Heavy Metal Levels in Vegetables and Soil Cultivated with Industrial Wastewater from Different Sites of Chunian and Jamber, District, Kasur

In human diet, vegetables play important role to maintain the physiological conditions. Due to anthropogenic activities and pollution, the food items become contaminated. The present study was performed to evaluate the level of heavy metals in the vegetables irrigated with wastewater across Chunian and Jamber, district, Kasur. Level of heavy metals from the study area like Zinc, Lead and chromium in the soil, water and vegetables was compared. The four sites of each city and 10 vegetables e.g. potato, radish, carrot, fenugreek, spinach, tomato, Onion, Turnip, Cauliflower, Pangalo were selected to conduct the experiment. The vegetables were irrigated with industrial wastewater and the concentration of heavy metals was measured by the atomic absorption spectrophotometer (AAS). We concluded that the level of heavy metals was beyond the FAO limits in irrigated water due to industrial waste. In Jamber and Chunian, the level of Zn and Pb was high and beyond the FAO safe limits in the all water sample, the level of Cr was much higher only in the water sample of one site from Jamber. The concentration of zinc was higher in soil samples as compared to lead and chromium. Zn and Pb in vegetables of study area were labeled as priority pollutants but this concentration was within the safe limits set by FAO. However, constant inspection of heavy metals is recommended to avoid accumulation in the food chain and thus avoid human health risks. Keywords: Atomic absorption spectrophotometer, Heavy metals, Industrial wastewater, Vegetables

Child tracking system using smartphone

The number of missing children and kidnapping is on the rise in recent years. Every parent wills definitely going through an agonizing experience to have their children missing. Therefore, there are many safety measurements to prevent this incident from happening. The help of modern technologies is one of the ways to reduce children missing and kidnapping. A child can be tracked by using the global positioning system (GPS) and global system for mobile communication (GSM) technology. Advanced child monitoring systems are expensive. Not all families have the same living standards. For this purpose, a low-cost child tracking system is proposed in this study. The implementation of the proposed approach is reported in real-time

Comparative Study of Sensorless Control Methods of PMSM Drives

Recently, permanent magnet synchronous motors (PMSMs) are increasingly used in high performance variable speed drives of many industrial applications. This is because the PMSM has many features, like high efficiency, compactness, high torque to inertia ratio, rapid dynamic response, simple modeling and control, and maintenance-free operation. In most applications, the presence of such a position sensor presents several disadvantages, such as reduced reliability, susceptibility to noise, additional cost and weight and increased complexity of the drive system. For these reasons, the development of alternative indirect methods for speed and position control becomes an important research topic. Many advantages of sensorless control such as reduced hardware complexity, low cost, reduced size, cable elimination, increased noise immunity, increased reliability and decreased maintenance. The key problem in sensorless vector control of ac drives is the accurate dynamic estimation of the stator flux vector over a wide speed range using only terminal variables (currents and voltages). The difficulty comprises state estimation at very low speeds where the fundamental excitation is low and the observer performance tends to be poor. The reasons are the observer sensitivity to model parameter variations, unmodeled nonlinearities and disturbances, limited accuracy of acquisition signals, drifts, and dc offsets. Poor speed estimation at low speed is attributed to data acquisition errors, voltage distortion due the PWM inverter and stator resistance drop which degrading the performance of sensorless drive. Moreover, the noises of system and measurements are considered other main problems. This paper presents a comprehensive study of the different methods of speed and position estimations for sensorless PMSM drives. A deep insight of the advantages and disadvantages of each method is investigated. Furthermore, the difficulties faced sensorless PMSM drives at low speeds as well as the reasons are highly demonstrated. Keywords: permanent magnet, synchronous motor, sensorless control, speed estimation, position estimation, parameter adaptation

Effects on egg production and quality of supplementing drinking water with calcium and magnesium

This study was conducted to appraise the effects on egg quality and production performance of laying hens when drinking water was supplemented with calcium (Ca) and magnesium (Mg). A total of 384 (64-week-old) Hy-line Brown laying hens were assigned at random to four treatments, which consisted of CON: unsupplemented drinking water; T1: drinking water + 2 mg/L Ca + 250 mg/L Mg; T2: drinking water + 4 mg/L Ca + 510 mg/L Mg /10 L; and T3: drinking water + 5 mg/L Ca and 760 mg/L Mg. The experiment lasted six weeks. Water intake increased linearly in week 1 with the rising levels of Ca and Mg in the drinking water. Increasing the Ca and Mg levels improved eggshell strength (week 2 (P =0.01), week 5 (P =0.01), and week 6 (P = 0.03), and eggshell thickness (week 6) (P =0.02) and reduced the rate at which eggs were broken (week 4) (P =0.01). The supplemental Ca and Mg did not affect egg production, egg weight, Haugh unit, albumen height, eggshell colour, and yolk colour compared with CON. Nor did they influence the Haugh unit and albumen height after storing for 1, 5, 10 and 15 days. In conclusion, adding Ca and Mg to the drinking water increased the thickness and strength of the eggshells

Effective extraction of cephalosporin C from whole fermentation broth of Acremonium chrysogenum utilizing aqueous two phase systems

The downstream processing of biotechnological products from fermentation broth is an important step of production and development of cost effective, efficient downstream processing of many biotechnological products. The present study was conducted by employing aqueous two phase systems (ATPSs) for the extraction of cephalosporin C (CPC) from whole fermentation broth of Acremonium chrysogenum. The biphasic system was prepared by mixing equal aliquots of 15% w/w polyethylene glycol (PEG) 3350 with 15% (NH4)2SO4. The effects of pH, neutral salts, temperature and centrifugal force on partitioning in ATPS to develop efficient extraction system for recovery of CPC from fermentation broth were also examined. The extraction efficiency was improved by enhancing the centrifugal force. Similarly centrifugation for 12.5 min also gave the maximum extraction. Improvement in the recovery yield was also observed by the addition of 0.1% NaCl. The concentration of CPC was determined by high performance liquid chromatography (HPLC). Slight modifications in the mobile phase from 10 to 5% MeOH improved CPC resolution. Further development of more inexpensive systems for extraction can be the future target of research.Keywords: Cephalosporin C, Acremonium chrysogenum, fermentation, aqueous two phase system (ATPS

Deficient methylation and formylation of mt-tRNA(Met) wobble cytosine in a patient carrying mutations in NSUN3.

Epitranscriptome modifications are required for structure and function of RNA and defects in these pathways have been associated with human disease. Here we identify the RNA target for the previously uncharacterized 5-methylcytosine (m(5)C) methyltransferase NSun3 and link m(5)C RNA modifications with energy metabolism. Using whole-exome sequencing, we identified loss-of-function mutations in NSUN3 in a patient presenting with combined mitochondrial respiratory chain complex deficiency. Patient-derived fibroblasts exhibit severe defects in mitochondrial translation that can be rescued by exogenous expression of NSun3. We show that NSun3 is required for deposition of m(5)C at the anticodon loop in the mitochondrially encoded transfer RNA methionine (mt-tRNA(Met)). Further, we demonstrate that m(5)C deficiency in mt-tRNA(Met) results in the lack of 5-formylcytosine (f(5)C) at the same tRNA position. Our findings demonstrate that NSUN3 is necessary for efficient mitochondrial translation and reveal that f(5)C in human mitochondrial RNA is generated by oxidative processing of m(5)C.This work was funded by the Medical Research Council (MRC; as part of the core funding for the Mitochondrial Biology Unit MC_U105697135 and by the G0801904 grant), the European Research Council (ERC; 310360), Cancer Research UK (CR-UK; C10701/ A15181), European Commission (FP7/2007-2013, under grant agreement number no.262055 (ESGI), as a Transnational Access project of the European Sequencing and Genotyping Infrastructure), core support grant from the Wellcome Trust and MRC to the Wellcome Trust-MRC Cambridge Stem Cell Institute, the European Commission (Horizon2020, under grant agreement number 633974), the Bundesministerium fur Bildung und Forschung (BMBF) (through the German Network for mitochondrial disorders (mitoNET, 01GM1113C) and through the European network for mitochondrial disorders (E-Rare project GENOMIT, 01GM1207)) and by EMBO (ALFT 701-2013).This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/ncomms1203

Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant

Coal-fired power plants have been used to meet the energy requirements in countries where coal reserves are abundant and are the key source of NOx emissions. Owing to the serious environmental and health concerns associated with NOx emissions, much work has been carried out to reduce NOx emissions. Sophisticated artificial intelligence (AI) techniques have been employed during the past few decades, such as least-squares support vector machine (LSSVM), artificial neural networks (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU), to develop the NOx prediction model. Several studies have investigated deep neural networks (DNN) models for accurate NOx emission prediction. However, there is a need to investigate a DNN-based NOx prediction model that is accurate and computationally inexpensive. Recently, a new AI technique, convolutional neural network (CNN), has been introduced and proven superior for image class prediction accuracy. According to the best of the author’s knowledge, not much work has been done on the utilization of CNN on NOx emissions from coal-fired power plants. Therefore, this study investigated the prediction performance and computational time of one-dimensional CNN (1D-CNN) on NOx emissions data from a 500 MW coal-fired power plant. The variations of hyperparameters of LSTM, GRU, and 1D-CNN were investigated, and the performance metrics such as RMSE and computational time were recorded to obtain optimal hyperparameters. The obtained optimal values of hyperparameters of LSTM, GRU, and 1D-CNN were then employed for models’ development, and consequently, the models were tested on test data. The 1D-CNN NOx emission model improved the training efficiency in terms of RMSE by 70.6% and 60.1% compared to LSTM and GRU, respectively. Furthermore, the testing efficiency for 1D-CNN improved by 10.2% and 15.7% compared to LSTM and GRU, respectively. Moreover, 1D-CNN (26 s) reduced the training time by 83.8% and 50% compared to LSTM (160 s) and GRU (52 s), respectively. Results reveal that 1D-CNN is more accurate, more stable, and computationally inexpensive compared to LSTM and GRU on NOx emission data from the 500 MW power plant

Techniques of EMG signal analysis: detection, processing, classification and applications

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications

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

Measurement of transverse energy at midrapidity in Pb-Pb collisions at root s(NN)=2.76 TeV

We report the transverse energy (ET) measured with ALICE at midrapidity in Pb-Pb collisions at root s(NN) = 2.76 TeV as a function of centrality. The transverse energy was measured using identified single-particle tracks. The measurement was cross checked using the electromagnetic calorimeters and the transverse momentum distributions of identified particles previously reported by ALICE. The results are compared to theoretical models as well as to results from other experiments. The mean ET per unit pseudorapidity (eta), , in 0%-5% central collisions is 1737 +/- 6(stat.) +/- 97(sys.) GeV. We find a similar centrality dependence of the shape of as a function of the number of participating nucleons to that seen at lower energies. The growth in at the LHC energies exceeds extrapolations of low-energy data. We observe a nearly linear scaling of with the number of quark participants. With the canonical assumption of a 1 fm/c formation time, we estimate that the energy density in 0%-5% central Pb-Pb collisions at root s(NN) = 2.76 TeV is 12.3 +/- 1.0 GeV/fm(3) and that the energy density at the most central 80 fm(2) of the collision is at least 21.5 +/- 1.7 GeV/fm(3). This is roughly 2.3 times that observed in 0%-5% central Au-Au collisions at root s(NN) = 200 GeV.Peer reviewe