Search Results (8,593)

IoT devices with limited resources, and in the absence of gateways, become vulnerable to various attacks, such as denial of service (DoS) and man-in-the-middle (MITM) attacks. Intrusion detection systems (IDS) are designed to detect and respond to these threats in IoT environments. While machine learning-based IDS have typically been deployed at the edge (gateways) or in the cloud, in the absence of gateways, the IDS must be embedded within the sensor nodes themselves. Available datasets mainly contain features extracted from network traffic at the edge (e.g., Raspberry Pi/computer) or cloud servers. We developed a unique dataset, named as Intrusion Detection in the Smart Homes (IDSH) dataset, which is based on features retrievable from microcontroller-based IoT devices. In this work, a Tree-based IDS is embedded into a smart thermostat for real-time intrusion detection. The results demonstrated that the IDS achieved an accuracy of 98.71% for binary classification with an inference time of 276 microseconds, and an accuracy of 97.51% for multi-classification with an inference time of 273 microseconds. Real-time testing showed that the smart thermostat is capable of detecting DoS and MITM attacks without relying on a gateway or cloud. Full article

Arbuscular mycorrhizal fungi (AMF) act as intermediaries between the root systems of host plants and the surrounding soil, offering various benefits to medicinal plants, such as promoting growth and enhancing quality. However, the host range of AMF in medicinal plants and the characteristics of plant–AMF networks in farmland ecosystems remain insufficiently studied. In the present study, we measured AMF colonization, species diversity, and soil properties of 31 medicinal plants at the Anguo Medicine Planting Base in Northwest China. The medicinal plant–AMF network was subsequently analyzed, and the growth-promoting effects of AMF on Astragalus mongholicus were examined. Spore density, species richness, and total colonization exhibited significant variation across different medicinal plant species. Glomus melanosporum, G. claroideum, and Septoglomus constrictum were the dominant species among 61 AMF species. Soil organic matter, phosphatase, available nitrogen, and glomalin-related soil proteins (GRSPs) were the main factors affecting the AMF composition. Structural equation models and a variation partitioning analysis suggested a highly plant species-specific pattern of AMF distribution patterns, where the host identities explained 61.4% of changes in spore density and 48.2% of AMF colonization. The soil nutrient availability and phosphatase activity also influenced AMF colonization. Our results confirmed glomalin as an important contributor to the soil carbon in farmland for cultivating medicinal plants. The medicinal plant–AMF symbiotic network exhibited highly nested patterns, a low specialized structure, high connectance, and low modularity, which suggested saturated AMF colonization and symbiosis stability provided by redundant plant–AMF associations. Despite the wide host range among medicinal plants, AMF inoculation revealed species-specific effects on the growth performance and active ingredient content levels in A. mongholicus, G. claroideum and Sep. constrictum induced the highest biomass and active ingredient content accumulation in A. mongholicus. These findings advance our understanding of AMF community dynamics in the rhizosphere of medicinal plants and offer valuable insights for optimizing medicinal plant cultivation practices. Full article

Solid-state electrolytes are widely anticipated to revitalize lithium-ion batteries with high energy density and safety. However, low ionic conductivity and high interfacial resistance at room temperature pose challenges for practical applications. This study combines the rigid oxide electrolyte LLZTO with the flexible polymer electrolyte poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) to achieve effective coupling of rigidity and flexibility. The semi-interpenetrating network structure endows the PEL composite solid electrolyte with excellent lithium-ion transport capabilities, resulting in an ionic conductivity of up to 5.1 × 10⁻⁴ S cm⁻¹ and lithium-ion transference number of 0.41. The assembled LiFePO₄/PEL/Li solid-state battery demonstrates an initial discharge capacity of 132 mAh g⁻¹ at a rate of 0.1 C. After 100 charge–discharge cycles, the capacity retention is 81%. This research provides a promising strategy for preparing composite solid electrolytes in solid-state lithium-ion batteries. Full article

Electric vehicles worldwide provide numerous key advantages in the energy sector. They are advantageous over fossil fuel vehicles in many aspects: for example, they consume no fuel, are economical, and only require charging the internal batteries, which power the motor for propulsion. Thus, due to their numerous advantages, research is necessary to improve the technological aspects that can enhance electric vehicles’ overall performance and efficiency. However, electric vehicle charging stations are the key hindrance to their adoption. Charging stations will affect grid stability and may lead to altering different parameters, e.g., power losses and voltage deviation when integrated randomly into the distribution system. The distributed generation, along with charging stations with the best location and size, can be a solution that mitigates the above concerns. Metaheuristic techniques can be used to find the optimal siting and sizing of distributed generations and electric vehicle charging stations. This review provides an exhaustive review of various methods and scientific research previously undertaken to optimize the placement and dimensions of electric vehicle charging stations and distributed generation. We summarize the previous work undertaken over the last five years on the multi-objective placement of distributed generations and electric vehicle charging stations. Key areas have focused on optimization techniques, technical parameters, IEEE networks, simulation tools, distributed generation types, and objective functions. Future development trends and current research have been extensively explored, along with potential future advancement and gaps in knowledge. Therefore, at the conclusion of this review, the optimization of electric vehicle charging stations and distributed generation presents both the practical and theoretical importance of implementing metaheuristic algorithms in real-world scenarios. In the same way, their practical integration will provide the transportation system with a robust and sustainable solution. Full article

With the increasing demand for expected data volume daily, current telecommunications infrastructure can not meet requirements without using enhanced technologies adopted by 5G and beyond networks. Due to their diverse features, 5G technologies and services will be phenomenal in the coming days. Proper planning procedures are to be adopted to provide cost-effective and quality telecommunication services. In this paper, we planned 5G network deployment in two frequency ranges, 3.5 GHz and 28 GHz, using a mixed cell structure. We used metaheuristic approaches such as Grey Wolf Optimization (GWO), Sparrow Search Algorithm (SSA), Whale Optimization Algorithm (WOA), Marine Predator Algorithm (MPA), Particle Swarm Optimization (PSO), and Ant Lion Optimization (ALO) for optimizing the locations of remote radio units. The comparative analysis of metaheuristic algorithms shows that the proposed network is efficient in providing an average data rate of 50 Mbps, can meet the coverage requirements of at least 98%, and meets quality-of-service requirements. We carried out the case study for an urban area and another suburban area of Kathmandu Valley, Nepal. We analyzed the outcomes of 5G greenfield deployment and 5G deployment using existing 4G infrastructure. Deploying 5G networks using existing 4G infrastructure, resources can be saved up to 33.7% and 54.2% in urban and suburban areas, respectively. Full article

With the evolution of cellular networks and wireless-local-area-network-based communication technologies, services for smart device users have appeared. With the popularity of 4G and 5G, smart device users can now consume larger bandwidths than before. Consequently, the demand for various services, such as streaming, online games, and video conferences, has increased. For improved quality of experience (QoE), streaming services utilize adaptive bitrate (ABR) algorithms to handle network bandwidth variations. ABR algorithms use network bandwidth history for future network bandwidth prediction, allowing them to perform efficiently when network bandwidth fluctuations are minor. However, in environments with frequent network bandwidth changes, such as wireless networks, the QoE of video streaming often degrades because of inaccurate predictions of future network bandwidth. To address this issue, we utilize the gated recurrent unit, a time series prediction model, to predict the network bandwidth accurately. We then propose a buffer-based ABR streaming technique that selects optimized video-quality settings on the basis of the predicted bandwidth. The proposed algorithm was evaluated on a dataset provided by Zeondo by categorizing instances of user mobility into walking, bus, and train scenarios. The proposed algorithm improved the QoE by approximately 11% compared with the existing buffer-based ABR algorithm in various environments. Full article

To solve the problem that traditional seed recognition methods are not completely suitable for needle-shaped seeds, such as Bidens L., in agricultural production, this paper proposes a model construction idea that combines the advantages of deep residual models in extracting high-level abstract features with multiscale feature extraction fusion, taking into account the depth and width of the network. Based on this, a multiscale feature fusion deep residual network (MSFF-ResNet) is proposed, and image segmentation is performed before classification. The image segmentation is performed by a popular semantic segmentation method, U²Net, which accurately separates seeds from the background. The multiscale feature fusion network is a deep residual model based on a residual network of 34 layers (ResNet34), and it contains a multiscale feature fusion module and an attention mechanism. The multiscale feature fusion module is designed to extract features of different scales of needle-shaped seeds, while the attention mechanism is used to improve the ability to select features of our model so that the model can pay more attention to the key features. The results show that the average accuracy and average F1-score of the multiscale feature fusion deep residual network on the test set are 93.81% and 94.44%, respectively, and the numbers of floating-point operations per second (FLOPs) and parameters are 5.95 G and 6.15 M, respectively. Compared to other deep residual networks, the multiscale feature fusion deep residual network achieves the highest classification accuracy. Therefore, the network proposed in this paper can classify needle-shaped seeds efficiently and provide a reference for seed recognition in agriculture. Full article

PCBs (printed circuit boards) are the core components of modern electronic devices, and inspecting them for defects will have a direct impact on the performance, reliability and cost of the product. However, the performance of current detection algorithms in identifying minor PCB defects (e.g., mouse bite and spur) still requires improvement. This paper presents the MDD-DETR algorithm for detecting minor defects in PCBs. The backbone network, MDDNet, is used to efficiently extract features while significantly reducing the number of parameters. Simultaneously, the HiLo attention mechanism captures both high- and low-frequency features, transmitting a broader range of gradient information to the neck. Additionally, the proposed SOEP neck network effectively fuses scale features, particularly those rich in small targets, while INM-IoU loss function optimization enables more effective distinction between defects and background, further improving detection accuracy. Experimental results on the PCB_DATASET show that MDD-DETR achieves a 99.3% mAP, outperforming RT-DETR by 2.0% and reducing parameters by 32.3%, thus effectively addressing the challenges of detecting minor PCB defects. Full article

Unmanned aerial systems (UASs) are increasingly playing a crucial role in earthquake emergency response and disaster assessment due to their ease of operation, mobility, and low cost. However, post-earthquake scenes are complex, with many forms of damaged buildings. UAS imagery has a high spatial resolution, but the resolution is inconsistent between different flight missions. These factors make it challenging for existing methods to accurately identify individual damaged buildings in UAS images from different scenes, resulting in coarse segmentation masks that are insufficient for practical application needs. To address these issues, this paper proposed DB-Transfiner, a building damage instance segmentation method for post-earthquake UAS imagery based on the Mask Transfiner network. This method primarily employed deformable convolution in the backbone network to enhance adaptability to collapsed buildings of arbitrary shapes. Additionally, it used an enhanced bidirectional feature pyramid network (BiFPN) to integrate multi-scale features, improving the representation of targets of various sizes. Furthermore, a lightweight Transformer encoder has been used to process edge pixels, enhancing the efficiency of global feature extraction and the refinement of target edges. We conducted experiments on post-disaster UAS images collected from the 2022 Luding earthquake with a surface wave magnitude (Ms) of 6.8 in the Sichuan Province of China. The results demonstrated that the average precisions (AP) of DB-Transfiner, ${AP}_{box}$ and ${AP}_{seg}$, are 56.42% and 54.85%, respectively, outperforming all other comparative methods. Our model improved the original model by 5.00% and 4.07% in ${AP}_{box}$ and ${AP}_{seg}$, respectively. Importantly, the ${AP}_{seg}$ of our model was significantly higher than the state-of-the-art instance segmentation model Mask R-CNN, with an increase of 9.07%. In addition, we conducted applicability testing, and the model achieved an average correctness rate of 84.28% for identifying images from different scenes of the same earthquake. We also applied the model to the Yangbi earthquake scene and found that the model maintained good performance, demonstrating a certain level of generalization capability. This method has high accuracy in identifying and assessing damaged buildings after earthquakes and can provide critical data support for disaster loss assessment. Full article

The severe contamination of groundwater supplies in rural areas is a global problem that requires strict environmental measures. Related to this, one of the most important challenges at present is the elimination of local sources of pollution. Therefore, this research examined the local water quality changes following the construction of the sewerage network, under the framework of long-term monitoring (2011–2022) in Báránd, Hungary, using water quality indices and GIS (Geographic Information System) techniques. In order to understand the purification processes and spatial and temporal changes, three periods were determined: the pre-sewerage period (2011–2014), the transitional period (2015–2018), and the post-sewerage period (2019–2022). Forty monitoring wells were included in the study, ensuring complete coverage of the municipality. The results revealed a high level of pollution in the area in the pre-sewerage period. Based on the calculated indices, an average of 80% of the wells were ranked in categories 4–5, indicating poor water quality, while less than 8% were classified in categories 1–2, indicating good water quality. No significant purification process was detected in the transitional period. However, marked changes were observed in the post-sewerage period as a result of the elimination of local sources of pollution. In the post-sewerage period, the number of monitoring wells ranked as excellent and good increased significantly. Additionally, the number of wells assigned to category 5 decreased markedly, compared to the reference period. The significant difference between the three periods was confirmed by the Wilcoxon test as well (p < 0.05). Based on interpolated maps, it was found that, in the post-sewerage period, an increasing section of the settlement had good or excellent water quality. In addition to an assessment of long-term tendencies, the annual fluctuations in the water quality of the wells were also examined. This showed that the purification processes do not occur in a linear pattern but are influenced by various factors (e.g., precipitation). Our results highlight the importance of protecting and improving groundwater resources in municipal areas and the relevance of long-term monitoring of water adequate management policy. Full article

This study investigates the integration of quantum computing, classical methods, and deep learning techniques for enhanced image processing in dynamic 6G networks, while also addressing essential aspects of copyright technology and detection. Our findings indicate that quantum methods excel in rapid edge detection and feature extraction but encounter difficulties in maintaining image quality compared to classical approaches. In contrast, classical methods preserve higher image fidelity but struggle to satisfy the real-time processing requirements of 6G applications. Deep learning techniques, particularly CNNs, demonstrate potential in complex image analysis tasks but demand substantial computational resources. To promote the ethical use of AI-generated images, we introduce copyright detection mechanisms that employ advanced algorithms to identify potential infringements in generated content. This integration improves adherence to intellectual property rights and legal standards, supporting the responsible implementation of image processing technologies. We suggest that the future of image processing in 6G networks resides in hybrid systems that effectively utilize the strengths of each approach while incorporating robust copyright detection capabilities. These insights contribute to the development of efficient, high-performance image processing systems in next-generation networks, highlighting the promise of integrated quantum-classical–classical deep learning architectures within 6G environments. Full article

This paper explores 6G technology for indoor positioning, focusing on accuracy and reliability using convolutional neural networks (CNN) with channel state information (CSI). Indoor positioning is critical for smart applications and the Internet of Things (IoT). 6G is expected to significantly enhance positioning performance through the use of higher frequency bands, such as terahertz frequencies with wider bandwidth. Preliminary results show that 6G-based systems are expected to achieve centimeter-level positioning accuracy due to the integration of advanced artificial intelligence algorithms and terahertz frequencies. In addition, this paper also investigates the impact of self-attention (SA) and channel attention (CA) mechanisms on indoor positioning systems. The combination of these attention mechanisms with conventional CNNs has been proposed to further improve the accuracy and robustness of localization systems. CNN with SA demonstrates a 50% reduction in RMSE compared to CNN by capturing spatial dependencies more effectively. Full article

This study investigates the rheological behavior of cellulose microfiber suspensions derived from kahili ginger stems (Hedychium gardnerianum), an invasive species, in two adhesive matrices: a commercial water-based adhesive (Coplaseal^®) and a casein-based adhesive made from non-food-grade milk, referred to as K and S samples, respectively. Rheological analyses were performed using oscillatory and rotational shear tests conducted at 25 °C, 50 °C, and 75 °C to assess the materials’ viscoelastic properties more comprehensively. Oscillatory tests across a frequency range of 1–100 rad/s assessed the storage modulus (G′) and loss modulus (G″), while rotational shear tests evaluated apparent viscosity and shear stress across shear rates from 0.1 to 1000 s⁻¹. Fiber-free samples consistently showed lower moduli than fiber-containing samples at all frequencies. The incorporation of fibers increased the dynamic moduli in both K and S samples, with a quasi-plateau observed at lower frequencies, suggesting solid-like behavior. This trend was consistent in all tested temperatures. As frequencies increased, the fiber network was disrupted, transitioning the samples to fluid-like behavior, with a marked increase in G′ and G″. This transition was more pronounced in K samples, especially above 10 rad/s at 25 °C and 50 °C, but less evident at 75 °C. This shift from solid-like to fluid-like behavior reflects the transition from percolation effects at low frequencies to matrix-dominated responses at high frequencies. In contrast, S samples displayed a wider frequency range for the quasi-plateau, with less pronounced moduli changes at higher frequencies. At 75 °C, the moduli of fiber-containing and fiber-free S samples nearly converged at higher frequencies, indicating similar effects of the fiber and matrix components. Both fiber-reinforced and non-reinforced suspensions exhibited pseudoplastic (shear-thinning) behavior. Fiber-containing samples exhibited higher initial viscosity, with K samples displaying greater differences between fiber-reinforced and non-reinforced systems compared to S samples, where the gap was narrower. Interestingly, S samples exhibited overall higher viscosity than K samples, implying a reduced influence of fibers on the viscosity in the S matrix. This preliminary study highlights the complex interactions between cellulosic fiber networks, adhesive matrices, and rheological conditions. The findings provide a foundation for optimizing the development of sustainable biocomposites, particularly in applications requiring precise tuning of rheological properties. Full article

Obesity is a chronic and complex disease defined by the excessive deposition of fat and is highly associated with oxidative stress. Adzuki bean saponins (ABS) showed anti-obesity activity in our previous in vivo study; however, the active saponins of adzuki beans and potential mechanisms are still unclear. This research aims to elucidate the anti-obesity effects of ABS in improving lipid metabolism and oxidative stress, exploring the effective ingredients and potential molecular mechanisms through UHPLC-QE-MS analysis, network pharmacology, bioinformatics, and in vitro experiments both in the 3T3-L1 cell line and HepG2 cell line. The results indicate that ABS can improve intracellular lipid accumulation, adipogenesis, oxidative stress, and mitochondrial damage caused by lipid accumulation including ROS generation, abnormal mitochondrial membrane potential, and ATP disorder. Fifteen saponin components were identified with the UHPLC-QE-MS analysis. The network pharmacology and bioinformatics analyses indicated that the PI3K/Akt signaling pathway is associated with the bioactive effect of ABS. Through Western blotting and immunofluorescence analysis, the anti-obesity effect of ABS is achieved through regulation of the PI3K/Akt/GSK3β/β-catenin signaling pathway and activation of downstream transcription factor c-Myc in the lipid accumulation cell model, and regulation of β-catenin signaling and inhibition of downstream transcription factor C/EBPα in the adipocyte cell model. These results illustrate the biological activity of ABS in improving fat metabolism and oxidative stress by restoring mitochondrial function through β-catenin signaling, the PI3K/Akt/GSK3β/β-catenin signaling pathway, laying the foundation for its further development. Full article

This study proposed applying artificial intelligence (AI) to predict the actual electrical conductivity (EC) of raw and pasteurized milk using moderate electric field (MEF) based on the electric field strength (EFS) and mass flow rate (MFR) along with modeling moisture content (MC) based on the EC. To this end, an artificial neural network (ANN) was implemented for conventionally (CP) and non-thermally (NP) pasteurized milk. The findings indicated no significant difference (p > 0.05) between the experimental and predicted data for EC and MC. The MFR and EFS affected the actual EC. The raw milk samples had an EC of 0.468812–0.46913 S/m and MC of 87.3218–87.35941%, while these values in NP pasteurized milk were 0.457441–0.638224 S/m and 87.33986–87.40851%. With correlation coefficients (R) of 0.736478106–0.951840323 and mean square errors (MSE) of 0.005539–0.0064, the ANN accurately predicted the raw and pasteurized milk MC based on the EC using the sixth-order polynomial model and the EC based on the EFS and MFR using a quadratic model. The EC of pasteurized milk by NP was significantly (p < 0.05) lower than that of CP and raw dairy by 15.44% and 11.30%, respectively. The results show that the EFS and MFR might be used for the online assessment of milk’s physical attributes (e.g., EC), followed by using the assessed parameter to determine other properties (e.g., MC) by developing AI approaches based on optimized models. These observations showcase the innovative use of ANN-based AI to predict milk’s EC and MC accurately. Integrating such AI platforms into non-thermal food processing could eventually develop more sustainable food production and enhance food security and quality through process innovation and sustainable manufacturing, contributing to the industrial revolution and sustainable development goals. Full article