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Sales and operations planning translates the requirements of the customers at the market place (related to new and/or existing products and services) into actionable tactical plans to drive the activities of the value chain of the organization. The present work aims to provide a multi-period and multi-perspective evaluation framework to compare the sales and operational performance (SOP) of firms in an emerging market. SOP is one of the frontline KPIs that describes the efficiency and effectiveness of the sales and operations planning. There is a scantiness in the extant literature about well-defined indicators to measure SOP. The current work fills the gap in the literature by developing a hybrid multi-criteria decision making (MCDM) framework utilizing the Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) and Evaluation based on Distance from Average Solution (EDAS) models for a novel application in assessing SOP. From the data analysis, it is also evident that there is a variations in the year wise ranking of the companies. However, all individual year wise rankings maintain statistically significant correlations with the aggregated ranking. For aggregation purpose, Borda Count Method is used. The companies like ITC Limited, Hindustan Unilever Ltd., Avanti Feeds Ltd., Britannia Industries Ltd., and Symphony Ltd. hold the top five positions on aggregate. The comparison with other MCDM models is made and sensitivity analysis is carried out. The present work is a first of its kind that would encourage the analysts and the policy makers to evaluate the sales and operational performance using a scientific way.

Soft Computing (SC) technique consisting of several fields of Artificial Intelligence namely, Artificial Neural Networks (ANNs), Fuzzy Logic (FL), Genetic Algorithms (GA), Machine Learning (ML) and Probabilistic Reasoning (PR) is a new paradigm in computing based on a creative mix of comparatively newer computing techniques. Here, we have taken into consideration several important schemes/ programs of selected sectors pertaining to different Central Ministries/ Departments of Government of India and observed that proper Knowledge Management System (KMS) can be developed and implemented in such cases using ANN based soft computing input output model as an effective tool for the same. Further, we have analyzed how various input and output parameters (both physical and financial) associated with each such selected scheme/ program are related to each other in the light of their basic objectives and the way these can be appropriately represented by an ANN based soft computing input output model in which the expected outputs corresponding to certain inputs can be calculated/ estimated.

Rationale: The current understanding of human lung development derives mostly from animal studies. While transcript-level studies have analyzed human donor tissue to identify genes expressed during...

In chronic obesity, hepatocytes become insulin resistant and exert important effects on systemic metabolism. Here we show that in early onset obesity (4 weeks high-fat diet), hepatocytes secrete exosomes that enhance insulin sensitivity both in vitro and in vivo. These beneficial effects were due to exosomal microRNA miR-3075, which is enriched in these hepatocyte exosomes. FA2H is a direct target of miR-3075 and small interfering RNA depletion of FA2H in adipocytes, myocytes and primary hepatocytes leads to increased insulin sensitivity. In chronic obesity (16–18 weeks of a high-fat diet), hepatocyte exosomes promote a state of insulin resistance. These chronic obese hepatocyte exosomes do not directly cause impaired insulin signalling in vitro but do promote proinflammatory activation of macrophages. Taken together, these studies show that in early onset obesity, hepatocytes produce exosomes that express high levels of the insulin-sensitizing miR-3075. In chronic obesity, this compensatory effect is lost and hepatocyte-derived exosomes from chronic obese mice promote insulin resistance. Ji and Luo et al. show that miR-3075 in hepatocyte-derived exosomes from mice at early stages of obesity improves insulin sensitivity in chronically obese mice, while hepatocyte exosomes from chronically obese mice induce insulin resistance.

Mutual funds are one of the safest options to invest money in the stock market or to buy assets. Past and present analysis of the mutual fund investing scenario is very crucial to gain an insight about the future performance. This paper aims to forecast the Net Asset Values of an efficient Indian Equity Mutual Funds portfolio using the Auto-Regressive Integrated Moving Average model. The Equity Mutual funds which are operational on or before January 2015 are considered and their monthly Net Asset Values are collected from January 2015 – December 2019. The portfolio is constructed by allocating weightage to the funds ensuring minimum risk. The stationarity of the Net Asset Value for each fund constituting the portfolio is tested using the Augmented Dickey-Fuller test. An appropriate Auto-Regressive Integrated Moving Average model is fitted to each of these funds and the models are validated using the Ljung Box test. The models with the lowest Akaike's Information Criteria value and significant coefficients are selected as the appropriate model. The Net Asset Values for the first six months of the year 2020 are forecasted for each fund using the selected model and the forecasts are found to be quite appropriate. Thus, this study will help investors in their future investments.

Background: The beneficial metabolic effects of the PPARγ agonist rosiglitazone (Rosi) are associated with anti-inflammatory changes in adipose tissue macrophages (ATMs) . One study aim was to characterize the in vivo Rosi-induced phenotypic switch of ATMs using a variety of macrophage markers, including the lipid-associated macrophage markers CD9 and Trem2. As a second aim, since ATM-derived exosomes can exert insulin-sensitizing or desensitizing effects dependent on ATM cell context, we assessed the effects of ATM exosomes from Rosi-treated obese mice on in vitro insulin actions. Research Design and Method: ATMs were isolated from 4 months HFD/obese mice treated for the last month with Rosi (60 mg/kg HFD) compared to HFD or chow-fed controls. Among CD11b+F4/80+ or CD64+ macrophages, we identified pro-inflammatory (“M1-like” CD11c+ and/or CD9+) , anti-inflammatory (“M2-like” CD11c-CD206+) and lipid-associated (CD9+ and/or Trem2+) ATMs by flow cytometry. Bone marrow-derived macrophages (BMDMs) were treated in vitro with µM Rosi for 24h. 3T3-L1 adipocytes, L6 myotubes, and primary hepatocytes were cultured 24h with exosomes isolated from ATMs of Rosi treated obese mice or in vitro Rosi treated BMDMs and then assessed for insulin sensitivity. Results: Rosi treatment reduced adipose tissue inflammation, as seen by reduced numbers of pro-inflammatory (“M1-like”) ATMs and increased the Trem2 signature in “M2-like” ATMs. Importantly, Rosi directly induced Trem2 expression in vitro in BMDMs. Additionally, exosomes isolated from Rosi-treated obese mice or M1 BMDMs increased glucose uptake and insulin signalling (pAKT) in adipocytes and myotubes and reduced glucose production in hepatocytes compared to control exosomes. Conclusion: Our data indicate that Trem2 is a PPARγ target gene. Since exosomes from Rosi treated ATMs or BMDMs enhance cellular insulin action, this could provide a new explanation for the well-known insulin-sensitizing effects of PPARγ agonists. Disclosure T.V.Rohm: None. F.C.G.Reis: None. R.Isaac: None. M.Paszek: None. G.Bandyopadhyay: None. J.M.Olefsky: None. Funding Swiss National Science Foundation (SNF) Early Postdoc.Mobility grant (to T.R.) .

Background & Aims Liver CRIg+ (complement receptor of the immunoglobulin superfamily) macrophages play a critical role in filtering bacteria and their products from circulation. Translocation of microbiota-derived products from an impaired gut barrier contributes to the development of obesity-associated tissue inflammation and insulin resistance. However, the critical role of CRIg+ macrophages in clearing microbiota-derived products from the bloodstream in the context of obesity is largely unknown. Methods We performed studies with CRIg–/–, C3–/–, cGAS–/–, and their wild-type littermate mice. The CRIg+ macrophage population and bacterial DNA abundance were examined in both mouse and human liver by either flow cytometric or immunohistochemistry analysis. Gut microbial DNA–containing extracellular vesicles (mEVs) were adoptively transferred into CRIg–/–, C3–/–, or wild-type mice, and tissue inflammation and insulin sensitivity were measured in these mice. After coculture with gut mEVs, cellular insulin responses and cGAS/STING-mediated inflammatory responses were evaluated. Results Gut mEVs can reach metabolic tissues in obesity. Liver CRIg+ macrophages efficiently clear mEVs from the bloodstream through a C3-dependent opsonization mechanism, whereas obesity elicits a marked reduction in the CRIg+ macrophage population. Depletion of CRIg+ cells results in the spread of mEVs into distant metabolic tissues, subsequently exacerbating tissue inflammation and metabolic disorders. Additionally, in vitro treatment of obese mEVs directly triggers inflammation and insulin resistance of insulin target cells. Depletion of microbial DNA blunts the pathogenic effects of intestinal EVs. Furthermore, the cGAS/STING pathway is crucial for microbial DNA–mediated inflammatory responses. Conclusions Deficiency of CRIg+ macrophages and leakage of intestinal EVs containing microbial DNA contribute to the development of obesity-associated tissue inflammation and metabolic diseases.

Mitigation of winding resonance triggered by oscillatory transient over voltages is one of the key challenges in designing high-voltage transformer windings. Internal resonance is typically an issue with high-voltage transformers with homogeneous winding, manifesting identical natural frequency for the uniform winding sections. Self-capacitance grading can be a possible remedy to resonance issue which incorporates diversification in the natural frequencies of winding disk coils. Practical implementation of this concept is however difficult due to economic and design constraints. This study proposes a practical method of implementing the concept of natural frequency diversification by use of ‘multi-graded inter-winding electrostatic shields’. The concept, theory, design and implementation of the proposed winding method have been presented in this study. A scaled-down prototype of a typical high-voltage transformer has been developed to illustrate the real-time implementation of the proposed method. Both simulation and experimental results established that the proposed winding can effectively suppress oscillatory characteristics under transient over voltages and overcome winding resonance.

Present-day interconnected power systems are prone to different types of disturbances which can affect the whole system and may even lead to blackouts. To get rid of these eventualities, effective online monitoring is carried out on a regular interval. This paper presents a very fast distributed method of calculating line flows in case of line outages. As a computational tool, one single server and two client computers are used in the present work. Since a power system is inherently distributed in nature and contains a number of subsystems connected through tie-lines, the client-/server-based distributed platform suits well with the power system structure. The overall computational work is done mainly by two client computers and server does the necessary job of coordination. Communication between different processes running in server and clients are carried out through the use of Berkeley Socket Interface.

An effort was made in forecasting year wise estimated foreign exchange reserves of India with the help of past data of relevant parameters, using back propagation type of artificial neural networks methodology of soft computing. The results indicates that in a particular financial year if we know the values of foreign direct investments, total expenditure on all plan schemes and special plan schemes of department of commerce, export to import ratio, average import duty rate and GDP of India, then estimation of the likely value of foreign exchange reserves for that particular year can be made by this method.

Agriculture is an important sector of the Indian economy. In the present paper an attempt has been made to theoretically explore the development of an agricultural knowledge management system (KMS) in respect of various micro irrigation techniques for agriculture, as well as relevant crop-/region-specific agricultural practices in different regions of the country, as the same has been observed to be very much necessary for the overall benefits of wider cross section of farmers, agricultural scientists, economists, and other stakeholders in the domain. It is further observed that artificial neural networks (ANNs), which are a part of soft computing techniques, can be used as a KMS tool for effective management of various sub sectors of agriculture. In this context, it has been shown that use of ANNs as a KMS tool can improve the effectiveness of applications of the above mentioned agricultural KMS by accurately forecasting the year-wise estimated yield of food grains of India with the help of past data of various relevant parameters.

Obesity is characterized by a state of chronic, unresolved inflammation in insulin-targeted tissues. Obesity-induced inflammation causes accumulation of proinflammatory macrophages in adipose tissue and liver. Proinflammatory cytokines released from tissue macrophages inhibits insulin sensitivity. Obesity also leads to inflammation-induced endoplasmic reticulum (ER) stress and insulin resistance. In this scenario, based on the data (specifically patterns) generated by our in vivo experiments on both diet-induced obese (DIO) and normal chow diet (NCD) mice, we developed an in silico state space model to integrate ER stress and insulin signaling pathways. Computational results successfully followed the experimental results for both DIO and NCD conditions. Chromogranin A (CgA) peptide catestatin (CST: hCgA 352 - 372 ) improves obesity-induced hepatic insulin resistance by reducing inflammation and inhibiting proinflammatory macrophage infiltration. We reasoned that the anti-inflammatory effects of CST would alleviate ER stress. CST decreased obesity-induced ER dilation in hepatocytes and macrophages. On application of Proportional-Integral-Derivative (PID) controllers on the in silico model, we checked whether the reduction of phosphorylated PERK resulting in attenuation of ER stress, resembling CST effect, could enhance insulin sensitivity. The simulation results clearly pointed out that CST not only decreased ER stress but also enhanced insulin sensitivity in mammalian cells. In vivo experiment validated the simulation results by depicting that CST caused decrease in phosphorylation of UPR signaling molecules and increased phosphorylation of insulin signaling molecules. Besides simulation results predicted that enhancement of AKT phosphorylation helps in both overcoming ER stress and achieving insulin sensitivity. These effects of CST were verified in hepatocyte culture model.

Agriculture is an important sector of the Indian economy. In the present paper an attempt has been made to theoretically explore the development of an agricultural knowledge management system (KMS) in respect of various micro irrigation techniques for agriculture, as well as relevant crop-/region-specific agricultural practices in different regions of the country, as the same has been observed to be very much necessary for the overall benefits of wider cross section of farmers, agricultural scientists, economists, and other stakeholders in the domain. It is further observed that artificial neural networks (ANNs), which are a part of soft computing techniques, can be used as a KMS tool for effective management of various sub sectors of agriculture. In this context, it has been shown that use of ANNs as a KMS tool can improve the effectiveness of applications of the above mentioned agricultural KMS by accurately forecasting the year-wise estimated yield of food grains of India with the help of past data of various relevant parameters.

The human lung plays vital roles in respiration, host defense and basic physiology. Recent technological advancements such as single-cell RNA sequencing and genetic lineage tracing have revealed novel cell types and enriched functional properties of existing cell types in lung. The time has come to take a new census. Initiated from members of the NHLBI-funded LungMAP Consortium and aided by experts in the lung biology community, we synthesized current data into a comprehensive and practical cellular census of the lung. Identities of cell types in the normal lung are captured in individual cell cards with delineation of function, markers, developmental lineages, heterogeneity, regenerative potential, disease links and key experimental tools. This publication will serve as the starting point of a live, up-to-date guide for lung research at https://lungmap.net/cell-cards-browser/. We hope that this Lung CellCards will promote the community-wide effort to establish, maintain and restore respiratory health.

Aim: Defects in hepatic glycogen synthesis contribute to post-prandial hyperglycaemia in type 2 diabetic patients. Chromogranin A (CgA) peptide Catestatin (CST: hCgA 352-372) improves glucose tolerance in insulin-resistant mice. Here, we seek to determine whether CST induces hepatic glycogen synthesis. Methods: We determined liver glycogen, glucose-6-phosphate (G6P), uridine diphosphate glucose (UDPG) and glycogen synthase (GYS2) activities; plasma insulin, glucagon, noradrenaline and adrenaline levels in wild-type (WT) as well as in CST knockout (CST-KO) mice; glycogen synthesis and glycogenolysis in primary hepatocytes. We also analysed phosphorylation signals of insulin receptor (IR), insulin receptor substrate-1 (IRS-1), phosphatidylinositol-dependent kinase-1 (PDK-1), GYS2, glycogen synthase kinase-3β (GSK-3β), AKT (a kinase in AKR mouse that produces Thymoma)/PKB (protein kinase B) and mammalian/mechanistic target of rapamycin (mTOR) by immunoblotting. Results: CST stimulated glycogen accumulation in fed and fasted liver and in primary hepatocytes. CST reduced plasma noradrenaline and adrenaline levels. CST also directly stimulated glycogenesis and inhibited noradrenaline and adrenaline-induced glycogenolysis in hepatocytes. In addition, CST elevated the levels of UDPG and increased GYS2 activity. CST-KO mice had decreased liver glycogen that was restored by treatment with CST, reinforcing the crucial role of CST in hepatic glycogenesis. CST improved insulin signals downstream of IR and IRS-1 by enhancing phospho-AKT signals through the stimulation of PDK-1 and mTORC2 (mTOR Complex 2, rapamycin-insensitive complex) activities. Conclusions: CST directly promotes the glycogenic pathway by (a) reducing glucose production, (b) increasing glycogen synthesis from UDPG, (c) reducing glycogenolysis and (d) enhancing downstream insulin signalling.

Portfolio formation holds paramount importance in the process of the investment decision making since, a single door investment (SDI) option is much riskier than a multiple door investment (MDI) option. Among available financial instruments, the stock market (SM) has allured investors because of its liquidity and growth opportunities. However, the effectiveness of the investment decision is largely reflected in the selection of the constituent elements of the portfolio by an investor while trading off risk and return. In this paper, after an initial level selection of for formulating a possible portfolio by using Perceptual Map (PM), we have applied DEA to calculate the efficiency of the stocks at the risk-return interface based on the market performance. In order to ascertain that the stock selection is logical and worthwhile, we further probe the fundamental performances over a time period of five consecutive financial years using the method of Multi-Criteria Decision Analysis (MCDA) framework based on the Complex Proportional Assessment (COPRAS) method, where, the criteria weights are calculated by using the entropy method. A consistency is visible in the yearly fundamental performances and a significant pattern with regard to the portfolio selection.

Contains fulltext : 206959.pdf (Publisher’s version ) (Closed access) We have previously shown that the chromogranin A (CgA)-derived peptide catestatin (CST: hCgA352-372) inhibits nicotine-induced secretion of catecholamines from the adrenal medulla and chromaffin cells. In the present study, we seek to determine whether CST regulates dense core (DC) vesicle (DCV) quanta (catecholamine and chromogranin/secretogranin proteins) during acute (0.5-h treatment) or chronic (24-h treatment) cholinergic (nicotine) or peptidergic (PACAP, pituitary adenylyl cyclase activating polypeptide) stimulation of PC12 cells. In acute experiments, we found that both nicotine (60 muM) and PACAP (0.1 muM) decreased intracellular norepinephrine (NE) content and increased (3)H-NE secretion, with both effects markedly inhibited by co-treatment with CST (2 muM). In chronic experiments, we found that nicotine and PACAP both reduced DCV and DC diameters and that this effect was likewise prevented by CST. Nicotine or CST alone increased expression of CgA protein and together elicited an additional increase in CgA protein, implying that nicotine and CST utilize separate signaling pathways to activate CgA expression. In contrast, PACAP increased expression of CgB and SgII proteins, with a further potentiation by CST. CST augmented the expression of tyrosine hydroxylase (TH) but did not increase intracellular NE levels, presumably due to its inability to cause post-translational activation of TH through serine phosphorylation. Co-treatment of CST with nicotine or PACAP increased quantal size, plausibly due to increased synthesis of CgA, CgB and SgII by CST. We conclude that CST regulates DCV quanta by acutely inhibiting catecholamine secretion and chronically increasing expression of CgA after nicotinic stimulation and CgB and SgII after PACAPergic stimulation.

Objectives: To investigate the impact of four dimensions on organizational culture in Heterogeneous Industries which includes: Training and Development, Reward and Recognition and Teamwork and Communication over employees commitment. Methods and Analysis: This research was conducted among employees covered under ten heterogeneous Industries located in Jharkhand, West Bengal, Bihar, Gujarat and Karnataka states in India. 242 random population samples were collected under this study. Initially, we have observed the Reliability analysis on the data set to make the information more reliable and authentic. Thereafter Factor analysis using statistical software (SPSS) version 20.2 was applied. Findings: On the basis of a response from the two categories of Management Staff (Jr Management and Senior with Middle Management) within ten Heterogeneous industries serving in the field of Service, Manufacturing and Infrastructure in different states in India, Reliability Analysis and Factor analysis was tested. A questionnaire developed by Lau and Idris, (2001) and Ooi and Arumugam (2006) have been evolved with some modification. Initially, the questionnaire has been framed on the basis of the Training and Development (TandD) with 6 Questions, Reward and Recognition(RandR) with 8 Questions, Team Work (TW) with 7 Questions, and Organizational Communication (OC) with 6 Questions and Employees Commitment (EC) with 10 Questions. After having the Reliability analysis and Factor Analysis on the basis of responses using 5 point Likert Scale (from 1 to5) of the relevant questions on the application of statistical tools showed reasonably significant on the determination of the attributes of various dimensions of organizational culture. Application: Empirical study covers both type of Industries and hence its outcome and implications will provide a better understanding of the cultural issue to such organizations and help them in carving out future strategy. Keywords: Heterogeneous Industries in India, Organizational Commitment, Organizational Culture, Reliability Analysis and Factor Analysis

BackgroundFor many practical hypothesis testing (H-T) applications, the data are correlated and/or with heterogeneous variance structure. The regressiont-test for weighted linear mixed-effects regression (LMER) is a legitimate choice because it accounts for complex covariance structure; however, high computational costs and occasional convergence issues make it impractical for analyzing high-throughput data. In this paper, we propose computationally efficient parametric and semiparametric tests based on a set of specialized matrix techniques dubbed as the PB-transformation. The PB-transformation has two advantages: 1. The PB-transformed data will have a scalar variance-covariance matrix. 2. The original H-T problem will be reduced to an equivalent one-sample H-T problem. The transformed problem can then be approached by either the one-sample Studentst-test or Wilcoxon signed rank test.ResultsIn simulation studies, the proposed methods outperform commonly used alternative methods under both normal and double exponential distributions. In particular, the PB-transformedt-test produces notably better results than the weighted LMER test, especially in the high correlation case, using only a small fraction of computational cost (3 versus 933 seconds). We apply these two methods to a set of RNA-seq gene expression data collected in a breast cancer study. Pathway analyses show that the PB-transformedt-test reveals more biologically relevant findings in relation to breast cancer than the weighted LMER test․.ConclusionsAs fast and numerically stable replacements for the weighted LMER test, the PB-transformed tests are especially suitable for “messy” high-throughput data that include both independent and matched/repeated samples. By using our method, the practitioners no longer have to choose between using partial data (applying paired tests to only the matched samples) or ignoring the correlation in the data (applying two sample tests to data with some correlated samples).

Microalgae are the unicellular or multicellular photosynthetic microorganisms that can efficiently fix carbon dioxide (CO2) from various sources such as the environment, industrial flue gas, and some carbonate salts. In the present study, one green microalgal strain and a cyanobacterial consortium were used separately for the sequestration of CO2 at different pHs (7–11), at different initial concentrations of CO2 (5–20%), and at various inoculum sizes (5–12.5%). The maximum sequestration of CO2 was found to be 74.37 ± 0.49% and 71.12 ± 0.05% at 5% and 15% CO2 for green algae and cyanobacterial consortium. The biomass generated after sequestration of CO2 was utilized for the synthesis of biomolecules.