Search Results (5,822)

Unmanned aerial vehicle (UAV) swarms have shown great potential in large-scale IoT (Internet of Things) and smart agriculture applications, particularly for cooperative monitoring and multi-target tracking in field environments. However, most existing coordination strategies assume ideal communication conditions, overlooking realistic network impairments such as congestion, packet loss, and latency. These impairments disrupt the timely exchange of information between UAVs and the ground base station, leading to delayed or lost control signals. As a result, coordination quality deteriorates and tracking performance is severely degraded in real-world deployments. To address this gap, we propose CSOOC (Communication-State Driven Online–Offline Coordination with Congestion Control), a hybrid control architecture that integrates centralized learning-based decision-making with decentralized rule-based policies to adapt UAV behaviors according to real-time network states. CSOOC consists of three key components: (1) an online module that enables centralized coordination under reliable communication, (2) an offline profit-driven mobility strategy based on local Gaussian maps for autonomous target tracking during communication loss, and (3) a congestion control mechanism based on STAR(Stratified Transmission and RTS/CTS), which combines temporal transmission desynchronization and RTS/CTS handshaking to enhance uplink reliability. We establish a unified co-simulation paradigm that connects network communication with swarm control and swarm coordination behavior. Experiments demonstrate that CSOOC achieves an average observation rate of 39.7%, surpassing baseline algorithms by 4.4–11.13%, while simultaneously improving network stability through significantly higher packet delivery ratios under congested conditions. These results demonstrate that CSOOC effectively bridges the gap between algorithmic performance in simulation and practical UAV swarm operations in communication-constrained environments. Full article

In this study, we explore the integration of a cost-effective triboelectric nanogenerator (TENG) with an large silicon PIN detector (diameter: 12 mm) for intelligent wireless recognition applications. Wireless communication eliminates the need for physical connections, enabling greater flexibility and scalability in deployment. It allows for seamless integration of AI systems into a wide range of environments without the constraints of wiring, reducing installation complexity and enhancing mobility. Additionally, we demonstrate the TENG’s functionality as an autonomous communication unit. The TENG is employed to convert various environmentally triggered signals into digital formats and to autonomously power optoelectronic devices, thus eliminating the need for an external power supply. By integrating optoelectronic components within the self-powered sensing system, the TENG can identify specific trigger information and reduce extraneous noise, thereby improving the accuracy of information transmission. Moreover wireless technology facilitates real-time data transmission and processing. This setup not only enhances the overall efficiency and adaptability of the system but also supports continuous operation in diverse and dynamic settings. This paper introduces a novel convolutional neural network-long short-term memory (CNN-LSTM) fusion neural network model. Utilizing the sensing system in combination with the CNN-LSTM neural network enables the collection and identification of variations in the flicker frequency and luminosity of optoelectronic devices. This capability allows for the recognition of environmental trigger signals generated by the TENG. The classification and recognition results of human body trigger signals indicate a recognition accuracy of 92.94%. Full article

With the rapid advancement of technology, data transmission security has become an increasingly critical issue. Visual Cryptography Scheme (VCS) provides a secure method for sharing secret images without complex computation—by stacking multiple shares, the secret image can be visually recognized. The earliest visual cryptography scheme was proposed. However, traditional VCS are limited to the encryption and decryption of a single secret. To address the evolving demands of modern information security, numerous enhanced VCS have been introduced by researchers, offering new perspectives and capabilities. This paper proposes a novel XOR-based visual cryptography scheme that supports fully independent secrets within a (k, n)-threshold framework for 2 ≤ k < n. In the proposed scheme, n shares can simultaneously encrypt C(n, k) distinct secrets. Each secret can be reconstructed by one subset of k shares out of the n, and all shares are designed to be meaningful images so as not to be identified as hiding a secret. This approach significantly enhances the flexibility of VCS, enabling its application in scenarios where different groups hold different secrets or where the reconstructed secret identifies the associated group, which can help administrators know which group has accessed the secret. As such, the proposed scheme is more suitable for a wide range of practical applications. Full article

Homography estimation of infrared and visible light images is a key visual technique that enables drones to perceive their environment and perform autonomous localization in low-altitude environments. Its potential lies in integration with edge computing and 5G technologies, enabling real-time control of drones within air–ground integrated networks. However, research on homography estimation techniques for low-altitude dynamic viewpoints remains scarce. Additionally, images in low-altitude scenarios suffer from issues such as blurring and jitter, presenting new challenges for homography estimation tasks. To address these issues, this paper proposes a light-weight homography estimation method, LFHomo, comprising two components: two anti-blurring feature extractors with non-shared parameters and a lightweight homography estimator, LFHomoE. The anti-blurring feature extractors introduce in-verse residual layers and feature displacement modules to capture sufficient contextual information in blurred regions and to enable lossless and rapid propagation of feature information. In addition, a spatial-reduction-based channel shuffle and spatial joint attention module is designed to suppress redundant features introduced by lossless transmission, allowing efficient extraction and refinement of informative features at low computational cost. The homography estimator LFHomoE adopts a CNN–GNN hybrid architecture to efficiently model geometric relationships between cross-modal features and to achieve fast prediction of homography matrices. Meanwhile, we construct and annotate an unregistered infrared and visible image dataset from drone perspectives for model training and evaluation. Experimental results show that LFHomo maintains great registration accuracy while significantly reducing model size and inference time. Full article

Air pollution monitoring systems use distributed sensors that record dynamic environmental conditions, often producing large volumes of heterogeneous and stochastic data. Efficient aggregation of this data is essential for reducing communication overhead while maintaining the quality of information for decision making. In this paper, we propose an unsupervised learning approach for soft clustering of sensors in air pollution monitoring systems. Our method utilizes the Expectation–Maximization algorithm, which is an unsupervised machine learning method and probabilistic technique, to cluster sensors into distinct sets corresponding to normal and polluted zones. This clustering is driven by the need for a dynamic data transmission policy: sensors in polluted zones must intensify their operation for detailed monitoring, while sensors in clean zones can reduce reporting rates and transmit condensed data summaries to alleviate network load and conserve energy. The cluster membership probability enables a tunable trade-off between data redundancy and monitoring accuracy. The high efficiency of the proposed AI-based clustering is validated by the simulation results. Under common pollution scenarios and with adequate sample sizes, the EM algorithm exhibits a relative error below 5%. The presented approach provides a foundation for a wide range of intelligent and adaptive data aggregation protocols. Full article

Background: Rabies is a fatal yet preventable zoonosis. In Saudi Arabia, uneven surveillance and limited public awareness may delay post-exposure prophylaxis (PEP). In Makkah, where residents regularly encounter free-roaming dogs, knowledge gaps could elevate exposure risks. Objectives: This study aims to assess public knowledge, attitudes, and post-bite practices regarding rabies, including wound washing and access to PEP among adult residents of the Makkah Region, and to examine associations with pet dog ownership. Methods: A cross-sectional survey was conducted in the Makkah Region (March–June 2025). An online validated bilingual questionnaire targeted residents ≥ 18 years via social media. Descriptive statistics, chi-square tests, 95% confidence intervals, and binomial logistic regression were applied in IBM SPSS v26; p < 0.05 was significant. Results: Of 523 respondents, 91.8% lived in Makkah city, 52.8% were female, and the age distribution was 18–24 years (44.2%), 25–34 years (35.6%), 35–44 years (12.0%), and ≥45 years (8.2%). Pet dog ownership was rare (1.9%), yet 39.4% reported stray dogs in their communities. Overall, 60.6% knew what rabies is and 63.7% knew it is vaccine-preventable, but 52.2% wrongly believed that transmission occurs only via dog bites. Hospitals (79.7%) and health centers (79.2%) were the most cited vaccination sites; social media was the dominant information source (74.6%). No significant association was found between pet ownership and rabies awareness (all p > 0.05). In multivariable regression (n = 509), adequate rabies knowledge increased the odds of an appropriate intended response (AOR 1.85, 95% CI: 1.27–2.68). Participants aged 30–40 years and those >50 years had significantly lower odds (AOR 0.45, 95% CI: 0.24–0.85 and AOR 0.23, 95% CI: 0.09–0.56, respectively). Conclusions: Despite moderate awareness, critical misconceptions and inconsistent first aid intentions persist. Priority actions include clear, locally adapted education on immediate wound washing and prompt PEP, standardized bite management pathways across facilities, reliable access to vaccines and immunoglobulin, and targeted social media micro-campaigns. By identifying public misconceptions, knowledge gaps, and preferred communication channels, this study provides baseline evidence to guide community awareness programs, intersectoral collaboration, and One Health-based surveillance essential for Saudi Arabia’s progress toward the global “Zero rabies by 2030” goal. Full article

In multibeam low-Earth-orbit (LEO) satellite systems, precoding has emerged as a key technology for mitigating co-channel interference (CCI) and for improving spectral efficiency (SE). However, its practical implementation is challenged by the difficulty of acquiring reliable instantaneous channel state information (iCSI) and by the high computational complexity induced by large-scale antenna arrays, making it incompatible with fixed codebook-based beamforming schemes commonly adopted in operational systems. In this analysis, we propose a beamspace transmission framework leveraging statistical CSI (sCSI) and achieves reduced computational complexity compared with antenna-domain precoding designs. Specifically, we first propose a low-complexity beam selection algorithm that selects a small subset of beams for each user terminal (UT) from a fixed beamforming codebook, using only the UTs’ two-dimensional (2D) angular information. To suppress CCI among beams, we then derive a beamspace weighted minimum mean square error (WMMSE) precoding scheme based on the equivalent beamspace channel matrix. The derivation employs an sCSI-based WMMSE (sWMMSE) formulation derived from an upper bound approximation of the ergodic sum rate, which provides a tighter estimate than the expected mean square error (MSE)-based lower bound approximation. Simulation results demonstrate that the proposed sCSI-based beamspace transmission scheme achieves a favorable trade-off between performance and computational complexity. Full article

In this study, we propose a multi-scale feature fusion network based on an improved RT-DETR model for the efficient detection of tomato leaf disease. Our model combines the multi-scale extended residual module by capturing contextual information at various scales and the multi-scale feature pyramid network by integrating feature information from different levels, which improves feature extraction capability and reduces the interference of complex backgrounds on feature extraction, thereby improving information transmission efficiency and the accuracy of the model. In addition, the novel loss function called adaptive focal loss (AFL) was used, which is based on traditional focal loss with the introduction of attenuation factors to focus the model’s attention to high-loss features to alleviate overfitting and of dynamic weight adjustment mechanisms to focus on the more important features during the training process to improve the overall learning performance. Another significant advantage of AFL is that it can more efficiently improve the detection accuracy on imbalanced datasets than on balanced datasets. These innovations optimized the learning strategy of the model, making AP@0.50 up to 97.9% on detecting the categories of tomato diseases. In addition, this model also achieves the high detection accuracy of 85.4% on other crop diseases. These results provide valuable references for agriculture applications. Full article

Using the Information Benefiting the People (IBP) policy as an exogenous shock to digital government construction, we investigate the impact of digital government on the Green Total Factor Productivity (GTFP) of listed forestry companies. Drawing on data for China’s A-share forestry firms from 2010 to 2023, our baseline findings reveal that digital government significantly promotes firms’ GTFP, and this result persists across a battery of robustness checks. Mechanism tests show that this effect can be explained by alleviated financial constraints and an increased level of corporate digital transformation; together with heterogeneity analysis, these results reveal both the transmission paths and the boundary conditions of the policy effect. In addition, the effect is more pronounced in small and medium-sized enterprises and in areas with lower levels of financial development and marketization, providing robust evidence for the above mechanisms. Our study offers important implications for the sustainable development of forestry enterprises. Full article

Virus diseases are among the major constraints in the production of food legumes in Africa, causing substantial crop losses. Common bean mosaic and black root, cowpea mosaic, chickpea stunt, faba bean necrotic yellows and stunt, groundnut rosette, and soybean mosaic are the six diseases considered economically significant in Africa. Past research enabled the description of the main characteristics of the causal viruses, including particle and genome properties, modes of transmission, host range, and virus–vector relationships. Such information in many cases assisted in developing effective diagnostics and disease management methods such as host resistance, chemical vector control, and cultural practices. Integrating two or more of these approaches is usually more effective. The major challenge, however, remains ensuring the adoption of such recommendations at a sufficiently large scale by many farmers to have an impact over wider geographical areas. Future work should focus on scaling up the adoption of available control technologies and generating new information, including epidemiological data, to support future management decisions. Furthermore, since the occurrence and significance of viruses on food legumes in many African countries are still not studied, large-scale surveys to identify viruses, establish their distribution and impact, and working out suitable control measures are required. Full article

Face masks are widely recognized as a key intervention to limit SARS-CoV-2 transmission, yet the distribution and persistence of viral RNA across different mask regions and layers remain poorly understood. To address this, we analyzed 185 masks collected from 60 SARS-CoV-2-positive individuals in Rio de Janeiro between December 2020 and September 2022. Masks were sectioned into anatomical regions (nose, mouth, sides) and structural layers (inner, middle, outer), and viral RNA was quantified using RT-qPCR. Samples with the highest viral loads were selected for partial sequencing of the spike gene, and paired analyses with swab samples were performed. Statistical comparisons included non-parametric tests and a linear mixed-effects model. Our results showed that the inner layer and nose region consistently harbored the highest viral RNA levels, with no significant differences between surgical and fabric masks. Viral load decreased by an estimated 39% per day, consistent with exponential decay. Sequencing confirmed identical viral genomes in masks and swabs and allowed identification of circulating variants, including Gamma and Omicron. These findings indicate that masks serve not only as effective physical barriers but also as non-invasive sources for genomic surveillance, providing insights into viral shedding patterns and informing strategies for monitoring and controlling SARS-CoV-2 transmission. Full article

Background/Objectives: This study assessed the risk associated with third-generation cephalosporin- and fluoroquinolone-resistant Salmonella from pork consumption by integrating phenotypic resistance profiles with genetic data to characterize the risks and transmission pathways. Methods: Salmonella were isolated from raw pork meat samples (n = 793) collected from fresh markets and hypermarkets across Bangkok during 2021–2022, of which 150 were extended-spectrum β-lactamase (ESBL)-producing and 31 were fluoroquinolone-resistant isolates. Phenotypic and genotypic resistance profiles were characterized. Quantitative antimicrobial resistance risk assessment (AMR RA) was conducted using a dose–response model. Results: Salmonella spp. was detected in 42.75% of pork samples, with a higher prevalence in fresh markets (75.5%) than in hypermarket samples and with concentrations ranging from 1.3 to 180 MPN/g. Twenty-eight percent of isolates were ESBL producers, with ciprofloxacin and levofloxacin resistance observed in 5.3% and 3.0%, respectively. The bla_CTX-M55 genes were located on conjugative plasmids. Whole genome sequencing revealed both vertical and horizontal gene transfer. IncHI2/N and IncC plasmids shared conserved backbones and resistance gene architectures, indicating horizontal dissemination of resistance genes. Phylogenomics suggested possible clonal transmission among pigs, pork, and humans. AMR RA estimated 88,194 annual illness cases per 100,000 people from ESBL-producing Salmonella and 61,877 from ciprofloxacin-resistant strain, compared with 95,328 cases predicted by QMRA from Salmonella contamination. Cooking pork at ≥64 °C for 3 min eliminated the risk in all scenarios. Sensitivity analysis identified initial contamination level and cooking temperature as key determinants. Conclusions: Raw pork meat consumption represents the highest risk, which can be mitigated by thorough cooking (>64 °C, ≥3 min), while integrating genomic data enhances AMR hazard identification, source attribution, and exposure assessment. Therefore, promoting well-cooked meat consumption and safe cooking practices, alongside the use of AMR genetic data to inform targeted interventions, is recommended. Full article

The one-parent-one-language (OPOL) approach has traditionally been considered a widely recommended strategy for heritage-language (HL) maintenance in bilingual families. However, alternative strategies, such as both parents consistently speaking the HL, may be equally or more effective. This study examines families’ language policies and their effectiveness in HL maintenance in Australia, where minority languages often hold lower status than English and receive minimal institutional support beyond the home. Using data from a nationwide survey of 280 families, we analyzed parental language-use patterns and their impact on HL transmission. Most mothers, who more often identified as primary caregivers, reported speaking a HL with their children, while secondary caregivers’ language use was varied. Families were categorized into four language-use approaches: OPOL, mixed-language use from one or both caregivers, HL-only from both caregivers, and single-caregiver only. Comparisons across these categories revealed that families following the HL-only and OPOL approaches reported significantly greater success in maintaining the HL than the other two groups, which showed no significant differences in self-reported outcomes. Follow-up analyses showed that Mixed-language families with high HL use achieved success comparable to HL-only and OPOL policies. Our findings suggest that language input is a central, but not exclusive, contributor to HL transmission. Families who reported higher perceived success showed strong commitment to HL maintenance, with caregivers likely reinforcing each other's efforts beyond direct language input. This study contributes to discussions on bilingual parenting and family language policy, providing empirical insights to inform HL maintenance strategies in diverse linguistic settings. Full article

Transmission lines in complex outdoor environments often suffer external damage in construction areas, severely affecting the stability of power systems. Traditional manual detection methods have problems of low efficiency and poor real-time performance. In deep learning-based detection methods, standard convolution has a large parameter count and computational complexity, making it difficult to deploy on edge devices; while lightweight depthwise separable convolution offers low computational cost, it suffers from insufficient feature extraction capability. This limitation stems from its independent processing of each channel’s information, making it unable to simultaneously meet the practical requirements for both lightweight design and high detection accuracy in transmission line monitoring applications. To address the above problems, this study proposes LFRE-YOLO, a lightweight external damage detection algorithm for transmission lines based on YOLOv10n. This study proposes LFRE-YOLO, a lightweight external damage detection algorithm based on YOLOv10n. First, we design a lightweight feature reuse and enhancement convolution (LFREConv) that overcomes the limitations of traditional depthwise separable convolution through cascaded dual depthwise convolution structure and residual connection mechanisms, significantly expanding the effective receptive field with minimal parameter increment and compensating for information loss caused by independent channel processing in depthwise convolution through feature reuse strategies. Second, based on LFREConv, we propose an efficient lightweight feature extraction module (LFREBlock) that achieves cross-channel information interaction enhancement and channel importance modeling. Additionally, we propose a lightweight feature reuse and enhancement detection head (LFRE-Head) that applies LFREConv to the regression branch, achieving comprehensive lightweight design of the detection head while maintaining spatial localization accuracy. Finally, we employ layer-adaptive magnitude-based pruning (LAMP) to prune the trained model, further optimizing the network structure through layer-wise adaptive pruning. Experimental results demonstrate significant improvements over YOLOv10n baseline: mAP50 increased from 92.0% to 94.1%, mAP50-95 improved from 66.2% to 70.2%, while reducing parameters from 2.27 M to 0.99 M, computational complexity from 6.5 G to 3.1 G, and achieving 86.9 FPS inference speed, making it suitable for resource-constrained edge computing environments. Full article

Based on the birefringence phenomenon of vortex beam in uniaxial crystals for optical path design, yttrium vanadate crystals and waveplates are used to realize coherent mixing of vortex beam. A crystal-type spatial light mixer applied to a vortex beam communication system is designed. The effects of beam polarization, waveplate optical axis, crystal transmittance, and other factors on the performance of the mixer are explored. Simulations show that the mixer output phase error is extremely small, the insertion loss is about 1.9 $\mathrm{dB}$ , and the overall loss is close to 36.6%. Finally, it is applied in the vortex optical coherent communication system, and the effectiveness of the optical mixer is experimentally verified with a phase deviation of 3°, a splitting ratio close to 1, and a mixing efficiency of 78.5%. Vortex beam mixer extracts information such as phase, amplitude, and polarization of the signal light by combining optical beams with orbital angular momentum modes. It enables mode multiplexing, topologically protected transmission, and high-order modulation. This technology is widely applied in space optical communication, high-speed fiber-optic systems, and quantum communication. Full article