Supply chain financing with advance selling under disruption

© 2019 The Authors. International Transactions in Operational Research © 2019 International Federation of Operational Research Societies We study a financing problem in a supply chain (SC) consisting of one supplier and one buyer under supply disruption. The supplier could face a disruption at its end which could effectively reduce its yield in case of disruption, thereby resulting in supply yield uncertainty. The retailer can finance the supplier using advance selling that can help mitigate the impact of disruption. We model this problem as a Stackelberg game, where the supplier as the leader announces the wholesale price and the retailer responds by deciding its optimal order quantity given stochastic demand and an exogenous fixed retail price. The supplier then commences production and a disruption can happen with a known probability. We assume that under disruption the quantity delivered is a fraction of the initial quantity ordered by the retailer. The retailer loses any unmet demand. We analyze three different scenarios of the Stackelberg game, namely no advance selling with disruption, advance selling without disruption, and advance selling with disruption. Our results indicate that advance selling can be used to mitigate the impact of supply disruption and at the same time could lead to an increase in the overall SCprofit

ASSESSMENT OF FEASIBILITY AND COMPLICATIONS OF LAPAROSCOPIC CHOLECYSTECTOMY IN CIRRHOTIC PATIENTS

Abstract :Introduction: From the era of absolute contraindication to the phase of preferred treatment, the technique of laparoscopic cholecystectomy advances with time. Here, we report our experience of laparoscopic cholecystectomy in 20 patients of liver cirrhosis. In our institute, laparoscopic cholecystectomy is the preferred choice for cholelithiasis in cirrhotic patient.Methods: In last 2 years, 180 laparoscopic cholecystectomies were performed and 20 patients were cirrhotic. Their data analyzed retrospectively in terms of preoperative optimization, operative technique and results.Results: Laparoscopic cholecystectomy was completed successfully in 19 patients and one was converted to open. Mean operative time was 54 minutes. No additional port was required in all cases. Calot's first dissection was performed in 18 patients and fundus first technique was used in 2 patients due to unclear anatomy. Liver bed bleeding was present in 16 patients, which was controlled effectively. Subhepatic drain was placed in 12 patients. There was no mortality. Morbidity  in two patients was worsening of ascites in one; and incisional hernia in other patient which was converted to open. Port site complications were not noted in any patient and there was no evidence of intraabdominal bleeding or bile leak postoperatively. Blood and component transfusion was required in 2 patients. Average length of hospital stay was 4.8 days.Conclusion: Though laparoscopic cholecystectomy may be difficult in cirrhotic patients but it is feasible and relatively safe. It offers many advantages in cirrhotic patients and associated with low morbidity when compared with open surgery.Keywords: cirrhosis, laparoscopic cholecystectomy, difficult cholecystectom

Loco-regionally advance breast cancer: evaluation of management of breast cancer with special reference to multimodal approach

Background: Breast cancer is one of the most common human neoplasms, accounting for approximately one-quarter of all cancers in females worldwide and 27% of cancers in developed countries with a western lifestyle. The aims of this study were to evaluate the management of loco-regionally advanced carcinoma of breast with special reference to multimodal approach.Methods: The study was conducted on patients with loco-regionally advanced carcinoma of breast, reporting for treatment in a large multi-specialty teaching institute. All patients of stage IIIB were initially treated with neo-adjuvant (induction) chemotherapy (3 cycles), except 4 patients in the study group offered surgery as initial treatment because of small tumor size with limited peu’d orange change in the skin. After this treatment all patients were reassessed with a thorough clinical examination and restaging work upto detect the response of the chemotherapy. All patients who achieved objective response (complete + partial) were offered surgery, followed by CT and RT.Results: About 60% of the patients were in stage IIIB and 32% in stage IIIA. Majority of the tumors were in T4 category (64%). In 28% cases ipsilateral fixed lymph nodes were found. Histopathological examination revealed 76% (38) patients with infiltrating duct carcinoma, 4 patients (8%) had comedo carcinoma, 2 patients (4%) had lobular carcinoma, 2 patients (4%) had medullary carcinoma and 4 patients (8%) had mucinous carcinoma. Both nonresponsive (NR) and disease progression (DP) patients were in stage III B group. About 76.9% patients of stage IIIB (20) achieved partial response. Only 1 patient (33.3%) developed local recurrence after 10 months of completion of treatment. Median disease free survival (DFS) period of this group is 30.2 months. Recurrence rate is stage IIIA patients was 27.7% and in stage IIIB 37.5%. Maximum numbers of disease free patients were found in T3N1 group (85.7%). Patient with N2 and T4 disease chances of recurrence was more than N1 and T3 lesions.Conclusions: Patients with LBAC who are able to complete treatment with chemotherapy, mastectomy, and postmastectomy radiation have a high probability of locoregional control. Neo-adjuvant chemotherapy can make inoperable locally advanced breast cancer operable and with the use of neo-adjuvant CT, breast conservation surgery is possible even in locally advanced breast cancer. Use of post-operative CT and RT can increase the disease free survival period. Use of multimodal treatment in the form of CT, surgery and radiotherapy can increase the disease free survival period in locally advanced breast cancer. The advent of successful multimodal regimens incorporating systemic treatment (chemotherapy or chemohormonal therapy) as well as local therapy (surgery and radiation) has significantly improved disease-free and overall survival as well as local-regional control. Longer follow-up of these conservatively treated patients will be needed, however, to determine whether local-regional control is preserved

Quality Time: A simple online technique for quantifying multicore execution efficiency

Abstract—In order to increase utilization, multicore pro-cessors share memory resources among an increasing number of cores. This sharing leads to memory interference, which in turn leads to a non-uniform degradation in the execution of concurrent applications, even in the presence of fairness mechanisms. Many utilities rely on application CPU Time both for measuring resource usage and inferring application progress. These utilities are therefore directly affected by the distorting effects of multicore interference on the representativeness of CPU Time as a proxy for progress. This makes reasoning about myriad properties from fairness, to QoS, to throughput optimality very difficult in consolidated environments, such as IaaS. We introduce the notion of Quality Time, which provides a measure of application progress analogous to CPU Time’s measure of resource usage, and we propose a simple online sampling-based technique to approximate Quality Time with high accuracy. We have implemented three user-space tools called Qtime, Qtop, and Qplacer. Qtime can attach to an application to calculate its Quality Time online, Qtop is a dashboard that monitors the Quality Times of all applications on the system, and Qplacer leverages Quality Time information to find better application placements and improve overall system quality. With Quality Time, we are able to reduce the error in inferring execution efficiency from 150.3 % to 25.1 % in the worst case and from 30.0 % to 7.5 % on average. Qplacer can increase average system throughput by 3.2 % when compared to static application placement. I

Large-scale inference and graph theoretical analysis of gene-regulatory networks in B. stubtilis

We present the methods and results of a two-stage modeling process that generates candidate gene-regulatory networks of the bacterium B. subtilis from experimentally obtained, yet mathematically underdetermined microchip array data. By employing a computational, linear correlative procedure to generate these networks, and by analyzing the networks from a graph theoretical perspective, we are able to verify the biological viability of our inferred networks, and we demonstrate that our networks' graph theoretical properties are remarkably similar to those of other biological systems. In addition, by comparing our inferred networks to those of a previous, noisier implementation of the linear inference process [17], we are able to identify trends in graph theoretical behavior that occur both in our networks as well as in their perturbed counterparts. These commonalities in behavior at multiple levels of complexity allow us to ascertain the level of complexity to which our process is robust to noise.Comment: 22 pages, 4 figures, accepted for publication in Physica A (2006

ASSESSMENT OF DEPRESSION AMONG MEDICAL STUDENTS OF PRIVATE UNIVERSITY IN BHOPAL, INDIA

ABSTRACT Introduction: Medical students repeatedly experience different stresses which render them more vulnerable to psychological problems that may affect their emotional, psychosocial and physical health. Objectives of the study were to determine the prevalence of depression and associated factors leading to depression among medical students at People's University

Investigation of direct and maternal genetic effects on days open in Jersey crossbred cattle

Estimates of (co)variance and genetic parameters for days open (DO) of Jersey crossbred cattle were estimated by restricted maximum likelihood (REML), fitting 6 animal models, including various combinations of maternal effects. Data on 792 records of 223 Jersey crossbred animals, descended from 51 sires and 170 dams were used. The direct heritability estimates for days open ranged from 0.04 to 0.10 depending on the model applied. The additive maternal effects varied from 0.06 to 0.09 in different models in this study, whereas the estimates of the fraction of variance due to maternal permanent environmental effects were practically negligible to very low (0– 4% of the phenotypic variance), irrespective of the models used. Results suggested that direct and maternal additive effects were important for this trait but, the low heritability estimates indicated little scope of genetic progress through selection for this trait

Data driven surrogate model-based optimization of the process parameters in electric discharge machining of D2 steel using Cu-SiC composite tool for the machined surface roughness and the tool wear

Electrical discharge machining (EDM) is mainly utilized for the die manufacturing and also used to machine the hard materials. Pure Copper, Copper based alloys, brass, graphite, steel are the conventional electrode materials for EDM process. While machining with the conventional electrode materials, tool wear becomes the main bottleneck which led to increased machining cost. In the present work, the composite tool tip comprises 80% Copper and 20% silicon carbide was used for the machining of hardened D2 steel. The powder metallurgy route was used to fabricate the composite tool tip. Electrode wear rate and surface roughness were assessed with respect to the different process parameters like input current, gap voltage, pulse on time, pulse off time and dielectric flushing pressure. During the analysis it was found that Input current (I p ), Pulse on time (T on ) and Pulse off time (T off ) were the significant parameters which were affecting the tool wear rate (TWR) while the I p , T on and flushing pressure affected more the surface roughness (SR). SEM micrograph reveals that increase in I p leads to increase in the wear rate of the tool. The data obtained from experiments were used to develop machine learning based surrogate models. Three machine learning (ML) models are random forest, polynomial regression and gradient boosted tree. The predictive capability of ML based surrogate models was assessed by contrasting the R 2 and mean square error (MSE) of prediction of responses. The best surrogate model was used to develop a complex objective function for use in firefly algorithm-based optimization of input machining parameters for minimization of the output responses