Evaluating Energy Security Performance in Pakistan and India through Aggregated Energy Security Performance Indicators (AESPI)

This article computes the energy security performance between two crucial South Asian neighboring countries in the region through aggregated energy security performance indicators (AESPI) using time series data of time spanning 1990-2018. The findings of this manuscript suggest that total primary energy supply, final energy consumption, household electricity consumption, share of non- carbon energy per total primary energy consumption, net energy import dependency Co2 emissions per capita and per GDP and residential energy consumption lead to make better energy security performance in Pakistan. However, total primary electricity consumption, total primary and final energy intensity, reserve production ratio of oil & gas and transformation losses adversely affect energy security performance in Pakistan. On another end, in India final energy consumption, total primary energy intensity, household electricity consumption, share of capacity of renewable energy per total electricity generation, share of renewable energy per final energy consumption, net energy import dependency and Co2 emissions per capita lead to improve energy security performance. Conversely, total primary energy supply, total primary electricity consumption, final energy intensity, transformation loss, reserve production ratio of oil & gas, share of non-carbon energy per total primary energy supply and Co2 emissions per GDP may cause to reduce energy security performance in India. In conclusion, the overall energy security performance is improved in both the countries by time, India (more improved than Pakistan) and Pakistan, as the findings of this manuscript suggest

Learning the Structure of Auto-Encoding Recommenders

Autoencoder recommenders have recently shown state-of-the-art performance in the recommendation task due to their ability to model non-linear item relationships effectively. However, existing autoencoder recommenders use fully-connected neural network layers and do not employ structure learning. This can lead to inefficient training, especially when the data is sparse as commonly found in collaborative filtering. The aforementioned results in lower generalization ability and reduced performance. In this paper, we introduce structure learning for autoencoder recommenders by taking advantage of the inherent item groups present in the collaborative filtering domain. Due to the nature of items in general, we know that certain items are more related to each other than to other items. Based on this, we propose a method that first learns groups of related items and then uses this information to determine the connectivity structure of an auto-encoding neural network. This results in a network that is sparsely connected. This sparse structure can be viewed as a prior that guides the network training. Empirically we demonstrate that the proposed structure learning enables the autoencoder to converge to a local optimum with a much smaller spectral norm and generalization error bound than the fully-connected network. The resultant sparse network considerably outperforms the state-of-the-art methods like \textsc{Mult-vae/Mult-dae} on multiple benchmarked datasets even when the same number of parameters and flops are used. It also has a better cold-start performance.Comment: Proceedings of The Web Conference 202

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

Mucosal Schwann cell hamartoma of the gallbladder

Mucosal Schwann cell hamartoma (MSCH) is a rare benign neurogenic tumor characterized by pure S100p positive spindle cell proliferation. Most cases occur in the distal colon. Involvement of the gall bladder is exceedingly rare. There have been no reports of recurrence or a syndromic association with MSCH. Herein, we describe a case of MSCH of the gallbladder in a 55-year-old female patient with prior history of gastrointestinal neurofibromas who presented with abdominal pain. MR imaging revealed choledocholithiasis, gallbladder thickening, and marked biliary and pancreatic ductal dilation. The patient subsequently underwent cholecystectomy with choledochoduodenostomy. Histologic evaluation of the gallbladder showed diffuse expansion of the mucosa with S100p positive cells with spindly nuclei and indistinct cytoplasmic borders and diagnosis of MSCH of the gallbladder was rendered

Identification of phytotoxins in different plant parts of Brassica napus and their influence on mung bean

Jabran, Khawar/0000-0001-8512-3330;WOS: 000436879200076PubMed: 29691745Plants in Brassica genus have been found to possess strong allelopathic potential. They may inhibit seed germination and emergence of subsequent crops following them in a rotation system. Series of laboratory and greenhouse experiments were conducted to determine the allelopathic impacts of Brassica napus L. against mung bean. We studied (1) the effects of aqueous extract (5%) of different plant parts (root, stem, leaf, flower, and whole plant) of B. napus, (2) the effects of leaf and flower extracts of B. napus at 0, 1, 2, 3, and 4% concentrations, and (3) the effect of residues of different B. napus plant parts and decomposition periods (0, 7, 14, and 21 days) on germination and seedling growth of mung bean. Various types of phenolics including quercitin, chlorogenic acid, p-coumeric acid, m-coumaric acid, benzoic acid, caffeic acid, syringic acid, vanillic acid, ferulic acid, cinamic acid, and gallic acid were identified in plant parts of B. napus. Among aqueous extracts of various plant parts, leaf and flower were found to have stronger inhibitory effects on germination and seedling growth traits of mung bean, higher concentrations were more toxic. The decomposition period changed the phtotoxic effect of residues, more inhibitory effect was shown at 14 days decomposition while decomposition for 21 days reduced inhibitory effect. The more total water-soluble phenolic was found in 5% (w/v) aqueous extract and 5% (w/w) residues of B. napus flowers at 14 days of decomposition (89.80 and 10.47 mg L-1), respectively. The strong inhibitory effects of B. napus should be managed when followed in rotation

A scalable architecture for geometric correction of multi-projector display systems

Multi-projector displays allow the realization of large and immersive projection environments by allowing the tiling of projections from multiple projectors. Such tiled displays require real time geometrical warping of the content that is being projected from each projector. This geometrical warping is a computationally intensive operation and is typically applied using high-end graphics processing units (GPUs) that are able to process a defined number of projector channels. Furthermore, this limits the applicability of such multi-projector display systems only to the content that is being generated using desktop based systems. In this paper we propose a platform independent FPGA based scalable hardware architecture for geometric correction of projected content that allows addition of each projector channel at a fractional increase in logic area. The proposed scheme provides real time correction of HD quality video streams and thus enables the use of this technology for embedded and standalone devices