Search Results (12,397)

To address the key issues in cooperative collision avoidance of multiple aircraft, such as unknown dynamics, external disturbances, and limited communication resources, this paper proposes a reference correction method based on the Artificial Potential Field-Control Barrier Function (APF-CBF) and combines it with a dynamic event-triggered mechanism to achieve efficient cooperative control. This paper adopts a Fuzzy Wavelet Neural Network (FWNN) to design a finite-time disturbance observer. By leveraging the advantages of FWNN, which integrates fuzzy logic reasoning and the time-frequency locality of wavelet basis functions, this observer can synchronously estimate system states and unknown disturbances, to ensure the finite-time uniformly ultimate boundedness of errors and break through the limitation of insufficient robustness in traditional observers. Meanwhile, the APF is embedded in the CBF framework. On the one hand, APF is utilized to intuitively describe spatial interaction relationships, thereby reducing reliance on prior knowledge of obstacles; on the other hand, CBF is used to strictly construct safety constraints to overcome the local minimum problem existing in APF. Additionally, the reference correction mechanism is combined to optimize trajectory tracking performance. In addition, this paper introduces a dynamic event-triggered mechanism, which adjusts the triggering threshold by real-time adaptation to error trends and mission phases, realizing “communication on demand”. This mechanism can reduce communication resource consumption by 49.8% to 69.8% while avoiding Zeno behavior. Theoretical analysis and simulation experiments show that the proposed method can ensure the uniformly ultimate boundedness of system states and effectively achieve safe collision avoidance and efficient formation tracking of multiple aircraft. Full article

Human papillomavirus (HPV) is a prime target for genome-editing therapy as its E6 and E7 oncogenes are crucial for cancer development and maintenance. A key challenge in CRISPR/Cas9 therapy is the off-target effects. This study utilized a double-nicking technique to introduce DNA breaks in the E6 and E7 regions of HPV16. From 146 gRNA candidates, 16 double-nicking pairs were selected. Multiple combinations of double-nicking (DN)-gRNA pairs were delivered to HPV16-positive cells via lentiviruses, followed by Cas9 nickase (Cas9n) expression. Combinations of 3–4 DN-gRNA pairs effectively killed HPV16-positive cells while sparing HPV-negative cells. Off-target effects were reduced by nearly three orders of magnitude. An “all-in-one” adenovirus (AdV) system expressing four gRNA pairs and Cas9n showed promise in inhibiting tumor growth in HPV16-positive cancer models, demonstrating its potential as a safe and effective treatment for HPV-induced tumors. Full article

Acinetobacter baumannii is an opportunistic pathogen notable for multidrug resistance and environmental persistence. We characterized a clinical isolate, HKAB-1, which exhibits pronounced virulence-associated traits despite being highly susceptible to all tested antibiotics. HKAB-1 exhibited superior growth in MH2B, serum and desiccating conditions, robust biofilm formation, and active motility. Whole-genome sequencing identified two heme utilization clusters, multiple siderophore biosynthesis pathways, and other virulence-associated genes. Gene expression analysis revealed significant upregulation of heme utilization and siderophore biosynthetic gene clusters under serum exposure, indicating activation of iron uptake pathways under host-like conditions. Biofilm-associated genes, including bap, PNAG biosynthetic genes, and type IV pili components, were notably upregulated in biofilm-forming cells, supporting their role in driving the enhanced biofilm phenotype. Conversely, adeB, encoding a major RND efflux pump, was markedly downregulated, potentially explaining its drug-susceptible phenotype. Comparative genomic analysis highlighted differences in genes related to nutrient transport, metabolic pathways, and membrane biogenesis that may underpin its enhanced growth. These findings point to a potential trade-off between antibiotic resistance and virulence, underscoring the importance of monitoring antibiotic-susceptible yet highly virulent A. baumannii isolates as potential reservoirs for resistance evolution. Further investigation is warranted to elucidate the mechanisms underlying this phenotypic balance. Full article

The rapid growth of Internet-connected devices integrating into our everyday lives has no end in sight. As more devices and sensor networks are manufactured, security tends to be a low priority. However, the security of these devices is critical, and many current research topics are looking at the composition of simpler techniques to increase overall security in these low-power commercial devices. Transmission security (TRANSEC) methods are one option for physical-layer security and are a critical area of research with the increasing reliance on the Internet of Things (IoT); most such devices use standard low-power Time-division multiple access (TDMA) or frequency-division multiple access (FDMA) protocols susceptible to reverse engineering. This paper provides a hardware validation of previously proposed techniques for the intentional injection of noise into the phase mapping process of a spread spectrum signal used within a receiver-assigned code division multiple access (RA-CDMA) framework, which decreases an eavesdropper’s ability to directly observe the true phase and reverse engineer the associated PRNG output or key and thus the spreading sequence, even at high SNRs. This technique trades a conscious reduction in signal correlation processing for enhanced obfuscation, with a slight hardware resource utilization increase of less than 2% of Adaptive Logic Modules (ALMs), solidifying this work as a low-power technique. This paper presents the candidate method, quantifies the expected performance impact, and incorporates a hardware-based validation on field-programmable gate array (FPGA) platforms using arbitrary-phase phase-shift keying (PSK)-based spread spectrum signals. Full article

Antimicrobial resistance (AMR) presents a global health challenge, necessitating comprehensive surveillance and intervention strategies. Wastewater-based epidemiology (WBE) is a promising tool that can be utilized for AMR monitoring by offering population-level insights into microbial dynamics and resistance gene dissemination in communities. This review (n = 29 papers) examines the current landscape of utilizing WBE for AMR surveillance with a focus on methodologies, findings, and gaps in understanding. Reported methods from the reviewed literature included culture-based, PCR-based, whole genome sequencing, mass spectrometry, bioinformatics/metagenomics, and antimicrobial susceptibility testing to identify and measure antibiotic-resistant bacteria and antimicrobial resistance genes (ARGs) in wastewater, as well as liquid chromatography-tandem mass spectrometry to measure antibiotic residues. Results indicate Escherichia coli, Enterococcus spp., and Pseudomonas spp. are the most prevalent antibiotic-resistant bacterial species with hospital effluent demonstrating higher abundances of clinically relevant resistance genes including bla, bcr, qnrS, mcr, sul1, erm, and tet genes compared to measurements from local treatment plants. The most reported antibiotics in influent wastewater across studies analyzed include azithromycin, ciprofloxacin, clindamycin, and clarithromycin. The influence of seasonal variation on the ARG profiles of communities differed amongst studies indicating additional factors hold significance when examining the conference of AMR within communities. Despite these findings, knowledge gaps remain, including longitudinal studies in multiple and diverse geographical regions and understanding co-resistance mechanisms in relation to the complexities of population contributors to AMR. This review underscores the urgent need for collaborative and interdisciplinary efforts to safeguard public health and preserve antimicrobial efficacy. Further investigation on the use of WBE to understand these unique population-level drivers of AMR is advised in a proposed framework to inform best practice approaches moving forward. Full article

To foster the performance of wireless communication while saving energy, the integration of Intelligent Reflecting Surfaces (IRS) into autonomous vehicle (AV) communication networks is considered a powerful technique. This paper proposes a novel IRS-assisted vehicular communication model that combines Lagrange optimization and Gradient-Based Phase Optimization to determine the optimal transmission power, optimal interference transmission power, and IRS phase shifts. Additionally, the proposed model help increase the Signal-to-Interference-plus-Noise Ratio (SINR) by utilizing IRS, which leads to maximizes energy efficiency and the achievable data rate under a variety of environmental conditions, while guaranteeing that resource limits are satisfied. In order to represent dense vehicular environments, practical constraints for the system model, such as IRS reflection efficiency and interference, have been incorporated from multiple sources, namely, Device-to-Device (D2D), Vehicle-to-Vehicle (V2V), Vehicle-to-Base Station (V2B), and Cellular User Equipment (CUE). A Lagrangian optimization approach has been implemented to determine the required transmission interference power and the best IRS phase designs in order to enhance the system performance. Consequently, a one-dimensional convolutional neural network has been implemented for the optimized data provided by this framework as training input. This deep learning algorithm learns to predict the required optimal IRS settings quickly, allowing for real-time adaptation in dynamic wireless environments. The obtained results from the simulation show that the combined optimization and prediction strategy considerably enhances the system reliability and energy efficiency over baseline techniques. This study lays a solid foundation for implementing IRS-assisted AV networks in real-world settings, hence facilitating the development of next-generation vehicular communication systems that are both performance-driven and energy-efficient. Full article

Traditional high-utility itemset (HUI) mining methods tend to overestimate utility for long itemsets, leading to biased results. High average-utility itemset (HAUI) mining addresses this problem by normalizing utility with itemset length. However, uniform utility thresholds fail to account for varying item importance. Recently, HAUI mining with multiple minimum utility thresholds (MMU) has been used for flexible utility evaluation. While the generalized HAUIM (GHAUIM) algorithm performs well, it requires two database scans and is limited to static datasets. Therefore, we developed a novel tree-based method that scans the database only once to improve efficiency by reducing storage and eliminating costly join operations. Additionally, pruning strategies and incremental updates were introduced to enhance scalability. The developed method outperformed GHAIM in efficiency. Full article

This paper aims to study a distribution decision support system (DSS) conceptual framework for textile logistics, combining the operational requirements of logistics enterprises in textile markets to optimize vehicle surplus tonnage usage and distribution flexibility, using the integrated computer-aided manufacturing definition (IDEF) method and developing a comprehensive conceptual framework for textile logistics distribution decisions, complemented by an in-depth analysis of its underlying database structure. Further, this paper constructs the model base and proposes two vehicle-loading models and their solving algorithms, including one model with constraints on the maximum loading rate and the other with constraints on the smallest vehicle numbers, with these algorithms implemented by linear programming in operational research and performed by programming techniques. This paper also constructs the method base and designs some methods, such as the method of vehicle surplus tonnage utilization, the method of vehicle-loading priority order selection, and the simultaneous loading method of multi-freight cargo and multiple vehicles; these methods are implemented by the database principle and technological or programming techniques. We use a test distribution DSS conceptual framework to run the data example and obtain a good test result. The findings indicate that the DSS conceptual framework can integrate the model and method bases and can also solve the hard problems of the use of surplus tonnage vehicles and simultaneous loading. Full article

The Honeycomb Distribution Network is a new distribution network architecture that utilizes the Smart Power-Exchange Station (SPES) to enable power interconnection and mutual assistance among multiple microgrids/distribution units, thereby supporting high-proportion integration of distributed renewable energy and promoting a sustainable energy transition. To promote the continuous and reliable operation of the Honeycomb Distribution Network, this paper proposes a Hierarchical Switching Control Strategy to address the issues of DC bus voltage (U_dc) fluctuation in the SPES of the Honeycomb Distribution Network, as well as the state of charge (SOC) and charging/discharging power limitation of the energy storage module (ESM). The strategy consists of the system decision-making layer and the converter control layer. The system decision-making layer selects the main converter through the importance degree of each distribution unit and determines the control strategy of each converter through the operation state of the ESM’s SOC. The converter control layer restricts the ESM’s input/output active power—this ensures the ESM’s SOC and input/output active power stay within the power boundary. Additionally, it combines the Flexible Virtual Inertia Adaptive (FVIA) control method to suppress U_dc fluctuations and improve the response speed of the ESM converter’s input/output active power. A simulation model built in MATLAB/Simulink is used to verify the proposed control strategy, and the results demonstrate that the strategy can not only effectively reduce U_dc deviation and make the ESM’s input/output power reach the stable value faster, but also effectively avoid the ESM entering the unstable operation area. Full article

Accurate and automated segmentation of white blood cells (WBCs) in whole slide images (WSIs) is a critical step in computational pathology. This study presents a comprehensive evaluation and enhancement of the StarDist algorithm, leveraging its star-convex polygonal modeling to improve segmentation precision in complex WSI datasets. Our pipeline integrates tailored preprocessing, expert annotations from QuPath, and adaptive learning strategies for model training. Comparative analysis with U-Net and Mask R-CNN demonstrates StarDist’s superiority across multiple performance metrics, including Dice coefficient (0.89), precision (0.99), and IoU (0.95). Visual evaluations further highlight its robustness in handling overlapping cells and staining inconsistencies. The study establishes StarDist as a reliable tool for digital pathology, with potential integration into clinical decision-support systems. In addition to Dice and IoU, metrics such as Aggregated Jaccard Index and Boundary F1-Score are gaining popularity for biomedical segmentation. Preprocessing techniques like Macenko stain normalization and adaptive histogram equalization can further improve generalizability. QuPath, an open-source digital pathology platform, was utilized to perform accurate WBC annotations prior to training and evaluation. Full article

Traditional pumped storage capacity configuration uses static, year-targeted approaches, leading under-capacity in the early planning stages—wasting renewable energy—and over-capacity in later stages, thus wasting resources. In order to solve the above problems, this article innovatively proposes a dynamic, time-sequenced construction timeline and annual capacity configuration strategy, synchronized with new energy and load development, enhancing sustainability through optimized investment allocation and efficient resource utilization. It presents a two-layer model that considers multiple scenario operational dispatch. The upper layer aims to minimize the curtailment of wind and solar energy, providing a planning scheme to the lower layer, which focuses on multi-scenario economic dispatch, taking into account the peak-valley difference indicators. The models co-iterate: lower-layer operational outcomes feed back to refine the upper-layer’s capacity plan. This process continues until the predicted curtailment calculated by the upper layer aligns closely with that observed in the lower-layer operational simulations, or until capacity changes stabilize, ultimately determining the optimal time-phased capacity configuration. Simulations on a provincial power grid during three typical scenarios in winter, transitional seasons, and summer, as well as extreme weather scenarios, confirm that timely, dynamic configuration strategy significantly enhances renewable absorption, proving the model’s effectiveness. Full article

Rapid urbanization in developing countries has widened the gap between urban and rural development, due to inefficient land markets and weak institutional systems in rural areas. China’s innovative “Urban–rural Construction Land Linkage” policy was designed to address this imbalance by encouraging rural land consolidation and creating a transferable development rights mechanism. While this approach has shown potential in improving the utilization efficiency of existing construction land and continuously supplying urban development space, concerns remain about its actual benefits to villagers and rural development, with some arguing it disrupts traditional livelihoods and favors government interests over rural needs. To respond to this debate, this study investigates two core questions: first, does China’s transferable land development rights (TDR) program genuinely improve rural welfare as intended; second, why does the theoretically preferred self-organized governance model sometimes fail in practice? To address these research questions, this paper develops a new analytical framework combining the IAD framework of Ostrom with the hierarchical institutional framework of Williamson to examine three implementation approaches in China’s TDR implementation: government-dominated, market-invested, and self-organized models. Based on case studies, surveys, and interviews across multiple regions, this study reveals distinct strengths and weaknesses in each approach in improving villagers’ lives. Government-dominated projects demonstrate strong resource mobilization but limited community participation. Market-based models show efficiency gains but often compromise equity. While self-organized initiatives promise greater local empowerment, they frequently face practical challenges including limited management capacity and institutional barriers. Furthermore, this study identifies the preconditional institutional environment necessary for successful self-organized implementation, including clear land property rights, financial support, and technical assistance. These findings advance global understanding of how to combine efficiency with fair outcomes for all stakeholders in land governance, which is particularly relevant for developing countries seeking to manage urban expansion while protecting rural interests. Full article

The development of generative AI Large Language Models (LLMs) raised the alarm regarding the identification of content produced by generative AI vs. humans. In one case, issues arise when students heavily rely on such tools in a manner that can affect the development of their writing or coding skills. Other issues of plagiarism also apply. This study aims to support efforts to detect and identify textual content generated using LLM tools. We hypothesize that LLM-generated text is detectable by machine learning (ML) and investigate ML models that can recognize and differentiate between texts generated by humans and multiple LLM tools. We used a dataset of student-written text in comparison with LLM-written text. We leveraged several ML and Deep Learning (DL) algorithms, such as Random Forest (RF) and Recurrent Neural Networks (RNNs) and utilized Explainable Artificial Intelligence (XAI) to understand the important features in attribution. Our method is divided into (1) binary classification to differentiate between human-written and AI-generated text and (2) multi-classification to differentiate between human-written text and text generated by five different LLM tools (ChatGPT, LLaMA, Google Bard, Claude, and Perplexity). Results show high accuracy in multi- and binary classification. Our model outperformed GPTZero (78.3%), with an accuracy of 98.5%. Notably, GPTZero was unable to recognize about 4.2% of the observations, but our model was able to recognize the complete test dataset. XAI results showed that understanding feature importance across different classes enables detailed author/source profiles, aiding in attribution and supporting plagiarism detection by highlighting unique stylistic and structural elements, thereby ensuring robust verification of content originality. Full article

As critical infrastructure for flood control and disaster mitigation, the completeness of a dam spatial database directly impacts regional emergency disaster response. However, existing dam data in some developing countries suffer from severe gaps and outdated information, particularly concerning small- and medium-sized dams, hindering rapid response during disasters. There is an urgent need to improve the physical dam database and implement dynamic monitoring. Yet, current remote sensing identification methods face limitations, including a lack of diverse dam samples, limited analysis of geographical factors, and low efficiency in full-image processing, making it difficult to efficiently enhance dam databases. To address these issues, this study proposes a dam extraction framework integrating comprehensive geographical factor analysis with deep learning detection, validated in Sindh Province, Pakistan. Firstly, multiple geographical factors were fused using the Random Forest algorithm to generate a dam existence probability map. High-probability candidate areas were delineated using dynamic threshold segmentation (precision: 0.90, recall: 0.76, AUC: 0.86). Subsequently, OpenStreetMap (OSM) water body data excluded non-dam potential areas, further narrowing the candidate areas. Finally, a dam image dataset was constructed to train a dam identification model based on YOLOv11, achieving an mAP50 of 0.85. This trained model was then applied to high-resolution remote sensing imagery of the candidate areas for precise identification. Ultimately, 16 previously unrecorded small and medium-sized dams were identified in Sindh Province, enhancing its dam location database. Experiments demonstrate that this method, through the synergistic optimization of geographical constraints and deep learning, significantly improves the efficiency and reliability of dam identification. It provides high-precision data support for dam disaster emergency response and water resource management, exhibiting strong practical utility and regional scalability. Full article

Background/Objectives: Chronic ulcers represent a major public health concern, being associated with substantial morbidity, impaired quality of life, and significant costs to healthcare systems. Against the backdrop of an aging population and increasing prevalence of chronic comorbid conditions, this study aimed to profile hospitalized patients with chronic ulcers in Romania and to examine their differential patterns of healthcare resource utilization. Methods: We conducted a retrospective analysis of the national administrative hospitalization database between 2017 and 2022, including adult patients with at least two admissions coded with a primary diagnosis of chronic ulcer. Sociodemographic, clinical, and healthcare utilization indicators were extracted, standardized, and analyzed using the K-means clustering algorithm to derive utilization-based phenotypes. Results: Two distinct patient clusters were identified: the first comprised predominantly elderly patients with multiple comorbidities, prolonged hospitalizations, and frequent readmissions, representing a high-burden profile; the second included relatively younger patients with fewer comorbidities, shorter hospital stays, and lower readmission rates, reflecting a more stable clinical profile. The high-burden cluster accounted for a disproportionate share of inpatient resource consumption, underscoring its impact on the healthcare system. Conclusions: These findings highlight the importance of early identification of potential high-burden patients, enabling the implementation of personalized care strategies and more efficient allocation of hospital resources, with the potential to improve health outcomes and support healthcare system sustainability. Full article