Search Results (147)

In complex environments, autonomous navigation for quadrotor drones presents challenges in terms of obstacle avoidance and path planning. Traditional artificial potential field (APF) methods are plagued by issues such as getting stuck in local minima and inadequate handling of dynamic obstacles. This paper introduces a layered obstacle avoidance structure that merges vortex artificial potential (VAPF) fields with reinforcement learning (RL) for motion control. This approach dynamically adjusts the target position through VAPF, strategically guiding the drone to avoid obstacles indirectly. Additionally, it employs the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to facilitate the training of the motion controller. Simulation experiments demonstrate that the incorporation of the VAPF effectively mitigates the issue of local minima and significantly enhances the success rate of drone navigation, reduces the average arrival time and the number of sharp turns, and results in smoother paths. This solution harmoniously combines the flexibility of VAPF methods with the precision of RL for motion control, offering an effective strategy for autonomous navigation of quadrotor drones in complex environments. Full article

The rapid progress of Large Language Models (LLMs) has greatly improved natural language tasks like code generation, boosting developer productivity. However, challenges persist. Generated code often appears “pseudo-correct”—passing functional tests but plagued by inefficiency or redundant structures. Many models rely on outdated methods like greedy selection, which trap them in local optima, limiting their ability to explore better solutions. We propose AnnCoder, a multi-agent framework that mimics the human “try-fix-adapt” cycle through closed-loop optimization. By combining the exploratory power of simulated annealing with the targeted evolution of genetic algorithms, AnnCoder balances wide-ranging searches and local refinements, dramatically increasing the likelihood of finding globally optimal solutions. We speculate that traditional approaches may struggle due to narrow optimization focuses. AnnCoder addresses this by introducing dynamic multi-criteria scoring, weighing functional correctness, efficiency (e.g., runtime/memory), and readability. Its adaptive temperature control dynamically modulates the cooling schedule, slowing cooling when solutions are diverse to encourage exploration, then accelerating convergence as they stabilize. This design elegantly avoids the pitfalls of earlier models by synergistically combining global exploration with local optimization capabilities. After conducting thorough experiments with multiple LLMs analyses across four problem-solving and program synthesis benchmarks—AnnCoder showcased remarkable code generation capabilities—HumanEval 90.85%, MBPP 90.68%, HumanEval-ET 85.37%, and EvalPlus 84.8%. AnnCoder has outstanding advantages in solving general programming problems. Moreover, our method consistently delivers superior performance across various programming languages. Full article

For many years, staphylococci have been detected mainly in infections of the skin and soft tissues, organs, bone inflammations, and generalized infections. Thromboembolic diseases have also become a serious plague of our times, which, as it turns out, are closely related to the toxic effects of staphylococci. Staphylococcus aureus, because of the presence of many different kinds of virulence factors, is capable of manipulating the host’s innate and adaptive immune responses. These include toxins and cofactors that activate host zymogens and exoenzymes, as well as superantigens, which are highly inflammatory and cause leukocyte death. Coagulases and staphylokinases can control the host’s coagulation system. Nucleases and proteases inactivate various immune defense and surveillance proteins, including complement components, peptides and antibacterial proteins, and surface receptors that are important for leukocyte chemotaxis. On the other hand, secreted toxins and exoenzymes are proteins that disrupt the endothelial and epithelial barrier as a result of cell lysis and disintegration of linking proteins, which ultimately increases the risk of thromboembolism. In this review, we discuss various virulence factors and substances that may inhibit their activity. Full article

In time-sensitive aerial missions such as urban surveillance, emergency response, and adversarial airspace operations, achieving rapid and reliable formation control of multi-UAV systems is crucial. This paper addresses the challenge of ensuring robust and efficient formation control under stringent time constraints. The proposed singularity-free prescribed-time formation (PTF) control scheme guarantees task completion within a user-defined time, independent of initial conditions and control parameters. Unlike existing scaling-based prescribed-time methods plagued by unbounded gains and fixed-time strategies with non-tunable convergence bounds, the proposed scheme uses fixed-time stability theory and systematic parameter tuning to avoid singularity issues while ensuring robustness and predictable convergence. The method also accommodates directed communication topologies and unknown external disturbances, allowing follower UAVs to track a dynamic leader and maintain the desired geometric formation. Finally, some simulation results demonstrate the effectiveness of the proposed control strategy, showcasing its superiority over existing methods and validating its potential for practical applications. Full article

There is growing interest in the design of maneuvering high-speed aircraft to fly within or at the edge of the atmosphere. We identify and develop novel quasi-static vehicle screening methodologies, suitable for use during preliminary design, to better predict an incipient loss of control due to the dynamic effects of feedback. We validate these metrics by reverse-engineering Neil Armstrong’s 1962 loss of control and inadvertent atmospheric skip while piloting the X-15. In 1962, then-extant flight dynamics screening methods did not forecast likely troubles. We assemble and refine a collection of predictive metrics which operate upon basic quasi-static aerodynamic data and predict the confluence of lateral/directional stability and controllability issues which plagued the flown mission. These tools, which leverage McRuer’s “equivalent stability derivative” approach, enable future engineers to make proactive design changes which can avoid lateral/directional instabilities developing at high speeds. Full article

The fast pace of urban development and increasing intensity of precipitation events have made managing urban stormwater an increasingly difficult challenge. Hydrologic models are commonly used to predict flows and assess the performance of stormwater controls, often based on a hypothetical yet standardized design storm. The Storm Water Management Model (SWMM) is widely used for simulating runoff in urban watersheds. However, calibration of SWMM, as with all hydrologic models, is often plagued with issues such as subjectivity, and an abundance of model parameters, leading to delays and inefficiencies in model development and application. Further development of modeling and simulation tools to aid in design is critical in improving the function of stormwater management systems. To address these issues, we developed an integration of PySWMM (a Python wrapper (tool) for SWMM) and Pymoo (a Python package for multi-objective optimization) to automate the SWMM calibration process. The tool was tested using a case study urban watershed in Fredericksburg, VA. This tool can employ either a single-objective or multi-objective approach to calibrate a SWMM model by minimizing the error between prediction and observed values. This tool uses performance metrics including Nash-Sutcliffe Efficiency (NSE), Percent Bias (PBIAS), and Root Mean Square Error (RMSE) Standardized Ratio (RSR) for both single-event and long-term continuous rainfall-runoff processes. During multi-objective optimization calibration, the model achieved NSE, PBIAS, and RSR values of 0.73, 17.1, and 0.52, respectively; while the validation period recorded values of 0.86, 13.1, and 0.37, respectively. Additionally, in the single-objective optimization test case, the model yielded NSE values of 0.68 and 0.73 for the calibration and validation, respectively. The tool also supports parallelized optimization algorithms and utilizes Application Programming Interfaces (APIs) to dynamically update SWMM model parameters, accelerating both model execution and convergence. The tool successfully calibrated the SWMM model, delivering reliable results with suitable computational performance. Full article

Globally, insect pests adversely affect approximately 75% of the most important crops. However, the widespread use of chemical insecticides has significant drawbacks, including non-specific biological activity, toxicity to humans, detrimental effects on beneficial insects, and the rapid development of resistance. In this context, prodigiosin—a tripyrrolic secondary metabolite produced by various microorganisms—emerges as a promising alternative due to its favourable properties, such as being non-toxic, environmentally safe, non-irritant, and non-allergenic, and having non-carcinogenic potential. Prodigiosin has demonstrated insecticidal efficiency against pests at various developmental stages. Studies suggest that prodigiosin inhibits enzymes like acetylcholine esterase, protease, and acid phosphatase and induces oxidative stress. This review explores the potential of prodigiosin as an eco-friendly insecticide, discussing its production, extraction, and purification processes and its advantages, disadvantages, and mechanism of action, and future perspectives. Special emphasis is given to using non-pathogenic strains to mitigate biosafety concerns. Full article

The high-performance control of permanent magnet synchronous motors hinges on precise rotor position information. However, traditional mechanical sensors are plagued by high costs, large dimensions, and low reliability. As a result, position sensorless control technology has emerged and is becoming a research hotspot in the field of motor control. This article comprehensively reviews the existing position sensorless control technologies for permanent magnet synchronous motors. First, the fundamental principles of classical methods based on the motor’s fundamental wave model and saliency effect are analyzed in detail. Second, the advantages, disadvantages, and applicable scenarios of various position sensorless control methods are summarized. Finally, the key issues that need to be addressed in future research are pointed out. Full article

This review compares 32 studies (2000–2024) on plant- and microbial-based organic disease management to control grapevine pests and diseases. A systematic literature search provided 24 studies on microbial agents and 8 on plant treatments. Their effectiveness against key pathogens, including downy mildew, powdery mildew, and gray mold, was compared. Microbial agents such as Candida sake inhibited Botrytis cinerea by up to 80% in the lab and Pseudomonas sp. dramatically reduced grapevine lesion lengths by 32–52% in field conditions, while Bacillus subtilis reduced powdery mildew by 96% in greenhouse conditions and A. pullulans reduced Ochratoxin A infection by 99% in field conditions. In laboratory conditions, C. guilliermondii A42 reduced grape rot to 8–22% and A. cephalosporium B11 reduced it to 16–82%, confirming A42’s greater efficacy. Plant-derived agents and essential oils, including lavender and cinnamon, suppressed 100% of pathogens in vitro, whereas copper coupled with plant-derived agents reduced disease incidence by up to 92% under field conditions. While promising, plant-derived agents are plagued by formulation instability, which affects shelf life and effectiveness, while microbial agents must be kept under stringent storage conditions and can be variable under different vineyard conditions. These limitations identify the requirement for a stronger formulation strategy and large field validations. Organic disease management offers several important benefits, such as environmental safety, biodegradability, compatibility with organic cultivation, and low pesticide dependence. The application of these agents in pest management systems is ecologically balanced, improves soil health, and enables sustainable vineyard management. Full article

The prospect of integrating every object under a unified infrastructure, which provides humans with the possibility to monitor, access, and control objects and systems, has played a significant role in the geometric growth of the Internet of Things (IoT) paradigm, across various applications. However, despite the numerous possibilities that the IoT paradigm offers, security and privacy within and between the different interconnected devices and systems are integral to the long-term growth of IoT networks. Various sophisticated intrusions and attack variants have continued to plague the sustainability of IoT technologies and networks. Thus, effective methodologies for the prompt identification, detection, and mitigation of these menaces are priorities for stakeholders. Recently, data-driven artificial intelligence (AI) models have been considered effective in numerous applications. Hence, in recent literature studies, various single and ensemble AI subset models, such as deep learning and reinforcement learning models, have been proposed, resulting in effective decision-making for the secured operation of IoT networks. Considering the growth trends, this study presents a critical review of recently published articles whereby learning models were proposed for IoT security analysis. The aim is to highlight emerging IoT security issues, current conventional strategies, methodology procedures, achievements, and also, importantly, the limitations and research gaps identified in those specific IoT security analysis studies. By doing so, this study provides a research-based resource for scholars researching IoT and general industrial control systems security. Finally, some research gaps, as well as directions for future studies, are discussed. Full article

Youth soccer academies are dominated by the older players in each annual age cohort because they are judged to be better at the time of selection. Failing to identify talented players because they are simply younger in their cohort is a problem of both discrimination and poor practice. One potential method for addressing such biases is to develop and use age- and size-corrected assessments of individual players using traits closely associated with match success. In this study, we quantified the relationship between age and size with individual passing and control performance in six different tests for 170 players between 10 and 20 years old from a Tier 1 academy in Brazil. Passing tests were significantly repeatable and performance varied among tests (df = 5; F = 432.2; p < 0.001). Overall passing performance (PC_P1)—based on all tests—was significantly positively associated with age (R² = 0.42, t = 10.67; p < 0.001), height (R² = 0.19, t = 6.13; p < 0.001) and mass (R² = 0.23, t = 6.90, p < 0.001). In addition, tests of passing and control could discriminate among groups of differing playing levels (test 1: F_(2,116) = 55.2, p < 0.001; test 3: F_(2,116) = 12.0, p < 0.001). Normative algorithms from this study can be used to compare athletes during selection trials and against an elite group, after taking age and size into account, and using such algorithms could vastly reduce the insipid age-biases that plague youth football. Full article

Viral infectious diseases pose a serious threat to global public health due to their high transmissibility, rapid mutation rates, and limited treatment options. Recent outbreaks of diseases such as plague, monkeypox, avian influenza, and coronavirus disease 2019 (COVID-19) have underscored the urgent need for efficient diagnostic and surveillance technologies. Focusing on viral infectious diseases that seriously threaten human health, this review summarizes and analyzes detection techniques from the perspective of combining viral surveillance and prevention advice, and discusses applications in improving diagnostic sensitivity and specificity. One of the major innovations of this review is the systematic integration of advanced biorecognition and detection technologies, such as bionanosensors, rapid detection test strips, and microfluidic platforms, along with the exploration of artificial intelligence in virus detection. These technologies address the limitations of traditional methods and enable the real-time monitoring and early warning of viral outbreaks. By analyzing the application of these technologies in the detection of pathogens, new insights are provided for the development of next-generation diagnostic tools to address emerging and re-emerging viral threats. In addition, we analyze the current progress of developed vaccines, combining virus surveillance with vaccine research to provide new ideas for future viral disease prevention and control and vaccine development, and call for global attention and the development of new disease prevention and detection technologies. Full article

This study aims to improve the state of financial distress, which plagues 64% of South Africa’s municipalities. This fiscal crisis has constrained the quality of basic service delivery to local communities. Even though municipal financial distress dominates South African news headlines, there is a gap in the existing research literature on how to address municipal financial distress practically. This study identified effective turnaround strategies to alleviate financial distress by analyzing regulatory audit reports and audit action plans of municipalities officially classified as financially distressed in May 2018 and subsequently improved their financial affairs. Effective turnaround strategies empower financially distressed municipalities to improve their financial viability by promoting accountability and restoring local communities’ trust in their democratically elected municipal councils. Effective turnaround was achieved through the introduction of internal controls, strengthening governance and oversight, and the implementation of adequate records management practices. This study was conducted in the context of local democratic theory, as elected municipal officials are accountable to residents for the manner in which taxpayers’ money is spent. It aims to assist financially distressed municipalities in becoming financially sustainable and empower them to deliver essential services to local communities in accordance with the United Nations’ Sustainable Development Goals. Full article

Parameter identification of a permanent magnet synchronous wind generator (PMSWG) is of great significance for condition monitoring, fault diagnosis, and robust control. However, the conventional multi-parameter identification approach for a PMSWG is plagued by deficiencies, including its sluggish identification speed, subpar accuracy, and susceptibility to local optimization. In light of these challenges, this paper proposes a distributed parameter identification framework based on intelligent algorithms. The proposed approach involves the deployment of SSA, DBO, and PSO algorithms, leveraging golden sine ratio and Gaussian variation strategies for multi-parameter optimization and performance enhancement. Second, the optimal solutions of each intelligent algorithm are aggregated to achieve overall optimization performance enhancement. The efficacy of the proposed method is substantiated by a 6 MW PMSWG parameter identification practice simulation result, which demonstrates its superiority. The proposed method was shown to identify parameters more quickly and effectively than the underlying algorithms, which is of great significance for condition monitoring, fault diagnosis, and robust control of the PMSWG. Full article

Locust adults can form gregarious swarms and cause locust plagues. Few studies have evaluated the efficacy of a mixture of two biocontrol agents for controlling locust adults. Here, we assessed the effects of the mixture of a protozoan biocontrol agent, Nsoema locustae, and a fungal agent, Aspergillus oryzae XJ-1, at two ratios against locust adults in the lab. Synergistic effects of the mixture were observed (χ² > χ² (df, 0.05) and P_o > P_E). The maximum mortality caused by an N. locustae–A. oryzae XJ-1 mixture was 92.67% on the 12th day after inoculation, much higher than those of each agent. In addition, the median survival times were significantly lower when locusts were exposed to the mixture than when they were exposed to N. locustae or A. oryzae XJ-1 alone (p < 0.05). Full article