Search Results (137)

In the forest ecosystems of Central European Russia, the influence of forest edges on the spatial distribution of Drosophilidae was studied for the first time. The research was conducted during the period of 2021–2022 in the Republic of Mordovia. Beer traps baited with fermented beer and sugar were used to collect Drosophilidae. Two study plots were selected, differing in their forest edges, tree stands, and adjacent open ecosystems. In both cases, the forest directly bordered an open ecosystem. Edges serve as transitional biotopes, where both forest and meadow (open area) faunas coexist. Knowing that many drosophilid species prefer forest habitats, we designated forest interior sites as control points. Traps were set at heights of 1.5 m (lower) and 7.5 m (upper) on trees. A total of 936 specimens representing 27 species were collected. Nine species were common across all traps, while ten species were recorded only once. At the forest edges, 23 species were captured across both heights, compared to 19 species in the forest interiors. However, the total abundance at the forest edges was 370 specimens, while it was 1.5 times higher in the forest interiors. Both abundance and species richness varied between plots. Margalef’s index was higher at the forest edges than in the forest interiors, particularly at 1.5 m height at the edge and at 7.5 m height in the forest interior. Shannon and Simpson indices showed minimal variation across traps at different horizontal and vertical positions. The highest species diversity was observed among xylosaprobionts (9 species) and mycetophages (8 species). All ecological groups were represented at the forest edges, whereas only four groups were recorded in the forest interiors, with the phytosaprophagous species Scaptomyza pallida being absent. In general, both species richness and drosophilid abundance increased in the lower strata, both at the forest edge and within the interior. Using the R package Indicspecies, we identified Gitona distigma as an indicator species for the forest edge and Scaptodrosophila rufifrons as an indicator for the forest interior in the lower tier for both plots. In addition, Drosophila testacea, D. phalerata, and Phortica semivirgo were found to be indicator species for the lower tier in both plots, while Leucophenga quinquemaculata was identified as an indicator species for the upper tier at the second plot. Full article

Efficient and intelligent spectrum access is crucial for meeting the diverse Quality of Service (QoS) demands of Vehicular Internet of Things (V-IoT) systems in next-generation cellular networks. This work proposes a novel reinforcement learning (RL)-based priority-aware spectrum management (RL-PASM) framework, a centralized self-learning priority-aware spectrum management framework operating through Roadside Units (RSUs). RL-PASM dynamically allocates spectrum resources across three traffic classes: high-priority (HP), low-priority (LP), and best-effort (BE), utilizing reinforcement learning (RL). This work compares four RL algorithms: Q-Learning, Double Q-Learning, Deep Q-Network (DQN), and Actor-Critic (AC) methods. The environment is modeled as a discrete-time Markov Decision Process (MDP), and a context-sensitive reward function guides fairness-preserving decisions for access, preemption, coexistence, and hand-off. Extensive simulations conducted under realistic vehicular load conditions evaluate the performance across key metrics, including throughput, delay, energy efficiency, fairness, blocking, and interruption probability. Unlike prior approaches, RL-PASM introduces a unified multi-objective reward formulation and centralized RSU-based control to support adaptive priority-aware access for dynamic vehicular environments. Simulation results confirm that RL-PASM balances throughput, latency, fairness, and energy efficiency, demonstrating its suitability for scalable and resource-constrained deployments. The results also demonstrate that DQN achieves the highest average throughput, followed by vanilla QL. DQL and AC maintain fairness at high levels and low average interruption probability. QL demonstrates the lowest average delay and the highest energy efficiency, making it a suitable candidate for edge-constrained vehicular deployments. Selecting the appropriate RL method, RL-PASM offers a robust and adaptable solution for scalable, intelligent, and priority-aware spectrum access in vehicular communication infrastructures. Full article

With the rapid advancement of Internet of Things (IoT) technology, intrusion detection systems (IDSs) have become pivotal in ensuring network security. However, the data produced by IoT devices is typically sensitive and tends to display non-independent and identically distributed (Non-IID) properties. These factors impose significant limitations on the application of traditional centralized learning. In response to these issues, this study introduces a novel IDS framework grounded in federated learning and knowledge distillation (KD), termed FD-IDS. The proposed FD-IDS aims to tackle issues related to safeguarding data privacy and distributed heterogeneity. FD-IDS employs mutual information for feature selection to enhance training efficiency. For Non-IID data scenarios, the system combines a proximal term with KD. The proximal term restricts the deviation between local and global models, while KD utilizes the global model to steer the training process of local models. Together, these mechanisms effectively alleviate the problem of model drift. Experiments conducted on both the Edge-IIoT and N-BaIoT datasets demonstrate that FD-IDS achieves promising detection performance across multiple evaluation metrics. Full article

The mechanism of low- to middle-rank coal seam gas accumulation in the Baode block on the eastern edge of the Ordos Basin is well understood. However, exploration efforts in the Shizuishan area on the western edge started later, and the current understanding of enrichment and accumulation rules is unclear. It is important to systematically study enrichment and accumulation, which guide the precise exploration and development of coal seam gas resources in the western wing of the basin. The coal seam collected from the Shizuishan area of Ningxia was taken as the target. Based on drilling, logging, seismic, and CBM (coalbed methane) test data, geological conditions were studied, and factors and reservoir formation modes of CBM enrichment were summarized. The results are as follows. The principal coal-bearing seams in the study area are coal seams No. 2 and No. 3 of the Shanxi Formation and No. 5 and No. 6 of the Taiyuan Formation, with thicknesses exceeding 10 m in the southwest and generally stable thickness across the region, providing favorable conditions for CBM enrichment. Spatial variations in burial depth show stability in the east and south, but notable fluctuations are observed near fault F1 in the west and north. These burial depth patterns are closely linked to coal rank, which increases with depth. Although the southeastern region exhibits a lower coal rank than the northwest, its variation is minimal, reflecting a more uniform thermal evolution. Lithologically, the roof of coal seam No. 6 is mainly composed of dense sandstone in the central and southern areas, indicating a strong sealing capacity conducive to gas preservation. This study employs a system that fuses multi-source geological data for analysis, integrating multi-dimensional data such as drilling, logging, seismic, and CBM testing data. It systematically reveals the gas control mechanism of “tectonic–sedimentary–fluid” trinity coupling in low-gentle slope structural belts, providing a new research paradigm for coalbed methane exploration in complex structural areas. It creatively proposes a three-type CBM accumulation model that includes the following: ① a steep flank tectonic fault escape type (tectonics-dominated); ② an axial tectonic hydrodynamic sealing type (water–tectonics composite); and ③ a gentle flank lithology–hydrodynamic sealing type (lithology–water synergy). This classification system breaks through the traditional binary framework, systematically explaining the spatiotemporal matching relationships of the accumulated elements in different structural positions and establishing quantitative criteria for target area selection. It systematically reveals the key controlling roles of low-gentle slope structural belts and slope belts in coalbed methane enrichment, innovatively proposing a new gentle slope accumulation model defined as “slope control storage, low-structure gas reservoir”. These integrated results highlight the mutual control of structural, thermal, and lithological factors on CBM enrichment and provide critical guidance for future exploration in the Ningxia Autonomous Region. Full article

ECG monitoring is central to the early detection of cardiac abnormalities. We compared 28 manually selected signal-specific features with 159 automatically extracted signal-independent descriptors from the MIT BIH Arrhythmia Database. ANOVA reduced features to the 10 most informative attributes, which were evaluated alone and in combination with the signal-specific features using Random Forest, SVM, and deep neural networks (CNN, RNN, ANN, LSTM) under an interpatient 80/20 split. Merging the two feature groups delivered the best results: a 128-layer CNN achieved 100% accuracy. Power profiling revealed that deeper models improve accuracy at the cost of runtime, memory, and CPU load, underscoring the trade-off faced in edge deployments. The proposed hybrid feature strategy provides beat-by-beat classification with a reduction in the number of features, enabling real-time ECG screening on wearable and IoT devices. Full article

Cyber threats that target Internet of Things (IoT) and edge computing environments are growing in scale and complexity, which necessitates the development of security solutions that are both robust and scalable while also protecting privacy. Edge scenarios require new intrusion detection solutions because traditional centralized intrusion detection systems (IDSs) lack in the protection of data privacy, create excessive communication overhead, and show limited contextual adaptation capabilities. This paper introduces the SecFedDNN framework, which combines federated deep learning (FDL) capabilities to protect edge–cloud environments from cyberattacks such as Distributed Denial of Service (DDoS), Denial of Service (DoS), and injection attacks. SecFedDNN performs edge-level pre-aggregation filtering through Layer-Adaptive Sparsified Model Aggregation (LASA) for anomaly detection while supporting balanced multi-class evaluation across federated clients. A Deep Neural Network (DNN) forms the main model that trains concurrently with multiple clients through the Federated Averaging (FedAvg) protocol while keeping raw data local. We utilized Google Cloud Platform (GCP) along with Google Colaboratory (Colab) to create five federated clients for simulating attacks on the TON_IoT dataset, which we balanced across selected attack types. Initial tests showed DNN outperformed Long Short-Term Memory (LSTM) and SimpleNN in centralized environments by providing higher accuracy at lower computational costs. Following federated training, the SecFedDNN framework achieved an average accuracy and precision above 84% and recall and F1-score above 82% across all clients with suitable response times for real-time deployment. The study proves that FDL can strengthen intrusion detection across distributed edge networks without compromising data privacy guarantees. Full article

The creation of surface relief gratings using hydrogels for label-free biomolecule detection represents a significant advance in the development of versatile, cutting-edge biosensors. Central to this innovation is the formulation of materials with enhanced mechanical properties, especially for applications in soft, wearable technologies. In this work, we have developed novel biofunctional hydrogels that incorporate slide-ring supramolecular structures into their network, enabling the production of surface relief gratings with superior mechanical characteristics for biomolecule detection without the need for labels. These hydrogels, functionalized with bovine serum albumin and goat anti-rabbit antibodies, demonstrated excellent selectivity and sensitivity toward anti-bovine serum albumin and rabbit IgGs in blood serum, evaluated using a label-free format. Remarkably, the new materials matched the analytical performance of conventional hydrogels based on static networks while offering dramatically improved toughness and elasticity, with a compressive modulus comparable to human skin. This demonstrates the potential of slide-ring hydrogels for fabricating robust, label-free biosensing platforms. Furthermore, the flexibility of this system allows for the incorporation of various recognition elements tailored to specific applications. Full article

This study examines the phytocenotic, phenotypic, phytochemical, antioxidant, and toxic effects of four geographically distinct populations of the traditionally used plant species Adonis volgensis Steven ex DC. from Central and Northern Kazakhstan. These populations, found in diverse habitats such as steppe-like forest edges and moist plains, coexist with species like Achillea nobilis L. and Artemisia absinthium L. Significant variations were observed in plant community composition and environmental stressors, including grazing and habitat degradation. Morphological analysis revealed that Population 2 exhibited greater vigor, while Population 3 was more constrained by local conditions, highlighting adaptive strategies influenced by both genetic and environmental factors. FTIR analysis of A. volgensis extracts revealed distinct solvent-specific profiles of bioactive compounds. Ethanol (EtOH) and ethyl acetate extracts were rich in phenolic and flavonoid compounds, whereas the chloroform (CHCl₃) extract was less effective in extracting phenolics, displaying weaker O–H bands. Phytochemical analysis showed notable variations in total phenolic content (TPC) and total flavonoid content (TFC). The highest TPC (89.351 ± 4.45 mg GAE/g d.w.) was found in the ethyl acetate extract from the Akmola region, while the highest TFC (33.811 ± 0.170 mg QE/g d.w.) was observed in the CHCl₃ extract from Kostanay region. Toxicity assessment using the Artemia salina lethality assay revealed significant mortality rates (88–96%) in CHCl₃ extracts of aerial parts, demonstrating a dose-dependent effect. These findings highlight the antioxidant and potential toxic properties of A. volgensis, emphasizing the importance of solvent selection in bioactive compound extraction for nutraceutical and pharmaceutical applications. Full article

Smart cities are widely regarded as a promising solution to urbanization challenges; however, environmental aspects such as outdoor thermal comfort and urban heat island are often less addressed than social and economic dimensions of sustainability. To address this gap, we developed and evaluated an affordable, scalable, and cost-effective weather station platform, consisting of a centralized server and portable edge devices to facilitate urban heat island and outdoor thermal comfort studies. This edge device is designed in accordance with the ISO 7726 (1998) standards and further enhanced with a positioning system. The device can regularly log parameters such as air temperature, relative humidity, globe temperature, wind speed, and geographical coordinates. Strategic selection of components allowed for a low-cost device that can perform data manipulation, pre-processing, store the data, and exchange data with a centralized server via the internet. The centralized server facilitates scalability, processing, storage, and live monitoring of data acquisition processes. The edge devices’ electrical and shielding design was evaluated against a commercial weather station, showing Mean Absolute Error and Root Mean Square Error values of 0.1 and 0.33, respectively, for air temperature. Further, empirical test campaigns were conducted under two scenarios: “stop-and-go” and “on-the-move”. These tests provided an insight into transition and response times required for urban heat island and thermal comfort studies, and evaluated the platform’s overall performance, validating it for nuanced human-scale thermal comfort, urban heat island, and bio-meteorological studies. Full article

Background: The issue of digital piracy is increasingly prevalent, with its proliferation further fueled by the widespread use of social media outlets such as WhatsApp, Snapchat, Instagram, Pinterest, and X. These platforms have become hotspots for the unauthorized sharing of copyrighted materials without due recognition to the original creators. Current techniques for digital watermarking are inadequate; they frequently choose less-than-ideal locations for embedding watermarks. This often results in a compromise on maintaining critical relationships within the data. Purpose: This research aims to tackle the growing problem of digital piracy, which represents a major risk to rights holders in various sectors, most notably those involved in entertainment. The goal is to devise a robust watermarking approach that effectively safeguards intellectual property rights and guarantees rightful earnings for those who create content. Approach: To address the issues at hand, this study presents an innovative technique for digital video watermarking. Utilizing the 2D-DWT along with the KAZE feature detection algorithm, which incorporates the Accelerated Segment Test with Zero Eigenvalue, scrutinize and pinpoint data points that exhibit circular symmetry. The KAZE algorithm pinpoints a quintet of stable features within the brightness aspect of video frames to act as central embedding sites. This research selects the chief embedding site by identifying the point of greatest intensity on a specific arc segment on a circle’s edge, while three other sites are chosen based on principles of circular symmetry. Following these procedures, the proposed method subjects videos to several robustness tests to simulate potential disturbances. The efficacy of the proposed approach is quantified using established objective metrics that confirm strong correlation and outstanding visual fidelity in watermarked videos. Moreover, statistical validation through t-tests corroborates the effectiveness of the watermarking strategy in maintaining integrity under various types of assaults. This fortifies the team’s confidence in its practical deployment. Full article

This study examines the impact of PVD coatings on cutting tool wear during the milling of 316L stainless steel under air cooling conditions. In the experiment, a carbide milling cutter coated with a nanocomposite nACo3 (AlTiSiN) coating was used. The coating was deposited using a next-generation device, the PLATIT π411PLUS, which features one central and three lateral rotating cathodes. The nanocomposite nACo3 coating obtained with this method exhibits exceptionally high structural density and excellent mechanical properties. The new generation of the nACo3 coating demonstrates improved surface properties and a lower friction coefficient compared to previous generations. The findings indicate that PVD nACo3 coatings significantly enhance wear resistance, extending tool life while maintaining acceptable surface quality. The optimal cutting time was determined to be approximately 90 min, after which a sharp increase in surface roughness and tool wear was observed. After 120 min of machining, substantial deterioration of surface quality parameters was recorded, suggesting increasing cutting forces and cutting edge degradation. SEM and EDS analyses revealed the presence of adhered material on the tool and sulfide inclusions in the microstructure of 316L stainless steel, which influenced the machining process. The nACo3 coating demonstrated high thermal and wear resistance, making it an effective solution for machining difficult-to-cut materials. This study suggests that selecting appropriate cutting parameters, tool geometry, protective coatings, and cooling strategies can significantly affect tool longevity and machining quality. The novelty of this research lies in the application of innovative nanocomposite PVD coatings during the milling of 316L stainless steel under air cooling conditions. These studies indicate potential future research directions, such as the use of minimum quantity lubrication (MQL) or cryogenic cooling as methods to reduce tool wear and improve post-machining surface quality. Full article

Different from conventional federated learning (FL), which relies on a central server for model aggregation, decentralized FL (DFL) exchanges models among edge servers, thus improving the robustness and scalability. When deploying DFL into the Internet of Things (IoT), limited wireless resources cannot provide simultaneous access to massive devices. One must perform client scheduling to balance the convergence rate and model accuracy. However, the heterogeneity of computing and communication resources across client devices, combined with the time-varying nature of wireless channels, makes it challenging to estimate accurately the delay associated with client participation during the scheduling process. To address this issue, we investigate the client scheduling and resource optimization problem in DFL without prior client information. Specifically, the considered problem is reformulated as a multi-armed bandit (MAB) program, and an online learning algorithm that utilizes contextual multi-arm slot machines for client delay estimation and scheduling is proposed. Through theoretical analysis, this algorithm can achieve asymptotic optimal performance in theory. The experimental results show that the algorithm can make asymptotic optimal client selection decisions, and this method is superior to existing algorithms in reducing the cumulative delay of the system. Full article

Community detection in complex networks remains a significant challenge due to noise, outliers, and the dependency on predefined clustering parameters. This study introduces GraphDBSCAN, an adaptive community detection framework that integrates an optimized density-based clustering method with an enhanced graph partitioning approach. The proposed method refines clustering accuracy through three key innovations: (1) a K-nearest neighbor (KNN)-based strategy for automatic parameter tuning in density-based clustering, eliminating the need for manual selection; (2) a proximity-based feature extraction technique that enhances node representations while preserving network topology; and (3) an improved edge removal strategy in graph partitioning, incorporating additional centrality measures to refine community structures. GraphDBSCAN is evaluated on real-world and synthetic datasets, demonstrating improvements in modularity, noise reduction, and clustering robustness. Compared to existing methods, GraphDBSCAN consistently enhances structural coherence, reduces sensitivity to outliers, and improves community separation without requiring fixed parameter assumptions. The proposed method offers a scalable, data-driven approach to community detection, making it suitable for large-scale and heterogeneous networks. Full article

Understanding the characteristics of urban plant multidimensional diversity and urban green spaces (UGSs) landscape patterns is the central theme of urban ecology, providing theoretical support for UGSs management and biodiversity conservation. Taking Changchun, a provincial city, as an example, a total of 240 plots were surveyed using the stratified random sampling method. We studied the effects of the urban park construction period on plant multidimensional diversities, landscape patterns of green spaces, and their associations in Changchun City, Northeast China. The results indicated that total woody species and tree species diversity attributes were both the highest in the construction period of 2001–2020 and lowest in the construction period before 1940. However, shrub species diversity attributes were completely the opposite. Diameter at the breast height (DBH) diversity index (H_d) was the highest in the construction period before 1940 and lowest in the construction period of 2001–2020. However, the height diversity index (H_h) showed the opposite trend. Phylogenetic structures of total woody species and tree species showed divergent patterns in parks constructed before 1940 and 1940–2000 period, while that in 2001–2020 period could not be determined. In contrast, the phylogenetic structure of the shrub species clustered across all construction periods. Landscape pattern metrics varied significantly among different construction periods. Total Area (TA) was the highest in the construction period of 2001–2020. The structural equation model (SEM) revealed that construction periods exerted significant direct effects on both multidimensional diversities and landscape patterns of green spaces. Specifically, construction periods indirectly affected tree species diversity through structural diversity and influenced shrub species’ phylogenetic diversity through shrub species diversity. What is more, Patch Density (PD), Edge Density (ED), and Aggregation Index (AI) correlated with H_h, which had a direct effect on the Shannon–Wiener diversity index of tree species (H′_t). Overall, the results indicated that species diversity can be enhanced through regulating landscape patterns, rationally selecting tree species, and optimizing plant configuration. These above results can provide scientific references for the configuration of plant communities and selection of tree species in urban parks, and offer important guidance for urban biodiversity conservation and enhancement. Full article

Pillar-to-pillar dashboards have become common in modern electric vehicles. These dashboards are made of liquid crystal displays (LCDs), of which backlight units (BLUs) are an integral part. Particulate contamination inside BLUs can lead to either an aesthetic or functional failure and is in consequence a part of quality control. Automatic optical inspection (AOI) was used to detect particulate matter to enable a process chain analysis to be carried out. The investigation showed that a high percentage of all contaminants originated from the assembly of the edge/side lightguide. The implementation of an additional cleaning process was the favored countermeasure to reduce the contaminants. The objective (cleanliness requirement) was to remove all contaminants larger than 100 µm from the lightguide with contactless (non-destructive) cleaning methods. The preferred cleaning methods of choice were compressed air and CO₂ snow jet cleaning. This work investigates the cleaning efficacy of both cleaning methods under consideration of the following impact factors: distance, orientation (inclination) and speed. The central question of this paper was as follows: would cleaning with compressed air be sufficient to meet the cleanliness requirements? In order to answer this question, a cleaning validation was carried out, based on a Box–Behnken design of experiments (DoE). To do so, representative test contaminants had to be selected in step one, followed by the selection of an appropriate measurement technology to be able to count the contaminants on the lightguide. In the third step, a test rig had to be designed and built to finally carry out the experiments. The data revealed that CO₂ was able to achieve a cleaning efficacy of 100% in five of the experiments, while the best cleaning efficacy of compressed air was 89.87%. The cleaning efficacy of compressed air could be improved by a parameter optimization to 94.19%. In contrast, a 100% cleaning efficacy is achievable with CO₂ after parameter optimization, which is what is needed to meet the cleanliness requirements. Full article