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To make up the insufficient computing power of remote medical devices and solve the transmission delay problem, edge computing has been widely studied in academic community. The application of edge computing in E-health makes it more convenient for medical providers to view the patient’electronic health records (EHRs), which not only improves the work efficiency of medical providers, but also provides patients with more personalized treatments. However,there are some security problems in the E-health system, such as EHRs leakage or tampering. Therefore, attribute-based encryption (CP-ABE) is widely used in the access control of EHRS. In order to reduce the computing cost of resource-constrained devices and achieve more flexible fine-grained access control, this paper proposes an effective ABE scheme that outsources part of the computing load to the fog nodes, which safely shares data with lower overheads. It also supports users revocation and attribute revocation. In addition, we combine smart contract and blockchain to share more fine-grained EHRs. The security proof and experimental results show that our scheme has higher efficiency to protect the patients’ EHRs privacy.

It has become a recent trend that large volumes of data are generated, stored, and processed across geographically distributed datacenters. When popular data parallel frameworks, such as MapReduce and Spark, are employed to process such geo-distributed data, optimizing the network transfer in communication stages becomes increasingly crucial to application performance, as the inter-datacenter links have much lower bandwidth than intra-datacenter links. In this article, we focus on exploiting the flexibility of multi-path routing for inter-datacenter flows of data analytic jobs, with the hope of better utilizing inter-datacenter links and thus improve job performance. We design an optimal multi-path routing and scheduling strategy to achieve the best possible network performance for all concurrent jobs, based on our formulation of an optimization problem that can be transformed into an equivalent linear programming (LP) problem to be efficiently solved. As a highlight of this article, we have implemented our proposed algorithm in the controller of an application-layer software-defined inter-datacenter overlay testbed, designed to provide transfer optimization service for Spark jobs. With extensive evaluations of our real-world implementation on Google Cloud, we have shown convincing evidence that our optimal multi-path routing and scheduling strategies have achieved significant improvements in terms of job performance.

Droplet injection methods are widely used in the applications of microelectronic manufacturing, biological engineering, and 3-D printing. This work presents a new jet dispenser driven by a single piezoelectric stack that enables the capability of drop-on-demand patterning under a high working frequency (500 Hz). Due to the special designs of the jet dispenser, a broad range of liquids, whose viscosities span more than four orders of magnitude (21-665 320 cps), can be jetted. Moreover, a coupled Coulomb damping physical model is proposed, which includes an electromechanical and a dynamic model. Both the Coulomb and the fluid-solid damping are considered in the dynamic model. In order to validate the results obtained by using MATLAB/Simulink, the experiments were carried out. The injection performance of this jet dispenser has been tested by employing a self-made jetting platform. The minimum volume of a jetting droplet is about 14.4 nL with liquid wax. The error of volume uniformity among droplets does not exceed 8%, and the error of angle trajectory is about 0.17°. Furthermore, the versatility of the jet dispenser is demonstrated by printing liquid lubricant, food, glue, silver past, and a ceramic slurry in predefined patterns.

In this study, the liquefied petroleum gas (LPG) accident in Wenling, Zhejiang Province, China, on June 13, 2020, was analyzed and simulated. An LPG tank truck overturned and collided with the concrete guardrail; the subsequent explosion of the tank released 25.36 t LPG. Shortly afterward, the LPG tank was shot into the air, and the gas cloud ignited, thereby triggering a fire and vapor cloud explosion (VCE). This accident killed 20 people, injured 175 people, and caused significant property loss. The accident timeline was established based on multiple accident images, and the accident process was discussed in detail. The distribution of tank debris, evaporation and spreading of the LPG pool, dispersion of the LPG gas cloud, and VCE were simulated with the EFFECTS and ALOHA software. The images of the accident scene helped to determine and analyze the accident process. In the particular case in which a tank is shot into the air with a continuous two-phase jet, the actual flight distance of the tank is much greater than the prediction of the debris distribution model. The gas cloud distribution simulated with the SLAB model approximately corresponds to a major part of the severely damaged zone. In this accident, the TNO multi-energy method and ALOHA provide relatively consistent predictions with the actual building damage distribution when models use a specific confined explosive mass.

To fully extract the potential of offshore wind power, the wind farms are built further from mainland and the capacity is growing larger. Low Frequency AC (LFAC) transmission System is a promising solution to the integration of large offshore wind farms in long distance. This paper conducts a comparative economic analysis of LFAC. Compared to 50 Hz High Voltage AC (HVAC) transmission, LFAC can reduce charging current in AC cables to enhance the transmission ability in long distance. Compared to High Voltage DC (HVDC) transmission, LFAC eliminates the large expense of offshore converter station. In addition, the topologies of AC/AC converter in LFAC also influences the economic performance significantly. By designing typical AC/AC converters based on modular multilevel technique in a unified way, this paper performs a full-scale economic comparison in different voltage level, active power and reactive power cases. Furthermore, the economic comparison based on capital cost of HVAC, HVDC and LFAC is comprehensively studied considering different transmission capacity and the AC/AC converter topologies. The economic range of HVAC, LFAC and HVDC application is obtained, and results show that LFAC is suitable for medium distance circumstances (around 80–200 km). The economic range of LFAC expands when power rating increases.

Xylitol has reported to decrease gingival inflammation and nasopharyngeal pneumonia, which indicated that xylitol may have potential application in respiratory diseases. Although some studies have reported the inhalation toxicity of xylitol, however, the longest period tested was only for 14 days. The inhalation toxicity of xylitol is insufficient. This work investigated the potential subacute toxicity of xylitol according to the OECD TG 412. Rats were randomly divided into a control group and different dosage groups (2 g/m3, 3 g/m3, 5 g/m3), and exposed for 6 hours/day, 5 days/week for 28 days. At the end of the exposure or recovery period, clinical signs, mortality, body weight, food consumption, hematology, blood biochemistry, gross pathology, organ weight, and histopathology were examined. Compared with the control group, rats of both sexes in the exposure groups exhibited no significant changes in body weight, organ mass, and food uptake. After the xylitol exposure, aspartate aminotransferase activity in the xylitol group (3 g/m3) was significantly higher than that in the control group, while other blood indicators and pathological changes of liver and the analysis of the recovery group showed no changes, suggesting that xylitol exerted no observable toxic effect on the liver. Finally, other observations including the histopathology of target organs and hematology also showed no alterations. These results indicated that xylitol had no significant inhalation toxicity at doses up to 5 g/m3. These subacute inhalation toxicity results of xylitol showed that its no-observed-adverse-effect concentration (NOAEC) in rats was determined to 5 g/m3.

The coal spontaneous combustion phenomenon seriously affects the safety production of coal mines. Aiming at the problem of complex coal molecular structure and incomplete reaction sequences at present, the mechanisms and thermodynamic parameters of coal spontaneous combustion chain reactions were explored by combining experimental detections and molecular simulations. First, the active groups on the surface of coal were obtained by Fourier transform infrared spectroscopy (FTIR), mainly including methyl (−CH3), methylene (−CH2), methyne (−CH), phenolic hydroxyl (−ArOH), alcohol hydroxyl (−ROH), carboxyl (−COOH), aldehyde (−CHO), and ether (−O−), and the coal molecular models containing functional groups and radicals were established. According to the charge density, electrostatic potential, and frontier orbital theories, the active sites and active bonds were obtained, and a series of reactions were given. The thermodynamic and structural parameters of each reaction were explored. In the chain initiation reaction stage, O2 chemisorption and the self-reaction of radicals play a leading role. In this stage, heat gradually accumulates and various radicals begin to generate, where the intramolecular hydrogen transfer reaction of a peroxide radical (−C–O–O·) can produce the key hydroxyl radical (−O·). In the chain propagation reaction stage, O2 and −O· continuously consume active sites to accelerate the reaction sequences and increase the temperature of coal, and index gases such as CO and CO2 generate, causing the chain cycle reactions to gradually form. The chain termination reaction stage is the formation of stable compounds such as ethers, esters, and quinones, which can inhibit the development of chain reactions. The results can further explain the reaction mechanism of coal spontaneous combustion and provide references for the development and utilization of chemical inhibitors.

Mining knowledge from imbalanced data is challenging due to the uneven distribution of classes and increasing dimensionality of data accumulated from real-life applications. Selecting informative features from imbalanced data is especially important for building an effective learning method. The global redundancy and the effect of imbalanced distribution need to be considered simultaneously. In this study, a feature selection method that considers the imbalanced distribution of classes in data is investigated by embedding the weighted constraint on the majority class into the global redundancy minimization GRM framework. Global redundancy minimization is acquired through an objective function that contains a feature redundancy matrix and feature scores. A new form of regularization to a within-class scatter matrix is first presented, which emphasizes the minority class and replaces the redundancy measurement approach. Then, after employing this new form of a within-class scatter matrix in GRM and taking the between-class distance as the GRM input score, a GRM-based discriminant feature selection algorithm (GRM-DFS) is proposed. Comparison studies on a within-class scatter matrix with different forms of regularization indicate that the proposed form of a within-class scatter matrix is effective when dealing with imbalanced data. Experiments on public imbalanced datasets are performed. The experimental results indicate that GRM-DFS is effective.

In recent years, supply chain risk management has been followed with interest due to the short life cycle of products. How to identify risk indicators can help evaluate risks on supply chains. Commonly adopted methods such as Fuzzy to determine the level of risks have limitations. In this paper, a framework of supply chain risk evaluation is first proposed and risk indicators are identified by theoretical surveys from 35 keywords and empirical analysis from 448 questionnaires. Moreover, both linguistic risk assessment model and Cloud model are used to evaluate risks of supply chain. The Cloud model evaluation results are between general risk and high risk but closer to high risk. In addition, Cloud expected value of risk is 6.54 which is within the high-risk range, and evaluation results are also high risk. It is shown that when the weights are the same, the cloud model can determine the priority of risk indicators, and reflect volatility and randomness comparing with other evaluation methods.

To compare advanced non-parallel transmission zoomed diffusion-weighted imaging (nonPTX zoom-DWI) to conventional DWI (conv-DWI) for the assessment of prostate cancer (PCa). This retrospective study included 98 patients who underwent conv-DWI, nonPTX zoom-DWI, and T2-weighted imaging of the prostate. The image qualities of the two DWI sets, including the distortion of the prostate and the existence of artifacts, were evaluated. To compare the overall PCa and clinically important PCa (ciPCa) detection ability between the sets, lesions were scored using the Prostate Imaging Reporting and Data System (PI-RADS) version 2. Apparent diffusion coefficient (ADC) values of the lesions were also measured and compared. The Mann–Whitney U test was used to compare continuous variables, and the χ2 test was used to compare categorical variables. Two-sided P values of

To the best of our knowledge, this is the largest clinical retrospective study in AN‐SAH patients, and is the first time to establish accurate predictive models paired with bleeding pattern. Background Angiogram‐negative subarachnoid hemorrhage (AN‐SAH) has a definite incidence of delayed cerebral ischemia (DCI) and poor clinical outcomes. The purpose is to screen independent factors and establish a nomogram to guide the clinical therapy and assess post‐discharge prognosis. Methods We identified 273 consecutive patients referred to our institute from 2013 to 2018 for AN‐SAH. A nomogram to predict poor outcomes was formulated based on the multivariable models of independent risk factors. The accuracy and discrimination of nomograms were determined in training and internal validation cohorts. Results The overall poor outcome rates of AN‐SAH were 14.3% and 8.7% at 3 months and 12 months, respectively. In addition, perimesencephalic AN‐SAH (PAN‐SAH) presented with a more unfavorable prognosis compared with non‐perimesencephalic AN‐SAH (NPAN‐SAH). The clinical prognosis was associated with the World Federation of Neurosurgical Societies scale (WFNS) (odds ratio, 3.82 [95% CI, 1.15‐12.67] for 3‐month outcome; and odds ratio, 31.69 [95% CI, 3.65‐275.43] for 12‐month outcome), Subarachnoid hemorrhage Early Brain Edema Score (SEBES) (odds ratio, 10.39 [95% CI, 1.98‐54.64] for 3‐month outcome; odds ratio, 10.01 [95% CI, 1.87‐53.73] for 12‐month outcome), and symptomatic vasospasm (odds ratio, 3.16 [95% CI, 1.03‐9.70] for 3‐month outcome; odds ratio, 5.15 [95% CI, 1.34‐19.85] for 12‐month outcome). The nomogram was constructed based on the above features, which represented great predictive value in clinical outcomes. Conclusions Symptomatic vasospasm, high WFNS, cerebral edema, and NPAN‐SAH after hemorrhage were associated with poor outcome of AN‐SAH. The nomogram with WFNS (3‐5), SEBES (3‐4), vasospasm, and NPAN‐SAH represented a practical approach to provide individualized risk assessment for AN‐SAH patients., To the best of our knowledge, this is the largest clinical retrospective study in AN‐SAH, and is the first time to establish accurate predictive models paired with bleeding pattern.

In nozzle-based three-dimensional printing, transitions are movements of the printing nozzle from a path endpoint to a path start-point. These transitions diminish printing quality by causing strings. A method to minimize the number of transitions based on direction-parallel line segments is presented in this paper. The endpoints of line segments were considered to be “cities” in converting the problem of minimizing the number of transitions into a traveling salesman problem (TSP). A genetic algorithm solver was developed by designing an oriented mutation method for the TSP. Compared with other algorithms for solving the TSP, our algorithm generates paths with fewer transitions. The algorithm was tested using several fused-deposition-modeling and weld arc additive manufacture examples to confirm that the generated paths were reasonable.

The foam generated in the CO2 flooding-produced fluids causes considerable oil and gas treatment problems, especially in the separation process. The foam in the gas-liquid separator is detrimental ...

Tazobactam is a β-lactamase inhibitor. In this work, a combination of continuous flow and batch experiments for the synthesis of tazobactam has been developed. The first three steps and the prepara...

Illness as the result of ingesting bacterially contaminated foodstuffs represents a significant annual loss of human quality of life and economic impact globally. Significant research investment has recently been made in developing new materials that can be used to construct food contacting tools and surfaces that might minimize the risk of cross-contamination of bacteria from one food item to another. This is done to mitigate the spread of bacterial contamination and resultant foodborne illness. Internet-based literature search tools such as Web of Science, Google Scholar, and Scopus were utilized to investigate publishing trends within the last 10 years related to the development of antimicrobial and antifouling surfaces with potential use in food processing applications. Technologies investigated were categorized into four major groups: antimicrobial agent-releasing coatings, contact-based antimicrobial coatings, superhydrophobic antifouling coatings, and repulsion-based antifouling coatings. The advantages for each group and technical challenges remaining before wide-scale implementation were compared. A diverse array of emerging antimicrobial and antifouling technologies were identified, designed to suit a wide range of food contact applications. Although each poses distinct and promising advantages, significant further research investment will likely be required to reliably produce effective materials economically and safely enough to equip large-scale operations such as farms, food processing facilities, and kitchens.

Stroke is a kind of cerebrovascular disease that heavily damages people’s life and health. The quantitative analysis of brain MRI images plays an important role in the diagnosis and treatment of stroke. Deep neural networks with massive data learning ability supply a powerful tool for lesion detection. In order to study the property of the stroke lesions and complete intelligent automatic detection, we collaborated with two authoritative hospitals and collected 5,668 brain MRI images of 300 ischemic stroke patients. All the lesion regions in the images were accurately labeled by professional doctors to ensure the authority and effectiveness of the data. Three categories of deep learning object detection networks including Faster R-CNN, YOLOV3, and SSD are applied to implement automatic lesion detection with the best precision of 89.77%. Meanwhile, statistical analysis of the locations, shapes of the lesions, and possible related diseases is conducted with valid conclusions. The research contributes to the intelligent assisted diagnosis and prevention and treatment of ischemic stroke.

Background: Time restricted feeding (TRF) involves deliberately restricting the times during which energy is ingested. Preliminary findings suggest that 8–10-h TRF improves sleep. However, the effects of shorter TRF windows (4–6 h) on sleep, remain unknown. Aims: This study compared the effects of 4-h versus 6-h TRF on sleep quality, duration, insomnia severity and the risk of obstructive sleep apnea. Methods: Adults with obesity ( n = 49) were randomized into one of three groups: 4-h TRF (eating only between 3 and 7 p.m.), 6-h TRF (eating only between 1 and 7 p.m.), or a control group (no meal timing restrictions) for 8 weeks. Results: After 8 weeks, body weight decreased ( p < 0.001) similarly by 4-h TRF (–3.9 ± 0.4 kg) and 6-h TRF (–3.4 ± 0.4 kg), versus controls. Sleep quality, measured by the Pittsburgh Sleep Quality Index (PSQI), did not change by 4-h TRF (baseline: 5.9 ± 0.7; week 8: 4.8 ± 0.6) or 6-h TRF (baseline: 6.4 ± 0.8; week 8: 5.3 ± 0.9), versus controls. Wake time, bedtime, sleep duration and sleep onset latency also remained unchanged. Insomnia severity did not change by 4-h TRF (baseline: 4.4 ± 1.0; week 8: 4.7 ± 0.9) or 6-h TRF (baseline: 8.3 ± 1.2; week 8: 5.5 ± 1.1), versus controls. Percent of participants reporting obstructive sleep apnea symptoms did not change by 4-h TRF (baseline: 44%; week 8: 25%) or 6-h TRF (baseline: 47%; week 8: 20%), versus controls. Conclusion: These findings suggest that 4- and 6-h TRF have no effect on sleep quality, duration, insomnia severity, or the risk of obstructive sleep apnea.

As a promising additive manufacturing method, direct energy deposition has been widely used to fabricate complex thin-walled parts. However, some defects such as uneven layer surface and excessive build-ups always occur in this process. In this study, a high-fidelity model adopting a novel laser/powder source is developed to simulate the detailed multi-layer deposition process. With layer surfaces being tracked in real-time, laser source model incorporates local incidence angle and defocusing amount to adjust absorbed energy and beam radius on the track surfaces. The powder catchment efficiency varies with the defocusing amount timely. The simulations demonstrate that build-ups are mainly induced by unstable linear powder feed rate and worsen due to laser/powder defocusing and accumulated heat. The layer surface unevenness results from powder positive defocusing, while molten pool dynamics, laser positive defocusing and accumulated heat plays a supplementary role. Eliminating methods are proposed based on the simulations and validated by experiments.

Mine water inrush seriously threatens the safety of coal mine production. Quick and accurate identification of mine water inrush sources is of great significance to preventing mine water hazards. This paper combined partial least squares-discriminate analysis (PLS-DA) with inrush water chemical composition to identify the source of water inrush from multiple aquifers in mines. The Renlou Coal Mine in the Linhuan mining area was selected for this study, and seven conventional water chemical compositions from 54 water samples in three aquifers were collected and tested, of which 45 water samples were used to establish the PLS-DA discriminant model, and nine were used to test the prediction effect. To improve model accuracy and predictive ability, hierarchical clustering analysis method was used to eliminate seven unqualified water samples to reduce the errors caused by improper data. PCA and PLS-DA methods were used to analyze and process the remaining water sample data, and on the basis of PCA analysis, the remaining 38 water samples were used to establish the PLS-DA discriminant model. The model was validated using permutation and external prediction tests. The research shows the following results: (1) Both PCA and PLS-DA methods can distinguish water samples from three different water sources, but the classification effect of PLS-DA was better than PCA because it can strengthen the difference of water chemical composition between different water sources. (2) The correct discrimination rate of the PLS-DA discriminant model was as high as 100%, and permutation tests showed that the model was not overfit. External validation found that the model had good stability and discrimination. (3) HCO3- and total dissolved solids (TDS) were the most important differential marker compositions that affected the discrimination results based on Variable Importance for the Projection (VIP) scores. The discriminant model established in this study combined the advantages of principal component analysis and multiple regression analysis, providing a new method for accurately identifying the sources of water inrush in mines.

With the flourishing of location based social networks, posting check-ins has become a common practice to document one's daily life. Users usually do not consider check-in records as violations of their privacy. However, through analyzing two real-world check-in datasets, our study shows that check-in records are vulnerable to linkage attacks. Specifically, adversary is able to uniquely re-identify over 52 $\sim$ ∼ 66 percent users in other anonymous mobility datasets and 60 $\sim$ ∼ 80 percent users have more than 60 percent probability leaking unreported mobility records. In addition, we further demonstrate that the privacy sensitivity of check-in records can be more accurately measured by including the information of additional mobility data compared with only looking at check-ins. Based on this observation, we design a partition-and-group framework to integrate the information of check-ins and additional mobility data to attain a novel privacy criterion— $k^{\tau, l}$ k τ , l -anonymity . It ensures adversaries with arbitrary background knowledge cannot use check-ins to re-identify users in other anonymous datasets or learning unreported mobility records. The proposed framework achieves favorable performance against state-of-art baseline in terms of improving check-in utility by 24 $\sim$ ∼ 57 percent while providing stronger privacy guarantee at the same time. We believe this study will open a new angle in attaining both privacy-preserving and useful check-in services.

BACKGROUND AND AIMS: This study examined if the weight loss and metabolic benefits of alternate day fasting (ADF) varies according to sex and menopausal status in adults with obesity. METHODS AND RESULTS: This secondary analysis pooled the data of men and women (n = 75) who participated in three 12-week ADF studies (500 kcal fast day; alternated with an ad libitum intake feast day). Body weight decreased in premenopausal women (−4.6 ± 3.2%), postmenopausal women (−6.5 ± 3.2%) and men (−6.2 ± 4.4%) (main effect of time, P < 0.001), with no difference between groups (no group × time interaction). Energy intake on fast days was higher than prescribed in all groups (~400-500 excess kcal consumed), with no differences between groups. Fat mass, lean mass, fasting insulin, and insulin resistance, and blood pressure decreased similarly in all groups (main effect of time, P < 0.05 for all comparisons). LDL cholesterol decreased more in postmenopausal versus premenopausal women (group × time interaction, P = 0.01). Fasting glucose, HDL cholesterol, and triglycerides remained unchanged in all groups. CONCLUSION: These findings suggest that the weight loss and metabolic benefits of ADF do not generally vary according to sex or menopausal status in adults with obesity.

Electric vehicles (EVs) are considered a promising alternative to conventional vehicles (CVs) to alleviate the oil crisis and reduce urban air pollution and carbon emissions. Consumers usually focus on the tangible cost when choosing an EV or CV but overlook the time cost for restricting purchase or driving and the environmental cost from gas emissions, falling to have a comprehensive understanding of the economic competitiveness of CVs and EVs. In this study, a life cycle cost model for vehicles is conducted to express traffic and environmental policies in monetary terms, which are called intangible cost and external cost, respectively. Battery electric vehicles (BEVs), fuel cell electric vehicles (FCEVs), and CVs are compared in four first-tier, four new first-tier, and 4 s-tier and below cities in China. The comparison shows that BEVs and FCEVs in most cities are incomparable with CVs in terms of tangible cost. However, the prominent traffic and environmental policies in first-tier cities, especially in Beijing and Shanghai, greatly increase the intangible and external costs of CVs, making consumers more inclined to purchase BEVs and FCEVs. The main policy benefits of BEVs and FCEVs come from three aspects: government subsidies, purchase and driving restrictions, and environmental taxes. With the predictable reduction in government subsidies, traffic and environmental policies present important factors influencing the competitiveness of BEVs and FCEVs. In first-tier cities, BEVs and FCEVs already have a competitive foundation for large-scale promotion. In new first-tier and second-tier and below cities, stricter traffic and environmental policies need to be formulated to offset the negative impact of the reduction in government subsidies on the competitiveness of BEVs and FCEVs. Additionally, a sensitivity analysis reveals that increasing the mileage and reducing fuel prices can significantly improve the competitiveness of BEVs and FCEVs, respectively.

Rolling bearings are one of the most vulnerable parts in rotating machines. This paper presents a novel approach to identify the rolling bearings fault based on variational mode decomposition (VMD) and Mahalanobis distance support vector machine (MDSVM). In this work, since the original vibration signal contains a lot of noise, we use wavelet threshold method to denoise the original vibration signal. The vibration signals are generally non-linear, to extract feature, VMD has been employed to reconstruct signals. When raw signals are decomposed by VMD, according to the center frequency of each decomposed mode, the number of modes is selected. Then we calculate the sample entropy of the decomposed modal component, which is considered as the feature and input of support vector machine (SVM). The Euclidean distance is usually used in the calculation of the Gaussian kernel function of the SVM, which cannot measure the distance between two samples accurately, so we combine the Mahalanobis distance with SVM, construct a Gaussian function kernel based on Mahalanobis distance, and propose a classifier model based on Mahalanobis distance Gaussian function kernel. The model integrates the parameter solutions of the Mahalanobis distance function and the support vector machine into the same framework, which makes full use of the advantages of both and makes it easier to get the solution of the parameters. Finally, all feature vectors are utilized to train improved SVM, with which the fault modes of rolling bearings are identified. The experimental results show that the proposed method has better diagnosing performance.

In view of the frequent occurrence of gas accidents in coal mines, the mechanism of oxygen-containing functional groups (OCFGs) in Danhou lignite adsorbing gas was studied by experiment and simulation. Elemental analysis, X-ray photoelectron spectroscopy (XPS), solid-state 13C nuclear magnetic resonance spectroscopy (13C-NMR), and adsorption experiment of CH4 were applied to establish the macromolecular model of Danhou lignite. Then, molecular mechanics (MM) and molecular dynamics (MD) were utilized to optimize the coal macromolecular model, and the density of coal was determined via adding periodic boundary conditions. The mechanism of gas adsorption by OCFGs was studied by grand canonical Monte Carlo (GCMC) and density functional theory (DFT). The results showed that the aromatic structures mostly exist in the form of pyrenes; the structure of aliphatic carbons are mostly methylene and methine groups; the alkanes are mostly long chains; oxygen atoms are mainly in the form of hydroxyl groups and ether groups; nitrogen atoms are mainly in the form of pyridines; and the density of Danhou lignite is 1.25 g/cm3. The isotherm adsorption curve and Langmuir adsorption curve have a good fit, a single coal molecule reaches saturation after absorbing four CH4 molecules, and the error between experiment and simulation is small. The results of DFT calculation showed that the adsorption of CH4 by OCFGs is affected by the adsorption positions and adsorption directions. Due to CH4 molecules are affected by different electrostatic forces, the adsorption capacities of OCFGs are different, and the order is carbonyl groups > ether bonds > hydroxyl groups > carboxyl groups. The results can be used for reference in the prevention and control of coal and gas outburst.

With the maturity of modern science and technology, such as networks and computers, Internet of Things has been widely used in various fields of industry, opening up a new situation for the development of the industry and creating a broader development platform. This paper systematically analyzes the characteristics and changes of the commodity structure, regional structure, market structure, and main structure of export management of agricultural products. The proposed algorithm uses computer technology, network technology, and remote communication technology, electronic, digital, and network—the entire business process of business. The empirical analysis shows that the structure of agricultural product export trade reflects the endowment and comparative advantage of agricultural resources in China. The proposed blockchain technology supports and guides agricultural export enterprises to develop their own brands, strengthen the quality and safety management of agricultural products, and innovate and expand the policy support system for optimizing the structure of agricultural product export trade. By optimizing the export mode of agricultural trade, we can achieve the purpose of increasing the export volume of agricultural products and the total value of trade exports. The experimental results show the effectiveness of the proposed algorithm, which can greatly improve the volume of trade exports and the total value of trade exports.

Step emulsification (SE) devices coupled with parallel generation nozzles are widely used in the production of large-scale monodisperse droplets, especially for droplet-based digital polymerase chain reaction (ddPCR) analysis. Although current ddPCR systems based on the SE method can provide a fully enclosed ddPCR scheme, high demands on chip fabrication and system control will increase testing costs and reduce its flexibility in ddPCR analysis. In this study, a compact SE device, integrating a smart SE chip into a reaction tube, was developed to prepare large-scale water-in-fluorinated-oil droplets for ddPCR analysis. The SE chip contained dozens of droplet-generation nozzles. By adjusting the nozzle height of the SE chip, monodisperse droplets in a picolitre to nanolitre vloume could be prepared at a production rate of tens to hundreds of microlitres per minute. Subsequently, we utilized such an integrated SE device to prepare monodisperse droplets for ddPCR experiments. The volume of PCR reagent and the number of droplets could be flexibly adjusted according to the requirements of the ddPCR analysis. The quantitative results showed that emulsions prepared by the SE device could achieve ddPCR detection with high accuracy, good repeatability, and an adaptive dynamic range, which also demonstrated the robustness and reliability of such devices in the droplet preparation. Thus, this compact SE device provides an inexpensive, flexible, and simplified droplet preparation method for digital PCR quantitative analysis.

To build a new green energy society, energy storage technology is becoming increasingly important and infiltrating into almost every aspect of our daily life. Lithium-ion batteries have been widely used because they have no memory effect and a high energy density. However, it is not clear whether the limited lithium resources can support such an energy system during its march towards our goal of sustainable development. To address this issue, sodium-ion and potassium-ion batteries are considered to be suitable alternative power sources because of their much more abundant reserves on our planet. The successful application of these two new battery systems requires the development of advanced electrode materials. Although many materials with promising performance have been reported, further improvements are still needed to achieve a high energy density and a long cycle life to meet the requirement for commercialization. Instead of categorizing different candidate anode materials according to their nanostructures or compositions, in this review, we aim to provide a comprehensive understanding of different effective strategies to address the specific drawbacks in these materials, including the construction of heterojunctions, the introduction of diffusion channels and heteroatom doping to enhance the ionic and electronic conductivity, and the strategic design of an unique structure or chemical composition to improve the stability of the electrode. We also summarize some challenges associated with these strategies, which need to be addressed in order to achieve a more intensive application of sodium-ion and potassium-ion batteries.

Solar-thermal desalination by interfacial evaporation that leverages abundant solar energy to convert saline water into clean freshwater has promised an exciting alternative to meet the grand challenges of water scarcity. Among a host of novel materials developed for efficient solar-thermal desalination, carbon-based materials are arguably the most competitive candidates for practical applications due to their superior biocompatibility. However, conventional carbon-based materials have the limitation of an unsatisfactory vapor generation performance under 1 sun illumination. Here, we report a hierarchical carbon nanostructure that is based on rational design of artificial hollow frameworks on natural cotton to achieve very favorable evaporation properties. Hierarchical water pathways of the evaporator rapidly replenish water as it evaporates. Water evaporation is facilitated by a thin-film effect in the hollow frameworks. The hollow carbonized cotton microfibers (HCMFs) evaporated water with an extremely high rate of 3.2 kg m−2 h−1 under 1 sun illumination, and exhibited outstanding solar desalination performance with excellent stability and durability. This new material design provides a novel approach to solar vapor generation for a broad range of practical applications.

Large fluctuations may occur on the energy supply and the load sides when large-scale renewable energies are integrated, leading to great challenges in power systems. The renewable power curtailment is especially numerous in the integrated electricity-heat energy system (IEHES) on account of electricity-heat coupling. The flexible resources (FRs) on both the energy supply and load sides are introduced into the optimal dispatch of the IEHES and further modeled to alleviate the renewable fluctuations in this paper. On the energy supply side, three kinds of FRs based on electricity-heat coordination are modeled and discussed. On the load side, the shiftable electricity demand resource is characterized. On this basis, the solution for FRs participating in IEHES dispatch is given, with goals of maximizing the renewable penetration ratio and lowering operation costs. Two scenarios are performed, and the results indicate that the proposed optimal dispatch strategy can effectively reduce the renewable energy curtailment and improve the flexibility of the IEHES. The contribution degrees of different FRs for renewable integration are also explored.

Aims Alcohol abuse induces multiple neuropathology and causes global burden to human health. Prefrontal cortex (PFC) is one of the most susceptible regions to alcohol-induced neuropathology. However, precise mechanisms underlying these effects on PFC remain to be elucidated. Herein, we investigated whether RIP1/RIP3/MLKL-mediated necroptosis was involved in the alcohol-induced PFC injury, and explored the effect that cannabinoid receptors (CBRs) exerted on the neurotoxicity of alcohol. Methods In this study, dynamic development of neuronal necroptosis in the PFC region was monitored after 95% (v/v) alcohol vapor administration for 15 and 30 days, respectively. Selective CBRs agonists or inverse agonists were pretreated according to the experimental design. All the PFC tissues were isolated and further examined by biochemical and histopathological analyses. Results It was found that chronic alcohol exposure increased the protein level of MLKL and also the phosphorylated levels of RIP1, RIP3 and MLKL in a time-dependent manner, all of which indicated the activation of necroptosis signaling. Particularly, compared to astrocytes, neurons from the PFC showed more prototypical necrotic morphology in response to alcohol insults. In parallel, an increased protein level of CB1R was also found after 15 and 30 days alcohol exposure. Administration of specific inverse agonists of CB1R (AM251 and AM281), but not its agonists or CB2R modulators, significantly alleviated the RIP1/RIP3/MLKL-mediated neuronal necroptosis. Conclusion We reported the involvement of RIP1/RIP3/MLKL-mediated necroptosis in alcohol-induced PFC neurotoxicity, and identified CB1R as a critical regulator of neuronal necroptosis that enhanced our understanding of alcohol-induced neuropathology in the PFC.

This paper studies the shear performance of the connection with the external stiffening ring between the square steel tubular column and unequal-depth steel beams. Two specimens of interior column connections were tested under low cyclic loading. The deformation characteristics and failure modes exhibited by the test phenomena can be summarized as: (1) two specimens all exhibited shear deformation in steel tube web of the panel zone and (2) weld fracture in the panel zone and plastic hinge failure at beam end were observed. Besides, load-displacement behaviors and strain distributions have been also discussed. The nonlinear finite element models were developed to verify the test results. Comparative analyses of the bearing capacity, failure mode, and load-paths between the equal-depth and unequal-depth beam models have been carried out.

Background Since 2017, the number of magnet ingestion cases has increased year over year in our hospital. Almost all of the ingested magnetic foreign bodies were magnetic beads, and most of the patients experienced intestinal perforations, causing substantial damage. Aim To summarize our experience with surgical treatment of multiple magnet ingestion in children. Methods The data for general surgeries were collected from January 2010 to April 2020, and the clinical characteristics, treatment methods, and outcomes were summarized and analyzed. Several typical cases were selected and discussed. Results Fifty-six cases of ingested magnetic foreign bodies were collected, of which 47 were magnetic beads. The average patient age was 4.7 ± 3.0 years old. The number of ingested magnetic foreign bodies ranged from 2 to 73. There were 26 cases with symptoms at the time of admission, including two cases of shock. Thirteen patients were discharged successfully following conservative treatment and 43 were treated by surgery. Laparotomy was the main method of operation. Laparoscopy was used in four cases, of which three were converted to open surgery, and one was treated successfully using surgery through the navel. Postoperative complications occurred in seven cases, incision infections were observed in six, and adhesive ileus was observed in one. Conclusion Clinicians need to summarize their experiences with treating magnetic foreign body ingestions in detail and carry out clinical research to reduce the damage to children.

The object detection in UAV application is a challenging task due to the diversity of target scales, variation of views, and complex backgrounds. To solve several challenges, including dense objects, objects with arbitrary orientation, and diversity of aspect ratios, this article proposes an end-to-end object detection method based on the convolutional neural network. In this article, the feature extraction performance is enhanced by utilizing a deep residual neural network. Multiscale feature maps are obtained through fusion with different convolutional layers, thus combining the high-level semantic information and low-level detail information. A rotation region proposal network is adopted to generate rotated regions, which makes the bounding box sensitive to dense objects in aerial images. Meanwhile, the RoIAlign is used and a convolution layer is appended in the classification stage, and focal loss is used in the classification stage. The proposed method focuses on arbitrary-oriented and dense objects in UAV images. After a comprehensive evaluation with several state-of-the-art object detection algorithms, the proposed method is proved to be effective to detect multiclass artificial objects in aerial images. Extensive experiments are conducted on the DOTA, VEDAI, and the VisDrone UAV image datasets, which demonstrate that the proposed method can obtain discriminative features through the improved multiscale feature extraction and the rotating region network. The results on the above datasets show that our method obtains gains in mean average precision compared with several state-of-the-art methods.

To study the adsorption and diffusion behavior of methane on coal macromolecules under different moisture contents, the macromolecular model of coking coal was established through elemental analysi...

A transversal coordinate frame based on a reference ellipsoid is proposed to solve problems associated with strapdown inertial navigation systems (SINSs) in polar regions. However, a commercial-grade SINS can still suffer from cumulative errors even if the transversal coordinate frame is used. To solve this problem, complementary sensors can be used to constitute an integrated navigation system. Because SINS navigational quantities are transformed to the transversal coordinate frame in polar regions, we analyze the performance of a global positioning system and a celestial navigation system in this paper when conducting the coordinate transformation. The derivation and evaluation results show how we reconcile the benefits in SINSs and the negative effects in attitude sensors when adopting the transversal coordinate frame.

We firstly fabricated CeO2 ceramic foams with tunable structure by using particle-stabilized bubbles as template, and designed their interconnected porous structure and even hierarchically porous structure, which endows them the penetration ability for gases or liquids. Hollow spheres with single-layer shell were innovatively selected as the pore-former, which allows for the formation of open pores on the cell wall. Moreover, 3D printing CeO2 particle-stabilized foams are realized with the aid of direct ink writing, which enables the production of CeO2 ceramic foams with complex shape. Highly porous CeO2 with relatively high compressive strength have been fabricated, the porosity of which varies from 81.0% to 92.0% while their excellent compressive strength ranges from 5.0 MPa to 20.0 MPa. Attributed to the hierarchical porous structure, uniform pore size distribution as well as densely assembled cell wall, 3D printing CeO2 ceramic foams possess superior mechanical performance at high porosity level.

Tourists’ environmentally responsible behavior (TERB) is crucial to the destination’s natural environment. Drawing on self-congruity theory, this study extends the existing literature on the underl...

To investigate the value of cardiovascular magnetic resonance tissue-tracking (CMR-TT) imaging in the differentiation of subendocardial and transmural myocardial infarction (MI) and determine whether strain parameters are enable to detect adverse left ventricular (LV) remodeling.Global peak circumferential, longitudinal, and radial strains (GPCS, GPLS, GPRS) and segmental peak circumferential, longitudinal, and radial strains (PCS, PLS, PRS) in accordance with the 16-segment model were all derived. All positive segments were divided into two groups according to transmural degree. All patients were dichotomized in accordance with the existence of LV remodeling, which was defined as infarct size (IS) > 24%.Patients with MI showed significant lower GPRS, GPCS, and GPLS than the control group (16.41% ± 8.92%, -8.77%± 3.51%, -7.54% ± 2.43% versus 32.41% ± 12.99%, -14.92% ± 3.32%, -11.50% ± 2.51%). Lower PRS [3.25% (-5.57, 7.835) versus 19.94% (12.50, 30.75), P < 0.001] and PCS (-3.81 ± 4.60% versus -8.97± 4.43%, P < 0.001) can be found in transmural infarcted segments compared to subendocardial infarcted segments. PLS between transmural and subendocardial infarcted segments (-4.03% ± 4.88% versus -4.34% ± 4.98%), without however statistical significance (P = 0.523). The optimal cutoff value for PRS in the discriminate diagnosis of MI was 8.97% with a sensitivity of 81.8% and specificity of 98.0%. The optimal cutoff value for PCS was -7.56% with a sensitivity of 83.6% and specificity of 72.1%. Receiver operating characteristic (ROC) analysis revealed an optimal cutoff GPRS of 15.45%, and GPCS of -6.72% yielded high diagnostic accuracy in the identification of remodeling, which was higher than left ventricular ejection fraction (LVEF).CMR-TT can differentiate between subendocardial and transmural infarction and detect LV remodeling, and the diagnostic value was superior to conventional functional parameters.

Objective The clinical outcomes, radiological parameters, complication rate and the cost of implants in two-level ACDF with and without screws in the intervening segment were compared. Methods A retrospective study of 68 patients who underwent 2-level ACDF from January 2014 to June 2016 was performed. The patients were divided into two groups: ACDF with screws in the intervening vertebra and those without screws in the intervening vertebra. Perioperative factors, clinical outcomes, postoperative complications, radiological parameters, and cost of the implants were evaluated in both groups. Results No statistical differences in clinical outcomes, operative blood loss, hospital stay, restoration of cervical lordosis or segment height, postoperative complications, and fusion rate were found between the ACDF (middle vertebra with screw) and ACDF (middle vertebra without screw) groups (P > 0.05). But the operative time of added screw placement and cost of implants in the ACDF (middle vertebra without screw) group were significantly less than the ACDF (middle vertebra with screw) group (P Conclusion Two kinds of screw placement with ACDF were found to be similar in terms of clinical outcomes. However, ACDF (middle vertebra without screw) was found to be superior to ACDF (middle vertebra with screw) in terms of the screw placement time and cost of implants.

Exposed aggregate is a typical feature of the abrasion erosion in stilling basin slabs concrete surface. Although a variety of underwater robots are designed for inspection, the exposed aggregate detection for identifying abrasion is often done by manual work. The scarcity of image samples, large differences in aggregate size, color and shape are the main difficulties in automatic detection. To address this problem, an improved Attention U-Net deep fully convolutional network-based detection method was proposed. To realize this method, underwater images in site were captured via a self-developed operated underwater robot. Through randomly separating and the cropping of the 128 underwater images, the 512×512 pixels images dataset was built according to the ratio of 8:1:1, including 408 training images, 52 validation images and 52 test images. After the data augmentation, loss function and the optimizer were carefully designed and selected, the proposed Attention U-Net architecture was evaluated on this dataset. For comparative research, the full convolution network (FCN) and U-Net network were trained with the same training and validation dataset. The performance comparison on the test dataset showed that the Attention U-Net architecture has better detection accuracy.

The development of digital pathology and progression of state‐of‐the‐art algorithms for computer vision have led to increasing interest in the use of artificial intelligence (AI), especially deep learning (DL)‐based AI, in tumor pathology. The DL‐based algorithms have been developed to conduct all kinds of work involved in tumor pathology, including tumor diagnosis, subtyping, grading, staging, and prognostic prediction, as well as the identification of pathological features, biomarkers and genetic changes. The applications of AI in pathology not only contribute to improve diagnostic accuracy and objectivity but also reduce the workload of pathologists and subsequently enable them to spend additional time on high‐level decision‐making tasks. In addition, AI is useful for pathologists to meet the requirements of precision oncology. However, there are still some challenges relating to the implementation of AI, including the issues of algorithm validation and interpretability, computing systems, the unbelieving attitude of pathologists, clinicians and patients, as well as regulators and reimbursements. Herein, we present an overview on how AI‐based approaches could be integrated into the workflow of pathologists and discuss the challenges and perspectives of the implementation of AI in tumor pathology.

The present study analyzed the clinicopathological features and prognosis in patients with idiopathic membranous nephropathy (IMN) with hypertension. In the hypertension group, significant differences were found in the age, hypertension history, systolic blood pressure, diastolic blood pressure (DBP), mean arterial pressure, albumin, serum creatinine, low-density lipoprotein, 24 h urine protein levels, calculated estimated glomerular filtration rate (e-GFR), glomerular sclerosis, segmental sclerosis, ischemic sclerosis, interstitial fibrosis, tubular atrophy and vascular lesion compared with the non-hypertension group (P

Fracture healing and growth of the bones are highly related to the stress level. Numerical analysis of stresses is the most effective means to determine the stress level, but it usually requires sufficient resolution to ensure an accurate description of geometry features of bones. In this paper, high-fidelity smoothed finite element method (S-FEM) skull models are created using computed tomography (CT) and micro-computed tomography (μCT) images of a juvenile pig skull. The material properties of the heterogeneous bone are modeled by a varying distribution of Young's modulus mapped to each element and smoothing domain to accurately capture the high heterogeneity. Different types of S-FEM models, including node-based, edge-based, and face-based, are developed for this high-fidelity modeling work. It is found that S-FEM has higher accuracy, in terms of displacements, stresses, and strain energy, compared to the traditional finite element method (FEM). Graphical abstract.

The development of polymer-based thermal management materials (TMMs) is crucial to address the frequency reduction or overheating damage of modern electronics caused by inadequate thermal conductivity of substrates. However, high interfacial thermal resistance between filler and filler or between filler and matrix creates paramount bottlenecks for the prominent thermal conduction in polymer-based composites. Herein, novel tanghulu-like MoS2–SiC nanowire (L-MoS2–SiCNW) and cattail-like H-MoS2–SiCNW hybrids are successfully fabricated via a facile hydrothermal method, and then incorporated into a green cellulose nanofiber (CNF) matrix by a vacuum-assisted filtration approach. MoS2 nanosheets tightly rooted to the surface of SiCNWs can bridge the gap between SiCNWs and increase the contact area between them, which consequently leads to the large reduction of interfacial thermal resistance. As a result, the maximum in-plane TC of 19.76 W m−1 K−1 is attained for the flexible CNF/H-MoS2–SiCNW film at a relatively low filler content of 22.5 vol%, which has an enhancement of 1408% in contrast to that of the pure CNF film. In addition, the volume resistivity of the flexible CNF/H-MoS2–SiCNW film is two orders of magnitude higher than that of pure CNF, which is far above the required resistance for electrical insulation. In the real TMM application measurement of CNF/H-MoS2–SiCNWs, the temperature of the CPU core with the CNF/H-MoS2–SiCNW heat spreader is elevated more slowly than that of the bare case (e.g.: Δ = 4.9 °C at 980 s). Thus, this investigation provides a valuable insight into the design and fabrication of favorable polymer-based TMMs for potential applications in microelectronics and advanced energy.

To research the problems of the rolling bearing fault diagnosis under different noises and loads, a dual-input model based on a convolutional neural network (CNN) and long-short term memory (LSTM) neural network is proposed. The model uses both time domain and frequency domain features to achieve end-to-end fault diagnosis. One-dimensional convolutional and pooling layers are utilized to extract the spatial features and retain the sequence features of the data. In addition, an LSTM layer is employed to extract the sequence features. Finally, a dense layer is applied for fault classification. To enhance recognition accuracy under different noises and loads, three techniques are applied to the proposed model, including taking time-frequency domain signals as input, using the CNN-LSTM model, and adopting the mini-batch and batch normalization methods. The Case Western Reserve University and Drivetrain Diagnostics Simulator data sets are used to construct experiments under different conditions, including varying loads and different noises. The proposed model can achieve a high fault recognition rate under variable load and noise conditions as well as satisfactory anti-noise and load adaptability.

In Real-time Bidding (RTB) based display advertising, demand side platforms (DSPs) estimate the click-through rate (CTR) of each advertisement impression, and then decide whether and how much to bid based on the information of the user and the advertiser. Typically, when a new campaign is launched, the CTR estimation module of the DSP needs to collect data to train an accurate estimator. The advertiser is charged for each ad impression in display advertising, therefore there is some cost for obtaining each training instance. Thus one crucial task is to actively train an accurate CTR estimator within the constraint of the budget. Traditional active learning algorithms fail to deal with such scenario because (i) acquiring training instances is implemented via performing real-time bidding for the corresponding auctions; (ii) RTB requires the bidding agent to make real-time decisions for sequentially coming bid requests; (iii) cost for each ad impression will be unveiled only after giving the bid price and winning the auction; (iv) training data gathered in post-bid stage has a strong bias towards the won impressions. In this paper, we propose a Bid-aware Active Real-time Bidding (BARB) algorithm to actively choose training instances by setting different bid prices for each ad auction, in order to efficiently train an accurate CTR estimation model within the budget constraint. The empirical study on different campaigns of three real-world datasets with three budget constraints shows the effectiveness of our proposed algorithm.