Search Results (1,751)

Benefiting from tunable emission from ultraviolet to near-infrared windows, long luminescence lifetimes, and exceptional photostability, rare earth (RE)-doped nanomaterials overcome the limitations of conventional dyes and quantum dots, enabling deep-tissue, high-resolution, and low-background imaging. As multifunctional fluorescent probes, RE-doped nanomaterials are driving the development of next-generation biomedical imaging. This review summarizes recent advances in the structural design of RE-doped nanomaterials, surface engineering for biocompatibility, and targeting strategies for improved performance, and highlights their integration into advanced imaging modalities, including NIR-I/II fluorescence, FLIM, PAI, super-resolution STED, multimodal FL/MRI/CT, X-ray-excited luminescence, and persistent luminescence. Meanwhile, mechanistic insights, material innovations, and comparative advantages are discussed. Furthermore, challenges related to quantum yield, scalable synthesis, imaging resolution, and clinical translation are considered, while future directions—centered on multifunctional probe design, NIR-II imaging, and AI-assisted data analysis—are proposed, offering a versatile platform for precise multimodal imaging with significant potential to advance early diagnosis, personalized therapy, and clinical applications. Full article

Construction materials like steel and concrete have been used for thousands of years; however, their industrial-scale production began relatively recently in the 19th century. These materials are still being improved as the drive to build taller buildings, longer bridges, larger dams, and similar engineering marvels keeps pushing boundaries and requirements to previously unimaginable values. Yet, testing and characterization of construction materials that make all that progress possible are overshadowed in scientific literature by more trendy materials such as graphene, composites, nanomaterials, smart materials, and biomaterials. The objective of this review was to identify, collect, and systematically analyze recent papers in which the researchers performed experimental testing on construction materials to document how state-of-the-art experimental practice extends beyond what standardized protocols prescribe. This paper covers Uniaxial Tensile Testing (UT), Compact Tension C(T), Uniaxial Compression (UC), and Single Edge Notched Bending SEN(B), as they are the most commonly used and best-suited techniques for construction material analysis. State-of-the-art papers featuring these techniques were systematically gathered using AI-assisted literature discovery tools, and their contributions beyond ISO and ASTM standards were identified and summarized. Using this review, material scientists and engineers can quickly discover the most influential and relevant papers with the actual experimental data and can apply the testing procedures described in these papers in their laboratories so they can compare their results with the previously published measurements and make an engineering decision based on appropriate comparisons. Full article

The introduction of minimally invasive surgery (MIS) marked a turning point in the history of medicine, driving one of the sharpest declines in surgical mortality and morbidity ever recorded—saving millions of lives and sparing an estimated one billion patients the suffering once inherent to large-incision surgery. Within a single generation, this once highly contested surgical innovation became the global standard of care, transforming surgical practice across disciplines and on a global scale. By every measure of public health, these outcomes place modern minimally invasive and robotic-assisted surgery as among the most consequential life-saving advances in modern medical history. This review examines the clinical impact and global dissemination of MIS, tracing its evolution from Camran Nezhat’s pioneering expansion of laparoscopy beyond diagnostics to complex therapeutic procedures across surgical disciplines. Drawing on decades of evidence across gynecology, general surgery, and urology, we show that MIS is associated with substantial reductions in perioperative mortality, major complications, blood loss, infections, thromboembolic events, postoperative pain, and length of hospital stay, while maintaining oncologic equivalence and improving functional and quality-of-life outcomes. Beyond these technical advances, MIS catalyzed a broader reimagining of surgery itself, challenging long-standing norms rooted in large-incision approaches and shifting the field toward precision, organ preservation, and pathology-directed intervention. These changes were accompanied by parallel advances in multiple domains, including in imaging, intraoperative visualization technologies, surgical anatomy, instrumentation, and nerve- and organ-sparing techniques—developments that collectively established the foundation for contemporary minimally invasive and robotic-assisted surgery. Collectively, these advances have contributed to the prevention of an estimated 10–20 million surgery-related deaths that would likely have occurred under the large-incision approaches of the past. Full article

Ensuring safety in autonomous vehicles (AVs) requires predictive control methods that can handle dynamic constraints, uncertain interactions, and real-time decision making. This review examines safety-oriented model predictive control (MPC) for AVs using a PRISMA-guided screening process. From 363 records published between January 2015 and March 2026, 101 peer-reviewed studies were selected for qualitative synthesis. The literature is organized into three domains: collision avoidance and risk mitigation, trajectory tracking and path following, and intersection and coordination tasks. Across these domains, MPC has evolved from nominal tracking and geometric avoidance toward risk-aware, robust, hierarchical, and learning-enhanced formulations. Unlike broader reviews on autonomous driving control, this review focuses specifically on safety-oriented MPC and compares the reviewed literature in terms of safety mechanisms, uncertainty treatment, validation practice, computational feasibility, and deployment limitations. The review shows that MPC remains one of the most versatile frameworks for AV safety, but the evidence base is weakened by heavy reliance on simulation, inconsistent safety metrics, limited validation under uncertainty, and uneven treatment of computational feasibility. The most promising directions are hybrid architectures that combine model-based safety guarantees with uncertainty-aware prediction, learning-assisted adaptation, and scalable coordination mechanisms. Full article

In this paper, we propose and evaluate a feature distillation technique for object detection under poor visibility conditions, and we analyze its impact when deployed on an FPGA platform. We demonstrate via extensive experiments how different detection architectures generalize across scenes, and we infer that a scale-permuted feature extraction is the ideal choice for detection tasks in unconstrained environments with an 11–12% gain. As verified by the experiments, image enhancement often fails to provide significant detection gains. We hence introduce a joint training in a scale-permuted student network that learns dehazed features from a dual teacher network without an explicit dehazing step. The student learns to replicate not only the teacher outputs but also the decision-making process of the teacher by using attention transfer. Although the overall goal is to produce a real-time system capable of providing driving assistance in challenging scenarios, the FPGA implementation of a scale-permuted network is the first of its kind. To achieve effective implementation of the model in FPGA technology, a high-level synthesis approach and model compression techniques are employed to obtain a deployment with a good trade-off between quality and memory footprint metrics. We develop two distilled models using the joint feature distillation technique and show that these perform better in poor visibility scenes when compared to other detectors with similar size or even bigger sizes in some cases. Our 8.5 M model shows an mAP gain of almost 1% compared to YOLOv10-M with 15 M parameters, on the Cityscapes Hazy dataset. On night images from the BDD dataset, our 8.5 M model shows an approximate mAP gain of 4% compared to YOLO26-S with 9.5 M parameters. We further perform cross-domain testing with the DriveIndia dataset to show that our models generalize well beyond the distillation distribution and can be used for generic driving scenarios. Full article

Electric vehicles (EVs) relying solely on lithium-ion (Li-ion) batteries face limitations related to range, mass, charging time, and battery downsizing. This study develops a dynamic system-level modeling framework for integrating an aluminum–air (Al–air) battery with a Li-ion traction battery within a MATLAB/Simulink electric vehicle platform. Two integration strategies were evaluated: (i) Al–air operation as a range extender activated through SOC-based control logic, and (ii) Al–air operation as an auxiliary power unit supplying non-traction loads. The Al–air subsystem was implemented using an experimentally informed polarization-based model coupled with aluminum consumption tracking and DC–DC converter integration. Vehicle performance was evaluated under UDDS, HWFET, WLTP, and FTP-75 drive cycles. Results show that coupling a 24.6 kWh Al–air pack with a downsized 20.3 kWh Li-ion pack enabled driving ranges of 379 km (UDDS), 523 km (HWFET), and 450 km (WLTP), exceeding the baseline full-capacity Li-ion configuration while reducing total battery-system mass by more than 50%. When operated as an auxiliary power unit under a constant 3 kW auxiliary load, the Al–air system increased the vehicle range by 44–96 km depending on the drive cycle. The results demonstrate the feasibility of Al–air-assisted dual-energy-storage architectures for extending the EV range while reducing dependence on large Li-ion battery packs. Full article

Polymer-based composites with magnetic properties are promising materials that are able to combine the usual polymer features (low density, high electrical resistance, enhanced flexibility, and processability, etc.) with magnetic properties typically associated with ferro- or ferrimagnetic metals, alloys or metal oxide. The combination of recycled NdFeB powders with additive manufacturing techniques based on material extrusion enables the production of magnetic composites. The novelty of this approach lies in the use of 3D printing supported by an external magnetic field, which is used to align the particles during the printing process and thus improve the final magnetic properties. This approach represents a sustainable strategy for the recovery of electronic waste, converting it into high-value-added magnetic materials intended for additive manufacturing applications. Micrometric particles made of a Neodymium–Iron–Boron (NdFeB) alloy are compounded with a flexible thermoplastic matrix made of polybutylene adipate-co-terephthalate (PBAT). The NdFeB alloy is recovered from permanent magnets of obsolete hard drives and is demagnetized, ground to powder under an inert atmosphere, and finally sieved to a particle size below 50 µm. The obtained powder is mixed with the polymer using a twin-screw extruder. The composite material containing the NdFeB particles is then processed to obtain a calibrated filament, used for the fused deposition modeling (FDM) three-dimensional (3D) printing of magnetic composites. To improve the composite’s ferromagnetic behavior, the particles were aligned along the stacking direction of the layers during the 3D FDM process by printing directly onto a permanent magnet placed on the build plate. Composites containing up to 50% by weight of recycled NdFeB powder were successfully processed using FDM technology, exhibiting increased stiffness, with the storage modulus rising from 123 to 178 MPa at 20 °C, while magnetic field-assisted printing increased the remanence from 11 to 28 emu/g and improved the reduced remanence from 0.21 to 0.49, corresponding to an estimated fourfold improvement in the magnetic energy product. Full article

Vulnerable Road Users (VRUs) are involved in a significant proportion of traffic fatalities, and they are highly exposed to severe injuries in urban traffic environments. For detecting and tracking VRUs, LiDAR technology offers precise 3D perception capabilities, overcoming challenges posed by their small size, dynamic behavior, and frequent presence in occluded or congested areas. This work aims to conduct a scoping review of LiDAR-based solutions for preventing and reducing accidents involving VRUs, synthesizing current methodologies, evaluating detection and tracking approaches, and identifying strategies to improve urban safety through data-driven interventions. An analysis of 49 publications indicates that effective monitoring of VRUs depends on a strategic balance between technological performance and practical limitations, such as system costs, calibration complexity, and hardware constraints. Privacy-preserving techniques, such as anonymization and LiDAR-based sensing, are essential to enable ethically responsible large-scale data collection. Full article

Grassland highways present a distinctive application context for human–machine co-driving (HMCODR), yet the evidence on takeover behavior in such environments remains limited. This study employed a driving simulator experiment to investigate how takeover scenarios, weather conditions, and driver gender influence eye-movement behavior during automated-driving takeovers on a representative Inner Mongolia grassland highway. 36 student participants completed a 2 (gender) × 3 (scenario: stationary vehicle ahead, ramp vehicle merging, and livestock intruding into lane) × 3 (weather: clear, rain–snow, and sandstorm) mixed experimental design. The mean fixation duration (MFD), fixation rate (FR), mean saccade duration (MSD), saccade amplitude (SA), and relative change in pupil area (RCPA) were recorded using a wearable eye tracker, and a mixed-design ANOVA was conducted based on these five metrics. The scenario and weather had significant effects on all eye-movement measurements (p < 0.05), with interactions observed for some indices. The ramp vehicle merging scenario elicited denser visual scanning, as reflected by a shorter MFD and a higher FR. In the livestock intruding into lane scenario, drivers exhibited a broader visual search, characterized by a longer MSD and a larger SA, along with greater pupil-area fluctuations (a higher RCPA); these effects tended to be more pronounced under rain–snow and sandstorm conditions. Across most of the condition combinations, the female drivers tended to show a higher FR, a shorter MFD, and a higher RCPA, whereas male drivers tended to show a slightly larger MSD and SA. Among all the eye-movement indices, RCPA showed a relatively more pronounced gender difference, with female drivers exhibiting values approximately 16–32% higher than male drivers. These findings extend the takeover research to grassland highway contexts and suggest that takeover assistance should place greater emphasis on hazard-relevant cues and timely gaze guidance under complex scenarios and adverse weather conditions, while the observed differences in the visual-response patterns may also inform more personalized prompting strategies to better support safety in high-risk takeover situations. Full article

To address the critical challenges of edge chipping and poor processing quality in sapphire precision dicing, this paper proposes a femtosecond laser filamentation-guided dicing technology. By systematically investigating the influence of pulse overlap rate, energy, and scan counts on damage evolution, the physical differences between 343 nm UV and 515 nm visible lasers in suppressing plasma shielding and breaking through processing saturation limits are revealed. The results indicate that an extremely high pulse overlap rate (>98%) significantly inhibits lateral energy dissipation and drives the efficient propagation of the filament deep along the optical axis; furthermore, the 343 nm laser demonstrates superior removal rates and localisation compared to the 515 nm laser. Using super-resolution imaging, the precision cleavage cross-section is clearly categorised into four evolutionary stages: general ablation, filament ablation, transition, and mechanical cleavage. To mitigate morphological degradation induced by multiple scans, a sacrificial-layer-assisted strategy is innovatively proposed to achieve spatial damage transfer and in situ self-polishing, effectively eliminating longitudinal damage striations and residual stress-induced hackles. Finally, taper-free, high-precision separation of 1 mm × 450 μm micro-units is successfully achieved on a 220-μm-thick sapphire wafer. This technology not only achieves ultra-low-loss dicing but also establishes a highly efficient, contamination-free in situ characterisation paradigm for buried structures in hard and brittle materials. Full article

Path planning is critical for multi-robot systems (MRS), directly affecting the operation efficiency, execution time, and operational cost. Despite extensive research and successful applications of multiple algorithms, achieving globally optimal solutions in cluttered or dynamic environments remains a significant challenge. Issues such as scalability with an increasing number of robots, computational efficiency, system robustness, and coordination complexity continue to drive the development of more reliable approaches. This study reviews modelling approaches, optimisation criteria, and solution algorithms based on the roadmap planning methods that are widely used for multi-robot path planning (MRPP). It focuses on three graph-based algorithms: MRPP algorithm, central algorithm (CA), and the optimisation central algorithm (OCA). These algorithms utilise visibility graphs (VG) for environment representation and Dijkstra’s algorithm for shortest path computation, while incorporating algebraic connectivity to improve coordination, safety, and scalability. In addition, the technological context and implementation platforms, including simulation environments, cloud robotics, and AI-based frameworks, are conceptually examined. The potential applications of these methods in assistive robotics are highlighted, particularly in supporting a safe and reliable navigation in healthcare and human-centred environments. The article synthesises theoretical and practical insights, identifies current limitations and challenges, and outlines future research directions for efficient, scalable, and robust MRPP. Full article

Volatile organic compounds (VOCs) represent a significant threat to both environmental quality and public health, driving the need for efficient abatement technologies. Herein, a series of PdCu dual single-atom catalysts supported on peroxide-modified attapulgite (ATP) were synthesized via a microwave-assisted solvothermal approach, and the effect of the Pd/Cu ratio on the catalytic oxidation of toluene was investigated. Results showed that the Pd1Cu1/ATP catalyst exhibited exceptional catalytic performance, achieving 99% toluene conversion at 240 °C under a high weight hourly space velocity of 20,000 mL·g⁻¹·h⁻¹. This high efficiency is attributed to the modification of ATP with hydrogen peroxide solution, which exposes abundant Si-OH, facilitating the immobilization of atomically dispersed atoms and enhancing the adsorption of toluene molecules. In addition, the strong metal–support interaction between the PdCu dual atoms and the ATP support significantly lowers the energy barrier of the reaction, thereby enhancing the low-temperature catalytic activity. In situ DRIFTS further elucidated the reaction pathway and intermediate evolution during toluene oxidation. This work offers an effective strategy for designing highly efficient dual single-atom catalysts for VOCs removal. Full article

Breakwater construction at meso-tidal ports fundamentally alters near-field hydrodynamics and drives harbor sedimentation, yet the three-dimensional mechanisms linking entrance geometry to sediment flux remain poorly quantified. Here, we apply a validated Delft3D tidal–sediment coupled model to Caofeidian Port, Bohai Bay, comparing pre-construction baseline conditions against four entrance width scenarios (400, 300, 250, and 200 m). Breakwater enclosure reduces depth-averaged harbor velocities by 61.9–63.2% during spring tides, while generating tip-jet velocities of 1.41–1.53 m s⁻¹ at the eastern breakwater head—exceeding pre-construction maxima by 14–18%. The eastern tip produces an ebb vortex (radius ~230 m; peak vorticity 0.034 s⁻¹) approximately 34% larger and 62% more intense than its flood counterpart, driving vortex-assisted sediment recirculation toward the harbor interior despite ebb-dominant background velocities. Reynolds flux decomposition confirms that the eastern tip-vortex sector contributes ~39% of net sediment import (advective component: −0.7%), directly quantifying vortex-assisted recirculation as an independent transport mechanism. Bed shear stress falls below the critical erosion threshold (${\tau }_{ce}$ = 0.22 Pa) across 76.8% of the harbor area during spring tides (robust lower bound ~60% under wave-coupling correction), creating a structurally stable depositional interior, while the near-entrance zone sustains persistent tidal-cycle resuspension. Asymmetric tidal pumping—flood-phase open-sea SSC of 0.088 kg m⁻³ versus ebb-phase harbor SSC of 0.032–0.041 kg m⁻³—drives net spring-tide sediment import of 14.8 × 10⁶ kg per cycle (wave-coupled upper bound: 17.8–19.2 × 10⁶ kg per cycle). Entrance width reduction from 400 to 300 m achieves a favorable sedimentation-to-water exchange trade-off (marginal efficiency ratio 1.23), whereas further reduction to 200 m indicates onset of hydraulic choking. The marginal efficiency ratio declines sharply from 1.23 (400 → 300 m) to 1.03 (300 → 250 m) to 1.01 (250 → 200 m), indicating a hydraulic transition within the 250–300 m range that warrants targeted refinement in future studies. Full article

With the wide application of artificial intelligence (AI) in the Internet of Vehicles (IoV), IoV is under pressure for data transmission and real-time sensing. Integrated sensing and communication (ISAC) is one of the key technologies to alleviate that pressure. Obstacles can cause communication disruptions and increased delays, hindering autonomous driving information acquisition and causing traffic hazards. The application of Reconfigurable Intelligent Surfaces (RISs) aims to solve this problem. This study focuses on RIS-assisted multi-base station (MBS) scenarios in the presence of obstacles. This study aims to maximize the communication rate, minimize the sensing error, and reduce the switching frequency by optimizing the RIS phase shift and beamforming. The problem is modeled as mixed integer nonlinear programming (MINLP) and further described as a Markov Decision Process (MDP). We use Long Short-Term Memory (LSTM) to predict the environmental state and propose two optimization algorithms, Multi-Factor Decision Deep Deterministic Policy Gradient (MFD-DDPG) and Mixed Discrete and Continuous Action DDPG (MDCA-DDPG). In the first algorithm, we consider multiple factors to make a switching decision and use DDPG to yield the optimal action. The second algorithm improves DDPG by outputting a discrete switching decision and a continuous optimized action simultaneously. Simulations show that the proposed algorithms significantly improve the system performance, and the communication rate is increased by more than 40% in specific multi-vehicle scenarios compared to the benchmark. Full article

Much of the research and proposed industrial deployment of Remote Operations (ROs) in support of automated vehicles is founded on the optimistic premise that in-vehicle standby drivers and Safety Officers (SOs) can easily be replaced with ROs, with some commercial models proposing that a single RO supervise over 30 vehicles. However, emerging evidence suggests that the RO task is fundamentally different from the in-vehicle driving task. Furthermore, communications latency and reliability constraints, coupled with fragmented attention and altered task demands, introduce distinctive human factor challenges. These include degraded situational awareness, increased cognitive workload, and reduced capacity for timely intervention. The result is a widening gap between what is commercially desirable and what may be operationally appropriate. This paper argues that the central question for remote operation in support of automated vehicles is not one of technical feasibility but of human-centred appropriateness, and debates which RO roles should continue to be developed and which should be constrained or avoided. We present a synthesis of research on remote vehicle operations, identifying recurring human-factor limitations and mapping them to proposed remote tasks. The paper concludes with targeted recommendations for designers, operators, and regulators intended to question the scaling of teleoperation models and to reframe the debate from “Can we teleoperate?” to “Under what conditions should we?” Full article