Search Results (850)

This study examines the interactions between healthy target cells, infected target cells, virus particles, and immune cells within an HIV model. The model exhibits two equilibrium points: an infection-free equilibrium and an infection equilibrium. Stability analysis shows that the infection-free equilibrium is locally asymptotically stable when ${\mathcal{R}}_0<1$. Further, it is unstable when ${\mathcal{R}}_0>1$. The infection equilibrium is locally asymptotically stable when ${\mathcal{R}}_0>1$. The structural and practical identifiabilities of the within-host model for HIV infection dynamics were investigated using differential algebra techniques and Monte Carlo simulations. The HIV model was structurally identifiable by observing the total uninfected and infected target cells, immune cells, and viral load. Monte Carlo simulations assessed the practical identifiability of parameters. The production rate of target cells $\left(\lambda \right)$, the death rate of healthy target cells $\left(d\right)$, the death rate of infected target cells $\left(\delta \right)$, and the viral production rate by infected cells $\left(\pi \right)$ were practically identifiable. The rate of infection of target cells by the virus $\left(\beta \right)$, the death rate of infected cells by immune cells $\left(\mathsf{\Psi }\right)$, and antigen-driven proliferation rate of immune cells $\left(b\right)$ were not practically identifiable. Practical identifiability was constrained by the noise and sparsity of the data. Analysis shows that increasing the frequency of data collection can significantly improve the identifiability of all parameters. This highlights the importance of optimal data sampling in HIV clinical studies, as it determines the best time points, frequency, and the number of sample points required to accurately capture the dynamics of the HIV infection within a host. Full article

Defect detection constitutes one of the most crucial processes in industrial production. With a continuous increase in the number of defect categories and samples, the defect detection model underpinned by deep learning finds it challenging to expand to new categories, and the accuracy and real-time performance of product defect detection are also confronted with severe challenges. This paper addresses the problem of insufficient detection accuracy of existing lightweight models on resource-constrained edge devices by presenting a new lightweight YoloV5 model, which integrates four modules, SCDown, GhostConv, RepNCSPELAN4, and ScalSeq. Here, this paper abbreviates it as SGRS-YoloV5n. Through the incorporation of these modules, the model notably enhances feature extraction and computational efficiency while reducing the model size and computational load, making it more conducive for deployment on edge devices. Furthermore, a cloud-edge collaborative defect detection system is constructed to improve detection accuracy and efficiency through initial detection by edge devices, followed by additional inspection by cloud servers. An incremental learning mechanism is also introduced, enabling the model to adapt promptly to new defect categories and update its parameters accordingly. Experimental results reveal that the SGRS-YoloV5n model exhibits superior detection accuracy and real-time performance, validating its value and stability for deployment in resource-constrained environments. This system presents a novel solution for achieving efficient and accurate real-time defect detection. Full article

Well placement optimization is one of the most important means to control the decline of oilfields and improve the recovery rate in the development process of deep and heterogeneous reservoirs, such as deep buried carbonate oil reservoirs. However, the mapping relationship from deployed well positions to actual profits is non-linear and multi-modal. At the same time, the injection and production relationship of new wells also affects the contribution of well positions to final profits. Currently, common algorithms include gradient-based and heuristic non-gradient algorithms, which have advantages, but face problems of high computational complexity, slow optimization speed, and difficulty in convergence. We propose an evolutionary algorithm for well placement optimization in carbonate reservoirs. This algorithm improves well placement optimization and computational speed by constraining the sampling process to effective sampling spaces, integrating prior knowledge to enhance sampling efficiency, strengthening local optima exploration, and utilizing parallel computing. Additionally, it refines the optimized variable content based on actual control factors, enhancing the algorithm’s robustness in practical applications. A case study from a carbonate reservoir in northwestern China demonstrated that this algorithm not only improved the performance by 50% compared to the classic DE algorithm but also achieved 15% higher optimization effectiveness than the current state-of-the-art algorithms. Full article

The continued evolution of H3 subtype avian influenza virus (AIV)—which crosses the interspecific barrier to infect humans—and the potential risk of genetic recombination with other subtypes pose serious threats to the poultry industry and human health. Therefore, rapid and accurate detection of H3 virus is highly important for preventing its spread. In this study, a method based on real-time reverse transcription recombinase-aided isothermal amplification (RT–RAA) was successfully developed for the rapid detection of H3 AIV. Specific primers and probes were designed to target the hemagglutinin (HA) gene of H3 AIV, ensuring highly specific detection of H3 AIV without cross-reactivity with other important avian respiratory viruses. The results showed that the detection limit of the RT–RAA fluorescence reading method was 224 copies/response within the 95% confidence interval, while the detection limit of the RT–RAA visualization method was 1527 copies/response within the same confidence interval. In addition, 68 clinical samples were examined and the results were compared with those of real-time quantitative PCR (RT–qPCR). The results showed that the real-time fluorescence RT–RAA and RT–qPCR results were completely consistent, and the kappa value reached 1, indicating excellent correlation. For visual detection, the sensitivity was 91.43%, the specificity was 100%, and the kappa value was 0.91, which also indicated good correlation. In addition, the amplified products of RT–RAA can be visualized with a portable blue light instrument, which enables rapid detection of H3 AIV even in resource-constrained environments. The H3 AIV RT-RAA rapid detection method established in this study can meet the requirements of basic laboratories and provide a valuable reference for the early diagnosis of H3 AIV. Full article

Indoor occupancy detection (IOD) via Wi-Fi sensing capitalizes on the varying patterns in CSI (Channel State Information) to estimate the number of people in a given area. However, the precision of such systems heavily depends on the quality of the CSI data, which can be degraded by noise and environmental factors. To address this issue, In this paper, we present a CSI preprocessing method to improve the accuracy of IOD systems using Wi-Fi sensing. Unlike existing preprocessing methods that use computationally complex signal processing or statistical techniques, we expand the dimension of CSI amplitude data into a three-channel vector through nonlinear transformation to amplify subtle differences between CSI data belonging to a different number of people. By drawing clearer boundaries between CSI data distributions belonging to a different number of people in a monitored area, our method improves the people-counting accuracy of a Wi-Fi sensing system. To ensure temporal consistency and improve data quality, we discretize the CSI measurements based on their transmission periods and aggregate consecutive measurements over a given time interval. These samples are then fed into a Convolutional Neural Network (CNN) specifically trained for the IOD task. Experimental results in diverse real-world scenarios verify that compared to the traditional methods, the enhanced feature representation capability of our approach leads to more accurate and robust sensing outcomes even in the most resource-constrained environment, where a commercial off-the-shelf CSI capture machine with only one antenna is used when a Wi-Fi sender with one transmit antenna sends packets periodically to the channel with the smallest Wi-Fi channel bandwidth. Full article

Distributed generation (DG) sources play a special role in the operation of active energy networks. The microgrid (MG) is known as a suitable substrate for the development and installation of DGs. However, the future of energy distribution networks will consist of more interconnected and complex MGs, called multi-microgrid (MMG) networks. Therefore, energy management in such an energy system is a major challenge for distribution network operators. This paper presents a new energy management method for the MMG network in the presence of battery storage, renewable sources, and demand response (DR) programs. To show the performance of each connected MG’s inefficient utilization of its available generation capacity, an index called unused power capacity (UPC) is defined, which indicates the availability and individual performance of each MG. The uncertainties associated with load and the power output of wind and solar sources are handled by employing the chance-constrained programming (CCP) optimization framework in the MMG energy management model. The proposed CCP ensures the safe operation of the system at the desired confidence level by involving various uncertainties in the problem while optimizing operating costs under Mixed-Integer Linear Programming (MILP). The proposed energy management model is assessed on a sample network concerning DC power flow limitations. The procured power of each MG and power exchanges at the distribution network level are investigated and discussed. Full article

Most existing approaches to ensuring water quality in water distribution systems (WDSs) are deterministic, i.e., they do not consider uncertainties, although they may have significant impacts on the water quality. It is well recognized that water demand represents a predominant uncertainty in a WDS. In addition, water age is often used as an important parameter to describe the water quality in a WDS and can be influenced by water demand and control elements such as pressure-reducing valves (PRVs). Therefore, the aim of this study is to carry out a probabilistic analysis of the impact of demand uncertainty on the water age in the distribution network. Based on the solution of deterministic optimization to minimize the water age, Monte Carlo simulation will be carried out by sampling the uncertain demand to evaluate the stochastic distribution of water age, as well as other operating variables like pressure and flow. As a result, the probability of violating the constraints of such variables can be determined, with the reliability of the operating strategy (e.g., the settings of the PRVs) given by deterministic optimization provided. In cases of low reliability, it is necessary to modify the operating strategy in order to decrease the probability of constraint violation. For this purpose, a chance-constrained optimization problem is formulated, and its benefits for ensuring the user-defined reliability are studied. A benchmark network is used to verify the proposed approach. Full article

The current manual inspection of transmission line images captured by unmanned aerial vehicles (UAVs) is not only time-consuming and labor-intensive but also prone to high rates of false detections and missed inspections. With the development of artificial intelligence, deep learning-based image recognition methods can automatically detect various defect categories of transmission lines based on images captured by UAVs. However, existing methods are often constrained by incomplete feature extraction and imbalanced sample categories, which limit the precision of detection. To address these issues, a novel method based on multi-strategy image processing and an improved deep network is proposed to conduct defect diagnosis of transmission lines. Firstly, multi-strategy image processing is proposed to extract the effective area of transmission lines. Then, a generative adversarial network is employed to generate images of transmission lines to enhance the trained samples’ diversity. Finally, the deep network GoogLeNet is improved by superseding the original cross-entropy loss function with a focal loss function to achieve the deep feature extraction of images and defect diagnosis of transmission lines. An actual imbalance transmission line dataset including normal, broken strands, and loose strands is applied to validate the effectiveness of the proposed method. The experimental results, as well as contrastive analysis, reveal that the proposed method is suitable for recognizing defects of transmission lines. Full article

Background: Moral Distress (MD) is a unique form of distress that occurs when people believe they know the ethically correct action to take but are constrained from doing so. Limited clinical experience and insufficient ethical knowledge contribute to nursing students’ MD, which can potentially cause negative outcomes. The aims of this study are: (1) to describe the MD intensity of nursing students, and (2) to analyze differences and associations between MD intensity and socio-demographic and academic variables. Methods: A cross-sectional study design with a convenience sample of the second, third, and delayed graduation students was included; only students willing to participate and who had attended their scheduled internships in the last six months were eligible for inclusion. To measure the level of MD, we used the It-ESMEE. We collected socio-demographic and academic variables. The data collection occurred from January 2024 to March 2024. Results: The students who adhered to the collection were N = 344. The findings reveal that the students perceived a high level of MD in situations related to clinical internship and class. They perceived higher levels of MD when nursing was not their first career choice, were separated or divorced, did not have children, and were not an employed student. The overall MD score is statistically significantly lower among students who had nursing as their first career choice (β = −0.267, p < 0.05), have children (β = −0.470, p < 0.01), and are employed (β = −0.417, p < 0.01). In contrast, being separated or divorced (β = 0.274, p < 0.01) was associated with a higher MD score. Conclusions: This study has some limitations: data reflect a local context, and the findings may not be generalizable to other regions or educational environments. Additionally, students’ recollections of their experiences could be influenced by the passage of time, and there may be a selection bias since only students willing to participate were included. The findings suggest that nursing education programs should incorporate more robust training in ethical decision-making and stress management to better prepare students for the moral challenges in their professional practice. Full article

Reconstructing cloud-covered regions in remote sensing (RS) images holds great promise for continuous ground object monitoring. A novel lightweight machine-learning method for cloud removal constrained by conditional information (SMLP-CR) is proposed. SMLP-CR constructs a multilayer perceptron with a presingle-connection layer (SMLP) based on multisource conditional information. The method employs multi-scale mean filtering and local neighborhood sampling to gain spatial information while also taking into account multi-spectral and multi-temporal information as well as pixel similarity. Meanwhile, the feature importance from the SMLP provides a selection order for conditional information—homologous images are prioritized over images from the same season as the restoration image, and images with close temporal distances rank last. The results of comparative experiments indicate that SMLP-CR shows apparent advantages in terms of visual naturalness, texture continuity, and quantitative metrics. Moreover, compared with popular deep-learning methods, SMLP-CR samples locally around cloud pixels instead of requiring a large cloud-free training area, so the samples show stronger correlations with the missing data, which demonstrates universality and superiority. Full article

X-ray CT imaging is an important three-dimensional non-destructive testing technique, which has been widely applied in various fields. However, segmenting image voxels as discrete material compositions may lose information below the voxel size. In this study, six samples with known volume fractions of compositions were imaged using laboratory micro-CT. Optical microscopic images of the samples reveal numerous small particles of compositions smaller than the CT voxel size within the samples. By employing the equivalent energy method to determine the X-ray beam energy for sample imaging experiments, data-constrained modelling (DCM) was used to obtain the volume fractions of different compositions in the samples for each voxel. The results demonstrated that DCM effectively captured information about compositions occupying CT voxels partially. The computed volume fractions of compositions using DCM closely matched the known values. The results of DCM and four automatic threshold segmentation algorithms were compared and analyzed. The results showed that DCM has obvious advantages in processing those samples containing a large number of particles smaller than the CT voxel size. This work is the first quantitative evaluation of DCM for laboratory CT image processing, which provides a new idea for multi-scale structure characterization of materials based on laboratory CT. Full article

Binary As_xSe_100−x alloys from the border of a glass-forming region (65 < x < 70) subjected to nanomilling in dry and dry–wet modes are characterized by the XRPD, micro-Raman scattering (micro-RS) and revised positron annihilation lifetime (PAL) methods complemented by a disproportionality analysis using the quantum–chemical cluster modeling approach. These alloys are examined with respect to tetra-arsenic biselenide As₄Se₂ stoichiometry, realized in glassy g-As₆₅Se₃₅, glassy–crystalline g/c-As₆₇Se₃₃ and glassy–crystalline g/c-As₇₀Se₃₀. From the XRPD results, the number of rhombohedral As and cubic arsenolite As₂O₃ phases in As-Se alloys increases after nanomilling, especially in the wet mode realized in a PVP water solution. Nanomilling-driven amorphization and reamorphization transformations in these alloys are identified by an analysis of diffuse peak halos in their XRPD patterning, showing the interplay between the levels of a medium-range structure (disruption of the intermediate-range ordering at the cost of an extended-range one). From the micro-RS spectroscopy results, these alloys are stabilized by molecular thioarsenides As₄Se_n (n = 3, 4), regardless of their phase composition, remnants of thioarsenide molecules destructed under nanomilling being reincorporated into a glass network undergoing a polyamorphic transition. From the PAL spectroscopy results, volumetric changes in the wet-milled alloys with respect to the dry-milled ones are identified as resulting from a direct conversion of the bound positron–electron (Ps, positronium) states in the positron traps. Ps-hosting holes in the PVP medium appear instead of positron traps, with ~0.36–0.38 ns lifetimes ascribed to multivacancies in the As-Se matrix. The superposition of PAL spectrum peaks and tails for pelletized PVP, unmilled, dry-milled, and dry–wet-milled As-Se samples shows a spectacular smoothly decaying trend. The microstructure scenarios of the spontaneous (under quenching) and activated (under nanomilling) decomposition of principal network clusters in As₄Se₂-bearing arsenoselenides are recognized. Over-constrained As_6·(2/3) ring-like network clusters acting as pre-cursors of the rhombohedral As phase are the main products of this decomposition. Two spontaneous processes for creating thioarsenides with crystalline counterparts explain the location of the glass-forming border in an As-Se system near the As₄Se₂ composition, while an activated decomposition process for creating layered As₂Se₃ structures is responsible for the nanomilling-driven molecular-to-network transition. Full article

To enhance agricultural productivity through the accurate detection of pests under the constrained resources of mobile devices, we introduce LP-YOLO, a bespoke lightweight object detection framework optimized for mobile-based insect pest identification. Initially, we devise lightweight components, namely LP_Unit and LP_DownSample, to serve as direct substitutes for the majority of modules within YOLOv8. Subsequently, we develop an innovative attention mechanism, denoted as ECSA (Efficient Channel and Spatial Attention), which is integrated into the network to forge LP-YOLO(l). Moreover, assessing the trade-offs between parameter reduction and computational efficiency, considering both the backbone and head components of the network, we use structured pruning methods for the pruning process, culminating in the creation of LP-YOLO(s). Through a comprehensive series of evaluations on the IP102 dataset, the efficacy of LP-YOLO as a lightweight object detection model is validated. By incorporating fine-tuning techniques during training, LP-YOLO(s)n demonstrates a marginal mAP decrease of only 0.8% compared to YOLOv8n. However, it achieves a significant reduction in parameter count by 70.2% and a remarkable 40.7% increase in FPS, underscoring its efficiency and performance. Full article

Recent technological advancements in conservation genetics and genomics have resulted in diverse tools for aiding the conservation of species. The precision and resolution of high throughput sequencing technologies provide valuable insights to aid conservation decisions, but these technologies are often financially unfeasible or unavailable in resource constrained countries. Inter-Simple Sequence Repeat (ISSR) markers, when combined with sensitive automated detection systems, provide a simple, cheap means to investigate genetic diversity and discriminate closely related species. Here, we apply this technology to assess genetic diversity and taxonomic delimitation in the Encephalartos eugene-maraisii species complex, a highly threatened, taxonomically dubious group of cycads in South Africa. Our analyses support the taxonomic singularity of E. dyerianus, E. dolomiticus and E. eugene-maraisii. Relationships between E. nubimontanus and E. cupidus remain uncertain. E. middelburgensis samples showed no clustering but had poor amplification success. This study demonstrates the suitability of automated ISSR fingerprinting as a method for plant conservation studies, especially in resource-constrained countries, and we make recommendations as to how this methodology can be effectively implemented. Full article

Industrial anomaly detection is constrained by the scarcity of anomaly samples, limiting the applicability of supervised learning methods. Many studies have focused on anomaly detection by generating anomaly images and adopting self-supervised learning approaches. Leveraging pre-trained networks on ImageNet has been explored to assist in this training process. However, achieving accurate anomaly detection remains time-consuming due to the network’s depth and parameter count not being reduced. In this paper, we propose a self-supervised learning method based on Feature Enhancement Patch Distribution Modeling (FEPDM), which generates simulated anomalies. Unlike direct training on the original feature extraction network, our approach utilizes a pre-trained network to extract multi-scale features. By aggregating these multi-scale features, we are able to train at the feature level, thereby adapting more efficiently to various network structures and reducing domain bias with respect to natural image classification. Additionally, it significantly reduces the number of parameters in the training process. Introducing this approach not only enhances the model’s generalization ability but also significantly improves the efficiency of anomaly detection. The method was evaluated on MVTec AD and BTAD datasets, and (image-level, pixel-level) AUROC scores of (95.7%, 96.2%), (93.4%, 97.6%) were obtained, respectively. The experimental results have convincingly demonstrated the efficacy of our method in tackling the scarcity of abnormal samples in industrial scenarios, while simultaneously highlighting its broad generalizability. Full article