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Abstract Shale oil development in Ordos Basin, China, primarily relies on the displacement of crude oil during the post-fracturing shut-in stage (PFSIS) of horizontal wells. Reservoir wettability significantly influences the shut-in duration and even the development approach. However, due to strong heterogeneity and super tight characteristics, the reservoir usually shows an mixed wettability, and it was usually hard to differentiate the wettability in different pore sizes. With this in mind, this study focuses on core samples from shale oil reservoir in the Longdong region of the Ordos basin to quantitatively analyze the reservoir wettability. Amott method combined with nuclear magnetic resonance is adopted in the paper to meed this end. And the optimal post-fracturing shut-in duration for Huachi and Heshui areas in the Longdong region are determined based on both wettability and field practice analysis as well as numerical simulations. Qualitative wettability evaluation reveals that the reservoir in the Longdong region is weakly oil-wet (oil-wet pores account for 58.9% and water-wet pores for 41.1%), and that larger pores are more water-wet, while smaller pores are more oil-wet. Field practice observes a noticeable two-stage decline in wellhead pressure, with pressure drop rates and water content decline rates following the order of neutral reservoir > weakly oil-wet reservoir > oil-wet reservoir during the post-fracturing shut-in stage. Numerical simulations indicate that the determination of the optimal post-fracturing shut-in duration for horizontal wells should consider reservoir properties, wettability, and injection volume. The final optimal shut-in durations for the Huachi and Heshui areas in the reservoir are determined to be 36 days and 43 days, respectively. Our study qualitatively distinguishes the wettability in different pores sizes and thus determines reasonable post-fracturing shut-in durations in different areas in Longdong region. The research has major implication for building a realistic method of wettability analysis in shale or tight oil reservoir.

Abstract Despite the growing interest of archiving information in synthetic DNA to confront data explosion, quantitatively querying the data stored in DNA is still a challenge. Herein, we present Search Enabled by Enzymatic Keyword Recognition (SEEKER), which utilizes CRISPR-Cas12a to rapidly generate visible fluorescence when a DNA target corresponding to the keyword of interest is present. SEEKER achieves quantitative text searching since the growth rate of fluorescence intensity is proportional to keyword frequency. Compatible with SEEKER, we develop non-collision grouping coding, which reduces the size of dictionary and enables lossless compression without disrupting the original order of texts. Using four queries, we correctly identify keywords in 40 files with a background of ~8000 irrelevant terms. Parallel searching with SEEKER can be performed on a 3D-printed microfluidic chip. Overall, SEEKER provides a quantitative approach to conducting parallel searching over the complete content stored in DNA with simple implementation and rapid result generation.

Abstract Simple, sensitive, and accurate molecular diagnostics are critical for preventing rapid spread of infection and initiating early treatment of diseases. However, current molecular detection methods typically rely on extensive nucleic acid sample preparation and expensive instrumentation. Here, a simple, fully integrated, lab‐in‐a‐magnetofluidic tube (LIAMT) platform is presented for “sample‐to‐result” molecular detection of virus. By leveraging magnetofluidic transport of micro/nano magnetic beads, the LIAMT device integrates viral lysis, nucleic acid extraction, isothermal amplification, and CRISPR detection within a single engineered microcentrifuge tube. To enable point‐of‐care molecular diagnostics, a palm‐sized processor is developed for magnetofluidic separation, nucleic acid amplification, and visual fluorescence detection. The LIAMT platform is applied to detect SARS‐CoV‐2 and HIV viruses, achieving a detection sensitivity of 73.4 and 63.9 copies µL−1, respectively. Its clinical utility is further demonstrated by detecting SARS‐CoV‐2 and HIV in clinical samples. This simple, affordable, and portable LIAMT platform holds promise for rapid and sensitive molecular diagnostics of infectious diseases at the point‐of‐care.

The PetroChina Changqing Oilfield Company has successfully achieved large-scale economical development of continental low-pressure freshwater lake interlayer shale oil in the Ordos Basin, China, targeting the gravity-flow sandstone thin interlayers in the organically rich mud shale formation of the seventh member of Yanchang Formation (Chang 7 Member) of Triassic,. Their significant discovery of the 1-billion-ton-level Qingcheng shale oil field, with a proven reserve of 1.052 × 109 t, has marked the establishment of China's first million-ton shale oil development zone. However, the extensive growth in production and construction has brought attention to notable challenges, including geological variances, low initial production, rapid decline, low recovery rates, and high development costs. Based on extensive field experience, this paper identifies several technological issues in shale oil development and offers comprehensive recommendations through systematic analysis. The encounter rate of horizontal shale oil wells, crucial for enhancing individual well productivity, can be improved both vertically and horizontally by prioritizing the extension direction of high-quality reservoirs during the deployment of horizontal wells. The contribution of fracturing fluid elasticity to the recovery rate is relatively low. Reservoir modification should not overemphasize large sand volume, fluid volume, or discharge. Additionally, optimizing parameters such as well spacing, vertical interlayer distribution, and fracture development is essential for maximizing the efficiency of fracturing scale, operation discharge, and other parameters. Variability in hydrocarbon source rock quality and the strength of diagenesis significantly impact oil saturation in sand bodies, affecting the selection of favorable areas and the distribution of high-quality reservoirs. Furthermore, the utilization of pre-fracturing with CO2 proves to be an effective approach in reducing viscosity and increasing the ultimate recovery rate. To ensure the efficient development of shale oil in the Changqing Oilfield, this paper emphasizes the necessity of deepening integrated geological studies, clarifying the main controlling factors of shale oil heterogeneous accumulation, and meticulously depicting three-dimensional sweet spots while exploring more effective development methodologies.

Abstract Nucleic acid detection powered by CRISPR technology provides a rapid, sensitive, and deployable approach to molecular diagnostics. While exciting, there remain challenges limiting its practical applications, such as the need for pre-amplification and the lack of quantitative ability. Here, we develop an asymmetric CRISPR assay for cascade signal amplification detection of nucleic acids by leveraging the asymmetric trans-cleavage behavior of competitive crRNA. We discover that the competitive reaction between a full-sized crRNA and split crRNA for CRISPR-Cas12a can induce cascade signal amplification, significantly improving the target detection signal. In addition, we find that CRISPR-Cas12a can recognize fragmented RNA/DNA targets, enabling direct RNA detection by Cas12a. Based on these findings, we apply our asymmetric CRISPR assay to quantitatively detect microRNA without the need for pre-amplification, achieving a detection sensitivity of 856 aM. Moreover, using this method, we analyze and quantify miR-19a biomarker in plasma samples from bladder cancer patients. This asymmetric CRISPR assay has the potential to be widely applied for simple and sensitive nucleic acid detection in various diagnostic settings.

Abstract Background This study aimed to compare the effects of single-pore non-liposuction near-infrared (NIR) endoscopic surgery and traditional open surgery for axillary sentinel lymph node biopsy (SLNB) in patients with early breast cancer (EBC). Methods The clinical pathological data of 61 patients with EBC who underwent axillary SLNB using indocyanine green (ICG) combined with carbon nanoparticle suspension (CNS) were retrospectively collected. Thirty patients received SLNB through single-pore non-liposuction NIR endoscopic surgery (endoscopic group), and the remaining 31 received SLNB through open-incision surgery (open group). The success rate, operation time, volume of intraoperative bleeding, postoperative axillary drainage, axillary extubation time, and the occurrence of postoperative complications were compared between the groups along with the total number of sentinel lymph nodes (SLNs), luminous SLNs, stained SLNs, and the pathological positivity rate of the SLNs. Results All patients underwent SLNB with a 100% success rate. SLNB operation times of the endoscopic group were longer than those of the open group (t = 3.963, P = 0.000), and the volume of axillary drainage was inferior (t = 3.035, P = 0.004). However, there were no differences in the intraoperative bleeding volumes, axillary extubation times, and postoperative complications (P > 0.05). In the Open group, the mean number of SLNs was 5.12 ± 2.16, and the pathological positivity rate was 13.53%; in the Endoscopic group, these numbers were 4.89 ± 1.73 and 12.39%. The mean number of SLNs detected (t = 0.458, P = 0.649) and the pathological positivity rates (χ2 = 0.058, P = 0.810) did not differ between the two groups. All 61 patients were followed for a median of 14.6 months. There were no local recurrences or distant metastases. Conclusions Our single-center results reveal that single-hole non-liposuction NIR endoscopic axillary SLNB is not inferior to open SLNB and may be an appropriate option for patients with early breast cancer who desire breast preservation with fewer incisions. Trial registration This retrospective study was “retrospectively registered” at the Sixth Affiliated Hospital of South China University of Technology (no. 2020105) and in National Medical Research Registration and Archival Information System ( https://www.medicalresearch.org.cn , number: MR-44-21-004727).

Since decoration is an essential part of buildings, the carbon emissions generated by decoration work should not be ignored. In recent years, prefabricated decoration has attracted much attention as efforts are made to pursue green, low-carbon, and waste-reducing buildings. However, research on carbon emissions assessment of prefabricated buildings has focused mainly on the structural aspect of prefabricated buildings, with few studies having considered prefabricated decoration. This study therefore focuses on assessing the carbon emissions of prefabricated decoration from the life cycle perspective of a case study residential building and explores the potential for reducing carbon emissions by decorating buildings with prefabricated components. The results show that using prefabricated decoration in the case study building reduced carbon emissions by 29.08% at the building material production stage compared to traditional decoration, and using an optimized design of prefabricated decoration, the building’s energy consumption over its design life could reduce carbon emissions by 1046 kgCO2/m2. These findings demonstrate the benefits of prefabrication decoration for reducing carbon emissions. This study provides decoration companies with robust data and insights to guide future decisions and practices, helping to transform and achieve the carbon neutrality goal for the building decoration industry.

Objective: To explore the value of CT radiomics characteristics in evaluating hepatic functional reserve of patients with liver cirrhosis. Methods: A total of 121 patients with clinically confirmed liver cirrhosis were retrospectively collected, who were graded as A, B and C according to Child-Pugh standard. 51 cases were grade A (group A) and 70 cases were grade B and C (group B). All patients underwent non-contrast and contrast enhanced CT scan using GE Discovery CT 750 HD scanner. Enhanced CT images of portal venous phase were selected, and the whole liver was delineated at the level of the left portal vein by 2 radiologists using Shukun Radiomics V94 software. Intraclass correlation coefficient (ICC) was applied to test the inter-group consistency of the results obtained by the 2 radiologists. All patients were randomly divided into the training set and the validation set at a ratio of 7∶3, and the whole liver was performed extraction process for radiomics characteristics. After dimensionality reduction, radiomics characteristics were acquired and the model was established by Logistic Regression (LR). Area Under ROC Curve (AUC) was used to evaluate the performance of the model. Results: The consistency test results of image delineation by two radiologists turned out well and the ICC were greater than 0.75. Finally, 17 radiomics characteristics were used to establish the evaluation model for hepatic functional reserve of patients with liver cirrhosis. The AUC of the training set was 0.84 while the accuracy，sensitivity and specificity was 0.78, 0.79, and 0.77，respectively. The AUC of the validation set was 0.77 while the accuracy，sensitivity and specificity was 0.71, 0.76, and 0.65，respectively. Conclusion: CT radiomics characteristics could be used to evaluate the hepatic functional reserve of patients with liver cirrhosis.

Background: Lung cancer is one of the most serious malignant tumors endangering human health and life. This study focused on evaluating the association between single nucleotide polymorphisms (SNPs) of the glutaminase (GLS) and lung cancer susceptibility in the Chinese Han population. Methods: A total of 684 lung cancer patients and 684 healthy individuals were enrolled. Five GLS SNPs (rs143584207 C/A, rs117985587 T/C, rs74271715 G/T, rs2355570 G/A, and rs6713444 A/G) were screened as candidate genetic loci. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated to assess the association between GLS SNPs and lung cancer susceptibility. False-positive report probability (FPRP) analysis further verified whether the positive results deserved attention. Finally, the multi-factor dimensionality reduction (MDR) method was applied to analyze the interactions between SNPs. Results: The overall analysis revealed that GLS rs143584207 and rs6713444 were significantly associated with lung cancer susceptibility. The subgroup and clinical information analyses further revealed that GLS rs143584207 and rs6713444 could remarkably reduce lung cancer susceptibility in different subgroups (age >60, females, body mass index (BMI)

Abstract Background Diabetic nephropathy is one of the most important complications in patients with diabetes. The etiology and pathogenesis of diabetic nephropathy remain unclear. Several studies have indicated that circular RNAs (circRNAs) play crucial regulatory roles in numerous human diseases and normal physiology; however, to date, no study has focused on the comprehensive expression profile of circRNAs in the kidneys of diabetic mice. Therefore, we aimed to identify differentially expressed circRNAs in diabetic mouse kidneys to explore the possible roles of dysregulated circRNAs in diabetic nephropathy development. Results Diabetic BKS-Leprem2Cd479/Nju (BKS-DB/Nju) mice and their nondiabetic wild-type littermates of C57BL/KsJ wild-type (WT) mice were used as experimental animals. Among all circRNAs identified by high-throughput RNA sequencing, four circRNAs were upregulated and ten were downregulated in diabetic mouse kidneys compared to those in nondiabetic mouse kidneys. After verification using quantitative reverse transcriptase polymerase chain reaction assays, we found that circR_1084, circR_182, circR_4, circR_50, circR_596, circR_897, and circR_203 were downregulated, whereas circR_627, circR_628, circR_735, and circR_801 were upregulated in the renal tissues of 8- and 16-week-old BKS-DB/Nju mice compared to those in WT mice. Conclusion We studied the circRNA expression profile in the kidneys of diabetic mice. Differentially expressed circRNAs may be useful as candidate biomarkers for diabetic nephropathy. Collectively, our results provide a novel theoretical basis for further investigation of the regulatory roles of circRNA in the etiology and pathogenesis of diabetic nephropathy.

Along with the number and the functional complexity of machines increase in the intelligent manufacturing system, the probability of faults will increase, which may lead to huge economic losses. Traditional passive or regular maintenance methods of solving the faults have the problems of low efficiency and huge resource consumption. Besides, traditional maintenance methods mostly contain single model, so all the prognostics and maintenance tasks of the intelligent manufacturing system can hardly be addressed at the same time. Therefore, this paper proposes a novel predictive maintenance (PDM) method based on the improved deep adversarial learning (LSTM-GAN). The long-short-term memory (LSTM) network can solve the disadvantage of vanishing gradients and the mode collapse from the generative adversarial network (GAN). The method can not only avoid the mode collapse of GAN but also realize the self-detection of abnormal data. Meanwhile, the predictive maintenance model includes two prediction models and a maintenance decision model. The prediction models can predict the state of the machine and the fault of the machine in advance. Then the maintenance decision model will arrange maintenance personnel and offer a plan of maintenance. Finally, a case study about predictive maintenance using LSTM-GAN in the intelligent manufacturing system is presented. The fault prediction accuracy of LTSM-GAN is as high as 99.68%. With the comparison between LSTM-GAN and other traditional methods, LSTM-GAN shows priority both in accuracy and efficiency. Moreover, the proposed PDM can reduce maintenance costs and downtime so that the life of machines in the intelligent manufacturing system will extend.

The job-shop scheduling problem (JSSP) is a complex combinatorial problem, especially in dynamic environments. Low-volume-high-mix orders contain various design specifications that bring a large number of uncertainties to manufacturing systems. Traditional scheduling methods are limited in handling diverse manufacturing resources in a dynamic environment. In recent years, artificial intelligence (AI) arouses the interests of researchers in solving dynamic scheduling problems. However, it is difficult to optimize the scheduling policies for online decision making while considering multiple objectives. Therefore, this paper proposes a smart scheduler to handle real-time jobs and unexpected events in smart manufacturing factories. New composite reward functions are formulated to improve the decision-making abilities and learning efficiency of the smart scheduler. Based on deep reinforcement learning (RL), the smart scheduler autonomously learns to schedule manufacturing resources in real time and improve its decision-making abilities dynamically. We evaluate and validate the proposed scheduling model with a series of experiments on a smart factory testbed. Experimental results show that the smart scheduler not only achieves good learning and scheduling performances by optimizing the composite reward functions, but also copes with unexpected events (e.g. urgent or simultaneous orders, machine failures) and balances between efficiency and profits.

The ongoing pandemic of coronavirus disease 2019 (COVID‐19) caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) has caused millions of deaths worldwide. However, most SARS‐CoV‐2 detection methods depend on time‐consuming sample preparation and large detection instruments. Herein, a method employing nonbleeding pH paper to achieve both RNA extraction and visual isothermal amplification is proposed, enabling rapid, instrument‐free SARS‐CoV‐2 detection. By taking advantage of capillary forces, pH‐paper‐based RNA extraction can be accomplished within 1 min without need for any equipment. Further, the pH paper can mediate dye‐free visual isothermal amplification detection. In less than a 46‐min sample‐to‐answer time, pH‐paper‐based extraction and visual detection (termed pH‐EVD) can consistently detect 1200 genome equivalents per microliter of SARS‐CoV‐2 in saliva, which is comparable to TaqMan probe‐based quantitative reverse transcription PCR (RT‐qPCR). Through coupling with a chemically heated incubator called a smart cup, the instrument‐free, pH‐EVD‐based SARS‐CoV‐2 detection method on 30 nasopharyngeal swab samples and 33 contrived saliva samples is clinically validated. Thus, the pH‐EVD method provides simple, rapid, reliable, low‐cost, and instrument‐free SARS‐CoV‐2 detection and has the potential to streamline onsite COVID‐19 diagnostics.

Rapid and early detection of SARS-CoV-2 will aid intervention to stop disease spread. Here the authors present a one-pot CRISPR-based rapid detection system with visual readout.

Abstract Background Leaves of the medicinal plant Ampelopsis grossedentata, which is commonly known as vine tea, are used widely in the traditional Chinese beverage in southwest China. The leaves contain a large amount of dihydromyricetin, a compound with various biological activities. However, the transcript profiles involved in its biosynthetic pathway in this plant are unknown. Results We conducted a transcriptome analysis of both young and old leaves of the vine tea plant using Illumina sequencing. Of the transcriptome datasets, a total of 52.47 million and 47.25 million clean reads were obtained from young and old leaves, respectively. Among 471,658 transcripts and 177,422 genes generated, 7768 differentially expressed genes were identified in leaves at these two stages of development. The phenylpropanoid biosynthetic pathway of vine tea was investigated according to the transcriptome profiling analysis. Most of the genes encoding phenylpropanoid biosynthesis enzymes were identified and found to be differentially expressed in different tissues and leaf stages of vine tea and also greatly contributed to the biosynthesis of dihydromyricetin in vine tea. Conclusions To the best of our knowledge, this is the first formal study to explore the transcriptome of A. grossedentata. The study provides an insight into the expression patterns and differential distribution of genes related to dihydromyricetin biosynthesis in vine tea. The information may pave the way to metabolically engineering plants with higher flavonoid content.

We consider a chemotaxis-haptotaxis system with $p$-Laplacian diffusion in three dimensional bounded domains. It is asserted that for any p>2, under the appropriate assumptions, the chemotaxis-haptotaxis system admits a global bounded weak solution if for initial data satisfies certain conditions.

Coronary arteriongraphy (CAG) is an accurate invasive technique for the diagnosis of coronary heart disease (CHD). However, its invasive procedure is not appropriate for the detection of CHD in the annual physical examination. With the successful application of machine learning (ML) in various fields, our goal is to perform selective integration of multiple ML algorithms and verify the validity of feature selection methods with personal clinical information commonly seen in the annual physical examination. In this study, a two level stacking based model is designed in which level 1 is base-level and level 2 is meta-level. The predictions of base-level classifiers is selected as the input of meta-level. The pearson correlation coefficient and maximum information coefficient are first calculated to find the classifier with the lowest correlation. Then enumeration algorithm is used to find the best combining classifiers which acquire the best result in the end. The Z-Alizadeh Sani CHD dataset which we use consists of 303 cases verified by CAG. Experimental results demonstrate that the proposed model obtains an accuracy, sensitivity and specificity of 95.43%, 95.84%, 94.44%, respectively for the detection of CHD. The proposed method can effectively aid clinicians to detect those with normal coronary arteries from those with CHD.

The depth development and widespread application of edge intelligence technology based on the Internet of Things has led to edge-cloud collaboration and related research. In recent years, with the rapid development of the Internet of Things and the formation of super-city groups, the management characteristics of enterprises with multiple manufacturing plants served for headquarters have become increasingly obvious. The problem of order dynamic fluctuations caused by personalized customization requirements has become more prominent, which makes it impossible to do global long-period prediction or real-time short-period response relied solely on the cloud or edge. Therefore, this paper proposes a production system scheduling framework under the edge-cloud collaborative paradigm based on the dynamic fluctuation of orders under these background, and builds an edge-cloud collaborative scheduling model, which guarantees real-time distributed scheduling at the edge. It enabled the cloud to periodically predict the total completion time of production tasks at the headquarters based on the value-added data uploaded by the edge, and to support more accurate and efficient scheduling at the edge based on the prediction results. Finally, an example analysis proved the rationality of the scheduling mechanism and the effectiveness of the scheduling model. The proposed method can provide a certain reference for task scheduling in the edge-cloud collaborative production paradigm.

There is a growing interest in automated diagnosis of coronary artery disease (CAD) with the application of machine learning (ML) methods to the body surface electrocardiograph (ECG). Although prior studies have documented associations of CAD with increased QT variability and ST-T segment abnormalities such as T-wave inversion and ST-segment elevation or depression, their efficacy in automated CAD detection has not been fully investigated. To validate their usefulness, a dataset containing related clinical characteristics and 5-min single-lead ECGs of 107 healthy controls and 93 CAD patients was first constructed. Based on this dataset, simultaneous analyses were then conducted in five scenarios, in which different ML algorithms were applied to classify the two groups with various features derived from the RR and QT interval time-series and ST-T segment waveforms. Compared with utilizing features obtained from the RR interval time-series, better classification results were achieved utilizing that obtained from the QT interval time-series. The classification results were elevated with combining utilization of features derived from both the RR and QT interval time-series. By further fusing features extracted from ST-T segment waveforms, the best performance was achieved with 96.16% accuracy, 95.75% sensitivity, and 96.40% specificity. Based the best performance, an automated CAD detection system was developed with extreme gradient boosting, an ensemble ML algorithm, and the residual neural network, namely, a deep learning method. The results of this study support the potential of information derived from the QT interval time-series and ST-T segment waveforms in ECG-based automated CAD detection.

The retrofitting of existing public buildings to save energy and reduce carbon emissions is a priority for China’s building sector. Accordingly, the Chinese government requires all public buildings to be energy retrofitted based on the Design Standard for Energy Efficiency of Public Buildings GB50189-2015. However, few studies have been conducted to assess the energy efficiency of this design standard in the hot summer and cold winter (HSCW) climate region of China. The aim of this study was therefore to provide sensitivity analysis for the thermal properties of the envelope of a typical public building energy retrofit in China’s HSCW climate region. The results show that the thermal performance of the existing envelope of the sample building was very poor, with heating and cooling energy consumption 18.94% higher than the GB90189-2015 baseline model. It was found that better optimized parameters could reduce the heating and cooling energy load by 28.26% compared with the parameters of the sample building. The findings from this study provide valuable references for local governments and practitioners eager to improve the energy efficiency of existing public buildings in China’s HSCW region.

With increasingly advanced Internet of Things (IoT) technology, the composition of workshop equipment has become more and more complex. Based on this, the rate of system performance degradation and the probability of fault have both increased. Owing to this, not only has the difficulty of constructing the remaining useful life (RUL) model increased but also the improvement in speed of maintenance personnel cannot keep up with the speed of equipment replacement. Therefore, an augmented reality (AR)-assisted prognostics and health management system based on deep learning for IoT-enabled manufacturing is proposed in this paper. Firstly, the feature extraction model based on Convolutional Neural Network-Particle Swarm Optimization (PSO-CNN) is proposed with the purpose of excavating the internal associations in large amounts of production data. Based on this, the high-accuracy RUL prediction is accomplished by Gate Recurrent Unit (GRU)-attention, which can capture the long-term and short-term dependencies of time series and successfully solve the gradient disappearance problem of RNN. Moreover, more attention will be paid to important content with the help of the attention mechanism. Additionally, high-efficiency maintenance guidance and visible instructions can be accomplished by AR. On top of this, the remote expert can offer help when maintenance personnel encounters tough problems. Finally, a real case was implemented in a typical IoT-enabled workshop, which validated the effectiveness of the proposed approach.

Abstract In this paper, we will show that solutions of the initial boundary value problem for the coupled system of Allen–Cahn/Cahn–Hilliard equations continuously depend on parameters of the system, and under some restrictions on the parameters all solutions of the initial boundary value problem for Allen–Cahn/Cahn–Hilliard equations tend to zero with an exponential rate as t→∞ $t\rightarrow\infty$.

Heart rate variability (HRV), systolic period variability (SPV), and diastolic period variability (DPV) have shown potential for assessing cardiac function. It is unknown whether the time delay between the myocardial electrical and mechanical activities (i.e., electromechanical delay, EMD) also possesses variability, and if it does, whether this EMD variability (EMDV) could render additional value for cardiac function assessment. In this paper, we extracted the beat-to-beat EMD from 5-min simultaneously recorded electrocardiogram and phonocardiogram signals in 30 patients with coronary artery disease (CAD) and 30 healthy control subjects, and studied its variability using the same methods as applied for HRV including time-domain measures [mean and standard deviation (SD)], frequency-domain measures [normalized low- and high-frequency (LFn, HFn) and LF/HF)], and nonlinear measures [sample entropy (SampEn), permutation entropy (PE), and dynamical patterns]. In addition, we examined whether the addition of EMDV could offer improved performance for distinguishing between the two groups compared to using the HRV, SPV, and DPV features. Support vector machine with 10-fold cross-validation was used for classification. Results showed increased SD of SPV, increased mean, SD and decreased SampEn of EMDV in CAD patients. Besides, the dynamical pattern analysis showed that CAD patients had significantly increased fluctuated patterns and decreased monotonous patterns in EMDV. In particular, the addition of EMDV indices dramatically increased the classification accuracy from 0.729 based on HRV, SPV, and DPV features to 0.958. Our results suggest promising of the EMDV analysis that could potentially be helpful for detecting CAD noninvasively.

Physiological signal variability can offer important insight into cardiovascular activity and clinical cardiovascular diseases. Heart rate variability (HRV) and pulse transit time variability (PTTV) are two important time series variabilities. However, combining HRV and PTTV can enhance the classification accuracy for heart failure which is unknown. In this paper, a simultaneous analysis of HRV and PTTV performed on both normal subjects and heart failure patients, was carried out, aiming to investigate the improvement of HRV-based heart failure detection with the assistant of PTTV analysis. Forty normal subjects and forty heart failure patients were enrolled. Standard limb lead-II electrocardiogram and radial artery pressure waveforms were synchronously recorded. HRV and PTTV analysis were performed on the acquired RR and PTT time series using the standard time- (MEAN, SDNN, and RMSSD), frequency- (LF, HF, and LF/HF), and non-linear (SD1, SD2, sample entropy, and fuzzy measure entropy) domain indices. The results showed that all HRV indices except MEAN (P = 0.1) and LF/HF (P = 0.9) showed significant differences (all P

Electrocardiogram (ECG) and phonocardiogram (PCG) signals reflect the electrical and mechanical activities of the heart, respectively. Although studies have documented that some abnormalities in ECG and PCG signals are associated with coronary artery disease (CAD), only few researches have combined the two signals for automatic CAD detection. This paper aims to differentiate between CAD and non-CAD groups using simultaneously collected ECG and PCG signals. To entirely exploit the underlying information in these signals, a novel dual-input neural network that integrates the feature extraction and deep learning methods is developed. First, the ECG and PCG features are extracted from multiple domains, and the information gain ratio is used to select important features. On the other hand, the ECG signal and the decomposed PCG signal (at four scales) are concatenated as a five-channel signal. Then, the selected features and the five-channel signal are fed into the proposed network composed of a fully connected model and a deep learning model. The results show that the classification performance of either feature extraction or deep learning is insufficient when using only ECG or PCG signal, and combining the two signals improves the performance. Further, when using the proposed network, the best result is obtained with accuracy, sensitivity, specificity, and G-mean of 95.62%, 98.48%, 89.17%, and 93.69%, respectively. Comparisons with existing studies demonstrate that the proposed network can effectively capture the combined information of ECG and PCG signals for the recognition of CAD.

We consider an initial-boundary-value problem for a class of p-biharmonic parabolic equation with logarithmic nonlinearity in a bounded domain. We prove that if $2

Abstract In this paper, we study a class of fourth-order parabolic equation with the logarithmic nonlinearity. By using the potential well method, we obtain the existence of the unique global weak solution. In addition, we also obtain results of decay and blow-up in the finite time for the weak solution.

As the COVID-19 pandemic continues, people are becoming infected at an alarming rate, individuals are unknowingly spreading disease, and more lives are lost every day. There is an immediate need for a simple, rapid, early and sensitive point-of-care testing for COVID-19 disease. However, current testing approaches do not meet such need. Recently, clustered regularly interspaced short palindromic repeats (CRISPR)-based detection methods have received substantial attention for nucleic acid-based molecular testing due to their simplicity, high sensitivity and specificity. This review explores the various CRISPR-based COVID-19 detection methods and related diagnostic devices. As with any emerging technology, CRISPR/Cas-based nucleic acid testing methods have several challenges that must be overcome for practical applications in clinics and hospitals. More importantly, these detection methods are not limited to COVID-19 but can be applied to detect any type of pathogen, virus, and fungi that may threaten humans, agriculture, and food industries in resource-limited settings. CRISPR/Cas-based detection methods have the potential to become simpler, more reliable, more affordable, and faster in the near future, which is highly important for achieving point-of-care diagnostics.

Relative permeability is a key index in resource exploitation, energy development, environmental monitoring, and other fields. However, the current determination methods of relative permeability are inefficient and invisible without considering wetting order and pore structure characteristics either. In this study, microfluidic experiments were designed for figuring out key factors impacting on the two-phase relative permeability. The optimized intelligent image recognition was established for saturation extraction. The deep learning was conducted for the prediction of two-phase permeability based on the inputs from microfluidic experiments and image recognition and optimized. Results revealed that phase saturation, wetting order, and pore topology were the key factors influencing the two-phase relative permeability, with the importance of 38.22%, 34.84%, and 26.94%, respectively. The deep learning-based relative permeability model performed well, with MSE

Clinically significant portal hypertension (CSPH) is associated with an incremental risk of esophageal varices and overt clinical decompensations. However, hepatic venous pressure gradient (HVPG) measurement, the gold standard for defining CSPH (HVPG≥10 mm Hg) is invasive and therefore not suitable for routine clinical practice. This study aims to develop and validate a radiomics-based model as a noninvasive method for accurate detection of CSPH in cirrhosis.The prospective multicenter diagnostic trial (CHESS1701, ClinicalTrials.gov identifier: NCT03138915) involved 385 patients with cirrhosis from five liver centers in China between August 2016 and September 2017. Patients who had both HVPG measurement and contrast-enhanced CT within 14 days prior to the catheterization were collected. The noninvasive radiomics model, termed rHVPG for CSPH was developed based on CT images in a training cohort consisted of 222 consecutive patients and the diagnostic performance was prospectively assessed in 163 consecutive patients in four external validation cohorts.rHVPG showed a good performance in detection of CSPH with a C-index of 0·849 (95%CI: 0·786–0·911). Application of rHVPG in four external prospective validation cohorts still gave excellent performance with the C-index of 0·889 (95%CI: 0·752–1·000, 0·800 (95%CI: 0·614–0·986), 0·917 (95%CI: 0·772–1·000), and 0·827 (95%CI: 0·618–1·000), respectively. Intraclass correlation coefficients for inter- and intra-observer agreement were 0·92–0·99 and 0·97–0·99, respectively.A radiomics signature was developed and prospectively validated as an accurate method for noninvasive detection of CSPH in cirrhosis. The tool of rHVPG assessment can facilitate the identification of CSPH rapidly when invasive transjugular procedure is not available. Keywords: Portal hypertension, Liver cirrhosis, Hepatic venous pressure gradient, Radiomics, Noninvasive

This paper studies the problem of scheduling a finite set of customers with stochastic service times for a single-server system. The objective is to minimize the waiting time of customers, the idle time of the server, and the lateness of the schedule. Because of the NP-hardness of the problem, the optimal schedule is notoriously hard to derive with reasonable computation times. Therefore, we develop a simulation based K-steps look-ahead selection method which can result in nearly optimal schedules within reasonable computation times. Furthermore, we study the different distributed service times, e.g., Exponential, Weibull and lognormal distribution and the results show that the proposed algorithm can obtain better results than the lag order approximation method proposed by Vink et al. (2015) [Vink, W., Kuiper, A., Kemper, B., & Bhulai, S. (2015). Optimal appointment scheduling in continuous time: The lag order approximation method. European Journal of Operational Research, 240(1), 213-219.]. Finally, a realistic appointment scheduling includes experiments to verify the good performance of the proposed method.

Abstract In this paper, we study the global existence and blow-up of solutions for an isothermal viscous Cahn–Hilliard equation with inertial term, which arises in isothermal fast phase separation processes. Based on the Galerkin method and the compactness theorem, we establish the existence of the global generalized solution. Using a lemma on the ordinary differential inequality of second order, we prove the blow-up of the solution for the initial-boundary problem.

Patch-based medical image registration has been well explored in recent decades. However, the patch fusion process can generate grid-like artifacts along the edge of patches for the following two reasons: firstly, in order to ensure the same size of input and output, zero-padding is used, which causes uncertainty in the edges of the output feature map during the feature extraction process; secondly, the sliding window extraction patch with different strides will result in different degrees of grid-like artifacts. In this paper, we propose an exponential-distance-weighted (EDW) method to remove grid-like artifacts. To consider the uncertainty of predictions near patch edges, we used an exponential function to convert the distance from the point in the overlapping regions to the center point of the patch into a weighting coefficient. This gave lower weights to areas near the patch edges, to decrease the uncertainty predictions. Finally, the dense displacement field was obtained by this EDW weighting method. We used the OASIS-3 dataset to evaluate the performance of our method. The experimental results show that the proposed EDW patch fusion method removed grid-like artifacts and improved the dice similarity coefficient superior to those of several state-of-the-art methods. The proposed fusion method can be used together with any patch-based registration model.

Tick species are considered the second leading vector of human diseases. Different ticks can transmit a variety of pathogens that cause various tick-borne diseases (TBD), such as Lyme disease. Currently, it remains a challenge to diagnose Lyme disease because of its non-specific symptoms. Rapid and accurate identification of tick species plays an important role in predicting potential disease risk for tick-bitten patients, and ensuring timely and effective treatment. Here, we developed, optimized, and tested a smartphone-based deep learning algorithm (termed “TickPhone app”) for tick identification. The deep learning model was trained by more than 2000 tick images and optimized by different parameters, including normal sizes of images, deep learning architectures, image styles, and training–testing dataset distributions. The optimized deep learning model achieved a training accuracy of ~90% and a validation accuracy of ~85%. The TickPhone app was used to identify 31 independent tick species and achieved an accuracy of 95.69%. Such a simple and easy-to-use TickPhone app showed great potential to estimate epidemiology and risk of tick-borne disease, help health care providers better predict potential disease risk for tick-bitten patients, and ultimately enable timely and effective medical treatment for patients.

Coronary heart disease (CHD) is the leading cause of cardiovascular death. This study aimed to propose an effective method for mining cardiac mechano-electric coupling information and to evaluate its ability to distinguish patients with varying degrees of coronary artery stenosis (VDCAS). Five minutes of electrocardiogram and phonocardiogram signals was collected synchronously from 191 VDCAS patients to construct heartbeat interval (RRI)–systolic time interval (STI), RRI–diastolic time interval (DTI), HR-corrected QT interval (QTcI)–STI, QTcI–DTI, Tpeak–Tend interval (TpeI)–STI, TpeI–DTI, Tpe/QT interval (Tpe/QTI)–STI, and Tpe/QTI–DTI series. Then, the cross sample entropy (XSampEn), cross fuzzy entropy (XFuzzyEn), joint distribution entropy (JDistEn), magnitude-squared coherence function, cross power spectral density, and mutual information were applied to evaluate the coupling of the series. Subsequently, support vector machine recursive feature elimination and XGBoost were utilized for feature selection and classification, respectively. Results showed that the joint analysis of XSampEn, XFuzzyEn, and JDistEn had the best ability to distinguish patients with VDCAS. The classification accuracy of severe CHD—mild-to-moderate CHD group, severe CHD—chest pain and normal coronary angiography (CPNCA) group, and mild-to-moderate CHD—CPNCA group were 0.8043, 0.7659, and 0.7500, respectively. The study indicates that the joint analysis of XSampEn, XFuzzyEn, and JDistEn can effectively capture the cardiac mechano-electric coupling information of patients with VDCAS, which can provide valuable information for clinicians to diagnose CHD.

Heart sound signals reflect valuable information about heart condition. Previous studies have suggested that the information contained in single-channel heart sound signals can be used to detect coronary artery disease (CAD). But accuracy based on single-channel heart sound signal is not satisfactory. This paper proposed a method based on multi-domain feature fusion of multi-channel heart sound signals, in which entropy features and cross entropy features are also included. A total of 36 subjects enrolled in the data collection, including 21 CAD patients and 15 non-CAD subjects. For each subject, five-channel heart sound signals were recorded synchronously for 5 min. After data segmentation and quality evaluation, 553 samples were left in the CAD group and 438 samples in the non-CAD group. The time-domain, frequency-domain, entropy, and cross entropy features were extracted. After feature selection, the optimal feature set was fed into the support vector machine for classification. The results showed that from single-channel to multi-channel, the classification accuracy has increased from 78.75% to 86.70%. After adding entropy features and cross entropy features, the classification accuracy continued to increase to 90.92%. The study indicated that the method based on multi-domain feature fusion of multi-channel heart sound signals could provide more information for CAD detection, and entropy features and cross entropy features played an important role in it.

Abstract Background Heart rate fluctuates beat-by-beat asymmetrically which is known as heart rate asymmetry (HRA). It is challenging to assess HRA robustly based on short-term heartbeat interval series. Method An area index (AI) was developed that combines the distance and phase angle information of points in the Poincaré plot. To test its performance, the AI was used to classify subjects with: (i) arrhythmia, and (ii) congestive heart failure, from the corresponding healthy controls. For comparison, the existing Porta’s index (PI), Guzik’s index (GI), and slope index (SI) were calculated. To test the effect of data length, we performed the analyses separately using long-term heartbeat interval series (derived from >3.6-h ECG) and short-term segments (with length of 500 intervals). A second short-term analysis was further carried out on series extracted from 5-min ECG. Results For long-term data, SI showed acceptable performance for both tasks, i.e., for task i p 0.05, d

Image dehazing is a technique to enhance the images acquired in poor weather conditions, such as fog and haze. Existing image dehazing methods are mainly based on dark channel prior. Since the dark channel is not reasonable for sky regions, a sky segmentation and region wised medium transmission based image dehazing method is proposed in this paper. Firstly, sky regions are segmented by quad-tree splitting based feature pixels detection. Then, a medium transmission estimation method for sky regions is proposed based on color characteristic observation of sky regions. The medium transmission is then filtered by an edge preserving guided filter. Finally, based on the estimated medium transmission, the hazed images are restored. Experimental results demonstrate that the performance of the proposed method is better than that of existing methods. The restored image is more natural, especially in the sky regions.

This article we study the Cahn-Hilliard/Allen-Cahn equation with degenerate mobility. Under suitable assumptions on the degenerate mobility and the double well potential, we prove existence of weak solutions, which can be obtained by considering the limits of Cahn-Hilliard/Allen-Cahn equations with non-degenerate mobility.

Myocardial ischemia in patients with coronary artery disease (CAD) leads to imbalanced autonomic control that increases the risk of morbidity and mortality. To systematically examine how autonomic function responds to percutaneous coronary intervention (PCI) treatment, we analyzed data of 27 CAD patients who had admitted for PCI in this pilot study. For each patient, five-minute resting electrocardiogram (ECG) signals were collected before and after the PCI procedure. The time intervals between ECG collection and PCI were both within 24 h. To assess autonomic function, normal sinus RR intervals were extracted and were analyzed quantitatively using traditional linear time- and frequency-domain measures [i.e., standard deviation of the normal-normal intervals (SDNN), the root mean square of successive differences (RMSSD), powers of low frequency (LF) and high frequency (HF) components, LF/HF] and nonlinear entropy measures [i.e., sample entropy (SampEn), distribution entropy (DistEn), and conditional entropy (CE)], as well as graphical metrics derived from Poincaré plot [i.e., Porta’s index (PI), Guzik’s index (GI), slope index (SI) and area index (AI)]. Results showed that after PCI, AI and PI decreased significantly (p < 0.002 and 0.015, respectively) with effect sizes of 0.88 and 0.70 as measured by Cohen’s d static. These changes were independent of sex. The results suggest that graphical AI and PI metrics derived from Poincaré plot of short-term ECG may be potential for sensing the beneficial effect of PCI on cardiovascular autonomic control. Further studies with bigger sample sizes are warranted to verify these observations.

Widely accessible, inexpensive, easy-to-use consumer 3D printers, such as desktop stereolithography (SLA) and fused-deposition modeling (FDM) systems are increasingly employed in prototyping and customizing miniaturized fluidic systems for diagnostics and research. However, these 3D printers are generally limited to printing parts made of only one material type, which limits the functionality of the microfluidic devices without additional assembly and bonding steps. Moreover, mating of different materials requires good sealing in such microfluidic devices. Here, we report methods to print hybrid structures comprising a hard, rigid component (clear polymethacrylate polymer) printed by a low-cost SLA printer, and where the first printed part is accurately mated and adhered to a second, soft, flexible component (thermoplastic polyurethane elastomer) printed by an FDM printer. The prescribed mounting and alignment of the first-printed SLA-printed hard component, and its pre-treatment and heating during the second FDM step, can produce leak-free bonds at material interfaces. To demonstrate the utility of such hybrid 3D-printing, we prototype and test three components: i) finger-actuated pump, ii) quick-connect fluid coupler, and iii) nucleic acid amplification test device with screw-type twist sealing for sample introduction.

Entropy measures that assess signals' complexity have drawn increasing attention recently in biomedical field, as they have shown the ability of capturing unique features that are intrinsic and physiologically meaningful. In this study, we applied entropy analysis to electroencephalogram (EEG) data to examine its performance in epilepsy detection based on short-term EEG, aiming at establishing a short-term analysis protocol with optimal seizure detection performance. Two classification problems were considered, i.e., 1) classifying interictal and ictal EEGs (epileptic group) from normal EEGs; and 2) classifying ictal from interictal EEGs. For each problem, we explored two protocols to analyze the entropy of EEG: i) using a single analytical window with different window lengths, and ii) using an average of multiple windows for each window length. Two entropy methods-fuzzy entropy (FuzzyEn) and distribution entropy (DistEn)-were used that have valid outputs for any given data lengths. We performed feature selection and trained classifiers based on a cross-validation process. The results show that performance of FuzzyEn and DistEn may complement each other and the best performance can be achieved by combining: 1) FuzzyEn of one 5-s window and the averaged DistEn of five 1-s windows for classifying normal from epileptic group (accuracy: 0.93, sensitivity: 0.91, specificity: 0.96); and 2) the averaged FuzzyEn of five 1-s windows and DistEn of one 5-s window for classifying ictal from interictal EEGs (accuracy: 0.91, sensitivity: 0.93, specificity: 0.90). Further studies are warranted to examine whether this proposed short-term analysis procedure can help track the epileptic activities in real time and provide prompt feedback for clinical practices.

In this paper, we prove the existence of attractor for a new mechanochemical model with Neumann boundary conditions on a bounded domain of space dimension n ≤ 3. Based on the regularity estimates for the semigroups and the classical existence theorem of global attractors, we prove that the mechanochemical model possesses a global attractor and Hk attractor.

This paper investigates the fatigue damage and cracking behavior of aluminum alloy 2024-T4 with different levels of prior corrosion. Damage evolution, crack initiation and propagation were experimentally analyzed by digital image correlation, scanning electron microscopy and damage curves. Prior corrosion is shown to cause accelerated damage accumulation, inducing premature fatigue crack initiation, and affecting crack nucleation location, crack orientation and fracture path. For the pre-corrosion condition, although multiple cracks were observed, only one corrosion-initiated primary crack dominates the failure process, in contrast to the plain fatigue cases, where multiple cracks propagated simultaneously leading to final coalescence and fracture. Based on the experimental observations, a mixed-mode fracture model is proposed and shown to successfully predict fatigue crack growth and failure from the single dominant localized corrosion region.

In this article, we study the initial-boundary-value problem for a sixth-order Cahn-Hilliard type equation $$displaylines{ u_t=D^2mu, cr mu=gamma D^4u-a(u)D^2u-frac{a'(u)}2|D u|^2+f(u)+ku_t, }$$ which describes the separation properties of oil-water mixtures, when a substance enforcing the mixing of the phases is added. The optimal control of the sixth order Cahn-Hilliard type equation under boundary condition is given and the existence of optimal solution to the sixth order Cahn-Hilliard type equation is proved.

We consider an initial-boundary problem for a sixth-order thin film equation, which arises in the industrial application of the isolation oxidation of silicon. Relying on some necessary uniform estimates of the approximate solutions, we prove the existence of radial symmetric solutions to this problem in the two-dimensional space. The nonnegativity and the finite speed of propagation of perturbations of solutions are also discussed.

Parasitic helminths such as schistosomes, as well as filarial and soil-transmitted nematodes, are estimated to infect at least a billion people worldwide, with devastating impacts on human health and economic development. Diagnosis and monitoring of infection dynamics and efficacy of treatment depend almost entirely on methods that are inaccurate, labor-intensive, and unreliable. These shortcomings are amplified and take on added significance in mass drug administration programs, where measures of effectiveness depend on accurate monitoring of treatment success (or failure), changes in disease transmission rates, and emergence of possible drug resistance. Here, we adapt isothermal molecular assays such as loop-mediated isothermal amplification (LAMP) to a simple, hand-held, custom-made field-ready microfluidic device that allows sensitive and specific detection of schistosome cell-free nucleic acids in serum and plasma (separated with a point-of-care plasma separator) from Schistosoma mansoni-infected mice. Cell-free S. mansoni DNA was detected with our device without prior extraction from blood. Our chip exhibits high sensitivity (~2 x 10(-17) g/μL), with a positive signal for S. mansoni DNA detectable as early as one week post infection, several weeks before parasite egg production commences. These results indicate that incorporation of isothermal amplification strategies with our chips could represent a strategy for rapid, simple, low-cost diagnosis of both pre-patent and chronic schistosome infections as well as potential monitoring of treatment efficacy.

Polyester fibers are used in various fields, due to their excellent mechanical and chemical stability. However, the lack of conductivity limits their application potential. In order to prepare conductive polyester fibers, silver is one of the most widely used materials to coat the surface of the fibers. This work aimed to prepare silver-coated polyester fibers by a continuous two-step method, which combined the operations of continuous electroless plating and electroplating. Meanwhile, we designed specialized equipment for the continuous plating of silver on the polyester fibers under a dynamic condition. The mechanical property, washability, electrical resistivity, and electrical conductivity of the resultant conductive polyester fibers obtained from different silver-plating conditions were also characterized. The results demonstrated that the conductive fibers prepared by continuous two-step silver plating equipment, had good electrical conductivity with better mechanical properties and washability.

In this paper, we study the initial boundary value problem for a class of metaparabolic equations. We establish the existence of solutions by the energy techniques. Some results on the regularity, blow-up and existence of global attractor are obtained.

Designs and applications of microfluidics-based devices for molecular diagnostics (Nucleic Acid Amplification Tests, NAATs) in infectious disease testing are reviewed, with emphasis on minimally instrumented, point-of-care (POC) tests for resource-limited settings. Microfluidic cartridges (‘chips’) that combine solid-phase nucleic acid extraction; isothermal enzymatic nucleic acid amplification; pre-stored, paraffin-encapsulated lyophilized reagents; and real-time or endpoint optical detection are described. These chips can be used with a companion module for separating plasma from blood through a combined sedimentation-filtration effect. Three reporter types: Fluorescence, colorimetric dyes, and bioluminescence; and a new paradigm for end-point detection based on a diffusion-reaction column are compared. Multiplexing (parallel amplification and detection of multiple targets) is demonstrated. Low-cost detection and added functionality (data analysis, control, communication) can be realized using a cellphone platform with the chip. Some related and similar-purposed approaches by others are surveyed.