Search Results (19,988)

Leak detection in oil and gas pipeline networks is a climacteric and frequent issue in the oil and gas field. Many establishments have long depended on stationary hardware or traditional assessments to monitor and detect abnormalities. Rapid technological progress; innovation in engineering; and advanced technologies providing cost-effective, rapidly executed, and easy to implement solutions lead to building an efficient oil pipeline leak detection and real-time monitoring system. In this area, wireless sensor networks (WSNs) are increasingly required to enhance the reliability of checkups and improve the accuracy of real-time oil pipeline monitoring systems with limited hardware resources. The real-time transient model (RTTM) is a leak detection method integrated with LoRaWAN technology, which is proposed in this study to implement a wireless oil pipeline network for long distances. This study will focus on enhancing the LoRa network parameters, e.g., node power consumption, average packet loss, and delay, by applying several machine learning techniques in order to optimize the durability of individual nodes’ lifetimes and enhance total system performance. The proposed system is implemented in an OMNeT++ network simulator with several frameworks, such as Flora and Inet, to cover the LoRa network, which is used as the system’s network infrastructure. In order to implement artificial intelligence over the FLoRa network, the LoRa network was integrated with several programming tools and libraries, such as Python script and the TensorFlow libraries. Several machine learning algorithms have been applied, such as the random forest (RF) algorithm and the deep extreme learning machine (DELM) technique, to develop the proposed model and improve the LoRa network’s performance. They improved the LoRa network’s output performance, e.g., its power consumption, packet loss, and packet delay, with different enhancement ratios. Finally, a hybrid deep extreme learning machine model was built and selected as the proposed model due to its ability to improve the LoRa network’s performance, with perfect prediction accuracy, a mean square error of 0.75, and an exceptional enhancement ratio of 39% for LoRa node power consumption. Full article

Mango production is fundamental to the agricultural economy, generating income and employment in various communities. Accurate estimation of its production optimizes the planning and logistics of harvesting; traditionally, manual methods are inefficient and prone to errors. Currently, machine learning, by handling large volumes of data, emerges as an innovative solution to enhance the precision of mango production estimation. This study presents an analysis of mango fruit detection using machine learning algorithms, specifically YOLO version 8 and Faster R-CNN. The present study employs a dataset consisting of 212 original images, annotated with a total of 9604 labels, which has been expanded to include 2449 additional images and 116,654 annotations. This significant increase in dataset size notably enhances the robustness and generalization capacity of the model. The YOLO-trained model achieves an accuracy of 96.72%, a recall of 77.4%, and an F1 Score of 86%, compared to the results of Faster R-CNN, which are 98.57%, 63.80%, and 77.46%, respectively. YOLO demonstrates greater efficiency, being faster in training, consuming less memory, and utilizing fewer CPU resources. Furthermore, this study has developed a web application with a user interface that facilitates the uploading of images from mango trees considered samples. The YOLO-trained model detects the fruits of each tree in the representative sample and uses extrapolation techniques to estimate the total number of fruits across the entire population of mango trees. Full article

Genetically diverse avian influenza viruses (AIVs) are maintained in wild aquatic birds with increasingly frequent spillover into mammals, yet these represent a small proportion of the overall detections. The isolation of AIVs in marine mammals, including seals, has been reported sporadically over the last 45 years. Prior to 2016, all reports of AIVs detected in seals were of low-pathogenicity AIVs. In spite of this, the majority of reported AIV outbreaks caused fatal respiratory diseases, with harbor seals particularly susceptible to infection. The H5 clade 2.3.4.4b highly pathogenic AIV (HPAIV) was detected in seals for the first time in 2016. Recently, many cases of mass seal die-offs have occurred because of 2.3.4.4b HPAIV and are attributed to spillover from wild bird species. The potential for seal-to-seal transmission has been considered after the mass mortality of southern elephant seals off the coast of Argentina. Close contact between seals and wild birds, the rapid evolution of H5N1 AIVs, and the possibility of efficient mammal-to-mammal transmission are increasing concerns due to the potential for the establishment of a marine mammal reservoir and public health risks associated with the pandemic potential of the virus. This manuscript details the detection of AIVs in the seal population, comparing interesting features of various subtypes with an emphasis on avian-to-mammal-to-mammal transmission. Phylogenetic characterizations of the representative seal isolates were performed to demonstrate the relationships within the different virus isolates. Furthermore, we demonstrate that the reassortment events between different LPAIVs occurred before and after the viruses reached the seal population. The reassortment of viral segments plays an important role in the evolution of influenza viruses. Taken together, these data report on the 45 year history between seals and AIVs. Full article

The tuning sensitivity towards CO₂ detection under in-field-like conditions was investigated using SnO₂-sensitive material deposited onto Al₂O₃ substrates provided with platinum electrodes with interdigital gaps of 100 µm and 30 µm. X-ray diffraction, low-magnification and high-resolution transmission electron microscopy, and electrical and contact potential difference investigations were employed to understand the sensing mechanism involved in CO₂ detection. The morpho-structural analysis revealed that the SnO₂ nanoparticles exhibit well-defined facets along the (110) and (101) crystallographic planes. Complex phenomenological investigations showed that moisture significantly affects the gas sensing performance. The experimental results corroborated the literature evidence, highlighting the importance of Pt within the interdigital electrodes subsequently reflected in the increase in the CO₂ sensing performance with the decrease in the interdigital gap. The catalytic efficiency is explained by the distribution of platinum at the gas-Pt-SnO₂ three-phase boundary, which is critical for enhancing the sensor performance. Full article

Tularemia is an acute infectious disease classified as a natural focal infection, requiring continuous monitoring of both human and animal morbidity, as well as tracking of pathogen circulation in natural reservoirs and vectors. These efforts are essential for a comprehensive prevention and containment strategy. The causative agent, Francisella tularensis, comprises three subspecies—tularensis, holarctica, and mediasiatica—which differ in their geographic distribution and virulence. The ability to directly detect the pathogen and differentiate between subspecies has enhanced diagnostics and allowed a more accurate identification of circulation areas. Real-time PCR protocols for identification of F. tularensis subspecies tularensis and holarctica have been developed, utilizing specific primers and probes that target unique genomic regions. In this study, we present the development of a new real-time PCR assay for the detection of Francisella spp. and differentiation of F. tularensis subsp. mediasiatica. The specificity of the assay was tested on DNA from 86 bacterial species across 31 families unrelated to Francisella spp., as well as on DNA collections of F. tularensis subsp. mediasiatica and F. tularensis subsp. holarctica. The limit of detection (LOD95%) for real-time PCR in detecting Francisella spp. was 0.297 fg (0.145 genomic equivalents, GE) for holarctica DNA and 0.733 fg (0.358 GE) for mediasiatica DNA. The LOD95% for subspecies differential identification of mediasiatica was 8.156 fg (3.979, GE). The high sensitivity and specificity of these developed protocols enable direct detection of pathogens in biological and environmental samples, thereby improving the efficiency of tularemia surveillance in Kazakhstan. Full article

Timely and accurate detection of pavement cracks, the most common type of road damage, is essential for ensuring road safety. Automatic image segmentation of cracks can accurately locate their pixel positions. This paper proposes a Multi-Scale Feature Enhanced U-shaped Network (MFE-UNet) for pavement crack detection. This network model uses a Residual Detail-Enhanced Block (RDEB) instead of a conventional convolution in the encoder–decoder process. The block combines Efficient Multi-Scale Attention to enhance its feature extraction performance. The Multi-Scale Gating Feature Fusion (MGFF) is incorporated into the skip connections, enhancing the fusion of multi-scale features to capture finer crack details while maintaining rich semantic information. Furthermore, we created a pavement crack image dataset named China_MCrack, consisting of 1500 images collected from road surfaces using smartphone-mounted motorbikes. The proposed network was trained and tested on the China_MCrack, DeepCrack, and Crack-Forest datasets, with additional generalization experiments on the BochumCrackDataset. The results were compared with those of the U-Net model, ResUNet, and Attention U-Net. The experimental results show that the proposed MFE-UNet model achieves accuracies of 82.95%, 91.71%, and 69.02% on three datasets, namely, China_MCrack, DeepCrack, and Crack-Forest datasets, respectively, and the F1_score is improved by 1–4% compared with other networks. Experimental results demonstrate that the proposed method is effective in detecting cracks at the pixel level. Full article

The efficient separation of coal and gangue remains a critical challenge in modern coal mining, directly impacting energy efficiency, environmental protection, and sustainable development. Current machine vision-based sorting methods face significant challenges in dense scenes, where label rewriting problems severely affect model performance, particularly when coal and gangue are closely distributed in conveyor belt images. This paper introduces CGDet (Coal and Gangue Detection), a novel compact convolutional neural network that addresses these challenges through two key innovations. First, we proposed an Object Distribution Density Measurement (ODDM) method to quantitatively analyze the distribution density of coal and gangue, enabling optimal selection of input and feature map resolutions to mitigate label rewriting issues. Second, we developed a Relative Resolution Object Scale Measurement (RROSM) method to assess object scales, guiding the design of a streamlined feature fusion structure that eliminates redundant components while maintaining detection accuracy. Experimental results demonstrate the effectiveness of our approach; CGDet achieved superior performance with AP50 and AR50 scores of 96.7% and 99.2% respectively, while reducing model parameters by 46.76%, computational cost by 47.94%, and inference time by 31.50% compared to traditional models. These improvements make CGDet particularly suitable for real-time coal and gangue sorting in underground mining environments, where computational resources are limited but high accuracy is essential. Our work provides a new perspective on designing compact yet high-performance object detection networks for dense scene applications. Full article

Inorganic polyphosphates and respective metabolic pathways and enzymes are important factors for yeast active growth in unfavorable conditions. However, particular proteins of polyphosphate metabolism remain poorly explored in this context. Here we report biochemical and transcriptomic characterization of the CRN/PPN2 yeast strain (derived from Ppn1-lacking CRN strain) overexpressing poorly studied Ppn2 polyphosphatase. We showed that Ppn2 overexpression significantly reduced lag phase in the alkaline medium presumably due to the ability of Ppn2 to efficiently hydrolyze inorganic polyphosphates and thus neutralize hydroxide ions in the cell. With RNA-Seq, we compared the molecular phenotypes of CRN/PPN2 and its parent CRN strain grown in YPD or alkaline medium and detected transcriptomic changes induced by Ppn2 overexpression and reflecting the adaptation to alkaline conditions. The core set of upregulated genes included several genes with a previously unknown function. Respective knockout strains (∆ecm8, ∆yol160w, ∆cpp3, ∆ycr099c) exhibited defects of growth or cell morphology in the alkaline medium, proving the functional involvement of the respective proteins in sustaining growth in alkaline conditions. Full article

The philosophy of herbal medicines is that they contain multiple active components that target many aspects of a given disease. This is in line with the recent multiple-target strategy adopted due to shortcomings with the previous “magic bullet”, single-target strategy. The complexity of biological systems means it is often difficult to elucidate the mechanisms of synergistic, additive, and/or multi-target (SAM) effects. However, the use of methodologies employing molecular docking offers some insight into these. The aim of this work was to review the uses of molecular-docking simulations in the detection and/or elucidation of SAM effects with herbal medicines. Molecular docking has revealed the potential for SAM effects with many different, individual herbal medicines. Docking can also improve the fundamental understanding of SAM effects as part of systems biology approaches, such as providing quantitative weightings for the connections within static networks or supplying estimates of kinetic parameters for dynamic metabolic networks. Molecular docking can also be combined with pharmacophore modeling in a hybrid method that greatly improves the efficiency of screening. Overall, molecular docking has been shown to be a highly useful tool to provide evidence for the efficacy of herbal medicines, previously only supported by traditional usage. Full article

This study presents a one-step multiplex reverse transcription polymerase chain reaction (RT-PCR) method for the simultaneous detection of multiple viruses affecting melon crops. Viruses such as Watermelon mosaic virus (WMV), Cucumber mosaic virus (CMV), Zucchini yellow mosaic virus (ZYMV), Squash mosaic virus (SqMV), Tobacco mosaic virus (TMV), Papaya ring spot virus (PRSV), and Melon yellow spot virus (MYSV) pose a great threat to melons. The mixed infection of these viruses is the most common observation in the melon-growing fields. In this study, we surveyed northern Xingjiang (Altay, Changji, Wujiaqu, Urumqi, Turpan, and Hami) and southern Xingjiang (Aksu, Bayingolin, Kashgar, and Hotan) locations in Xinjiang province and developed a one-step multiplex RT-PCR to detect these melon viruses. The detection limits of this multiplex PCR were 10³ copies/μL for ZYMV and MYSV and 10² copies/μL for WMV, SqMV, PRSV, CMV, and TMV. The detection results in the field showed 242 samples were infected by one or more viruses. The multiplex RT-PCR protocol demonstrated rapid, simultaneous, and relatively effective detection of viruses such as WMV, CMV, ZYMV, SqMV, TMV, PRSV, and MYSV. The technique is designed to identify these melon viruses in a single reaction, enhancing diagnostic efficiency and reducing costs, thus serving as a reference for muskmelon anti-virus breeding in Xinjiang. Full article

Index modulation (IM) is considered a promising approach for fifth-generation wireless systems due to its spectral efficiency and reduced complexity compared to conventional modulation techniques. However, IM faces difficulties in environments with unpredictable channel conditions, particularly in accurately detecting index values and dynamically adjusting index assignments. Deep learning (DL) offers a potential solution by improving detection performance and resilience through the learning of intricate patterns in varying channel conditions. In this paper, we introduce a robust detection method based on a hybrid DL (HDL) model designed specifically for orthogonal frequency-division multiplexing with IM (OFDM-IM) in challenging channel environments. Our proposed HDL detector leverages a one-dimensional convolutional neural network (1D-CNN) for feature extraction, followed by a bidirectional long short-term memory (Bi-LSTM) network to capture temporal dependencies. Before feeding data into the network, the channel matrix and received signals are preprocessed using domain-specific knowledge. We evaluate the bit error rate (BER) performance of the proposed model using different optimizers and equalizers, then compare it with other models. Moreover, we evaluate the throughput and spectral efficiency across varying SNR levels. Simulation results demonstrate that the proposed hybrid detector surpasses traditional and other DL-based detectors in terms of performance, underscoring its effectiveness for OFDM-IM under uncertain channel conditions. Full article

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline and memory loss. While the precise causes of AD remain unclear, emerging evidence suggests that messenger RNA (mRNA) dysregulation contributes to AD pathology and risk. This study examined exosomal mRNA expression profiles of 15 individuals diagnosed with AD and 15 healthy controls from Barranquilla, Colombia. Utilizing advanced bioinformatics and machine learning (ML) techniques, we identified differentially expressed mRNAs and assessed their predictive power for AD diagnosis and AD age of onset (ADAOO). Our results showed that ENST00000331581 (CADM1) and ENST00000382258 (TNFRSF19) were significantly upregulated in AD patients. Key predictors for AD diagnosis included ENST00000311550 (GABRB3), ENST00000278765 (GGTLC1), ENST00000331581 (CADM1), ENST00000372572 (FOXJ3), and ENST00000636358 (ACY1), achieving > 90% accuracy in both training and testing datasets. For ADAOO, ENST00000340552 (LIMK2) expression correlated with a delay of ~12.6 years, while ENST00000304677 (RNASE6), ENST00000640218 (HNRNPU), ENST00000602017 (PPP5D1), ENST00000224950 (STN1), and ENST00000322088 (PPP2R1A) emerged as the most important predictors. ENST00000304677 (RNASE6) and ENST00000602017 (PPP5D1) showed promising predictive accuracy in unseen data. These findings suggest that mRNA expression profiles may serve as effective biomarkers for AD diagnosis and ADAOO, providing a cost-efficient and minimally invasive tool for early detection and monitoring. Further research is needed to validate these results in larger, diverse cohorts and explore the biological roles of the identified mRNAs in AD pathogenesis. Full article

Machine learning (ML) techniques have permeated various domains, offering intelligent solutions to complex problems. ML has been increasingly explored for applications in active power filters (APFs) due to its potential to enhance harmonic compensation, reference signal generation, filter control optimization, and fault detection and diagnosis. This paper reviews the most recent applications of ML in APFs, highlighting their abilities to adapt to nonlinear load conditions, improve fault detection and classification accuracy, and optimize system performance in real time. However, this paper also highlights several limitations of these methods, such as the high computational complexity, the need for extensive training data, and challenges with real-time deployment in distributed power systems. For example, the marginal improvements in total harmonic distortion (THD) achieved by ML-based methods often do not justify the increased computational overhead compared to traditional control methods. This review then suggests future research directions to overcome these limitations, including lightweight ML models for faster and more efficient control, federated learning for decentralized optimization, and digital twins for real-time system monitoring. While traditional methods remain effective, ML-based solutions have the potential to significantly enhance APF performance in future power systems. Full article

The automatic and accurate segmentation of bladder tumors is a key step in assisting urologists in diagnosis and analysis. At present, existing Transformer-based methods have limited ability to restore local detail features and insufficient boundary segmentation capabilities. We propose FEBE-Net, which aims to effectively capture global and remote semantic features, preserve more local detail information, and provide clearer and more precise boundaries. Specifically, first, we use PVT v2 backbone to learn multi-scale global feature representations to adapt to changes in bladder tumor size and shape. Secondly, we propose a new feature exploration attention module (FEA) to fully explore the potential local detail information in the shallow features extracted by the PVT v2 backbone, eliminate noise, and supplement the missing fine-grained details for subsequent decoding stages. At the same time, we propose a new boundary enhancement and refinement module (BER), which generates high-quality boundary clues through boundary detection operators to help the decoder more effectively preserve the boundary features of bladder tumors and refine and adjust the final predicted feature map. Then, we propose a new efficient self-attention calibration decoder module (ESCD), which, with the help of boundary clues provided by the BER module, gradually and effectively recovers global contextual information and local detail information from high-level features after calibration enhancement and low-level features after exploration attention. Extensive experiments on the cystoscopy dataset BtAMU and five colonoscopy datasets have shown that FEBE-Net outperforms 11 state-of-the-art (SOTA) networks in segmentation performance, with higher accuracy, stronger robust stability, and generalization ability. Full article

Semi-supervised learning has gained significant attention in the field of remote sensing due to its ability to effectively leverage both a limited number of labeled samples and a large quantity of unlabeled data. An effective semi-supervised learning approach utilizes unlabeled samples to enforce prediction consistency under minor perturbations, thus reducing the model’s sensitivity to noise and suppressing false positives in change-detection tasks. This principle underlies consistency regularization-based methods. However, while these methods enhance noise robustness, they also risk overlooking subtle but meaningful changes, leading to information loss and missed detections. To address this issue, we introduce a simple yet efficient method called Sample Inflation Interpolation (SII). This method leverages labeled sample pairs to mitigate the information loss caused by consistency regularization. Specifically, we propose a novel data augmentation strategy that generates additional change samples by combining existing supervised change samples with calculated proportions of change areas. This approach increases both the quantity and diversity of change samples in the training set, effectively compensating for potential information loss and reducing missed detections. Furthermore, to prevent overfitting, small perturbations are applied to the generated sample pairs and their labels. Experiments conducted on two public change detection (CD) datasets validate the effectiveness of our proposed method. Remarkably, even with only 5% of labeled training data, our method achieves performance levels that closely approach those of fully supervised learning models. Full article