Improvement Of Discrimination Power And Weight Dispersion In Multi-Criteria Data Envelopment Analysis

Lack of discrimination power and poor weight dispersion remain major issues in Data Envelopment Analysis (DEA). Since the initial multiple criteria DEA (MCDEA) model developed in the late 1990s, only goal programming approaches; that is, the GPDEA-CCR and GPDEA-BCC were introduced for solving the said problems in a multi-objective framework. Kekurangan keupayaan mendiskriminasi dan kelemahan pengagihan pemberat kekal sebagai isu utama dalam Analisis Penyampulan Data (DEA). Semenjak model DEA berbilang kriteria (MCDEA) pertama yang dibentuk pada akhir tahun 1990an, hanya pendekatan pengaturcaraangol; yakni, GPDEA-CCR dan GPDEA-BCC telah diperkenalkan bagi menyelesaikan masalah berkenaan dalam konteks berbilang kriteria

Model Order Reduction in Porous Media Flow Simulation and Optimization

Subsurface flow modeling and simulation is ubiquitous in many energy related processes, including oil and gas production. These models are usually large scale and simulating them can be very computationally demanding, particularly in work-flows that require hundreds, if not thousands, runs of a model to achieve the optimal production solution. The primary objective of this study is to reduce the complexity of reservoir simulation, and to accelerate production optimization via model order reduction (MOR) by proposing two novel strategies, Proper Orthogonal Decomposition with Discrete Empirical Interpolation Method (POD-DEIM), and Quadratic Bilinear Formulation (QBLF). While the former is a training-based approach whereby one runs several reservoir models for different input strategies before reducing the model, the latter is a training-free approach. Model order reduction by POD has been shown to be a viable way to reduce the computational cost of flow simulation. However, in the case of porous media flow models, this type of MOR scheme does not immediately yield a computationally efficient reduced system. The main difficulty arises in evaluating nonlinear terms on a reduced subspace. One way to overcome this difficulty is to apply DEIM onto the nonlinear functions (fractional flow, for instance) and to select a small set of grid blocks based on a greedy algorithm. The nonlinear terms are evaluated at these few grid blocks and interpolation based on projection is used for the rest of them. Furthermore, to reduce the number of POD-DEIM basis and the error, a new approach is integrated in this study to update the basis online. In the regular POD-DEIM work flow all the snapshots are used to find one single reduced subspace, whereas in the new technique, namely the localized POD-DEIM, the snapshots are clustered into different groups by means of clustering techniques (k-means), and the reduced subspaces are computed for each cluster in the online (pre-processing) phase. In the online phase, at each time step, the reduced states are used in a classifier to find the most representative basis and to update the reduced subspace. In the second approach in order to overcome the issue of nonlinearity, the QBLF of the original nonlinear porous media flow system is introduced, yielding a system that is linear in the input and linear in the state, but not in both input and state jointly. Primarily, a new set of variables is used to change the problem into QBLF. To highlight the superiority of this approach, the new formulation is compared with a Taylor's series expansion of the system. At this initial phase of development, a POD-based model reduction is integrated with the QBLF in this study in order to reduce the computational costs. This new reduced model has the same form as the original high fidelity model and thus preserves the properties such as stability and passivity. This new form also facilitates the investigation of systematic MOR, where no training or snapshot is required. We test these MOR algorithms on the SPE10 and the results suggest twofold runtime speedups for a case study with more than 60,000 grid blocks. In the case of the QBLF, the results suggests moderate speedups, but more investigation is needed to accommodate an efficient implementation. Finally, MOR is integrated in the optimization work flow for accelerating it. The gradient based optimization framework is used due to its efficiency and fast convergence. This work flow is modified to include the reduced order model and consequently to reduce the computational cost. The water flooding optimization is applied to an offshore reservoir benchmark model, UNISIM-I-D, which has around 38,000 active grid blocks and 25 wells. The numerical solutions demonstrate that the POD-based model order reduction can reproduce accurate optimization results while providing reasonable speedups

Relationship among plasma adipokines, insulin and androgens level as well as biochemical glycemic and lipidemic markers with incidence of PCOS in women with normal BMI

Polycystic ovary syndrome (PCOS) is an endocrine disorder in women. Omentin-1 and vaspin are secretary adipokines that are produced by the visceral adipose tissue. These levels change in obese women with PCOS. The aim of this study is to investigate whether omentin and vaspin levels change in nonobese PCOS subjects. This study is a cross-sectional case control study in which 39 women with PCOS were picked out for this study. The inclusion criteria were based on the Rotterdam 2003 diagnostic criteria. The control group consisted of 39 women with normal pelvic sonographic reports having regular menstruation and showing no signs of infertility. The fasting plasma glucose (FPG), triglyceride (TG), Chol, and high-density lipoprotein cholesterol (HDL-C), insulin, testosterone, omentin and vaspin were measured by the enzymatic methods. The differences within these groups were calculated by the un-paired t-test and the Mann–Whitney test. The results from this study show a significant increase in the amount of insulin, testosterone, homeostasis model assessments for insulin resistance, TG and lower HDL in the patient group. No significant differences were seen in omentin, vaspin, FPG, Cho, low-density lipoprotein, very low-density lipoprotein cholesterol, blood urea nitrogen, Cr and homeostasis model assessments for B cell function levels between groups. Results show that PCOS is not a determinant of decreased omentin and vaspin plasma levels and those high androgen level and insulin resistances are warning signs of PCOS

Preparation of Polyaniline-Clay Nanoadditive and Investigation on Anticorrosion Performance in Epoxy Coating

The corrosion protection of mild steel by a newly developed epoxy-based coating system containing inherently conducting nanopolyaniline-clay as a nanoadditive has been studied. Polyaniline-clay anticorrosion nanoadditive (PCNA) was obtained by the direct mixing method of nanopolyaniline (0.03 wt.%) and organo-modified clay (3 wt.%) at atmospheric pressure, and XRD technique was used to study d-spacing of clay platelets in the prepared nanoadditive. PCNA was dispersed in polyaminoamide hardener matrix and was used for epoxy coating (EPCNA) preparation. The particle size of the polyaniline in hardener was determined using dynamic light scattering technique (DLS). The results revealed that the particles were in the range of 50–58 nm. The degree of exfoliation and distribution and particles size were studied by XRD and TEM in the final dried film. The corrosion protection ability of EPCNA was compared to an epoxy coating containing pure nanopolyaniline (ENPN) using electrochemical impedance spectroscopy (EIS) and salt spray methods. In addition, an investigation on the morphology of metal-coating interface by scanning electron microscopy (SEM) technique in ENPN and EPCNA samples after salt spray test showed stable oxide layer formation for ENPN and a dense stable oxide layer for EPCNA on metal surface. The results showed that the PCNA nanoadditive enhanced corrosion protection effect in comparison to pure nanopolyaniline (NPN) in the epoxy coating.</span

Association study of ESR1 rs9340799, rs2234693, and MMP2 rs243865 variants in Iranian women with premature ovarian insufficiency: A case-control study

Background: Primary ovarian insufficiency (POI) is a rare disease clinically characterized by ovarian follicles depletion or dysfunction and menopause before the age of 40 yr as the cut-off age for POI. It is a complex disease, and its etiology involves several factors. However, genetic factors have a predominant role in the susceptibility to the disease. Objective: This study aims to investigate the polymorphisms of rs243865 in the matrix metallopeptidase 2 (MMP2) gene and rs2234693 and rs9340799 in the estrogen receptor 1 (ESR1) gene with susceptibility to POI in Iranian women under 35 yr. Materials and Methods: This case-control study was performed on 150 women with POI and 150 healthy women who were referred to Yazd Reproductive Sciences Institute, Yazd, Iran between May-October 2020. The genotyping of ESR1 rs9340799, rs2234693, and MMP2 rs243865 polymorphism was done using tetra-amplification refractory mutation system-polymerase chain reaction. In addition, haplotype analysis and linkage disequilibrium were investigated by SNPanalyzer software. Results: Our study revealed the frequency of rs243865 TT, CC genotypes in the MMP2 gene and rs2234693 CC, TT; and rs9340799 GG, AA in the ESR1 gene were more prevalent in the case group compared to the control group. In addition, ESR1 rs2234693 and rs9340799 genotypes showed significant association with the development of the disease in our population. Among 4 haplotypes for 2 polymorphisms in the ESR1 gene, rs2234693T/rs9340799A haplotype was associated with conferring risk to POI. Conclusion: ESR1 rs2234693 and rs9340799 polymorphism were strongly associated with our population’s POI. Key words: Matrix metalloproteinase-2, Estrogen receptor alpha, Primary ovarian insufficiency, Female infertility

Evaluation of Microbial Resistance Pattern in Children with Urinary Tract Infection in Bushehr between 2017 and 2018

Background and Aim: Urinary tract infection is one of the most common childhood illnesses that can lead to complications such as hypertension and kidney failure. The aim of this study was to evaluate microbial resistance and sensitivity and to determine the relationship between urinary tract abnormalities and prior antibiotic use with microbial resistance. Methods: This is a descriptive-analytic study on 90 patients with a positive urine culture. Urine culture samples were taken using one of the sampling methods (midstream clean catch, catheterization, urine bag, suprapubic aspiration) and ultrasonography was requested for all patients to evaluate urinary system abnormalities. Also, a history of prior antibiotic use was asked and recorded. Results: Of all patients, 55.6% showed E.coli and 44.4% showed other bacteria in urine culture. 97.7% of patients' cultures were sensitive to imipenem, 82.2% to nitrofurantoin, and 77.8% to cefixime. 65% of patients' cultures showed resistance to nalidixic acid, 56.7% to co-trimoxazole, and 38.9% to ceftriaxone. There was a significant relationship between cefixime and amikacin antibiotic resistance with abnormal ultrasound and there was a significant relationship between antibiotic resistance to cefixime, ceftriaxone, co-trimoxazole, and duration of prior antibiotic use (p-value &lt;0.05). Conclusion: The most common pathogen in UTI was E.coli. The highest sensitivity was to imipenem, nitrofurantoin, and cefixime, and the highest resistance was to nalidixic acid, co-trimoxazole, and ceftriaxone. There was a relationship between urinary tract abnormalities and prior antibiotic use with microbial resistance, so it is suggested to use kidney ultrasound in all patients with urinary tract infection

An advanced framework for leakage risk assessment of hydrogen refueling stations using interval-valued spherical fuzzy sets (IV-SFS)

The extensive population growth calls for substantial studies on sustainable development in urban areas. Thus, it is vital for cities to be resilient to new situations and adequately manage the changes. Investing in renewable and green energy, including high-tech hydrogen infrastructure, is crucial for sustainable economic progress and for preserving environmental quality. However, implementing new technology needs an effective and efficient risk assessment investigation to minimize the risk to an acceptable level or ALARP (As low as reasonably practicable). The present study proposes an advanced decision-making framework to manage the risk of hydrogen refueling station leakage by adopting the Bow-tie analysis and Interval-Value Spherical Fuzzy Sets to properly deal with the subjectivity of the risk assessment process. The outcomes of the case study illustrate the causality of hydrogen refueling stations' undesired events and enhance the decision-maker's thoughts about risk management under uncertainty. According to the findings, jet fire is a more likely accident in the case of liquid hydrogen leakage. Furthermore, equipment failure has been recognized as the most likely cause of hydrogen leakage. Thus, in order to maintain the reliability of liquid hydrogen refueling stations, it is crucial that decision-makers develop a trustworthy safety management system that integrates a variety of risk mitigation measures including asset management strategies

Modeling the Consequences of the Auditors' Leaving the Public Accounting Profession: Is There a Brain Drain in Auditing?

Many recent professional reports have reported that professional turnover is a primary concern for the audit profession. Also, based on the information received from the Securities and Exchange Organization by March 2019, the average retention period for auditors who worked in their trusted firms was 574 days, which is too low compared to global statistics. This study investigates why auditors leave public accounting and the consequences of auditor departures. We find that audit competency is negatively associated with a departure decision. Specifically, audit partners, managers, and auditors generating more audit revenues and providing higher quality audits have a lower likelihood of departure. Therefore, this study examines why auditors leave the formal accounting profession and the consequences of auditors leaving the profession. In order to achieve this goal, the present study uses a data theory strategy based on interviews with 18 experts in the auditing profession in 2022 who were improbably and purposefully selected using the snowball method. Finally, the developed model includes four categories of conditions (including individual factors, job factors, internal organizational links, organizational characteristics, perceived organizational climate, organizational job attitudes, audit fees, and career advancement), context (including macro-level factors). At the professional level, actions and consequences were presented and conceptualized. Finally, it can be said that the present study can provide specific and interesting perspectives for the auditing profession, auditing firms, and legislators to use in relation to the performance analysis of the profession