Search Results (1,348)

Traditional complementary metal-oxide-semiconductor (CMOS) logic gates serve as the fundamental building blocks of modern computing, operating through the electron charge manipulation wherein binary information is encoded as distinct high- and low-voltage states. However, as physical dimensions approach the quantum limit, conventional logic gates encounter fundamental bottlenecks, including power consumption barriers, memory limitations, and a significant increase in static power dissipation. Consequently, the pursuit of novel low-power computing methodologies has emerged as a research hotspot in the post-Moore era. Logic gates based on magnetic skyrmions constitute a highly promising candidate in this context. Magnetic skyrmions, nanoscale quasiparticles endowed with topological protection, offer ideal carriers for information transmission due to their exceptional stability and mobility. In this work, we provide a concise overview of the current development status and underlying operating principles of magnetic skyrmion logic gates across various magnetic materials, including ferromagnetic, synthetic antiferromagnetic, and antiferromagnetic systems. The introduction of magnetic skyrmion-based logical operations represents a paradigm shift from traditional Boolean logic to architectures integrating memory and computation, as well as brain-inspired neuromorphic computing. Although significant challenges remain in the synthesis of materials, fabrication, and detection, magnetic skyrmion-based logic computing holds considerable potential as a future ultra-low-power computing technology. Full article

Symbiotic radio (SR) has recently emerged as a promising paradigm for enabling spectrum- and energy-efficient massive connectivity in low-power Internet-of-Things (IoT) networks. By allowing passive backscatter devices (BDs) to coexist with active primary link transmissions, SR significantly improves spectrum utilization without requiring dedicated spectrum resources. However, most existing studies on multi-tag multiple-input multiple-output (MIMO) SR systems assume homogeneous traffic demands among BDs and primarily focus on rate-based performance metrics, while neglecting system-level task completion time (TCT) optimization under heterogeneous data requirements. In this paper, we investigate a joint performance optimization framework for a multi-tag MIMO symbiotic radio network. We first formulate a weighted sum-rate (WSR) maximization problem for the secondary backscatter links. The original non-convex WSR maximization problem is transformed into an equivalent weighted minimum mean square error (WMMSE) problem, and then solved by a block coordinate descent (BCD) approach, where the transmit precoding matrix, decoding filters, backscatter reflection coefficients are alternatively optimized. Second, to address the transmission delay imbalance caused by heterogeneous data sizes among BDs, we further propose a rate weight adaptive task TCT minimization scheme, which dynamically updates the rate weight of each BD to minimize the overall TCT. Simulation results demonstrate that the proposed framework significantly improves the WSR of the secondary system without degrading the primary link performance, and achieves substantial TCT reduction in multi-tag heterogeneous traffic scenarios, validating its effectiveness and robustness for MIMO symbiotic radio networks. Full article

Background/Objectives: Transmission electron microscopy (TEM) is an essential tool for diagnosing renal diseases. It produces high-resolution visualization of glomerular and mesangial ultrastructural features. However, manual interpretation of TEM images is labor-intensive and prone to interobserver variability. In this study, we introduced and evaluated deep learning architectures based on YOLOv8-OBB for automated detection of six ultrastructural features in kidney biopsy TEM images: glomerular basement membrane, mesangial folds, mesangial deposits, normal podocytes, podocytopathy, and subepithelial deposits. Methods: Building on our previous work, we propose a modified YOLOv8-OBB architecture that incorporates three major refinements: a grayscale input channel, a high-resolution P2 feature pyramid with refinement blocks (FPRbl), and a four-branch oriented detection head designed to detect small-to-large structures at multiple image scales (feature-map strides of 4, 8, 16, and 32 pixels). We compared two pretrained variants: our previous YOLOv8-OBB model developed with a grayscale input channel (GSch) and four additional feature-extraction layers (4FExL) (Pretrained + GSch + 4FExL) and the newly developed (Pretrained + FPRbl). Results: Quantitative assessment showed that our previously developed model (Pretrained + GSch + 4FExL) achieved an F1-score of 0.93 and mAP@0.5 of 0.953, while the (Pretrained + FPRbl) model developed in this study achieved an F1-score of 0.92 and mAP@0.5 of 0.941, demonstrating strong and clinically meaningful performance for both approaches. Qualitative assessment based on expert visual inspection of predicted bounding boxes revealed complementary strengths: (Pretrained + GSch + 4FExL) exhibited higher recall for subtle or infrequent findings, whereas (Pretrained + FPRbl) produced cleaner bounding boxes with higher-confidence predictions. Conclusions: This study presents how targeted architectural refinements in YOLOv8-OBB can enhance the detection of small, low-contrast, and variably oriented ultrastructural features in renal TEM images. Evaluating these refinements and translating them into a web-based platform (Renal-AI) showed the clinical applicability of deep learning-based tools for improving diagnostic efficiency and reducing interpretive variability in kidney pathology. 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

Low-illumination conditions significantly degrade the performance of vision-based garbage recognition systems in practical smart city applications. To address this issue, this paper presents a garbage recognition framework that combines low-light image enhancement with attention-guided feature learning. A multi-branch low-light enhancement network (MBLLEN) is employed as the enhancement backbone, and a Convolutional Block Attention Module (CBAM) is integrated to alleviate local over-enhancement and guide feature responses under uneven illumination. The enhanced images are then used as inputs for a deep learning-based garbage classification model. In addition, a lightweight encryption mechanism is considered at the system level to support secure data transmission in practical deployment scenarios. Experiments conducted on a self-collected low-light garbage dataset show that the proposed framework achieves improved image quality and recognition performance compared with baseline approaches. These results suggest that integrating low-light enhancement with attention-guided feature learning can be beneficial for garbage recognition tasks under challenging illumination conditions. Full article

New nano-biomaterials for specific dentistry applications have been developed thanks to contributions from materials science. The present work aims to characterize the physicochemical properties of a composite nanomaterial scaffold in block form for maxillofacial bone regeneration applications. The scaffold was composed of block-shaped elements and consisted of a mixture of nano-hydroxyapatite, β-tricalcium phosphate, and type I collagen of bovine origin. Collagen I molecule is biodegradable, biocompatible, easily available, and a natural bone matrix component. The biomaterial was analyzed using a range of methods, including scanning electron microscopy (SEM), transmission electron microscopy (TEM), chemical composition microanalysis, and X-Ray diffractometry (XRD). The wettability was measured. This was carried out by measuring the contact angle of a 0.9% NaCl solution on the surface. Differential scanning calorimetry (DSC) was used to measure the phase transformation temperatures. In the SEM and TEM analyses, it was possible to identify the layers of the materials and, with microanalysis, quantify their chemical composition. The XRD spectra showed the presence of nano-hydroxyapatite and ß-TCP. Wettability testing revealed that the material is highly hydrophilic, and BM-MSC culture analyses demonstrated that the biomaterial can promotes cell adhesion and interaction. The higher wettability is due to the higher density of the porous material observed in the SEM analysis. The results of the DSC testing showed that the sample analyzed undergoes endothermic transitions and transformation between 25 and 150 °C. The first phase transformation during heating occurs at 61.1 °C, which is above body temperature. The findings demonstrated that the composite was devoid of any contamination arising from manufacturing processes. It can be concluded that the n-HA/β-TCP and type 1 collagen are free of manufacturing contaminants. They also have high wettability, which increases the spreading of body fluids on the biomaterial’s surface and its interactions with cells and proteins. This makes them suitable for clinical application. Full article

Influenza A virus (IAV) continues to present a threat to public health, highlighting the need for safe and multi-target antivirals. In this study, anti-influenza activity, airborne transmission blocking capacity, and immunomodulatory effects of Cnidium monnieri polysaccharides (CMP) were evaluated. Cytotoxicity in A549 cells was assessed by CCK-8 (CC₅₀ = 8.49 mg/mL), antiviral efficacy against A/California/04/2009 (CA04) by dose–response (EC₅₀ = 1.63 mg/mL), and the stage of action by time-of-addition assays (pre-, co-, post-treatment). A guinea pig model infected with CA04 was used for testing the effect of pre-exposure CMP on transmission, with readouts including nasal-wash titers, seroconversion, lung index, and tissue titers (EID₅₀). RT-qPCR was employed to quantify the mRNA expression levels of proinflammatory cytokines, including TNF-α, IL-1β, and IL-6, in lung tissue, while Western blot analysis was performed to assess the expression and phosphorylation status of key proteins involved in the NF-κB signaling pathway. CMP suppressed viral replication in vitro within non-cytotoxic ranges, and pre-treatment—rather than co- or post-treatment—significantly reduced titers and cytopathic effect, consistent with effects at pre-entry steps and/or host priming. In vivo, pre-exposure CMP lowered nasal shedding, reduced aerosol transmission (3/6 seroconverted vs. 6/6 controls), decreased lung indices, and diminished tissue viral loads; IAV was undetectable in trachea at 7 days post-infection in pre-exposed animals, and nasal-turbinate titers declined relative to infection controls. Moreover, during in vivo treatment in mice, CMP significantly suppressed the levels of inflammatory cytokines (TNF-α, IL-1β, and IL-6) in lung tissue. This effect was mechanistically associated with CMP-mediated regulation of the NF-κB signaling pathway, leading to attenuation of inflammatory responses. These data indicate that CMP combines a favorable in vitro safety and efficacy profile with inhibition of airborne spread in vivo, supporting further mechanistic, pharmacokinetic, and fractionation studies toward translational development. Full article

Background: Malaria remains a major public health concern in Africa, due to the persistence of Plasmodium falciparum gametocytes that sustain transmission post treatment. This study evaluated the effects of artemether–lumefantrine (AL) alone compared with AL combined with a single low-dose of primaquine (SLD-PQ) on gametocyte clearance and infectivity to Anopheles arabiensis post treatment. Methods: A prospective cohort and entomological study were conducted from January to September 2025 in Northwest Ethiopia. Ninety-six microscopically confirmed cases of P. falciparum gametocytemia mono-infection were proportionally assigned to both treatment groups. Follow-up assessments were conducted on days 3, 7, 14, and 28, and mixed-species infections were assessed using molecular diagnostic assays. Additionally, membrane feeding assays (MFAs) were performed to evaluate mosquito infectivity post treatment. Results: Gametocyte prevalence declined faster with AL + SLD-PQ (15.2% on day 3; 0% by day 7) compared to AL alone (28.9% on day 3: p = 0.001; 12.2% by day 7: p = 0.033). Higher baseline gametocyte density strongly predicted mosquito infection (95% in high vs. 59% moderate and 33% low). On day 3 post treatment, 28.6% of cases treated with AL only showed confirmed mosquito infection, compared to 6.8% in the AL + SLD-PQ group (p = 0.001). By day 7, 7.3% of cases remained infectious in the AL-only group, while none were detected in the AL+ SLD-PQ group (p = 0.01). Conclusions: High baseline gametocyte density strongly correlated with increased infectivity. Adding SLD-PQ markedly accelerates gametocyte clearance and completely blocks post-treatment transmission. Submicroscopic gametocytemia contributed to residual transmission in the AL-only group. Incorporation of SLD-PQ alongside AL, in line with WHO recommendations, is advised to enhance post-treatment transmission blocking, with continued surveillance. Full article

Changes in climate and ocean pollution has prioritized monitoring of ocean surface behavior. Ocean drifters, which are floating sensors that record position and velocity, help track ocean dynamics. However, environmental events such as oil spills can cause abnormal behavior, making anomaly detection critical. Unsupervised learning, combined with deep learning and advanced data handling, is used to detect unusual behavior more accurately on the NOAA Global Drifter Program dataset, focusing on regions of the West Coast and the Mexican Gulf, for time periods spanning 2010 and 2024. Using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), pseudo-labels of anomalies are generated to train both a one-dimensional Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network. The results of the two models are then compared with bootstrapping with block shuffling, as well as 10 trials with bar chart summaries. The results show nuance, with models outperforming the other in different contexts. Between the four spatiotemporal domains, a difference in the increasing rate of anomalies is found, showing the relevance of the suggested pipeline. Beyond detection, data reliability and efficiency are addressed: a RAID-inspired recovery method reconstructs missing data, while delta encoding and gzip compression cut storage and transmission costs. This framework enhances anomaly detection, ensures reliable recovery, and reduces energy consumption, thereby providing a sustainable system for timely environmental monitoring. Full article

The Silicon Alley model is enhancing neighborhood competitiveness through cultural and technological innovation, while the living experience of its inhabitants serves as a critical foundation for sustainable development. This study investigates neighborhoods developed under the Silicon Alley framework. It explores theoretical models and practical pathways that use inclusive design to enhance public facilities and service strategies, ultimately aiming to build a sustainable living experience system. Utilizing a combined LDA-DEMATEL-ISM-MICMAC methodology, the research first identifies seven key factors influencing living experience from multi-source texts, spanning social, technological, emotional, and governance dimensions. It then reveals the cause-effect relationships, hierarchical structure, and driver-dependency mechanisms among these factors. The findings indicate that sustainable collaborative governance acts as a fundamental driver, diversified community experience and urban attractiveness serve as intermediate transmission factors, while Elderly-Friendly Livelihood Experience, Digital Block Experience, Artistic Life Scene Experience, and Local Cultural and Historical Experience function as surface-level outcome factors. The study proposes short-term priorities focusing on collaborative governance and social integration, and long-term strategies emphasizing livelihood services and cultural identity. These recommendations are intended to enhance neighborhood living experience, promote inclusive and sustainable urban renewal, and provide both theoretical insights and practical guidance for achieving sustainable neighborhood development. Full article

Real-time affine transformation, a core operation for image correction and registration of industrial cameras and scanners, faces challenges including the high computational cost of interpolation and inefficient data access. In this study, we propose a reconfigurable accelerator architecture based on a heterogeneous system-on-chip (SoC). The architecture decouples tasks into control and data paths: the ARM core in the processing system (PS) handles parameter matrix generation and scheduling, whereas the FPGA-based acceleration module in programmable logic (PL) implements the proposed PATRM algorithm. By integrating multiplication-free design and affine matrix properties, PATRM adopts Q15.16 fixed-point computation and AXI4 burst transmission for efficient block data prefetching and pipelined processing. Experimental results demonstrate 25 frames per second (FPS) for $2095\times 2448$ resolution images, representing a 128.21 M pixel/s throughput, which is 5.3× faster than the Block AT baseline with a peak signal-to-noise ratio (PSNR) exceeding 26 dB. Featuring low resource consumption and dynamic reconfigurability, the accelerator meets the real-time requirements of industrial scanner correction and other high-performance image processing tasks. Full article

With the explosive growth of global data traffic, long-distance fiber optic transmission systems are continuously evolving towards higher capacity and longer distances. However, to overcome the high complexity of fiber dispersion compensation algorithms, various dispersion compensation techniques have emerged. This paper aims to systematically review and summarize dispersion compensation algorithms in long-distance fiber optic transmission. First, we briefly introduce the physical mechanism of fiber dispersion. Then, this paper focuses on digital domain compensation algorithms, dividing them into two major categories: compensation algorithms without penalty and with penalty. For compensation algorithms without penalty, we elaborate on traditional block processing strategies such as Overlap-Save (OLS), and various enhanced strategies combining intelligent filter segmentation and optimized frequency domain workflows. For compensation algorithms with penalty, we focus on analyzing a scheme that redesigns chromatic dispersion compensation (CDC) algorithm into a hardware-friendly structure using geometric clustering of taps, and finite-impulse-response (FIR) filters based on frequency response approximating the ideal inverse chromatic dispersion (CD) transfer function. By numerical simulation, we analyze the core principles, computational complexity, and compensation performance of each type of algorithm. Finally, this paper summarizes the limitations and development trends of existing dispersion compensation algorithms, pointing out that low-complexity and small-scale deployment algorithm structures will be an important research direction in the future. Full article

Network slicing in 5G systems enables different Quality of Service (QoS) for heterogeneous Vehicle-to-Everything (V2X) applications, yet efficiently allocating resource blocks from both 5G base stations and roadside units (RSUs) across multiple slices remains challenging. Existing approaches either pre-assign users to slices or rely on population-based metaheuristic algorithms that cannot guarantee deterministic real-time performance within the stringent 20 ms latency requirements of vehicular networks. This study formulates the resource allocation problem as an integer programming model that jointly optimizes slice selection and resource allocation to maximize weighted system transmission rate while satisfying heterogeneous QoS constraints. We develop a constructive heuristic algorithm that employs a hierarchical allocation strategy prioritizing 5G resources before RSU resources, coupled with a backfilling mechanism to exploit the remaining resource block capacity. Numerical experiments across abundant 5G and limited resource scenarios demonstrate the algorithm’s effectiveness. First, comparing against Random baseline validates the optimization model’s value, achieving 21.4–24.9% higher weighted throughput in an abundant 5G scenario and 42.5–51.0% improvement under a limited resource scenario. Second, performance evaluation with 500 users shows the proposed constructive heuristic achieves optimal solutions in abundant 5G resource scenarios and 3.5–5.7% optimality gaps in limited resource scenarios, while maintaining an execution time of under 20 ms, which satisfies real-time requirements and executes faster than Gurobi, Simulated Annealing and Round-Robin. Third, scalability analyses across 400–700 users demonstrate favorable performance scaling, as the optimality gap decreases from 5.3% to 3.4% with execution times consistently below 20 ms. The proposed heuristic achieves the highest service admission count while maintaining near-optimal system weighted transmission rate performance, ranking second only to Gurobi solver. Compared with other baseline algorithms, the proposed heuristic delivers a superior balance between solution quality and computational efficiency, confirming its real-time feasibility for large-scale V2X network deployments. Full article

Due to the high mobility, flexibility, and low cost, unmanned aerial vehicles (UAVs) can provide an efficient way for provisioning data communication and computing offloading services for massive Internet of Things (IoT) devices, especially in remote areas with limited infrastructure. However, current transmission schemes for unmanned aerial vehicle-assisted Internet of Things (UAV-IoT) predominantly employ polling scheduling, thus not fully exploiting the potential multiuser diversity gains offered by a vast number of IoT nodes. Furthermore, conventional opportunistic scheduling (OS) or opportunistic beamforming techniques are predominantly designed for downlink transmission scenarios. When applied directly to uplink IoT data transmission, these methods can incur excessive uplink training overhead. To address these issues, this paper first proposes a low-overhead multi-UAV uplink OS framework based on channel reciprocity. To avoid explicit massive uplink channel estimation, two scheduling criteria are designed: minimum downlink interference (MDI) and the maximum downlink signal-to-interference-plus-noise ratio (MD-SINR). Second, for a dual-UAV deployment scenario over Rayleigh block fading channels, we derive closed-form expressions for both the average sum rate and the asymptotic sum rate based on the MDI criterion. A degrees-of-freedom (DoF) analysis demonstrates that when the number of sensors, K, scales as ${\rho }^{\alpha }$, the system can achieve a total of $2\alpha$ DoF, where $\alpha \in \left[0,1\right]$ is the user-scaling factor and $\rho$ is the transmitted signal-to-noise ratio (SNR). Third, for a three-UAV deployment scenario, the Gamma distribution is employed to approximate the uplink interference, thereby yielding a tractable expression for the average sum rate. Simulations confirm the accuracy of the performance analysis for both dual- and three-UAV deployments. The normalized error between theoretical and simulation results falls below 1% for K > 30. Furthermore, the impact of fading severity on the system’s sum rate and DoF performance is systematically evaluated via simulations under Nakagami-m fading channels. The results indicate that more severe fading (a smaller m) yields greater multiuser diversity gain. Both the theoretical and simulation results consistently show that within the medium-to-high SNR regime, the dual-UAV deployment outperforms both the single-UAV and three-UAV schemes in both Rayleigh and Nakagami-m channels. This study provides a theoretical foundation for the adaptive deployment and scheduling design of UAV-assisted IoT uplink systems under various fading environments. Full article

Background: Whooping cough caused by Bordetella pertussis is re-emerging despite high vaccination coverage, with rising incidence in adolescents and adults in the acellular vaccine (aP) era. This narrative review synthesizes evidence on the drivers of this paradox and their implications for pertussis control. Methods: We conducted a structured (but not fully systematic) literature search and narrative synthesis of PubMed, Web of Science, and Embase for publications from January 2000 to February 2025 using terms related to “Bordetella pertussis,” “pertussis resurgence,” “acellular vaccine,” “waning immunity,” “ptxP3,” “pertactin-deficient,” “macrolide resistance,” and “whole-genome sequencing.” English-language, peer-reviewed studies, surveillance reports, genomic analyses, and immunological investigations were included. About 1900 records met broad eligibility criteria and were screened, and key studies were selected for narrative synthesis. Results: The resurgence appears to result from three convergent factors: (1) waning and non-sterilizing aP-induced immunity, which allows bacterial colonization and transmission; (2) vaccine-driven genomic evolution of B. pertussis, marked by global dominance of the ptxP3 lineage and widespread pertactin-deficient (PRN−) strains; and (3) emergence of macrolide-resistant clones, exemplified by the MT28-Shanghai strain. Whole-genome sequencing (WGS) has been central for defining these processes and clonal sweeps under combined vaccine and antibiotic pressure, supporting a three-driver framework of waning aP immunity, vaccine-driven evolution, and macrolide resistance. Conclusions: Pertussis resurgence illustrates pathogen adaptation to human interventions. Effective mitigation requires WGS-integrated global surveillance, re-evaluation of vaccine formulations to keep pace with antigenic change, and strengthened antibiotic stewardship, alongside development of next-generation vaccines that induce durable mucosal immunity and block transmission. Full article