An Intelligent Cloud Service Composition Optimization Using Spider Monkey and Multistage Forward Search Algorithms

Web service composition allows developers to create and deploy applications that take advantage of the capabilities of service-oriented computing. Such applications provide the developers with reusability opportunities as well as seamless access to a wide range of services that provide simple and complex tasks to meet the clients’ requests in accordance with the service-level agreement (SLA) requirements. Web service composition issues have been addressed as a significant area of research to select the right web services that provide the expected quality of service (QoS) and attain the clients’ SLA. The proposed model enhances the processes of web service selection and composition by minimizing the number of integrated Web Services, using the Multistage Forward Search (MSF). In addition, the proposed model uses the Spider Monkey Optimization (SMO) algorithm, which improves the services provided with regards to fundamentals of service composition methods symmetry and variations. It achieves that by minimizing the response time of the service compositions by employing the Load Balancer to distribute the workload. It finds the right balance between the Virtual Machines (VM) resources, processing capacity, and the services composition capabilities. Furthermore, it enhances the resource utilization of Web Services and optimizes the resources’ reusability effectively and efficiently. The experimental results will be compared with the composition results of the Smart Multistage Forward Search (SMFS) technique to prove the superiority, robustness, and effectiveness of the proposed model. The experimental results show that the proposed SMO model decreases the service composition construction time by 40.4%, compared to the composition time required by the SMFS technique. The experimental results also show that SMO increases the number of integrated ted web services in the service composition by 11.7%, in comparison with the results of the SMFS technique. In addition, the dynamic behavior of the SMO improves the proposed model’s throughput where the average number of the requests that the service compositions processed successfully increased by 1.25% compared to the throughput of the SMFS technique. Furthermore, the proposed model decreases the service compositions’ response time by 0.25 s, 0.69 s, and 5.35 s for the Excellent, Good, and Poor classes respectively compared to the results of the SMFS Service composition response times related to the same classes.

Optimised Reduction of Surgical Gloves Pinholes using Forward Search Method

This research investigates the factors that affect the existence of pinholes in surgical gloves during the manufacturing process. Since eight factors affect the existence of pinholes in surgical gloves, a two-level fractional factorial design 28-4 was used to study the main effects and the first-order interactions of the multiple variables. Multiple linear regressions are used to model the data. This paper also examines the presence of influential points in the data using the influential measures in linear regression such as Cook’s Distance, DFFITS, DFBETAS, Studentized Residual, Standardized Residual, Hadi's measure, and the robust forward search. The impact of influential points is further assessed through deletion of potential influential points and model selection using adjusted R2, information criterion, and stepwise selection to see whether these influential points significantly improved the existing model.

Non-standard neutrino and Z′ interactions at the FASERν and the LHC

We study the impact of non-standard neutrino interactions in the context of a new gauge boson Z′ in neutral-current deep-inelastic scattering performed in ForwArd Search ExpeRiment-ν (FASERν) and in monojet production at the Large Hadron Collider (LHC). We simulate the neutral-current deep-inelastic neutrino-nucleon scattering νN → νN at FASERν in the presence of an additional Z′ boson, and estimate the anticipated sensitivities to the gauge coupling in a wide range of Z′ mass. At the LHC, we study the effect of Z′ on monojet production, which can be enhanced in regions with large missing transverse momenta. We then use the recent results from ATLAS with an integrated luminosity of 139 fb−1 to improve the limits on the gauge coupling of Z′. We interpret such limits on Z′ gauge couplings as bounds on effective non-standard neutrino interactions. We show that the FASERν and the LHC results cover the medium and high energy scales, respectively, and complement one another.

Research on Particle Collision Forward Search Algorithm and the CFD-DEM Variable Time Step Coupling Calculation Method

In CFD-DEM coupling calculations, an excessively large selection for particle calculation time step affects the calculation accuracy, and an extremely small selection affects the calculation efficiency. A search ball is constructed by taking each target particle as the center particle with the fastest displacement in the calculation domain. Subsequently, the particles that may collide are screened to establish a search list, and a forward search method is used to determine particle collisions. Finally, a particle calculation time step is proposed. The improved DEM method, which automatically adjusts the collision time, resolves the contradiction between particle calculation time step selection, accuracy, and efficiency. The relative error between the numerical simulation results of particle collision and the theoretical solution was less than 3%. The three calculation time steps selected in this study can guarantee excellent calculation accuracy and efficiency. For multi-particle and fluid coupling simulations, the traditional CFD-DEM method selects 10-7s or less in the calculation time step to obtain an accurate solution. The method proposed in this paper selects 10-5s to obtain an accurate solution, which increased the calculation efficiency by 19.8%.

Clustering functional data using forward search based on functional spatial ranks with medical applications

Cluster analysis of functional data is finding increasing application in the field of medical research and statistics. Here we introduce a functional version of the forward search methodology for the purpose of functional data clustering. The proposed forward search algorithm is based on the functional spatial ranks and is a data-driven non-parametric method. It does not require any preprocessing functional data steps, nor does it require any dimension reduction before clustering. The Forward Search Based on Functional Spatial Rank (FSFSR) algorithm identifies the number of clusters in the curves and provides the basis for the accurate assignment of each curve to its cluster. We apply it to three simulated datasets and two real medical datasets, and compare it with six other standard methods. Based on both simulated and real data, the FSFSR algorithm identifies the correct number of clusters. Furthermore, when compared with six standard methods used for clustering and classification, it records the lowest misclassification rate. We conclude that the FSFSR algorithm has the potential to cluster and classify functional data.

A Multi-mRNA Prognostic Signature for Anti-TNFα Therapy Response in Patients with Inflammatory Bowel Disease

Background: Anti-TNF-alpha (anti-TNFα) therapies have transformed the care and management of inflammatory bowel disease (IBD). However, they are expensive and ineffective in greater than 50% of patients, and they increase the risk of infections, liver issues, arthritis, and lymphoma. With 1.6 million Americans suffering from IBD and global prevalence on the rise, there is a critical unmet need in the use of anti-TNFα therapies: a test for the likelihood of therapy response. Here, as a proof-of-concept, we present a multi-mRNA signature for predicting response to anti-TNFα treatment to improve the efficacy and cost-to-benefit ratio of these biologics. Methods: We surveyed public data repositories and curated four transcriptomic datasets (n = 136) from colonic and ileal mucosal biopsies of IBD patients (pretreatment) who were subjected to anti-TNFα therapy and subsequently adjudicated for response. We applied a multicohort analysis with a leave-one-study-out (LOSO) approach, MetaIntegrator, to identify significant differentially expressed (DE) genes between responders and non-responders and then used a greedy forward search to identify a parsimonious gene signature. We then calculated an anti-TNFα response (ATR) score based on this parsimonious gene signature to predict responder status and assessed discriminatory performance via an area-under-receiver operating-characteristic curve (AUROC). Results: We identified 324 significant DE genes between responders and non-responders. The greedy forward search yielded seven genes that robustly distinguish anti-TNFα responders from non-responders, with an AUROC of 0.88 (95% CI: 0.70–1). The Youden index yielded a mean sensitivity of 91%, mean specificity of 76%, and mean accuracy of 86%. Conclusions: Our findings suggest that there is a robust transcriptomic signature for predicting anti-TNFα response in mucosal biopsies from IBD patients prior to treatment initiation. This seven-gene signature should be further investigated for its potential to be translated into a predictive test for clinical use.

Smart Visualization of Mixed Data

In this work, we propose a new protocol that integrates robust classification and visualization techniques to analyze mixed data. This protocol is based on the combination of the Forward Search Distance-Based (FS-DB) algorithm (Grané, Salini, and Verdolini 2020) and robust clustering. The resulting groups are visualized via MDS maps and characterized through an analysis of several graphical outputs. The methodology is illustrated on a real dataset related to European COVID-19 numerical health data, as well as the policy and restriction measurements of the 2020–2021 COVID-19 pandemic across the EU Member States. The results show similarities among countries in terms of incidence and the management of the emergency across several waves of the disease. With the proposed methodology, new smart visualization tools for analyzing mixed data are provided.