Search Results (10,042)

Digital village construction (DVC) is an important tool for promoting rural revitalization and increasing farmers’ income. This paper selects panel data at the county level and employs the difference-in-differences (DID) method, combined with mediation effect models, heterogeneity tests, and multi-dimensional robustness tests, to systematically explore the impact of DVC on farmers’ income and its internal transmission path. According to the research, the DVC has a positive impact on farmers’ income at the 1% significance level, a conclusion that remains valid after robustness tests such as PSM-DID and substitution of the explained variable. Industrial restructuring, agricultural mechanization, and enterprise agglomeration are positively significant at the 5%, 1%, and 1% levels, respectively, indicating that these three are the core intermediary mechanisms for increasing farmers’ income, promoting farmers’ income growth by releasing structural dividends, efficiency dividends, and agglomeration dividends, respectively. The income-increasing effect of DVC exhibits significant heterogeneity, being positively significant at the 5% and 1% levels in areas with a deep digital divide and non-grain-producing areas, but not significant in areas with a shallow digital divide and major grain-producing areas. Therefore, policy recommendations are to optimize resource allocation, broaden income-increasing pathways, and implement differentiated policies. Full article

Cotton, a globally significant crop grown in over 100 countries, sustains a $40 billion market and provides employment for over 350 million people worldwide. However, plastic contamination remains a persistent challenge within the industry, degrading cotton fiber quality and disrupting ginning. Manual inspection and optical machine-vision systems struggle when plastic fragments are concealed by fibers or lack sufficient color contrast. To address these challenges, we developed an ultrasonic phased-array imaging system operating at 40 kHz under field-programmable gate array (FPGA) control. Transmitter elements emit pulsed ultrasound along radial paths, separate reflection receivers record echo amplitudes to form acoustic images, and a set of transmission receivers captures signal attenuation, which is overlaid onto the reflection-based image to highlight potential contaminants. In preliminary laboratory-based tests on both seed cotton and lint samples, the system successfully detected visually obscured plastic fragments as small as $2\mathrm{cm}\times 2\mathrm{cm}$ with an angular resolution limit of $\pm 3^{\circ }$. Distinct reflection peaks and corresponding attenuation overlays were produced across the field of view, validating the system’s detection capabilities. These results demonstrate the feasibility of using ultrasonic imaging to reveal concealed plastics in cotton processing. Integrating this approach with existing optical methods could enhance contaminant-removal workflows and improve overall fiber quality and processing efficiency. Full article

Varroa destructor (Bee mite) and Deformed Wing Virus are primary causes of honeybee colony collapse. This study developed an automated AI-based vision inspection system for detecting bee mites and deformed bees using the YOLO algorithm. The system integrates an RGB camera, a beecomb rotation motor, and an image transmission module to enable automated dual-sided image acquisition of the beecomb. The image characteristics of normal bees, bee mites, and deformed bees were analyzed, and YOLO-based object detection models were developed to classify them. Six YOLO models—based on YOLOv8 and YOLOv11 architectures across three model sizes (nano, small, and large)—were evaluated on 405 test images (6441 objects). The proposed system reduced the inspection time from 240 s required for manual method to 20 s per beecomb, achieving 12-fold efficiency improvement. Comparative analysis showed model-task specialization: YOLOv8l excelled in detecting small bee mites (F1: 92.5%, mAP[0.5]: 92.1%), while YOLOv11s achieved the highest performance for morphologically diverse deformed bees (F1: 95.1%). Error analysis indicated that detection performance was influenced by morphological characteristics. Deformed bee detection errors correlated with overlap in wing-to-body ratio: DB Type II exhibited 18.6% miss rate, while DB Type III achieved perfect detection. In bee mite detection, a sensitivity–specificity trade-off was observed: YOLOv11l had the lowest false negatives (2.5%) but highest false positives, while YOLOv8l demonstrated superior discrimination. These results demonstrate the practical potential of the proposed system for field deployment in apiaries, supporting early pest diagnosis and improved colony health management. The model-task specialization framework provides guidance for architecture selection based on object characteristics. Future work will focus on multi-location validation and real-time monitoring integration. Full article

This paper presents a high-performance and resource-efficient Field Programmable Gate Array (FPGA)-based architecture for satellite telemetry transmission systems. The proposed design implements a flexible channel coding chain, including Reed–Solomon (R-S) encoding, convolutional encoding, symbol interleaving, pseudo-randomization, and Attached Synchronization Marker (ASM) insertion, in accordance with CCSDS recommendations. The architecture is fully integrated and configurable, allowing dynamic selection of coding schemes without requiring structural modifications. The system is implemented on a modern FPGA platform with a 32-bit AXI4-Stream interface at 110 MHz, reaching an effective throughput of up to 1.76 Gbps. Experimental results demonstrate reliable timing with positive setup and hold margins, allowing the system to operate at approximately 130 MHz. Power consumption is measured using Switching Activity Interchange Format (SAIF)-based switching activity, providing a realistic estimate of programmable logic power consumption. The total on-chip power is about 1.77 W for individual coding modes. It rises to 1.91 W in the concatenated setup, which is the worst-case scenario. The results show that the proposed architecture efficiently uses resources, runs reliably at high speeds, and exhibits predictable power consumption. This makes it well suited for high-reliability and energy-constrained satellite communication systems. resources are used. Full article

Optical-fibre daylighting systems (OFDS) harvest solar energy as a renewable lighting resource by delivering sunlight deep into green buildings. This emerging technology for sustainable infrastructure reduces electric-lighting demand; however, reported performance is difficult to compare across heterogeneous designs, metrics, and validation practices. Therefore, a PRISMA 2020–reported systematic literature review (SLR) of OFDS studies from three databases (Google Scholar, Scopus, and Web of Science; 2000–2025) was conducted, synthesising primary research that quantifies system- or component-level performance, with a focus on (i) plastic optical fibre (POF) transmission characteristics; and (ii) POF-based illuminance model validation. After de-duplication and screening, 106 primary studies were included, and two meta-analyses were performed where data were harmonised from 29 studies in total. Across reported POF configurations, attenuation ranged from 150 to 800 dB/km with a pooled mean of 332.8 dB/km, corresponding to a mean 1 m transmission of 92.7% and median design length scales of ∼3.7 m for 80% transmission and ∼11.6 m to half-power. Across illuminance validation datasets, models showed high linear agreement with experimental measurements (coefficient of determination ($R^2$) = 0.99; slope = 0.99) but typically underpredicted illuminance (geometric mean ratio = 1.16; mean absolute error (MAE) = 27.3 lux; mean absolute percentage error (MAPE) = 17.6%). These findings underscore the need for a standardised evaluation framework, including consistent metric definitions, robust uncertainty reporting, and reusable validation datasets to enable variance-weighted synthesis, while also identifying short-run POF routing as a key lever for improving system efficiency. In addition to providing the OFDS research agenda, this study serves as a roadmap for the industrial development of daylighting systems for green buildings based on harvesting solar energy, with its novelty lying in the PRISMA-guided evidence synthesis and quantitative meta-analytic consolidation of POF transmission and illuminance-validation performance. Full article

Against the backdrop of the accelerated development of the low-altitude economy and the structural transformation of modern logistics systems, systematically elucidating the key driving factors and their interaction structure is paramount for optimizing operational efficiency, promoting sustainable industry growth, and enhancing policy effectiveness. Integrating an extensive literature review with expert consultations, this study constructs a comprehensive indicator system of influencing factors for the coordinated development of the low-altitude economy and sustainable modern logistics. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is employed to characterize the causal relationships and influence directions among the factors. Empowered by these findings, an Analytic Network Process (ANP) model is established to calculate refined weights, forming a hybrid DEMATEL–ANP analytical framework. The results indicate that technological factors and institutional factors constitute the primary driving layer of the system. Specifically, System Integration and Operational Technology, Flight Control and Scheduling Capability, as well as the Standardisation of Airspace Management and the Completeness of the Regulatory and Standards Framework, exert pivotal influences on the systemic evolution. Social factors and infrastructure factors primarily function as the outcome and feedback layers, with their effectiveness contingent upon the maturity of the core driving elements. Further hybrid weight analysis demonstrates that the ranking of key influencing factors exhibits high stability and robustness. The coordinated development process presents a progressive transmission characteristic from “technology–institution” to “market–application” providing targeted practical guidance for promoting the sustainable and high-quality synergy between the low-altitude economy and modern logistics. Full article

Candida parapsilosis is a major cause of healthcare-associated infections due to its persistence on abiotic surfaces and efficient transmission via healthcare workers’ hands. This study evaluated the antifungal efficacy and safety of clinically relevant antiseptics against 60 C. parapsilosis clinical isolates using a surface carrier test designed to simulate contamination and disinfection events on hospital surfaces. Antifungal activity was assessed by logarithmic reduction (log₁₀) assays on surface carriers and by minimum inhibitory concentration (MIC) testing. Potential synergistic interactions between antiseptics and selected phytochemicals were investigated using checkerboard assays, and toxicity was evaluated in vivo using Caenorhabditis elegans. Surface carrier assays showed that 70% ethanol and 0.5% alcoholic chlorhexidine (CHG) achieved the highest fungicidal activity, with reductions of up to 5 log₁₀ after 1 min exposure at 25 °C. Polyhexamethylene guanidine hydrochloride (PHMGH) displayed consistently low MIC values (0.4–0.9 ppm) and intermediate surface activity. CHG combined with eugenol or menthol produced strong synergistic interactions, reducing CHG MICs from up to 6250 ppm to as low as 20 ppm (>300-fold). Toxicity assays revealed a narrow safety margin for CHG, whereas PHMGH showed a more gradual concentration-dependent toxicity profile. These findings highlight clinically relevant differences in antiseptic performance and identify combination strategies that may reduce CHG exposure while maintaining antifungal efficacy. Full article

This work presents an enhancement to the Dynamic Traffic Management (DTM) framework aimed at reducing signaling overhead between SDN controllers in multi-domain cloud environments. This extension is based on the ability to transmit information regarding the amount of balanced traffic and the optimal transfer pattern. In the baseline periodic mode, the system regularly exchanges the compensation vector ($\overrightarrow{C}$) and the reference pattern ($\overrightarrow{R}$). To minimize communication, we define non-periodic modes that restrict $\overrightarrow{C}$ updates and eliminate $\overrightarrow{R}$ transmission entirely. Within these restricted signaling modes, we further distinguish between reactive and proactive operational schemes. Our experimental results demonstrate that reducing the visibility of optimization goals ($\overrightarrow{R}$ and only sign of $\overrightarrow{C}$) and cutting signaling frequency in this manner maintains a comparable level of cost-efficiency. Specifically, the initial evaluation shows that DTM typically decreases transit costs by 8% to 15%, with maximum savings reaching up to 29% when compared to the worst-case default BGP path scenario. These findings suggest that the DTM mechanism can maintain its economic efficiency even with significantly reduced inter-domain coordination. Full article

The use of solar photovoltaic technology is among the most promising approaches to achieving SDG7—Affordable and Clean Energy—which seeks to provide modern, reliable, sustainable, and efficient energy for everyone globally, especially in developing areas with high irradiation, where both energy access and decarbonization are major challenges. South Khorasan Province, Iran, is one of the most highly irradiated regions in the world. However, despite the abundance of solar resources, most previous research in Iran on solar potential has focused on technical potential, with little emphasis on actual energy consumption patterns and economic viability. To the best of our knowledge, this is the first demand-driven assessment at the county level and the first national-scale implementation of the MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution) method for selecting solar energy sites in Iran. A spatially explicit integrated framework based on GIS-MARCOS was established for each of the eleven counties of South Khorasan Province, and five benefits were used as criteria (solar irradiance, population, per capita electrical consumption in residential, industrial, and agricultural sectors). Objective weights were calculated using Shannon’s Entropy. The analysis indicates that residential electricity demand emerges as the most influential factor in the prioritization process. Therefore, the counties of Birjand, Qaenat, and Tabas were identified as top priority counties, while counties with high irradiation levels but low demand (for example, Boshruyeh) received the least priority. These results clearly indicate the need to transition from irradiation-based to demand-based planning to minimize transmission losses and maximize the ability to integrate solar-generated electricity into the electric power grid. This proposed methodology provides a transferable decision-support tool for other high-irradiation, demand-heterogeneous regions around the globe. Full article

The industrial metaverse is a digital twin space that integrates the real world with virtual environments through bidirectional synchronization. It supports critical services, such as time-sensitive machine control and large-scale collaboration, which require Time-Sensitive Networking and scalable Data Distribution Services. DDS, developed by the Object Management Group, provides excellent scalability and diverse QoS policies but struggles to guarantee transmission delay and jitter for time-critical applications. TSN, based on IEEE 802.1 standards, addresses these challenges by ensuring time-criticality. However, current research lacks comprehensive integration mechanisms for DDS and TSN, particularly from the viewpoints of semantics and system framework. Additionally, there is no adaptive QoS mapping converting the abstract DDS QoS policies to the sophisticated TSN QoS parameters. This paper presents a novel DDS-over-TSN framework that incorporates three key functions to address these challenges. First, Cross-layer QoS Mapping automates correspondences between DDS and TSN parameters, deriving technical constraints from standard documentation through retrieval-augmented generation. Second, Semantic Priority Estimation extracts substantial priority levels by utilizing language model embedding vectors as high-dimensional feature extractors. Third, Adaptive Resource Allocation performs dynamic bandwidth distribution for each priority level through reinforcement learning. Simulation results reveal over 99% mapping accuracy and 97% consistency in priority extraction. The applied Deep Reinforcement Learning paradigm allocated 99% of required resources to high-priority classes and reduced resource wastage by 15% compared to conventional methods. This methodology meets industrial requirements by ensuring both deterministic real-time performance and efficient resource isolation. Full article

This research-related study examines the relevance of mud pulse telemetry (MPT) systems and their intersection with drilling performance, focusing on data transmission signal propagation performance and overall operation under different drilling parameters conditions, with an additional focus on drilling fluid flow rate and downhole pressure conditions. The novelty of this study lies in the investigation of adjustments to drilling operating parameters that could potentially improve the transmission of telemetry signals during drilling, in real time, without requiring mechanical or functional modifications to the MPT system itself. Improvements on transmission performance in situations where the data rate may be limited are also addressed, presenting an alternative through possible propagation velocity improvements to counterbalance it. A detailed chronological technical scientific literature review details important parts on analyses of pressure pulse propagation velocities focused on data transmission. A systematic experimental approach was developed and put into practice to evaluate the MPT systems in regard to tendencies on transmission performances, emphasizing pressure pulse propagation velocity. The laboratory-scale experiments were conducted at the Institute of Drilling Engineering and Fluid Mining (IBF) from the Technical University Bergakademie Freiberg (TUBAF), namely the Flow-loop Research Facility, to assess the impact of fluid flow rate (and subsequent pressure) on data transmission efficiency. Experimental results demonstrate that increasing the flow rate significantly speeds up signal propagation. In the performed experiments, for the mud siren configuration, increasing the flow rate from 15 to 25 m³/h improved the data transmission performance by approximately, at minimum, 18%, while for the positive mud pulse system, an increase in flow rate from 11.5 to 14 m³/h resulted in a propagation velocity rise of about 19%. The results also showed that higher concentrations of glycerin in the working fluid reduced the propagation velocity, confirming the influence of the fluid’s rheological properties on telemetry performance. At the end, in the presented case study, for 6 bps data rate configurations and for a transmission of a 40-bit string, it was demonstrated that the propagation time from downhole to the surface could potentially represent approximately 40% of the total time demanded for transmitting the desired information (generation plus propagation time). It was verified that an increment of 0.02208 m³/s (350 gpm) could lead to shortening eventual surveying procedures by 1–2 s, and that it could equally represent 1.137 bps. This is a relevant outcome, since, without any physical or functional alteration to the MPT system, one could have the data transmission performance improved, an approach not yet analyzed in the literature nor at the industrial park. These results, added to the detailed literature investigation and interaction with field personnel, indicate that the drilling fluid flow rate is a critical operational parameter affecting both the telemetry signal transmission speed and the overall drilling efficiency. Increasing the flow rate can reduce survey transmission time and decrease operational exposure to drilling hazards, such as drill string sticking. The results provide quantitative information applicable in optimizing measurement-drilling telemetry and help support the development of integrated drilling optimization strategies that balance drilling performance with real-time data transmission assurance in deep drilling operations. Full article

Visible light communication (VLC)-based Li-Fi systems suffer from limitations in transmission capacity expansion due to the restricted modulation bandwidth of LEDs. In this study, a latent representation-based NRZ-OOK Li-Fi transmission framework that exploits the statistical feature distribution of the latent space is proposed to improve transmission efficiency without expanding the physical bandwidth. An autoencoder is employed to transform input images into low-dimensional latent vectors, which are then quantized and modulated for transmission. At the receiver, hard decision and inverse quantization are performed, and the image is reconstructed through a trained decoder by leveraging the distribution characteristics of the latent representation. The effective transmission capacity gain $G_{\mathrm{c}\mathrm{a}\mathrm{p}}$ is defined to quantify the amount of representable information relative to the original data under the same physical link resources according to the latent dimension, achieving up to a 49-fold data representation efficiency. The experimental results over practical optical links (0.5–1.5 m) showed that, in short-range conditions, larger latent dimensions maintained higher reconstruction PSNR, whereas under channel degradation conditions, smaller latent dimensions exhibited higher robustness, demonstrating a performance inversion phenomenon. Furthermore, it was confirmed that the dominant factor governing reconstruction performance shifts from the representational capability of the data to error accumulation characteristics depending on the channel condition. These results suggest that the latent representation-based transmission framework is an effective Li-Fi strategy that can simultaneously consider transmission efficiency and channel robustness through information representation optimization in bandwidth-limited environments. Full article

Wireless sensor networks using battery-powered, low-cost sensors, due to their non-rechargeability and strictly limited energy resources, are more sensitive to energy efficiency than other networks of this type. Clustered wireless sensor networks address this problem. In these networks, the most energy-intensive communication, i.e., a long-range one, is carried out via designated nodes, called cluster head nodes, while other cluster nodes communicate with their cluster heads. Cluster head node selection is handled by appropriate routing protocols, and newly designed protocols are first tested in simulations. Among the simulators of cluster head selection protocols, those implemented in a MATLAB environment play an important role, and among these, those implementing a first-order radio model to estimate the energy cost of transmission, both at the transmitter and at the receiver, play a particularly important role. This paper presents and discusses the energy aspects of MATLAB-based open-source wireless sensor network environments that employ the first-order radio model for the simulation of cluster head selection protocols. Current MATLAB-based open-source simulators of cluster head selection protocols were inventoried and analyzed. The review results showed that the first-order radio model had been used in its classic form for years, with the same default parameters. Although the simulators were written using different programming paradigms, precluding simple copy-and-paste, the first-order radio model was generally similar. However, there were exceptions to this rule. A hard exception is the simulator for a body-area wireless sensor network, which only implements a version of the first-order radio model specific to that environment. Soft exceptions are two simulators of the popular cluster head selection protocol, which implemented only half the functionality of the classic first-order radio model. On the one hand, this demonstrates both the widespread use of a conservative approach to the model, which ensures relatively easy repeatability of simulation results, and, on the other hand, the flexibility of the model, which allows its extension to other environments. Finally, the limitations of the model are presented and directions for future research are indicated. Full article

Quantum image transmission over noisy communication channels remains a challenge due to the fragility of quantum states and their susceptibility to channel impairments. Existing quantum encoding schemes often exhibit limited noise resilience, while advanced approaches introduce computational and implementation complexity. To address these limitations, this paper proposes a circular polarization-based quantum encoding framework for image transmission over error-prone channels. In the proposed approach, source images are compressed and source-encoded using standard image coding formats, including the joint photographic experts group (JPEG) standard and the high-efficiency image file format (HEIF), and converted into classical bitstreams. The resulting bitstreams are protected using channel coding and mapped onto quantum states via circular polarization representations, where left- and right-hand circularly polarized states encode binary information. The encoded quantum states are transmitted over noisy quantum channels to model channel impairments. At the receiver, appropriate quantum decoding and channel decoding operations are applied to recover the classical bitstream, followed by source decoding to reconstruct the image. The performance of the proposed framework is evaluated using image quality metrics, including peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and universal quality index (UQI). Simulation results demonstrate that the proposed circular polarization-based encoding scheme outperforms existing quantum image encoding techniques, achieving channel SNR gains of 4 dB over state-of-the-art Hadamard-based encoding and 3 dB over frequency-domain quantum encoding methods under severe noise conditions. These results indicate that circular polarization-based quantum encoding provides improved noise robustness and reconstruction fidelity for practical quantum image transmission systems. Full article

The load characteristics of the helicopter tail rotor system are critical to flight safety and handling performance, and flight testing remains the most direct and reliable means to obtain authentic load data. In this paper, the well-established Wheatstone bridge strain measurement method is adopted to carry out accurate load testing on the helicopter tail rotor system. The tail rotor assembly mainly consists of the tail rotor shaft, pitch link, and tail rotor blades, which undertake different load transfer tasks during flight. Under actual operating conditions, the tail rotor shaft bears significant axial tension as well as combined lateral and vertical bending moments; the pitch link is primarily subjected to alternating axial tension and compression; and the tail rotor blades withstand complex loads including flapping bending, lagwise bending, and torsional moments. According to the distinct stress characteristics and force transmission paths of each component, targeted flight test maneuvers are reasonably designed. These maneuvers include steady-level flight at low, medium, and high speeds, zigzag climbing flight, near-ground side-rear flight, as well as deceleration-to-sprint and obstacle slope maneuvers specified in ADS-33E. Key flight parameters are selected for in-depth analysis to reveal the load distribution and dynamic variation patterns of the tail rotor under typical operating conditions. On this basis, a helicopter load risk test point matrix is established to identify high-risk working conditions and key monitoring positions. This study provides a solid theoretical and data foundation for subsequent flight test monitoring and structural strength verification. It effectively reduces flight test risks, improves monitoring efficiency and accuracy, and helps cut down the human, material, and financial costs associated with flight test monitoring. The research results can also provide important references for the design optimization and safety evaluation of helicopter tail rotor systems. Full article