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Software comprehension helps us to maintain software better. The developers often use documentation and mutual communication to understand software. However, the incompletion of documentation usually causes some problems such that we cannot acquire the detailed information of software. So researchers are now using key classes as starting points for software understanding and propose several effective methods for key class identification. Such methods are mostly implemented using supervised and unsupervised learning methods; however, they may be subject to class imbalance, insufficient amount of data, and lack of accuracy. In this paper, we propose a novel approach, called COSPA (identifying key Classes in Object-Oriented Software using Preference Aggregation), to identify key classes in software. Our approach is inspired by the social choice theory and uses Kemeny-Young method to find a preference that minimizes the total difference between different pairs of sequences returned by three key class identification methods (i.e., a-index, InDeg, and OSE). Further, we rank the classes in descending order according to that preference set, and a small number of top-ranked classes are considered as key candidates. To better evaluate the performance of our approach, we apply it to six Java software systems and compare it with nine state-of-the-art approaches. In the experiments, we use Friedman to evaluate all methods in the whole set of research subjects; results show that, though no single method works always best in all subject systems, our method indeed performs better than others.

With the continued development of the new energy vehicle industry, two-stage isolated AC/DC converters are widely used because of their simple topology and easy control characteristics. In this study, we investigate the front-stage Buck power factor correction (PFC) converter and rear-stage full-bridge converter. The main circuit design and component selection were completed through a detailed analysis of the circuit characteristics. In terms of the control strategy, the front-stage adopting PI control and parameter adaptive terminal sliding mode control strategy were proposed for the rear-stage full-bridge converter. This new compound control strategy ensures an optimal regulation of the system under different operating conditions. Simulation analysis verified the correctness of the system topology and control strategy. Based on an analysis of the main parameters of the system, a low-power experimental prototype was trial-produced. The experimental results show that under the same load switching conditions, the parameter-adaptive terminal sliding mode control enhanced faster dynamic regulation and stronger robustness than the conventional PI control. The study is also a good reference in terms of engineering work.

Abstract Gas drainage is carried out based on output from each coal bed throughout commingling production of coalbed methane (CBM). A reasonable drainage process should therefore initially guarantee main coal bed production and then enhance gas output from other beds. Permanent damage can result if this is not the case, especially with regard to fracture development in the main gas-producing coal bed and can greatly reduce single well output. Current theoretical models and measuring devices are inapplicable to commingled CBM drainage, however, and so large errors in predictive models cannot always be avoided. The most effective currently available method involves directly measuring gas output from each coal bed as well as determining the dominant gas-producing unit. A dynamic evaluation technique for gas output from each coal bed during commingling CBM production is therefore proposed in this study. This technique comprises a downhole measurement system combined with a theoretical calculation model. Gas output parameters (i.e., gas-phase flow rate, temperature, pressure) are measured in this approach via a downhole measurement system; substituting these parameters into a deduced theoretical calculation model then means that gas output from each seam can be calculated to determine the main gas-producing unit. Trends in gas output from a single well or each seam can therefore be predicted. The laboratory and field test results presented here demonstrate that calculation errors in CBM outputs can be controlled within a margin of 15% and therefore conform with field use requirements.

Machine learning has been pervasively used in a wide range of applications due to its technical breakthroughs in recent years. It has demonstrated significant success in dealing with various complex problems, and shows capabilities close to humans or even beyond humans. However, recent studies show that machine learning models are vulnerable to various attacks, which will compromise the security of the models themselves and the application systems. Moreover, such attacks are stealthy due to the unexplained nature of the deep learning models. In this survey, we systematically analyze the security issues of machine learning, focusing on existing attacks on machine learning systems, corresponding defenses or secure learning techniques, and security evaluation methods. Instead of focusing on one stage or one type of attack, this paper covers all the aspects of machine learning security from the training phase to the test phase. First, the machine learning model in the presence of adversaries is presented, and the reasons why machine learning can be attacked are analyzed. Then, the machine learning security-related issues are classified into five categories: training set poisoning; backdoors in the training set; adversarial example attacks; model theft; recovery of sensitive training data. The threat models, attack approaches, and defense techniques are analyzed systematically. To demonstrate that these threats are real concerns in the physical world, we also reviewed the attacks in real-world conditions. Several suggestions on security evaluations of machine learning systems are also provided. Last, future directions for machine learning security are also presented.

Abstract Background Visceral (VS) fat depot is known to have defective adipogenic functions compared to subcutaneous (SC) fat, but its mechanism of origin is unclear. Objective We tested our hypothesis that the degree of oxidative stress in adipose-derived stem cells (ASCs) from these depots may account for this difference. Methods ASCs were isolated from VS (omental region) and SC (abdominal region) fat depots of human subjects undergoing bariatric surgery. ASCs from VS and SC fat were investigated for their cellular characteristics in reactive oxygen species (ROS), metabolism, gene expression, proliferation, senescence, migration, and adipocyte differentiation. ASCs were also treated with antioxidant ascorbic acid (vitamin C). Results We found that human VS-derived ASCs exhibit excessive oxidative stress characterized by high reactive oxygen species (ROS), compared to SC-derived ASCs. Gene expression analyses indicate that the VS-ASCs exhibit higher levels of genes involved in pro-oxidant and pro-inflammatory pathways and lower levels of genes in antioxidant and anti-inflammatory pathways. VS-ASCs have impaired cellular functions compared to SC-ASCs, such as slower proliferation, early senescence, less migratory activity, and poor adipogenic capability in vitro. Treatment with ascorbic acid decreased ROS levels drastically in VS-ASCs. Ascorbic acid treatment substantially improved proliferation, senescence, migration, and adipogenic capacities of compromised ASCs caused by high ROS. Conclusions This finding suggests the fat depot-specific differences of cellular defects originating from stem cell population. Considering clinical potentials of human ASCs for cell therapies, this also offers a possible strategy for improving their therapeutic qualities through antioxidants.

Researchers have proposed many approaches to identify key classes in software from the perspective of complex networks, such as CONN-TOTAL-W, PageRankBR, and ElementRank, which can effectively help developers understand software. However, these approaches tend to rely on a single metric when measuring the importance of classes. They do not consider the aggregation of multiple metrics to select the winner classes that rank high in majority metrics. In this work, we propose a key class identification approach using Markov-Chain-based ranking aggregation, namely CRA. First, CRA constructs a weighted directed class coupling network (WDCCNet) to describe the software and further applies existing approaches on WDCCNet to calculate class importance. Second, CRA filters out some metrics according to specific rules and uses the Markov chain to aggregate the remaining metrics. When the state probability distribution reaches a fixed point and does not change anymore, the classes in the software are sorted in a descending order according to the probability distribution, and the top-15% classes are treated as key classes. To evaluate the CRA approach, we compare it with 10 baseline approaches available on 6 pieces of software. Empirical results show that our approach is superior to the baselines according to the average ranking of the Friedman Test.

Abstract Introduction Hemorrhagic transformation (HT) is a complex and multifactorial complication among patients with acute ischemic stroke (AIS), and the inflammatory response has been considered as a risk factor for HT. We aimed to evaluate the stratification of FAR (fibrinogen‐to‐albumin ratio), an inflammatory biomarker, in HT patients. Methods A total of 256 consecutive stroke patients with HT and 256 age‐ and gender‐matched stroke patients without HT were included in this study. HT during hospitalization was diagnosed by follow‐up imaging assessment and was classified into hemorrhagic infarction (HI) and parenchymal hematoma (PH) according to the recommendations of European Cooperative Acute Stroke Study II classification. Blood samples were obtained at admission. Results Higher levels of FAR were observed in patients with HT compared with the non‐HT group [10.29 (8.39–12.95) vs. 8.60 (7.25–10.8), p .05]. Patients were assigned to groups of high FAR (≥9.51) and low FAR (

The poor dynamic performance problem of a Full-Bridge converter under a traditional control strategy is investigated in this study. A new parameter adaptive terminal sliding mode control policy is developed for a Full-Bridge DC-DC converter, by combining the integral part with the power function and differential function in the design of the sliding surface. In theory, the steady-state error of the system can approach zero within a short time. To manage the un-ideal situation after using a fixed value of power γ, an improved γ adaptive algorithm is proposed. The system output is tracked and γ is adjusted in real time. The effect of the system can be guaranteed always in an optimal state. Finally, simulation results are provided to verify the performance of the proposed design method under different conditions.

Abstract Objective Reduced thiamine (vitamin B1) had been reported to be associated with cognitive impairment caused by Alzheimer disease. Our study is to explore the association between thiamine and cognitive impairment after acute ischemic stroke. Materials and Methods One hundred and eighty two patients with acute cerebral infarction were recruited within the first 24 hr after admission. Thiamine and other vitamin Bs of peripheral blood samples were measured. Patients were divided into with poststroke cognitive impairment (PSCI) and non‐PSCI according to the score of MMSE and the degree of education. Results Reduced thiamine (

Abstract Adipogenesis is essential in in vitro experimentation to assess differentiation capability of stem cells, and therefore, its accurate measurement is important. Quantitative analysis of adipogenic levels, however, is challenging and often susceptible to errors due to non-specific reading or manual estimation by observers. To this end, we developed a novel adipocyte quantification algorithm, named Fast Adipogenesis Tracking System (FATS), based on computer vision libraries. The FATS algorithm is versatile and capable of accurately detecting and quantifying percentage of cells undergoing adipogenic and browning differentiation even under difficult conditions such as the presence of large cell clumps or high cell densities. The algorithm was tested on various cell lines including 3T3-L1 cells, adipose-derived mesenchymal stem cells (ASCs), and induced pluripotent stem cell (iPSC)-derived cells. The FATS algorithm is particularly useful for adipogenic measurement of embryoid bodies derived from pluripotent stem cells and was capable of accurately distinguishing adipogenic cells from false-positive stains. We then demonstrate the effectiveness of the FATS algorithm for screening of nuclear receptor ligands that affect adipogenesis in the high-throughput manner. Together, the FATS offer a universal and automated image-based method to quantify adipocyte differentiation of different cell lines in both standard and high-throughput workflows.

Background: Hemorrhagic transformation (HT) is a complication that may cause neurological deterioration in patients with acute ischemic stroke. Both neutrophil and platelet have been associated with the stroke progression. The aim of this study was to explore the relationship between neutrophil-to-platelet ratio (NPR) and HT after acute ischemic stroke.Methods: A total of 279 stroke patients with HT were consecutively recruited. HT was diagnosed using magnetic resonance imaging (MRI) or computed tomography (CT) and classified into hemorrhagic infarction (HI) and parenchymal hematoma (PH). Blood samples for neutrophil and platelet counts were obtained at admission. Meanwhile, 270 age- and gender-matched controls without HT were included for comparison.Results: Among the patients with HT, 131 patients had PH and 148 patients had HI. NPR was higher in patients with PH than those with HI or non-HT [36.8 (23.7–49.2) vs. 26.6 (17.9–38.3) vs. 19.1 (14.8–24.8), P < 0.001]. After adjustment for potential confounders, high NPR remained independently associated with the increased risk of HT (OR = 2.000, 95% CI: 1.041–3.843, P = 0.037). NPR (>39.9) was independently associated with PH (OR = 2.641, 95% CI: 1.308–5.342, P = 0.007).Conclusions: High NPR was associated with the increased risk of HT especially PH in patients with acute ischemic stroke.