During fracturing operations, special situations are often encountered. For example, the insufficient proppant-carrying capacity of fracturing fluid can cause quartz sand or ceramsite to settle near the wellbore and form a sand plug. Alternatively, excessive sand injection intensity can lead to severe accumulation of injected sand near the wellbore and also form a sand plug. These special situations are reflected in the fracturing operation curve as an abnormal increase in oil pressure over a short period of time. If not handled promptly, they can have unimaginable consequences. Sand plugs in fracturing operations, characterized by their speed and unpredictability, often form rapidly, within about 20 s. Conventional methods for on-site sand-plug warnings during fracturing include the oil pressure−time double logarithmic slope method and the net pressure−time double logarithmic slope method. Although these methods respond quickly, their warning results are unstable and vary significantl... [more]

The number of aerobic mesophilic bacteria in milk is one of the indicators of the hygienic quality of milk. The aim of this work was to determine such aerobic mesophilic bacteria and their number in raw unpreserved milk and milk preserved with sodium azide. In 40 collected samples, the total number of aerobic mesophilic bacteria was determined using the classical method of counting colonies on a nutrient medium according to the international standard HRN EN ISO 4833-1:2013. The results showed a trend of decreasing the number of grown colonies in milk preserved with sodium azide. MALDI-TOF mass spectrometry also successfully identified 392 bacterial colonies in raw unpreserved milk samples and 330 colonies in preserved milk samples. Of these, 30 genera and 54 bacterial species were identified in the raw unpreserved milk samples, while 27 genera and 41 bacterial species were identified in the preserved samples. By using a collective approach, the present study provided a more detailed in... [more]

With the increasing scale of photovoltaic (PV) power stations, timely anomaly detection through analyzing the PV output power curve is crucial. However, overlooking the impact of external factors on the expected power output would lead to inaccurate identification of PV station anomalies. This study focuses on the discrepancy between measured and expected PV power generation values, using a dual classification system. The system leverages two-dimensional Gramian angular field (GAF) data and curve features extracted from one-dimensional time series, along with attention weights from a CNN network. This approach effectively classifies anomalies, including normal operation, aging pollution, and arc faults, achieving an overall classification accuracy of 95.83%.

This study introduces a novel process identification method aimed at overcoming the challenge of accurately estimating process models when faced with deterministic disturbances, a common limitation in conventional identification methods. The proposed method tackles the difficult modeling problems due to deterministic disturbances by representing the disturbances as a linear combination of Laguerre polynomials and applies an integral transform with frequency weighting to estimate the process model in a numerically robust and stable manner. By utilizing a least squares approach for parameter estimation, it sidesteps the complexities inherent in iterative optimization processes, thereby ensuring heightened accuracy and robustness from a numerical analysis perspective. Comprehensive simulation results across various process types demonstrate the superior capability of the proposed method in accurately estimating the model parameters, even in the presence of significant deterministic distur... [more]

The rock mass is susceptible to instability and damage during cavern construction. The blast-induced cracking process of the rock mass contains a wealth of information about the precursors of instability, and the identification of fracture nucleation signals is a prerequisite for effective hazard warning. A laboratory mechanical test and microseismic (MS) monitoring were carried out in the Baihetan Cavern to investigate the fracture nucleation process in the rock mass. MS monitoring shows that pre-existing microcracks were closed or new cracks were generated under the action of high stress, which caused the migration of microcracks. As the crack density increases, the fracture interaction gradually increases. The study of the rock fracture nucleation mechanism helps to reveal the MS sequences during the rock fracture process, and the fore-main shock was found in the MS sequence during access tunnel excavation. This study can effectively provide guidance for the early warning of rock ma... [more]

Double casing joints are flexible pipe joints used for connecting aircraft fuel pipelines, which can compensate for the displacement and corner of the connected pipes and have complex mechanical characteristics. However, it is difficult to use sensors to directly measure the mechanical connection parameters of flexible joints. In this paper, we construct a coupling dynamics model and parameter identification of a double casing joint. Firstly, we analyze the structure and working principle of double-layer casing joints and establish the dynamics model of a single-layer flexible joint based on the transfer matrix method. Then, we deduce the coupling matrix of the inner and outer pipeline according to the deformation coordination conditions combined with matrix dimension extension. We establish the coupling dynamics model of flow−solid coupling of double casing joints. Furthermore, parameters such as equivalent stiffness and damping of each motion of the double casing joint in the casing... [more]

Culinary herbs and spices are widely used in daily diets. Pakistan’s flora is enriched with phytochemicals due to a diverse range of land. Phytochemicals, including volatile and non-volatile compounds, have captured much interest due to their numerous health advantages and significance in daily diet. The present study aimed to conduct in-depth metabolomic profiling of Pakistani-grown fenugreek leaves (Trigonella foenum-graecum), fennel seeds (Foeniculum vulgare), mint leaves (Mentha royleana), coriander seeds (Coriandrum sativum) and basil leaves (Ocimum basilicum) by using liquid chromatography−mass spectrometry (LC-MS/MS) and gas chromatography−mass spectrometry (GC-MS). The first study was conducted to optimize extraction using different solvents (methanol, ethanol, chloroform, acetone, and water). Total phenolic content (TPC), total flavonoid content (TFC), and total condensed tannins (TCT) were quantified along with the antioxidant and anti-diabetic activities. The highest TPC (12... [more]

Solutions for enhancing parameter identification effects for multivariate equation-error systems in random interference and parameter coupling conditions are considered in this paper. For the purpose of avoiding the impact of colored noises on parameter identification precision, an appropriate filter is utilized to process the autoregressive moving average noise. Then, the filtered system is transformed into a number of sub-identification models based on system output dimensions. Founded on negative gradient search, a new multivariate filtering algorithm employing a partial coupling approach is proposed, and a conventional gradient algorithm is derived for comparison. Parameter identification for multivariate equation-error systems has a high estimation accuracy and an efficient calculation speed with the application of the partial coupling approach and the data filtering method. Two simulations are performed to reveal the proposed method’s effectiveness.

The precipitation of struvite from wastewater is a potential alternative for the recovery of nutrients, especially phosphorus, which is an essential macronutrient for agriculture but can be harmful to the environment when improperly disposed of in water bodies. In addition, struvite has elements of great added value for agricultural activity (P, N, and Mg) and is, therefore, considered a sustainable alternative fertilizer. In its formation process, several intervening physicochemical factors may be responsible for the production yield levels. Optimization processes can help to define and direct the factors that truly matter for precipitation. In this context, a sequential design of experiments (DOE) methodology was applied to select and optimize the main struvite precipitation factors in wastewater. Initially, a screening was performed with eight factors with the aid of Plackett−Burman design, and the factors with a real influence on the process were identified. Then, a Doehlert design... [more]

Proteases play a role in different processes for protozoans and for the free-living amoeba Acanthamoeba. Some of these processes are related to pathogenicity and to encystment. In this study we describe the discovery of a protease with antimicrobial activity produced by Acanthamoeba. To identify it, we developed a novel zymogram using bacteria as an in-gel substrate that can help identify proteins capable of bacterial degradation. We used chromatography to isolate the proteases and showed that it quickly degrades in the environment. Additionally, we identified overexpressed proteases during encystment. The study of proteases from Acanthamoeba can serve several purposes including new antimicrobial proteins that the amoeba can use for potentially predigesting prokaryotes. Secondly, it can help with the identification of potential new therapies against Acanthamoeba infection.

The battery power state (SOP) is the basic indicator for the Battery management system (BMS) of the battery energy storage system (BESS) to formulate control strategies. Although there have been many studies on state estimation of lithium-ion batteries (LIBs), aging and temperature variation are seldom considered in peak power prediction during the whole life of the battery. To fill this gap, this paper aims to propose an adaptive peak power prediction method for power lithium-ion batteries considering temperature and aging is proposed. First, the Thevenin equivalent circuit model is used to jointly estimate the state of charge (SOC) and SOP of the lithium-ion power battery, and the variable forgetting factor recursive least squares (VFF-RLS) algorithm and extended Kalman filter (EKF) are utilized to identify the battery parameters online. Then, multiple constraint parameters including current, voltage, and SOC were derived, considering the dependence of the polarization resistance of... [more]

Select porous carbonate cores are used to carry out water-flooding oil micro-CT flooding experiments, and use image processing to separate oil, water, microfacies, and rock skeleton. The gray value is used to determine the distribution position of the microfacies sub-resolution remaining oil. The gray image resolution is improved by the SRCNN method to improve the pore identification accuracy. The distribution and evolution law of the sub-resolution remaining oil after the displacement is determined by the oil-water distribution results. Using the SRCNN method, the pore recognition accuracy of the original scanned images of the two samples was increased by 47.88 times and 9.09 times, respectively. The sub-resolution residual oil and the macro-pore residual oil were determined from the CT scan images after the brine was saturated and divided into five categories. With the increase in the displacement ratio, the columnar and droplet residual oil of the low-permeability samples first incr... [more]

Machine learning is the main technical means for lithofacies logging identification. As the main target of shale oil spatial distribution prediction, mud shale petrography is subjected to the constraints of stratigraphic inhomogeneity and logging information redundancy. Therefore, choosing the most applicable machine learning method for different geological characteristics and data situations is one of the key aspects of high-precision lithofacies identification. However, only a few studies have been conducted on the applicability of machine learning methods for mud shale petrography. This paper aims to identify lithofacies using commonly used machine learning methods. The study employs five supervised learning algorithms, namely Random Forest Algorithm (RF), BP Neural Network Algorithm (BPANN), Gradient Boosting Decision Tree Method (GBDT), Nearest Neighbor Method (KNN), and Vector Machine Method (SVM), as well as four unsupervised learning algorithms, namely K-means, DBSCAN, SOM, and... [more]

Implementing intelligent identification of faults in hydroelectric units helps in the timely detection of faults and taking measures to minimize economic losses. Therefore, improving the accuracy of fault signal recognition has always been a research focus. This study is based on the improved empirical mode decomposition (EMD) theory to study the denoising and feature extraction of vibration signals of hydroelectric units and uses the backpropagation neural network (BPNN) to establish corresponding connections between signal features and vibration fault states. The improved EMD in this study can improve the performance of noise reduction processing and contribute to the accurate identification of vibration faults. The vibration fault identification criteria can adopt three dimensionless feature parameters: peak skewness coefficient, valley skewness coefficient, and kurtosis coefficient of the second- and third-order components of the signal, with recognition rates and accuracy reaching... [more]

This article aims to enhance technological advancements in the classification of polyethylene terephthalate (PET) bottle plastic, positively impacting sustainable development and providing effective solutions for collection centers (CC) in Mexico. Three experimental designs and machine learning tools for data processing were developed. The experiments considered three factors: bottle size, liquid volume, and bottle labels. The first experiment focused on determining the sensor distance from post-consumer PET bottles. The second experiment aimed to evaluate the sensor’s detection ability with varying liquid levels, while the third experiment assessed its detection capability for bottle labels. A digital lux meter integrated with a microcontroller was developed to monitor illuminance in post-consumer PET bottles containing liquid as they moved through a conveyor belt at an average rate of three bottles per second. The implemented methodology successfully detected liquids inside transpare... [more]

Pesticide residues have long been a significant aspect of food safety, which has always been a major social concern. This study presents research and analysis on the identification of pesticide residue fast detection cards based on the enzyme inhibition approach. In this study, image recognition technology is used to extract the color information RGB eigenvalues from the detection results of the quick detection card, and four regression models are established to quantitatively predict the pesticide residue concentration indicated by the quick detection card using RGB eigenvalues. The four regression models are linear regression model, quadratic polynomial regression model, exponential regression model and RBF neural network model. Through study and comparison, it has been shown that the exponential regression model is superior at predicting the pesticide residue concentration indicated by the rapid detection card. The correlation value is 0.900, and the root mean square error is 0.106.... [more]

In this paper, we propose a damage identification method based on minimum mean square error estimation for a wind tunnel flexible plate condition monitoring system. Critical structural members of important equipment are large in size, and the measurement systems used to monitor their condition are often complex. The proposed damage identification method is based on the minimum mean squared error estimator and the generalized likelihood ratio test. It introduced activation function to generate the standard deviation of the data, which can then simulate the sensor output. A single sensor damage only affects a single dimension of the output data matrix of the measurement system. However, structural damage affects the output of multiple sensors. The damage identification method proposed in this paper can not only distinguish the sensor damage from the structure damage, but also locate the damaged sensor or structure damage location. This method can identify the measurement system output an... [more]

The Gulong shale oil reservoir is situated in freshwater to slightly saline lacustrine basins mainly consisting of a pure shale geological structure, which is quite different from other shale reservoirs around the world. Currently, the development of Gulong shale oil mainly relies on hydraulic fracturing, while the subsequent shut-in period for imbibition has been proven to be an effective method for enhancing shale oil recovery. To clarify the characteristics of the fluid occurrence space and the variation in the fluid occurrence during saltwater imbibition in Gulong shale, this paper carried out porosity and permeability tests on Gulong shale cores and analyzed the fluid occurrence space characteristics and imbibition oil recovery based on nuclear magnetic resonance (NMR). In the porosity and permeability tests, T2 distributions were used to correct the porosity measured by the saturation method to obtain the NMR porosity. Combined with the identification of fractures in shale cores... [more]

CO2 is a potential fluid for absorbing and accumulating thermal energy; an accurate and fast calculation method for the heat capacity is essential for the study of the flow state near the critical point. However, the calculation of the heat capacity near the critical point by the equations suggested by NIST can easily be divergent, such as for CO2, nitrogen, methane, etc. Therefore, an explicit fitting equation was studied. The fitting equation, which used density and temperature as variables and contained three constants, was derived from the nature of heat capacity change (molecular kinetic energy and potential energy). Based on the heat capacity data of the NIST WebBook, the heat capacity of CO2 is taken as the example for the equation deduction and parameter fitting. The three constants were defined in order by Origin fitting software. By this new approach, it is found that the heat capacity at the critical point is below 1% deviant from that of the NIST WebBook. Moreover, the heat... [more]

Accurate parameter estimation of photovoltaic (PV) cells is crucial for establishing a reliable cell model. Based on this, a series of studies on PV cells can be conducted more effectively to improve power output; an accurate model is also crucial for the operation and control of PV systems. However, due to the high nonlinearity of the cell and insufficient measured current and voltage data, traditional PV parameter identification methods are difficult to solve this problem. This article proposes a parameter identification method for PV cell models based on the radial basis function (RBF). Firstly, RBF is used to de-noise and predict the data to solve the current problems in the parameter identification field of noise data and insufficient data. Then, eight prominent meta-heuristic algorithms (MhAs) are used to identify the single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM) parameters of PV cells. By comparing the identification accuracy of the three models... [more]

This manuscript presents the remote monitoring of the main parameters in the 18O isotope separation technological process. It proposes to monitor the operation of the five cracking reactors in the isotope production system, respectively, the temperature in the preheating furnaces, the converter reactors and the cracking reactors. In addition, it performs the monitoring of the two separation columns from the separation cascade structure, respectively, the concentrations of the produced 18O isotope and the input nitric oxides flows. Even if the production process is continuously monitored by teams of operators, the professionals who designed the technical process and those who can monitor it remotely have the possibility to intervene with the view of making the necessary adjustments. Based on the processing of experimental data, which was gathered from the actual plant, the proposed original model of the separation cascade functioning was developed. The process computer from the monitori... [more]

Limitations regarding process design, optimization, and control often occur when using particular process simulators. With the implementation of connection methodologies, integrated tools could be made by coupling popular process simulation software with each other or with programming environments. In the current paper, we summarized and categorized the existing research regarding the application of multi-software engineering in the chemical industry, with an emphasis on software connections. CAPE-OPEN, COM, OPC, and native integration were discussed in detail, with the intention to serve as a guide for choosing the most suitable software combination and connection. These hybrid systems can handle complex user-defined problems and can be used for decision support, performing custom unit operations, operator training, process optimization, building control systems, and developing digital twins. In this work, we proposed the use of process simulator Aspen HYSYS linked together with the n... [more]

Identifying the gender of a person and his age by way of speaking is considered a crucial task in computer vision. It is a very important and active research topic with many areas of application, such as identifying a person, trustworthiness, demographic analysis, safety and health knowledge, visual monitoring, and aging progress. Data matching is to identify the gender of the person and his age. Thus, the study touches on a review of many research papers from 2016 to 2022. At the heart of the topic, many systematic reviews of multimodal pedagogies in Age and Gender Estimation for Adaptive were undertaken. However, no current study of the theme concerns connected to multimodal pedagogies in Age and Gender Estimation for Adaptive Learning has been published. The multimodal pedagogies in four different databases within the keywords indicate the heart of the topic. A qualitative thematic analysis based on 48 articles found during the search revealed four common themes, such as multimodal... [more]

Batch processes in the biopharmaceutical and chemical manufacturing industries often develop new products to meet changing market demands. When the dynamic models of these new products are trained, dynamic modeling with limited data for each product can lead to inaccurate results. One solution is to extract useful knowledge from past historical production data that can be applied to the product of a new grade. In this way, the model can be built quickly without having to wait for additional modeling data. In this study, a subspace identification combined common feature learning scheme is proposed to quickly learn a model of a new grade. The proposed modified state-space model contains common and special parameter matrices. Past batch data can be used to train common parameter matrices. Then, the parameters can be directly transferred into a new SID model for a new grade of the product. The new SID model can be quickly well trained even though there is a limited batch of data. The effec... [more]

CDK1 (cyclin dependent kinase 1) is a key regulator of the cell cycle and is frequently dysregulated in cancer, making it a promising target for anticancer therapy. Securigera securidaca L. (S. securidaca) seeds, traditionally used in folk medicine for various ailments including cancer, were examined for their potential as CDK1/Cks2 inhibitors using in silico approaches. A total of 14 phytocompounds was identified in the GC/MS chromatogram, with gingerone being the most abundant at 25.67% and hippeastrine the least at 2%. Major constituents of the essential extract, including gingerol, eugenol, α-curcumene, and gingerol, showed high values and made up 52% of the total content of the volatile extract. Molecular docking and ADMET studies suggested that hippeastrine and naringenin are potential hit candidates against CDK1, exhibiting good drug-like properties and molecular interactions with desirable pharmacokinetic and toxicological characteristics close to dinaciclib. Furthermore, molec... [more]