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The success of an organization or a particular activity is evaluated through the measurement of key performance indicators (KPIs). The aim of this paper is to analyze and predict the indicators of healthcare performance using grey systems theory. Recent advancements in science and technology have made the healthcare industry extremely efficient at collecting data using electronic claims systems such as electronic health records. Therefore, collecting field level primary data becomes easier and accumulate them to generate secondary data for research purpose and to get an insight of the organization performance is absolutely necessary. Our research analyzes the KPIs of a hospital based on a secondary data source. Since, secondary data contains uncertainty and sometimes poor information, grey prediction model suits best to make a prediction model in this regard. Conventional grey model has considerable drawbacks while making a rigorous prediction model. For this, we apply an improved grey prediction model to predict the KPIs of the healthcare performance indicators. Several error measures in our model give a best fit of the data and allow prediction of the KPIs. The prediction model gives good estimates of the quantitative indicators and produced error rate within an acceptable range. We observe that the KPIs of bed turnover rate (BTR) and bed occupancy rate (BOR) have an increasing trend, whereas the KPIs of average length of stay (ALOS), hospital death rate (HDR) and hospital infection rate (HIR) show a decreasing trend over time. The main contribution of this research is a grey-based prediction model that can provide managers with the information they need to evaluate and predict the performance of a hospital. The research indicates that managers should give greater priority to the indicators which will result in better patients’ satisfaction and improved profit margin. Healthcare managers striving towards better performance will now have an empirical basis upon which to formulate and adjust their strategies, after analyzing the predicted value.

Electrochemical mechanisms in unbuffered solutions are difficult to analyze because of the presence of second order reactions. If the protonation reaction is irreversible, then the second order reverse reaction is not kinetically relevant. For protonation on N- or O-atoms, the reaction is often reversible, and leads to an EC reversible mechanism. Rotating ring-disk electrode (RRDE) voltammetry is ideal to investigate this reaction because it is possible to focus on the limiting currents. The limiting currents are neither sensitive to the solution resistance, nor to electron transfer kinetics, but they are sensitive to reaction rates and equilibrium constants in solution. In this report, the theory for the EC reversible mechanism was developed for RRDE voltammetry. Two cases were examined. The first case (Case 1) is the situation where A is reduced to A−, and is then reversibly converted to a new product. An example of this is the reduction of A in a buffered media, where the reduction product is converted to its conjugate acid (HB + A− ⇆ B− + HA). In a buffered media, both the forward and reverse reactions are pseudo-first order. The second case (Case 2) is the situation where the forward reaction is generally (pseudo)first-order, and reverse reaction is second order. The theory was then applied to the reaction of Fe(OEP) (NO)- with 2,6-dichlorophenol (2,6-dcp) in THF in buffered and unbuffered media.

Supply chain disruptions management has attracted significant attention among researchers and practitioners. The paper aims to examine the effect of potential market demand disruptions on price and service level for competing retailers. To investigate the effect of potential demand disruptions, we consider both a centralized and a decentralized supply chain structure. To analyze the decentralized supply chain, the Manufacturing Stackelberg (MS) game theoretical approach was undertaken. The analytical results were tested using several numerical analyses. It was shown that price and service level investment decisions are significantly influenced by demand disruptions to retail markets. For example, decentralized decision makers tend to lower wholesale and retail prices under potential demand disruptions, whereas a proactive retailer needs to increase service level with an increased level of possible disruptions. This research may aid managers to analyze disruptions prone market and to make appropriate decision for price and service level. The manufacturer or the retailers will also be able to better determine when to close a market based on the proposed analysis by considering anticipated disruptions. The benefits and usefulness of the proposed approach are explained through a real-life case adopted from a toy supply chain in Bangladesh.

The extreme use of commercial fertilizers (CF) for maximum agricultural crop production leads to severe environmental pollution, reduces land fertility as well as directly or indirectly affects human health. Besides, it lowers down the nutrient use efficiency (NUE) by decreasing the rate of crop production with the increased rate of fertilization which expedites the cost of production. In this review, we have explored the recent advancements of slow and/or controlled released fertilizers and their impacts on crop production in terms of the growth, development, and yield of agricultural crop plants. However, we have found that slow-release fertilizers (SRFs) cost much because their pattern of nutrients release is not in a controlled manner that leads to nutrient loss into the ecosystems. Contrarily, the controlled released fertilizers (CRFs) releases their nutrients with a controlled pattern, however, the size of both SRFs and CRFs are not always specific for the particular crop plants, therefore, there would have a great chance to be aggregated of SFs and/or CRFs into the plant rhizosphere due to lower uptake by the plants causing the plant toxicity and polluting the groundwater and environments. Thus, from this review, an innovative nano-fertilizers that could be named as controlled released nano-fertilizers (CRNFs) has been proposed to be developed that will regulate the nutrient release with the plant’s demand with time, increase the NUE and reduce the environmental pollution suggesting the sustainable crop production. Moreover, we believe that the industrialization of CRNFs could replace CF which will greatly contribute to the cleaner agricultural production process.

Wastes of leather industries are substantially polluting the environment; therefore, to ameliorate the situation using environment-friendly dehairing process, an extracellular protease producing bacterium highly capable of dehairing cattle hide was isolated from soil. The bacterium was capable of producing extracellular protease which could dehair cattle hide within 8–10 h only. Scanning electron microscopic analysis of the dehaired hide obtained from enzymatic process showed that hair pores were free of any hair residues and grain surface was found to be smoother than conventional dehairing process. The optimum temperature for bacterial growth and enzyme production was found to be around 37 °C and crude enzyme was found to have optimum activity at pH 8.5. Complete inhibition of the enzyme activity by phenylmethylsulfonyl fluoride (PMSF) indicated that the enzyme could be serine protease. Molecular phylogenetic analysis of 1430 nt 16S rRNA gene sequence by MEGA 6.0 placed the isolated bacterium in the same clad with Bacillus subtilis. Phylogenetic analysis of full length 16S-23S Internal Transcribed Spacer (ITS) region and partial sequence of 23S rRNA gene also showed that the bacterium could be a member of Bacillus subtilis family. This potential bacterium can be used in the leather industries as an eco-friendly alternative to chemical dehairing.

Green supply chain management is attracting increasing attention as a way to decrease the adverse environmental effects of industries worldwide. However, considering the context of an emerging economy like Bangladesh, green supply chain management is still in its inception and has not been widely embraced in the textile industry, and therefore barriers hindering its adoption in emerging economy context demand a comprehensive investigation. This research reviews the viewpoints and hurdles in adopting green supply chain management practices in the context of the Bangladeshi textile industry. A questionnaire survey of Bangladeshi textile practitioners of operations and supply chain management division, having a sample size of thirty, was undertaken to identify the barriers, and a hierarchical cluster analysis technique was used in the detailed analysis of this data. Opinions were sought from experts on the significance of the resulting clusters, considering the relative importance of the barriers. Fifteen barriers to the adoption of green supply chain management were identified in the review of the literature, with these barriers then analyzed by using the data collected from Bangladeshi textile industry practitioners. The research indicates that the most important barrier is that there is low demand from customers and financial constraint resulting from short term little financial benefit to businesses, with lack of government regulations also a commonly faced barrier in adopting green supply chain initiatives. This study will provide valuables insights to practitioners and relevant policy makers about the barriers prevailing in the emerging economies towards the adoption of green supply chain management practices, which, in turn, can guide to undertake appropriate steps for alleviating those barriers.

A new anthraquinone along with 10 known compounds were isolated from the leaves of Aloe sinkatana Reynolds (Aloaceae), and their structures were elucidated as the new compound 2,8-dihydroxy-6-(hydroxymethyl)-1-methoxyanthracene-9,10-dione (1) and the known compounds Aloe-emodin (2), feralolide (3), 1-hydroxy-5-methoxy-3-methyl-9,10 dihydroanthracene 9,10-dione (4), β-sitosterol (5), β-sitosterol with glycosidic bond (6), microdontin (7), homoaloins A (8) and B (9) and aloins A (10) and B (11). Characterization of compounds 1–9 was based on spectral analyses and comparison with reported data, particularly the new compound 1 was identified by 1D- and 2D NMR, mass spectroscopic and X-ray crystallography analyses. Antiglycation activity of the extracts and isolated compounds were carried out using the hemoglobin-δ-gluconolactone and glucose–bovine serum albumin assays. The results obtained showed that MeOH and EtOAc extracts as well as compound 1 showed an inhibitory effect on early stage protein glycation. Compound 1 also showed significant inhibitory effects against glucose-induced advanced glycation end-products.

The method of Barker is a popular scheme for determination of activity coefficients from total pressure measurements. A comprehensive review of this method is presented in this study. While discussing this technique various aspects of (vapor + liquid) equilibrium (VLE) data reduction process including types of algorithms applied, roles of saturated vapor pressures and equilibrium vapor compositions data, and types of objective functions used are analyzed. Activity coefficient or liquid state models frequently used in VLE data reduction are shown and their comparisons are investigated. More so, advantages and limitations of Barker’s method are demonstrated.