Multi-product multi-chance-constraint stochastic inventory control problem with dynamic demand and partial back-ordering: A harmony search algorithm

In this paper, a multiproduct inventory control problem is considered in which the periods between two replenishments of the products are assumed independent random variables, and increasing and decreasing functions are assumed to model the dynamic demands of each product. Furthermore, the quantities of the orders are assumed integer-type, space and budget are constraints, the service-level is a chance-constraint, and that the partial back-ordering policy is taken into account for the shortages. The costs of the problem are holding, purchasing, and shortage. We show the model of this problem is an integer nonlinear programming type and to solve it, a harmony search approach is used. At the end, three numerical examples of different sizes are given to demonstrate the applicability of the proposed methodology in real world inventory control problems, to validate the results obtained, and to compare its performances with the ones of both a genetic and a particle swarm optimization algorithms.     

Structural Characterization of Oat Bran (1→3), (1→4)-β-D-Glucans by Lichenase Hydrolysis Through High-Performance Anion Exchange Chromatography with Pulsed Amperometric Detection

Cereal mixed linkages (1 → 3) (1 → 4)-β-D-glucan is a linear polysaccharide composed of glucose units. Oat β-glucan is a natural polymer. The main products of β-glucanase are oligosaccharides with DP3 and DP4, i.e., 3-Ob-cellobiosyl-D-glucose and 3-Ob-cellotriosyl-D-glucose, which represent over 90% of the molecule. Keeping in mind all the benefits of oat bran, the present study was planned to investigate the structural properties of oat bran, high-performance anion exchange chromatography with pulsed amperometric detection was used to examine these oligosaccharides. The structural analysis of oat bran of two oat varieties revealed that the ratio of soluble and insoluble triose to tetraose in β-glucan fraction was 1.44 and 1.78, respectively, for Avon variety; while the ratio of soluble and insoluble triose to tetraose in β-glucan fraction for Sargodha-81 was 1.49 and 1.77. The major units determined were cellotriose and cellotetraose. Other units cellopentaose and hexaoses were also existed but in minor fractions. Lichenase hydrolysis high-performance anion exchange chromatography with pulsed amperometric detection appeared to be the best choice for structural analysis of purified samples of mixed-linkage β-glucan.     

Effect of Gamma Rays on Starch Extracted from Irradiated Wheat Flour

Irradiated starch was more retrograded than the unirradiated samples. The amylose fractions were increased on the expence of the amylopectin fractions of starch under the influence of gamma rays. Oxidation of starch with periodate ions was enhanced by gamma irradiation as indicated by the formation of greater amounts of formic acid in the irradiated samples. This result indicated that destruction in amylopectin had occured and formed small branched fragments. The decrease in intrinsic viscosity due to radiation, showed a straight line relation, when plotted versus irradiation doses, with a slope of 5.2 × 10⁻⁷. This indicates that a specific slope could be obtained which characterized gamma ray doses on wheat flour.     

Modification of the optical and structural properties of ZnO nanowires by low-energy Ar+ ion sputtering

The effects of low-energy (≤2 kV) Ar⁺ irradiation on the optical and structural properties of zinc oxide (ZnO) nanowires (NWs) grown by a simple and cost-effective low-temperature technique were investigated. Both photoluminescence spectra from ZnO NW-coated films and cathodoluminescence analysis of individual ZnO NWs demonstrated obvious evidences of ultraviolet/visible luminescent enhancement with respect to irradiation fluence. Annihilation of the thinner ZnO NWs after the ion bombardment was appreciated by means of high-resolution scanning electron microscopy and transmission electron microscopy (TEM), which results in an increasing NW mean diameter for increasing irradiation fluences. Corresponding structural analysis by TEM pointed out not only significant changes in the morphology but also in the microstructure of these NWs, revealing certain radiation-sensitive behavior. The possible mechanisms accounting for the decrease of the deep-level emissions in the NWs with the increasing irradiation fluences are discussed according to their structural modifications.     

Effects of silymarin on biochemical and oxidative stress markers in end‐stage renal disease patients undergoing peritoneal dialysis

Introduction End‐stage renal disease (ESRD) patients especially those undergoing dialysis are vulnerable to several complications, in particular those related to oxidative stress. Silymarin is an herbal medicine commonly used as an antioxidant in different pathologies. Methods To evaluate the effect of silymarin on biochemical and oxidative stress markers, 50 ESRD patients undergoing peritoneal dialysis were randomly divided into two groups of silymarin (n = 28) and control (n = 22) and received silymarin (140 mg every 8 hours) or placebo for 2 months, respectively. Ferric reducing antioxidant power and total 8‐iso‐prostaglandin F_2α were measured in plasma, while catalase enzyme activity was measured in erythrocytes of both groups before and after treatment. Findings Ferric reducing antioxidant power values after treatment were significantly decreased in silymarin group compared to before treatment values (17.2 ± 2.9 and 15.9 ± 3.1 µM equivalent of quercetin/dL, respectively, P < 0.05). Conversely, catalase levels were increased 17.3% after silymarin consumption, while it was decreased 9.1% in control group. Further, hemoglobin (from 10.94 ± 2.17 to 11.54 ± 2.03 g/dL, P < 0.05) and albumin levels (from 3.48 ± 0.67 to 3.61 ± 0.53 g/dL, P < 0.05) were significantly increased after silymarin administration. Discussion It is concluded that silymarin could be regarded as a supplementary therapy for ESRD patients undergoing peritoneal dialysis in order to reduce complications.     

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.     

Thermal optimization of solar dish collector for indirect vapor generation

This work presented the performance analysis of a solar parabolic concentrator prototype. The purpose of this paper is to achieve most quantity of vapor production with different water flows. The principal component of the solar concentrator is a new absorber concept that absorbs reflected solar rays and transports it to a heat exchanger in order to generate vapor. Climatic conditions, inlet/outlet oil temperatures of the tubular solar heat exchanger, water tank temperature, and inlet/outlet water temperatures of the mixed heat exchanger were recorded experimentally during three days in November 2018. The absorbed energy, losses energy, concentrated energy, and vapor heat energy of the system were determined. Results of this work, the solar system provides thermal energy efficiency varied from 60% to 70% and a concentration factor around 350 for three water mass flow rates. In this experiment, the optimum value of vapor mass is 6 kg/h with 0.016 kg/s of water flow. Consequently, to achieve the most quantity of vapor, the water flow should be decreased.     

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.