Precise Values Research Articles

Optimized XGBoost Model with Whale Optimization Algorithm for Detecting Anomalies in Manufacturing

Anomalies and defects in the manufacturing process hinder operating efficiency and product quality. The Whale Optimization Algorithm (WOA) optimizes the XGBoost model for better anomaly identification by iteratively refining hyperparameters. Experiments using real-world manufacturing datasets prove proposed model works. Comparing the proposed model to traditional anomaly detection methods shows its superior performance in industry patent concept. The optimized XGBoost model's interpretability and anomaly detection features are also discussed. In this paper, WOA is applied in this work to optimize hyperparameters of XGBoost, a robust gradient boosting technique for accurate anomaly detection in manufacturing systems. Optimized XGBoost gained 1.00 precision value, 0.9 recall value and 0.96 f1-score for class 0.0 and gained a 0.95 precision value, 1.00 recall value, and a 0.97 f1-score for class 1.0. The proposed model gained 0.993 Train Score and 0.964 Test Score. Our findings suggest that integrating XGBoost with the WOA may uncover manufacturing process irregularities. Optimization improves detection accuracy and provides a flexible and interpretable framework, helping modern industrial processes maintain quality and efficiency. This research encourages machine learning optimization for industrial patent applications, advancing anomaly detection methods.

Identifying Potential Natural Antibiotics from Unani Formulas through Machine Learning Approaches.

The Unani Tibb is a medical system of Greek descent that has undergone substantial dissemination since the 11th century and is currently prevalent in modern South and Central Asia, particularly in primary health care. The ingredients of Unani herbal medicines are primarily derived from plants. Our research aimed to address the pressing issues of antibiotic resistance, multi-drug resistance, and the emergence of superbugs by examining the molecular-level effects of Unani ingredients as potential new natural antibiotic candidates. We utilized a machine learning approach to tackle these challenges, employing decision trees, kernels, neural networks, and probability-based methods. We used 12 machine learning algorithms and several techniques for preprocessing data, such as Synthetic Minority Over-sampling Technique (SMOTE), Feature Selection, and Principal Component Analysis (PCA). To ensure that our model was optimal, we conducted grid-search tuning to tune all the hyperparameters of the machine learning models. The application of Multi-Layer Perceptron (MLP) with SMOTE pre-processing techniques resulted in an impressive accuracy precision and recall values. This analysis identified 20 important metabolites as essential components of the formula, which we predicted as natural antibiotics. In the final stage of our investigation, we verified our prediction by conducting a literature search for journal validation or by analyzing the structural similarity with known antibiotics using asymmetric similarity.

Improved YOLOv7 Electric Work Safety Belt Hook Suspension State Recognition Algorithm Based on Decoupled Head

Safety is the eternal theme of power systems. In view of problems such as time-consuming and poor real-time performance in the correct use of seat belt hooks by manual supervision operators in the process of power operation, this paper proposes an improved YOLOv7 seat belt hook suspension state recognition algorithm. Firstly, the feature extraction part of the YOLOv7 backbone network is improved, and the M-Spatial Pyramid Pooling Concurrent Spatial Pyramid Convolution (M-SPPCSPC) feature extraction module is constructed to replace the Spatial Pyramid Pooling Concurrent Spatial Pyramid Convolution (SPPCSPC) module of the backbone network, which reduces the amount of computation and improves the detection speed of the backbone network while keeping the sensory field of the backbone network unchanged. Second, a decoupled head, which realizes the confidence and regression frames separately, is introduced to alleviate the negative impact of the conflict between the classification and regression tasks, consequently improving the network detection accuracy and accelerating the network convergence. Ultimately, a dynamic non-monotonic focusing mechanism is introduced in the output layer, and the Wise Intersection over Union (WioU) loss function is used to reduce the competitiveness of high-quality anchor frames while reducing the harmful gradient generated by low-quality anchor frames, which ultimately improves the overall performance of the detection network. The experimental results show that the mean Average Precision (mAP@0.5) value of the improved network reaches 81.2%, which is 7.4% higher than that of the original YOLOv7, therefore achieving better detection results for multiple-state recognition of hooks.

YOLOv5s-Based Image Identification of Stripe Rust and Leaf Rust on Wheat at Different Growth Stages.

Stripe rust caused by Puccinia striiformis f. sp. tritici and leaf rust caused by Puccinia triticina, are two devastating diseases on wheat, which seriously affect the production safety of wheat. Timely detection and identification of the two diseases are essential for taking effective disease management measures to reduce wheat yield losses. To realize the accurate identification of wheat stripe rust and wheat leaf rust during the different growth stages, in this study, the image-based identification of wheat stripe rust and wheat leaf rust during different growth stages was investigated based on deep learning using image processing technology. Based on the YOLOv5s model, we built identification models of wheat stripe rust and wheat leaf rust during the seedling stage, stem elongation stage, booting stage, inflorescence emergence stage, anthesis stage, milk development stage, and all the growth stages. The models were tested on the different testing sets in the different individual growth stages and in all the growth stages. The results showed that the models performed differently in disease image identification. The model based on the disease images acquired during an individual growth stage was not suitable for the identification of the disease images acquired during the other individual growth stages, except for the model based on the disease images acquired during the milk development stage, which had acceptable identification performance on the testing sets in the anthesis stage and the milk development stage. In addition, the results demonstrated that wheat growth stages had a great influence on the image identification of the two diseases. The model built based on the disease images acquired in all the growth stages produced acceptable identification results. Mean F1 Score values between 64.06% and 79.98% and mean average precision (mAP) values between 66.55% and 82.80% were achieved on each testing set composed of the disease images acquired during an individual growth stage and on the testing set composed of the disease images acquired during all the growth stages. This study provides a basis for the image-based identification of wheat stripe rust and wheat leaf rust during the different growth stages, and it provides a reference for the accurate identification of other plant diseases.

Multi-Feature-Filtering-Based Road Curb Extraction from Unordered Point Clouds

Road curb extraction is a critical component of road environment perception, being essential for calculating road geometry parameters and ensuring the safe navigation of autonomous vehicles. The existing research primarily focuses on extracting curbs from ordered point clouds, which are constrained by their structure of point cloud organization, making it difficult to apply them to unordered point cloud data and making them susceptible to interference from obstacles. To overcome these limitations, a multi-feature-filtering-based method for curb extraction from unordered point clouds is proposed. This method integrates several techniques, including the grid height difference, normal vectors, clustering, an alpha-shape algorithm based on point cloud density, and the MSAC (M-Estimate Sample Consensus) algorithm for multi-frame fitting. The multi-frame fitting approach addresses the limitations of traditional single-frame methods by fitting the curb contour every five frames, ensuring more accurate contour extraction while preserving local curb features. Based on our self-developed dataset and the Toronto dataset, these methods are integrated to create a robust filter capable of accurately identifying curbs in various complex scenarios. Optimal threshold values were determined through sensitivity analysis and applied to enhance curb extraction performance under diverse conditions. Experimental results demonstrate that the proposed method accurately and comprehensively extracts curb points in different road environments, proving its effectiveness and robustness. Specifically, the average curb segmentation precision, recall, and F1 score values across scenarios A, B (intersections), C (straight road), and scenarios D and E (curved roads and ghosting) are 0.9365, 0.782, and 0.8523, respectively.

A Comparative Study for Evaluating the Groundwater Inflow and Drainage Effect of Jinzhai Pumped Storage Power Station, China

Various hydrogeological problems like groundwater inflow, water table drawdown, and water pressure redistribution may be encountered in the construction of hydraulic projects. How to accurately predict the occurrence of groundwater inflow and assess the drainage effect during construction are still challenging problems for engineering designers. Taking the Jinzhai pumped storage power station (JPSPS) of China as an example, this paper aims to use different methods to calculate the water inflow rates of an underground powerhouse and evaluate the drainage effect caused by tunnel inflow during construction. The methods consist of the analytical formulas, the site groundwater rating (SGR) method, and the Signorini type variational inequality formulation. The results show that the analytical methods considering stable water table may overestimate the water inflow rates of caverns in drained conditions, whereas the SGR method with available hydro-geological parameters obtains a qualitative hazard assessment in the preliminary phase. The numerical solutions provide more precise and reliable values of groundwater inflow considering complex geological structures and seepage control measures. Moreover, the drainage effects, including a seepage-free surface, pore water pressure redistribution, and hydraulic gradient, have been accurately evaluated using various numerical synthetic cases. Specifically, the faults intersecting on underground caverns and drainage structures significantly change the groundwater flow regime around caverns. This comparative study can not only exactly identify the capabilities of the methods for cavern inflow in drained conditions, but also can comprehensively evaluate the drainage effect during cavern construction.

Named Entity Recognition for Equipment Fault Diagnosis Based on RoBERTa-wwm-ext and Deep Learning Integration

Equipment fault diagnosis NER is to extract specific entities from Chinese equipment fault diagnosis text, which is the premise of constructing an equipment fault diagnosis knowledge graph. Named entity recognition for equipment fault diagnosis can also provide important data support for equipment maintenance support. Equipment fault diagnosis text has complex semantics, fuzzy entity boundaries, and limited data size. In order to extract entities from the equipment fault diagnosis text, this paper presents an NER model for equipment fault diagnosis based on RoBERTa-wwm-ext and Deep Learning network integration. Firstly, this model uses the RoBERTa-wwm-ext to extract context-sensitive embeddings of text sequences. Secondly, the context feature information is obtained through the BiLSTM network. Thirdly, the CRF is combined to output the label sequence with a constraint relationship, improve the accuracy of sequence labeling task, and complete the entity recognition task. Finally, experiments and predictions are carried out on the constructed dataset. The results show that the model can effectively identify five types of equipment fault diagnosis entities and has higher evaluation indexes than the traditional model. Its precision, recall, and F1 value are 94.57%, 95.39%, and 94.98%, respectively. The case study proves that the model can accurately recognize the entity of the input text.

An innovative attention infused- BiRecurrenTwin network assisted hybrid segmentation technique for accurate heart disease prediction

Heart disease is a noteworthy global health concern, profoundly affecting public health and individual well-being. Early prediction and diagnosis are crucial for reducing its negative impact. This work focuses on developing an effectual heart disease detection employing advanced optimization approaches and data processing. The proposed system incorporates a hybrid segmentation algorithm, Hybrid K-means-Fuzzy-C-means (HKMFCM) clustering, which assigns membership degrees to data points, enabling soft clustering and accommodating data uncertainty. Additionally, the Artificial Bee Colony (ABC) Optimization method is applied for feature selection, mimicking the foraging behavior of honeybees to identify the most discriminative attributes from the dataset. This optimization algorithm iteratively explores the feature space to select features that enhance the model's predictive accuracy. Furthermore, a novel classification architecture, termed the Attention-infused BiRecurrenTwin Network, is introduced to accurately predict heart disease based on segmented and extracted patient data profiles. This classifier leverages both Bidirectional Gated Recurrent Unit (BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM) networks, with their bidirectional nature capturing both past and future contexts, thus increasing the classifier's capability to detect subtle temporal patterns in patient data. In addition, the proposed system addresses traditional approaches limitations through its advanced components like HKMFCM (effectively manages data uncertainty by enabling soft clustering), ABC Optimization technique (enabling an iterative, global search for the most discriminative attributes) Attention-infused BiRecurrenTwin Network (surpasses conventional classifiers by capturing both past and future temporal patterns in patient data). The simulation outcomes demonstrate that the developed system attains improved performance, with accuracy, ROC, F-measure, precision, recall, sensitivity, and specificity values of 96.09%, 97%, 95%, 96.6%, 94.3%, 95.14%, and 97.05%, respectively which is higher than the baseline models with an average of 7.75 % increase in accuracy. These results indicate that the developed predictive models show promise in accurately classifying individuals based on their extracted features, thereby facilitating the early recognition of heart disease.

Unsupervised machine learning model for detecting anomalous volumetric modulated arc therapy plans for lung cancer patients.

Volumetric modulated arc therapy (VMAT) is a new treatment modality in modern radiotherapy. To ensure the quality of the radiotherapy plan, a physics plan review is routinely conducted by senior clinicians; however, this process is less efficient and less accurate. In this study, a multi-task AutoEncoder (AE) is proposed to automate anomaly detection of VMAT plans for lung cancer patients. The feature maps are first extracted from a VMAT plan. Then, a multi-task AE is trained based on the input of a feature map, and its output is the two targets (beam aperture and prescribed dose). Based on the distribution of reconstruction errors on the training set, a detection threshold value is obtained. For a testing sample, its reconstruction error is calculated using the AE model and compared with the threshold value to determine its classes (anomaly or regular). The proposed multi-task AE model is compared to the other existing AE models, including Vanilla AE, Contractive AE, and Variational AE. The area under the receiver operating characteristic curve (AUC) and the other statistics are used to evaluate the performance of these models. Among the four tested AE models, the proposed multi-task AE model achieves the highest values in AUC (0.964), accuracy (0.821), precision (0.471), and F1 score (0.632), and the lowest value in FPR (0.206). The proposed multi-task AE model using two-dimensional (2D) feature maps can effectively detect anomalies in radiotherapy plans for lung cancer patients. Compared to the other existing AE models, the multi-task AE is more accurate and efficient. The proposed model provides a feasible way to carry out automated anomaly detection of VMAT plans in radiotherapy.

Concordant inter-laboratory derived concentrations of ceramides in human plasma reference materials via authentic standards

In this community effort, we compare measurements between 34 laboratories from 19 countries, utilizing mixtures of labelled authentic synthetic standards, to quantify by mass spectrometry four clinically used ceramide species in the NIST (National Institute of Standards and Technology) human blood plasma Standard Reference Material (SRM) 1950, as well as a set of candidate plasma reference materials (RM 8231). Participants either utilized a provided validated method and/or their method of choice. Mean concentration values, and intra- and inter-laboratory coefficients of variation (CV) were calculated using single-point and multi-point calibrations, respectively. These results are the most precise (intra-laboratory CVs ≤ 4.2%) and concordant (inter-laboratory CVs < 14%) community-derived absolute concentration values reported to date for four clinically used ceramides in the commonly analyzed SRM 1950. We demonstrate that calibration using authentic labelled standards dramatically reduces data variability. Furthermore, we show how the use of shared RM can correct systematic quantitative biases and help in harmonizing lipidomics. Collectively, the results from the present study provide a significant knowledge base for translation of lipidomic technologies to future clinical applications that might require the determination of reference intervals (RIs) in various human populations or might need to estimate reference change values (RCV), when analytical variability is a key factor for recall during multiple testing of individuals.

Beyond the root: Geometric characterization for the diagnosis of syndromic heritable thoracic aortic diseases

Syndromic heritable thoracic aortic diseases (sHTAD), such as Marfan (MFS) or Loeys–Dietz (LDS) syndromes, involve high risk of life threatening aortic events. Diagnosis of syndromic features alone is difficult, and negative genetic tests do not necessarily exclude a genetic or hereditary condition. Periodic 3D imaging of the aorta is recommended in patients with aortic disease. Thus, an imaging-based approach aimed at identifying unique features of aortic geometry can be highly effective for diagnosing sHTAD and assessing risk. In this study, we present a method that can help identify the manifestations of sHTAD by focusing on the entire geometry of the thoracic aorta, rather than only using measurements of dilation of the aortic root. We analyze the geometric phenotype of 97 patients with genetically confirmed sHTAD (79 MF and 18 LDS) and of 45 healthy volunteers, using 3D aorta meshes obtained from phase contrast-enhanced magnetic resonance angiograms computed from 4D flow cardiac magnetic resonance. We build a geometric encoding of the aorta, based on a vessel coordinate system, and use several mathematical models to discriminate between controls and patients with sHTAD: a baseline scenario, based on aortic root dimensions only, a descriptor typically used in sHTAD patients; a low dimensional scenario, with a reduce encoding using principal component analysis; and a high-dimensional scenario, which included the full coefficient representation for geometry encoding, aiming to capture finer geometric details. The results indicate that considering the anatomy of the whole thoracic aorta can improve predictive ability. We achieve precision and sensitivity values over 0.8, with a specificity of over 70% in all the models used, while a single value classifiers (based only on aortic root diameter) demonstrated a trade-off between sensitivity and specificity. Using the mathematical properties of the vessel coordinate system representation, feature importance is mapped onto a set of anatomical traits that are used by the models to do the classification, thus providing interpretability of the results. This analysis indicates that in addition to the diameter of the aortic root, aortic elongation and a narrowing of the descending thoracic aorta may be markers of positive sHTAD.

Particle size by design: Standardizing measurements for complex topical drug product assessment

The physicochemical and biopharmaceutical properties of drug substances and dosage forms can be significantly influenced by particle size. However, the diversity of equivalent particle diameters generated by different methods poses a fundamental challenge in particle size analysis. This study aimed to develop an Analytical Quality by Design (AQbD) approach to accurately assess the particle size of a complex formulation – clobetasol propionate (CP) 0.5 mg/g cream – through automated microscopy (AM) and laser light diffraction (LD). Additionally, Raman spectroscopy was utilized to determine the chemical composition of the formulation particles. In the AQbD approach, prior knowledge was considered for the construction of the Ishikawa diagram and estimate failure mode and effects analysis (FMEA). The methods were developed following the ICH Q8-Q10, and ICH Q14 guidelines, and validated according to ICH Q2, ISO 13320:2020, and EP2.9.31./USP<429>. Results indicate that a trade-off between the techniques must be established for a particle size by design: while LD offers higher throughput and more precise values at the expense of peak resolution and broadening, AM has higher variability but more reliable information in terms of size and shape analysis. The validated methods successfully demonstrated the implementation of an AQbD method in the definition of particle size methods.

DDLA: a double deep latent autoencoder for diabetic retinopathy diagnose based on continuous glucose sensors.

The current diagnosis of diabetic retinopathy is based on fundus images and clinical experience. However, considering the ineffectiveness and non-portability of medical devices, we aimed to develop a diagnostic model for diabetic retinopathy based on glucose series data from the wearable continuous glucose monitoring system. Therefore, this study developed a novel method, i.e., double deep latent autoencoder, for exploring glycemic variability influence from multi-day glucose data for diabetic retinopathy. Specifically, the model proposed in this research could encode continuous glucose sensor data with non-continuous and variable length via the integration of a data reorganization module and a novel encoding module with fragmented-missing-wise objective function. Additionally, the model implements a double deep autoencoder, which integrated convolutional neural network, long short-term memory, to jointly capturing the inter-day and intra-day glucose latent features from glucose series. The effectiveness of the proposed model is evaluated through a cross-validation method to clinical datasets of 765 type 2 diabetes patients. The proposed method achieves the highest accuracy value (0.89), precision value (0.88), and F1 score (0.73). The results suggest that our model can be used to remotely diagnose and screen for diabetic retinopathy by learning potential features of glucose series data collected by wearable continuous glucose monitoring systems.

ECMTrans-net: Multi-Class Segmentation Network Based on Tumor Tissue in Endometrial Cancer Pathology Images

Endometrial cancer has the second highest incidence of malignant tumors in the female reproductive system, and accurate and efficient endometrial cancer pathology image analysis is one of the important research components of computer-aided diagnosis. However, endometrial cancer pathologic images have the challenges of smaller solid tumors, lesion areas varying in morphology, and difficulty distinguishing solid and non-solid tumors, which would impact the accuracy of subsequent pathological analyses. Therefore, an Endometrial Cancer Multi-class Transformer Network (ECMTrans-net) is proposed to improve the segmentation accuracy of endometrial cancer pathology images. On the one hand, an ECM-Attention is proposed, which can sequentially infer attention maps along three separate dimensions: channel, local spatial, and global spatial, and multiply the attention maps and the input feature map for adaptive feature refinement, solving the problems of the small size of solid tumors and similar characteristics of solid tumors to non-solid tumors and further improving the accuracy of segmentation of solid tumors. On the other hand, an ECM-Transformer is proposed, which can fuse multi-class feature information and dynamically adjust the receptive field, solving the issue of complex tumor features. Experiments on the solid tumor endometrial cancer pathological (ST-ECP) dataset show that the ECMTrans-net performs superior to state-of-the-art image segmentation methods, and the average values of Accuracy, MIoU, Precision, and Dice were 0.952, 0.927, 0.931 and 0.901, respectively.

Time-guided convolutional neural networks for spatiotemporal urban flood modelling

Urban flood modelling is key to understand flood risks and develop effective interventions in flood management. Deep learning (DL), known for its robust and automatic feature extraction capabilities, has been applied for urban flood predictions. However, the hybrid spatiotemporal structure of conventional DL-enabled urban flood models is limited in terms of accuracy and efficiency. To address this gap, this study develops a new DL model guided by time information. This model uses a classic CNN (Convolution Neural Network) architecture, Unet, as its backbone. Time information is integrated into inputs via an extra channel to specify the desired prediction time, facilitating the simulation of the spatiotemporal flood process. Additionally, a modified loss function is formulated to tackle the sample imbalance problem between flooded and non-flooded sites. The model performance is assessed in an urban area in Dalian, China with a total of 18 rainfall events of varying return periods. The model attains average precision and recall values of 0.90 and 0.81, respectively, across different time steps during various events. Furthermore, the model exhibits transferability in ungauged regions where a high influence of surrounding environments on local flood processes is identified by Grad-CAM (Gradient-weighted Class Activation Mapping) analysis. The results show that the new Unet model has great promise in efficiently providing accurate spatiotemporal flood simulations. The time-guided Unet model can serve as practical tools for rapid flood simulation in urban areas.

Determination of naturally derived propionic, benzoic, and sorbic acids in seafood, meats, and fruits during storage

Propionic acid, benzoic acid, and sorbic acid are common preservatives in processed foods, but their natural concentrations in raw foods are not well understood. In Korea, if propionic acid or benzoic acid exceeds certain levels in processed plant-based foods, manufacturers must prove they are naturally occurring to avoid recalls. This regulation applies to both local and imported products, with non-compliance leading to economic costs and damage to brand reputation. To address this, further research is needed to build a database of naturally occurring preservatives in raw foods and develop food safety standards. This study quantified the levels of propionic, benzoic, and sorbic acids in 37 raw foods stored at different temperatures for 2 weeks, using gas chromatography and high-performance liquid chromatography. The changes in acid concentrations during storage were examined, with preservatives identified by mass spectrometry. Sensory evaluation and total viable count were used to assess spoilage in seafood and meat samples. The matrix effects were evaluated, and the quantification methods were validated using seven food matrices. The intra- and interday accuracies and precisions (% relative standard deviation, %RSD) values met the Codex guidelines. In general, the propionic acid contents of most seafood and meat samples increased during storage, with the highest content being found in manila clams. Interestingly, the fresh sea squirt sample contained a higher propionic acid content (456mg/kg) than the other samples. However, benzoic acid and sorbic acid were not detected in most samples, with the exception of the jujube and apple samples.

An efficient face and mask detection by embedded device via thermal imaging camera

Abstract Since 87.9% of COVID-19 patients develop a fever symptom, the mandatory wearing of masks, temperature screening, and isolation of individuals with fever are the most import methods for community epidemic prevention. This study develops a thermal imaging thermometer embedded system with face, face mask, and temperature detection capabilities using low-end CPU devices. The hardware includes a Raspberry Pi 4, FLIR Lepton 3.5 LWIR Micro Thermal Camera Module, display screen, and alarm. The system is characterized by its low cost and lightweight design, and it only needs to detect necessary areas around the face area during face mask and temperature detection, effectively avoiding external interference and further enhancing the effectiveness of epidemic prevention. The face detection model is based on the YOLO-Fastest architecture and has been improved, referred to as Pi-fast. In terms of performance on the training and testing sets, our proposed Pi-fast achieves Mean Average Precision (mAP) values of 99.64% and 93.18%, F1 score values of 0.94 and 0.87, and Intersection over Union (IoU)values of 78.07% and 65.98%, respectively. Regarding system performance, the frame rate of FLIR Lepton 3.5 Thermal Camera Module hardware specifications is 8.7 FPS. When using YOLO-Fastest and proposed Pi-fast model for detection on the proposed thermal imaging system, the frame rates are 6.58 FPS and 7.32FPS, respectively. The proposed Pi-fast model outperforms YOLO-Fastest about 0.74 FPS (11.2%). The system can detect objects at a distance of about 7 meters from the lens in thermographic camera with a resolution of only 160×120 pixels, making it suitable for multi-person screening and improving the screening process’s efficiency. The system can effectively perform temperature screening and face mask detection, making it suitable for office buildings, schools, medical clinics, and other public areas. During the severe epidemic period, the proposed thermal imaging embedded system that was actually used for epidemic prevention work at I-Shou University, improving screening efficiency and reducing labour costs.

Partial molar volumes of 1–1 electrolytes at high T and P: correlations and predictions

Knowledge of the partial molar volumes of aqueous ions allows accurate calculation of the pressure dependence of equilibrium constants, solubility of minerals, etc., thus being useful for thermodynamic modeling of hydrothermal processes. This study analyzed methods to correlate and predict the values of the partial molar volumes at infinite dilution, V2o, for 1–1 electrolytes and singly charged ions at elevated T and P. Since the precise experimental values of the dielectric constant of water are measured only up to 873 K, we were interested only in non-electrostatic ways to correlate V2o data. First of all, we compiled the V2o values at T > 373 K for the following 1–1 electrolytes: HCl, LiCl, LiI, LiNO3, LiOH, NaF, NaCl, NaBr, NaI, NaNO3, NaOH, NaHCO3, NaClO4, NaH2PO4, NaTr (Tr stands for triflate), KF, KCl, KBr, KI, KNO3, KOH, CsBr, and NH4Cl. Relations, following from the “density” model and from the Fluctuation Solution Theory (FST) were employed to analyze data. It was concluded that at the current state of knowledge of V2o the FST-relations for electrolytes are recommended mainly to reject strongly deviating experimental outliers. However, the “density” model provides a simple and fairly accurate way to describe the compiled set of data with only two parameters for each ion, n and Vhc, values of which were evaluated for the following singly-charged ions: H+, Li+, Na+, K+, Cs+, NH4+, F-, Cl-, Br-, I-, OH–, NO3–, H2PO4-, HCO3–, ClO4-, Tr- (Tr = triflate). Following Mesmer et al. (1988), we consider the fitting parameters Vhc and n to be related to the intrinsic volume of the ion and to the number of water molecules transferred from the bulk water to a hydration shell around the ion, respectively.Although the values of n and Vhc were fitted from data at temperatures from 373 to 673 K and water densities, do, from 0.5 to 1.1 g cm−3, apparently, they predict realistic V2o values at temperatures up to 1600 K and do ranges from 0.2 to 1.6 g cm−3.

Simultaneous Determination of Methotrexate Concentrations in Human Plasma and Cerebrospinal Fluid Using Two-Dimensional Liquid Chromatography: Applications in Primary Central Nervous System Lymphoma.

In this study, we aimed to develop a method for the simultaneous quantification of methotrexate (MTX) samples extracted from human plasma and cerebrospinal fluid (CSF), using two-dimensional liquid chromatography (2D-LC). Furthermore, we intended to verify whether intravenous mannitol could increase MTX concentration in the CSF of patients. The mobile phase of PUMP1 consisted of 10.0 mmol/L ammonium acetate and acetonitrile. PUMP2 solution consisted of an aqueous solution of 10.0 mmol/L ammonium acetate. The mobile phase of PUMP3 comprised 50.0 mmol/L ammonium acetate and acetonitrile, with a flow rate of 1.0 mL/min. The developed method was successfully employed to simultaneously determine drug levels in plasma and CSF from the patients treated with MTX. CSF samples were obtained by lumbar puncture 0.5 - 2 h after starting the high-dose methotrexate (HD-MTX) infusion (over 4 h) and immediately before the intrathecal (IT) administration of MTX. Venous blood samples were drawn 4 h after the start of infusion. The calibration curve was linear, with a range of 0.07 - 2.38 µmol/L for CSF samples and a range of 0.11 - 5.51 µmol/L for plasma samples. Precision (> 95%) and accuracy (> 97%) were within the acceptance criteria for each quality control (QC) level. Inter- and intra-day accuracy and precision values met the acceptance criteria for each QC level. The correlation between MTX concentrations in the plasma and CSF was moderate (r = 0.502). No significant difference was observed in MTX concentration in CSF between patients using intravenous mannitol and those not using intravenous mannitol (P = 0.682). The developed method was useful for therapeutic drug monitoring of MTX and suitable for assessing the risks and benefits of chemotherapy in patients with primary central nervous system lymphoma. Intravenous mannitol did not increase MTX concentration in the CSF of patients.

Evaluation of asphalt anti-cracking performance of SBS polymer with SCB method and deep learning

In recent years, there have been unprecedented developments in artificial intelligence. Object detection, voice recognition, face recognition etc. are some of the artificial intelligence applications. In this study, an auxiliary method for the automatic detection of cracks, one of the main deterioration problems on highways, is proposed. The crack formation of hot mix asphalts is investigated with an image processing method modeled with Attention SegNet architecture. Styrene-butadiene-styrene (SBS), the most widely used additive in bitumen modification, was used at 2 %, 3 %, and 4 % ratios to modify 50/70 bitumen. Semi-circular asphalt specimens obtained with SBS modified bitumen were subjected to a semicircular bending (SCB) test and fracture performance was investigated. The effects of different temperature, notch size and additive on crack detection performance are evaluated. In the experimental study, maximum load, fracture energy, fracture toughness (KIC) values were obtained at low temperature, and resistance values against crack propagation were obtained by applying the J-integral method at intermediate temperature. The results demonstrated that with the addition of SBS, the fracture strength and maximum load values increased at each temperature value, with the 4 % SBS mixture offering the highest performance. Moreover, the image segmentation performed with SegNet provided high accuracy and precision values for cracks. It was observed that the accuracy values of the image processing methods decreased at low temperature, while at high temperature, higher accuracy values were obtained as the cracking rate.