Choledochal cyst--a different disease in newborns and infants

Abstract We report experience of managing Choledochal Cyst (CC) in different paediatric ages. Eleven neonates and infants (aged 0-8 months) and 24 paediatric cases (aged 2.5 - 18 years) were managed over 24 years (1988 to 2012). Neonates and infants presented with jaundice, acholic stools and abdominal mass whereas most of the paediatric cases presented with intermittent non-specific abdominal pain. Morphology of CC was mostly cystic in neonates whereas it was fusiform in majority (62%) of paediatric cases. Biliary amylase was high and correlated with the presence of abnormal pancreaticobiliary junction (PBJ) in 20 /24 paediatric patients. Obstruction at the lower end of bile duct, liver fibrosis and cirrhosis were common in neonates. In conclusion, CC in newborns and infants is different and mimic correctable Biliary Atresia (BA). Early excision of CC and biliary reconstruction is promising in neonates, infants and children and it can be performed with minimal morbidity

Well Control in Extended Reach Drilling (ERD) Well by Using WELLPLAN Software

The oil and gas industry has developed rapidly by introduced new various technologies. Directional, horizontal, Extended Reach Drilling (ERD) and multilateral wells has been used in the industry for economical and technical reasons. Even though technologies are well developed in the last decade, but these wells still have high levels of risk in drilling and completion. Well control is one of the important issues because improper well control will lead to a blowout which is the most feared operational hazards and expensive cost. The key elements for the success and further development of ERD projects are the ability to continue developing new technology while at the same time adopting a technical limit approach to performance delivery. For this study, the project focused on well control in ERD well by using Halliburton‟s software, WELLPLAN. WELLPAN is very useful software which is provides various functionalities such as torque drag analysis, analyze hydraulics, analyze surge/swab pressures and ECD‟s, investigate well control and etc. This project is focused on investigate well control using the Well Control Analysis Module. The Well Control module can be used to determined predicted kick type, estimate influx volume and kick tolerance, evaluate pressure and generate kill sheet. Besides, the theoretical calculations also were performed to compare the results with WELLPLAN. Two equations are identical to find the suitable kill rate. Based on this study with literature review, well control procedures for extended-reach wells are as follows: Once a kick is detected and confirmed, perform a “hard” shut-in of the well. When the pressure is stabilized, record SIDPP, SICP and pit gain and start circulate immediately using the Driller‟s Method. In order to remove the gas from the horizontal section, the kill rate should be 1/3 to ½ of the rate in drilling circulation flowrate

Short term energy consumption forecasting using neural basis expansion analysis for interpretable time series

Smart grids and smart homes are getting people\u27s attention in the modern era of smart cities. The advancements of smart technologies and smart grids have created challenges related to energy efficiency and production according to the future demand of clients. Machine learning, specifically neural network-based methods, remained successful in energy consumption prediction, but still, there are gaps due to uncertainty in the data and limitations of the algorithms. Research published in the literature has used small datasets and profiles of primarily single users; therefore, models have difficulties when applied to large datasets with profiles of different customers. Thus, a smart grid environment requires a model that handles consumption data from thousands of customers. The proposed model enhances the newly introduced method of Neural Basis Expansion Analysis for interpretable Time Series (N-BEATS) with a big dataset of energy consumption of 169 customers. Further, to validate the results of the proposed model, a performance comparison has been carried out with the Long Short Term Memory (LSTM), Blocked LSTM, Gated Recurrent Units (GRU), Blocked GRU and Temporal Convolutional Network (TCN). The proposed interpretable model improves the prediction accuracy on the big dataset containing energy consumption profiles of multiple customers. Incorporating covariates into the model improved accuracy by learning past and future energy consumption patterns. Based on a large dataset, the proposed model performed better for daily, weekly, and monthly energy consumption predictions. The forecasting accuracy of the N-BEATS interpretable model for 1-day-ahead energy consumption with day as covariates remained better than the 1, 2, 3, and 4-week scenarios

Forecasting Energy Consumption Demand of Customers in Smart Grid Using Temporal Fusion Transformer (TFT)

Energy consumption prediction has always remained a concern for researchers because of the rapid growth of the human population and customers joining smart grids network for smart home facilities. Recently, the spread of COVID-19 has dramatically increased energy consumption in the residential sector. Hence, it is essential to produce energy per the residential customers\u27 requirements, improve economic efficiency, and reduce production costs. The previously published papers in the literature have considered the overall energy consumption prediction, making it difficult for production companies to produce energy per customers\u27 future demand. Using the proposed study, production companies can accurately have energy per their customers\u27 needs by forecasting future energy consumption demands. Scientists and researchers are trying to minimize energy consumption by applying different optimization and prediction techniques; hence this study proposed a daily, weekly, and monthly energy consumption prediction model using Temporal Fusion Transformer (TFT). This study relies on a TFT model for energy forecasting, which considers both primary and valuable data sources and batch training techniques. The model\u27s performance has been related to the Long Short-Term Memory (LSTM), LSTM interpretable, and Temporal Convolutional Network (TCN) models. The model\u27s performance has remained better than the other algorithms, with mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) of 4.09, 2.02, and 1.50. Further, the overall symmetric mean absolute percentage error (sMAPE) of LSTM, LSTM interpretable, TCN, and proposed TFT remained at 29.78%, 31.10%, 36.42%, and 26.46%, respectively. The sMAPE of the TFT has proved that the model has performed better than the other deep learning models

Designing a relational model to identify relationships between suspicious customers in anti-money laundering (AML) using social network analysis (SNA)

The stability of the economy and political system of any country highly depends on the policy of anti-money laundering (AML). If government policies are incapable of handling money laundering activities in an appropriate way, the control of the economy can be transferred to criminals. The current literature provides various technical solutions, such as clustering-based anomaly detection techniques, rule-based systems, and a decision tree algorithm, to control such activities that can aid in identifying suspicious customers or transactions. However, the literature provides no effective and appropriate solutions that could aid in identifying relationships between suspicious customers or transactions. The current challenge in the field is to identify associated links between suspicious customers who are involved in money laundering. To consider this challenge, this paper discusses the challenges associated with identifying relationships such as business and family relationships and proposes a model to identify links between suspicious customers using social network analysis (SNA). The proposed model aims to identify various mafias and groups involved in money laundering activities, thereby aiding in preventing money laundering activities and potential terrorist financing. The proposed model is based on relational data of customer profiles and social networking functions metrics to identify suspicious customers and transactions. A series of experiments are conducted with financial data, and the results of these experiments show promising results for financial institutions who can gain real benefits from the proposed model

INVESTIGATING LETHAL EFFECT OF DIFFERENT BOTANICALS AGAINST OXYCARENUS LAETUS KIRBY UNDER LABORATORY CONDITIONS

For last few years, dusky cotton bug, Oxycarenus laetus (Hemiptera: Lygaeidae) has become an emerging pest of cotton crop threatening cotton production in Pakistan. Onset of insecticidal resistance demands the use the alternate approaches for the control of O. laetus. Plant-based botanicals have the potential to suppress O. laetus at different concentrations. The findings of present study showed that highest mortality (53.13%, 70.83% and 96.91%) of O. leatus was recorded at 1.5%, 2.5% and 5% concentrations after 72h of treatment with N. tabacum. However, O. sanctum harbored lowest mortality (38.10%, 37.50% and 52.91%) at all tested concentrations. Consequently, Nicotiana tabacum was proved as exhibiting competent insecticidal properties for the control of O. laetus

PERAN GURU DALAM PEMBENTUKAN KARAKTER DISPLIN

The character of discipline is one of the most important characters for every student to have. Therefore, the teacher must carry out his role in shaping the character of student discipline. This study aims to analyze the teacher's role in the formation of disciplinary character. This study uses a qualitative approach to the type of field research. The subjects of this study were class teachers who were determined by purposive sampling technique. The data collection technique used is to explain what roles are played by the teacher in shaping the disciplinary character of students in the new normal era. In addition, the results of the research also show that there are several inhibiting factors in the formation of disciplinary character

PERAN GURU DALAM PEMBENTUKAN KARAKTER DISPLIN

The character of discipline is one of the most important characters for every student to have. Therefore, the teacher must carry out his role in shaping the character of student discipline. This study aims to analyze the teacher's role in the formation of disciplinary character. This study uses a qualitative approach to the type of field research. The subjects of this study were class teachers who were determined by purposive sampling technique. The data collection technique used is to explain what roles are played by the teacher in shaping the disciplinary character of students in the new normal era. In addition, the results of the research also show that there are several inhibiting factors in the formation of disciplinary character

A new approach to seasonal energy consumption forecasting using temporal convolutional networks

There has been a significant increase in the attention paid to resource management in smart grids, and several energy forecasting models have been published in the literature. It is well known that energy forecasting plays a crucial role in several applications in smart grids, including demand-side management, optimum dispatch, and load shedding. A significant challenge in smart grid models is managing forecasts efficiently while ensuring the slightest feasible prediction error. A type of artificial neural networks such as recurrent neural networks, are frequently used to forecast time series data. However, due to certain limitations like vanishing gradients and lack of memory retention of recurrent neural networks, sequential data should be modeled using convolutional networks. The reason is that they have strong capabilities to solve complex problems better than recurrent neural networks. In this research, a temporal convolutional network is proposed to handle seasonal short-term energy forecasting. The proposed temporal convolutional network computes outputs in parallel, reducing the computation time compared to the recurrent neural networks. Further performance comparison with the traditional long short-term memory in terms of MAD and sMAPE has proved that the proposed model has outperformed the recurrent neural network

Comparing System of Wheat Intensification with Normal Practices Under Different Levels of Organic and Inorganic Fertilizer in Southeast Region of Afghanistan

A field experiment was conducted to study the influence of NPK and FYM under normal practice and system of wheat intensification (SWI). The soil of the experimental area was sandy loam with pH (8.4); and available N (117.3 kg ha-1), medium in available P (13.85 kg ha-1) and high in available K (270 kg ha-1). Mazar 99 variety of wheat was chosen for the study. The experiment was laid out in split plot design with 24 treatments combination and three replications on a plot size of 1.5 x 3 m. Different cropping system (Broadcast method of sowing and system of wheat intensification) and different levels of NPK (50, 75 and 100%) were taken in main plot. Different levels of farm yard manure (0, 20, 40, 60, 80 and 100%) were assigned to sub plot in a split plot design. Application of 100 percent RDF under system of wheat intensification significantly influenced growth and growth attributes of wheat at different crop growth stages. Application of 100% RDF under system of wheat intensification (SWI) registered significantly higher plant height (23.4, 52.3, 77.7 and 82.9 cm), and dry matter accumulation (60, 257, 753 and 964 gram) at tillering, blooming, flowering and maturity stages and number of leaves (1130.0, 1722.3 and 2020.8) and number of tillers (187.9, 280.2 and 310) of wheat was also registered higher in same treatments at tillering, blooming and flowering stages of wheat respectively as compared to rest of the treatments. Different treatments of cropping system and different levels of RDF and FYM significantly influenced yield and yield attributes of wheat. Among the cropping system, M4 (100% RDF + SWI) registered significantly higher grain yield (3794.3 kg ha-1) and straw yield (6096.8 kg ha-1) as compared to rest of the treatments. Application of 100 percent farm yard manure recorded significant and maximum grain yield (3553.7 kg ha-1) as compared to rest of the treatments. While, the minimum grain yield (3060.8 kg ha-1) was recorded in S1 due to application of 0% FYM. Similarly, application of 100 percent farm yard manure recorded significantly higher straw yield (5935.5 kg ha-1) as compared to rest of the treatments. However, the lower grain yield (3060.8 kg ha-1) and straw yield (5373.4 kg ha-1) was observed in S1 due to application of zero percent farm yard manure. The interaction of 100% RDF + SWI with 100 % FYM showed highest grain yield (4060.0 kg ha-1) and straw yield (6450.0 kg ha-1) as compared to rest of the treatments. vOn the basis of economic analysis it is concluded that wheat cv. ‘Mazar 99’ sown under system of wheat intensification treated by 100% recommended dose of fertilizer (120-60-60 kg NPK/ha) accompanied with 20% N through FYM proved to be the most remunerative dose which will increase the grain yield of wheat by 33 percent as compared to M1S1 due to application of 100% RDF + 0% FYM under broadcast method of sowing. However, SWI will increase the net return by 36 percent as compared to broadcast method of sowing