N4-methylcytosine (4mC) is a critical epigenetic modification that plays a pivotal role in the regulation of a multitude of biological processes, including gene expression, DNA replication, and cellular differentiation. Traditional experimental methods for detecting DNA N4-methylcytosine sites are time-consuming, labor-intensive, and costly, making them unsuitable for large-scale or high-throughput research. Computational methods for identifying DNA N4-methylcytosine sites enable the rapid and cost-effective analysis of DNA 4mC sites across entire genomes. In this study, we focus on the identification of DNA 4mC sites in the mouse genome. Although there are already some computational methods that can predict DNA 4mC sites in the mouse genome, there is still significant room for improvement in accurately predicting them due to their inability to fully capture the multifaceted characteristics of DNA sequences. To address this issue, we propose a new deep learning predictor called Mus4mCP... [more]

Sustainable and green waste management has become increasingly crucial due to the rising volume of waste driven by urbanization and population growth. Deep learning models based on image recognition offer potential for advanced waste classification and recycling methods. However, traditional image recognition approaches usually rely on single-label images, neglecting the complexity of real-world waste occurrences. Moreover, there is a scarcity of recognition efforts directed at actual municipal waste data, with most studies confined to laboratory settings. Therefore, we introduce an efficient Query2Label (Q2L) framework, powered by the Vision Transformer (ViT-B/16) as its backbone and complemented by an innovative asymmetric loss function, designed to effectively handle the complexity of multi-label waste image classification. Our experiments on the newly developed municipal waste dataset “Garbage In, Garbage Out”, which includes 25,000 street-level images, each potentially containing... [more]

In additive manufacturing, such as Selective Laser Melting (SLM), identifying fabrication defects poses a significant challenge. Existing identification algorithms often struggle to meet the precision requirements for defect detection. To accurately identify small-scale defects in SLM, this paper proposes a deep learning model based on the original YOLOv5 network architecture for enhanced defect identification. Specifically, we integrate a small target identification layer into the network to improve the recognition of minute anomalies like keyholes. Additionally, a similarity attention module (SimAM) is introduced to enhance the model’s sensitivity to channel and spatial features, facilitating the identification of dense target regions. Furthermore, the SPD-Conv module is employed to reduce information loss within the network and enhance the model’s identification rate. During the testing phase, a set of sample photos is randomly selected to evaluate the efficacy of the proposed model... [more]

The eEF2K, a member of the α-kinase family, plays a crucial role in cellular differentiation and the stability of the nervous system. The development of eEF2K inhibitors has proven to be significantly important in the treatment of diseases such as cancer and Alzheimer’s. With the advancement of big data in pharmaceuticals and the evolution of molecular generation technologies, leveraging artificial intelligence to expedite research on eEF2K inhibitors shows great potential. Based on the recently published structure of eEF2K and known inhibitor molecular structures, a generative model was used to create 1094 candidate inhibitor molecules. Analysis indicates that the model-generated molecules can comprehend the principles of molecular docking. Moreover, some of these molecules can modify the original molecular frameworks. A molecular screening strategy was devised, leading to the identification of five promising eEF2K inhibitor lead compounds. These five compound molecules demonstrated e... [more]

Since the “13th Five-Year Plan”, the exploration of large-scale structural oil and gas reservoirs in the Bohai oilfield has become more complex, and the exploration of igneous oil and gas reservoirs has become the focus of current attention. At present, igneous rock reservoir fluid identification methods are mainly based on the evaluation method of logging single parameter construction, which is primarily a qualitative identification due to lithology, physical property, and engineering factors. Accurate acquisition of interference logging data, and multi-parameter coupling and recording coupling methods are few, lacking systematic and comprehensive evaluation and analysis of logging data. Since conventional logging data in the study area have difficulty accurately and quickly identifying reservoir fluid properties, a systematic analysis was conducted of three factors: lithology, physical properties, and engineering, as well as a variety of logging parameters (gas measurement, three-dim... [more]

As a distinct type of reservoir, tuffaceous tight reservoirs have attracted much attention. However, previous studies on tuffaceous tight reservoirs formed in the burial diagenetic stage are few, particularly regarding the genesis of micropores, which restricts the in-depth exploration of tuffaceous tight oil. According to thin section observation, scanning electron microscopy (SEM) identification, X-ray diffraction (XRD) experiments, elemental analyses, porosity and permeability tests, and pore structure analyses, the micropore characteristics of the Carboniferous tuffaceous tight reservoir formed by burial dissolution in the Santanghu Basin, NW China, are studied. In addition, the cause of the tuff micropore formation and its geological significance are also researched in this paper. The results are as follows: (1) The tuffaceous tight reservoir formed by burial dissolution mainly consists of quartz, feldspar, dolomite, and clay minerals. The reservoir space mainly consists of interg... [more]

Recently, the “sweet spot” of a fractured reservoir, controlled by a strike-slip fault, has been found and become the favorable target for economic exploitation of deep (>4500 m) tight gas reservoirs in the Sichuan Basin, Southwestern China. However, hidden faults of small vertical displacements (<20 m) are generally difficult to identify using low signal−noise rate seismic data for deep subsurfaces. In this study, we propose a seismic processing method to improve imaging of the hidden strike-slip fault in the central Sichuan Basin. On the basis of the multidirectional and multiscale decomposition and reconstruction processes, seismic information on the strike-slip fault can be automatically enhanced to improve images of it. Through seismic processing, the seismic resolution increased to a large extent enhancing the fault information and presenting a distinct fault plane rather than an ambiguous deflection of the seismic wave, as well as a clearer image of the sectional seismic attr... [more]

Overflow is one of the complicated working conditions that often occur in the drilling process. If it is not discovered and controlled in time, it will cause gas invasion, kick, and blowout, which will bring inestimable accidents and hazards. Therefore, overflow identification and early warning has become a hot spot and a difficult problem in drilling engineering. In the face of the limitations and lag of traditional overflow identification methods, the poor application effect, and the weak mechanisms of existing models and methods, a method of series fusion of feature data obtained from physical models as well as sliding window and random forest machine learning algorithm models is proposed. The overflow identification and early warning model of managed pressure drilling based on a series fusion data-driven model is established. The research results show that the series fusion data-driven model in this paper is superior to the overflow identification effect of other feature data and a... [more]

The massive integration of distributed renewable energy sources and nonlinear power electronic equipment has given rise to power quality issues such as waveform distortion, voltage instability, and increased harmonic components. Nowadays, the pollution of power quality is becoming increasingly severe, posing a potential threat to the security of the power grid and the stable operation of electrical equipment. Due to the presence of significant noise interference in the collected signals, existing methods still face issues such as low accuracy in disturbance identification and high computational complexity. To address these problems, this paper proposes a hybrid data-driven strategy that can significantly improve the accuracy and speed of identification. Firstly, the wavelet packet transform (WPT) method is employed to denoise the power disturbance signals. Subsequently, the local mean decomposition (LMD) algorithm is used to adaptively decompose the nonlinear and complex time series in... [more]

Stuck pipe refers to the accidental phenomenon whereby drilling tools are stuck in a well during the drilling process and cannot move freely due to various reasons. As a result, the stuck pipe can consume a lot of manpower and material resources. With the development of artificial intelligence, the intelligent prediction and identification of stuck pipe risk has gradually advanced. However, there are usually only a few stuck samples, so the intelligent model is not sufficient to excavate the stuck feature law, and then the model overfitting phenomenon occurs. Regarding the above issue, this paper proposed a limited incident dataset method based on data augmentation. Firstly, in terms of data processing, by applying percentage scaling and random dithering to the original data and combining it with GAN to generate new data, the training dataset was effectively extended, solving the problem of insufficient sample size. Then, in the selection and training of the intelligent model, an LSTM... [more]

Modeling the non-linear dynamics of a system from measurement data accurately is an open challenge. Over the past few years, various tools such as SINDy and DySMHO have emerged as approaches to distill dynamics from data. However, challenges persist in accurately capturing dynamics of a system especially when the physical knowledge about the system is unknown. A promising solution is to use a hybrid paradigm, that combines mechanistic and black-box models to leverage their respective strengths. In this study, we combine a hybrid modeling paradigm with sparse regression, to develop and identify models simultaneously. Two methods are explored, considering varying complexities, data quality, and availability and by comparing different case studies. In the first approach, we integrate SINDy-discovered models with neural ODE structures, to model unknown physics. In the second approach, we employ Multifidelity Surrogate Models (MFSMs) to construct composite models comprised of SINDy-discover... [more]

Machine learning presents opportunities to improve the scale-specific accuracy of mechanistic models in a data-driven manner. Here we demonstrate the use of a machine learning technique called Sparse Identification of Nonlinear Dynamics (SINDy) to improve a simple mechanistic model of algal growth. Time-series measurements of the microalga Chlorella Vulgaris were generated under controlled photobioreactor conditions at the University of Technology Sydney. A simple mechanistic growth model based on intensity of light and temperature was integrated over time and compared to the time-series data. While the mechanistic model broadly captured the overall growth trend, discrepancies remained between the model and data due to the model's simplicity and non-ideal behavior of real-world measurement. SINDy was applied to model the residual error by identifying an error derivative correction term. Addition of this SINDy-informed error dynamics term shows improvement to model accuracy while maint... [more]

The determination of the design space (DS) in a pharmaceutical process is a crucial aspect of the quality-by-design (QbD) initiative which promotes quality built into the desired product. This is achieved through a deep understanding of how the critical quality attributes (CQAs) and process parameters (CPPs) interact that have been demonstrated to provide quality assurance. For computational inexpensive models, the original process model can be directly deployed to identify the design space. One such crucial process is the Tablet Press (TP), which directly compresses the powder blend into individual units of the final product or adds dry or wet granulation to meet specific formulation needs. In this work, we identify the design space of input variables in a TP such that there is a (probabilistic) guarantee that the tablets meet the quality constraints under a set of operating conditions. A reduced-order model of TP is assigned for this purpose where the effects of lubricants and glidan... [more]

This paper presents an algorithm for developing sparse surrogate models that satisfy mass/energy conservation even when the training data are noisy and violate the conservation laws. In the first step, we employ the Bayesian Identification of Dynamic Sparse Algebraic Model (BIDSAM) algorithm proposed in our previous work to obtain a set of hierarchically ranked sparse models which approximate system behaviors with linear combinations of a set of well-defined basis functions. Although the model building algorithm was shown to be robust to noisy data, conservation laws may not be satisfied by the surrogate models. In this work we propose an algorithm that augments a data reconciliation step with the BIDSAM model for satisfaction of conservation laws. This method relies only on known boundary conditions and hence is generic for any chemical system. Two case studies are considered-one focused on mass conservation and another on energy conservation. Results show that models with minimum bia... [more]

To address challenges in the detection of wind turbine blade damage images, characterized by complex backgrounds and multiscale feature distribution, we propose a method based on an enhanced YOLOV8 model. Our approach focuses on three key aspects: First, we enhance the extraction of small target features by integrating the CBAM attention mechanism into the backbone network. Second, the feature fusion process is refined using the Weighted Bidirectional Feature Pyramid Network (BiFPN) to replace the path aggregation network (PANet). This modification prioritizes small target features within the deep features and facilitates the fusion of multiscale features. Lastly, we improve the loss function from CIoU to EIoU, enhancing sensitivity to small targets and the perturbation resistance of bounding boxes, thereby reducing the gap between computed predictions and real values. Experimental results demonstrate that compared with the YOLOV8 model, the CBAM-BiFPN-YOLOV8 model exhibits improvement... [more]

With the development of ultra-long-range visual sensors, the application of unsupervised person re-identification algorithms to them has become increasingly important. However, these algorithms inevitably generate noisy pseudo-labels, which seriously hinder the performance of tasks over a large range. Mixup, a data enhancement technique, has been validated in supervised learning for its generalization to noisy labels. Based on this observation, to our knowledge, this study is the first to explore the impact of the mixup technique on unsupervised person re-identification, which is a downstream task of contrastive learning, in detail. Specifically, mixup was applied in different locations (at the pixel level and feature level) in an unsupervised person re-identification framework to explore its influences on task performance. In addition, based on the richness of the information contained in the person samples to be mixed, we propose an uncertainty-aware mixup (UnA-Mix) method, which red... [more]

We proposed a novel detection method for identifying joint defects in the brazing process between copper tubes and stainless steel using a convolutional neural network (CNN) model. The brazing joints were created using high-frequency induction heating equipment, and infrared thermal imaging cameras were employed to capture the thermal data generated during the jointing process. The experiments involved 15.88 mm diameter copper tubes commonly used in plate heat exchangers, stainless-steel tubes, and filler metal containing 20% Ag. The thermal data were obtained with a resolution of 80 × 80 pixels per frame, resulting in 4796 normal joint data and 5437 defective joint data collected over 100 high-frequency induction-heating brazing experiments. A total of 10,233 thermal imaging data were categorized into 6548 training data, 1638 validation data, and 2047 test data for the development of the predictive model. We designed CNN models with varying hyperparameters, specifically the number of... [more]

When overhead lines are impacted by lightning, the traveling wave of the fault contains a wealth of fault information. The accurate extraction of feature quantities from transient components and their classification are fundamental to the identification of lightning faults. The extraction process may involve modal aliasing, optimal wavelet base issues, and inconsistencies between the lightning strike distance and the fault point. These factors have the potential to impact the effectiveness of recognition. This paper presents a method for identifying lightning strike faults by utilizing Kullback−Leibler (KL) divergence enhanced Variational Mode Decomposition (VMD) and Symmetric Geometry Mode Decomposition (SGMD) improved with Permutation Entropy (PE) to address the aforementioned issues. A model of a 220 kV overhead line is constructed using real faults to replicate scenarios of winding strike, counterstrike, and short circuit. The three-phase voltage is chosen and then subjected to Kar... [more]

Lithology identification is the fundamental work of oil and gas reservoir exploration and reservoir evaluation. The lithology of volcanic reservoirs is complex and changeable, the longitudinal lithology changes a great deal, and the log response characteristics are similar. The traditional lithology identification methods face difficulties. Therefore, it is necessary to use machine learning methods to deeply explore the corresponding relationship between the conventional log curve and lithology in order to establish a lithology identification model. In order to accurately identify the dominant lithology of volcanic rock, this paper takes the Carboniferous intermediate basic volcanic reservoir in the Hongche fault zone as the research object. Firstly, the Synthetic Minority Over-Sampling Technique−Edited Nearest Neighbours (SMOTEENN) algorithm is used to solve the problem of the uneven data-scale distribution of different dominant lithologies in the data set. Then, based on the extreme... [more]

The Ordos Basin has abundant resources in its tight sandstone reservoirs, and the use of well logging technology stands out as a critical element in the exploration and development of these reservoirs. Unlike conventional reservoirs, the commonly used interpretation models are not ideal for evaluating tight sandstone reservoirs through logging. In order to accurately evaluate parameters and identify fluid properties in the tight sandstone reservoirs of the Ordos Basin, we propose the adaption of conventional logging curves. This involves establishing an interpretation model that integrates the response characteristics of logging curves to tight sandstone reservoirs in accordance with the principles of logging. In this approach, we create interpretation models specifically for shale content, porosity, permeability, and saturation within the tight sandstone reservoir. Using the characteristics of the logging curves and their responses, we apply a mathematical relationship to link these p... [more]

Microfiltration is an important process in the pharmaceutical industry. Filter selection and validation is a time-consuming and expensive process. Quality by design approach is important for product safety. The article covers the instrumentalization and process control of a laboratory-scale dead-end microfiltration layout. The layout is a downscale model of the actual production line, and the goal is filter validation and analysis of process parameters, which may influence filter operation. Filter size, fluid pressure, valve plunger speed, and timing issues were considered. The focus is on the identification of the most influential process parameters and their influence on the repeatability of pressure oscillations caused by valve opening. The goal was to find the worst-case scenario regarding pressure oscillations and, consequently, filter energy intake. The layout was designed as compact as possible to reduce pressure losses between the filter and valve. Valve-induced pressure oscill... [more]

Data fusion, which involves integrating information from multiple sources to achieve a specific objective, is an essential area of contemporary scientific research. This article presents a multimodal fusion system for object identification in point clouds in a controlled environment. Several stages were implemented, including downsampling and denoising techniques, to prepare the data before fusion. Two denoising approaches were tested and compared: one based on neighborhood technique and the other using a median filter for each “x”, “y”, and “z” coordinate of each point. The downsampling techniques included Random, Grid Average, and Nonuniform Grid Sample. To achieve precise alignment of sensor data in a common coordinate system, registration techniques such as Iterative Closest Point (ICP), Coherent Point Drift (CPD), and Normal Distribution Transform (NDT) were employed. Despite facing limitations, variations in density, and differences in coverage among the point clouds generated by... [more]

Polymerization products are indispensable for our daily life, and the relevant modeling process plays a vital role in improving product quality. However, the model identification of the related process is a difficult point in industry due multivariate, nonlinear and time-varying characteristics. As for the conventional offline subspace identification methods, the identification accuracy may be not satisfying. To handle such a problem, an enhanced on-line recursive subspace identification method is presented on the basis of principal component analysis and sliding window (RSIMPCA-SW) in this paper to obtain the state space model for polymerization. In the proposed on-line subspace identification approach, the initial L-factor is acquired by the LQ decomposition of the sampled historical data, firstly, and then it is updated recursively through the bona fide method after the new data have been handled by the sliding window rule. Subsequently, principal component analysis (PCA) is introdu... [more]

In order to precisely ascertain the temperature at the hot spot within the intermediate joint of a three-core cable, this study focused on a 10 kV three-core cable joint as its primary subject. A three-dimensional finite element model of the cable joint was constructed, enabling the calculation of both the steady-state hot spot temperature field distribution and the transient temperature rise curve of the joint. Employing a one-dimensional transient thermal path model for the cable body, a radial inversion model for the cable core temperature was established. Through simulating the transient temperature field of the cable joint under varying currents, a fitting relationship was determined for the axial temperature points of the cable core. Subsequently, an inversion perception model was devised to calculate the hot spot temperature of the cable joint based on temperature measurements at specific points on the outer surface of the cable. Under both continuous and periodic loads, the inv... [more]

After a mine water inrush occurs, it is crucial to quickly identify the source of the water inrush and the key control area, and to formulate accurately efficient water control measures. According to the differences in water chemical characteristics of four aquifers in the Fenyuan coal mine, the concentrations of K+~Na+, Ca2+, Mg2+, Cl−, SO42−, and HCO3− were taken as water source identification indexes. A decision tree classification model based on the C4.5 algorithm was adopted to visualize the chemical characteristics of a single water source and extract rules, and intuitively obtained the discrimination conditions of a single water source with Mg2+, Ca2+, and Cl− as important variables in the decision tree: Mg2+ < 39.585 mg/L, Cl− < 516.338 mg/L and Mg2+ ≥ 39.585 mg/L, Ca2+ < 160.860 mg/L. Factor analysis and Fisher discriminant theory were used to eliminate the redundant ion variables, and the discriminant function equations of the two, three, and four types of mixed wate... [more]