Multi-Echelon Data Envelopment Analysis Variable Returns to Scale Models for Performance Evaluation of Supply Chains

This paper develops a variable returns to scale multi-echelon data envelopment analysis (DEA) model to measure the efficiency of supply chain. The model is constructed at first with the assumption of serial sequence in a supply chain. The inputs of one stage become the output of the other stage in the multi-echelon structure. The traditional variable returns to scale model of DEA is modified to fit in the multi-echelon structure. The developed model helps to evaluate the supply network in a coordinated manner. It also provides helpful insights as how to improve the supply network performance

A Tale of Two Transients: GW 170104 and GRB 170105A

We present multi-wavelength follow-up campaigns by the AstroSat CZTI and GROWTH collaborations in search of an electromagnetic counterpart to the gravitational wave event GW 170104. At the time of the GW 170104 trigger, the AstroSat CZTI field of view covered 50.3% of the sky localization. We do not detect any hard X-ray (>100 keV) signal at this time, and place an upper limit of $\approx 4.5\times {10}^{-7}\,\mathrm{erg}\,{\mathrm{cm}}^{-2}\,{{\rm{s}}}^{-1}$, for a 1 s timescale. Separately, the ATLAS survey reported a rapidly fading optical source dubbed ATLAS17aeu in the error circle of GW 170104. Our panchromatic investigation of ATLAS17aeu shows that it is the afterglow of an unrelated long, soft GRB 170105A, with only a fortuitous spatial coincidence with GW 170104. We then discuss the properties of this transient in the context of standard long GRB afterglow models

Daksha: On Alert for High Energy Transients

We present Daksha, a proposed high energy transients mission for the study of electromagnetic counterparts of gravitational wave sources, and gamma ray bursts. Daksha will comprise of two satellites in low earth equatorial orbits, on opposite sides of earth. Each satellite will carry three types of detectors to cover the entire sky in an energy range from 1 keV to >1 MeV. Any transients detected on-board will be announced publicly within minutes of discovery. All photon data will be downloaded in ground station passes to obtain source positions, spectra, and light curves. In addition, Daksha will address a wide range of science cases including monitoring X-ray pulsars, studies of magnetars, solar flares, searches for fast radio burst counterparts, routine monitoring of bright persistent high energy sources, terrestrial gamma-ray flashes, and probing primordial black hole abundances through lensing. In this paper, we discuss the technical capabilities of Daksha, while the detailed science case is discussed in a separate paper.Comment: 9 pages, 3 figures, 1 table. Additional information about the mission is available at https://www.dakshasat.in

Science with the Daksha High Energy Transients Mission

We present the science case for the proposed Daksha high energy transients mission. Daksha will comprise of two satellites covering the entire sky from 1~keV to $>1$~MeV. The primary objectives of the mission are to discover and characterize electromagnetic counterparts to gravitational wave source; and to study Gamma Ray Bursts (GRBs). Daksha is a versatile all-sky monitor that can address a wide variety of science cases. With its broadband spectral response, high sensitivity, and continuous all-sky coverage, it will discover fainter and rarer sources than any other existing or proposed mission. Daksha can make key strides in GRB research with polarization studies, prompt soft spectroscopy, and fine time-resolved spectral studies. Daksha will provide continuous monitoring of X-ray pulsars. It will detect magnetar outbursts and high energy counterparts to Fast Radio Bursts. Using Earth occultation to measure source fluxes, the two satellites together will obtain daily flux measurements of bright hard X-ray sources including active galactic nuclei, X-ray binaries, and slow transients like Novae. Correlation studies between the two satellites can be used to probe primordial black holes through lensing. Daksha will have a set of detectors continuously pointing towards the Sun, providing excellent hard X-ray monitoring data. Closer to home, the high sensitivity and time resolution of Daksha can be leveraged for the characterization of Terrestrial Gamma-ray Flashes.Comment: 19 pages, 7 figures. Submitted to ApJ. More details about the mission at https://www.dakshasat.in

Allogeneic Hematopoietic Cell Transplantation for Blastic Plasmacytoid Dendritic Cell Neoplasm: A CIBMTR Analysis

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare hematological malignancy with a poor prognosis and considered incurable with conventional chemotherapy. Small observational studies reported allogeneic hematopoietic cell transplantation (allo-HCT) offers durable remissions in patients with BPDCN. We report an analysis of patients with BPDCN who received an allo-HCT, using data reported to the Center for International Blood and Marrow Transplant Research (CIBMTR). We identified 164 patients with BPDCN from 78 centers who underwent allo-HCT between 2007 and 2018. The 5-year overall survival (OS), disease-free survival (DFS), relapse, and nonrelapse mortality (NRM) rates were 51.2% (95% confidence interval [CI], 42.5-59.8), 44.4% (95% CI, 36.2-52.8), 32.2% (95% CI, 24.7-40.3), and 23.3% (95% CI, 16.9-30.4), respectively. Disease relapse was the most common cause of death. On multivariate analyses, age of ≥60 years was predictive for inferior OS (hazard ratio [HR], 2.16; 95% CI, 1.35-3.46; P = .001), and higher NRM (HR, 2.19; 95% CI, 1.13-4.22; P = .02). Remission status at time of allo-HCT (CR2/primary induction failure/relapse vs CR1) was predictive of inferior OS (HR, 1.87; 95% CI, 1.14-3.06; P = .01) and DFS (HR, 1.75; 95% CI, 1.11-2.76; P = .02). Use of myeloablative conditioning with total body irradiation (MAC-TBI) was predictive of improved DFS and reduced relapse risk. Allo-HCT is effective in providing durable remissions and long-term survival in BPDCN. Younger age and allo-HCT in CR1 predicted for improved survival, whereas MAC-TBI predicted for less relapse and improved DFS. Novel strategies incorporating allo-HCT are needed to further improve outcomes

Benchmark optimization and attribute identification for improvement of container terminals

The aim of this paper is to optimize the benchmarks and prioritize the variables of decision-making units (DMUs) in data envelopment analysis (DEA) model. In DEA, there is no scope to differentiate and identify threats for efficient DMUs from the inefficient set. Although benchmarks in DEA allow for identification of targets for improvement, it does not prioritize targets or prescribe level-wise improvement path for inefficient units. This paper presents a decision tree based DEA model to enhance the capability and flexibility of classical DEA. The approach is illustrated through its application to container port industry. The method proceeds by construction of multiple efficient frontiers to identify threats for efficient/inefficient DMUs, provide level-wise reference set for inefficient terminals and diagnose the factors that differentiate the performance of inefficient DMUs. It is followed by identification of significant attributes crucial for improvement in different performance levels. The application of this approach will enable decision makers to identify threats and opportunities facing their business and to improve inefficient units relative to their maximum capacity. In addition, it will help them to make intelligent investment on target factors that can improve their firms' productivity.Data envelopment analysis Decision tree Context DEA Container terminals

Identification of bioactive molecules from Triphala (Ayurvedic herbal formulation) as potential inhibitors of SARS-CoV-2 main protease (Mpro) through computational investigations

© 2022 The Author(s)Severe acute respiratory syndrome coronavirus disease (SARS-CoV-2) induced coronavirus disease 2019 (COVID-19) pandemic is the present worldwide health emergency. The global scientific community faces a significant challenge in developing targeted therapies to combat the SARS-CoV-2 infection. Computational approaches have been critical for identifying potential SARS-CoV-2 inhibitors in the face of limited resources and in this time of crisis. Main protease (Mpro) is an intriguing drug target because it processes the polyproteins required for SARS-CoV-2 replication. The application of Ayurvedic knowledge from traditional Indian systems of medicine may be a promising strategy to develop potential inhibitor for different target proteins of SARS-CoV-2. With this endeavor, we docked bioactive molecules from Triphala, an Ayurvedic formulation, against Mpro followed by molecular dynamics (MD) simulation (100 ns) to investigate their inhibitory potential against SARS-CoV-2. The top four best docked molecules (terflavin A, chebulagic acid, chebulinic acid, and corilagin) were selected for MD simulation study and the results obtained were compared to native ligand X77. From docking and MD simulation studies, the selected molecules showed promising binding affinity with the formation of stable complexes at the active binding pocket of Mpro and exhibited negative binding energy during MM-PBSA calculations, indication their strong binding affinity with the target protein. The identified bioactive molecules were further analyzed for drug-likeness by Lipinski's filter, ADMET and toxicity studies. Computational (in silico) investigations identified terflavin A, chebulagic acid, chebulinic acid, and corilagin from Triphala formulation as promising inhibitors of SARS-CoV-2 Mpro, suggesting experimental (in vitro/in vivo) studies to further explore their inhibitory mechanisms