Quantitative Prediction Model Research Articles

Physicochemical characterization of thermally oxidized rapeseed oil: An insight into combining acoustic diagnostic technique and chemometrics

This study aimed to apply an ultrasonic pulse-echo system to characterize thermally oxidized rapeseed oil. After 60 h of heating treatment (165 ± 5°C), the physicochemical (density, viscosity, acid value, iodine value and polar compounds) and acoustic properties (velocity, acoustic impedance, maximum amplitude of the first echo, difference in amplitude, and area under the curve) of rapeseed oil were measured. Support vector machine, random forest (RF), and backpropagation neural network algorithms were used to establish quantitative prediction models for viscosity and polar compounds based on the acoustic properties of rapeseed oil. The results indicated significant correlations between acoustic impedance and viscosity (R=0.70), as well as between acoustic impedance and polar compounds (R=0.79). Heating treatment reduced oil unsaturation and led to the formation of oxidative and polymeric compounds, which in turn increased the velocity and impedance, while decreasing the other three acoustic features. The RF model yielded the best performance in predicting viscosity (R2=0.7944) and polar compounds (R2=0.8385). These findings highlight that ultrasonic technology not only accurately predicts key quality parameters, but also provides a rapid, non-destructive, and cost-effective alternative to traditional methods for characterizing thermally oxidized rapeseed oil.

Qualitative and quantitative assessment of apple quality using bulk optical properties in combination with machine learning and chemometrics techniques

This study aimed to understand the quantitative relationship between the bulk optical properties (BOP), soluble solids content (SSC), and fruit firmness (FF) of apples, along with the qualitative discrimination of apple cultivar and shelf-life. The absorption coefficient (μa) and reduced scattering coefficient (μs′) of 200 apples from four cultivars during 36-days shelf-life were determined using the single integrating sphere technique in 500-1000 nm. Partial least squares regression (PLSR) and random forest (RF) algorithms were used to establish quantitative prediction models for SSC and FF based on the BOP of apples. The results indicated that the PLSR models based on μɑ and μs′ were optimal for quantitative prediction of SSC (R2p=0.749, RMSEP=0.507) and FF (R2p=0.745, RMSEP=0.571), respectively. RF and linear discriminant analysis (LDA) were used to establish qualitative models for discriminating apple cultivar and shelf-life, demonstrating that the RF model based on μɑ and μɑ+ μs′ had the highest accuracy for the determination of apple cultivar and shelf-life, respectively, with the prediction set reaching 93.2 % and 85.7 %. Overall, RF was better than LDA for qualitative discrimination; however, it was less effective than PLSR for quantitative modeling.

Rapid Detection of Adulteration in Minced Lamb Meat Using Vis-NIR Reflectance Spectroscopy

In view of the phenomenon that adulterated lamb with other animal-derived meats in the market could not be quickly identified, this study used visible near-infrared spectroscopy combined with chemometric methods to quickly identify and quantify lamb rolls adulterated with chicken, duck, and pork. The spectra of the visible–near-infrared band (350–1000 nm) and near-infrared band (1000–1700 nm) of 360 lamb samples, which were mixed with chicken, duck, pork, and 10% lamb oil separately in different increasing proportions, were collected. It was found that the qualitative models of heterogeneous meat (adulterated with chicken, duck, and pork) in lamb were constructed by the combination of first derivative and multiplicative scatter correction (MSC); the accuracy of the validation set reached 100%; the meantime accuracy of the cross-validation set reached 100% (pure lamb), 98.3% (adulterated with chicken), 98.7% (adulterated with duck), and 97.3% (adulterated with pork). Furthermore, the correlation coefficient (R2c) of the adulterated chicken, pork, and duck quantitative prediction models reached 0.972 (chicken), 0.981 (pork), and 0.985 (duck). In summary, the use of Vis NIR can identify lamb meat mixed with chicken, duck, and pork and can quantitatively predict the content of adulterated meat.

Can individual functional improvements be predicted in osteoarthritic patients after total knee arthroplasty?

PurposeTotal knee arthroplasty (TKA) is an effective treatment for advanced osteoarthritis, and achieving optimal outcomes can be challenging due to various influencing factors. Previous research has focused on identifying factors that affect postoperative functional outcomes. However, there is a paucity of studies predicting individual postoperative improvement following TKA. Therefore, a quantitative prediction model for individual patient outcomes is necessary.Materials and methodsDemographic data, radiologic variables, intraoperative variables, and physical examination findings were collected from 976 patients undergoing TKA. Preoperative and 1-year postoperative Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores were assessed, and multivariate regression analysis was conducted to identify significant factors influencing one-year WOMAC scores and changes in WOMAC scores. A predictive model was developed on the basis of the findings.ResultsThe predictive accuracy of the model for 1-year WOMAC scores was poor (all adjusted R2 < 0.08), whereas the model for changes in WOMAC scores demonstrated strong predictability (all adjusted R2 > 0.75). Preoperative WOMAC scores, sex, and postoperative knee range of motion significantly affected all pain, stiffness, and physical function aspects of the WOMAC scores (all P < 0.05). Age, cerebrovascular disease, and patellar resurfacing were associated with changes in physical function (all P < 0.05).ConclusionsThe developed quantitative model demonstrated high accuracy in predicting changes in WOMAC scores after TKA. The identified factors influencing postoperative improvement in WOMAC scores can assist in optimizing patient outcomes after TKA.

The Genetic Characteristics of FT-MIRS-Predicted Milk Fatty Acids in Chinese Holstein Cows

Fourier Transform Mid-Infrared Spectroscopy (FT-MIRS) can be used for quantitative detection of milk components. Here, milk samples of 458 Chinese Holstein cows from 11 provinces in China were collected and we established a total of 22 quantitative prediction models in milk fatty acids by FT-MIRS. The coefficient of determination of the validation set ranged from 0.59 (C18:0) to 0.76 (C4:0). The models were adopted to predict the milk fatty acids from 2138 cows and a new high-throughput computing software HiBLUP was employed to construct a multi-trait model to estimate and analyze genetic parameters in dairy cows. Finally, genome-wide association analysis was performed and seven novel SNPs significantly associated with fatty acid content were selected, investigated, and verified with the FarmCPU method, which stands for “Fixed and random model Circulating Probability Unification”. The findings of this study lay a foundation and offer technical support for the study of fatty acid trait breeding and the screening and grouping of characteristic dairy cows in China with rich, high-quality fatty acids. It is hoped that in the future, the method established in this study will be able to screen milk sources rich in high-quality fatty acids.

DSA Quantitative Analysis and Predictive Modeling of Obliteration in Cerebral AVM following Stereotactic Radiosurgery.

Stereotactic radiosurgery is a key treatment modality for cerebral AVMs, particularly for small lesions and those located in eloquent brain regions. Predicting obliteration remains challenging due to evolving treatment paradigms and complex AVM presentations. With digital subtraction angiography (DSA) being the gold standard for outcome evaluation, radiomic approaches offer potential for more objective and detailed analysis. We aimed to develop machine learning modeling using DSA quantitative features for post-SRS obliteration prediction. A prospective registry of patients with cerebral AVMs was screened to include patients with digital prestereotactic radiosurgery DSA. Anterior-posterior and lateral views were retrieved and manually segmented. Quantitative features were computed from the lesion ROI. Following feature selection, machine learning models were developed to predict unsuccessful 2-year total obliteration using processed radiomics features in comparison with clinical and radiosurgical features. When we evaluated through area under the receiver operating characteristic curve (AUROC), accuracy, area under the precision-recall curve F1, recall, and precision, the best performing model predictions on the test set were interpreted using the Shapley additive explanations approach. DSA images of 100 included patients were retrieved and analyzed. The best-performing clinical radiosurgical model was a gradient boosting classifier with an AUROC of 68% and a recall of 67%. When we used radiomics variables as input, the AdaBoost classifier had the best evaluation metrics with an AUROC of 79% and a recall of 75%. The most important clinico-radiosurgical features, ranked by model contribution, were lesion volume, patient age, treatment dose rate, the presence of seizure at presentation, and prior resection. The most important ranked radiomics features were the following: gray-level size zone matrix, gray-level nonuniformity, kurtosis, sphericity, skewness, and gray-level dependence matrix dependence nonuniformity. The combination of radiomics with machine learning is a promising approach for predicting cerebral AVM obliteration status following stereotactic radiosurgery. DSA could enhance prognostication of stereotactic radiosurgery-treated AVMs due to its high spatial resolution. Model interpretation is essential for building transparent models and establishing clinically valid radiomic signatures.

Navigating OpenDOAR Distribution: A Deep Dive into Predictive Modeling and Trend Forecasting

Effective navigation and strategic foresight are imperative in the dynamic realm of academic repositories. This study aims to comprehensively understand global scholarly communication dynamics by analyzing repository distribution and forecasting future trends using predictive modeling methodologies. Employing a mixed-methods approach, this research blends quantitative analysis and predictive modeling techniques. It encompasses phases such as distribution analysis of repositories across continents, examination of repository counts across countries using GeoPandas and Matplotlib, optimization of Long short-term memory (LSTM) networks, application of Random Forest Regressor models, and time series analysis using Autoregressive Integrated Moving Average (ARIMA) models. The study reveals profound disparities in repository counts across continents, reflecting variations in research infrastructure. Visualizations vividly depict geographical patterns and disparities in repository distribution. Predictive modeling techniques offer insights into future trends in repository development, empowering decision-makers with strategic insights. While this study provides valuable insights, limitations exist, such as the scope limited to OpenDOAR repositories and the need for further refinement in predictive modeling techniques; future research should explore additional factors influencing repository development.

Quantitative impact damage of apple based on hyperspectral imaging combined with mechanical parameters and size correction.

In order to solve the problem of decreasing the accuracy of quantitative prediction of damage of fruits resulting in the size difference of fruits, the spectral correction method based on the size difference of fruits was adopted. To provide richer theoretical knowledge for the quality detection of fruits and the design of damage reduction programs in reality. First, the undamaged spectra of the group of apples with better performance of the model were selected as the reference spectra by analyzing and comparing the modeling results of the prediction models of mechanical parameters with the single fruit diameter groups. The spectral correction coefficient was calculated with the formulas, and the damage spectra of three groups of apples were size-corrected by this coefficient to build the mechanical parameter models. Finally, the corrected spectra were screened for characteristic wavelengths by competitive adaptive reweighting and uninformative variable elimination algorithms. The results of study showed that the correlation coefficients of the prediction set of the models were improved by 2.1%-13% and the root mean square errors were reduced by 16%-51% with the spectrally corrected models compared with the precorrection models. Therefore, the size correction method can be used to eliminate the effect of size difference on the mechanical parameter models to improve the applicability of the quantitative damage prediction models, and it can provide the theoretical guidance to design the loss-reducing protective measures and the agricultural mechanized operation process.

Rapid Lactic Acid Content Detection in Secondary Fermentation of Maize Silage Using Colorimetric Sensor Array Combined with Hyperspectral Imaging

Lactic acid content is a crucial indicator for evaluating maize silage quality, and its accurate detection is essential for ensuring product quality. In this study, a quantitative prediction model for the change of lactic acid content during the secondary fermentation of maize silage was constructed based on a colorimetric sensor array (CSA) combined with hyperspectral imaging. Volatile odor information from maize silage samples with different days of aerobic exposure was obtained using CSA and recorded by a hyperspectral imaging (HSI) system. Subsequently, the acquired spectral data were subjected to preprocessing through five distinct methods before being modeled using partial least squares regression (PLSR). The coronavirus herd immunity optimizer (CHIO) algorithm was introduced to screen three color-sensitive dyes that are more sensitive to changes in lactic acid content of maize silage. To minimize model redundancy, three algorithms, such as competitive adaptive reweighted sampling (CARS), were used to extract the characteristic wavelengths of the three dyes, and the combination of the characteristic wavelengths obtained by each algorithm was used as an input variable to build an analytical model for quantitative prediction of the lactic acid content by support vector regression (SVR). Moreover, two optimization algorithms, namely grid search (GS) and crested porcupine optimizer (CPO), were compared to determine their effectiveness in optimizing the parameters of the SVR model. The results showed that the prediction accuracy of the model can be significantly improved by choosing appropriate pretreatment methods for different color-sensitive dyes. The CARS-CPO-SVR model had better prediction, with a prediction set determination coefficient (RP2), root mean square error of prediction (RMSEP), and a ratio of performance to deviation (RPD) of 0.9617, 2.0057, and 5.1997, respectively. These comprehensive findings confirm the viability of integrating CSA with hyperspectral imaging to accurately quantify the lactic acid content in silage, providing a scientific and novel method for maize silage quality testing.

Development of the Quantitative Property-Consequence Relationship Model for Prediction of Hydrogen Leakage and Dispersion Using Response Surface Method and Artificial Neural Network Approaches.

Hydrogen, an environmentally friendly and highly regarded future energy source, can form flammable vapor clouds upon leakage, which may transition into explosion. Predicting the dispersion behavior of hydrogen is crucial for preventing such incidents. This study aims to develop a quantitative property-consequence relationship (QPCR) model using the response surface method (RSM) and artificial neural network (ANN) to swiftly and accurately predict dispersion behavior. Initially, 8 variables were defined from source and dispersion models, constructing a data set through 6,561 PHAST simulations. Subsequently, the RSM-BBD (Box-Behnken design) and ANN-BPNN (Backpropagation neural network) models were developed, alongside a hybrid model incorporating BPNN after excluding four low-influence variables based on analysis of variance (ANOVA). All models achieved an R2 value exceeding 0.99. The hybrid model notably reduced computational costs by 97% compared to ANN-BPNN and exhibited lower mean square error (MSE). These results introduce a cost-effective approach for high-accuracy QPCR modeling and highlight the viability of diverse statistical methods.

Full connected layer model with self-attention to hourly predict heparin dosage for perioperative cardiac surgery patients

BackgroundAnticoagulant therapy for patients who underwent cardiac surgery is a challenge. Both inadequate and excessive anticoagulation can cause fatal complications. Previous studies failed to provide real-time guidance for heparin pump speed adjustment. This study intended to provide a quantitative prediction model to optimize heparin dosage for cardiac surgery patients based on machine learning method. MethodsPatients who underwent cardiac surgery and admitted to intensive care unit in Peking Union Medical College Hospital (PUMCH) from January 2013 to December 2023 were retrospectively analyzed. In order to reach target activated partial thromboplastin time (aPTT), linear regression, SVM, XGBoost, LSTM, GRU, FC (Full Connected Layer) and FC + self-attention models were used to make hourly adjustment recommendation for administrations of heparin pump speed. Mean absolute square, and absolute percentage errors were used to evaluate the reliability of the models. SHAP method and feature cumulative effect were used to interpret the features of the FC + self-attention model. Safety and economic evaluation based on clinical compliance of this real-world data-oriented model was further analyzed. ResultsA total of 1080 patients including 112,554 heparin pump administrations were included in this study. Among seven candidate models, FC + self-attention model yielded the lowest mean absolute error of 0.9388 and 1.1325 in test and validation cohort. Gap to target aPTT, thrombin time, history of coronary heart disease, previous duration of arterial fibrillation and prothrombin activity were identified as important features affecting heparin adjustment. High compliance to FC + self-attention model may increase percentage of normal therapeutic time and decrease supratherapeutic therapeutic time and reducing blood draw until two consecutive normal therapeutic stabilization of aPTT. ConclusionsThis FC + self-attention model is potentially applicable for giving recommendation for healthcare providers to optimize heparin dosage for cardiac surgery patients.

Quantitative classification evaluation model for tight sandstone reservoirs based on machine learning

Tight sandstone reservoirs are a primary focus of research on the geological exploration of petroleum. However, many reservoir classification criteria are of limited applicability due to the inherent strong heterogeneity and complex micropore structure of tight sandstone reservoirs. This investigation focused on the Chang 8 tight reservoir situated in the Jiyuan region of the Ordos Basin. High-pressure mercury intrusion experiments, casting thin sections, and scanning electron microscopy experiments were conducted. Image recognition technology was used to extract the pore shape parameters of each sample. Based on the above, through grey relational analysis (GRA), analytic hierarchy process (AHP), entropy weight method (EWM) and comprehensive weight method, the relationship index Q1 between initial productivity and high pressure mercury injection parameters and the relationship index Q2 between initial productivity and pore shape parameters are obtained by fitting. Then a dual-coupled comprehensive quantitative classification prediction model for tight sandstone reservoirs was developed based on pore structure and shape parameters. A quantitative classification study was conducted on the target reservoir, analyzing the correlation between reservoir quality and pore structure and shape parameters, leading to the proposal of favourable exploration areas. The research results showed that when Q1 ≥ 0.5 and Q2 ≥ 0.5, the reservoir was classified as type I. When Q1 > 0.7 and Q2 > 0.57, it was classified as type I1, indicating a high-yield reservoir. When 0.32 < Q1 < 0.47 and 0.44 < Q2 < 0.56, was classified as type II. When 0.1 < Q1 < 0.32 and 0.3 < Q2 < 0.44, it was classified as type III. Type I reservoirs exhibit a zigzag pattern in the northwest part of the study area. Thus, the northwest should be prioritized in actual exploration and development. Additionally, the initial productivity of tight sandstone reservoirs showed a positive correlation with the porosity, permeability, sorting coefficient, coefficient of variation, and median radius. Conversely, it demonstrated a negative correlation with the median pressure and displacement pressure. The perimeters of pores, their circularity, and the length of the major axis showed a positive correlation with the porosity, permeability, sorting coefficient, coefficient of variation, and median radius. On the other hand, they exhibited a negative correlation with the median pressure and displacement pressure. This study quantitatively constructed a new classification and evaluation system for tight sandstone reservoirs from the perspective of microscopic pore structure, achieving an overall model accuracy of 93.3%. This model effectively predicts and evaluates tight sandstone reservoirs. It provides new guidance for identifying favorable areas in the study region and other tight sandstone reservoirs.

Reflectance spectroscopy analysis and lithium content estimation in lithium-rich rocks and stream sediments: Insights from Tuanjie Peak, Western Kunlun, China

Lithium, a rare metal of strategic importance, has garnered heightened global attention. This investigation delves into the laboratory visible-near infrared and short-wavelength infrared reflectance (VNIR-SWIR 350 nm–2500 nm) spectral properties of lithium-rich rocks and stream sediments, aiming to elucidate their quantitative relationship with lithium concentration. This research seeks to pave new avenues and furnish innovative technical solutions for probing sedimentary lithium reserves. Conducted in the Tuanjie Peak region of Western Kunlun, Xinjiang, China, this study analyzed 614 stream sediments and 222 rock specimens. Initial steps included laboratory VNIR-SWIR spectral reflectance measurements and lithium quantification. Following the preprocessing of spectral data via Savitzky-Golay (SG) smoothing and continuum removal (CR), the absorption positions (Pos2210nm, Pos1910nm) and depths (Depth2210, Depth1910) in the rock spectra, as well as the Illite Spectral Maturity (ISM) of the rock samples, were extracted. Employing both the Successive Projections Algorithm (SPA) and genetic algorithm (GA), wavelengths indicative of lithium content were identified. Integrating the lithium-sensitive wavelengths identified by these feature selection methods, A quantitative predictive regression model for lithium content in rock and stream sediments was developed using partial least squares regression (PLSR), support vector regression (SVR), and convolutional neural network (CNN). Spectral analysis indicated that lithium is predominantly found in montmorillonite and illite, with its content positively correlating with the spectral maturity of illite and closely related to Al-OH absorption depth (Depth2210) and clay content. The SPA algorithm was more effective than GA in extracting lithium-sensitive bands. The optimal regression model for quantitative prediction of lithium content in rock samples was SG-SPA-CNN, with a correlation coefficient prediction (Rp) of 0.924 and root-mean-square error prediction (RMSEP) of 0.112. The optimal model for the prediction of lithium content in stream sediment was SG-SPA-CNN, with an Rp and RMSEP of 0.881 and 0.296, respectively. The higher prediction accuracy for lithium content in rocks compared to sediments indicates that rocks are a more suitable medium for predicting lithium content. Compared to the PLSR and SVR models, the CNN model performs better in both sample types. Despite the limitations, this study highlights the effectiveness of hyperspectral technology in exploring the potential of clay-type lithium resources in the Tuanjie Peak area, offering new perspectives and approaches for further exploration.

Optimizing the effects of potato size and shape on near-infrared prediction models of potato quality using a linear-nonlinear algorithm

The potato size and shape can affect the accuracy of predicting potato quality using near-infrared (NIR) spectroscopy. This study used NIR spectroscopy and a linear-nonlinear algorithm to eliminate the influence of potato size and shape on the accuracy of the prediction model for potato starch and moisture content. Savitzky-Golay (SG) filtering and four dimensionality reduction algorithms (iterative variable subset optimization (IVSO), variable combination population analysis- iteratively retaining informative variables (VCPA-IRIV), bootstrapping soft shrinkage (BOSS), and principal component analysis (PCA)) were used to optimize the NIR spectrum and extract spectral data. Partial least squares (PLS) linear regression and a nonlinear model (convolutional neural network–bi-directional long short-term memory (CNN-BiLSTM)) were used to establish and compare 52 quantitative prediction models. The optimum prediction model was the SG-IVSO-PLS-CNN-BiLSTM. Its correlation coefficient of prediction (Rp), root mean square error of prediction (RMSEP), and relative percent deviation (RPD) were 0.949, 1.350 %, and 3.172 for predicting the moisture content and 0.937, 1.110 %, and 2.863 for predicting the starch content. The SG-IVSO-PLS-CNN-BiLSTM eliminated the influence of potato size and shape on the accuracy of the prediction model. This method is suitable for predicting potato quality in the potato processing industry.

Sectoral carbon emission prediction and spatial modeling framework: A local climate zone-based case study of the Guangdong-Hong Kong-Macao Greater Bay Area

Understanding the spatio-temporal pattern of carbon emission (CE) is prerequisite for formulating carbon reduction policies. Previous studies emphasized quantitative analysis of CE inventory while ignoring sectoral spatial distribution. This study fills this gap by developing a framework for coupling the CE quantitative prediction model with the sectoral CE spatial model based on the Long-range Energy Alternatives Planning (LEAP) model, spatial proxy data and local climate zone (LCZ). The framework's sectoral CE results reveal a great varied landscape within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), one of the leading bay areas in the world with rapid urbanization and emphasis on low-carbon development, under four carbon reduction scenarios. By 2060, the CN3 scenario that considers both energy-supply and consumption sides, predicts a drastic emission cut to 35.76 million tons, just 10 % of the business as usual (BAU) scenario's forecast, mainly from transportation (29.45 million tons) and industry (9.34 million tons) sectors. Besides, compared with the common CE spatial products, the spatial simulation results of sectoral CE in our framework present detailed spatial differences at the jurisdictional level. The findings are conducive for governments to formulate accurate CE reduction and optimization strategies of the cities towards to the 2060 carbon neutrality.

Quantitative predictive model for screening optimal processing methods of Polygonati rhizoma

Polygonati rhizoma (Huangjing in Chinese) is a common clinical tonic with the traditional effects of tonifying Qi, nourishing Yin. However, the lack of precise control of processing parameters has led to the uneven quality of processed Huangjing. A prediction model using the CRITIC method optimizes processing by correlating method, component contents, and biological activity, ensuring consistent quality and efficacy.

A novel nomogram to predict psoriatic arthritis in patients with plaque psoriasis.

To construct a predictive model for Psoriatic Arthritis (PsA) based on clinical and ultrasonic characteristics in patients with plaque psoriasis (PsP). Demographic, clinical, and ultrasound data were collected from patients with PsP and PsA between May 2019 and December 2022. A total of 212 patients with PsP and 123 with PsA in the training cohort, whereas the validation cohort comprised 91 patients with PsP and 49 with PsA. The multivariate logistic regression identified nail psoriasis (odds ratio [OR] 1.88, 95%CI: 1.07-3.29), synovitis (OR 18.23, 95% CI: 4.04-82.33), enthesitis (OR 3.71, 95%CI: 1.05-13.14), and bone erosion (OR 11.39, 95%CI: 3.05-42.63) as effective predictors for PsA. The area under the curve was 0.750 (95%CI, 0.691-0.806) and 0.804 (95%CI, 0.723-0.886) for the training and validation cohorts, respectively. The Hosmer-Lemeshow goodness-of-fit test showed good consistency for both the training cohort (p = 0.970) and the validation cohort (p = 0.967). Calibration curves also indicated good calibration for both cohorts. The DCA revealed that the predictive model had good clinical utility. We have developed a quantitative, intuitive, and convenient predictive model based on nail psoriasis, synovitis, enthesitis, and bone erosion to assess the risk of PsA in patients with plaque psoriasis.

Complementary testing and machine learning techniques for the characterization and prediction of middle Permian tight gas sandstone reservoir quality in the northeastern Ordos Basin, China

In this study, an integrated approach for diagenetic facies classification, reservoir quality analysis and quantitative wireline log prediction of tight gas sandstones (TGSs) is introduced utilizing a combination of fit-for-purpose complementary testing and machine learning techniques. The integrated approach is specialized for the middle Permian Shihezi Formation TGSs in the northeastern Ordos Basin, where operators often face significant drilling uncertainty and increased exploration risks due to low porosities and micro-Darcy range permeabilities. In this study, detrital compositions and diagenetic minerals and their pore type assemblages were analyzed using optical light microscopy, cathodoluminescence, standard scanning electron microscopy, and X-ray diffraction. Different types of diagenetic facies were delineated on this basis to capture the characteristic rock properties of the TGSs in the target formation. A combination of He porosity and permeability measurements, mercury intrusion capillary pressure and nuclear magnetic resonance data was used to analyze the mechanism of heterogeneous TGS reservoirs. We found that the type, size and proportion of pores considerably varied between diagenetic facies due to differences in the initial depositional attributes and subsequent diagenetic alterations; these differences affected the size, distribution and connectivity of the pore network and varied the reservoir quality. Five types of diagenetic facies were classified: (ⅰ) grain-coating facies, which have minimal ductile grains, chlorite coatings that inhibit quartz overgrowths, large intergranular pores that dominate the pore network, the best pore structure and the greatest reservoir quality; (ⅱ) quartz-cemented facies, which exhibit strong quartz overgrowths, intergranular porosity and a pore size decrease, resulting in the deterioration of the pore structure and reservoir quality; (ⅲ) mixed-cemented facies, in which the cementation of various authigenic minerals increases the micropores, resulting in a poor pore structure and reservoir quality; (ⅳ) carbonate-cemented facies and (ⅴ) tightly compacted facies, in which the intergranular pores are filled with carbonate cement and ductile grains; thus, the pore network mainly consists of micropores with small pore throat sizes, and the pore structure and reservoir quality are the worst. The grain-coating facies with the best reservoir properties are more likely to have high gas productivity and are the primary targets for exploration and development. The diagenetic facies were then translated into wireline log expressions (conventional and NMR logging). Finally, a wireline log quantitative prediction model of TGSs using convolutional neural network machine learning algorithms was established to successfully classify the different diagenetic facies.

Modeling and analysis of surface integrity transition in cutting of Sip/Al composites based on coordination deformation effect of particle-matrix

With the wide application of Sip/Al composites in key fields, it is critical to understand the material removal behavior and surface integrity evolution mechanism of Sip/Al composites. In this paper, based on the nanoindentation and scratching experiments, the coordination deformation behavior of Si particles and the Al matrix were investigated at the micro-scale, and the mechanism and conditions of surface integrity transition (SIT) of Sip/Al composites were elucidated. Then, taking into account the feature of particle fragmentation and detachment caused by interfacial failure, an analytical model of specific cutting energy for Sip/Al composites cutting under different material removal modes was established. The competitive relationship between ductile deformation and surface defect formation was investigated, and a quantitative prediction model for the critical depth of SIT was proposed. The results show that the evolution of the material removal behavior of Sip/Al composites is a consequence of the coordination deformation the Al matrix and Si particles. The material removal mode of Sip/Al composites transitions from ductile removal to quasi-brittle removal with particle fragmentation and detachment, and there is an obvious SIT characteristic. The prediction results of critical depth are in agreement with the experimental results, with an average error of less than 9.8 %. Moreover, an appropriate increase in cutting speed can effectively enhance the surface integrity of Sip/Al composites. This research contributes to a deeper understanding of the low-damage machining mechanism of Sip/Al composites.

Assessment of comprehensive PAH index in roasted Tan lamb by two-dimensional correlation spectroscopy combined with hyperspectral imaging.

Thermally processed meat may contain harmful compounds, including polycyclic aromatic hydrocarbons (PAHs). This study constructed, for the first time, the comprehensive PAH index (CPI) concentration (phenanthrene [26.47%], acenaphthene [21.83%], pyrene [18.64%], fluoranthene [17.11%], fluorene [8.49%], and anthracene [7.46%]). A visible near-infrared (Vis-NIR) hyperspectral image (HSI) system was employed to detect CPI in 150 roasted Tan lamb samples. Furthermore, two-dimensional correlation spectra were used to identify spectral features and reveal the order of chemical bond changes under the characteristic peaks at 579-737-631-449nm. The results indicated that competitive adaptive reweighted sampling-multiple linear regression quantitative prediction model worked the best with calibration set coefficient of determination of 0.9161, calibration set coefficient of root mean square error of 2.3426µg/kg, R-squared prediction of 0.8469, and root mean square error of prediction of 2.4119µg/kg. Finally, PAH content distributions were visualized using the best prediction model. This study aimed to propose a feasible method for CPI in roasted Tan lamb detection based on Vis-NIR HSI coupled with multivariate analysis methods.