals is frequently hindered by severe fiscal and personnel constraints. Consequently, evidence regarding the real-world clinical effectiveness of artificial intelligence software as a medical device (AI-SaMD) for predicting deterioration in such resource-constrained settings remains limited. Methods: We conducted a retrospective analysis on a multicenter, staggered-implementation study evaluating 164,761 eligible adult general ward admissions across three secondary hospitals in South Korea. The intervention involved deploying an AI-SaMD (DeepCARS), which utilizes four routine vital signs to predict ward IHCA within 24 h. The primary outcome was ward IHCA. Secondary outcomes included in-hospital mortality and length of stay (LOS). Exploratory analyses investigated the mechanisms of clinical outcomes by evaluating lead-times to interventions, outcomes in sepsis subgroups, changes in care directives, and post-arrest neurological outcomes. Results: AI-SaMD implementation was associated with a 21% reduction in ward IHCA incidence (adjusted rate ratio 0.79; 95% CI, 0.65–0.96; p = 0.016) and a 15% reduction in in-hospital mortality (aRR 0.85; 95% CI, 0.79–0.90; p < 0.001), alongside significantly shorter hospital and intensive care unit LOS. These associations were also observed in patients with sepsis (IHCA aRR 0.71; 95% CI, 0.54–0.93; p = 0.013). Lead-times to critical care intervention and to antibiotic escalation were numerically shorter in the AI-SaMD group by 16.3 h (p = 0.066) and 2.6 h (p = 0.523); poor neurological outcome at discharge among ward IHCA cases was 85/108 (78.7%) in the AI-SaMD group versus 63/102 (61.8%) in the standard-care group (aRR 1.13; 95% CI, 0.99–1.33; p = 0.058); and the full-code death rate did not differ between groups (aRR 0.94; 95% CI, 0.76–1.15)—none of these additional analyses reached statistical significance. Conclusions: In secondary hospitals unable to operate an RRS due to fiscal limitations, implementation of an AI-SaMD as an additional informational layer was associated with lower ward IHCA and in-hospital mortality. The AI-SaMD may serve as an actionable and scalable additional safety layer for general-ward patients in resource-constrained environments where RRS infrastructure is not feasible. Although this was a multicenter, large-scale study, the present analysis was retrospective and quasi-experimental in design; rigorous randomized studies are needed to confirm these associations. Full article

Fatty acid abundance is a key parameter for evaluating the quality of edible oils. This study developed a rapid and non-destructive method for predicting palmitic acid content in edible oils by combining visible-near-infrared (Vis-NIR) reflectance spectroscopy with deep learning models. A total of 1740 reflectance spectra in the range of 350–2500 nm were collected from 87 brands of edible oils, including peanut, soybean, corn, sunflower, rapeseed, sesame, and olive oils. Reference values of palmitic acid content were determined via gas chromatography–mass spectrometry (GC-MS). Two conventional machine learning models (SVR and KNN) and four deep learning models (1D-CNN, 1D-ResNet, 1D-Inception, and 1D-Inception-ResNet) were developed and compared using both full-spectrum data and CARS selected characteristic wavelengths. Among the full-spectrum models, the designed 1D-ResNet model achieved the best performance, with the determination coefficient of prediction ($R_p^2$) of 0.9027 and the root mean square error of prediction ($RMS{E}_p$) of 1.13 in the prediction dataset. The proposed 1D-Inception-ResNet model yielded the best prediction results based on the 91 selected informative wavelengths via competitive adaptive reweighted sampling (CARS), achieving an $R_p^2$ of 0.9825 and an $RMS{E}_p$ of 0.4804 in the prediction dataset. The experimental results indicated that Vis-NIR reflectance spectroscopy combined with informative wavelength selection and deep learning models provided an effective strategy for rapid prediction of palmitic acid content in edible oils. Full article

Chimeric antigen receptor T-cell (CAR-T) therapy has emerged as the most transformative cellular immunotherapy modality, with its evolutionary trajectory intrinsically coupled to advances in immune receptor structure–function paradigms. Recent technological breakthroughs have yielded unprecedented mechanistic insights into immune receptors. Cryo-electron microscopy, single-cell omics, and structural biology have revealed the molecular architecture and functional dynamics of key receptors, including T-cell receptors (TCRs) and B-cell receptors (BCRs). This comprehensive review systematically integrates the latest discoveries in immune receptor structure–function relationships, emphasizing the mechanistic underpinnings of receptor diversity generation, signal transduction networks, and their direct translational impact on CAR-T therapeutic optimization. We critically examine the innovative design principles governing fourth-generation CAR-T cells, delineate breakthrough strategies for overcoming solid tumor immunoresistance, and analyze the synergistic potential of CAR-T and TCR-T technological convergence. Particular attention is devoted to elucidating how fundamental immune receptor research can be harnessed to address the tripartite challenges of safety, efficacy, and persistence that currently constrain CAR-T clinical applications. This review establishes a mechanistic framework for developing next-generation CAR-T technologies grounded in immune receptor biology and provides strategic insights for accelerating cellular immunotherapy clinical translation. Full article

Elevators exemplify mechatronics by integrating mechanical, electrical, and software systems. This paper discusses a four-story tabletop elevator model developed to demonstrate mechatronics and automation concepts in engineering education. The system utilized an Arduino MEGA microcontroller, 3D-printed components, an integrated servo motor, and standard electronics to replicate commercial elevator logic. The physical design features a ball screw linear actuator for vertical motion. It replicates dual-door systems with one door on the moving car and fixed doors at each floor that open simultaneously upon arrival. Development included designing the physical model, prototyping control algorithms, and integrating hardware and software. The model successfully demonstrated key functions: automatic dual-door operation, safety interlocks, smooth inter-floor motion, responsive floor-selection buttons with LED feedback, and efficient routing algorithms prioritizing requests based on current direction and location. Performance testing confirmed that the model accurately replicates modern elevator behavior and serves as an effective educational tool. Full article