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Remittances are the funds sent by individuals to their home country from the host country and are the main source of foreign funding after FDI. Remittances are a crucial source of subsistence for the countries that are still in developing phase. Due to of the increasing scale and effect on the origin country, the international remittance flows into the developing country have gained the attention of numerous analysts from all over the world. This study examines the role of foreign remittances in poverty alleviation utilizing the panel data set of Asian countries, precisely nine developing countries. For the dependent variable this study uses poverty gap and the independent variables are Remittances as percentage of GDP, Per capita GDP, External debt as percentage of GNI and Trade openness as percentage of GDP. The findings reveal that foreign emittances have a significant role in alleviating poverty gap. The results of the random effect model showed that a 1% increase in international remittances as percentage of GDP leads to a decrease of 65.932% in poverty gap at $1.90 poverty line in the sample developing economies from 1990-2019. In addition, the results indicate, trade openness and per capita GDP decrease poverty and external debt seems to increase poverty.

Using the high-frequency data of Bitcoin, this study aims to model the time-varying volatility identified in the residuals of the heterogeneous autoregressive (HAR) model of realized volatility using the symmetric, asymmetric and long-memory generalized autoregressive conditional heteroscedastic models (GARCH) models. We further extended these models by incorporating jumps and continuous components in the realized volatility estimators and investigating the impact of the inverse leverage effect. The Diebold Mariano and model confidence set test confirm that the forecasting performance of HAR-type models can be effectively improved by these innovations. The long memory HAR-GARCH model with jumps and continuous components provided better forecasting accuracy for Bitcoin volatility as compared to other realized volatility models. The findings of this study may benefit individual investors and risk managers who wish to minimize risks and diversify their portfolios to maximize profits in Bitcoin’s investment.

Accurate prediction of river discharge is essential for the planning and management of water resources. This study proposes a novel hybrid method named HD-SKA by integrating two decomposition techniques (termed as HD) with support vector regression (SVR), K-nearest neighbor (KNN) and ARIMA models (combined as SKA) respectively. Firstly, the proposed method utilizes local mean decomposition (LMD) to decompose the original river discharge series into sub-series. Next, ensemble empirical mode decomposition (EEMD) is employed to further decompose the LMD-based sub-series into intrinsic mode functions. Further, the EEMD decomposed components are used as inputs in three data-driven models to predict river discharge respectively. The prediction of all components is then aggregated to obtain the results of HD-SVR, HD-KNN and HD-ARIMA models. The final prediction is obtained by taking the average prediction of these models. The proposed method is illustrated using five rivers in Indus Basin System. In five case studies, six models were built to compare the performance of the proposed HD-SKA model. The data analysis results show that the HD-SKA model performs better than all other considered models. The Diebold-Mariano test confirms the superiority of the proposed HD-SKA model over ARIMA, SVR, KNN, EEMD-ARIMA, EEMD-KNN, and EEMD-SVR models.

The widespread human-robot interaction is increasing progressively as robots have made the life of everyone easy-going and comfortable. In this work, we have analysed the behaviour and characteristics of various types of robots. We have also studied the outgrowing relation between robotics and humans. In our analysis, we also have a selection of aspects of this field, which are done by the numerous technologists as well as scientists. We are interested in exploring the functioning of the human brain by generating a functioning system that resolves problems and gives satisfactory results. Artificial intelligence is a vast field that is also pushing its way in the domain of healthcare, business and quality assurance. Various researches disclose that the corporate sector is joining artificial intelligence to estimate the supply-demand concept and automate human resource systems. The public sector is also developing different intelligent machines for security surveillance and malfunction detection of critical systems like nuclear reactors. Artificial intelligence and robotics are also phenomenal to implement the law and order enforcement without any danger. As artificial intelligence is growing, employment in this domain is also increasing due to the high demand of intelligent machines in each sector worldwide. Our primary focus is to delve into the relationship between humans and robots.

This paper compares a number of stochastic volatility (SV) models for modeling and predicting the volatility of the four most capitalized cryptocurrencies (Bitcoin, Ethereum, Ripple, and Litecoin). The standard SV model, models with heavy-tails and moving average innovations, models with jumps, leverage effects and volatility in mean were considered. The Bayes factor for model fit was largely in favor of the heavy-tailed SV model. The forecasting performance of this model was also found superior than the other competing models. Overall, the findings of this study suggest using the heavy-tailed stochastic volatility model for modeling and forecasting the volatility of cryptocurrencies.

26 March, 2018 Accepted: 17 September, 2019Abstract: Resistivity survey is very well known for the exploration of groundwater and to determine the depth ofbedrock. Generally, in Pakistan local drillers rarely use resistivity meter due to high cost of commercially availableequipment. Therefore, most of the wells for groundwater are drilled without any feasibility survey, which causeseconomic and time loss. An inexpensive resistivity meter has been developed that can help the local community toconduct a survey for groundwater. This designed equipment is handy, portable, easy to operate and can bemanufactured locally. This equipment costs 500 US Dollars (USD), whereas commercially available equipment costs2500 to 50000 USD. The designed portable device comprises of a 12V DC battery, an inverter, multiplier circuit,DPDT switch and electrodes. A 12V DC battery is fed to an inverter to achieve AC supply of 220V. The achieved ACvoltage is rectified to DC-voltage which is further enhanced up-to 1300 volts using voltage multiplier circuit. This highDC voltage is called High Voltage Direct Current (HVDC). HVDC is switched at very low frequency of 1Hz.Automatic switching is being accomplished by means of DPDT relay and its control circuitry. HVDC at low frequencyis applied to the earth through electrodes to determine resistivity for different materials lying inside the ground with apenetration depth of 100 meters. This portable instrument would be useful to map surface lithological layers, determinequality of groundwater and bedrock level in accurate and inexpensive way.

Five non-linear functions, i.e. Gompertz, Logistic, Negative exponential, Brody and Bertalanffy, and multivariate adaptive regression splines (MARS) data mining algorithm were implemented with the objective to describe the body weight-age relationship of Harnai sheep of Balochistan, Pakistan. The data comprised of 1317 records of body weight from birth to 1 year were provided from Multi-Purpose Sheep Research Station Loralai, Balochistan. Each non-linear function and MARS algorithm were fitted to the data of male and female, single and twin and all lambs. Comparison among different non-linear models was based using the adjusted coefficient of determination ([Formula: see text]), Durbin-Watson statistic (DW), root mean square error (RMSE), Akaike's and Bayesian information criteria (AIC and BIC) and the coefficient of correlation (r) between observed and fitted live body weight. The best fit was provided by the Brody model in terms of the highest [Formula: see text] and r values and lowest RMSE, AIC and BIC values in male and female, single and twin and all lambs followed by Bertalanffy, Gompertz, Negative exponential and Logistic model in order of their goodness. The negative correlation between asymptotic weight and maturing rate inferred that animals with smaller mature weight mature fast. Though males and singles were found heavier at mature weight than females and twins, respectively, they mature more slowly. The results of the study suggested the use of the Brody model to accurately describe the weight-age relationship of Harnai sheep. The present study also showed a very high predictive performance of the MARS data mining algorithm for describing the growth of sheep. In conclusion, MARS algorithm may be a good alternative for breeders aiming at describing the weight-age relationship of Harnai sheep.

The year 2020 has changed the living style of people all around the world. Corona pandemic has affected the people in all fields of life economically, physically, and mentally. This dataset is a collection of published articles discussing the effect of COVID and SARS on the social sciences from 2003 to 2020. This dataset collection and analysis highlight the significance and influential aspects, research streams, and themes in this domain. The analysis provides top journals, highly cited articles, mostly used keywords, top affiliation institutes, leading countries based on the citation, potential research streams, a thematic map, and future directions in this area of research. In the future, this dataset will be helpful for every researcher and policymakers to proceed as a starting point to identify the relevant research based on the analysis of 18 years of research in this domain.

A significant type of Lung cancer today know to the world is called Malignant Mesothelioma (MM). MM is associated with an inferior prognosis, and the majority of patients do not show symptoms. The etiology of MM is essential for the identification of disease. Clinical results provide a better way for the treatment of disease. Typically, costly imaging and laboratory resources, i.e. (X-rays, Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) scans, biopsies, and blood tests) are required for the identification of MM risk factors. Furthermore, these methods are often expensive and invasive. The primary purpose of this work is to explore risk factors of MM. The dataset consists of healthy and mesothelioma patients, but only mesothelioma patients were selected for the identification of symptoms. The raw data set has been pre-processed, and then the Apriori method was utilized for association rules with various configurations. The pre-processing task involved the removal of duplicated and irrelevant attributes, balanced the dataset, numerical to the nominal conversion of attributes in the dataset and creating the association rules in the dataset. Strong associations of disease's factors; asbestos exposure, erythrocyte sedimentation rate, duration of time for asbestos exposure and Pleural to serum LDH ratio determined via Apriori algorithm. The identification of risk factors associated with MM may prevent patients from going into the high danger of the disease. This will also help to control the comorbidities associated with MM, which are cardiovascular diseases, cancer-related emotional distress, diabetes, anemia, and hypothyroidism.

A throughput is a measure of successful data transmission through a terminal in a given time. There is a significant number of surveys that have been published to measure the performance of MAC protocols and each MAC channel. There is a substantial need to perform a comparative analysis of achieved results that can provide a comprehensible result for the end-users. In our work, we have applied different methods to obtain the throughput of contention-free and contention-based protocols. We have provided a brief comparison between different protocols of the same class to accentuate the efficient protocols. Our paper incorporates collision avoidance MAC channels, as the collision avoidance techniques provide better usability of protocols than the collision detection techniques. We have also proposed an efficient way to increase the life span of the Low-energy Adaptive Clustering Hierarchy (LEACH) protocol using Time Division Multiple Access (TDMA). The goal to obtain the maximum effectiveness of a network can be achieved by maximal utilization of the throughput. In this paper, we have provided a brief comparison of different MAC protocols by using the throughput parameter, and we have also proposed a new method to increase the efficiency of LEACH protocol.

New technologies are being invented and energy demand is increasing. Growth of population has always been and will remain one of the major causes of energy demand. Science is therefore looking for new major and minor energy resources to keep world in progress. The main focus of energy engineering and technology in the field of energy generation is to harvest energy by any mean from any source. A theoretical research is introduced in this paper which will contribute its reasonable share in the field of renewable and green energy sector. This energy generating system is named as energy generating gymnasium system (EGGS). The core idea behind this energy harvesting system is that, the human being is also a source of renewable energy and it is possible to harness electrical energy from people by the use of EGGS. Human energy is wasted when excessive calories of body are burnt during exercise in gymnasiums to achieve the desired fitness. EGGS will provide an opportunity to return expended energy in the form of electrical energy from gymnasium equipment and cardiovascular machines. This electrical energy will be cheap and also green since it will not emit any carbon dioxide (CO 2 ) gas during the process. This system can increase the potential of renewable energy area and the electrical energy generated from EGGS can be sold back to the national utility via micro grids (MG). The proposed system will be very beneficial for such countries that are facing energy crises as well as the third world countries. Authors have discussed a gymnasium machine named as “Pull-down machine” and proposed a theoretical modification to make it as an energy generating gymnasium machine (EGGM) in the presented research. Citation: Saeed, M., Saleem, I., and Iqbal, F. (2019). Theoretical Design of Energy Generating Gymnasium Pull-down Machine for Green, Renewable and Sustainable Energy Production. Trends in Renewable Energy, 5, 47-59. DOI: 10.17737/tre.2019.5.1.0086 Â

The present study was conducted with the aimed to determine the frequency of anemia and lack of hemoglobin in human pregnancy at Bolan Medical College Hospital Quetta during 2017. As many as 625 pregnant women patients were examined, of these 350 were found anemic. Blood samples of randomly selected three hundred and fifty pregnant women of age group 17-44 years were analyzed, and was classified under first trimester (21), second trimester (58) and third trimester (271). The data was analyzed to estimate the frequency of anemia during pregnancy ranging from mild: 288 (46.1%) to moderate: 54 (8.6%) and severe: 8 (1.3%). Findings revealed that anemia was found to be prevalent in third trimester (271) as compared to the second (58) and first (21) trimester of pregnancy. Chi-square test was applied at 5% level of significance to check the association between age groups, parity and gestation age (G age). Significant association was found with anemic level at p-value

This paper studies the association between price of crude oil and the Pakistani Rupee-US Dollar exchange. Asymmetric power autoregressive conditional heteroscedastic (APARCH) model is used to measure the influence of oil price on the nominal exchange rate using daily data of extreme oil price volatility (2006 – 2013). This model is found to fit the data well and the results reveal a high degree of volatility persistence and leverage effect in returns. This study also establishes a positive association between currency exchange rate and oil price. These findings provide insight into the transmission link between the global oil market and exchange rate.

The monthly average temperature series of Quetta – Pakistan from 1950 – 2000 is examined. A straight line is fitted to the data and seasonal variation and trend in temperature for each month of the year were obtained. An overall model is constructed as large variations in the monthly slopes were observed. In order to describe the seasonal pattern and trend in temperature, corresponding to the different months, both sine/cosine waves and sine/cosine waves multiplied by the time were included in the model as independent variables. The lag-1 autocorrelation was found in the residual of the model and hence another model was fitted to the pre-whiten series that shows a good fit ( ) and is free from correlated residuals. Both parametric and non-parametric tests applied to each month temperature show significant trend in all months except February and March.

The asymptotic distributions of squared and absolute residual autocorrelations for GARCH model estimated by M-estimators are derived. Two diagnostic tests are developed which can be used to check the adequacy of GARCH model fitted by using M-estimators. Simulation results show that the empirical sizes of both tests are close to the nominal size in most of the cases. The power of test based on absolute residual autocorrelation is found better than test based on squared residual autocorrelations. Our results reveal that there are estimators that can fit GARCH-type models better than the commonly used quasi-maximum likelihood estimator under non normal errors. An application to real data set is also presented.

This paper is concerned with the estimation, forecasting and evaluation of Value-at-Risk (VaR) of Karachi Stock Exchange before and after the global financial crisis of 2008 using Bayesian method. The generalized autoregressive conditional heteroscedastic (GARCH) models under the assumption of normal and heavy-tailed errors are used to forecast one-day-ahead risk estimates. Various measures and backtesting methods are employed to evaluate VaR forecasts. The observed number of VaR violations using Bayesian method is found close to the expected number of violations. The losses are also found smaller than the competing Maximum Likelihood method. The results showed that the Bayesian method produce accurate and reliable VaR forecasts and can be preferred over other methods.

This is a study of twenty six subjects who presented to Sir Ganga Ram Hospital, Lahore 1999-2001 and were cases of primary amenorrhoea 20(76.9%) of patient presented between 15-25 years of age. Five (19.2%) had lower abdominal pain. Secondary sex characters were developed in 69.2%. Uterus was absent in 4(15.5%) and ovaries were present in 17(65.5%). FSH was high >40mIU in 12(46.5%) and low

A prospective case control study was carried out in Gynae Unit-II, Sir Ganga Ram Hospital, Lahore from December, 2002 to December, 2003. Total 60 cases were included in study. 30 were cases of primigravida < 19 years of age (study group and 30 cases > 20 years of age (control group) were studied after 24 weeks of gestation. The mean age for study group was 18.03±0.89 and control group was 24.23±2.80. 8 subjects (26%) of PIH in study group and 3(10%) were in control group. 2 subjects (6%) of UTI in study group and 0 case in control group. 6(20%) subjects were anemic in study group and only 1(3%) was in control (6%) no case in control group. Antepartum haemorrhage in 2 subjects (83%) of normal vaginal delivery in study group there were 25 cases (83%) while 22 subjects, (73%) in control group. Instrumental delivery in 1(3%) subject in study group. 3 subjects (10%) of postpartum haemorrhage were found in study group and only 1(3%) in control group. Puerperal infection was present in 4 subjects (13%) study group in 2 subjects (6%) in control group. 10 neonates (33%) were of low birth weight in study group, while 6 neonates (60%) in study group not seen in control group. We concluded teenage pregnancy is high risk pregnancy.

In this paper, robust M-estimation of multivariate GARCH models are considered. The simplified GARCH model is chosen that involves the estimation of only univariate GARCH models, and hence easy to estimate, and does not put additional constraints on the model. The results of Monte Carlo simulations showed that accurate estimates of conditional correlations can be obtained using these robust estimators when the errors are heavy-tailed. We also investigate the forecasting performance of the class of robust estimators in predicting value-at-risk using various evaluation measures and collect empirical evidences of the better predictive potential of estimators such as LAD and B-estimator over the widely-used quasi-maximum likelihood estimator for the estimation and prediction of multivariate GARCH models. Applications to real data sets are also presented.

In this paper, we investigate the performance of a class of M-estimators for both symmetric and asymmetric conditional heteroscedastic models in the prediction of value-at-risk. The class of estimators includes the least absolute deviation (LAD), Huber’s, Cauchy and B-estimator, as well as the well-known quasi maximum likelihood estimator (QMLE). We use a wide range of summary statistics to compare both the in-sample and out-of-sample VaR estimates of three well-known stock indices. Our empirical study suggests that in general Cauchy, Huber and B-estimator have better performance in predicting one-step ahead VaR than the commonly used QMLE.

With the Development of New Technologies in Silkworm Rearing though Sericulture Has now Emerged as a Main Profession and a Major Cash Crop for the Rural People of the India in Tropical Areas but in Temperate Belt Like Kashmir the Constraints Faced by the Sericulture Farmers/silkworm Rearers Are More and these Are Responsible for Yield Gaps which Have to Be Considered Seriously and Accordingly the Extension Services Need to Be Modulated and Implemented. Exploitable Yield Gaps in Border Areas Are Often Caused by Various Factors Including Physical, Biological, Socio-Economic and Institutional Constraints which Can Be Effectively Improved through Participatory and Holistic Approaches. Kashmir Is a Univoltine Area where for Generations only one Crop in Spring Season Is Taken between May and June. Spring (May – June, 2009) Data of Kandi Kupwara J&K India (Border Area) Revealed Varying Cocoon Yields from 7 to 13 Kgs Dry Cocoons per 100 DFLS with Cocoon Price Varying from Rs. 172-430 per Kg. these Facts and Figures Prove that these Silkworm Rearers by Realizing the Potential of Sericulture (a New Culture in a Border Area) Increased their Income Substantially from Rs. 720 to Rs.5590/100DFLS. it Was Also Found that Sericulture Productivity Can Be Further Increased by Planting Improved Varieties of Mulberry as it Has Been Observed that where, Very Good Quality Mulberry Leaf and Inputs Are Available, the Sericulture Productivity Is Reasonably Good. the Present Communication, Therefore, Discusses the Extension Strategies and New Technologies to Improve the Essential Knowledge and Skills to the Sericulture Farmers to Improve the Yield and Profitability of Sericulture.