An empirical study on the relationship between working capital management and profitability: A case study of Mehregan Sangesar Company

This study examines the relationship between the working capital management and profitability for a real-world case study in Iran over the period 2004-2012. There are three components associated with working capital including account payable period, inventory turnover period and receivable account period. The study uses cash conversion cycle to investigate the impacts of working capital management on profitability, simultaneously. We use Pearson correlation ratios as well as regression techniques to study different hypotheses. The result indicates an inverse relationship between variables of working capital and profitability. It means if account receipt, cash conversion cycle and period of debt payment increase, the profitability of this company will decrease so managers can create more value that is positive for shareholders by decreasing period of debt payment, period of inventory turnover and period of demand collection

Centrosymmetric graphs and a lower bound for graph energy of fullerenes

The energy of a molecular graph G is defined as the summation of the absolute values of the eigenvalues of adjacency matrix of a graph G. In this paper, an infinite class of fullerene graphs with 10n vertices, n ≥ 2, is considered. By proving centrosymmetricity of the adjacency matrix of these fullerene graphs, a lower bound for its energy is given. Our method is general and can be extended to other class of fullerene graphs

Histopatological and biochemical effects of subcute toxicity of lemon balm hydroalcoholic extract on liver and kidney tissues in the surri mice

Background and aims: Lemon balm (Melissa officinalis) in many countries is used as a nerve calming, spasmolytic, and antibacterial agent. Anyway, more investigations are needed to determine toxic effects of this plant. In this study, we investigated the effects of lemon balm hydroalcoholic extract on some biochemical enzymes and tissue changes (in kidney and liver) in mice. Methods: In this experimental study, 21 male mice were randomly divided into three groups of seven mice each. Group I was treated with normal saline as control and experimental groups II and III were treated with hydroalcoholic extract of lemon balm daily for two weeks intraperitoneally in doses of 0.450 and 1.350 g/kg, respectively. At the end of the experimental period (15 days), blood samples were obtained from the heart and centrifuged, and then the sera were evaluated to determine the concentrations of aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, creatinine, and urea using autoanalyzer and commercial kits. In addition, the liver and kidney tissues were removed and examined after fixation in formalin buffer 10% and hematoxyline-eosin staining. The data were analyzed at a significance level of p<0.05 by one-way ANOVA, Tukey's post hoc test, and Kruskal-Wallis. Results: Lemon balm caused a significant reduction in alanine aminotransferase and alkaline phosphatase levels compared to the control group. Also, it showed a dose-dependent effect. Furthermore, lemon balm extract had no effect on the amount of urea and creatinine compared to the control group. Histopathological changes of the liver and kidney in the groups that received different doses of the extract showed mild, moderate, and severe tissue injuries. Conclusion: The biochemical analysis in this study indicates that the extract of lemon balm causes liver tissue damage in mice therefore, plant’s consumption in high doses should be avoided

Biochemical, liver and renal toxicities of Melissa officinals hydroalcoholic extract on balb/C mice

Introduction: Melissa officinalis is usually used as antispasmodic, antiaxiety and antibacterial agent. However, its toxicity has not been evaluated, yet. In this study biochemical, liver and renal toxicities of Melissa officinals hydroalcoholic extract were evaluated in balb/C mice. Methods: In an experimental study, 21 balb/C male mice were randomly designated to three equal groups. Group I was treated with normal saline and groups II and III were respectively treated with 0.450 and 1.350 g/kg, hydroalcoholic extract of Melissa officinals daily for two weeks, intraperitoneally. Then on 15th day of the experiment, blood samples were obtained from the heart. The blood was centrifuged and then the sera were evaluated for alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, urea and creatinine, using autoanalyzer and commercial kits. The liver and kidney tissues were also hystopathologically evaluated. The data were analyzed by one-way ANOVA, Tukey&rsquo;s post hoc test, and Kruskal-Wallis at a significance level of p&lt;0.05. Results: Melissa officinals dose dependently caused a significant reduction in alkaline phosphatase and alanine aminotransferase levels compared to the control group. Furthermore, Melissa officinals extract had no effect on the amount of urea and creatinine compared to the control group. The liver and kidney histopathological changes in the groups that received different doses of the extract showed mild, moderate, and severe tissue injuries. Conclusion: The biochemical analysis in this study indicates that the extract of Melissa officinals causes liver tissue damage in mice; therefore, its consumption in high doses should be avoided.</p

Diagnostic values of serum levels of pepsinogens and gastrin-17 for screening gastritis and gastric cancer in a high risk area in Northern Iran

Background: Gastric cancer (GC) is the second cause of cancer related death in the world. It may develop by progression from its precancerous condition, called gastric atrophy (GA) due to gastritis. The aim of this study was to evaluate the accuracy of serum levels of pepsinogens (Pg) and gastrin-17 (G17) as non-invasive methods to discriminate GA or GC (GA/GC) patients. Materials and Methods: Subjects referred to gastrointestinal clinics of Golestan province of Iran during 2010 and 2011 were invited to participate. Serum levels of PgI, PgII and G17 were measured using a GastroPanel kit. Based on the pathological examination of endoscopic biopsy samples, subjects were classified into four groups: normal, non-atrophic gastritis, GA, and GC. Receiver operating curve (ROC) analysis was used to determine cut-off values. Indices of validity were calculated for serum markers. Results: Study groups were normal individuals (n=74), non-atrophic gastritis (n=90), GA (n=31) and GC patients (n=30). The best cut-off points for PgI, PgI/II ratio, G17 and HP were 80 μg/L, 10, 6 pmol/L, and 20 EIU, respectively. PgI could differentiate GA/GC with high accuracy (AUC=0.83; 95%CI: 0.76-0.89). The accuracy of a combination of PgI and PgI/II ratio for detecting GA/GC was also relatively high (AUC=0.78; 95%CI: 0.70-0.86). Conclusions: Our findings suggested PgI alone as well as a combination of PgI and PgI/II ratio are valid markers to differentiate GA/GC. Therefore, Pgs may be considered in conducting GC screening programs in high-risk areas

Correlation Between Low Bone Density and Disease Activity in Patients with Ulcerative Colitis

BACKGROUND Different clinical and epidemiological studies using dual-energy X-ray absorptiometry have shown an increased prevalence of low bone mineral density in patients with inflammatory bowel diseases. The aim of this study was to assess the correlation between bone density and the disease activity in patient swith ulcerative colitis.KEYWORDS Ulcerative colitis; Z-score; Bone densitometry; Low bone densit

A comparison between two different FPGA-based topologies of first order sigma-delta modulator

Integrating sigma-delta modulator (SDM) in FPGA causes to have quantization noise inside interested bandwidth. This paper compares and analyzes between two possible FPGA-based SDM structures which are non-noise shaper SDM and noise-shaper SDM. The difference between these two structures is in the integrator block. All other SDM constitutive components are the same for both structures. First-order noise-shaper SDM with its maximum integration for FPGA implementation is reported. The advantages of noise-shaper SDM over non-noise shaper SDM are the elimination of input peak signal error as well as the increment of input voltage range. Moreover, higher suppression in signal harmonics results in better reconstruction of input signal at the output. However, the most important outcome is the improvement of quantization noise shaping out of the interested bandwidth which results higher SNR at the output

Application of Data and Geospatial Analysis in Energy and Health Systems

Having sustainable energy and health systems are the main factors in the vision plan of every country. Both of these systems are correlated with a variety of frameworks, including social, physical, technological, political, and economic factors. Therefore, different types of analytics methods can be implemented to develop the required assessments for those who make plans since understanding the effect of such factors individually, also their interactions, and the overall effect is crucial. With this regard, applications of data analysis and geospatial techniques in both energy, and health systems have recently gained attention. The proposed research here deployed advanced data analytics methods and Geographic Information Systems (GIS) to study energy and health systems, obtain understandings and make predictions, also analyze the corresponding data based on their spatial location and organizing multiple layers of information into visualizations. The proposed research is comprised of two sections. First, Geographic Information Systems and different data analytics methods were used to evaluate the potential of Municipal Solid Waste (MSW) as a renewable energy source in the state of Illinois. Our results demonstrated that Illinois is capable of producing 6,295,385.77 MWH annual energy using incineration technology from MSW. Also, using Anaerobic Digestion (AD) technology in MSW management would enable the state to be capable of producing more than 1,140,493,710,450.00 Litres biogas per year. Second, we expanded the application of data and geospatial analysis in the health system and deployed advanced data analytics methods, geographical information system (GIS), and predictive epidemiological models to analyze the anti-contagion policies implemented by the states across the country to slow the spread of COVID-19. Also, by implementing a meta-analysis in conjunction with multi-criteria decision-making methods, a Lung Cancer Risk Index (LCRI) was produced representing the probability of individuals getting lung cancer. The methods that have been developed for the extended applications of data and geospatial analysis in health can be used for various complex decision making and index generating purposes in engineering disciplinary such as additive manufacturing to evaluate the effect of process factors (e.g., injection, concentration, material characteristics, speed, temperature and so forth) individually and collectively to optimize the process and increase the performance