Search Results (1,660)

The exponential growth of autonomous systems demands robust security mechanisms that can operate within the extreme constraints of real-time embedded environments. This paper introduces SMART DShot, a groundbreaking machine learning-enhanced framework that transforms the security landscape of unmanned aerial vehicle motor control systems through seamless integration of adaptive timing correction and real-time anomaly detection within Digital Shot (DShot) communication protocols. Our approach addresses critical vulnerabilities in Electronic Speed Controller (ESC) interfaces by deploying four synergistic algorithms—Kalman Filter Timing Correction (KFTC), Recursive Least Squares Timing Correction (RLSTC), Fuzzy Logic Timing Correction (FLTC), and Hybrid Adaptive Timing Correction (HATC)—each optimized for specific error characteristics and attack scenarios. Through comprehensive evaluation encompassing 32,000 Monte Carlo test iterations (500 per scenario × 16 scenarios × 4 algorithms) across 16 distinct operational scenarios and PolarFire SoC Field-Programmable Gate Array (FPGA) implementation, we demonstrate exceptional performance with 88.3% attack detection rate, only 2.3% false positive incidence, and substantial vulnerability mitigation reducing Common Vulnerability Scoring System (CVSS) severity from High (7.3) to Low (3.1). Hardware validation on PolarFire SoC confirms practical viability with minimal resource overhead (2.16% Look-Up Table utilization, 16.57 mW per channel) and deterministic sub-10 microsecond execution latency. The Hybrid Adaptive Timing Correction algorithm achieves 31.01% success rate (95% CI: [30.2%, 31.8%]), representing a 26.5% improvement over baseline approaches through intelligent meta-learning-based algorithm selection. Statistical validation using Analysis of Variance confirms significant performance differences (F(3,1996) = 30.30, p < 0.001) with large effect sizes (Cohen’s d up to 4.57), where 64.6% of algorithm comparisons showed large practical significance. SMART DShot establishes a paradigmatic shift from reactive to proactive embedded security, demonstrating that sophisticated artificial intelligence can operate effectively within microsecond-scale real-time constraints while providing comprehensive protection against timing manipulation, de-synchronization, burst interference, replay attacks, coordinated multi-channel attacks, and firmware-level compromises. This work provides essential foundations for trustworthy autonomous systems across critical domains including aerospace, automotive, industrial automation, and cyber–physical infrastructure. These results conclusively demonstrate that ML-enhanced motor control systems can achieve both superior security (88.3% attack detection rate with 2.3% false positives) and operational performance (31.01% timing correction success rate, 26.5% improvement over baseline) simultaneously, establishing SMART DShot as a practical, deployable solution for next-generation autonomous systems. Full article

Glycyrrhiza uralensis (G. uralensis), a leguminous plant, is an important medicinal and economic plant in saline–alkaline soils of arid regions in China. Its main bioactive components include liquiritin, glycyrrhizic acid, and flavonoids, which play significant roles in maintaining human health and preventing and adjuvantly treating related diseases. However, the cultivation of G. uralensis is easily restricted by adverse soil conditions in these regions, characterized by high salinity, high alkalinity, and nutrient deficiency. This study investigated the impacts of four multistrain microbial inoculants (Pa, Pb, Pc, Pd) on the growth performance and bioactive compound accumulation of G. uralensis in moderately saline–sodic soil. The aim was to screen the most beneficial inoculant from these strains, which were isolated from the rhizosphere of plants in moderately saline–alkaline soils of the Hexi Corridor and possess native advantages with excellent adaptability to arid environments. The results showed that inoculant Pc, comprising Pseudomonas silesiensis, Arthrobacter sp. GCG3, and Rhizobium sp. DG1, exhibited superior performance: it induced a 0.86-unit reduction in lateral root number relative to the control, while promoting significant increases in single-plant dry weight (101.70%), single-plant liquiritin (177.93%), single-plant glycyrrhizic acid (106.10%), and single-plant total flavonoids (107.64%). Application of the composite microbial inoculant Pc induced no significant changes in the pH and soluble salt content of G. uralensis rhizospheric soils. However, it promoted root utilization of soil organic matter and nitrate, while significantly increasing the contents of available potassium and available phosphorus in the rhizosphere. High-throughput sequencing revealed that Pc reorganized the rhizospheric microbial communities of G. uralensis, inducing pronounced shifts in the relative abundances of rhizospheric bacteria and fungi, leading to significant enrichment of target bacterial genera (Arthrobacter, Pseudomonas, Rhizobium), concomitant suppression of pathogenic fungi, and proliferation of beneficial fungi (Mortierella, Cladosporium). Correlation analyses showed that these microbial shifts were linked to improved plant nutrition and secondary metabolite biosynthesis. This study highlights Pc as a sustainable strategy to enhance G. uralensis yield and medicinal quality in saline–alkali ecosystems by mediating microbe–plant–nutrient interactions. Full article

The aim of this study is to evaluate the effects of different types of sourdough (I to IV), developed with a specific starter culture (including Lactiplantibacillus plantarum, Levilactobacillus brevis, and Candida famata), on bread fermentation performance and shelf-life. Real-time tracking of multiple parameters (pH, dough rising, ethanol release, and total titratable acidity) was monitored by a smart fermentation oven. The impact of the different treatments on the lactic acid, acetic acid, and ethanol content of the breads were quantified by high performance liquid chromatography analysis. In addition, the bio-preservation capacity of the breads contaminated with fungi was analyzed. The results show that liquid sourdough (D3: Type 2) and backslopped sourdough (D4: Type 3) increased significantly (p < 0.05) in dough rise, dough acidification (lower pH, higher titratable acidity), production of organic acids (lactic and acetic), and presented the optimal fermentation quotient. These findings were substantiated by chemometric analysis, which successfully clustered the starters based on performance and revealed a strong positive correlation between acetic acid production and dough-rise, highlighting the superior heterofermentative profile of D3 and D4. These types of sourdough also stood out for their antifungal capacity, preventing the visible growth of Aspergillus niger and Penicillium commune for up to 10 days after inoculation. Full article

In the era of smart agriculture, accurate identification of plant species is critical for effective crop management, biodiversity monitoring, and the sustainable use of medicinal resources. However, existing deep learning approaches often underperform when applied to fine-grained plant classification tasks due to the lack of domain-specific, high-quality datasets and the limited representational capacity of traditional architectures. This study addresses these challenges by introducing a novel, well-curated leaf image dataset consisting of 39 classes of medicinal and aromatic plants collected from the Aromatic and Medicinal Plant Research Station in Odakkali, Kerala, India. To overcome performance bottlenecks observed with a baseline Convolutional Neural Network (CNN) that achieved only 44.94% accuracy, we progressively enhanced model performance through a series of architectural innovations. These included the use of a pre-trained VGG16 network, data augmentation techniques, and fine-tuning of deeper convolutional layers, followed by the integration of Squeeze-and-Excitation (SE) attention blocks. Ultimately, we propose a hybrid deep learning architecture that combines VGG16 with Batch Normalization, Gated Recurrent Units (GRUs), Transformer modules, and Dilated Convolutions. This final model achieved a peak validation accuracy of 95.24%, significantly outperforming several baseline models, such as custom CNN (44.94%), VGG-19 (59.49%), VGG-16 before augmentation (71.52%), Xception (85.44%), Inception v3 (87.97%), VGG-16 after data augumentation (89.24%), VGG-16 after fine-tuning (90.51%), MobileNetV2 (93.67), and VGG16 with SE block (94.94%). These results demonstrate superior capability in capturing both local textures and global morphological features. The proposed solution not only advances the state of the art in plant classification but also contributes a valuable dataset to the research community. Its real-world applicability spans field-based plant identification, biodiversity conservation, and precision agriculture, offering a scalable tool for automated plant recognition in complex ecological and agricultural environments. Full article

This paper proposes a hierarchical cooperative tracking control method for multi-missile formations under dynamic event-triggered mechanisms, addressing parameter uncertainties and saturated overload constraints. The proposed hierarchical structure consists of a reference-trajectory generator and a trajectory-tracking controller. The reference-trajectory generator considers communication and collaboration among multiple interceptors, imposes saturation constraints on virtual control inputs, and generates reference trajectories for each receptor, effectively suppressing aggressive motions caused by overload saturation. On this basis, a radial basis function neural network (RBFNN) combined with a sliding-mode disturbance observer is adopted to estimate unknown external disturbances and unmodeled dynamics, and the finite-time convergence of the disturbance observer is proved. A tracking controller is then designed to ensure precise tracking of the reference trajectory by missile. This approach not only reduces communication and computational burdens but also effectively avoids Zeno behavior, enhancing the practical feasibility and robustness of the proposed method in engineering applications. The simulation results verify the effectiveness and superiority of the proposed method. Full article

High penetration of photovoltaic (PV) generation presents new challenges for voltage regulation in distribution networks (DNs), primarily due to output intermittency and constrained reactive power capabilities. This paper introduces a distributed voltage control method leveraging reactive power compensation from PV inverters. Instead of relying on centralized computation, the proposed method allows each inverter to make local decisions using real-time voltage measurements and delayed communication with neighboring PV nodes. To account for practical asynchronous communication and feedback delay, a Distributed Online Primal–Dual Push–Sum (DOPP) algorithm that integrates a fixed-step delay model into the push–sum coordination framework is developed. Through extensive case studies on a modified IEEE 123-bus system, it has been demonstrated that the proposed method maintains robust performance under both static and dynamic scenarios, even in the presence of fixed feedback delays. Specifically, in static scenarios, the proposed strategy rapidly eliminates voltage violations within 50–100 iterations, effectively regulating all nodal voltages into the acceptable range of [0.95, 1.05] p.u. even under feedback delays with a delay step of 10. In dynamic scenarios, the proposed strategy ensures 100% voltage compliance across all nodes, demonstrating superior voltage regulation and reactive power coordination performance over conventional droop and incremental control approaches. Full article

Community detection is widely recognized as a crucial area of research in network science. In recent years, multi-objective evolutionary algorithms (MOEAs) have been extensively employed in community detection tasks. Continuous coding is able to transform the discrete problem into a continuous one. However, conventional continuous coding methodologies frequently disregard the relationships between node structures, resulting in low-quality encoded populations that subsequently diminish community detection performance. Furthermore, continuous coding needs to be decoded into to label-based coding during the optimization process to compute objective functions. To alleviate this, we design the surrogate model adaptive switching strategy that selects the optimal surrogate model for the task. Subsequently, the surrogate-assisted evolutionary multi-objective community detection algorithm with core node learning is proposed. The core node learning method is employed to enhance the connection between nodes in augmented sequential coding, which helps initialize the population using the node similarity matrix. The core nodes of the network are subsequently identified based on node weights, which can be utilized to construct a surrogate model between the continuous coding and the objective function. The surrogate model is updated during the optimization process, which effectively improves both the accuracy and efficiency of community detection tasks. Experimental results obtained from synthetic and real-world networks demonstrate that the proposed algorithm exhibits superior performance compared to seven community detection algorithms. Full article

This research investigates resonator width optimization for simultaneously enhancing electrical performance and mechanical reliability in wideband RF MEMS filters through systematic evaluation of three configurations: 0% (L1), 60% (L2), and 100% (L3) matching ratios between cap and bottom wafers using Au-Au thermocompression bonding. The study demonstrates that resonator width alignment significantly influences both electromagnetic field coupling and bonding interface integrity. The L3 configuration with complete width matching achieved optimal RF performance, demonstrating 3.34 dB insertion loss across 4.5 GHz bandwidth (25% fractional bandwidth), outperforming L2 (3.56 dB) and L1 (3.10 dB), while providing enhanced electromagnetic wave coupling and minimized contact resistance. Mechanical reliability testing revealed superior bonding strength for the L3 configuration, withstanding up to 7.14 Kgf in shear pull tests, significantly exceeding L1 (4.22 Kgf) and L2 (2.24 Kgf). SEM analysis confirmed uniform bonding interfaces with minimal void formation (~180 nm), while Q-factor measurements showed L3 achieved optimal loaded Q-factor (Q_L = 3.31) suitable for wideband operation. Comprehensive environmental testing, including thermal cycling (−50 °C to +145 °C) and humidity exposure per MIL-STD-810E standards, validated long-term stability across all configurations. This investigation establishes that complete resonator width matching between cap and bottom wafers optimizes both electromagnetic performance and mechanical bonding reliability, providing a validated framework for developing high-performance, reliable RF MEMS devices for next-generation communication, radar, and sensing applications. Full article

China’s rapid urbanization has driven tremendous socioeconomic development while posing new forms of social–spatial inequalities that challenge environmental sustainability and spatial justice. This study investigates urban park-accessibility patterns across 10 s-tier provincial capital cities in China, examining how these patterns relate to housing-price dynamics to reveal diverse manifestations of social–spatial (in)justice. Using comprehensive spatial analysis grounded in distributive justice principles, we measure park accessibility through multiple metrics: distance to the nearest park, park size, and the number of parks within a 15 min walk from residential communities. Our findings reveal significant variation in park accessibility across these cities, with distinctive patterns emerging in the relationship between housing prices and park access that reflect different forms of social–spatial exclusion and inclusion. While most cities demonstrate an unbalanced spatial distribution of parks, they exhibit different forms of this disparity. Some cities show consistent park access across housing-price categories, while others display correlations between high housing prices and superior park accessibility. We argue that these divergent patterns reflect each city’s unique combination of economic development trajectory, politically strategic positioning within national urban hierarchies, and geographical constraints. Through this comparative analysis of second-tier cities, this study contributes to broader understandings of social–spatial (in)justice and urban environmental inequalities within China’s urbanization process, highlighting the need for place-specific approaches to achieving equitable access to urban amenities. Full article

The rise in online communication platforms has significantly increased exposure to harmful discourse, presenting ongoing challenges for digital moderation and user well-being. This paper introduces the EsCorpiusBias corpus, designed to enhance the automated detection of sexism and racism within Spanish-language online dialogue, specifically sourced from the Mediavida forum. By means of a systematic, context-sensitive annotation protocol, approximately 1000 three-turn dialogue units per bias category are annotated, ensuring the nuanced recognition of pragmatic and conversational subtleties. Here, annotation guidelines are meticulously developed, covering explicit and implicit manifestations of sexism and racism. Annotations are performed using the Prodigy tool (v1. 16.0) resulting in moderate to substantial inter-annotator agreement (Cohen’s Kappa: 0.55 for sexism and 0.79 for racism). Models including logistic regression, SpaCy’s baseline n-gram bag-of-words model, and transformer-based BETO are trained and evaluated, demonstrating that contextualized transformer-based approaches significantly outperform baseline and general-purpose models. Notably, the single-turn BETO model achieves an ROC-AUC of 0.94 for racism detection, while the contextual BETO model reaches an ROC-AUC of 0.87 for sexism detection, highlighting BETO’s superior effectiveness in capturing nuanced bias in online dialogues. Additionally, lexical overlap analyses indicate a strong reliance on explicit lexical indicators, highlighting limitations in handling implicit biases. This research underscores the importance of contextually grounded, domain-specific fine-tuning for effective automated detection of toxicity, providing robust resources and methodologies to foster socially responsible NLP systems within Spanish-speaking online communities. Full article

The Industrial Internet of Things (IIoT), a key enabler of Industry 4.0, integrates advanced communication technologies with the industrial economy to enable intelligent manufacturing and interconnected systems. Secure and reliable identity authentication in the IIoT becomes essential as connectivity expands across devices, systems, and domains. Blockchain technology presents a promising solution due to its decentralized, tamper-resistant, and traceable characteristics, facilitating secure and transparent identity verification. However, current blockchain-based cross-domain authentication schemes often lack a lightweight design, rendering them unsuitable for latency-sensitive and resource-constrained industrial environments. This paper proposes a lightweight cross-domain authentication scheme that combines blockchain with Chebyshev chaotic mapping. Unlike existing schemes relying heavily on Elliptic Curve Cryptography or bilinear pairing, our design circumvents such computationally intensive primitives entirely through the algebraic structure of Chebyshev polynomials. A formal security analysis using the Real-Or-Random (ROR) model demonstrates the scheme’s robustness. Furthermore, performance evaluations conducted with Hyperledger Fabric and the MIRACL cryptographic library validate the method’s effectiveness and superiority over existing approaches in terms of both security and operational efficiency. Full article

Swarm robots often need to cover the designated area to complete specific tasks. While robots possess local perception and limited communication capabilities, they struggle to handle coverage issues in dynamic environments. This paper proposes a self-organizing algorithm for swarm robots based on Dynamic Virtual Force (DVF) to cover dynamic areas. Robots in the swarm can locally perceive their surrounding robots and dynamically select adjacent ones to generate virtual repulsion, thereby controlling their movement. The algorithm enables swarm robots to be rapidly and evenly deployed in unknown areas, adapt to dynamic area changes, and solve the problem of symmetrical robot distribution during coverage. It also allows for adaptive coverage of different density areas, divided as needed. Experimental validation across 20 benchmark scenarios (including obstacles, dynamic boundaries, and multi-density zones) demonstrates that the DVF method outperforms existing approaches in coverage rate, total robot movement distance, and coverage uniformity. The results validate its effectiveness and superiority in addressing area coverage problems. By addressing these challenges, the DVF algorithm can be widely applied to forest firefighting, oil spill cleanup in the ocean, and other swarm robot tasks. Full article

Non-orthogonal multiple access (NOMA) technology can effectively improve spectrum efficiency, unmanned aerial vehicle (UAV) communication has the advantage of flexible deployment, and reconfigurable intelligent surface (RIS) can intelligently control the wireless transmission environment. Traditional communication systems have problems such as limited coverage and low spectrum efficiency in complex scenarios. However, a key challenge in deploying RIS-assisted NOMA-UAV communication systems lies in how to jointly optimize the UAV flight trajectory, power allocation strategy, and RIS phase offset to achieve the maximum average achievable rate for users. The non-convex nature of the optimization complicates the problem, making it challenging to find an efficient solution. Based on this, this paper presents a RIS-assisted NOMA-UAV communication system consisting of one UAV, one RIS, and two ground users. To achieve the maximum average rate for users, the UAV flight trajectory, power allocation strategy, and RIS phase offset are jointly optimized. For the non-convex problem, we decompose it into three sub-problems based on its inherent structural characteristics and use an alternating iterative approach to gradually converge to a feasible solution. The simulation results demonstrate that the proposed scheme offers significant advantages in the application scenario. Compared to other benchmark schemes, it delivers superior performance improvements to the communication system and offers higher practical value. Full article

Reconfigurable intelligent surfaces (RISs), recognized as one of the most promising key technologies for sixth-generation (6G) mobile communications, are characterized by their minimal energy expenditure, cost-effectiveness, and straightforward implementation. In this study, we develop a novel communication channel model that integrates RIS-enabled base stations with unmanned ground vehicles. To enhance the system’s adaptability, we implement a fluid antenna system (FAS) at the unmanned ground vehicle (UGV) terminal. This innovative model demonstrates exceptional versatility across various wireless communication scenarios through the strategic adjustment of active ports. The inherent dynamic reconfigurability of the FAS provides superior flexibility and adaptability in air-to-ground communication environments. In the paper, we derive and study key performance characteristics like the autocorrelation function (ACF), validating the model’s effectiveness. The results demonstrate that the RIS-FAS collaborative scheme significantly enhances channel reliability while effectively addressing critical challenges in 6G networks, including signal blockage and spatial constraints in mobile terminals. Full article

Background/Objectives: Leishmaniasis is a zoonotic and anthropozoonotic disease with significant public health impact worldwide and is classified as a neglected tropical disease. The search for new affordable treatments, particularly oral and/or topical ones that are easy to administer and have fewer side effects, remains a priority for the scientific community in this field of research. In previous investigations, 3-alkoxy-1-benzyl-5-nitroindazole derivatives showed remarkable in vitro results against Leishmania species, and predictions of absorption, distribution, metabolism, excretion, and toxicity properties, as well as pharmacological scores, of the compounds classified as active were superior to those of amphotericin B, indicating their potential as candidates for in vivo studies. Therefore, the aim of the present study was to evaluate the in vivo antileishmanial activity of the indazole derivatives NV6 and NV16. Methods: The compounds were administered intralesionally at concentrations of 10 and 5 mg/kg in a BALB/c mouse model of cutaneous leishmaniasis caused by Leishmania amazonensis. To evaluate the efficacy of the compounds, indicators such as lesion size, ulcer area, lesion weight, and parasitic load were determined. Amphotericin B was used as a positive control. Results: The compound NV6 showed leishmanicidal activity comparable to that observed with amphotericin B, with a significant reduction in lesion development and parasite load, while NV16 caused a reduction in ulcer area. Conclusions: These results provide strong evidence for the antileishmanial activity of NV6 and support future studies to improve its pharmacokinetic profile, as well as the investigation of combination therapies with other chemotherapeutic agents currently in use. Full article