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A significant corridor under the "Belt and Road Initiative" is announced by the government of China for numerous transport and energy projects. China focuses greatly on the sustainability of these projects, in accordance with Green Investment Principles. For this, environmental, social, governance, and technological considerations are taken into account. This paper offers a novel assessment of the impact of green sustainability under BRI and the current emission scenario in Pakistan. The question that needs to be addressed here is whether the projects under Belt and Road Initiative are as environment friendly as they claim. In the first phase of this study, we review theories establishing evidence based on existing literature to determine the influence of Belt and Road on economic development. In the next phase, we use Environment Kuznets Curve and apply estimates from Johansson's Cointegration Test to quantify the present environmental scenario of Pakistan and probable environmental risk portrayed by the China-Pakistan Economic Corridor (CPEC). This paper helps in determining the relationship between carbon emission and three major variables including energy consumption, economic growth, and foreign direct investment (FDI). Cointegration analysis is applied on time series data comprising of a 40 years' period from 1979 to 2019. The paper finds that (i) the belt and road corridors could substantially improve trade, foreign investment, and living conditions for citizens in participating countries; (ii) moreover, this study confirms the presence of one cointegrated equation suggesting the presence of a long term relationship between the CO2 and independent variables and a short run association running from EnC to CO2. The study also proposes a Green BRI Model which formulates a body that devises environmental performance standards, reviews procedures, and ensures that they get implemented on all CPEC projects. The Belt and Road Initiative increases emissions among participating countries. China and corridor economies need to adopt strict policy reforms that increase transparency, expand trade, improve debt sustainability, and mitigate environmental, social, and corruption risks. [ABSTRACT FROM AUTHOR]

In this paper, an approach is presented for the demand-side management of residential loads in the urban areas of Pakistan using a battery storage system at the feeder level. The proposed storage system will be installed by a private distributor to supply affordable electricity during peak hours. The experimental data used to carry out this research work are the Pakistan Residential Energy Consumption (PRECON) data set. The households of the data set are categorized based on electric power usage through K-means clustering. The clusters are expanded for feeder synthesis to represent small-scale, medium-scale, and large-scale consumption. This expansion is performed through uniform distribution in a Monte Carlo simulation. The techno-economic analysis for the installation of a battery storage system is carried out for each feeder using SAM. The results of the research project elucidated that the load factors of the feeders representing small-scale, medium-scale, and large-scale consumption improved by 1%, 6%, and 7% by using the optimally sized batteries of 50 kW (670 kWh), 90 kW (1207 kWh), and 100 kW (1360 kWh), respectively. The distributor profit and the consumer utility bill savings ranged from US$12 k to US$25 k. The results proved the validity of the used approach to simultaneously reduce the consumer bill, maximize the distributor profit, and improve the feeder load factor. The novelty of this work lies in the location and in the way the system modeling has been performed with limited data. [ABSTRACT FROM AUTHOR]

The solar photovoltaic (PV) power forecast is crucial for steady grid operation, scheduling, and grid electricity management. In this work, numerous time series forecast methodologies, including the statistical and artificial intelligence-based methods, are studied and compared fastidiously to forecast PV electricity. Moreover, the impact of different environmental conditions for all of the algorithms is investigated. Hourly solar PV power forecasting is done to confirm the effectiveness of various models. Data used in this paper is of one entire year and is acquired from a 100 MW solar power plant, namely, Quaid-e-Azam Solar Park, Bahawalpur, Pakistan. This paper suggests recurrent neural networks (RNNs) as the best-performing forecasting model for PV power output. Furthermore, the bidirectional long-short-term memory RNN framework delivered high accuracy results in all weather conditions, especially under cloudy weather conditions where root mean square error (RMSE) was found lowest 0.0025, R square stands at 0.99, and coefficient of variation of root mean square error (RMSE) Cv was observed 0.0095%. [ABSTRACT FROM AUTHOR]

Fintech revolutionized the traditional banking business models in emerging countries. The effect of fintech on banks' operating efficiency and risk-taking behavior is still inconclusive. The study is aimed at exploring the effect of fintech products on banks' operating efficiency and risk-taking behavior. The study used a quantitative research approach by collecting secondary data from annual reports of 50 commercial banks from emerging countries, namely, China, India, Pakistan, and Bangladesh, for the period 2014 to 2021. The study used panel data for path analysis and structural equation modeling (SEM) to test the theoretical mediation model by using STATA. The results show that the fintech product reduces the bank's risk-taking behavior by enhancing the bank's operating efficiency. The path analysis results show that operating efficiency mediates the relationship between fintech products and bank risk-taking behavior in emerging countries. The paper offers useful recommendations for central bank and commercial bank policymakers. The study is also beneficial for commercial banks that use fintech solutions to increase operational effectiveness and reduce risk. The study is the first empirical investigation into the connection between the growth of fintech products, bank operational effectiveness, and risk-taking behavior in developing nations. [ABSTRACT FROM AUTHOR]

Extreme value theory (EVT) is useful for modeling the impact of crashes or situations of extreme stress on investor portfolios. EVT is mostly utilized in financial modeling, risk management, insurance, and hydrology. The price of gold fluctuates considerably over time, and this introduces a risk on its own. The goal of this study is to analyze the risk of gold investment by applying the EVT to historical daily data for extreme daily losses and gains in the price of gold. We used daily gold prices in the Pakistan Bullion Market from August 1, 2011 to July 30, 2021. This paper covers two methods such as Block Maxima (BM) and Peak Over Threshold (POT) modeling. The risk measures which are adopted in this paper are Value at Risk (VaR) and Expected Shortfall (ES). The point and interval estimates of VaR and ES are obtained by fitting the Generalized Pareto (GPA) distribution. Moreover, in this paper, return-level forecasting is also included for the next 5 and 10 years by analyzing the Generalized Extreme Value (GEV) distribution. [ABSTRACT FROM AUTHOR]

A microzonation study deals with the classification of hazards in a town or city in terms of surface ground motions that result from amplification and resonance frequency in soils against seismic tremors. This paper presents the result of a microzonation study in terms of resonance frequency and peak amplitude for Lahore city, Pakistan. In order to recognize the local soil effects of the covered geology at 159 sites in Lahore city, the horizontal-to-vertical spectral ratio (HVSR) Nakamura method was implemented. A fuzzy C-mean (FCM) clustering algorithm was adopted to obtain the best cluster solution of the analyzed HVSR parameters. The results of the Silhouette Index suggest that the FCM clustering solution of observation data points is more consistent. The results of clustering reveal three solutions. Clusters 1 and 2 reveal that a major part of the research area possesses low to moderate frequencies (0.66–1.03 Hz) with a peak amplitude of 2.25–4.38 mm, indicating the presence of soft to hard rock and thick alluvial sedimentary cover. Cluster 3 reveals the presence of soft to compact rocks (with frequencies and amplitudes of 0.73–1.03 Hz and 3.02–4.11 mm, respectively) overlaying the bedrock. Lahore city has 60% of soil cover with an amplitude of 2–3 mm (for the central part) and about 40% of 3–4 mm in the northern, southern, and southwest portions. According to the NEHRP soil classification code of 1997, a major part of the city has stiff nature of the soil, while a few places reveal the presence of very dense soil. The maps produced in this study will provide expected ground motion-related useful information to reduce the seismic risk for infrastructure in Lahore city. [ABSTRACT FROM AUTHOR]

Patient record keeping plays a vital role in diagnoses and cures. Due to a shortage of time, most doctors write prescriptions manually in Pakistan. At times, it becomes difficult for pharmacists to read prescriptions properly. As a result, they may dispense the wrong medicine. This might cause risky and deadly effects on the patient's health. This paper proposes an online handwritten medical prescription recognition system that lets doctors write prescriptions on a tablet using a stylus and automatically recognizes the medicine. We use signature verification techniques to recognize the doctor's handwriting to overcome the problem of misinterpretation of the medicine name by the pharmacist. The proposed system stores different features like the pen coordinates, time, and several pen-ups and pen-downs. Besides using features already proposed in the literature for signature verification, we propose some new features that greatly enhance recognition accuracy. We built a dataset of 24 medicine names from two users and compared results using newly proposed features. We have obtained 84%, 78%, 77.47% 77.31%, 74.17%, 60%, 38.5%, 68%, and 61.64% accuracies for 9 users using SVM classifier. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has shocked nations due to its exponential death rates in various countries. According to the United Nations (UN), in Russia, there were 895, in Mexico 303, in Indonesia 77, in Ukraine 317, and in Romania 252, and in Pakistan, 54 new deaths were recorded on the 5th of October 2021 in the period of months. Hence, it is essential to study the future waves of this virus so that some preventive measures can be adopted. In statistics, under uncertainty, there is a possibility to use probability models that leads to defining future pattern of deaths caused by COVID-19. Based on probability models, many research studies have been conducted to model the future trend of a particular disease and explore the effect of possible treatments (as in the case of coronavirus, the effect of Pfizer, Sinopharm, CanSino, Sinovac, and Sputnik) towards a specific disease. In this paper, varieties of probability models have been applied to model the COVID-19 death rate more effectively than the other models. Among others, exponentiated flexible exponential Weibull (EFEW) distribution is pointed out as the best fitted model. Various statistical properties have been presented in addition to real-life applications by using the total deaths of the COVID-19 outbreak (in millions) in Pakistan and Afghanistan. It has been verified that EFEW leads to a better decision rather than other existing lifetime models, including FEW, W, EW, E, AIFW, and GAPW distributions. [ABSTRACT FROM AUTHOR]

The failure of electric feeders is a common problem in the summer season in Pakistan. In this article, one of the troubling aspects of the electric power system of Pakistan (Multan city) has been studied. The time lapses between the breakdown of electric feeders of the city have been modeled by suggesting an inverse Rayleigh-exponential distribution. The parameters of the distribution are estimated in both the frequentist and Bayesian paradigms. Since the Bayes estimators under informative priors are not attained in the closed form, this paper provides a comparative analysis of the Bayes estimators under Lindley and Tierney–Kadane approximation methods. The simulation study and the real-life data set assessed the validity of the model and the superiority of the Bayes estimators over the maximum likelihood estimators. [ABSTRACT FROM AUTHOR]

The unequal water distribution in the universe has resulted in more than 2 billion people living in water-stressed areas. Globally, withdrawals of water resources are still below the critical level. Pakistan is also affected by this serious problem. In the context of environmental evaluation, irrigation scheduling, and water resource management, evapotranspiration predictions are crucial. The evapotranspiration estimation methods may need to be evaluated daily or monthly to understand the climate change effects in the local areas. This paper investigates spatial-temporal interpolation of evapotranspiration data from 41 Pakistani meteorological stations. To estimate evapotranspiration, we have used the average time series data between 2006 and 2015 on temperature, relative humidity, wind speed, and solar radiation. We developed a new modified Hargreaves equation to estimate evapotranspiration and evaluate the performance of different models. We found that our modified Hargreaves model could perform better than the original Hargreaves model. To analyze trends in different seasons, such as rainy, dry, and annual, FAO-56 Penman-Monteith, Blaney-Criddle, and Hargreaves-Samani models are used. We used Ordinary Kriging for Functional Data to map and predict evapotranspiration spatially and temporally in various locations. [ABSTRACT FROM AUTHOR]

In developing countries, the chances of fraud in written documents are comparatively high. Therefore, comparison of fountain pen inks is especially imperative in examination of forensic questioned documents. We have investigated the use of the gas chromatography-mass spectrometry technique in profiling and discrimination of fountain pen ink used in Pakistan for forensic purpose. The main purpose of this study was to discriminate different Pakistani fountain pen inks. The datum for Pakistani inks of fountain pen is not obtainable. In this research study, blue, black, and green colors fountain pen inks commercially used in Pakistan have been extracted from paper using micropunch and then investigated using the gas chromatography-mass spectrometry technique. Gas chromatography-mass spectrometry (GC-MS) was used to differentiate various brands of different colors of fountain pen inks based on their chemical composition. Molecular ion peaks for different components were obtained, and components were identified on the basis of detected ions. Results have been calculated and compared in terms of discriminating power (D.P.). The D.P. for blue, black, and green inks of fountain pen was 1.0 by using the gas chromatography-mass spectrometry technique. [ABSTRACT FROM AUTHOR]

The production data of mineral resources are noisy, nonstationary, and nonlinear. Therefore, some techniques are required to address the problem of nonstationarity and complexity of noises in it. In this paper, two hybrid models (EMD-CEEMDAN-EBT-MM and WA-CEEMDAN-EBT-MM) flourish to improve mineral production prediction. First, we use empirical mode decomposition (EMD) and wavelet analysis (WA) to denoise the data. Second, ensemble empirical mode decomposition (EEMD) and complete ensemble empirical mode decomposition (CEEMDAN) are used for the decomposition of nonstationary data into intrinsic mode function (IMF). Then, empirical Bayesian threshold (EBT) is applied on noise dominant IMFs to consolidate noises, which are further used as input in the data-driven model. Next, other noise-free IMFs are used in the stochastic model as input for the prediction of minerals. At last, the predicted IMFs are ensemble for final prediction. The proposed strategy is exemplified using Pakistan's four major mineral resources. To measure the prediction performance of all the models, three methods, that is, mean relative error, mean square error, and mean absolute percentage error, are used. Our proposed framework WA-CEEMDAN-EBT-MM has shown improvement with minimum mean absolute percentage error value compared to other existing models in prediction accuracy for all four minerals. Therefore, our proposed strategy can predict the noisy and nonstationary time-series data with an efficient mechanism. Hence, it will be helpful to the policymakers for making policies and planning in mineral resource management. [ABSTRACT FROM AUTHOR]

The extreme value theory (EVT) has been used to model and measure the distribution of extreme minima of Byco Petroleum in the Pakistan stock market over the period from 2005 to 2012. This paper covers the investigation of distributions that are mostly used in finance including the generalized extreme value (GEV), generalized logistics (GL), and generalized Pareto (GPA) distribution. L-moment ratio diagram is being used to find the appropriate distributions among the distributions. L-moment diagram depicts that GEV and GL distributions are suitable to represent the extremes of Byco Petroleum Pakistan Limited. Thereafter, the probability weighted moment (PWM) method has been used in order to estimate the parameters of probability distributions. Furthermore, Anderson–Darling (AD) goodness-of-fit test is employed to test the goodness of fit among GEV and GL distributions, and it is clear from the results that the GL distribution is more reliable and applicable for extreme minima of Byco Petroleum Company in the Pakistan stock exchange market. EVT and traditional methods are used for value-at-risk (VaR) analysis. The analysis indicates that EVT methods are more suitable for risk measurement in comparison with traditional methods. [ABSTRACT FROM AUTHOR]

In this paper, we set forth a framework for solving a multiattribute group decision-making (MAGDM) problem, namely, the selection of a firm for participation in a Saudi oil refinery project in Pakistan. This project will prove a key success factor for the economic growth of Pakistan due to its enormous economic impact on the energy sector, industrial development, commerce, transportation, and so on. This multiplicity justifies that several intricate components comprising both intrinsic and external attributes should be adequately evaluated for the selection of such a firm, that is, the formulation of this question as a MAGDM problem. Nonbinary evaluation with two-dimensional ambiguity and uncertainty in the parameters are general concerns in modern literature, and they fit into this problem. Within this context, one of the most superior and amenable theories (complex spherical fuzzy N -soft sets, henceforth C S F N S f S s) shall be used to formulate a new comprehensive method, known as complex spherical fuzzy N -soft-VIKOR (C S F N S f -VIKOR) method. According to the general spirit of the benchmark technique, the normalized Euclidean distances and the weights of the attributes are jointly handled, and as consequence, two main features ("maximum group utility" and "minimum individual regret") are acquired. The coefficient strategy with reference to group utility measure and individual regret measure of opponents are employed for the compromise measure. Armed with this novel tool, we single out the most feasible firm according to the preference order of the alternatives examined by the decision-makers on the subject of linear normalized weights of experts and attributes. Furthermore, a comparative analysis justifies the C S F -VIKOR method, and some results prove its capabilities and validity. Moreover, a sensitivity test certifies the stability of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we set forth a framework for solving a multiattribute group decision-making (MAGDM) problem, namely, the selection of a firm for participation in a Saudi oil refinery project in Pakistan. This project will prove a key success factor for the economic growth of Pakistan due to its enormous economic impact on the energy sector, industrial development, commerce, transportation, and so on. This multiplicity justifies that several intricate components comprising both intrinsic and external attributes should be adequately evaluated for the selection of such a firm, that is, the formulation of this question as a MAGDM problem. Nonbinary evaluation with two-dimensional ambiguity and uncertainty in the parameters are general concerns in modern literature, and they fit into this problem. Within this context, one of the most superior and amenable theories (complex spherical fuzzy N -soft sets, henceforth C S F N S f S s) shall be used to formulate a new comprehensive method, known as complex spherical fuzzy N -soft-VIKOR (C S F N S f -VIKOR) method. According to the general spirit of the benchmark technique, the normalized Euclidean distances and the weights of the attributes are jointly handled, and as consequence, two main features ("maximum group utility" and "minimum individual regret") are acquired. The coefficient strategy with reference to group utility measure and individual regret measure of opponents are employed for the compromise measure. Armed with this novel tool, we single out the most feasible firm according to the preference order of the alternatives examined by the decision-makers on the subject of linear normalized weights of experts and attributes. Furthermore, a comparative analysis justifies the C S F -VIKOR method, and some results prove its capabilities and validity. Moreover, a sensitivity test certifies the stability of the proposed method. [ABSTRACT FROM AUTHOR]

Accurate estimation of the mining process is vital for the optimal allocation of mineral resources. The development of any country is precisely connected with the management of mineral resources. Therefore, the forecasting of mineral resources contributed much to management, planning, and a maximum allocation of mineral resources. However, it is challenging because of its multiscale variability, nonlinearity, nonstationarity, and high irregularity. In this paper, we proposed two revised hybrid methods to address these issues to predict mineral resources. Our methods are based on denoising, decomposition, prediction, and ensemble principles that are applied to the production of mineral resource time-series data. The performance of the proposed methods is compared with the existing traditional one-stage model (without denoised and decomposition strategies) and two-stage hybrid models (based on denoised strategy), and three-stage hybrid models (with denoised and decomposition strategies). The performance of these methods is evaluated using mean relative error (MRE), mean absolute error (MAE), and mean square error (MSE) as evaluation measures for the production of four principle mineral resources of Pakistan. It is concluded that the proposed framework for the prediction of mineral resources indicated better performance as compared to other existing one-stage, two-stage, and three-stage models. Furthermore, the prediction accuracy of the revised hybrid model is improved by reducing the complexity of the production of mineral resource time-series data. [ABSTRACT FROM AUTHOR]

Ensuring international transport corridors' safety is essential for countries along the routes. For the sustainable development of international transport channels, this paper discusses how to evaluate the operational risks and how to alleviate the adverse effects caused by emergencies. First, an indicator system of international transport corridors' operational risks was constructed, which consists of 30 indicators from five dimensions of politics, economy, society, safety, and technology. Secondly, a comprehensive scoring approach combined with a network analysis method was applied to examine the effects caused by operational hazards. On this basis, the quantitative method and rigorous statistical analysis were used to evaluate the China-Pakistan-Iran-Turkey International Transport Corridor in the south line of the Silk Road Economic Belt, whose operational risks from 2010 to 2019 were analyzed. Finally, the operational risk index was discussed in detail. Results demonstrate the following. (1) During the China-Pakistan-Iran-Turkey International Transport Corridor, the Pakistan section has the highest operational risk index, and the next culprit is Turkey, followed by Iran. The Chinese section has the lowest operational risk index. (2) Pakistan has been trapped in severe political risks and security risks in recent years. Turkey suffers from severe security risks due to the Kurdish problem. Iran ranks first in terms of economic risk and social risk. Special attention should be paid to the natural risks for China. (3) Pakistan's operational risks will exhibit consistently high in the future, while China appears downwards in long-term trend. The operational risk index in Iran is on the rise. Turkey shows a flattening of and then a slight decline in the foreseeable future. [ABSTRACT FROM AUTHOR]

Infectious diseases like COVID-19 spread rapidly and have led to substantial economic loss worldwide, including in Pakistan. The effect of weather on COVID-19 spreading needs more detailed examination, as some studies have claimed to mitigate its spread. COVID-19 was declared a pandemic by WHO and has been reported in about 210 countries worldwide, including Asia, Europe, the USA, and North America. Person-to-person contact and international air travel between the nations were the leading causes behind the spreading of SARS-CoV-2 from its point of origin, besides the natural forces. However, further spread and infection within the community or country can be aided by natural elements, such as the weather. Therefore, the correlation between COVID-19 and temperature can be better elucidated in countries like Pakistan, where SARS-CoV-2 has affected at least 0.37 million people. This study collected Pakistan's COVID-19 infection and mortality data for ten months (March–December 2020). Related weather parameters, temperature, and humidity were also obtained for the same course of time. The collected data were processed and used to compare the performance of various time series prediction models in terms of mean squared error (MSE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE). This paper, using the time series model, estimates the effect of humidity, temperature, and other weather parameters on COVID-19 transmission by obtaining the correlation among the total infected cases and the number of deaths and weather variables in a particular region. Results depict that weather parameters hold more influence in evaluating the sum number of cases and deaths than other factors like community, age, and the total population. Therefore, temperature and humidity are salient parameters for predicting COVID-19 affected instances. Moreover, it is concluded that the higher the temperature, the lesser the mortality due to COVID-19 infection. [ABSTRACT FROM AUTHOR]

Infectious diseases like COVID-19 spread rapidly and have led to substantial economic loss worldwide, including in Pakistan. The effect of weather on COVID-19 spreading needs more detailed examination, as some studies have claimed to mitigate its spread. COVID-19 was declared a pandemic by WHO and has been reported in about 210 countries worldwide, including Asia, Europe, the USA, and North America. Person-to-person contact and international air travel between the nations were the leading causes behind the spreading of SARS-CoV-2 from its point of origin, besides the natural forces. However, further spread and infection within the community or country can be aided by natural elements, such as the weather. Therefore, the correlation between COVID-19 and temperature can be better elucidated in countries like Pakistan, where SARS-CoV-2 has affected at least 0.37 million people. This study collected Pakistan's COVID-19 infection and mortality data for ten months (March–December 2020). Related weather parameters, temperature, and humidity were also obtained for the same course of time. The collected data were processed and used to compare the performance of various time series prediction models in terms of mean squared error (MSE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE). This paper, using the time series model, estimates the effect of humidity, temperature, and other weather parameters on COVID-19 transmission by obtaining the correlation among the total infected cases and the number of deaths and weather variables in a particular region. Results depict that weather parameters hold more influence in evaluating the sum number of cases and deaths than other factors like community, age, and the total population. Therefore, temperature and humidity are salient parameters for predicting COVID-19 affected instances. Moreover, it is concluded that the higher the temperature, the lesser the mortality due to COVID-19 infection. [ABSTRACT FROM AUTHOR]

Infectious diseases like COVID-19 spread rapidly and have led to substantial economic loss worldwide, including in Pakistan. The effect of weather on COVID-19 spreading needs more detailed examination, as some studies have claimed to mitigate its spread. COVID-19 was declared a pandemic by WHO and has been reported in about 210 countries worldwide, including Asia, Europe, the USA, and North America. Person-to-person contact and international air travel between the nations were the leading causes behind the spreading of SARS-CoV-2 from its point of origin, besides the natural forces. However, further spread and infection within the community or country can be aided by natural elements, such as the weather. Therefore, the correlation between COVID-19 and temperature can be better elucidated in countries like Pakistan, where SARS-CoV-2 has affected at least 0.37 million people. This study collected Pakistan's COVID-19 infection and mortality data for ten months (March–December 2020). Related weather parameters, temperature, and humidity were also obtained for the same course of time. The collected data were processed and used to compare the performance of various time series prediction models in terms of mean squared error (MSE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE). This paper, using the time series model, estimates the effect of humidity, temperature, and other weather parameters on COVID-19 transmission by obtaining the correlation among the total infected cases and the number of deaths and weather variables in a particular region. Results depict that weather parameters hold more influence in evaluating the sum number of cases and deaths than other factors like community, age, and the total population. Therefore, temperature and humidity are salient parameters for predicting COVID-19 affected instances. Moreover, it is concluded that the higher the temperature, the lesser the mortality due to COVID-19 infection. [ABSTRACT FROM AUTHOR]

Solar energy is directly converted into electrical energy by using photovoltaic (PV) panels. The efficiency of PV panel varies with its orientation and tilt angle with the horizontal plane. In this paper, we investigate the optimum tilt angle of solar panels installed at Sukkur IBA University. The optimum angle for tilted surfaces varying from 0° to 90° in steps of 1° was calculated for the values of which the daily total solar radiation was maximum for a specific period. It was found that the optimum tilt angle changed between 0° and 61.1° throughout the year in Sukkur IBA University, Sindh Pakistan (latitude = 27.7268 ° N, longitude = 68.8191 ° E). For calculating irradiance, optimal fixed (15 and 29.5 degrees) and variable tilt angles are used for every month of year 2019. The irradiance calculated at 15 degrees tilt angle is compared with the fixed angle of 29.5 and variable angles. It was found that optimal tilt angle for the region of Sukkur located in northern Pakistan is to be 29.5 degrees. [ABSTRACT FROM AUTHOR]

It is a common practice to improve the punctuality of a railway service by the addition of time margins during the planning process of a timetable. Due to the capacity constraints of the railway network, a limited amount of time margins can be inserted. The paper presents a model and heuristic technique to find the better position for the limited amount of time margins (headway buffers and running time supplements) in a train timetable. The aim of reallocating the time margins is to adjust an existing timetable to minimize the sum of train delays at the event of the operational disturbances. The model consists of two basic parts. Firstly, the paper treats the train timetable as a Directed Arc Graph (DAG) with the aggregation concept and proposes a heuristic technique known as Critical Time Margins Allocation (CTMA), which is based on the critical path method (CPM), to reallocate the time margins. Secondly, the paper evaluates the original and modified timetable under different disturbed situations. The case study is developed on a hypothetical small railway network and a practical timetable of single-line train timetable for the track segment of Rawalpindi to Lalamusa, Pakistan. The results show that the timetable modified with the CTMA reduces the total delay time by an average of 3.25% for the small railway network and 5.18% for the large dataset. It suggests that adding the time supplements to the proper positions in a timetable can reduce the delay propagation and increase the robustness of the timetable. [ABSTRACT FROM AUTHOR]

In order to maximize both the life cycle and efficiency of a reactor core, it is essential to find the optimum loading pattern. In the case of research reactors, a loading pattern can also be optimized for flux at an irradiation site. Therefore, the development of a general-use methodology for core loading optimization would be very valuable. In this paper, general-use multiobjective core reloading pattern optimization is performed using modified genetic algorithms (MGA). The developed strategy can be applied for the constrained optimization of research and power reactor cores. For an optimal reactor core reloading design strategy, an intelligent technique GA is coupled with the Monte Carlo (MC) code SuperMC developed by the FDS team in China for nuclear reactor physics calculations. An optimal loading pattern can be depicted as a configuration that has the maximum keff and maximum thermal fluxes in the core of the given fuel inventory keeping in view the safety constraints such as limitation on power peaking factor. The optimized loading patterns for Pakistan Research Reactor-1 (PARR-1) have been recommended using the implemented strategy by considering the constraint optimization, i.e., to maximize the keff or maximum thermal neutron flux while maintaining low power peaking factor. It has been observed that the developed intelligent strategy performs these tasks with a reasonable computational cost. [ABSTRACT FROM AUTHOR]

Background. Despite substantial progress in the treatment of hepatitis C through the use of direct-acting antivirals which have been shown to cure the disease, complementary and alternative medicines (CAM) are popular among patients as a substitute or complement of allopathic medicines. This study aimed to explore the perspectives of patients and CAM practitioners on the use of CAM for the treatment of hepatitis C in Pakistan. Methods. A cross-sectional design was adopted. Participants (CAM practitioners and patients) were recruited from the capital and two provinces: Khyber Pakhtunkhwa and Punjab of Pakistan. A survey using paper-based questionnaires, each specific for patients and CAM practitioners, was conducted to gather information pertaining to demography, disease status, treatment history, and participants' perspectives (about the disease, reasons to switch to CAM, and referring source). Results. A total of 417 respondents (n = 284 patients, n = 133 practitioners) were recruited. Of the total patients, 170 (59.9%) had started CAM during the previous three months. There were 168 (59.2%) of the total patients who had used allopathic treatments for hepatitis C prior to their use of CAM. The confidence in CAM (24.6%), high cost (19%), and unbearable side effects (52.1%) of allopathic medicines were the main reasons to switch to CAM treatment. Majority (49.3%) of the patients were referred to CAM on the recommendations of relatives or care givers (17.3%) whereas only 9.5% were referred by health care professionals. Out of 133 practitioners, 48 (36.1%) were practicing herbal medicines. From practitioners' perspectives, club-moss (Lycopodium clavatum) was the best treatment option for hepatitis C. The majority, 73 (54.9%), of the patients had chosen to use CAM because of the side effects of allopathic medicines. Patients who had previous "good experience" with CAM were the most common referral source (56.4%) for CAM use in hepatitis C. Conclusions. Patients' beliefs in CAM, side effects of allopathic therapy, high cost of allopathic medicines, and referrals from previous CAM users are key factors in the switching of hepatitis C patients to CAM. [ABSTRACT FROM AUTHOR]

Background. Despite substantial progress in the treatment of hepatitis C through the use of direct-acting antivirals which have been shown to cure the disease, complementary and alternative medicines (CAM) are popular among patients as a substitute or complement of allopathic medicines. This study aimed to explore the perspectives of patients and CAM practitioners on the use of CAM for the treatment of hepatitis C in Pakistan. Methods. A cross-sectional design was adopted. Participants (CAM practitioners and patients) were recruited from the capital and two provinces: Khyber Pakhtunkhwa and Punjab of Pakistan. A survey using paper-based questionnaires, each specific for patients and CAM practitioners, was conducted to gather information pertaining to demography, disease status, treatment history, and participants' perspectives (about the disease, reasons to switch to CAM, and referring source). Results. A total of 417 respondents (n = 284 patients, n = 133 practitioners) were recruited. Of the total patients, 170 (59.9%) had started CAM during the previous three months. There were 168 (59.2%) of the total patients who had used allopathic treatments for hepatitis C prior to their use of CAM. The confidence in CAM (24.6%), high cost (19%), and unbearable side effects (52.1%) of allopathic medicines were the main reasons to switch to CAM treatment. Majority (49.3%) of the patients were referred to CAM on the recommendations of relatives or care givers (17.3%) whereas only 9.5% were referred by health care professionals. Out of 133 practitioners, 48 (36.1%) were practicing herbal medicines. From practitioners' perspectives, club-moss (Lycopodium clavatum) was the best treatment option for hepatitis C. The majority, 73 (54.9%), of the patients had chosen to use CAM because of the side effects of allopathic medicines. Patients who had previous "good experience" with CAM were the most common referral source (56.4%) for CAM use in hepatitis C. Conclusions. Patients' beliefs in CAM, side effects of allopathic therapy, high cost of allopathic medicines, and referrals from previous CAM users are key factors in the switching of hepatitis C patients to CAM. [ABSTRACT FROM AUTHOR]

The modern logistics industry has opened new strategic perspectives in establishing its interrelation with economic growth. In recent years, understanding such an overlap has become a policy issue considering ever-increasing factors and their influence on this relation. Most existing studies have explored this interaction from a general perspective, or for developed countries. This paper explores time-series analysis of the dynamic variables and their inter-related influence in both the short and long run on the relationship between modern logistic industry and economic growth—a more specific perspective, particularly for developing countries. Accordingly, we exemplify our analysis by employing the vector autoregression (VAR) model to the most updated time series data of investment in the logistics industry and the economic growth of Pakistan from 1990 to 2018. The empirical findings endorse the previous studies' outcomes and recognize the importance of sustainable economic development concerning continuously improving the logistics industry. However, a unidirectional relation is observed that economic growth leads to developing the logistics industry—economic growth exerts a significant demand-pull effect on Pakistan's logistics. It implies that logistic industrial development is comparatively quicker in the geographical areas where economic growth is higher than those areas where economic growth is low. To conclude this study's findings, logistics industry reforms should prioritize the selected geographical areas in improving the economy that would lead to the modern logistics industry's development. As the model adopts Pakistan's context, the overall statistical analysis can be generalized to other developing economies. These results would be of particular interest to strategy makers working in developing countries and help them design and develop modern transportation and logistics, coupled with interlinked technological factors, which would attract investment in the logistics industry for sustainable economic development. [ABSTRACT FROM AUTHOR]

The problem of energy crisis and environmental pollution has been mitigated by the generation and use of solar power; however, the choice of locations for solar power plants is a difficult task because the decision-making process includes political, socio-economic, and environmental aspects. Thus, several adverse consequences have been created by the choice of suboptimal locations. The objective of this paper is to address the integrated qualitative and quantitative multicriteria decision-making framework for the selection of solar power plant locations. Neutrosophic sets (NSs) are the latest extension of the ordinary fuzzy sets. The main characteristic of the neutrosophic sets is satisfying the condition that the sum of the truth, indeterminacy, and falsity grades must be at least zero and at most three. In this research, we establish novel operational laws based on the Yager t-norm and t-conorm under neutrosophic environments (NE). Furthermore, based on these Yager operational laws, we develop a list of novel aggregation operators under NE. In addition, we design an algorithm to tackle the uncertainty to investigating the best solar power plant selection in five potential locations in Pakistan. A numerical example of solar power plant location problem is considered to show the supremacy and effectiveness of the proposed study. Also, a detailed comparison is constructed to evaluate the performance and validity of the established technique. [ABSTRACT FROM AUTHOR]

The accuracy and consistency of streamflow prediction play a significant role in several applications involving the management of hydrological resources, such as power generation, water supply, and flood mitigation. However, the nonlinear dynamics of the climatic factors jeopardize the development of efficient prediction models. Therefore, to enhance the reliability and accuracy of streamflow prediction, this paper developed a three-stage hybrid model, namely, IVL (ICEEMDAN-VMD-LSTM), which integrated improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN), variational mode decomposition (VMD), and long short-term memory (LSTM) neural network. Monthly data series of streamflow, temperature, and precipitation in the Swat River Watershed, Pakistan, from January 1971 to December 2015 was used as a case study. Firstly, the correlation analysis and the two-stage decomposition approach were employed to select suitable inputs for the proposed model. ICEEMDAN was employed as a first decomposition stage, to decompose the three data series into intrinsic mode functions (IMFs) and a residual component. In the second decomposition stage, the component of high frequency (IMF1) was decomposed by VMD, as the second decomposition. Afterward, all the components obtained through the correction analysis and the two-stage decomposition approach were predicted by using the LSTM network. Finally, the predicted results of all components were aggregated, to formulate an ensemble prediction for the original monthly streamflow series. The predicted results showed that the performance of the proposed model was superior to the other developed models, in respect of several evaluation benchmarks, demonstrating the applicability of the proposed IVL model for monthly streamflow prediction. [ABSTRACT FROM AUTHOR]

The electrical infrastructure around the globe is expanding at a rapid rate for the sake of fulfilling power demands in the domestic, commercial and entertainment industries aiming to boost the living standards. In this regard, renewable energy sources (RES) are globally accepted potential candidates for maintaining inexhaustible, clean, and reliable electricity with a supplementary feature of economic prospect. The efficiency of power distribution at reduced cost to the consumers can be further enhanced by introducing a two-way billing system so-called net-metering which has the potential to overcome issues such as voltage regulation, power blackouts, overstressed grid and need for expensive storage systems thereby making it beneficial for the grid and the end user. This envisioning has encouraged the Government of Pakistan to install net-metering infrastructure at places which accommodate surplus renewable energy reserves. According to the Electric Power Act 1997, the National Electric Power Regulatory Authority (NEPRA) issued the net-metering rules and regulations in September 2015 by the endorsement of Federal Government which allowed the distribution companies in Pakistan to buy surplus electricity units generated by the consumers in order to partly reimburse the units imported from the utility grid. The aim behind this research work is to promote renewable energy utilization through net-metering mechanism in order to achieve maximum power. The export of units from consumer side to utility grid and vice versa can be made through bidirectional energy meter. In this paper, a solar net-metering analysis has been carried out on ETAP software to determine its benefits in a distribution network. Different scenarios have been investigated, and it is concluded that solar net-metering technique has multiple influential benefits, e.g., improvement in voltage regulation, reduction in transmission and distribution losses, increase in power availability, less billing to consumers, and reduction of loading on utility grid. [ABSTRACT FROM AUTHOR]

Precise and reliable hydrological runoff prediction plays a significant role in the optimal management of hydropower resources. Nevertheless, the hydrological runoff practically possesses a nonlinear dynamics, and constructing appropriate runoff prediction models to deal with the nonlinearity is a challenging task. To overcome this difficulty, this paper proposes a three-stage novel hybrid model, namely, CVS (CEEMDAN-VMD-SVM), by coupling the support vector machine (SVM) with a two-stage signal decomposition methodology, combining complete ensemble empirical decomposition with additive noise (CEEMDAN) and variational mode decomposition (VMD), to obtain inclusive information of the runoff time series. Hydrological runoff data of the Swat River, Pakistan, from 1961 to 2015 were taken for prediction. CEEMDAN decomposes the runoff time series into subcomponents, and VMD performs further decomposition of the high-frequency component obtained after CEEMDAN decomposition to improve the prediction activity. Afterward, the SVM algorithm was applied to the decomposed subcomponents for the prediction purpose. Finally, four statistical indices are utilized to measure the performance of the CVS model compared with other hybrid models including CEEMDAN-VMD-MLP (multilayer perceptron), CEEMDAN-SVM, VMD-SVM, CEEMDAN-MLP, VMD-MLP, SVM, and MLP. The CVS model performs better during the training period by reducing RMSE by 71.28% and 40.06% compared with MLP and CEEDMAD-VMD-SVM models, respectively. However, during the testing period, the error reductions include RMSE by 68.37% and 35.33% compared with MLP and CEEDMAD-VMD-SVM models, respectively. The results highlight that the CVS model outperforms other models in terms of accuracy and error reduction. The research also highlights the superiority of other hybrid models over standalone in predicting the hydrological runoff. Therefore, the proposed hybrid model is applicable for the nonlinear features of runoff time series with feasibility for future planning and management of water resources. [ABSTRACT FROM AUTHOR]

This paper investigates the spatiotemporal variability in hydrometeorological time-series to evaluate the current and future scenarios of water resources availability from upper Indus basin (UIB). Mann–Kendall and Sen's slope estimator tests were used to analyze the variability in the temperature, precipitation, and streamflow time-series data at 27 meteorological stations and 34 hydrological stations for the period of 1963 to 2014. The time-series data of entire study period were divided into two equal subseries of 26 years each (1963–1988 and 1989–2014) to assess the overlapping aspect of climate change acceleration over UIB. The results showed a warming pattern at low altitude stations, while a cooling tendency was detected at high-altitude stations. An increase in streamflow was detected during winter and spring seasons at all hydrological stations, whereas the streamflow in summer and autumn seasons exhibited decreasing trends. The annual precipitation showed a significant decreasing trend at ten stations, while a significant increasing trend was observed at Kohat station during second subseries of the study period. The most significant winter drying trends were observed at Gupis, Chitral, Garidopatta, and Naran stations of magnitude of 47%, 13%, 25%, and 18%, respectively, during the second subseries. The annual runoff exhibited significant deceasing trends over Jhelum subbasin at Azad Pattan, Chinari, Domel Kohala, Muzaffarabad, and Palote, while within Indus basin at Chahan, Gurriala, Khairabad, Karora, and Kalam in the second time-series. It is believed that the results of this study will be helpful for the decision-makers to develop strategies for planning and development of future water resources projects. [ABSTRACT FROM AUTHOR]

Despite hybrids being grown on 30–40 percent of the maize area in Pakistan, the retail price of hybrid maize seed is high in Pakistan compared with its neighbors in South Asia and beyond. Hence, this paper analyzes the adoption and impact of hybrid maize on livelihoods using a cross-sectional dataset collected from 822 maize growers in Pakistan. The data were collected from two types of farmers: adopters and nonadopters of hybrid maize, from four major provinces of Pakistan (Punjab, Sindh, KPK, and Balochistan). We use the bivariate probit to analyze the factors influencing the adoption of hybrid maize and the propensity score-matching (PSM) approach to analyze the impact of hybrid maize adoption on livelihood of maize growers, as PSM helps correct sample selection biasedness. The empirical result shows that farm size, farm and household assets, the level of education of farmers, access to market, and social networks positively influence the adoption of hybrid maize in Pakistan. The results from PSM revealed that hybrid maize adopters had higher grain yields in the range of 94–124 kgs per hectare as compared with nonadopters. Similarly household income levels were more in the range of Pakistani rupees 2,176–3,518, while the poverty levels were lower in the range of 2-3 percent for hybrid maize adopters. As hybrid maize adoption has had a positive impact on the livelihood of farmers, policies should aim to scale up the adoption of hybrid maize through enhancing the supply and lowering the seed cost through research and subsidy programs, thereby enabling poor farmers in remote areas to adopt hybrid maize varieties. [ABSTRACT FROM AUTHOR]

In this paper, thermal modeling of a typical rural house in Pakistan has been done using BEopt, to determine the hourly load profile. Using the load data, the design of a stand-alone PV system has been completed using HOMER Pro. The designed system consists of a 5.8 kW PV with eight batteries of 12 V, 255 Ah, and a 1.4 kW inverter. The system analyses show that such system can support mainly lighting and appliance load in a rural house. The dynamic model of the designed system has been simulated in MATLAB-Simulink. Perturbation and observation-based algorithm has been used for maximum power extraction from PV. Simulation results indicate that the system can provide a stable voltage and frequency for the domestic load. The method and analysis presented here can be used for the PV system design for other parts of the world. [ABSTRACT FROM AUTHOR]

The ongoing load-shedding and energy crises due to mismanagement of energy produced by different sources in Pakistan and increasing dependency on those sources which produce energy using expensive fuels have contributed to rise in load shedding and price of energy per kilo watt hour. In this paper, we have presented the linear programming model of 95 energy production systems in Pakistan. An improved multiverse optimizer is implemented to generate a dataset of 100000 different solutions, which are suggesting to fulfill the overall demand of energy in the country ranging from 9587 MW to 27208 MW. We found that, if some of the power-generating systems are down due to some technical problems, still we can get our demand by following another solution from the dataset, which is partially utilizing the particular faulty power system. According to different case studies, taken in the present study, based on the reports about the electricity short falls been published in news from time to time, we have presented our solutions, respectively, for each case. It is interesting to note that it is easy to reduce the load shedding in the country, by following the solutions presented in our dataset. Graphical analysis is presented to further elaborate our findings. By comparing our results with state-of-the-art algorithms, it is interesting to note that an improved multiverse optimizer is better in getting solutions with lower power generation costs. [ABSTRACT FROM AUTHOR]

Wheat is planted through the broadcast method on 7.8 million ha and irrigated through low-efficiency flood basin irrigation methods. With decreased water availability, there is a need to adopt water use efficient planting techniques. The current paper uses two-year trials data set as well as farmer survey data to demonstrate benefits of ridge-furrow planting wheat and farmer perception regarding the technology in Pakistan. During 2014–15 and 2015–16, ridge-furrow planting of wheat was compared with farmer practice of planting on flat surface through broadcasting at 162 sites located in 15 districts of Pakistan. In a survey, 134 farmers shared their experiences about ridge-furrow planting. Data collected from field trials and demonstrations showed that better tillering and crop growth contributed towards 12% higher wheat grain yield with ridge-furrow planting in comparison with the farmer practice of flat planting in all 15 districts during both years. Furrow-irrigated ridge planting of wheat helped farmers in saving of 30–35% water in comparison with farmer practice. Farmers adopting ridge-furrow wheat planting earned USD 100.63 per hectare more profit than nonadopter farmers. Adoption of this technology on one million hectare can help in saving 1 million acre foot of water and produce additional 0.36 million tons of wheat per year. [ABSTRACT FROM AUTHOR]

Atmospheric aerosols are considered to be an important constituent of Earth’s atmosphere because of their climatic, environmental, and health effects. Therefore, while studying the global climate change, investigation of aerosol concentrations and properties is essential both at local and regional levels. In this paper, we have used relatively long-term Aerosol Robotic Network (AERONET) data during September 2006–August 2014 to analyze aerosol properties such as aerosol optical depth at 500 nm (AOD), Ångström exponent (440–870 nm) (AE), refractive index (RI), and asymmetry parameter over Karachi, a coastal megacity of Pakistan. The average annual values of AOD and AE were found to be 0.48 ± 0.20 and 0.59 ± 0.29, respectively. The peak (0.88 ± 0.31) AOD was recorded in July with corresponding AE of 0.30 ± 0.22 representing reasonably higher concentration of coarse size particles over Karachi. The cluster analysis using the scatter plot between absorption AE and extinction AE revealed that desert dust prevailed in the atmosphere of Karachi in spring and summer, while biomass burning aerosols dominate in autumn and winter. The peak values of volume concentrations of coarse and fine-mode particulate matter were found in summer and autumn, respectively. Also, we found significant growing trend in single-scattering albedo with wavelength, indicating the domination of dust particles during summer and spring. The peak value of the real part of the RI was observed in spring (1.53) and modest in winter (1.50). On the contrary, the peak value of the imaginary part of the RI was observed to be constantly elevated in winter and lesser in spring. [ABSTRACT FROM AUTHOR]

The core purpose of this paper was to analyze the trend analysis of Pakistan railways from the year 1950 to 2015, using the principal component analysis method and industrial life cycle theory. Industrial life cycle theory, the development trend analysis of Pakistan railway industry, entails four stages: introduction, growth, maturity, and decline. The results indicated that railway industry in Pakistan was at its pinnacle in the middle of the seventies and thereafter the decline of railway industry was observed. The main reasons behind the decline were underinvestment, political interference, and the rise of the same-sector competitor, the National Logistics Cell (NLC). From the year 2011, it experienced an upward trend of combined utility curve and showed a new round of industry life cycle. To revive its previous glory, the current government has proposed a development document for Pakistan railways, Vision 2025. It is envisaged that railways share in transportation will be increased from current 4% to 20% by the year 2025, which seems to be an onerous challenge for the organization. [ABSTRACT FROM AUTHOR]