A Bayesian approach to design of adaptive multi-model inferential sensors with application in oil sand industry

In the context of process industries, online monitoring of quality variables is often restricted by inadequacy of measurement techniques or low reliability of measuring devices. Therefore, there has been a growing interest in the development of inferential sensors to provide frequent online estimates of key process variables on the basis of their correlation with real-time process measurements. Representation of multi-modal processes is one of the challenging issues that may arise in the design of inferential sensors. In this paper, Bayesian procedures for the development and implementation of adaptive multi-model inferential sensors are presented. It is shown that the application of a Bayesian scheme allows for accommodating the overlapping operating modes and facilitating the inclusion of prior knowledge. The effectiveness of the proposed procedures are first demonstrated through a simulation case study. The efficacy of the method is further highlighted by a successful industrial application of an adaptive multi-model inferential sensor designed for real-time monitoring of a key quality variable in an oil sands processing unit.     

Variant versus invariant time to total forgetting: the learn–forget curve model revisited

Understanding and quantifying the learning–forgetting process helps predict the performance of an individual (or a group of individuals), estimate labor costs, bid on new and repeated orders, estimate costs of strikes, schedule production, develop training programs, set time standards, and improve work methods [IIE Trans. 29 (1997) 759]. Although there is agreement that the form of the learning curve is as presented by [J. Aeronaut. Sci. 3 (1936) 122], scientists and practitioners have not yet developed a full understanding of the behavior and factors affecting the forgetting process. The paucity of research on forgetting curves has been attributed to the practical difficulties involved in obtaining data concerning the level of forgetting as a function of time [IIE Transactions 21 (1989) 376]. The learn–forget curve model (LFCM) was shown to have many advantages over other theoretical models that capture the learning–forgetting relationship. However, the deficiency of the LFCM is in the assumption that the time for total forgetting is invariant of the experience gained prior to interruption. This paper attempts to correct this deficiency by incorporating the findings of [Int. J. Ind. Ergon. 10 (1992) 217] into the LFCM. Numerical examples are used to illustrate the behavior of the modified LFCM (MLFCM) and compare results to those of the LFCM.     

Wear characteristics of commercial gas turbine components

Wear is one of the common degradations in a commercial gas turbine. A stainless steel grade 304 (SS 304) fuel nozzle and its collar made of nickel-based superalloy (Hastelloy X) are two vital components that normally suffer from rapid wear. During an installation, the fuel nozzle is slotted into the collar of the combustion liner. In operation, these two surfaces are subjected to high pressure from the fuel combustion, hence continuously rubbing against each other causes vibrations. During the start–stop operation, these surfaces had undergone a large relative motion. The vibration is the main cause of the wear occurrence on the surfaces. Physical properties of the worn surfaces were obtained through visual observations: wear measurement, hardness and microstructure examination. Through visual observations, fretting wear was mainly suspected as the dominant wear mode, particularly after 8,000 of running hours at high temperature and vibration. In short, this paper discusses the preliminary findings of wear on the fuel nozzle and its collar. It also discusses the changes in the mechanical properties before and after the operation. Solutions for mitigating the problem were discussed.     

Assessing T-Cell Immunity in Kidney Transplant Recipients with Absent Antibody Production after a 3rd Dose of the mRNA-1273 Vaccine

The vulnerable population of kidney transplant recipients (KTRs) are low responders to COVID-19 vaccines, so specific immune surveillance is needed. The interferon-gamma (IFN-γ) release assay (IGRA) is effective in assessing T cell-mediated immunity. We assessed SARS-CoV-2-directed T cell responses in KTRs with absent antibody production after a third dose of the mRNA-1273 vaccine, using two different IGRAs. A cohort of 57 KTRs, who were actively followed up, received a third dose of the mRNA-1273 vaccine. After the evaluation of humoral immunity to SARS-CoV-2, 14 seronegative patients were tested with two commercial IGRAs (SD Biosensor and Euroimmun). Out of 14 patients, one and three samples were positive by IGRAs with Euroimmun and SD Biosensor, respectively. The overall agreement between the two assays was 85.7% (κ = 0.444). In addition, multivariate linear regression analysis showed no statistically significant association between the IFN-γ concentration, and the independent variables analyzed (age, gender, years since transplant, total lymphocytes cells/mcl, CD3+ cells/mcl, CD3+ CD4+ cells/mcl, CD3+ CD8+ cells/mcl, CD19+ cells/mcl, CD3-CD16+CD56+ cells/mcl) (p > 0.01). In a vulnerable setting, assessing cellular immune response to complement the humoral response may be advantageous. Since the two commercial IGRAs showed a good agreement on negative samples, the three discordant samples highlight the need for further investigations.     

Bacillus subtilis: As an Efficient Bacterial Strain for the Reclamation of Water Loaded with Textile Azo Dye,Orange II

The azo dye orange II is used extensively in the textile sector for coloring fabrics. High concentrations of it are released into aqueous environments through textile effluents. Therefore, its removal from textile wastewater and effluents is necessary. Herein, initially, we tested 11 bacterial strains for their capabilities in the degradation of orange II dye. It was revealed in the preliminary data that B. subtilis can more potently degrade the selected dye, which was thus used in the subsequent experiments. To achieve maximum decolorization, the experimental conditions were optimized whereby maximum degradation was achieved at: a 25 ppm dye concentration, pH 7, a temperature of 35 °C, a 1000 mg/L concentration of glucose, a 1000 mg/L urea concentration, a 666.66 mg/L NaCl concentration, an incubation period of 3 days, and with hydroquinone as a redox mediator at a concentration of 66.66 mg/L. The effects of the interaction of the operational factors were further confirmed using response surface methodology, which revealed that at optimum conditions of pH 6.45, a dye concentration of 17.07 mg/L, and an incubation time of 9.96 h at 45.38 °C, the maximum degradation of orange II can be obtained at a desirability coefficient of 1, estimated using the central composite design (CCD). To understand the underlying principles of degradation of the metabolites in the aliquot mixture at the optimized condition, the study steps were extracted and analyzed using GC-MS(Gas Chromatography Mass Spectrometry), FTIR(Fourier Transform Infrared Spectroscopy), ¹H and carbon 13 NMR(Nuclear Magnetic Resonance Spectroscopy). The GC-MS pattern revealed that the original dye was degraded into o-xylene and naphthalene. Naphthalene was even obtained in a pure state through silica gel column isolation and confirmed using ¹H and ¹³C NMR spectroscopic analysis. Phytotoxicity tests on Vigna radiata were also conducted and the results confirmed that the dye metabolites were less toxic than the parent dye. These results emphasize that B. subtilis should be used as a potential strain for the bioremediation of textile effluents containing orange II and other toxic azo dyes.     

Green Tea Catechins: Nature’s Way of Preventing and Treating Cancer

Green tea’s (Camellia sinensis) anticancer and anti-inflammatory effects are well-known. Catechins are the most effective antioxidants among the physiologically active compounds found in Camellia sinesis. Recent research demonstrates that the number of hydroxyl groups and the presence of specific structural groups have a substantial impact on the antioxidant activity of catechins. Unfermented green tea is the finest source of these chemicals. Catechins have the ability to effectively neutralize reactive oxygen species. The catechin derivatives of green tea include epicatechin (EC), epigallocatechin (EGC), epicatechin gallate (ECG) and epigallocatechin gallate (EGCG). EGCG has the greatest anti-inflammatory and anticancer potential. Notably, catechins in green tea have been explored for their ability to prevent a variety of cancers. Literature evidence, based on epidemiological and laboratory studies, indicates that green tea catechins have certain properties that can serve as the basis for their consideration as lead molecules in the synthesis of novel anticancer drugs and for further exploration of their role as pharmacologically active natural adjuvants to standard chemotherapeutics. The various sections of the article will focus on how catechins affect the survival, proliferation, invasion, angiogenesis, and metastasis of tumors by modulating cellular pathways.     

Enhanced Reversible Zinc Ion Intercalation in Deficient Ammonium Vanadate for High-Performance Aqueous Zinc-Ion Battery

Ammonium vanadate with bronze structure (NH4V4O10) is a promising cathode material for zinc-ion batteries due to its high specific capacity and low cost. Howeve...     

Healthcare knowledge sharing among a community of specialized physicians

Healthcare organizations strive to make the best use of their organizational knowledge. The collective know-hows of the medical workers directly affect the quality of the delivered healthcare services. This study addresses the healthcare knowledge-sharing among a community of specialized physicians. An extensive study of the literature on knowledge-sharing in industries generally and healthcare organizations specifically were presented. Six focal elements were detected in previous attempts to address the knowledge-sharing status in healthcare organizations. Additionally, three previous models for healthcare knowledge management were analyzed. The studied literature along with the three studied models helped in constructing the framework and suggesting a suitable research methodology for primary data collection. Qualitative approach of in-depth interview technique was used for interviewing eight specialized physicians. The data collected from the interviewees were then analyzed and produced explanatory themes and codes. These themes are physicians’ acquisition of medical knowledge, staff participating in the knowledge sharing, knowledge-sharing culture, ICT-based knowledge sharing and top management involvement. The findings resulted in recognizing four considerations which ought to be taken into account for successful knowledge-sharing activities and learning initiatives in the healthcare organization. Conclusions and recommendations for future studies were presented based on the implications of this research study.     

Gene subset selection using an iterative approach based on genetic algorithms

Microarray data are expected to be useful for cancer classification. However, the process of gene selection for the classification contains a major problem due to properties of the data such as the small number of samples compared with the huge number of genes (higher-dimensional data), irrelevant genes, and noisy data. Hence, this article aims to select a near-optimal (small) subset of informative genes that is most relevant for the cancer classification. To achieve this aim, an iterative approach based on genetic algorithms has been proposed. Experimental results show that the performance of the proposed approach is superior to other previous related work, as well as to four methods tried in this work. In addition, a list of informative genes in the best gene subsets is also presented for biological usage.     

Design of dual‐band microstrip patch antenna with right‐angle triangular aperture slot for energy transfer application

This work focusing on the dual‐band antenna design with rectifying circuit for energy transfer system technology for enhancement gain performance. The air gap technique is applied on this microstrip antenna design work to enhance the antenna gain. The work begins with designing and analyzing the antenna via the CST Microwave Studio software. After validation on acceptable performance in simulation side is obtained, the return loss, S₁₁ of the antenna is measured using vector network analyzer equipment. The rectifier circuit is used to convert the captured signal to DC voltage. This projected dual‐band antenna has successfully accomplished the target on return loss of ?44.707 dB and ?32.163 dB at dual resonant frequencies for 1.8 GHz and 2.4 GHz, respectively. This proposed antenna design benefits in low cost fabrication and has achieved high gain of 6.31 dBi and 7.82 dBi for dual‐band functioning frequencies.