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The proliferation of Internet of Things (IoT) devices and their integration into critical infrastructure and business operations has rendered them susceptible to malware and cyber-attacks. Such malware presents a threat to the availability and reliability of IoT devices, and a failure to address it can have far-reaching impacts. Due to the limited resources of IoT devices, traditional rule-based detection systems are often ineffective against sophisticated attackers. This paper addressed these issues by designing a new framework that uses a machine learning (ML) algorithm for the detection of malware. Additionally, it also employed sequential detection architecture and evaluated eight malware datasets. The design framework is lightweight and effective in data processing and feature selection algorithms. Moreover, this work proposed a classification model that utilizes one support vector machine (SVM) algorithm and is individually tuned with three different optimization algorithms. The employed optimization algorithms are Nuclear Reactor Optimization (NRO), Artificial Rabbits Optimization (ARO), and Particle Swarm Optimization (PSO). These algorithms are used to explore a diverse search space and ensure robustness in optimizing the SVM for malware detection. After extensive simulations, our proposed framework achieved the desired accuracy among eleven existing ML algorithms and three proposed ensemblers (i.e., NRO_SVM, ARO_SVM, and PSO_SVM). Among all algorithms, NRO_SVM outperforms the others with an accuracy rate of 97.8%, an F1 score of 97%, and a recall of 99%, and has fewer false positives and false negatives. In addition, our model successfully identified and prevented malware-induced attacks with a high probability of recognizing new evolving threats.

Small nucleolar RNA host gene 15 (SNHG15) plays an oncogenic role in many cancers. However, the role of SNHG15 in bladder cancer (BLCA) remains unclear. In this study, the regulation of SNHG15 on the activities of BLCA cells (T24 and RT112) was investigated. In detail, super-enhancers (SEs), differentially expressed genes, and functional enrichment were detected by bioinformatic analyses. Mutant cell lines lacking SNHG15-SEs were established using CRISPR-Cas9. Relative gene expression was detected by quantitative polymerase chain reaction (qPCR), western blot, in situ hybridization, and immunohistochemistry assays. Cell senescence, apoptosis, viability, and proliferation were measured. Chromatin immunoprecipitation (ChIP)-qPCR and luciferase reporter gene assays were conducted to analyze the interactions between genes. A novel super-enhancer of SNHG15 (SNHG15-SEs) was discovered in several BLCA datasets. The deletion of SNHG15-SEs resulted in a significant downregulation of SNHG15. Mechanistically, the core active region of SNHG15-SEs recruited the transcription factor FOSL1 to facilitate the SNHG15 transcription, thereby inducing the proliferation and metastasis of BLCA cells. Deletion of SNHG15-SEs inhibited the growth and metastasis of T24 and RT112 cells by inactivating the WNT/CTNNB1 pathway activation. Overexpression of FOSL1 in SNHG15-SEs restored the cell proliferation and metastasis. Next, a xenograft mouse model showed that SNHG15-SEs deletion inhibited the proliferation and metastasis of BLCA cells in vivo. Collectively, our data indicate that SNHG15-SEs recruit FOSL1 to promote the expression of SNHG15 which interacts with CTNNB1 in the nucleus to activate the transcription of ADAM12, leading to the malignance of BLCA cells.

Unconstrained optimization problems often arise from mining of big data and scientific computing. On the basis of a modified gradient-difference, this article aims to present a new three-term conjugate gradient algorithm to efficiently solve unconstrained optimization problems. Compared with the existing nonlinear conjugate gradient algorithms, the search directions in this algorithm are always sufficiently descent independent of any line search, as well as having conjugacy property. Using the standard Wolfe line search, global and local convergence of the proposed algorithm is proved under mild assumptions. Implementing the developed algorithm to solve 750 benchmark test problems available in the literature, it is shown that the numerical performance of this algorithm is remarkable, especially in comparison with that of the other similar efficient algorithms.

Globally, prostate cancer remains a leading cause of mortality and morbidity despite advances in treatment. Research on prostate cancer has primarily focused on the malignant epithelium, but the tumor microenvironment has recently been recognized as an important factor in the progression of prostate cancer. Cancer-associated fibroblasts (CAFs) play an important role in prostate cancer progression among multiple cell types in the tumor microenvironment. In order to develop new treatments and identify predictive and prognostic biomarkers for CAFs, further research is needed to understand the mechanism of action of prostate cancer and CAF. In this work, we performed the single-cell RNA sequence analysis to obtain the biomarkers for CAFs, and ten genes were finally regarded as the marker genes for CAFs. Based on the ssGSEA algorithm, the prostate cancer cohort was divided into low- and high-CAFs groups. Further analysis revealed that the CAFs-score is associated with many immune-related cells and immune-related pathways. In addition, between the low- and high-CAFs tissues, a total of 127 hub genes were discovered, which is specific in CAFs. After constructing the prognostic prediction model, SLPI, VSIG2, CENPF, SLC7A1, SMC4, and ITPR2 were finally regarded as the key genes in the prognosis of patients with prostate cancer. Each patient was assigned with the risk score as follows: SLPI* 0.000584811158157081 + VSIG2 * -0.01190627068889 + CENPF * -0.317826812875334 + SLC7A1 * -0.0410213995358753 + SMC4 * 0.202544454923637 + ITPR2 * -0.0824652047622673 + TOP2A * 0.140312081524807 + OR51E2 * -0.00136602095885459. The GSVA revealed the biological features of CAFs, many cancer-related pathways, such as the adipocytokine signaling pathway, ERBB signaling pathway, GnRH signaling pathway, insulin signaling pathway, mTOR signaling pathway and PPAR signaling pathway are closely associated with CAFs. As a result of these observations, similar transcriptomics may be involved in the transition from normal fibroblasts to CAFs in adjacent tissues. As one of the biomarkers for CAFs, CENPF can promote the proliferation ability of prostate cancer cells. The overexpress of CENPF could promote the proliferation ability of prostate cancer cells. In conclusion, we discuss the potential prognostic and therapeutic value of CAF-dependent pathways in prostate cancer.

With the explosive growth of data at the edge in the Industrial Internet of Things (IIoT), edge devices are increasingly performing more data processing tasks to alleviate the load on cloud servers. To achieve this goal, Programmable Logic Controllers (PLCs) are gradually transitioning into edge PLCs. However, efficiently executing a large number of computational tasks in memory-limited edge PLCs is a significant challenge. Therefore, there is a need to design an efficient memory allocation strategy for edge PLCs. This paper proposes a dynamic memory allocation strategy for edge PLCs. It adopts an approach of organizing memory into small blocks to handle memory requests from real-time tasks and utilizes a well-performing dynamic programming method for resource allocation problems to handle memory requests from non-real-time tasks. This approach ensures real-time performance while improving the efficiency of non-real-time task processing. In the simulation experiments, the algorithm implemented based on this allocation strategy is compared with the default method and several open-source memory allocators. The experimental results demonstrate that the proposed algorithm, on average, improves the speed of real-time task processing by 13.7% and achieves a maximum speed improvement of 17.0% for non-real-time task processing. The experimental results show that the allocation strategy effectively improves memory allocation efficiency in memory-limited environments.

Chinese herbal medicine (CHM), which includes herbal slices and proprietary products, is widely used in China. Shenqi Dihuang (SQDH) is a traditional Chinese medicine (TCM) formula with ingredients that affect tumor growth. Despite recent advances in prognosis, patients with renal cell carcinoma (RCC) cannot currently receive curative treatment. The present study aimed to explore the potential target genes closely associated with SQDH. The gene expression data for SQDH and RCC were obtained from the TCMSP and TCGA databases. The SQDH-based prognostic prediction model reveals a strong correlation between RCC and SQDH. In addition, the immune cell infiltration analysis indicated that SQDH might be associated with the immune response of RCC patients. Based on this, we successfully built the prognostic prediction model using SQDH-related genes. The results demonstrated that CCND1 and NR3C2 are closely associated with the prognosis of RCC patients. Finally, the pathways enrichment analysis revealed that response to oxidative stress, cyclin binding, programmed cell death, and immune response are the most enriched pathways in CCND1. Furthermore, transcription regulator activity, regulation of cell population proliferation, and cyclin binding are closely associated with the NR3C2.

Previous work has shown that helical domain walls can form between states of different spin-polarization during a ferromagnetic spin transition in the fractional quantum Hall regime. Here, the authors study the transport through a single helical domain wall and find strong deviations from a simplified theory of weakly interacting edge channels.

Renal cell carcinoma (RCC) is one of the leading causes of death in men. Messenger ribonucleic acid (mRNA) vaccines may be an attractive means to achieve satisfactory results. Cancer immunotherapy is a promising cancer treatment strategy. However, immunotherapy is not widely used in renal cell carcinoma, as only a few patients show a positive response. The present study aimed to identify potential antigens associated with renal cell carcinoma to develop an anti-renal cell carcinoma mRNA vaccine. Moreover, the immune subtypes of renal cell carcinoma cells were determined. The Cancer Genome Atlas (TCGA) analysis revealed gene expression profiles and clinical information. Antigen-presenting cells infiltrated the immune system using Tumor Immune Estimation Resource (TIMER) tool (http://timer.cistrome.org/). GDSC (Genomics of Drug Sensitivity in Cancer) database were used to estimate drug sensitivity. The 13 immune-related genes discovery could be targets for immunotherapy in renal cell carcinoma patients, as they were associated with a better prognosis and a higher level of antigen-presenting cells. These immune subtypes have significant relationships with immunological checkpoints, immunogenic cell death regulators, and RCC prognostic variables. Furthermore, DBH-AS1 was identified as a potential antigen for developing an mRNA vaccine. The CCK8 assay demonstrated that the proliferative capacity of 786-O and Caki-1 cells overexpressing DBH-AS1 was higher than in the control group. In addition, transwell assay revealed that 786-O and Caki-1 cells overexpressing DBH-AS1 showed higher invasion capacity compared with control. This study provides a theoretical basis for the development of mRNA vaccines. Our findings suggest that DBH-AS1 could be potential antigens for developing RCC mRNA vaccines.

The small molecule drug 5-fluorouracil (5-FU) is widely used in the treatment for gastric cancer (GC), however, it exerts poor efficacy and is associated with acquired and intrinsic resistance. Focal adhesion kinase (FAK), a non-receptor tyrosine kinase, plays a key role in adhesion, migration, and proliferation of gastric carcinoma cells, suggesting that this kinase may be a promising therapeutic target. Differentially expressed FAK in GC tissue was detected by RT-qPCR and TCGA database analysis. To investigate the biological functions of FAK, loss-of-function experiments were performed. CCK-8 assay, colony formation assay, flow cytometry, dual-luciferase reporter assays, and western blot assays were conducted to determine the underlying mechanisms of FAK in 5-FU chemosensitivity in GC. FAK is overexpressed in GC patients, and positively correlated with poor prognosis. The use of shRNA interference to target FAK decreased proliferation and increased apoptosis of GC cells in vitro. Importantly, FAK silencing enhanced the therapeutic efficacy of 5-FU, leading to reduced tumor growth in vivo. We further demonstrated that FAK silencing increased 5-FU-induced caspase-3 activity, and promoted p53 transcriptional activities. Clinical data also has shown that patients with higher levels of FAK had significantly shorter overall survival (OS) and time to first progression (FP) than those with lower levels of FAK. These findings indicate that FAK plays a critical role in 5-FU chemosensitivity in GC, and the use of FAK inhibitors as an adjunct to 5-FU might be an effective strategy for patients who undergo chemotherapy.

In this paper, a new spectral scaling memoryless Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is developed for solving large scale unconstrained optimization problems, where the scaling parameter is chosen so as to minimize all the eigenvalues of search direction matrices. The search directions in this algorithm are proved to satisfy the approximate Dai-Liao conjugate condition. With this advantage of the search directions, a scaling memoryless BFGS update formula is constructed and an algorithm is developed by incorporating acceleration strategy of line search and restart criterion. Under mild assumptions, global convergence of the algorithm is proved. Numerical tests demonstrate that the developed algorithm is more robust and efficient in solving large scale benchmark test problems than the similar ones in the literature.

We retrospectively reviewed data for 286 patients with varicocele who underwent microsurgical subinguinal varicocelectomy from March 2015 to May 2017 in Shanghai General Hospital (Shanghai, China). In this surgical approach, the testis was delivered, and the gubernacular and external cremasteric veins were stripped. In addition, the spermatic cord was delivered downward with continuous double traction away from the external ring. The remaining procedure was similar to the conventional approach. We followed patients for at least 3 months and evaluated postoperative semen parameters, pain symptoms, and complications. We excluded data for 32 men due to inadequate follow-up (

Using quantitative structure-activity relationship (QSAR) and molecular docking to study the interaction between levofloxacin and thiadiazole histone deacetylase inhibitors. The molecular electrical distance vector (MEDV) and multiple linear regression (MLR) were used to study the relationship between the structure and activity of compounds. The three equations obtained by multiple linear regression, their R2 = 0.976, 0.985 and 0.976, and their Rcv2 = 0.949, 0.977 and 0.932. The structures that affect HDAC1 and HDAC6 activity are -O- and -S-, and the structures that affect HDAC2 are -C- and -N-. Finally, molecular docking is used to study the binding of receptors and drug molecules to provide guidance for future drug design.

Abstract Thickness‐dependent chemical and physical properties have gained tremendous interest since the emergence of two‐dimensional (2D) materials. Despite attractive prospects, the thickness‐controlled synthesis of ultrathin nanosheets remains an outstanding challenge. Here, a chemical vapor deposition (CVD) route is reported to controllably synthesize high‐quality PtSe2 nanosheets with tunable thickness and explore their thickness‐dependent electronic and magnetotransport properties. Raman spectroscopic studies demonstrate all Eg, A1g, A2u, and Eu modes are red shift in thicker nanosheets. Electrical measurements demonstrate the 1.7 nm thick nanosheet is a semiconductor with room temperature field‐effect mobility of 66 cm2 V−1 s−1 and on/off ratio of 106. The 2.3–3.8 nm thick nanosheets show slightly gated modulation with high field‐effect mobility up to 324 cm2 V−1 s−1 at room‐temperature. When the thickness is over 3.8 nm, the nanosheets show metallic behavior with conductivity and breakdown current density up to 6.8 × 105 S m–1 and 6.9 × 107 A cm−2, respectively. Interestingly, magnetoresistance (MR) studies reveal magnetic orders exist in this intrinsically non‐magnetic material system, as manifested by the thickness‐dependent Kondo effect, where both metal to insulator transition and negative MR appear upon cooling. Together, these studies suggest that PtSe2 is an intriguing system for both developing novel functional electronics and conducting fundamental 2D magnetism study.

Isothermal compression behaviors of as-extruded AA 2055 alloy (T6 state) were studied at temperature of 320, 380, 440 and 500 °C with strain rate of 0.001, 0.01, 0.1 and 1 s−1 by a Gleeble-3800 testing machine. A modified Johnson–Cook model fitted by polynomial and power-exponential functions were established to describe the flow stress of the alloy. The constitutive models fitted by higher-order polynomials were more accurate than the ones fitted by second-order polynomial and power-exponential functions. The constitutive model fitted by a fourth-order polynomial was chosen for the optimal constitutive model in order to balance the prediction accuracy and model complexity. The modified Johnson–Cook constitutive model could predict the flow stress well, especially in high-temperature zone (around 500 °C) and low-temperature zone (around 320 °C). The dynamic precipitation and dissolution of the T1 phase during hot compression were discussed. The unusual dynamic precipitation of the T2 phase was investigated during hot compression by XRD and TEM. The massive dense fine precipitates effectively pinned dislocations or subgrain boundaries to accelerate DRV but suppressed DRX, leading to a low frequency of HAGBs in compressed samples.

Lipreading refers to the task of decoding the text content of a speaker based on visual information about the movement of the speaker’s lips. With the development of deep learning in recent years, lipreading has attracted extensive research. However, the deep learning method requires a lot of computing resources, which is not conducive to the migration of the system to edge devices. Inspired by the work of Spiking Neural Networks (SNNs) in recognizing human actions and gestures, we propose a lipreading system based on SNNs. Specifically, we construct the front-end feature extractor of the system using Liquid State Machine (LSM). On the other hand, a heuristic algorithm is used to select appropriate parameters for the classifier in the backend. On small-scale lipreading datasets, our recognition accuracy achieves good results. We claim that our network performs better in terms of accuracy and ratio of learned parameters compared to other networks, and has superior advantages in terms of network complexity and training cost. On the AVLetters dataset, our model achieves a 5% improvement in accuracy over traditional methods and a 90% reduction in parameters over the state-of-the-art.

PurposeWe aimed to construct an immune-related long noncoding ribonucleic acids (irlncRNA) signature to evaluate the prognosis of patients without specific expression level of these irlncRNA.MethodsThe raw transcriptome data were downloaded from The Cancer Genome Atlas (TCGA), irlncRNAs were filtered out using an online immune related gene database and coexpression analysis, differently expressed irlncRNA (DEirlncRNA) pairs were identified by univariate analysis. The areas under curve (AUC) were compared and the Akaike information criterion (AIC) values of receiver operating curve (ROC) was counted, the most optimal model was constructed to divide bladder cancer patients into high- and low-risk groups usingõ the cut-off point of ROC. Then, we evaluated them from multiple perspectives, such as survival time, clinic-pathological characteristics, immune-related cells infiltrating, chemotherapeutics efficacy and immune checkpoint inhibitors.Results14 DEirlncRNA pairs were included in this signature. Patients in high-risk groups demonstrated apparent shorter survival time, more aggressive clinic-pathological characteristics, different immune-related cells infiltrating status, lower chemotherapeutics efficacy.ConclusionThe irlncRNA signature demonstrated a promising prediction value for bladder cancer patients and was important in guiding clinical treatment.

Breast cancer is one of the leading causes of cancer-related deaths in women. Chemotherapy remains one of the main clinical treatments for breast cancer. However, this therapy has appreciable side effects. Nanoscale carriers, such as metal nanoparticles and liposomes, are being widely utilized as drug delivery vehicles to achieve precise targeting of tumor cells. In this study, we designed a novel upconversion nanocarriers (UCNCs) host-guest complexation system based on a photolabile capping-like molecule. UCNCs containing epirubicin (EPI) were grafted with a dimer-targeting peptide via cyclodextrin-adamantine host-guest complexation. After entering breast tumor cells with the guidance of the dimer-targeting peptide, the core of the UCNCs upconverted near-infrared light to ultraviolet light which subsequently triggered the intracellular on-demand release of epirubicin. The precise and efficient delivery and release of epirubicin inside breast cancer cells significantly inhibited cancer cells proliferation, migration, and invasion in vitro and decreased the tumor size in vivo. We believe that this UCNCs system is a promising platform for the precise and controllable delivery of various chemotherapy drugs for clinical cancer treatments.

It has been shown that reciprocal cross allodiploid lineage with sub-genomes derived from the cross of Megalobrama amblycephala (BSB) × Culter alburnus (TC) generates the variations in phenotypes and genotypes, but it is still a challenge to deeply mine biological information in the transcriptomic profile of this lineage owing to its genomic complexity and lack of efficient data mining methods. In this paper, we establish an optimization model by non-negative matrix factorization approach for deeply mining the transcriptomic profile of the sub-genomes in hybrid fish lineage. A new so-called spectral conjugate gradient algorithm is developed to solve a sequence of large-scale subproblems such that the original complicated model can be efficiently solved. It is shown that the proposed method can provide a satisfactory result of taxonomy for the hybrid fish lineage such that their genetic characteristics are revealed, even for the samples with larger detection errors. Particularly, highly expressed shared genes are found for each class of the fish. The hybrid progeny of TC and BSB displays significant hybrid characteristics. The third generation of TC-BSB hybrid progeny (BTF3 and TBF3) shows larger trait separation.

Recovery of waste electrical and electronic equipment (WEEE) plays an important role in protecting environment and conserving resources. Design of a more efficient WEEE recovery system is an imperative need for the relevant decision-makers, such as alleviation of overcapacity or insufficient recycling in many developing countries. In this paper, we optimize the WEEE recovery network which is associated with recycling prices and government subsidies by a nonlinear mixed integer programming approach. An integrated model is first proposed to formulate a design problem of WEEE recovery network, being involved with collection centers, two types of transfer stations, processing centers, incineration plants, landfill plants, secondhand product markets, and government subsides. The recycling prices and the transported quantities of WEEE (the number of batches) are endogenous variables of the model, being subject to a number of practical constraints. For solving this model, an algorithm is developed based on the branch and bound method. Scenario analysis and numerical experiments indicate that: (1) appropriate capacities of transfer stations can be provided by the proposed model for designing an environmentally and economically efficient WEEE recycling system, especially for alleviating the existing overcapacity or insufficient recycle. (2) An optimal governmental subsidy can be obtained in virtue of the proposed model and algorithm. (3) Diversity of transportation modes and permission of more than one mode in the same delivery route can greatly reduce the cost of recycling WEEE. (4) Preferred awareness of environmental protection can increase the profit and the recycled quantities, as well as reduction of the total recycling cost.

With rapid development of technology and improvement of living standards, the per capita holding of automobiles greatly increases, and the amount of end-of-life vehicles (ELVs) becomes larger and larger such that it is valuable to investigate an effective strategy for recycling ELVs from the viewpoints of environmental protection and resource utilization. In this paper, an optimization model with fuzzy and stochastic parameters is built to formulate the transportation planning problems of recycling ELVs in polymorphic uncertain environment, where the unit processing and transportation costs, the selling price of reused items, and the fixed cost are all fuzzy, while the demand in secondary market and the production capacity are random owing to features underlying the practical data. For this complicated polymorphic uncertain optimization model, a unified compromising approach is proposed to hedge the uncertainty of this model such that some powerful optimization algorithms can be applied to make an optimal recycling plan. Then, an interactive algorithm is developed to find a compromising solution of the uncertain model. Numerical results show efficiency of the algorithm and a number of important managerial insights are revealed from the proposed model by scenario analysis and sensitivity analysis.

In this paper, we develop two algorithms to solve a nonlinear system of symmetric equations. The first is an algorithm based on modifying two Broyden–Fletcher–Goldfarb–Shanno (BFGS) methods. One of its advantages is that it is more suitable to effectively solve a small-scale system of nonlinear symmetric equations. In contrast, the second algorithm chooses new search directions by incorporating an approximation method of computing the gradients and their difference into the determination of search directions in the first algorithm. In essence, the second one can be viewed as an extension of the conjugate gradient method recently proposed by Lv et al. for solving unconstrained optimization problems. It was proved that these search directions are sufficiently descending for the approximate residual square of the equations, independent of the used line search rules. Global convergence of the two algorithms is established under mild assumptions. To test the algorithms, they are used to solve a number of benchmark test problems. Numerical results indicate that the developed algorithms in this paper outperform the other similar algorithms available in the literature.

We report the fabrication of polyvinylidene fluoride (tetrabutyl titanate)/polyvinyl pyrrolidone ((tetrabutyl titanate))-graphene quantum dots [PVDF(TBT)/PVP(TBT)-GQDs] film photocatalyst with enhanced photocatalytic performance. The polyvinylidene fluoride (tetrabutyl titanate)/polyvinyl pyrrolidone ((tetrabutyl titanate)) [PVDF(TBT)/PVP(TBT)] film was first prepared with a dual-electrospinning method and then followed by attaching graphene quantum dots (GQDs) to the surface of the composite film through a hydrothermal method. Later, part of the PVP in the composite film was dissolved by a hydrothermal method. As a result, a PVDF(TBT)/PVP(TBT)-GQDs film photocatalyst with a larger specific surface area was achieved. The photocatalytic degradation behavior of the PVDF(TBT)/PVP(TBT)-GQDs film photocatalyst was examined by using Rhodamine B as the target contaminant. The PVDF(TBT)/PVP(TBT)-GQDs photocatalyst showed a higher photocatalytic efficiency than PVDF(TBT)-H2O, PVDF(TBT)/PVP(TBT)-H2O, and PVDF(TBT)-GQDs, respectively. The enhanced photocatalytic efficiency can be attributed to the broader optical response range of the PVDF(TBT)/PVP(TBT)-GQDs photocatalyst, which makes it useful as an effective photocatalyst under white light irradiation.

In multi-criteria group decision-making (MCGDM), one of the most important problems is to determine the weights of criteria and experts. This paper intends to present two Min-Max models to optimize the point estimates of the weights. Since each expert generally possesses a uniform viewpoint on the importance (weighted value) of each criterion when he/she needs to rank the alternatives, the objective function in the first model is to minimize the maximum variation between the actual score vector and the ideal one for all the alternatives such that the optimal weights of criteria are consistent in ranking all the alternatives for the same expert. The second model is designed to optimize the weights of experts such that the obtained overall evaluation for each alternative can collect the perspectives of the experts as many as possible. Thus, the objective function in the second model is to minimize the maximum variation between the actual vector of evaluations and the ideal one for all the experts, such that the optimal weights can reduce the difference among the experts in evaluating the same alternative. For the constructed Min-Max models, another focus in this paper is on the development of an efficient algorithm for the optimal weights. Some applications are employed to show the significance of the models and algorithm. From the numerical results, it is clear that the developed Min-Max models more effectively solve the MCGDM problems including the ones with incomplete score matrices, compared with the methods available in the literature. Specifically, by the proposed method, (1) the evaluation uniformity of each expert on the same criteria is guaranteed; (2) The overall evaluation for each alternative can collect the judgements of the experts as many as possible; (3) The highest discrimination degree of the alternatives is obtained.

(Tb0.6Y0.4)3Al5O12 transparent ceramics were successfully fabricated by solid-state reactive sintering using Tb4O7, Y2O3, and α-Al2O3 powders as raw materials. The effect of (Tb+Y)/Al ratio on microstructure evolution and densification process was investigated in detailed. The results showed that the grain growth kinetics were significantly affected by (Tb+Y)/Al ratio. Al-rich and Tb-rich phases appeared in part of the samples of different ratios. Particularly, excess aluminum increased the diffusing process, leading to a higher densification rate, while samples with excess terbium ratios displayed a smaller grain size and lower relative density. The optical quality was highly related to the amount of the secondary phase produced by different (Tb+Y)/Al ratios. Finally, (Tb0.6Y0.4)3Al5O12 transparent ceramics have been fabricated through pre-sintering in vacuum, followed by hot isostatic sintering (HIP), and the best transmittance of sample with a 4 mm thickness was approximately 78% at 1064 nm.

A smoothing inexact Newton method is presented for solving nonlinear complementarity problems. Different from the existing exact methods, the associated subproblems are not necessary to be exactly solved to obtain the search directions. Under suitable assumptions, global convergence and superlinear convergence are established for the developed inexact algorithm, which are extensions of the exact case. On the one hand, results of numerical experiments indicate that our algorithm is effective for the benchmark test problems available in the literature. On the other hand, suitable choice of inexact parameters can improve the numerical performance of the developed algorithm.

This paper describes a disturbance acceleration adaptive estimate and correction approach for an attitude reference system (ARS) so as to improve the attitude estimate precision under vehicle movement conditions. The proposed approach depends on a Kalman filter, where the attitude error, the gyroscope zero offset error and the disturbance acceleration error are estimated. By switching the filter decay coefficient of the disturbance acceleration model in different acceleration modes, the disturbance acceleration is adaptively estimated and corrected, and then the attitude estimate precision is improved. The filter was tested in three different disturbance acceleration modes (non-acceleration, vibration-acceleration and sustained-acceleration mode, respectively) by digital simulation. Moreover, the proposed approach was tested in a kinematic vehicle experiment as well. Using the designed simulations and kinematic vehicle experiments, it has been shown that the disturbance acceleration of each mode can be accurately estimated and corrected. Moreover, compared with the complementary filter, the experimental results have explicitly demonstrated the proposed approach further improves the attitude estimate precision under vehicle movement conditions.

A modified spectral PRP conjugate gradient method is presented for solving unconstrained optimization problems. The constructed search direction is proved to be a sufficiently descent direction of the objective function. With an Armijo-type line search to determinate the step length, a new spectral PRP conjugate algorithm is developed. Under some mild conditions, the theory of global convergence is established. Numerical results demonstrate that this algorithm is promising, particularly, compared with the existing similar ones.

A new approach was proposed to reformulate the biobjectives optimization model of portfolio management into an unconstrained minimization problem, where the objective function is a piecewise quadratic polynomial. We presented some properties of such an objective function. Then, a class of penalty algorithms based on the well-known conjugate gradient methods was developed to find the solution of portfolio management problem. By implementing the proposed algorithm to solve the real problems from the stock market in China, it was shown that this algorithm is promising.

In this paper, the reliability characteristics of a parallel system are investigated. The parallel system under consideration is made up of three active units that run in parallel, with two of them having to be operational in order for the system to work. The main purpose of this study is to quantify/examine the effect of online and offline preventive maintenance. Preventive maintenance is carried out on the systems in two ways: online and offline preventive maintenance. After the first unit of each system fails, online preventive maintenance is performed. Following the failure of the second unit of each system, offline preventive maintenance is performed. Partial and complete failures are the two types of failures that may occur. Both systems can undergo exponential failure and repair. Using supplementary variable technique, Laplace transform, and Copula repair approach, the system of first-order differential equations associated with system effectiveness, which are crucial to this research, is established and resolved. Tables and graphs are used to illustrate the important findings based on assumed numerical values. System designers, programmers, and maintenance supervisors will be able to create and maintain more crucial systems with the assistance of this research paper. [ABSTRACT FROM AUTHOR]

Objective: This study aims to investigate the molecular mechanisms of Huangqi {Astragalus) in the treatment of premature ovarian insufficiency (POI) using network pharmacology and molecular docking methods. Methods: The active compounds and target proteins of Huangqi were screened from the TCMSP database, while the disease target proteins associated with POI were obtained from the Genecards database. The Venny online tool was used to identify the overlapping genes between the two datasets. Cytoscape software was used to construct a gene regulatory network for the active ingredients of Huangqi. The protein-protein interaction (PPI) network and core genes were selected based on the STRING database and CytoNCA plug-in of Cytoscape. GO (gene ontology) enrichment and KEGG (Kyoto encyclopedia of genes and genomes) pathway enrichment analyses of the overlapping genes were performed using the DAVID database. Molecular docking techniques were used to further elucidate the molecular mechanisms underlying the therapeutic effects. Human ovarian granulosa-like tumor cells (KGN) underwent serum starvation (SS) treatment to induce POI cell models and then received quercetin treatment, and experimentally validated the network pharmacology prediction results by TUNEL staining and Western blotting assay. Results: The TCMSP database provided 32 active compounds and 552 target genes associated with Huangqi, while totally 730 disease target genes associated with POI were screened out from the disease database. Of these, 62 genes were found to be shared between the two datasets. The top 6 compounds based on degree values were quercetin, isorhamnetin, kaempferol, mairin, formononetin and astragaloside IV. The 10 core genes identified were ALB, INS, AKT1, ACTB, TP53, TNF, ESR1, CASP3, STAT3 and PTEN. These potential targets were mainly involved in regulating the JAK2/STAT3 and PI3K/Akt signaling pathway. Molecular docking results revealed strong binding interactions between the compounds and the key proteins STAT3 and Akt1 in the JAK-STAT and PI3K-Akt signalling pathways. The results of in vitro experiments revealed that quercetin could activate the JAK2/STAT3 pathway and inhibit cell apoptosis. Conclusions: Network pharmacology and molecular docking analysis showed that Astragalus could regulate the response of ovarian cells to estradiol, inhibit apoptosis and regulate the JAK-STAT and PI3K-Akt signalling pathways in the treatment of early onset ovarian dysfunction. [ABSTRACT FROM AUTHOR]