E-Business Transformation at the Crossroads: Sears\u27 Dilemma

It was December 2002, and Garry Kelly, the newly appointed CIO of Sears, Roebuck & Company, looked out of his office window and contemplated the issues he needed to discuss in the management committee meeting the following day. Garry had arrived at Sears only a few weeks ago when the company was at a critical juncture. Sears’ net income in 2001 had fallen to $735 million on a revenue level of$41.1 billion. These figures reflected only half of the profits it had recorded two years earlier, on a similar level of sales. Sears also faced intense competition from rival retailers across the nation, new dot-com e-tailers as well as from the specialty stores that had been eroding the profit base for the last couple of years. Investors, stakeholders, and employees were anxiously looking for signs of turnaround at the giant in the U.S. retailing industry

E-Business Transformation at the Crossroads: Sears’ Dilemma

This teaching case discusses the challenges facing Sears, Reobeck and Co., a leading retailer in United States, in its efforts to transform itself into an effective brick-and-click organization. In face of intense competition from other retailers and online e-tailers, Sears has continually expanded its online efforts in e-business transformation. This case traces the key e-business initiatives taken by Sears and highlights significant managerial challenges that were encountered during the formulation and execution of an effective e-business transformation strategy. The case presents the issues faced by a new CIO who had taken over the technology and e-business affairs at Sears at the end of 2002

Clinical profile of congenital diaphragmatic hernia and their short-term outcome in a tertiary care neonatal unit: A retrospective study

Background: Recent developments in the antenatal diagnosis, surgical techniques, and neonatal intensive care had widely increased survival rates in neonates with congenital diaphragmatic hernia (CDH) in the western world. In developing countries, however, high mortality in neonates with CDH still continues to be a challenge. Objective: The aim of this study is to study the clinical profile of neonates with CDH and to analyze the various factors affecting mortality. Materials and Methods: In this retrospective study, 148 babies with a diagnosis of diaphragmatic hernia admitted to a tertiary care neonatal unit in South India, from the year 2010 to 2015, were reviewed. Results: The total survival rate was 58.1%, and the operative survival rate was 85.1%. Prenatal diagnosis was made in only 7 cases, and of these, 5 (71.42%) survived. Higher mortality was associated with age at admission <24 h, low Apgar score, early onset of respiratory distress, right-sided CDH, presence of persistent pulmonary hypertension of newborn, and high FiO2 requirement at the time of admission, during stabilization and surgery (p<0.01). Significantly higher mortality was also observed among babies who required positive-pressure ventilation during transport, required inotropes during hospital course (p<0.01), and had low PaO2, high PCO2, and high oxygenation index (p<0.01). Conclusion: Babies admitted to hospital within 24 h of age and who manifested early had a poor outcome indicating severe disease. Antenatal diagnosis of this condition should improve to prevent delay in stabilization and poor outcome

Optimization of Green Infrastructure Practices in Industrial Areas for Runoff Management: A Review on Issues, Challenges and Opportunities

In urbanized lands, industrial areas are generally located close to residential and commercial areas due to ease of access for material and human resources. These industrial areas annually discharge large volumes of contaminated stormwater to receiving waters. Green Infrastructure (GI) practices, which were initially introduced as a land conservation strategy to enhance green space in urban areas, can provide benefits in source control of runoff generated in industrial areas with higher percentage of impermeable surfaces. Even though industrial areas across the world are currently looking at the applications of GI to reduce the impacts of excessive runoff and mitigate flash floods, several debates exist in optimization of these practices for such areas. In the current practice, optimal selection of GI practices for such areas are generally conducted based on expert judgement, and there are no systematic methodologies currently available for this process. This paper presents a review on various issues, challenges, and opportunities in the optimum applications of GI practices for industrial areas. The Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis conducted in this review by focusing on the applications of GI practices for industrial areas, helped to identify the existing research gaps for the optimization. Furthermore, the review showed the importance of engaging the multi-disciplinary stakeholders in the GI optimization process for industrial areas. In conclusion, the present review highlights the importance of introducing a systematic methodology for the optimum applications of GI practices for industrial areas to manage stormwater

Identification of putative substrates and inhibitors for Glutathione S-transferases using computational methods

Glutathione S-transferases (GSTs) comprise a family of enzymes that utilizes glutathione (GSH) in many enzymatic reactions that involved in transformation of several compounds including therapeutic drug molecules and carcinogens. In addition, GSTs influence cellular survival and proliferation, by repressing apoptosis signal-regulating kinase 1 (ASK1) thus affecting the activation of p38 mitogen-activated protein kinase (MAPK) and c-Jun N-terminal kinase (JNK) in response to various intra and extracellular stresses. Molecules inhibiting the function of GSTs received attention as an adjuvant therapy to the highly toxic electrophilic agents to avoid usage of high doses and toxicity for better outcomes. There is no detailed in silico analysis exists in literature to describe the binding patterns of known inhibitors to all GST isoforms. This study is aimed at providing details of binding patterns of known and putative substrates (Busulfan, Treosulfan, SS-EBDM, SS-DEB), inhibitors (Ethacrynic acid, Sulfolane and Curcumin) with predominately-expressed seven isoforms of GST (Alpha1, Alpha2, Pi1, Mu1, Mu2, Mu5 and Theta1). In silico methodology include six steps namely (a) Retrieval of three-dimensional structure of GSTs and Ligand molecules from RCSB-PDB and NCBI-PubChem databases, (b) Protein and Ligand preparation using Auto Dock Tools (ADT), (c) Receptor grid preparation based on known binding site (Direct docking protocol) of GSTs using AutoDock/Vina plugin in PyMOL, (d) Preparation of Auto Dock Vina configuration file, (e) Running of docking calculation using Auto Dock Vina and (f) Analysis of docking results using ADT, PyMOL and LigPlus programs. Molecular docking studies of substrates/inhibitors are performed with both Apo and GSH bounded forms of GSTs. Structural parameters such as estimated free energy of binding (ΔH), estimated inhibition constant (Ki), binding orientation, intermolecular interactions were noted for all the docking interaction models. Then the parameters were compared against each substrate or inhibitor for the affinity towards a selective GST isoform. Out of the three putative or known inhibitors screened, Curcumin showed a significant high binding affinity towards all the classes of GSTs, particularly GST Alpha1 (ΔH: -9.7 kcal/mol and Ki: 0.08 µM). Ethacrynic acid also showed better binding affinity towards GST Alpha1 (ΔH: -7.6 kcal/mol and Ki: 2.7 uM). Sulfolane did not exhibited a stronger affinity towards all the seven GST isoforms. Busulfan and Treosulfan exhibited a reasonable binding affinity towards GST Alpha1 (ΔH: -5.2 and -5.3 kcal/mol) and weakened affinity for the remaining six GST isoforms. Thus, treosulfan could be a possible substrate for GST Alpha1. Manual inspection of three-dimensional structures of the docking complexes revealed that binding-sites for inhibitor and substrate are different. In an on-going study, we are evaluating the inhibitory potential of Curcumin and Ethacrynic acid against GSTs in in vitro studies. The detailed description of the binding interactions may be useful to screen new putative GST substrates and inhibitors. Presence or absence of variants in these binding pockets also can define the amount of inhibitor required and the affinity and potency of an inhibitor and or substrate. This poster is presented at " ESPT 2017 in Catania, Italy from Oct 4th-7th 2017

An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer

In comparison to all other malignancies, breast cancer is the most common form of cancer, among women. Breast cancer prediction has been studied by several researchers and is considered a serious threat to women. Clinicians are finding it difficult to create a treatment approach that will help patients live longer, due to the lack of solid predictive models. Rates of this malignancy have been observed to rise, more with industrialization and urbanization, as well as with early detection facilities. It is still considerably more prevalent in very developed countries, but it is rapidly spreading to developing countries as well. The purpose of this work is to offer a report on the disease of breast cancer in which we used available technical breakthroughs to construct breast cancer survivability prediction models. The Machine Learning (ML) techniques, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT) Classifier, Random Forests (RF), and Logistic Regression (LR) is used as base Learners and their performance has been compared with the ensemble method, eXtreme Gradient Boosting (XGBoost).  For performance comparison, we employed the k-fold cross-validation method to measure the unbiased estimate of these prediction models. The results indicated that XGBoost outperformed with an accuracy of 97.81% compared to other ML algorithms

An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer

In comparison to all other malignancies, breast cancer is the most common form of cancer, among women. Breast cancer prediction has been studied by several researchers and is considered a serious threat to women. Clinicians are finding it difficult to create a treatment approach that will help patients live longer, due to the lack of solid predictive models. Rates of this malignancy have been observed to rise, more with industrialization and urbanization, as well as with early detection facilities. It is still considerably more prevalent in very developed countries, but it is rapidly spreading to developing countries as well. The purpose of this work is to offer a report on the disease of breast cancer in which we used available technical breakthroughs to construct breast cancer survivability prediction models. The Machine Learning (ML) techniques, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT) Classifier, Random Forests (RF), and Logistic Regression (LR) is used as base Learners and their performance has been compared with the ensemble method, eXtreme Gradient Boosting (XGBoost).  For performance comparison, we employed the k-fold cross-validation method to measure the unbiased estimate of these prediction models. The results indicated that XGBoost outperformed with an accuracy of 97.81% compared to other ML algorithms

TaLoS: secure and transparent TLS termination inside SGX enclaves

We introduce TaLoS1, a drop-in replacement for existing transport layer security (TLS) libraries that protects itself from a malicious environment by running inside an Intel SGX trusted execution environment. By minimising the amount of enclave transitions and reducing the overhead of the remaining enclave transitions, TaLoS imposes an overhead of no more than 31% in our evaluation with the Apache web server and the Squid proxy