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Software-as-a-Service is becoming the prevalent way of software delivery. The popularisation of microservices architecture and containers has facilitated the efficient development of complex SaaS applications. Yet, for average SaaS vendors, there are a lot of challenges in managing microservices at a large scale while meeting the Quality-of-Service constraints. In this paper, we present SmartVM, a business Service-Level-Agreement (SLA) aware, microservice-centric deployment framework, designed to streamline the process of building and deploying dynamically-scalable microservices that can handle traffic spikes in a cost-efficient manner. We also compare our approach with traditional monolithic and the state-of-the-art microservice deployment approaches. The evaluation results show our approach advances in deployment cost, resource utilisation, and SLA compliance. [ABSTRACT FROM AUTHOR]

Background: Diabetic retinopathy (DR), a prevalent complication in patients with type 2 diabetes, has attracted increasing attention. Recent studies have explored a plausible association between retinopathy and significant liver fibrosis. The aim of this investigation was to develop a sophisticated machine learning (ML) model, leveraging comprehensive clinical datasets, to forecast the likelihood of significant liver fibrosis in patients with retinopathy and to interpret the ML model by applying the SHapley Additive exPlanations (SHAP) method. Methods: This inquiry was based on data from the National Health and Nutrition Examination Survey 2005–2008 cohort. Utilizing the Fibrosis-4 index (FIB-4), liver fibrosis was stratified across a spectrum of grades (F0-F4). The severity of retinopathy was determined using retinal imaging and segmented into four discrete gradations. A ten-fold cross-validation approach was used to gauge the propensity towards liver fibrosis. Eight ML methodologies were used: Extreme Gradient Boosting, Random Forest, multilayer perceptron, Support Vector Machines, Logistic Regression (LR), Plain Bayes, Decision Tree, and k-nearest neighbors. The efficacy of these models was gauged using metrics, such as the area under the curve (AUC). The SHAP method was deployed to unravel the intricacies of feature importance and explicate the inner workings of the ML model. Results: The analysis included 5,364 participants, of whom 2,116 (39.45%) exhibited notable liver fibrosis. Following random allocation, 3,754 individuals were assigned to the training set and 1,610 were allocated to the validation cohort. Nine variables were curated for integration into the ML model. Among the eight ML models scrutinized, the LR model attained zenith in both AUC (0.867, 95% CI: 0.855–0.878) and F1 score (0.749, 95% CI: 0.732–0.767). In internal validation, this model sustained its superiority, with an AUC of 0.850 and an F1 score of 0.736, surpassing all other ML models. The SHAP methodology unveils the foremost factors through importance ranking. Conclusion: Sophisticated ML models were crafted using clinical data to discern the propensity for significant liver fibrosis in patients with retinopathy and to intervene early. Practice implications: Improved early detection of liver fibrosis risk in retinopathy patients enhances clinical intervention outcomes. [ABSTRACT FROM AUTHOR]

Durability is one of the technical bottlenecks restricting fuel cell electric vehicle development. As a result, significant time and resources have been invested in research related to this area worldwide. Current durability research mainly focuses on the single cell and stack levels, which is quite different from the usage scenarios of actual vehicles. There is almost no research on developing durability test cycles on the fuel cell system level. This paper proposes a universal model for developing a durability test cycle for fuel cell system based on the China automotive test cycle. Large-scale comparison tests of the fuel cell systems are conducted. After 1000 h test, the output performance degradation of three mass-produced fuel cell system is 14.49%, 9.59%, and 4.21%, respectively. The test results show that the durability test cycle proposed in this paper can effectively accelerate the durability test of the fuel cell system and evaluate the durability performance of the fuel cell system. Moreover, the methodology proposed in this paper could be used in any other test cycles such as NEDC (New European Driving Cycle), WLTC (Worldwide Harmonized Light Vehicles Test Procedure), etc. And it has comprehensive application value and are significant for reducing the cost of durability testing of fuel cell systems and promoting the industrialization of fuel cell electric vehicles. [ABSTRACT FROM AUTHOR]

Al-Baali et al. (Comput. Optim. Appl. 60:89–110, 2015) have proposed a three-term conjugate gradient method which satisfies a sufficient descent condition and global convergence. In this paper, we extend this method to a family of three-term conjugate gradient projection methods with a restart procedure and their relaxed-inertial versions for constrained nonlinear pseudo-monotone equations. The accelerated gradient-descent method MSM and the relaxed-inertial strategy are incorporated into the proposed methods to obtain better computational performance. The global convergence of the extended methods are established theoretically without the Lipschitz continuity of the underlying mapping. Numerical results on constrained nonlinear equations show that the extended methods with different settings are efficient. The applicability and efficiency of the extended methods in the regularized decentralized logistic regression and sparse signal restoration problems are also presented and verified. [ABSTRACT FROM AUTHOR]

The development of a hydrogen energy-based society is becoming the solution for more and more countries. Fuel cell electric vehicles are the best carriers for developing a hydrogen energy-based society. The current research on hydrogen leakage and the diffusion of fuel cell electric vehicles has been sufficient. However, the study of hydrogen safety has not reduced the safety concerns for society and government management departments, concerning the large-scale promotion of fuel cell electric vehicles. Hydrogen safety is both a technical and psychological issue. This paper aims to provide a comprehensive overview of fuel cell electric vehicles' hydrogen dispersion and the burning behavior and introduce the relevant work of international standardization and global technical regulations. The CFD simulations in tunnels, underground car parks, and multistory car parks show that the hydrogen escape performance is excellent. At the same time, the research verifies that the flow, the direction of leakage, and the vehicle itself are the most critical factors affecting hydrogen distribution. The impact of the leakage location and leakage pore size is much smaller. The relevant studies also show that the risk is still controllable even if the hydrogen leakage rate is increased ten times the limit of GTR 13 to 1000 NL/min and then ignited. Multi-vehicle combustion tests of fuel cell electric vehicles showed that adjacent vehicles were not ignited by the hydrogen. This shows that as long as the appropriate measures are taken, the risk of a hydrogen leak or the combustion of fuel cell electric vehicles is controllable. The introduction of relevant standards and regulations also indirectly proves this point. This paper will provide product design guidelines for R&D personnel, offer the latest knowledge and guidance to the regulatory agencies, and increase the public's acceptance of fuel cell electric vehicles. [ABSTRACT FROM AUTHOR]

Abstract: A spark discharge plasmatron was designed. Hydrogen was produced through partial oxidation reforming of DME by spark discharge plasma at atmospheric pressure and ambient temperature. The effects of voltage of electric source, arc frequency, air-to-DME ratio (by mole), DME feed flow rate and arc length on gas concentrations (Vol%), output power of electric source, energy consumption and thermal efficiency were investigated. The experimental results show that when arc frequency is 120Hz, air-to-DME ratio is 2, DME feed flow rate is 20mL/min, voltage of electric source is 12V and arc length is 9mm, hydrogen concentration is 7%, hydrogen yield is 8.5%, energy consumption is 75.5kJ/L-H2 and thermal efficiency is 4.2%. The output power of electric source is different at different arc frequency. [Copyright &y& Elsevier]