Search Results (2,557)

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in childhood, characterized by persistent inattention, hyperactivity, and impulsivity. This narrative review aims to synthesize and critically evaluate recent large-scale magnetic resonance imaging (MRI) studies to clarify the neuroanatomical and functional brain alterations associated with ADHD in children. By addressing current gaps in understanding, this work seeks to identify reliable neurobiological markers that could improve diagnostic accuracy and guide personalized interventions. The literature reveals that large-scale structural MRI studies consistently report abnormal development in total cortical volume and surface area, prefrontal cortex volume, and basal ganglia volume in children with ADHD. Moreover, gray matter alterations show significant age-dependent effects, with the degree of impairment potentially serving as neurobiological markers. Diffusion magnetic resonance imaging studies reveal disrupted white matter microstructures in regions such as the left uncinate fasciculus, superior and inferior longitudinal fasciculi, corpus callosum, cingulum, and internal capsule. Importantly, these white matter abnormalities often persist into adulthood, highlighting their clinical relevance. Functional MRI findings indicate reduced global connectivity within core hubs of the default mode network in children with ADHD. Furthermore, deficits in inhibitory control identified via fMRI may represent one of the neurofunctional signatures that differentiates ADHD from typically developing controls. By consolidating evidence from large-scale multimodal MRI studies, this review provides a comprehensive understanding of the neurodevelopmental alterations in ADHD and underscores their potential utility for improving diagnosis and treatment. Full article

The widespread adoption of Electric Vehicles (EVs) is critically dependent on the deployment of efficient charging infrastructure. However, existing facility location models typically treat charging duration as an exogenous parameter, thereby neglecting the traveler’s autonomy to make trade-offs between service time and energy needs based on their Value of Time (VoT). This study addresses this theoretical gap by developing a heterogeneous network design model that endogenizes both charging mode selection and continuous charging duration decisions. A bi-objective optimization framework is formulated to minimize the weighted sum of infrastructure capital expenditure and users’ generalized travel costs. To ensure computational tractability for large-scale networks, an exact linearization technique is applied to reformulate the resulting Mixed-Integer Non-Linear Program (MINLP) into a Mixed-Integer Linear Program (MILP). Application of the model to the Hubei Province highway network reveals a convex Pareto frontier between investment and service quality, providing quantifiable guidance for budget allocation. Empirical results demonstrate that the marginal return on infrastructure investment diminishes rapidly. Specifically, a marginal budget increase from the minimum baseline yields disproportionately large reductions in system-wide dwell time, whereas capital allocation beyond a saturation point yields diminishing returns, offering negligible service gains. Furthermore, sensitivity analysis indicates an asymmetry in technological impact: while extended EV battery ranges significantly reduce user dwell times, they do not proportionally lower the capital required for the foundational infrastructure backbone. These findings suggest that robust infrastructure planning must be decoupled from anticipations of future battery breakthroughs and instead focus on optimizing facility heterogeneity to match evolving traffic flow densities. Full article

We present an adaptive governance engine for blockchain-enabled Vehicular Ad Hoc Networks (VANETs) that regulates the latency–energy–coherence trade-off under rapid topology changes. The core contribution is an Ideal Information Cycle (an operational abstraction of information injection/validation) and a modular VANET Engine implemented as a real-time control loop in NS-3.35. At runtime, the Engine monitors normalized Shannon entropies—informational entropy S over active transactions and spatial entropy $H_{\mathrm{spatial}}$ over occupancy bins (both on $[0,1]$)—and adapts the consensus mode (latency-feasible PoW versus signature/quorum-based modes such as PoS/FBA) together with rigor parameters via calibrated policy maps. Governance is formulated as a constrained operational objective that trades per-block resource expenditure (radio + cryptography) against a Quality-of-Information (QoI) proxy derived from delay/error tiers, while maintaining timeliness and ledger-coherence pressure. Cryptographic cost is traced through counted operations, $E_{\mathrm{crypto}}=e_h{n}_{\mathrm{hash}}+e_{\mathrm{sig}}{n}_{\mathrm{sig}}$, and coherence is tracked using the LCP-normalized definition $D_{\mathrm{ledger}}\left(t\right)$ computed from the longest common prefix (LCP) length across nodes. We evaluate the framework under urban/highway mobility, scheduled partitions, and bounded adversarial stressors (Sybil identities and Byzantine proposers), using 600 s runs with 30 matched random seeds per configuration and 95% bias-corrected and accelerated (BCa) bootstrap confidence intervals. In high-disorder regimes ($S\gtrsim 0.8$), the Engine reduces total per-block energy (radio + cryptography) by more than 90% relative to a fixed-parameter PoW baseline tuned to the same agreement latency target. A consensus-first triggering policy further lowers agreement latency and improves throughput compared with broadcast-first baselines. In the emphasized urban setting under high mobility ($v=30$ m/s), the Engine keeps agreement/commit latency in the sub-100 ms range while maintaining finality typically within sub-150 ms ranges, bounds orphaning (≤10%), and reduces average ledger divergence below $0.07$ at high spatial disorder. The main evaluation is limited to $N\le 100$ vehicles under full PHY/MAC fidelity. PoW targets are intentionally latency-feasible and are not intended to provide cryptocurrency-grade majority-hash security; operational security assumptions and mode transition safeguards are discussed in the manuscript. Full article

The current-mode logic (CML) driver has evolved alongside integrated circuit (IC) technology. Its typical structure contains a tail current source, differential amplifying transistors, and load resistors. It is widely used in modern optical transceivers and other serial link transceivers, and is compatible with various processes, including CMOS, SiGe BiCMOS, and InP DHBT. The basic performance indicators of CML driver include gain, bandwidth, power, and total harmonic distortion (THD). For different application scenarios, different tail currents and load resistance are required. Nowadays, as the performance requirements for drivers in various applications continue to increase, more techniques need to be employed to balance high speed, high output amplitude, high linearity, and low power, such as bandwidth expansion techniques, linearity improvement techniques, and gain control techniques. In this review, the electrical characteristics of basic CML circuits are highlighted and compared with other interface level standards. The advancement of CML drivers is summarized. Emerging CML structures and performance enhancement technologies are introduced and analyzed. Design considerations are concluded in terms of the challenges faced by high-speed drivers. The review provides comparative study and comprehensive reference for designers. Full article

Focusing on the challenges posed by the strong randomness, weak manifestation, and difficulty in diagnosing intermittent faults (IFs) in high-density integrated circuits (HDICs)—often induced by bonding wire defects—this paper takes the GPIO interfaces of a typical DSP chip as the research object. It systematically analyzes the response characteristics of intermittent short-circuit and open-circuit faults and proposes a hybrid intelligent diagnosis method based on the Sparrow Search Algorithm-optimized Variational Mode Decomposition and Attention-based Support Vector Machine (SSA–VMD–Attention–SVM). A dedicated fault injection circuit is designed to accurately replicate IFs and acquire the power supply current response signals. The Sparrow Search Algorithm (SSA) is employed to adaptively optimize the parameters of Variational Mode Decomposition (VMD) for effective extraction of frequency-domain features from fault signals. A three-level attention mechanism is introduced to adaptively weight multi-domain features, thereby highlighting the key fault components. Finally, the Support Vector Machine (SVM) is utilized to achieve high-precision fault classification under small-sample conditions. Experimental results demonstrate that the proposed method achieves a diagnostic accuracy of 97.78% for intermittent short-circuit and open-circuit faults in the GPIO interfaces of the DSP chip, significantly outperforming traditional methods and exhibiting notable advantages in terms of diagnostic accuracy, robustness, and interpretability. Full article

A YOLOv8-based deep learning model was developed to address real-time detection and dynamic regulation needs of the electrohydrodynamic atomization process. An EHDA experimental system was built to obtain images of six typical atomization modes, forming a dataset with 6000 images. After annotation and mosaic augmentation, the dataset served as the training data for the model. The YOLOv8 adopts a “backbone-neck-head” architecture to extract and fuse features, decouple classification and detection, and optimize performance. Experimental results demonstrate that on the test set, the model attains a precision value, recall rate, and mAP50 of 0.995, alongside an mAP50-95 of 0.8. Additionally, its prediction accuracy exceeds 0.99 across all operational modes. Compared with 10 models, it has the best precision and mAP50, as well as low computational complexity, combining high accuracy and lightweight advantages, which can be effectively used for real-time detection of EHDA modes. Full article

Climate change has exacerbated flood risks for urban infrastructure, rendering sewage treatment facilities (STFs) particularly vulnerable due to their typical low-lying topographic placement. However, conventional flood risk assessment methodologies often rely solely on physical hazard parameters such as inundation depth, neglecting the functional interdependencies and operational criticality of individual treatment units. To address this limitation, this study proposes the Integrated Hydro-Operational Risk Assessment (IHORA) framework. The IHORA framework synthesizes 2D hydrodynamic modeling with a modified Hazard and Operability Study(HAZOP) study to systematically identify unit-specific physical failure thresholds and employs the Analytic Hierarchy Process (AHP) to quantify the relative operational importance of each process based on expert elicitation. The framework was applied to an underground STF under both fluvial flooding and internal structural breach scenarios. The results revealed a significant risk misalignment in traditional assessments; vital assets like electrical facilities were identified as high-risk hotspots despite moderate physical exposure, due to their high operational weight. Furthermore, Cause–Consequence Analysis (CCA) was utilized to trace cascading failure modes, bridging the gap between static risk metrics and dynamic emergency response protocols. This study demonstrates that the IHORA framework provides a robust scientific basis for prioritizing mitigation resources and enhancing the operational resilience of environmental facilities. Full article

Accurate quantification of aircraft emissions and their uncertainties is essential for well-informed policy-making, air quality management, and the development of sustainable airport strategies. This study addresses uncertainties in aircraft emission estimates implemented for local air pollutants with hourly resolution at six European airports. Publicly available flight-tracking data were used to determine aircraft movements and types, but they typically lack detailed information on aircraft engine models, thus contributing to uncertainties in emission factors. Times-in-mode for take-off, climb-out, and approach modes followed International Civil Aviation Organization (ICAO) recommendations, while taxi times, known to vary between airports, were modeled using statistical distributions derived from Eurocontrol, and the contribution of taxi time to overall uncertainty in emission estimates was investigated. Monte Carlo simulation combined with Sobol sensitivity analysis identified the relative contribution of each uncertainty source. On average, the results indicate an uncertainty of 23% for CO, 34% for HC, 7% for NO_x, and 21% for PM across the airports analyzed. Overall, the proposed methodology introduces a novel framework utilizing publicly available, hourly resolved flight-tracking data with robust uncertainty analysis to estimate airport-level emissions with enhanced reliability, providing crucial information for local air quality assessment and policy development. Full article

With the widespread diffusion of personal Internet of Things (IoT) devices, Electromagnetic Side-Channel Attacks (EM-SCAs), which exploit electromagnetic emissions to uncover critical data such as cryptographic keys, are becoming extremely common. Existing shielding approaches typically rely on bulky or opaque materials, which limit integration in modern IoT environments; this motivates the need for a transparent, lightweight, and easily integrable solution. Thus, to address this threat, we propose the use of electromagnetic metasurfaces with shielding capabilities, fabricated with an optically transparent conductive film. This film can be easily integrated into glass substrates, offering a novel and discrete shielding solution to traditional methods, which are typically based on opaque dielectric media. The paper presents two proof-of-concept case studies for shielding against EM-SCAs. The first one investigates the design and fabrication of a passive metasurface aimed at shielding emissions from chip processors in IoT devices. The metasurface is conceived to attenuate a specific frequency range, characteristic of the considered IoT processor, with a target attenuation of 30 dB. At the same time, the metasurface ensures that signals from 4G and 5G services are not affected, thus preserving normal wireless communication functioning. Conversely, the second case study introduces an active metasurface for dynamic shielding/transmission behavior, which can be modulated through diodes according to user requirements. This active metasurface is designed to block undesired electromagnetic emissions within the 150–465 MHz frequency range, which is a common band for screen gleaning security threats. The experimental results demonstrate an attenuation of approximately 10 dB across the frequency band when the shielding mode is activated, indicating a substantial reduction in signal transmission. Both the case studies highlight the potential of transparent metasurfaces for secure and dynamic electromagnetic shielding, suggesting their discrete integration in building windows or other environmental structural elements. Full article

Composite laminates designed for additive manufacturing require efficient material distribution to minimize weight while maintaining structural integrity. Traditional topology optimization methods, however, produce continuous density fields incompatible with layer-based fabrication. This work presents Selective Reinforcement Optimization (SRO), a stress-driven methodology that converts uniformly loaded laminate layers into localized reinforcement regions, or “patches”, at critical stress concentrations. The approach employs layer-wise statistical analysis of Tsai–Wu failure indices to identify high-variance layers; applies DBSCAN clustering to extract spatially coherent stress regions while rejecting artificial concentrators; and generates CAD-compatible and manufacturing-ready boundary geometries through a custom concave hull algorithm. The method operates iteratively in dual modes: lightweighting progressively removes full layers and replaces them with localized regions when the structure is safe, while strengthening adds reinforcement without layer removal when failure criteria are approached. Case studies demonstrate weight reductions of 10–30% while maintaining failure indices below unity, with typical convergence achieved within 100 iterations. Unlike classical topology optimization, which requires extensive post-processing, SRO directly outputs discrete patch geometries compatible with composite additive manufacturing, offering a computationally efficient and production-oriented framework for the automated design of layered composite structures. Full article

To address the problem that transformer inrush currents under no-load and energization conditions can easily trigger misoperations of differential protection, this paper proposes a multi-feature identification method for transformer inrush current based on adaptive variational mode decomposition. Traditional methods typically rely on fixed physical features or single criteria, making them sensitive to operating condition variations and prone to misclassification or missed detection under complex disturbances, with limited generalization capability. The proposed method first performs adaptive VMD decomposition of current waveforms under different operating conditions. On this basis, time-domain, frequency-domain, and nonlinear features are extracted to comprehensively characterize the signal’s amplitude, spectral, and complexity information. Then, by combining the ReliefF algorithm with forward stepwise feature selection, the method reduces feature dimensionality while maintaining high discriminative power and low redundancy. Using the VMD-ReliefF-EEFO-SVM classification model, the approach achieves efficient and accurate discrimination between inrush currents and fault currents. Simulation results demonstrate that the proposed identification method adapts well to various operating conditions and exhibits strong robustness and versatility. Full article

This paper describes a composite control approach that improves the accuracy and dynamic performance of the control of a voice-coil-driven, two-dimensional fast steering mirror (FSM). Strong nonlinearity, perturbation of parameters, unmodeled dynamics and external disturbances typically compromise the performance of the FSM. The proposed controller combines a tracking differentiator (TD), linear extended state observer (LESO), and a double-integral global fast terminal-sliding mode control (DIGFTSMC). The TD corrects the reference command signal, and the LESO approximates and counteracts system disturbances. The sliding surface is then equipped with the double-integral operators and an improved adaptive reaching law (IARL) to enhance tracking accuracy, response speed and robustness. Prior to physical experiments, systematic numerical simulations were conducted for five control algorithms across four typical test scenarios, verifying the proposed controller’s feasibility and preliminary performance advantages. It is found through experimentation that the proposed controller lowers the time esterified by the step response adjustment by 81.0% and 48.4% more than the PID controller and the DIGFTSMC approach with no IARL, respectively, and the proposed controller enhances error control when tracking sinuoidal signals and multisinusoidal signals. Simulation results consistently align with experimental trends, confirming the proposed controller’s superior convergence speed, tracking precision, and disturbance rejection capability. Furthermore, it cuts the angular movement swing by an average of over 44% through dismissing needless vibration interruptions as compared to other sliding mode control techniques. Experimental results demonstrate that the proposed composite control approach significantly enhances the disturbance rejection, control accuracy, and dynamic tracking performance of the voice-coil-driven FSM system. Full article

To evaluate the application potential of bamboo in cold regions, this study systematically compared the differences in the effects of freeze–thaw cycles on the longitudinal compressive properties of moso bamboo (Phyllostachys edulis) and Chinese fir (Cunninghamia lanceolata). By subjecting the materials to 0, 5, and 10 standard freeze–thaw cycles, the evolution patterns were analyzed from three aspects: mechanical properties, failure modes, and apparent color. The results show that bamboo exhibits significantly superior freeze–thaw resistance: after 10 cycles, bamboo retained 95.4% of its compressive strength (decreasing from 50.2 MPa to 47.9 MPa), whereas the strength of Chinese fir decreased by 14.2% (from 46.7 MPa to 40.0 MPa). The elastic modulus of bamboo remained stable, while that of Chinese fir decreased by 30.86%. Load–displacement curves revealed that bamboo displayed a ductile plateau after failure, whereas Chinese fir exhibited a linear drop-off. Analysis of failure modes further highlighted the intrinsic differences between the materials: bamboo primarily underwent progressive buckling of fiber bundles, forming typical accordion-like folds; Chinese fir mainly showed brittle failures such as end crushing and longitudinal splitting. Color characterization indicated that the lightness index L of the bamboo outer skin (bamboo green) decreased by 26.1%, while the chromaticity indices a (red) and b* (yellow) increased significantly, showing the most notable changes; the color of Chinese fir and the bamboo inner skin (bamboo yellow) remained relatively stable. This study demonstrates that natural bamboo outperforms Chinese fir in terms of frost resistance, toughness, and strength retention in the short term. The findings provide important experimental evidence and design references for promoting the application of bamboo in engineering projects in cold regions. Full article

Ensuring friction stir welding (FSW) joint quality typically relies on ultrasonic testing (UT) and radiographic testing (RT), but achieving complete coverage is challenging, and echo-based defect discrimination becomes difficult in dissimilar joints. Laser ultrasonics is a promising non-contact technique that remotely assesses weld quality and provides high spatial resolution at the generation and detection points. This study establishes a laser-ultrasonic method for defect detection in dissimilar Cu–Al FSW joints. Slit-like artificial defects (0.1–2.5 mm deep in 5 mm thick plates) were introduced at the Al-side interface of specimens fabricated with an Al-offset tool. Experiments and numerical simulations were used to evaluate wave modes and irradiation configurations, focusing on intensity-attenuation ratios of specific wave types, including longitudinal and Rayleigh waves. On the non-slit surface, attenuation of reflected longitudinal waves enabled detection of defects ≥0.5 mm deep. On the slit surface, Rayleigh-wave attenuation allowed identification of defects as shallow as 0.1 mm, although slit-side irradiation may be less practical during joining. These results demonstrate that defect identification in dissimilar materials can be achieved by evaluating wave-intensity attenuation rather than relying solely on the presence of reflected echoes, suggesting potential for implementing laser ultrasonics in in-process monitoring of FSW joints. Full article

Although previous studies composited response of sea surface temperature (SST) to typhoon sea surface wind (SSW) forcing around typhoon center, how SST responded spatiotemporally along the typhoon track over the ocean remains unclear. Through Empirical Orthogonal Function (EOF) analysis, several isolated typhoons in the Western North Pacific (WNP) from 2021 to 2024 were investigated. Two SSW forcing modes and two SST response modes were identified. The first SSW mode spatially reflects the overall distribution of SSW along the track, centering at its maturation position. And the first SST mode exhibits a high spatial correlation ($\left|R\right|>0.85$) with this SSW mode. The second SSW mode displays a distinct track-long scale dipole pattern along the path of the typhoon, representing its intensity variation during the “development–maturation–decay” lifecycle. Similarly, the second SST response mode shows a significant but lower correlation with this second SSW mode. Both corresponding SST response modes typically lag behind their respective wind-forcing by approximately 2 to 4 days, indicating that these SST response modes are direct reactions to SSW forcing. These cases implies that two track-long scale SSW modes are generally present during the lifecycle of typhoons and that their corresponding SST responses are dominated accordingly. Full article