Generalized linear model based monitoring methods for high-yield processes

Emerge in technology brought well-organized manufacturing systems to produce high-quality items. Therefore, monitoring and control of products have become a challenging task for quality inspectors. From these highly efficient processes, produced items are mostly zero-defect and modeled based on zero-inflated distributions. The zero-inflated Poisson (ZIP) and zero-inflated Negative Binomial (ZINB) distributions are the most common distributions, used to model the high-yield and rare health-related processes. Therefore, data-based control charts under ZIP and ZINB distributions (i.e., Y-ZIP and Y-ZINB) are proposed for the monitoring of high-quality processes. Usually, with the defect counts, few covariates are also measured in the process, and the generalized linear model based on the ZIP and ZINB distributions are used to estimate their parameters. In this study, we have designed monitoring structures (i.e., PR-ZIP and PR-ZINB) based on the ZIP and ZINB regression models which will provide the monitoring of defect counts by accounting the single covariate. Further, proposed model-based charts are compared with the existing data-based charts. The simulation study is designed to access the performance of monitoring methods in terms of run length properties and a case study on the number of flight delays between Atlanta and Orlando during 2012–2014 is also provided to highlight the importance of the stated research.     

Alternative approach to compute shear amplification in high‐rise reinforced concrete core wall buildings using uncoupled modal response history analysis procedure

The seismic response of the high‐rise reinforced concrete (RC) wall structures is really complicated as several vibration modes other than the fundamental mode normally contribute significantly to the response—commonly recognized as ‘higher mode effects’. Response spectrum analysis (RSA) procedure, which can account for higher mode effects, is usually employed to compute the seismic design demand for the high‐rise structures. Recent studies show that the inelastic seismic force demands obtained from the rigorous nonlinear response history analysis procedure are much larger than the seismic force design demands obtained from the code‐based RSA procedure for the high‐rise RC wall structures. Though, the nonlinear response history analysis procedure is widely accepted for its ability to provide the most accurate estimate of nonlinear seismic responses, the obtained responses are generally so complex that it is quite difficult for engineers to grasp the overall picture of the responses and gain some insight into them and use them to understand the cause of high seismic demands. Another important issue related to the nonlinear seismic response prediction of the high‐rise RC wall structures is the realistic and accurate numerical modeling of RC walls. In this study, a simplified but reasonably accurate procedure called the uncoupled modal response history analysis procedure is used to interpret the complex nonlinear behavior of high‐rise RC wall structures. Moreover, a finite element model based on modified compression field theory is employed for accurate numerical modeling of RC walls by incorporating the axial‐flexure‐shear interaction. This study, by making use of a better computer modeling approach and an in‐depth analysis by modal decomposition, aims to resolve some of the unanswered questions regarding realistic prediction of nonlinear seismic demands of high‐rise structures.     

IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19

The prediction of human diseases, particularly COVID-19, is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment. To deal with the prediction and diagnosis of COVID-19, we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System (IoMTSM-HMFIS). The proposed system determines the various factors like fever, cough, complete blood count, respiratory rate, Ct-chest, Erythrocyte sedimentation rate and C-reactive protein, family history, and antibody detection (lgG) that are directly involved in COVID-19. The expert system has two input variables in layer 1, and seven input variables in layer 2. In layer 1, the initial identification for COVID-19 is considered, whereas in layer 2, the different factors involved are studied. Finally, advanced lab tests are conducted to identify the actual current status of the disease. The major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19; the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for quarantining. MATLAB-2019a tool is used to conduct the simulation. The COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%. Finally, to achieve improved performance, the analysis results of the system were shared with experts of the Lahore General Hospital, Lahore, Pakistan.     

Enhancement in Physical Properties of Silver-Doped Fe–Ni Invar Nano-alloy Using Chemical Reduction Method

Journal of Superconductivity and Novel Magnetism - Silver-substituted Fe–Ni nano invar alloy is a new and innovative field of research due to their interesting invar, magnetic and electrical...     

Prediction of Cloud Ranking in a Hyperconverged Cloud Ecosystem Using Machine Learning

Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet. The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric. In a hyperconverged cloud ecosystem environment, building high-reliability cloud applications is a challenging job. The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings. The emergence of cloud computing is significantly reshaping the digital ecosystem, and the numerous services offered by cloud service providers are playing a vital role in this transformation. Hyperconverged software-based unified utilities combine storage virtualization, compute virtualization, and network virtualization. The availability of the latter has also raised the demand for QoS. Due to the diversity of services, the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical, common, and impactful parameters. It is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various CSPs. This research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters: service quality, downtime of servers, and outage of cloud services.     

Marine Water Quality Assessment Of Synthetic-Based Drilling Waste Discharges

Drilling muds or fluids used in drilling operations serve several functions: they lubricate the drill bit; maintain borehole pressure; clean and condition the hole; and flush rock cuttings to the surface. Drilling mud also adheres to rock cuttings, as can formation oil. Barite is used as a weighting agent, which approximately makes up approximately 33% of synthetic mud. It contains traces of heavy metals, which contribute to the toxicity of drilling waste. As environmental issues attract increasing public awareness, the treatment and disposal of spent drilling fluids and contaminated rock cuttings from drilling operations have become sensitive issues for offshore operations around the world. Within the last decade, the oil and gas industry has introduced synthetic-based drilling fluids as substitutes for oil-based fluids. This pollution prevention measure has been complemented by improvements in solid control systems technology. This paper adopts the current U.S. Environmental Protection Agency methodology for a hypothetical case study of an oil field on the East Coast of Canada to determine the water quality impacts of disposal of drilling wastes. The concentration of pollutants at the edge of the mixing zone in the water column and pore water are estimated and compared with Canadian water quality guidelines for protection of aquatic life.     

Effect of silica fume on the concrete-steel bond

Silica fume is used as an admixture to concrete to improve the engineering properties. Additions of silica fume improve the bond between concrete and steel reinforcing bars. The authors describe a laboratory study which explores the ultimate pullout load of steel reinforcing bars from concrete enhanced with silica fume. Les fumees de silice utilisees comme adjuvant ameliorent les proprietes techniques du beton et notamment l'adherence entre le beton et les fers d'armature. Les auteurs decrivent une etude de laboratoire qui porte sur la resistance a l'arrachement des fers d'armature dans du beton traite aux fumees de silice.     

Weibull parameters for wind speed distribution in Saudi Arabia

The shape and scale parameters of a Weibull density distribution function are calculated for 10 locations in Saudi Arabia. The daily mean wind speed data from 1970 to mid-1990 are used for this purpose. It is found that the numerical values of the shape parameter vary between 1.7 and 2.7, whereas the value of the scale parameter is found to vary between 3 and 6. It is also concluded from this study that wind data are very well represented by the Weibull distribution function.     

Effects of treatment with low molecular weight phenol formaldehyde resin on the surface characteristics of oil palm (Elaeis quineensis) stem veneer

Even though oil palm (Elaeis quineensis) stem (OPS) is highly potential as an alternative raw material in wood industry, it possesses some inferior characteristics. One of the critical weaknesses is a high degree of veneer surface roughness that resulted in high resin consumption during plywood manufacture. The objective of this study was to investigate effects of treatment with low molecular weight phenol formaldehyde (LMWPF) resin on the wettability and surface roughness of OPS veneer. OPS veneers were segregated into two categories namely outer and inner layer veneer, prior to soaking in LMWPF resin solution to obtain weight percent gain of 16–20%. The wettability of OPS veneers was assessed with contact angle measurement according to the sessile drop method. The veneer surface roughness was evaluated by determining the average roughness (R_a), mean peak-to-valley height (R_z), and maximum roughness (R_max) using a stylus profilometer in accordance with DIN standard 4768. The results show that the effect of LMWPF resin treatment on the surface roughness of the veneers is statistically significant. The technique used in the study was able to enhance the surface properties as well as improved the physical and mechanical properties of OPS plywood.