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The optimization of most combinatorial problems is NP-hard, which can be solved by heuristic algorithms to obtain approximately optimal solutions, especially for large-scale problems. Many heuristic algorithms can apply the 2-opt local search to find better solutions. In complete 2-opt local search, every valid neighboring solutions of swapping mechanism will be compared, which is very time consuming especially when a sequential algorithm is adopted. Nowadays, graphic processing units (GPUs) have evolved into powerful and flexible parallel computation platforms that have been widely used to accelerate the solving of NP-hard problems. Therefore, in this study, we focus on how to use GPUs to solve the single-row facility layout problem (SRFLP) with the 2-opt-based simulated annealing (SA) heuristic algorithm. As far as we know, this study is the first to solve SRFLP by using a GPU. After analyzing the fitness function and the move gains calculation between parent and child solutions, we propose a prefix-sum formula table to eliminate a large amount of replicated computation. To calculate move gains for the 2-opt local search during each iteration, many GPU threads are created and they construct and lookup the table in parallel. According to experimental results, if the prefix-sum formula table is not adopted, the GPU version outperforms the sequential CPU counterpart with the best speedup of 123. However, if the table is used in the GPU version, the best speedup can reach up to 3208. Because the proposed parallelization approach with the prefix-sum formula table is based on the features of the 2-opt local search and the SRFLP fitness function, it can be applied to any 2-opt-based heuristic algorithm for solving SRFLP even though the SA algorithm is used in this study.

Extreme learning machine (ELM) has fast learning speed and perfect performance, at the same time, ELM provides a unified learning framework with a widespread type of feature mappings which can be applied in multiclass classification applications directly. These advantages make ELM become one of the best classification algorithms, and ELM has attracted great attention in supervised learning and semi-supervised learning. However, noise and outliers of data are usually existed in the real world, which will affect the performance of ELM. To improve the robustness and classification performance of ELM, we propose the Kernel Risk-Sensitive Mean p-power Loss Based Hyper-graph Regularized Robust Extreme Learning Machine (KRP-HRELM) method. On the one side, as a nonlinear similarity measure defined in the reproducing kernel space, the kernel risk-sensitive mean p-power loss (KRP) can effectively weaken the negative effects caused by noise and outliers. Therefore, the KRP is introduced into ELM to enhance its robustness. Then, the application of hyper-graph can help the ELM to explore higher-order geometric structures among more sampling points, thereby obtaining more comprehensive data information. In addition, to obtain a more sparsity network model, the L2,1-norm is used to constrain the output weight. On the other side, improving the practical application ability of KRP-HRELM is also the focus of our research, so KRP-HRELM is extended to semi-supervised learning, which is called the semi-supervised KRP-HRELM (SS-KRP-HRELM). Notably, the results of the robustness experiment have proved that our method has extraordinary robustness. At the same time, by using four evaluation measures such as accuracy, recall, precision, and F1-measure, to evaluate the classification results, we can find that our method has obtained better classification performance than other advanced methods.

Frontal impacts are the most frequent crash types and may generate extreme body in white (BIW) deformations. Vehicles are subjected to impact evaluation rating protocols such as 100 % Front Rigid Barrier (FRB) impact, 40 % Offset Deformable Barrier (ODB) impact and 25 % overlap (small overlap) impact. This paper proposes a collaborative optimization process using optimal Latin hypercube design (Opt LHD) and response surface methodology (RSM), to improve the vehicle crashworthiness in the frontal impacts, by considering displacement oriented structure (DOS). Upper and lower engine bay structure were considered herein to increase the lateral vehicle displacement during small overlap impact and decrease the impact force that transferred to the passenger compartment in the frontal impacts. The optimal results indicated that the acceleration of the B-pillar was reduced 0.4g in the FRB impact, the intrusion of the firewall was reduced by 55.13 % in the ODB impact, and the intrusion of passenger compartment during small overlap impact was decreased by an average of 53.88 %, with a maximum percentage of 71.41 % around left toepan, leading to an IIHS rating to acceptable from poor. The proposed crashworthiness design approach is effective in vehicle structure optimization for better frontal impacts performance.

Recently, extreme learning machine (ELM) has attracted a lot of attention due to its high performance and extreme speed. However, how to improve the robustness of ELM has always been a problem. Considering the problems of noise and outliers in the experimental data, in this paper, we introduce the kernel risk-sensitive mean p-power error (KRP) into ELM and propose a robust ELM method named kernel risk-sensitive mean p-power error based robust extreme learning machine (KRPELM), on the basis of the high efficiency and robustness of KRP. In KRPELM, KRP function instead of square loss is integrated into ELM as the loss function, which can improve the robustness of ELM to noise and outliers. We also propose an efficient iterative adjustment strategy to optimize KRPELM. Nine benchmark datasets are utilized to verify the classification performance of the proposed KRPELM. In addition, we apply the proposed method to the classification of cancer samples. The experimental results on five cancer gene expression datasets show that KRPELM can identify different cancer types more accurately.

Introduction. Against the background of marketing and globalization of higher education, many organizations and enterprises not only face stiff competition and quality problems in the market. Despite all the current challenges, they need to implement brand management tools. The urgent task for universities was how to apply the brand's marketing strategy to the brand management of universities, implement the optimal distribution of tangible and intangible resources, create a good brand image, expand popularity and reputation and gain competitive advantage.The hypothesis of scientific research is that for the formation of an effective brand it is necessary to use the tools of strategic marketing.The aim is to study the theoretical and methodological foundations of brand formation in educational institutions using modern competitive marketing strategies.The research methodology is a comparative analysis to reflect trends in the development of the brand of education; systematization and classification – for the classification of structural elements of the object of study; system approach – for the formation of theoretical foundations of regulation of brand management processes.Results. Based on the law on higher education and reliance on the mechanism and legislation of the market, this article examines brand management and argues that university brands should be optimized, coordinated and integrated to take full advantage of internal and external resources. It is necessary to strengthen the concept of brand management, explain the position of the university brand, create a strong university brand, strengthen the dominant university brand, create a crisis management system for university brands, increase the basic competitiveness of universities and gain greater benefits.Conclusions. University brand management should be based on the marketing strategy of enterprise brands, make full use of their own resources, strengthen the concept of brand management, determine the position of the university brand and create a strong university brand. universities, the university brand is undoubtedly a dazzling edge of the sword.

In this paper, the quantitative evaluation of the explosion effect based on the fuzzy comprehensive evaluation method is proposed to describe the qualitative evaluation results. The selected state characteristic parameters are expressed by two kinds of membership functions, fuzzy normal and triangular distribution membership functions, and preliminary evaluation results are obtained. The validity index of the maximum membership principle is used to assess the accuracy of the evaluation results of two algorithms, and a relevant approaching degree is chosen to optimize the results. The entire evaluation process selects eleven indicators to form an evaluation set, including the boulder yield, root rate, flying distance of flyrock, explosive consumption, postcracking distance, detonator unit consumption, vibration velocity, loose coefficient, cast distance, throw rate, and blasted volume per meter of hole. Part of the indicator parameters are derived from field test monitoring, and another part of the indicator parameters are derived from numerical simulation. The simulation process uses the user-defined material interface function provided by LS-DYNA. And the numerical model of slope blasting is established by embedding the evolution relationship of tensile and compressive damage into the elastoplastic constitutive material. The evaluation method proposed in this paper is used to evaluate the postexplosion effect of Zijin Mountain gold-copper mine slope cast blasting. The results demonstrate that the fuzzy normal distribution membership function can correlate the state characteristic information and evaluation index effectively, and the working condition after explosion can be reflected accurately. Additionally, the influencing factors can be ranked by the importance degrees according to the calculated value of the evaluation index.

Small overlap accidents of sedans often result in severe injuries. In this article, a crashworthiness design method, including engine-room energy management, suspension safety design and passenger ...

ObjectivesTo investigate the value of contrast-enhanced computer tomography (CT)-based on radiomics in discriminating high-grade and low-grade hepatocellular carcinoma (HCC) before surgery.MethodsThe retrospective study including 161 consecutive subjects with HCC which was approved by the institutional review board, and the patients were divided into a training group (n = 112) and test group (n = 49) from January 2013 to January 2018. The least absolute shrinkage and selection operator (LASSO) was used to select the most valuable features to build a support vector machine (SVM) model. The performance of the predictive model was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity.ResultsThe SVM model showed an acceptable ability to differentiate high-grade from low-grade HCC, with an AUC of 0.904 in the training dataset and 0.937 in the test dataset, accuracy (92.2% versus 95.7%), sensitivity(82.5% versus 88.0%), and specificity (92.7% versus 95.8%), respectively.ConclusionThe machine learning-based radiomics reflects a better evaluating performance in differentiating HCC between low-grade and high-grade, which may contribute to personalized treatment.

Objective:. Cryoglobulinemia often causes systemic vasculitis, thereby damaging to skin and internal organs including kidneys, even life-threatening. This review aimed to introduce the advances in understanding, detection, and treatment of this disease in recent years, with a particular concern to clinical practice. Data sources:. All the data in this review were from the English or Chinese literature in the PubMed and China National Knowledge Infrastructure databases as of March 2019. Study selection:. This review selected important original articles, meaningful reviews, and some reports on cryoglobulinemia published in recent years and in history, as well as the guidelines for treatment of underlying diseases which lead to cryoglobulinemia. Results:. Diagnosis of cryoglobulinemia relies on serum cryoglobulin test, in which to ensure that the blood sample temperature is not less than 37°C in the entire pre-analysis phase is the key to avoid false negative results. Cryoglobulinemic vasculitis (Cryo Vas), including cryoglobulinemic glomerulonephritis (Cryo GN), usually occurs in types II and III mixed cryoglobulinemia, and can also be seen in type I cryoglobulinemia caused by monoclonal IgG3 or IgG1. Skin purpura, positive serum rheumatoid factor, and decreased serum levels of C4 and C3 are important clues for prompting types II and III Cryo Vas. Renal biopsy is an important means for diagnosis of Cryo GN, while membranous proliferative GN is the most common pathological type of Cryo GN. In recent years, great advances have been made in the treatment of Cryo Vas and its underlying diseases, and this review has briefly introduced these advances. Conclusions:. Laboratory examinations of serum cryoglobulins urgently need standardization. The recent advances in the diagnosis and treatment of Cryo Vas and GN need to be popularized among the clinicians in related disciplines.

In recent years, Extreme Learning Machine (ELM) has attracted extensive attention in various research fields. To improve the performance of ELM, we propose an Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss (DKRSLELM) method in this paper. The Kernel Risk-Sensitive Loss (KRSL) is integrated into the objective function of ELM. This is because KRSL not only can effectively reduce the influence of noise and outliers, but also can eliminate the redundant neurons of ELM. In the experiment, we conduct a classification experiment on cancer integration data-sets. The experimental results indicate that our method can effectively improve the classification performance of ELM.

Background As a machine learning method with high performance and excellent generalization ability, extreme learning machine (ELM) is gaining popularity in various studies. Various ELM-based methods for different fields have been proposed. However, the robustness to noise and outliers is always the main problem affecting the performance of ELM. Results In this paper, an integrated method named correntropy induced loss based sparse robust graph regularized extreme learning machine (CSRGELM) is proposed. The introduction of correntropy induced loss improves the robustness of ELM and weakens the negative effects of noise and outliers. By using the L2,1-norm to constrain the output weight matrix, we tend to obtain a sparse output weight matrix to construct a simpler single hidden layer feedforward neural network model. By introducing the graph regularization to preserve the local structural information of the data, the classification performance of the new method is further improved. Besides, we design an iterative optimization method based on the idea of half quadratic optimization to solve the non-convex problem of CSRGELM. Conclusions The classification results on the benchmark dataset show that CSRGELM can obtain better classification results compared with other methods. More importantly, we also apply the new method to the classification problems of cancer samples and get a good classification effect.

Background Large scale randomized trials have demonstrated that bevacizumab in addition to chemotherapy as first-line or second-line treatment has significant survival benefits. We aim to explore the clinical impact of bevacizumab in combination with chemotherapy in first-line or second-line in patients with colorectal cancer (CRC). Methods The medical records of patients with CRC who received bevacizumab at first or second-line of treatment were collected retrospectively. The primary outcome of the study was to evaluate the efficacy of bevacizumab in combination with chemotherapy by survival endpoints i.e. overall survival (OS) and progression-free survival (PFS) and the secondary outcome was to evaluate its safety by incidence of adverse events (AE). Results Fifty-one patients with CRC had met the selection criteria for treatment with bevacizumab to either cetuximab or FOLFOX or both. The median age was 54 years. During follow-up, ten patients had exhibited progression after treatment while 5 patients died. The median OS and PFS of the overall population were not reached. The Cox proportional regression analysis revealed no significant prognostic factors of OS and PFS for treatment with bevacizumab in various demographic subgroups. The 1-year PFS rates of all 51 patients was 76%. The 1-year and 3-year OS rates for all 51 patients were 95% and 88%, respectively. Toxicities were usually mild in nature, with nausea, vomiting, hand and foot syndrome, neutropenia, asthenia and palpitation being the commonly reported adverse events. Conclusion In this real-world setting, the efficacy and safety of bevacizumab in combination with chemotherapy is limited and further research is warranted as to whether bevacizumab with chemotherapy is an optimal treatment as first-line or second-line therapy in Chinese CRC patients.

Previous researches have found that chronic neck pain was closely related to depression. However, the relation between chronic neck pain, sleep quality, exercise and depression remains unclear. This study aims to evaluate the association between these factors, based on the assumption that sleep quality and exercise are potential mediators in the relationship between chronic neck pain and depression. This cross-sectional study enrolled 231 patients with chronic neck pain who were referred to outpatient clinics in department of neurology between July and December 2016. Data analysis was performed using a structural equation model to evaluate the mediating effects of sleep quality and exercise frequency on the relation between chronic neck pain intensity and depression. A positive correlation between neck pain intensity and depression was unveiled. In addition, sleep quality was identified as a significant positive mediator between neck pain intensity and depression, and exercise can help relieve depression. Poor sleep quality leads to an increase of the symptoms of depression for people with high neck pain intensity which can be effectively relieved via additional exercise.

It is very important to train practical ability of the students in logistics engineering. In the traditional practice teaching, often only pay attention to the software operation of the laboratory and neglect the practical training that combines with the actual enterprises in order to help the enterprises solve their practical problems. This will lead to the disconnection between the students and the reality, which prevents the students from applying the theory and expertise they has learned. To cultivate innovative talents in logistics, students need to integrate more practical problems, and theoretically and practically analyze and solve them. In this paper, we design the teaching of logistics system planning and simulation practice in the logistics system planning and simulation course. We proposed how to analyze and design the production logistics system in combination with actual cases, and simulate the actual system. Through the case design, it can improve the students’ ability to solve the practical problems in combination with actual case. This design can provide the instructors the practical case of the related course, and also give references for how to improve the practical and innovative ability of logistics engineering students.

Combined with Auto Encoder (AE), Extreme Learning Machine Auto Encoder (ELM-AE) has attracted the interest of researchers in recent years. Considering the classification tasks of single-cell Ribonucleic Acid sequencing (scRNA-seq) data, in this paper, we propose a novel supervised learning method based on ELM-AE, which is named Robust Graph Regularized Extreme Learning Machine Auto Encoder (RGELMAE). The method introduces L2,1-norm minimization on loss function to improve the robustness, and combines with the manifold regularization framework to explore the internal local structure between data points. Finally, RGELMAE is applied to the classification tasks of scRNA-seq data. The experimental results indicate that our method can effectively extract the key information representing the original data, and improve the classification performance of ELM.

Switch-capacitor battery equalizer (SCBE) is widely used because of its simple structure, small PCB area, and no inductance components. Its balance current is sawtooth or trapezoid waveform is significantly different from traditional charger’s DC waveform. From many non-DC charging literatures, it can be found that different frequency and waveform shape will cause the significantly different battery charging performance. Therefore, this paper explores the near-optimal balance current frequency and waveform shape of the SCBE from electrochemical impedance to optimize its battery balance performance. Further, the LC resonant battery equalizer (LC-RBE) is proposed to perform battery balancing with a sine wave. Experiments have shown that the better efficiency and lower battery temperature rise are obtained when the LC-RBE work with the battery minimum ac impedance frequency.

Kernel Risk-Sensitive Loss (KRSL) is a nonlinear similarity measure defined in kernel space, which enable the gradient based method to achieve higher accuracy while effectively weakening the negative effects caused by noise and outliers. Defined as a kernel function expectation between two random variables, KRSL has been successfully applied in robust machine learning and signal processing. Extreme Learning Machine, as one of the most popular methods of machine learning, has attracted great attention in supervised learning and semi-supervised learning. However, when the data contains noise and outliers, the manifold structure of the data is not considered or the neural network structure is too complex, the performance of traditional ELM methods will decline. Therefore, based on KRSL, hyper-graph regularization and L 2,1 -norm, we first propose a more robust ELM method named Kernel Risk-Sensitive Loss Based Hyper-graph Regularized Robust Extreme Learning Machine (KRSL-HRELM). In KRSL-HRELM, KRSL is introduced into ELM to enhance its ability to handle noise and outliers. Moreover, the hyper-graph regularization is integrated into the method to learn the higher-order geometric structure information between the data. In addition, the L 2,1 -norm is introduced to constrain the output weight matrix to obtain a sparse network model. Inspired by other semi-supervised ELM methods, we extend KRSL-HRELM to semi-supervised learning and propose its semi-supervised version semi-supervised KRSL-HRELM (SS- KRSL-HRELM). Empirical studies on a large number of real-world datasets show that the proposed methods are competitive with other advanced supervised or semi-supervised learning methods in terms of robustness and efficiency.

During the mining process, many energy resources are produced additionally, which have a promising prospect to supply electricity, heat and cooling energy. However, different energy inertia and diverse uncertainties from photovoltaic, wind turbine and gushing water outputs will pose challenges to the operation of a coal mine integrated energy system. This paper constructs a coal mine integrated energy system operation model considering energy resources recovery. Coal mine integrated energy system consists of energy supply subsystem, energy recovery subsystem, and energy storage subsystem, and it integrates multiple energies such as electricity, heat and cooling. A two-stage robust stochastic optimization method is proposed to adapt to the uncertainties. At the day-ahead stage decision, the optimal operation problem is solved at long time intervals to determine the heat dispatching. Meanwhile, the intra-day stage decision is focused on dealing with diverse uncertainties at short time intervals to enhance operational robustness. Heat energy equipment is scheduled at intra-day stage with the day-ahead dispatching result. A real case of coal mine is demonstrated to validate the effectiveness and robustness of the proposed method. The result reveals that the proposed method, compared to the conventional methods, can fully improve the economic benefits, the energy recovery and the system operational robustness.

Podocyte injury critically contributes to the pathogenesis of obesity-related glomerulopathy (ORG). Recently, lipid accumulation and inflammatory responses have been found to be involved in podocyte injury. This study is to explore their role and relationship in podocyte injury of ORG. In animal experiments, the ORG mice developed proteinuria, podocyte injury, and hypertriglyceridemia, accompanied with deregulated lipid metabolism, renal ectopic lipid deposition, activation of NOD-like receptor protein 3 (NLRP3) inflammasome, and secretion of IL-1

More than 60% of China’s coal seams have the feature of low permeability, which severely restricts the gas drainage efficiency when using traditional drilling method. The development of enhanced coalbed methane (ECBM) technologies is essential to prevent coal and gas outburst (CGO) disasters caused by high levels of coal bed methane (CBM). This paper proposes an ECBM technology by liquid carbon dioxide (LCO2-ECBM), based on permeability improvement by thermal damage and CO2-CH4 displacement effect. In-situ experiment conducted in the No. 6 coal seam of Zhangji mine shows the effectiveness of LCO2-ECBM technology, which had drainage efficiency 1.9 times higher than that of the traditional drilling method. The mechanisms of LCO2-ECBM recovery were theoretically deduced and experimentally verified. The evolution of adsorbed pores (ADP) and seepage pores (SEP) in coal samples before and after LCO2 saturation were revealed. The ADP specific area, SEP volume, and permeability in the saturated coal samples were both greater than those of the raw ones. Moreover, the periodic features of binary-gas competitive adsorption in ADP and SEP were revealed by the variation of the CH4 concentration in the displacement test. The feasibility of LCO2-ECBM technology was proved by in-situ experiment and laboratory tests, and it provided a new means for preventing CGO hazards.

With the development of cancer research, various gene expression datasets containing cancer information show an explosive growth trend. In addition, due to the continuous maturity of single-cell RNA sequencing (scRNA-seq) technology, the protein information and pedigree information of a single cell are also continuously mined. It is a technical problem of how to classify these high-dimensional data correctly. In recent years, Extreme Learning Machine (ELM) has been widely used in the field of supervised learning and unsupervised learning. However, the traditional ELM does not consider the robustness of the method. To improve the robustness of ELM, in this paper, a novel ELM method based on L 2 , 1 -norm named L 2 , 1 -Extreme Learning Machine ( L 2 , 1 -ELM) has been proposed. The method introduces L 2 , 1 -norm on loss function to improve the robustness, and minimizes the influence of noise and outliers. Firstly, we evaluate the new method on five UCI datasets. The experiment results prove that our method can achieve competitive results. Next, the novel method is applied to the problem of classification of cancer samples and single-cell RNA sequencing datasets. The experimental results on The Cancer Genome Atlas (TCGA) datasets and scRNA-seq datasets prove that ELM and its variants has great potential in the classification of cancer samples.

The utilization of liquid CO2 for enhanced coalbed methane recovery has great environmental and commercial potential to sink greenhouse gas emissions and enchance CH4 extraction. A comprehensive understanding of mechanical properties and micro-scale changes to coal saturated with liquid CO2 would fill the knowledge gap regarding CO2 utilization and be greatly beneficial to industry. The present study employed extensive experimentation using uniaxial compression test, acoustic emission (AE) monitoring, Brazilian splitting test, scanning electron microscopy, and energy dispersive X-ray diffraction spectrometry to determine the damage features of coal microstructure, mechanical characteristics, failure modes, and minerals variation between original and liquid CO2-saturated coal samples. The pre-existing cracks were enlarged and more cracks were generated in the saturated coal samples than in the raw samples. The liquid CO2-saturated coal samples exhibited larger plastic deformation and lower compressive strength in unstable crack propagation stage and more AE counts in rupture stage than those of unsaturated samples. The average compressive strength and tensile strength of the saturated samples were 21% and 27% lower than that of the raw samples. More ragged failure cracks were formed in the liquid CO2-saturated coal specimens than in the unsaturated coal samples. The failure modes of the saturated coal samples were inclined to ductile fracture as opposed to brittle fracture in the unsaturated coal samples. The width and number of micro-cracks on the saturated samples were both increased, and the amount of partial minerals that filled in the micro-cracks reduced after liquid CO2 saturation, which indicated an interaction between minerals in coal and liquid CO2 during the saturation process.

In allusion to such problems as real-time requirement dissatisfaction and significant recognition difference caused by dimension difference existing in the imaging and recognition algorithm for pedestrian in dark scene, a fast head detection and recognition method for pedestrian at night based on fast support vector machine (FC-SVM) algorithm optimization and entropy weight is established in this paper according to relevant principle of statistics. Based on entropy weight, this method aims at improving the extraction process based on histogram gradient features in order to establish threebranch SVM for the deep recognition of pedestrian at night; meanwhile, FC-SVM algorithm is combined to optimize the recognition calculation overhead in order to ensure the real-time property of the recognition algorithm. Furthermore, the falsely detected pedestrians are evaluated on the basis of the head detection mode so as to improve pedestrian imaging matching accuracy. The simulation result shows that this method can not only effectively recognize FIR target of pedestrian at night, but also effectively adapt to such different application environments as urban and suburban areas on the basis of ensuring the real-time requirement for pedestrian recognition, thus presenting good practicability.

The report of the Nineteenth National Congress of the Communist Party of China put forward that we should implement the "Healthy China Strategy" and actively deal with the aging of the population, The problem between the rapid increase in the number of the elderly and their living environment needs to be paid attention to and solved urgently, and creating a new pension environment has become a hot spot. In this study, the methods of ergonomics measurement, investigation and analysis and data statistical analysis were used to obtain the data related to the human body size of the elderly, and a set of reference standards for the size of aging facilities were put forward. Taking Huanggang Sanatorium in Duanzhou District of Zhaoqing City as an example, the indoor of nursing homes environment was designed with the standard design dimensions garden chairs and aromatic plants. In this environment, the relationship between the different concentrations of the fragrant plants and the physical health indicators of the elderly was quantitatively analyzed through the research methods of human physiology. Meanwhile, the outdoor environment of nursing homes with the standard design dimensions display garden facilities was also designed. The study provides an objective data and scientific basis for the construction of green natural recuperation environment and the study of landscape recuperation function in the future.

Airy beams have received considerable attention due to their unique features on propagation, including non-spreading, self-healing, self-accelerating, and parabolic trajectories. Here in this work we study the propagation of linearly polarized Airy beams with an added spherical phase in uniaxial crystal orthogonal to the optical axis. Based on the beam transmission theory in uniaxial crystals, the analytical expressions for the intensity distribution of the beams in different view planes are derived. Numerical calculations are performed and some novel propagation features are presented graphically. It is shown that the Airy beam with an added spherical phase remains linearly polarized but cannot keep other properties unchanged during propagation in uniaxial crystal. Such a beam maintains its intensity profile in the near-field, then with the propagation distance increasing, converts into the Gaussian-Airy beams with different orientations at two specified distances which are codetermined by the extraordinary and ordinary refractive index of the crystal (namely ne and no) and the radius of the spherical phase, and most impressively, forms a mirror-like reflection profile in the far field, i.e., the intensity pattern in the far field returns to the initial Airy beam profile while its orientation on the transversal plane is reversed along the bisector line of the second and fourth quadrant. Note that the intensity pattern successively experiences two mirror transformations along the x and y coordinate axis when passing through these two critical positions, which can give rise to the mirror reflection effect for the whole Airy beam. Moreover, we further demonstrate that the sequences of these two mirror transformations are in close relation with the relative size between ne and no. Therefore, the results obtained in this paper reveal new propagation features in anisotropic medium of Airy beams with added spherical phase and provide a novel route to controlling propagation properties like the pattern profile and orientation of the Airy beams through choosing appropriate anisotropic materials and the radius of the spherical phase factor. Considering that it is easy to obtain an Airy beam with an added spherical phase which can be realized with an Airy beam through an ideal lens, our investigation may lead to potential applications in many fields where the ability to change profile and orientation of the intensity pattern and the ability to determine the refractive index of anisotropic medium are both required.

In smart factory, automatic processing mode with high-tech content has become the main method of the modern sorting system. In small manufacturing enterprises, most of the warehouse sorting system is not done with automation but by manual labor. However, along with the increase in demand for products and the expansion of production scale, an automated warehouse sorting system is necessary. Automated sorting system is a typical discrete logistics system. Applying modern logistics simulation technology can help to analyze and evaluate the actual systems. In this paper, we take a small scale bag manufacturing enterprise for example, to design an automated sorting system in the warehouse. Applying discrete event simulation, model and simulate the system with the Automod as one of the most popular logistics simulation tools. Through conducting the experiments and analyzing the experimental results, verify the feasibility of our design and put forward the improvement of the system. The proposed method can provide the references for other small manufacturing enterprises and help business managers to make decisions.

The paper chooses four trade competitiveness assessment indices to compare the international competitiveness of Computer and Information Service(C&IS) trade between South Korea, China and Japan. The results show that the competitiveness of China is the strongest, Japan secondary and Korea the weakest among the three countries. However, the competitiveness of Korea can be promoted the most quickly and it has exceeded Japan according to TC and NXRCA indices. Meanwhile, there was still a huge gap between the three countries and the two world strong countries?India and Ireland. The main findings of empirical research on influencing factors show that domestic market demand and trade opening degree are the two most important factors influencing the competitiveness of three-country C&IS trade. At the same time, improving the utilization of C&IS and realizing good interaction of C&IS trade and electronic information, communication and other relevant and auxiliary industries can also contribute to the competitiveness improvement.

Background and objective: The fact that mineralocorticoid receptor antagonists reduce structural and functional alterations induced by cyclosporine A (CsA) indicates that aldosterone plays a key role in chronic CsA nephrotoxicity. We and other researchers have reported local renal aldosterone synthesis. To investigate local renal aldosterone’s role in chronic CsA nephrotoxicity, we evaluated the effect of eplerenone (Epl) on renal structural damage and renal dysfunction in adrenalectomized (ADX) rats, and assessed whether the therapeutic benefit was associated with reduction of transforming growth factor-β1 (TGF-β1), connective tissue growth factor (CTGF), plasminogen activator inhibitor type 1 (PAI-1) and collagen I (COL-I) expression. Methods: Male Sprague-Dawley rats fed a normal-sodium diet were divided in four groups: sham-ADX, ADX, CsA, or Epl. Rats in the ADX, CsA and Epl groups were adrenalectomized first. Aldosterone, sodium and potassium levels in serum and urine were measured on the second day. Two weeks later, vehicle (sham-ADX and ADX group), CsA (25mg/kg/d), or CsA and Epl (100 mg/ kg/d) combination was administrated, respectively. After six weeks, urinary protein, creatinine clearance (C cr ), tubulointerstitial fibrosis (TIF), aldosterone level in kidney, and renal aldosterone synthase CYP11B2, COL-I, TGF-β1, CTGF and PAI-1 gene expression levels were determined. Results: On the second day after surgery, adrenalectomized rats showed undetectable aldosterone with natriuresis, hyponatremia, decreased urinary potassium excretion and hyperpotassemia. CsA reduced C cr , induced urinary proteins and up-regulated COL-I, TGF-β1, CTGF and PAI-1 gene expression with a significant development of TIF. Eplerenone administration prevented TIF and COL-I, TGF-β1 and PAI-1 up-regulation but did not improve renal function. Conclusion: Our results suggest local renal aldosterone is an important mediator of renal injury induced by CsA.

A novel algorithm for maximum power point tracking (MPPT) in solar power-generating systems was proposed and compared with the conventional methods of hill climbing, perturbation and observation, and incremental conductance. The three conventional algorithms are prone to divergence under low irradiance levels (

In recent years, direct current power (DC) supply systems have been applied in various types of industrial equipment. As a common practice, an alternating current(AC)/DC converter will be introduced at the system front end to convert mains into DC power sources for system operation. However, when connecting to a power grid, under the influence of load-end characteristics, the phase difference between the voltage and current of the input AC power source will increase. This decreases the power factor (PF), reduces power quality, and affects the stability of system operation. In this paper, three types of circuit topologies (traditional, bridgeless, and interleaved boost) for power factor correction converters are compared and discussed. Moreover, features as well as pros and cons of these topologies are analysed. Finally, a prototype for each topology is studied and implemented. Further, the efficiency, PF, current harmonic distortion, and ripple are compared and examined based on experimental results.

Rationale IgG4-related disease (IgG4-RD) is a systemic autoimmune disease and mixed cryoglobulinemia may be caused by autoimmune diseases. However, so far only 1 case of IgG4-RD complicated with mixed cryoglobulinemia is reported. Our case further confirms the close relationship between these 2 diseases. Patient concerns A 55-year-old female was admitted because of dry mouth and teeth falling off. Diagnoses The patient was diagnosed as IgG4-related sialadenitis (IgG4-RS) complicated with type III mixed cryoglobulinemia. IgG4-RS was confirmed by elevated serum IgG4 levels and diffuse IgG4 plasmocyte infiltration and storiform fibrosis in the interstitium of labial gland. Type III mixed cryoglobulinemia was confirmed by positive serum cryoglobulins and no monoclonal immunoglobulin in serum and urine. Interventions and outcomes After treatment with prednisone and cyclophosphamide, serum cryoglobulins rapidly turned negative with the remission of IgG4-RS. Lessons Type III mixed cryoglobulinemia can be caused by IgG4-RS, and the underlying mechanisms need to be further explored.

In this paper, the ac impedance analysis is used to explore the optimal discharging frequency for a Li-ion battery. Experiments indicate that the optimal discharging frequency is at the minimum ac impedance frequency fZmin for a conventional pulse-current (PC) discharging. In addition, a sinusoidal current (SC) discharging strategy is also proposed to achieve better performances. This SC discharging strategy improves the discharging capacity, discharging efficiency, and rising temperature of the Li-ion battery by about 1.3%, 1.32%, and 41.9%, respectively, as compared with the traditional constant-current discharging.