Search Results (4,192)

The accelerating industrialization process of China expanded coal consumption and induced the depletion of resource reserves. Meanwhile, vast amounts of coal resources are “trapped” since they are located beneath buildings, railways, and water bodies, which is termed the “three-limitation” problem in China. In order to minimize the damage of coal extraction to two villages in Weibei Coalfield, China, a modified room-and-pillar method is integrated with backfilling. This work conducted a series of numerical tests in order to determine the optimal design of this integration in the Jinqiao coal mine, and field verification was carried out. The result shows that the widths of both the pillar and backfill body have an influence on the surface subsidence, but the subsidence is controlled to be within a low extent by the proposed method. Additionally, the backfill body becomes a stress concentration area, induced by the transmission of the weight of overlying strata from the gob area. Plastic failure is concentrated near the top of the backfill body and exhibits shear characteristics, while the immediate roof experiences less damage, primarily in the form of tensile failure. As the width of the backfill body decreases, the tensile and shear failures in the immediate roof gradually diminish, reducing the impact on the overlying strata. The protection of village buildings can therefore be guaranteed. Full article

This paper investigates secrecy solutions for integrated sensing and communication (ISAC) systems, leveraging the combination of a reflecting intelligent surface (RIS) and an unmanned aerial vehicle (UAV) to introduce new degrees of freedom for enhanced system performance. Specifically, we propose a secure ISAC system supported by a UAV-mounted RIS, where an ISAC base station (BS) facilitates secure multi-user communication while simultaneously detecting potentially malicious radar targets. Our goal is to improve parameter estimation performance, measured by the Cramér–Rao bound (CRB), by jointly optimizing the UAV position, transmit beamforming, and RIS beamforming, subject to constraints including the UAV flight area, communication users’ quality of service (QoS) requirements, secure transmission demands, power budget, and RIS reflecting coefficient limits. To address this non-convex, multivariate, and coupled problem, we decompose it into three subproblems, which are solved iteratively using particle swarm optimization (PSO), semi-definite relaxation (SDR), majorization–minimization (MM), and alternating direction method of multipliers (ADMM) algorithms. Our numerical results validate the effectiveness of the proposed scheme and demonstrate the potential of employing UAV-mounted RIS in ISAC systems to enhance radar sensing capabilities. Full article

Hemoparasitic diseases represent a significant problem with a considerable impact on tropical and subtropical areas of the world. These conditions cause economic losses associated with multi-organic failure and even the death of animals. In these areas, the hemoparasites are transmitted in an enzootic cycle when infectious cattle, such as persistently infected animals, including cows, contribute to the success of transmission. However, the factors associated with transmission have always been considered environmental issues, disregarding herd management and practices. In this sense, we conducted a cross-sectional study sampling 360 female cattle older than one year to identify infectious cattle using the PCR technique. We employed a dichotomic questionnaire for association analyses in 150 herds of the southern Andean region of Colombia. Overall prevalence with infectious cattle was 52.5% for Babesia spp., Anaplasma spp., and Trypanosoma spp., and the significant risk factors (p < 0.05) included geographic area, animal weight, purchase of cattle for fattening, disinfection of clothing after contact with neighboring animals, self-medication, separation of animals in pens, supply of mineralized salt, presence of livestock from other owners on the farm, prevention of joint trauma, documented milking routine, and sending blood samples for analysis. These practices permitted the maintenance of persistently infected animals and their movement to shed the agents to other animals in the presence of vectors. This suggests the importance of implementing comprehensive control and training measures to reduce the infectious cattle and, therefore, the profitability of dual-purpose livestock farms in the Andean region of southwestern Colombia. Full article

In radio frequency identification (RFID), differences in spectrum policies and tag misreading in different countries are the two main issues that limit its application. To solve these problems, this article proposes a composite right/left-handed transmission line (CRLH-TL)-based reconfigurable antenna for ultra-high frequency near-field and far-field RFID reader applications. The CRLH-TL is achieved using a periodically capacitive gap-loaded parallel plate line. By deploying the CRLH-TL operating at zeroth-order resonance, a loop antenna with in-phase radiating current is obtained, which contributes to a strong and uniform H-field and a horizontally polarized omnidirectional radiation pattern. By introducing additional tunable components, frequency and reading range reconfigurabilities are enabled. The frequency tuning range is from 833 MHz to 979 MHz, which covers the worldwide UHF RFID band. Moreover, each operation mode has a narrow frequency band, which means it can operate without violating different countries’ radio frequency policy and reduce the design difficulty of designing multiple versions of a reader. Both the near-field interrogation zone and maximum far-field reading distance of the antenna are adjustable. The near-field interrogation zone is 400 mm × 400 mm × 50 mm and can be further confined. The tuning range for far-field reading distance is from 2.71 m to 0.35 m. Full article

In existing coverage challenges within wireless sensor networks, traditional sensor perception models often fail to accurately represent the true transmission characteristics of wireless signals. In more complex application scenarios such as warehousing, residential areas, etc., this may lead to a large gap between the expected effect of actual coverage and simulated coverage. Additionally, these models frequently neglect critical factors such as sensor failures and malfunctions, which can significantly affect signal detection. To address these limitations and enhance both network performance and longevity, this study introduces a perception model that incorporates path loss and false alarm probability. Based on this perception model, the optimization objective function of the WSN node optimization coverage problem is established, and then the intelligent optimization algorithm is used to solve the objective function and finally achieve the optimization coverage of sensor nodes. The study begins by deriving a logarithmic-based path loss model for wireless signals. It then employs the Neyman–Pearson criterion to formulate a maximum detection probability model under conditions where the cost function and prior probability are unknown, constraining the false alarm rate. Simulated experiments are conducted to assess the influence of various model parameters on detection probability, providing comparative analysis against traditional perception models. Ultimately, an optimization model for WSN coverage, based on combined detection probability, is developed and solved using an intelligent optimization algorithm. The experimental results indicate that the proposed model more accurately captures the signal transmission and detection characteristics of sensor nodes in WSNs. In the coverage area of the same size, the coverage of the model constructed in this paper is compared with the traditional 0/1 perception model and exponential decay perception model. The model can achieve full coverage of the area with only 50 nodes, while the exponential decay model requires 54 nodes, and the coverage of the 0/1 model is still less than 70% at 60 nodes. According to the simulation experiments, it can be basically proved that the WSN node optimization coverage strategy based on the proposed model provides an effective solution for improving network performance and extending network lifespan. Full article

Unmanned aerial vehicle (UAV)-based wireless sensor networks (WSNs) hold great promise for supporting ground-based sensors due to the mobility of UAVs and the ease of establishing line-of-sight links. UAV-based WSNs equipped with mobile edge computing (MEC) servers effectively mitigate challenges associated with long-distance transmission and the limited coverage of edge base stations (BSs), emerging as a powerful paradigm for both communication and computing services. Furthermore, incorporating simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) as passive relays significantly enhances the propagation environment and service quality of UAV-based WSNs. However, most existing studies place STAR-RISs in fixed positions, ignoring the flexibility of STAR-RISs. Some other studies equip UAVs with STAR-RISs, and UAVs act as flight carriers, ignoring the computing and caching capabilities of UAVs. To address these limitations, we propose an energy-efficient aerial STAR-RIS-aided computing offloading and content caching framework, where we formulate an energy consumption minimization problem to jointly optimize content caching decisions, computing offloading decisions, UAV hovering positions, and STAR-RIS passive beamforming. Given the non-convex nature of this problem, we decompose it into a content caching decision subproblem, a computing offloading decision subproblem, a hovering position subproblem, and a STAR-RIS resource allocation subproblem. We propose a deep reinforcement learning (DRL)–successive convex approximation (SCA) combined algorithm to iteratively achieve near-optimal solutions with low complexity. The numerical results demonstrate that the proposed framework effectively utilizes resources in UAV-based WSNs and significantly reduces overall system energy consumption. Full article

A communication network integrating multiple modes can effectively support the sustainable development of next-generation wireless communications. Integrated sensing, communication, and power transfer (ISCPT) represents an emerging technological paradigm that not only facilitates information transmission but also enables environmental sensing and wireless power transfer. To achieve optimal beamforming in transmission, it is crucial to satisfy multiple constraints, including quality of service (QoS), radar sensing accuracy, and power transfer efficiency, while ensuring fundamental system performance. The presence of multiple parametric constraints makes the problem a non-convex optimization challenge, underscoring the need for a solution that balances low computational complexity with high precision. Additionally, the accuracy of channel state information (CSI) is pivotal in determining the achievable rate, as imperfect or incomplete CSI can significantly degrade system performance and beamforming efficiency. Deep reinforcement learning (DRL), a machine learning technique where an agent learns by interacting with its environment, offers a promising approach that can dynamically optimize system performance through adaptive decision-making strategies. In this paper, we propose a DRL-based ISCPT framework, which effectively manages complex environmental states and continuously adjusts variables related to sensing, communication, and energy harvesting to enhance overall system efficiency and reliability. The achievable rate upper bound can be inferred through robust, learnable beamforming in the ISCPT system. Our results demonstrate that DRL-based algorithms significantly improve resource allocation, power management, and information transmission, particularly in dynamic and uncertain environments with imperfect CSI. Full article

Internet congestion control focuses on balancing effective network utilization with the avoidance of congestion. When bottleneck bandwidth and network buffer capacities are exceeded, congestion typically manifests as packet loss. Additionally, when packets remain in buffers for too long, a queueing delay occurs. Most existing congestion control algorithms aim to solve this as a constraint satisfaction problem, where constraints are defined by bandwidth or queueing delay limits. However, these approaches often emphasize finding feasible solutions over optimal ones, which often lead to under-utilization of available bandwidth. To address this limitation, this article leverages Little’s Law to derive a closed-form optimality equation for congestion control. This optimality equation serves as the foundation for developing a new algorithm, TCP QtColFair, designed to optimize the sending rate. TCP QtColFair is evaluated against two widely deployed congestion control algorithms: TCP CUBIC, which utilizes a cubic window growth function to enhance performance in high-bandwidth, long-distance networks and TCP BBR (Bottleneck Bandwidth and Round-trip propagation time), developed by Google to optimize data transmission by estimating the network’s bottleneck bandwidth and round-trip time. In terms of avoiding queueing delays and minimizing packet loss, TCP QtColFair outperforms TCP CUBIC and matches TCP BBR’s performance when network buffers are large. For effective network utilization, TCP QtColFair outperforms both TCP BBR and TCP CUBIC. TCP QtColFair achieves an effective utilization of approximately 96%, compared to just above 94% for TCP BBR and around 93% for TCP CUBIC. Full article

In recent years, the internet has developed rapidly. With the popularity of social media, uploading and backing up digital images has become the norm. A huge number of digital images are circulating on the internet daily, and issues related to information security follow. To protect intellectual property rights, digital watermarking is an indispensable technology. However, the common lossy compression technology in the network transmission process is a big problem for watermarking. This paper describes an innovative semi-blind watermarking method with the use of deep convolutional generative adversarial networks (DCGANs) for hiding and extracting watermarks from JPEG-compressed images. The proposed method achieves an average peak signal-to-noise ratio (PSNR) of 49.99 dB, a structural similarity index (SSIM) of 0.95, and a bit error rate (BER) of 0.008 across varying JPEG quality factors. The process is based on an embedder, decoder, generator, and discriminator. It allows watermarking, decoding, or reconstruction to be symmetric such that there is less distortion and durability is improved. It constructs a specific generator for each image and watermark that is supposed to be protected. Experimental results show that, with the variety of JPEG quality factors, the restored watermark achieves a remarkably low corrupted rate, outstripping recent deep learning-based watermarking methods. Full article

The transport of freshly post-harvested fruit to its collection point is mainly achieved using trailers over uneven terrain, which generates impacts and vibrations that negatively affect the quality of the fruit. Although some solutions to mitigate these effects have been proposed in previous studies, none of them are applied directly to the source of the problem, i.e., the transport boxes. In this context, metamaterial sheets inspired by the design of quasi-zero stiffness isolators (QZSs) open up the possibility of exploring ways of vibration isolation thanks to their associated nonlinear characteristics. In this work, ABS sheets with different internal geometries were manufactured and compared as possible bottoms of transport boxes. Vibration reduction not only protects the physical integrity of the fruit, avoiding visible damage such as bumps or bruises, but also preserves its chemical properties, such as texture and freshness, which directly impacts its shelf life and presentation for sale. The design variables analyzed for these geometries included the number of ribs, their thickness and their angle of inclination. In these specimens, their behavior to impact-type forces and their experimental dynamic behavior were studied using an electromagnetic shaker against a sinusoidal signal and against the uniaxial vibration recorded at the base of a trailer in a real rural route. The results showed that the specimens with a rib angle of 30° and a thickness of 0.4 mm showed the best impact performance and a higher amplification of vibration transmissibility in the steady state. In the presence of the signal recorded on the route, transmissibility reduction percentages between 13% and 19% were obtained in the principal acceleration impact. Full article

The process of establishing relay protection and automation (RPA) settings for electric power systems (EPSs) entails complex calculations of operating modes. Traditionally, these calculations are based on symmetrical components, which require the building of equivalent circuits of various sequences. This approach can lead to errors both when identifying the operating modes and when modeling the RPA devices. Proper modeling of measuring transformers (MTs), symmetrical component filters (SCFs), and circuits connected to them effectively solves this problem, enabling the configuration of relay protection and automation systems. The methods of modeling the EPS in phase coordinates are proposed to simultaneously determine the operating modes of high-voltage networks and secondary circuits connected to the current and voltage transformers. The MT and SCF models are developed to concurrently identify the operating modes of secondary wiring circuits and calculate the power flow in the controlled EPS segments. This method is effective in addressing practical problems related to the configuration of the relay protection and automation systems. It can also be used when establishing cyber–physical power systems. For a comprehensive check of the adequacy of the MT models, 140 modes of the electric power system were determined which corresponded to time-varying traction loads. Based on the results of calculating the complexes of currents and voltages at the MT terminals, parametric identification of the power transmission line was performed. Based on this, the model of this transmission line was adjusted; repeated modeling was carried out, and errors were calculated. The modeling results showed a high accuracy when calculating the modules and phases of voltages using the identified model. The average error value for current modules was 0.6%, and for angles, it was 0.26°. Full article

Aim: Malaria in pregnancy is a global health problem because it causes anemia in the mother and may result in abortion, stillbirth, uterine growth retardation, and low birth weight in the newborn. The purpose of this study was to assess the effects of HEI on knowledge and adherence to intermittent preventive treatment of malaria among pregnant women at secondary health facilities in Benue State, Nigeria. Methods: This quasi-experimental study included pre-, intervention, and post-intervention. The study recruited 871 pregnant women (436 study and 435 control) using multistage sampling. The study used a semi-structured questionnaire (pre- and post-test), follow-up checklist, and health education module. Participants self-administered the semi-structured questionnaire with 57 open-ended and closed-ended questions. Results: About 41% had high malaria awareness, but 93.9% did throughout pregnancy and intermittent preventive treatment (IPT) after health education intervention (HEI). The majority (93.8%) understood malaria transmission methods after HEI. 95.3% understood malaria symptoms after HEI. The HEI shows 95.6% of participants knew a lot about malaria during pregnancy. Post-HEI, 95% knew malaria prophylaxis. After HEI, 95.4% knew malaria-prevention drugs. Intermittent Preventive treatment (IPT) pregnancy dosages were known by 94.3% of respondents post-HEI. Post-HEI, 95.4% of responders knew the interval between IPT dosages, compared to 59.2% pre-HEI. After HEI, 95% of respondents were aware of IPT adverse effects, up from 29.2% pre-HEI. Pre-HEI showed. Conclusions: Results demonstrate HEI promotes malaria IPT adherence during pregnancy. A health education proves a veritable interventional strategy in influencing a mother’s understanding of malaria IPT, level of adherence to IPT, and drug adherence to directly observed therapy of IP while pregnant. Thus, nurses and midwives should increase IPT health education during antenatal clinic visits to increase its uptake and adherence among pregnant women and reduce malaria burden and death. Sulfadoxine/pyrimethamine (SP) for malaria in pregnancy (MiP) IPT must be distributed by the state health ministry to all health facilities—tertiary, secondary, primary, faith-based, and private. Full article

As the Internet of Things (IoT) expands globally, the challenge of signal transmission in remote regions without traditional communication infrastructure becomes prominent. An effective solution involves integrating aerial, terrestrial, and space components to form a Space–Air–Ground Integrated Network (SAGIN). This paper discusses an uplink signal scenario in which various types of data collection sensors as IoT devices use Unmanned Aerial Vehicles (UAVs) as relays to forward signals to low-Earth-orbit satellites. Considering the fairness of resource allocation among IoT devices of the same category, our goal is to maximize the minimum uplink channel capacity for each category of IoT devices, which is a multi-objective optimization problem. Specifically, the variables include the deployment locations of UAVs, bandwidth allocation ratios, and the association between UAVs and IoT devices. To address this problem, we propose a multi-objective evolutionary algorithm that ensures fair resource distribution among multiple parties. The algorithm is validated in eight different scenario settings and compared with various traditional multi-objective optimization algorithms. The experimental results demonstrate that the proposed algorithm can achieve higher-quality Pareto fronts (PFs) and better convergence, indicating more equitable resource allocation and improved algorithmic effectiveness in addressing this issue. Moreover, these pre-prepared, high-quality solutions from PFs provide adaptability to varying requirements in signal collection scenarios. Full article

The demand for high-capacity communication systems has grown exponentially in recent decades, constituting a technological field in constant change. Data transmission at high rates, reaching tens of Gb/s, and over distances that can reach hundreds of kilometers, still faces barriers to improvement, such as distortions in the transmitted signals. Such distortions include chromatic dispersion, which causes a broadening of the transmitted pulse. Therefore, the development of solutions for the adequate recovery of such signals distorted by the complex dynamics of the transmission channel currently constitutes an open problem since, despite the existence of well-known and efficient equalization techniques, these have limitations in terms of processing time, hardware complexity, and especially energy consumption. In this scenario, this paper discusses the emergence of photonic neural networks as a promising alternative for equalizing optical communication signals. Thus, this review focuses on the applications, challenges, and opportunities of implementing integrated photonic neural networks for the scenario of optical signal equalization. The main work carried out, ongoing investigations, and possibilities for new research directions are also addressed. From this review, it can be concluded that perceptron photonic neural networks perform slightly better in equalizing signals transmitted over greater distances than reservoir computing photonic neural networks, but with signals at lower data rates. It is important to emphasize that photonics research has been growing exponentially in recent years, so it is beyond the scope of this review to address all existing applications of integrated photonic neural networks. Full article

The symmetrical linear Diophantine fuzzy Hamacher aggregation operators play a fundamental role in many decision-making applications. The selection of a cyber security system is of paramount importance for maintaining digital assets. It necessitates a comprehensive review of threat landscapes, vulnerability assessments, and the specific needs of the organization in order to ensure the implementation of effective security measures. Smart grid (SG) technology uses modern communication and monitoring technologies to enhance the management and regulation of electricity production and transmission. However, greater dependence on technology and connection creates new vulnerabilities, exposing SG communication networks to large-scale attacks. Unlike previous surveys, which often give broad overviews of SG design, our research goes a step further, giving a full architectural layout that includes major SG components and communication linkages. This in-depth review improves comprehension of possible cyber threats and allows SGs to analyze cyber risks more systematically. To determine the best cybersecurity strategies, this study introduces a multi-criteria group decision-making (MCGDM) approach using the linear Diophantine fuzzy Hamacher prioritized aggregation operator (LDFHPAO). In real-world applications, aggregation operators (AOs) are essential for information fusion. This research presents innovative prioritized AOs designed to address MCGDM problems in uncertain environments. We developed the LDF Hamacher prioritized weighted average (LDFHPWA) and LDF Hamacher prioritized weighted geometric (LDFHPWG) operators, which address the shortcomings of traditional operators and provide a more robust modeling approach for MCGDM challenges. This study also outlines key characteristics of these new prioritized AOs. An MCGDM approach incorporating these operators is proposed and demonstrated to be effective through an example that compares and selects the optimal cybersecurity. Full article