Search

This study explores the dynamics of government spending composition in Pakistan, focusing on its impact on economic growth and poverty reduction. The study analyzes subcategories of public expenditures, including current and capital spending, and functional classifications such as education, health, physical infrastructure, and defence. Using the ARDL framework, the study finds a long-run relationship between public spending and economic growth, with capital spending positively impacting GDP growth while current expenditures have a negative effect. Education and physical infrastructure expenditures contribute positively to economic growth, while health expenditures negatively impact the long run. The study also disaggregates health and education expenditures to analyze their economic impacts. Expenditures on research and curriculum development in education and subsidiary transportation services positively affect economic growth. However, primary and secondary education expenses and tertiary education expenditures do not show a significant growth impact. Similarly, expenditures on hospital services, patient health services, and research and development in health do not contribute to economic growth. The study concludes that the composition of public expenditures is related to poverty reduction, with total government spending and investments in education, health, and physical infrastructure playing a significant role in poverty reduction.

In developed markets, the documented enhancement of investment efficiency due to financial reporting quality has yet to address the question of whether such a correlation persists in emerging, frontier, and diverse markets. This study investigates the association between financial reporting quality and investment efficiency across 21,741 publicly listed firms spanning 36 countries worldwide, encompassing developed, emerging, frontier, and other markets. Comprehensive accounting data spanning the years 2000 to 2022 is gathered for all listed firms in 40 industries across these 36 countries having 166,453 firms-year observations. Causal connections are examined through fixed-effect regression analysis, supplemented by additional tests and robustness checks utilizing alternative proxies. Concerns about endogeneity are mitigated through 2SLS analysis. The results reveal a positive impact of financial reporting quality on investment efficiency for firms in developed, emerging, frontier, and other markets. Our exploration of both over-investment and under-investment scenarios demonstrates a more pronounced link between financial reporting quality and investment efficiency in the underinvestment scenario. These findings contribute to the existing body of evidence, indicating that beyond its influence on investment efficiency in developed markets, the relationship between financial reporting quality and investment efficiency holds true globally. This encompasses emerging, frontier, and other markets, characterized by varying levels of reporting quality and financial frameworks

This study reconnoiters the impact of oil price fluctuations on the macroeconomic performance of 95 oil-importing countries for the period 1981-2023. Due to an unobserved heterogeneity among the countries, the fixed effect approach of panel data estimation is employed. This approach is advantageous because it controls for the country-specific time invariant characteristics. According to our empirical findings, oil price fluctuations substantially affect GDP and Inflation. However, the coefficient on unemployment remains statistically insignificant. Our findings suggest that governmental policies should focus on reducing oil dependency by promoting renewable energy sources for improved macroeconomic performance and sustainable development.

RINGKASAN Affandi Muhammad Azhar. 2023. Pengaruh Model Pembelajaran collaborative Learning dan Pemanfaatan Media Trainer Kelistrikan Terhadap Kualitas Keaktifan Proses dan Hasil Pembelajaran siswa pada mata pelajaran PKKR Kelas 12 di SMK Brantas Karangkates. Skripsi Jurusan Teknik Mesin dan Industri Fakultas Teknik Universitas Negeri Malang. Pembimbing (1) Bapak Dr. Amat Nyoto M.Pd (2) Bapak M. Ihwanudin S.Pd M.Pd. Tujuan dari penelitian ini adalah untuk mengetahui pengaruh model pembelajaran collaborative learning dan pemanfaatan media trainer kelistrikan terhadap kualitas keaktifan proses dan hasil pembelajaran siswa pada mata pelajaran PKKR kelas XII TKRO di SMK Brantas Karangkates.Penelitian ini termasuk dalam jenis penelitian quasi experiment.Subjek penelitian siswa kelas XII TKRO 1 dan 5 semester gasal tahun ajaran 2023/2024 yang berjumlah 60 siswa. Penelitian dilakukan dalam 3 pertemuan dengan dilakukan post-test dan observasi pada tahap awal dan akhir. Teknik pengumpulan data dengan lembar observasi tes hasil belajar dan dokumentasi. Metode analisis yang digunakan deskriptif kuantitatif. Hasil penelitian menunjukkan (1) Tidak terdapat pengaruh yang signifikan penggunaan metode Collaborative Learning terhadap kualitas keaktifan proses belajar mata pelajaran Pemeliharaan Kelistrikan Kendaraan Ringan Otomotif di SMK Brantas Karangkates diperoleh nilai sig.(2 tailed) yaitu 0 91 gt 0 05 maka Ho diterima dan Ha ditolak atau tidak terdapat pengaruh yang signifikan. (2) Tidak terdapat pengaruh yang signifikan penggunaan metode Collaborative Learning terhadap hasil pembelajaran mata pelajaran Pemeliharaan Kelistrikan Kendaraan Ringan Otomotif di SMK Brantas Karangkates diperoleh nilai sig 2 tailed atau signifikansi yaitu 0 353 gt 0 05 maka Ho diterima dan Ha ditolak atau tidak terdapat pengaruh yang signifikan. (3) Terdapat pengaruh yang signifikan pemanfaatan media trainer kelistrikan terhadap kualitas proses belajar mata pelajaran Pemeliharaan Kelistrikan Kendaraan

Research endeavors focused on examining genetic diversity play a pivotal role in advancing agricultural practices, as they furnish valuable insights for breeding programs and the preservation of germplasm. Wheat holds significant global importance as a cereal crop, serving as a primary source of sustenance for almost one-third of the global human population. In the current study, a total of 80 bread wheat genotypes were characterized using the start codon targeted (SCoT) marker system. A set of 14 polymorphic primers yielded a cumulative count of 399 bands with an average of 28.05 bands per primer. The range of diversity markers, such as polymorphism information content (0.26–0.59), effective numbers of alleles (1.27–1.59), Shannon’s information index (0.30–0.51), and gene diversity (0.18–0.34), provided evidence of elevated genetic differences within the examined germplasm. According to the findings of the molecular variance analysis, greater genetic variations are found within populations (92%). The model-based structural approach partitioned the 80 bread wheat genotypes into two distinct populations (Population A and B). The diversity indices based on the structures of the populations revealed that population A had greater diversity compared to population B. Both the principal coordinate analysis and the neighbor-joining analysis also differentiated the 80 different bread wheat genotypes based on their respective collection points. The present crop diversity offers great opportunities for enhancing crop quality and increasing tolerance to environmental challenges, both of which will lead to a reduction in the problem of food insecurity.

Penggunaan ponsel pada orang tua diyakni menjadi faktor yang dapat memicu renggangnya kedekatan keluarga serta permasalahan psikologis remaja. Muncul fenomea parental phubbing suatu bentuk pengabaian dengan menggunakan ponsel terhadap pengasuhan yang dilakukan orang tua dan diyakini dapat berdampak pada berbagai aspek psikologis termasuk di antaranya perasaan kesepian. Penelitian ini bertujuan untuk mengetahui sejauhmana pengaruh dari parental phubbing terhadap perasaan kesepian remaja dengan menggunakan desain penelitian kuantitatif dengan model regresi. Partisipan penelitian terdiri dari 168 remaja di kota Malang yang tinggal bersama orang tua dan berusia 12 ndash 21 tahun. Pengukuran variabel parental phubbing menggunakan skala phubbing yang dimodifikasi dari Generic Scale of Being Phubbed (GSBP) sedangkan perasaan kesepian menggunakan skala hasil modifikasi dari University of California Los Angles (UCLA) Loneliness Scale Version-3. Pengujian hasil modifikasi skala menggunakan uji validitas isi dengan Aiken rsquo s V dan uji keterbacaan uji validitas konstrak dengan analisis faktor dan uji reliabilitas dengan menghitung koefisien alpha ( alpha ). Diperoleh hasil penelitian 1) Validitas isi berdasarkan penilaian expert judgement pada skala GSBP memiliki nilai Aiken berkisar 0.83 ndash 1.00 sedangkan skala UCLA Loneliness Scale Version-3 memiliki nilai Aiken berkisar 0.91 ndash 1.00. Uji keterbacaan pada kedua skala dengan menggunakan 5 partisipan mendapatkan nilai rerata berkisar 2.05 ndash 3 yaitu mudah untuk dipahami. 2) Terdapat 4 item pada skala GSBP yang memiliki nilai factor loading ( lambda ) dibawah 0.5 sehingga dianggap tidak valid yaitu item nomor 1 2 3 4 sedangkan pada skala UCLA Loneliness Scale Version-3 item yang yang tidak valid ditujukan pada nomor 3 6 7 8 17. 3) Reliabilitas kedua skala adalah 0.92 untuk GSBP dan 0.82 untuk UCLA Loneliness Scale Version-3 4) Hasil analisis regresi menunjukkan bahwa pengabaian yang dilakukan orang tua dengan ponsel

The flowability and compressibility properties were determined for two types of cocoa powders, which are used commercially in chocolate industries; alkalized and natural cocoa powders. Physical and flow properties have a very significant role in the flow behavior, which relates to the handling, transportation, and storage of bulk powders. Throughout the research, it was observed that the properties of both cocoa powders are similar. The objectives of this work are to compare the compressibility and flowability between alkalized and natural cocoa powders and to design the minimum opening size of hopper for mass flow based on their flow properties. Jenike's mathematical methodology is applied during the research for the design of conical hoppers for mass flow. Besides, powder flow analysis using powder flow analyzer is one of the testing methodologies in this research. In addition, direct shear box tests were performed with three different normal stresses. The alkalized cocoa powder needs 1.366 m in diameter for the opening of the hopper, while the natural cocoa powder needs 1.531 m. From the results of the study conducted, it was concluded that both cocoa powders are very cohesive and have poor flow properties. This research reveals that the physical and flow properties of two main types of cocoa powder contributed a lot to the process of hopper design, enhance process understanding, and also applicable for analyzing the powder behavior at any stage in industry. Practical applications: Cocoa powder is the essential ingredient of chocolate manufacturing and other food industry having a chocolate‐like flavor including beverages, biscuits, desserts, and so forth. In chocolate industry, the flow behavior of cocoa powders can affect manufacturing efficiency, which directly affects the production capacity and product quality in terms of its weight and content uniformity. In this study, the critical design parameters such as hopper angle and size of opening were determined

The moisture sorption isotherms of Clinacanthus nutans (C. nutans) herbal leaves were established at three different temperatures of 30°C, 40°C, and 50°C, and water activity between 0.3–0.9 using a static gravimetric method. The results showed that equilibrium moisture content of C. nutans increased with increasing water activity at a constant temperature for both desorption and adsorption isotherms. In contrast, the equilibrium moisture content decreased when the temperature increased at a constant water activity for all isotherms. The sorption isotherms exhibited Type III behaviour or J-shaped curve according to Brunauer-Emmett-Teller (BET) classification. It was found that Guggenheim-Anderson-de Boer (GAB) and Peleg models were the best-fit to the experimental moisture isotherms. The maximum net isosteric heat of sorption values were found to be 70 kJ/mol and 13 kJ/mol for desorption and adsorption respectively. The moisture sorption isotherms established are essential for optimum design of C. nutans dryer and storage conditions.

Konsep Teknologi Informatika ini disusun secara lengkap dengan tujuan untuk memudahkan para pembaca memahami isi buku ini. Buku ini membahas tentang Konsep Teknologi Informatika dan Komunikasi (TIK), konsep sistem komputer, konsep jaringan komputer dan internet, konsep algoritma dan pemrograman, dan dampak sosial informatika.

Family planning is essential for sustainable development, particularly for a lower middle income country like Pakistan, the fifth most densely inhabited country in the world. Women's empowerment leads to a dynamic role in family planning decisions. Based on the data of PDHS 2017-18 and by taking a sample of 12339 married women, this study aims to explore the connection between women empowerment and family planning decisions in Pakistan. A summative women empowerment index was calculated for the five dimensions (economic empowerment, financial inclusion, participation in decision making, self-esteem, and awareness status). Findings of binary logistics showed that women's empowerment (as an index) has a positive effect on the use of contraceptives. Every dimension of women's empowerment except her financial inclusion positively affects family planning decisions. Household wealth status increases the probability of using contraceptives. Due to regional disparities, contraceptive usage is shallow in Sindh, Balochistan, AJK, and FATA.

In today's global landscape, it is crucial to have a comprehensive understanding of carbon dioxide (CO2) emissions in developing nations such as Pakistan. This knowledge is vital in informing effective policymaking and promoting sustainable development. This study investigates the impact of renewable energy (RE) on environmental degradation in Pakistan from 1990 to 2021. This study utilized the renowned ARDL econometrics to analyze the impact of renewable energy consumption (REC), economic growth (EG), trade (TR), financial development (FD), energy consumption (EC) and FDI on carbon emission. The study's findings confirm the presence of a hypothesis in the case of Pakistan. Additionally, the results indicate that certain factors have a negative impact on carbon emissions while others have a positive impact. The study's findings indicate that embracing renewable energy sources, enhancing energy efficiency, and enacting legislative measures to decrease CO2 emissions are the most effective strategies for tackling climate change. Additionally, the study proposed that policymakers should prioritize encouraging and supporting using renewable energy sources in Pakistan. This involves establishing a conducive environment for investment in renewable energy projects, providing financial support, and promoting research and development in the renewable energy sector.

Agriculture is a critical component of many economies, and the livelihood of farmers is often highly dependent on their ability to manage crop and livestock production risks. This study provides a comprehensive synthesis of the existing literature, shedding light on the intricate dynamics shaping farmers' decisions to invest in agricultural insurance. Based on the literature survey made for 54 relevant research studies (conducted between 2004 and 2023), this study presents an extensive review of research on factors affecting farmers' willingness to pay for livestock insurance. The study aggregates and analyzes findings from many sources, offering valuable insights for policymakers, insurance providers, and researchers. The study's findings inform the design of targeted interventions and awareness campaigns to enhance the financial protection of farmers, ensuring their stability in the face of economic and climatic uncertainties. In this way, the findings are significant in the context of global agricultural resilience and food security.

Making a better estimate of Inflation can enable us to make a better guesstimate about its economic repercussions. In modern monetary economics, the standard Phillips Curve model (PCM), the New Keynesian Phillips curve model (NPCM), and the incomplete competition model (ICM) are the alternative econometric models specified to forecast Inflation. The present study intends to identify the appropriate inflation model based on its forecasting performance with its different specifications for Pakistan's Economy. PCM includes the output gap and unemployment rate, NPCM has forward-looking expectations and uses labor income share instead of the output gap, and ICM identifies the importance of incomplete information on labor and product markets and uses some error correction term (ECT) to forecast Inflation. The relevant ECT has overcome the omitted variable bias. ICM is better in visualization forecasting and has lower root mean square error and mean absolute percentage error than other inflation models. In conclusion, the wage-price dynamics model (ICM) offers the best prospect of a successful inflation forecast in the case of Pakistan.

The accuracy and hardware efficiency of a neural system depends critically on the choice of an activation function. Rectified linear unit (ReLU) is a contemporary activation function that yields high accuracy and allows the construction of efficient neural chips, but it results in a lot of dead neurons, especially at the output layer. This problem is more pronounced in case of multichannel, multiclass classification. This is due to the fact that ReLU cancels out negative values altogether, as a result of which the corresponding values cannot be successfully backpropagated. This phenomenon is referred to as the dying ReLU problem. In this article, we present a novel ‘surrogate gradient’ learning scheme in order to solve gradient vanishing and the dying ReLU problems. To the best of our knowledge, this is the first learning scheme that enables the use of ReLU for all network layers while solving the Dying ReLU problem. We also present a high-performance inference engine that uses ReLU-based actuators for all the network layers in order to achieve high hardware efficiency. The design is excellent for online learning as well, since the derivative of the activation is equal to a constant and can be implemented using low-complexity components. The proposed technique significantly outperforms various contemporary schemes for the CIFAR-10 dataset, and can successfully yield about 98.39% accuracy on MNIST dataset while using less than 159k synapses. Moreover, the proposed hardware implementation is able to perform about 218 giga operation per second (GOPS) while consuming only about 3.95 slice registers and 25.89 slice look-up tables per synapse on a low-end Virtex 6 FPGA. The system is able to operate at a clock frequency of 93.2 MHz.

Southeast Asian countries have seen substantial economic growth over the years, but they have not been able to maintain environmental quality at the same time. Non-renewable sources constitute a significant proportion of energy consumption in the ASEAN which can have repercussions for long-term sustainable development. While the impacts of energy consumption and economic growth on environmental quality have been studied before, literature is quiet about the nexus between globalization, renewable and non-renewable energy, economic growth, and CO2 emissions in the ASEAN context. To fill in this gap in the literature, the present study estimates the effect of globalization, economic growth, and renewable and non-renewable energy on CO2 emission under the umbrella of the EKC hypothesis over the 1995 to 2020 period. Due to the presence of cross-sectional dependence and heterogeneity of slope parameters, second-generation techniques of co-integration, unit root, and long and short-run estimations are used. According to the findings of CS-ARDL estimation, non-renewable energy and globalization contribute to environmental deterioration, whereas renewable energy has a positive contribution to environmental quality improvement in ASEAN countries. Moreover, the findings prove the validation of the EKC hypothesis in the selected economies. The study concludes that the region is developing at the expense of environmental quality while also pursuing enormous globalization initiatives. The policy implications and directions of the findings for sustainable development are discussed.

Child malnutrition is of utmost concern since deprivation in early childhood often causes irreversible damage to physical and mental health, reduces learning at school, and leads to lower incomes as an adult. The study's primary purpose is to investigate the relationship between economic growth and malnutrition in Pakistan. There is a general view that if economic growth increases, malnutrition decreases. The present study examined the impact of economic growth on malnutrition in Pakistan. The study is based on time series data taking from the period of 1985 to 2021. The data are obtained from World Development Indicator (WDI) and United Nations Children’s Fund (UNICEF). Six variables are used in the study, as malnutrition is the dependent variable while economic growth, immunization, health expenditures, mother education, and improved water are independent variables. The non-linear Auto-Regressive Distributed Lag Model (NARDL) is used for the empirical analysis. The results also show the long-run relationship between economic growth and malnutrition; if economic growth increases, malnutrition decreases in children.

Population planning is inevitable for a sustainable and secure future of a lower middle-income economy, like, Pakistan which stands fifth populous economy across the globe and has high population growth rate. Based on Pakistan Demographic and Health Survey 2017-18 data and by covering a sample of 12339 ever-married females, the study intends to investigate the correlates of family planning services use in Pakistan as well as the use of contraceptives across the regions and districts of Pakistan. An exploration of 34 studies (spanning over a time from 1989 to 2021), specifically on Pakistan, with respect to family planning services utilization, have found that the use of family planning services was an outcome of a woman’s education, awareness status, wealth or economic status, religion, husband’s approval, area of residence, number of living children, prevalence of the desired family planning method, desire for more children, subsidized healthcare services and other social or individual factors. In Pakistan, the average contraceptive use rate was recorded as between 19-49 percent across the regions and between 0-71 percent across the districts. The region Islamabad Capital Territory (ICT) had the highest contraceptive usage rate, i.e., 49%, followed by: Punjab (42%), Khyber Pakhtunkhwa (KPK) and Gilgit Baltistan (39% each), Sindh and Azad Jammu and Kashmir (AJK) (34% each), Federally Administered Tribal Areas (FATA) (24%), and Balochistan (19%). Amongst the districts, top three districts with highest contraceptive usage were Jafarabad, Sheikhupura and Jhang whereas the districts of Rajanpur, Tor Ghar and Kohlu were lying at the bottom. Region-wise disaggregation found that in the province of Punjab and Sindh, the range of contraceptive use across the districts was respectively ranged between 0-70 percent and 8-60 percent. Likewise, in KPK and Balochistan provinces, the range of contraceptive use had been recorded between 0-59 percent and between 2-71 percent. While

Purpose: Governments rely on taxes, but a high tax rate can slow economic growth. Fiscal policy objectives can be achieved most effectively by lowering tax collection costs and boosting economic growth through efficient taxation. This study aims to find the impact of tax revenue on Pakistan's economic growth. Design/Methodology/Approach: The time series dataset spanning 1985–2021 is used for the current analysis. GDP is used as the dependent variable, while tax revenue and other fiscal policy variables like government spending, inflation, gross fixed capital formation and current account balance are used as the explanatory variables. The stationarity of the data is checked using the ADF test. The results of the ARDL bound test, which is used to determine whether there is a long-term link between the variables and a short-term relationship, indicate a long-term relationship. Findings: Current analysis reveals that tax revenue and inflation have a negative and significant impact while government expenditures and gross fixed capital formation have a positive and significant impact on the economic growth of Pakistan. Implications/Originality/Value: According to the study's results, Pakistan's government should ensure that tax rates are set at the right level to bring in enough money to pay for government spending that helps the economy grow.

The main objective of the current study is to quantify the research productivity in Library and Information Science (LIS) Services produced by Pakistani authors during the period of ten years from 2011 to 2020. The retrospective research method by applying the bibliometric technique has been used. The data of the publications on LIS Services by Pakistani authors has been browsed from various online and print sources. The selected bibliographic parameters of publications were inserted in Microsoft Excel-2016 for data analysis. The findings have been presented in tabular and graphic formats. A total of 1,258 documents were identified, contributed by 3,110 authors including multiple counts with an average of 2.47 authors per document. As a distinct name, a total of 858 authors were recognized and the share of international authors was slightly more than one-fifth (21%). About one-third of the total documents were published in the last two years of the study. Eighty-three percent of the documents were the results of research collaboration and the two-author pattern was found the preferred authorship pattern. The majority of documents (91%) were published as journals articles, followed by conference papers, book chapters and books. The highest number of papers were published in Pakistan Library and Information Science Journal, followed by Library Philosophy and Practice (e-journal). Sixty-four percent of the authors contributed in one paper each while 69 authors contributed ten or more than ten papers each. Dr. Rubina Bhatti, Dr. Kanwal Ameen and Dr. Khalid Mahmood were found the top-three most productive authors with 103, 94 and 77 publications, respectively. The introduction of LIS doctorate programs and the establishment of the National Digital Library of Higher Education Commission put a healthy impact on the growth of publications. There is a need to inspire the practicing librarians that they should come forward to contribute to the research progress to develop LIS

Modern deep learning schemes have shown human-level performance in the area of medical science. However, the implementation of deep learning algorithms on dedicated hardware remains a challenging task because modern algorithms and neuronal activation functions are generally not hardware-friendly and require a lot of resources. Recently, researchers have come up with some hardware-friendly activation functions that can yield high throughput and high accuracy at the same time. In this context, we propose a hardware-based neural network that can predict the presence of cancer in humans with 98.23% accuracy. This is done by making use of cost-efficient, highly accurate activation functions, Sqish and LogSQNL. Due to its inherently parallel components, the system can classify a given sample in just one clock cycle, i.e., 15.75 nanoseconds. Though this system is dedicated to cancer diagnosis, it can predict the presence of many other diseases such as those of the heart. This is because the system is reconfigurable and can be programmed to classify any sample into one of two classes. The proposed hardware system requires about 983 slice registers, 2,655 slice look-up tables, and only 1.1 kilobits of on-chip memory. The system can predict about 63.5 million cancer samples in a second and can perform about 20 giga-operations per second. The proposed system is about 5–16 times cheaper and at least four times speedier than other dedicated hardware systems using neural networks for classification tasks.

Cloud service providers acquire the computing resources and allocate them to their clients. To effectively utilize the resources and achieve higher user satisfaction, efficient task scheduling algorithms play a very pivotal role. A number of task scheduling technique have been proposed in the literature. However, majority of these scheduling algorithms fail to achieve efficient resource utilization that causes them to miss tasks deadlines. This is because these algorithms are not resource and deadline-aware. In this research, a Resource and deadline Aware Dynamic Load-balancer (RADL) for Cloud, tasks have been presented. The proposed scheduling scheme evenly distribute the incoming workload of compute-intensive and independent tasks at run-time. In addition, RADL approach has the capability to accommodate the newly arrived tasks (with shorter deadlines) efficiently and reduce task rejection. The proposed scheduler monitors/updates the task and VM status at run-time. Experimental results show that the proposed technique has attained up to 67.74%, 303.57%, 259.2%, 146.13%, 405.06%, and 259.14% improvement for average resource utilization, meeting tasks deadlines, lower makespan, task response time, penalty cost, and task execution cost respectively as compared to the state-of-the-art tasks scheduling heuristics using three benchmark datasets.

Based on Pakistan Demographic and Health Survey (PDHS) 2017-18 data covering a sample of 15068 currently married females aged 15-49 years, the study intends to investigate the women empowerment situation across the regions and districts of Pakistan as well as to explore the factors affecting women empowerment in Pakistan. An index of women empowerment (ranging from score 0 to 7) was constructed by taking the sum of the seven indicators on the five dimensions: woman's participation in decision making, awareness, employment, self-esteem, and financial inclusion status. The extent of women empowerment regarding each particular indicator is gauged (in percentage) at a national level as well as at regional and district levels. Regarding each particular indicator of women's empowerment, spatial differences exist across the country at the regional and district levels. At the country level, women empowerment is poor in Pakistan. Nearly 86% of respondent women said they were currently not working (unemployed). Similarly, 92% said they did not have an account in a bank or financial institution. At the same time, 57% percent of women reported that they do not own a mobile phone, 87% indicated that they never used the internet, and 40% stated that they were not watching television. At the country level, the women empowerment index's mean score was 2.14. The mean score of the women empowerment index at the regional level stood between 0.52 (in FATA) and 3.43 (in ICT), whereas at the district level, the mean score of the index stood between 0.03 (in South Waziristan) and 4.82 (in Hunza). Out of a total of 143 districts in Pakistan, in 13 districts, the mean score of the women empowerment index remained less than 1. Findings of the ordered logit regression showed that women's higher education, age, husband's higher education, household wealth status, female headship of household, number of living children, and belongingness to the urban area are positively associated with the empo

Pakistan is the world’s 5th most populated country. This growing population is causing numerous social and environmental problems. By the increase in population, energy demand is increasing day by day. Pakistan, where majority of the population is living in rural areas is using uncleaned cooking fuel due to which CO2 emission is increasing that is further a cause of environmental degradation. Based on the data (for the period from 1980 to 2019) of World Development Indicators, this study intends to examine the association between urban population, and energy use with CO2 emission. Unit root test was applied to check stationarity of data and after checking the stationarity status of variables, ARDL (Auto regressive distributive lag model) techniques was applied on data. The outcomes of the study showed that trade openness, urbanization, and energy consumption have significant and positive effects on environmental degradation. This study suggest that government must plan population and take steps towards the use of clean cooking fuels to control environmental degradation.

Higher education is important for the social and economic development of an economy. In the recent past, the higher education sector has developed many folds in Pakistan. However, many aspects of the higher education sector are unexplored and need scientific examination. For example, how far the administrative services which aim to facilitate students and academic staff are efficient? The efficiency of administrative services is vital for the generation of human capital in the form of students with enhanced capabilities. In addition, efficient administrative services lead to efficient academic services leading to the growth of quality research and development in the country. The current study aims to examine the efficiency of administrative services between academia and students. Data is collected through a survey from seven female hostels of International Islamic University (IIU), Islamabad, Pakistan. Efficiency is examined through students’ perceptions about the quality of administrative services being provided. Students’ response reveals their views about administrative services in IIUI. The analysis includes several relevant dimensions of administrative service such as accommodation facilities, social services like sports and health, general aspects like cafeteria and foods, library, and laboratory facilities, etc. Importance Performance Analysis (IPA) is used to evaluate the efficiency perception and importance of academic services. Based on the empirical results, the nature of inefficiencies is identified to find possible dimensions for improvements.

China Pakistan Economic Corridor (CPEC) is an amalgam that addresses employability in Pakistan. The perception of creating millions of jobs in the next few years is vital for strengthening the relationship between China and Pakistan. This economic corridor aims to enhance job opportunities, which may increase the general public's education level and standard of living. This paper aims to estimate the perception of the employability impact of Bahawalpur's solar power project and Sahiwal's coal power plant within a radius of 15km2. This study measures the employability impact perceived by the workers through qualitative and descriptive procedures. The data is collected from employees of both the power plants. This study contributes to the existing literature, explains the negative and positive perceptions regarding CPEC projects, and is concluded in support of CPEC and suggests that uplifting to technical skills of the local labor force should be of utmost priority in such types of projects. The study also suggests developing a linkage between education institutes and industry to make the required skill set available to provide more abundant opportunities to the local employees.

Contemporary development economists are focusing more on the influence of trade openness and institutional quality on economic development in developing nations. Effective institutions are thought to be essential for increasing economic progress, especially in developing countries. Trade liberalization has been increasing since the mid-1980s. The term "trade openness" refers to the removal or reduction of trade obstacles between countries and the promotion of free trade. Twenty years ago, a consensus emerged that trade openness strongly promoted economic growth. Developing economies including Pakistan are rapidly moving to free trade due to its numerous advantages. From 1984 to 2018, this research sought to uncover the relationships between institutional quality, trade openness, and Pakistan's economic growth. To achieve the goals of this research, state-of-the-art econometric methodologies were applied to various findings. Descriptive statistics are used to determine the data's center of tendency and skewness. To check the stationary problem of all series, ADF and Philips-Perron unit root tests are used. Both tests recommended using the ARDL bounds test. Two indices were created for this study: one to gauge the institutional quality and the other to measure trade openness. The main factors in Pakistan's economic growth are trade openness and institutional quality, according to empirical findings. According to this empirical research, trade liberalization will only be successful if policymakers seek to improve the quality of Pakistan's political and economic institutions

The recent re-emergence of agrarian crises in Punjab has again drawn the attention of policymakers towards the viability of alternative crops. In this context, the study attempts production and marketing of one of the alternatives and viable crops (i.e., Olive) considered for the farmers in Punjab, Pakistan. The study was carried out in the Pothwar region of Punjab, and a total of 100 farmers were interviewed from four districts of Pothwar. The study results revealed that olive is a profitable crop since the benefit-cost ratio, net present value, and internal rate of return at a 10% rate of discount were 2.20, Rs. 263,338.00, and 31%, respectively. The findings also revealed that the channel with the least number of intermediaries was more efficient than the channels with more intermediaries. The study suggested that to take advantage of this new crop, controlled marketplaces with improved marketing facilities are needed to allow farmers to minimize their transport costs and achieve better prices for their products. Besides, linking fresh produce with agro-processing industries can go a long way in ensuring the returns from this crop.

Environment degradation is a very important issue in developing nations and a lot of research had done to examine the factors of environmental degradation but these studies were missed some important factors which are covered by this study. By examining the effect of economic growth and energy in the presence of renewable energy consumption and technology innovation on environment degradation for ASEAN nations. Panel ARDL (which is PMG and MG) is used to estimate the model, and the advantage of this model is it gives both the long and short-run estimates of the model which helps to understand the situation in both short as well as long run. The results confirm that economic growth, Population, trade, and renewable energy increase the carbon emission level in ASEAN nations. While technology innovation decreased carbon emission levels which means technology innovation helps to keep the environment healthy and clean. Hence, economic growth helps the nations to improve their energy mode from non-renewable to renewable energy, which meets the energy demand by keeping the environment clean.

Nowadays, economic growth has again gained global attention because of the uncertainty in global economic conditions and attracts the focus of regulators and recent research studies. In this scenario, this study examines the role of tourism growth and foreign direct investment (FDI) on the economic growth of South Asian countries. This study has used the interest rate and population growth as the control variables. The secondary data has been extracted from the world development indicators (WDI) from 2001 to 2020. The fixed effect model (FEM) and generalized method of moments (GMM) are run to test the linkage among the variables. The results expose that tourism growth, FDI, interest rate, and population growth have a positive and significant effect on the economic growth in South Asian countries. The results provide guidelines to the regulators and furnish policies regarding economic growth for tourism growth.

For the last two decades, environmental degradation has become the most serious concern at multiple levels and has gained special attention of literature and policymakers. Strict implementation of the evidence-based policy solutions could serve as a regulatory tool to gradually overcome this issue. The current study examines the role of environmental governance (environmental-related taxes) and green energy (renewable energy consumption and production) on the carbon dioxide (CO2) emission of the ten most CO2 emitting countries. The study has used economic growth as the control variable. Data has been extracted from the world development indicators (WDI) from 2012 to 2020. The study employed the robust standard error and fixed effect model (FEM) to examine the linkage among the variables under consideration. The results indicated that environmental governance and green energy have a significant role in reducing environmental degradation in the ten most CO2 emitting countries in the world. The findings of this empirical and evidence-based study suggest to the relevant environmental management authorities, administrative bodies, and policymakers to incorporate environmental governance and green energy in their environmental management strategies.

Liquidity management is critical for a company's financial management and profitability. This study intends to analyze the effect of liquidity management on the functioning of 15 companies from the textile sector listed at the Pakistan Stock Exchange (PSE) for five years. The secondary data was obtained from the annual reports of these companies. Correlation and regression analysis were applied for data analysis. The results reveal that the current ratio and creditor payment period have a negative and considerable effect on the return on assets (ROA). In contrast, the quick ratio and inventory sales period positively correlate with profitability (ROA). Yet, the debtor collection period has a worse and exceptional impact on the firms' profitability. As a result, it is suggested that the textile industry in Pakistan should consider liquidity management as an important tool to increase profitability.

Purpose: The relationship between capital account liberalization and economic growth has been a fervently discussed subject among economists and policy-makers. The role of institutions is imperious to comprehensively investigate the impact of financial openness on growth. The objective of the study is to inspect the nexus between financial liberalization and economic growth after incorporating the contribution of institutional quality. Methodology: A panel of data on 17 emerging market economies (EMEs) is used for the period 1995-2019. We employ the GMM technique by using different de facto and de jure measures of financial liberalization along with institutional variables. Findings: The empirical results illustrate that better quality institutions strengthen the connection between capital account liberalization and output growth in the emerging World. Implications: The policymakers should focus on the more beneficial nature of financial liberalization such as FDI. Also, the policy should be aiming at availing the services of efficient human resources with proper institutional infrastructure.

Contaminated site management is currently a critical problem area all over the world, which opens a wide discussion in the areas of policy, research and practice at national and international levels. Conventional site management and remediation techniques are often aimed at reducing the contaminant levels to an acceptable level in a short period of time at low cost. Owing to the fact that the conventional approach may not be sustainable as it overlooks many ancillary environmental effects, there is an immense need of “sustainable” or “green” approaches. Green approaches address environmental, social and economic impacts throughout the remediation process and are capable of conserving the natural resources and protecting air, water and soil quality through reduced emissions and other waste burdens. This paper presents a methodology to quantify the environmental footprint of a cleanup for a hypothetical contaminated site by using the US Environmental Protection Agency’s (EPA) Spreadsheet for Environmental Footprint Assessment (SEFA). The hypothetical contaminated site is selected from a metropolitan city of Pakistan and the environmental footprint of the cleanup is analyzed under three different scenarios: cleanup without any renewable energy sources at all, cleanup with a small share of renewable energy sources, and cleanup with a large share of renewable energy sources. It is concluded that integration of renewable energy sources into the remedial system design is a promising idea which can reduce CO2, NOx, SOx, PM and HAP emissions up to 68%.

This paper covers previous studies' deficiencies and re-examine the theoretical model using a heterogeneous panel GMM technique, which overcomes cross-section dependency. In the current sample of developing nations, developed two models'; model 1 consists of the domestic output gap, and the second model includes the foreign output gap. According to model 1, foreign globalization and imports boost the inflation level in developing countries and disaggregation analysis (low, lower-middle, and upper-middle-income countries). The output gap impedes inflation in overall, lower-middle, and upper-middle-income countries, while it boosts inflation in low-income nations. And unemployment level increases the inflation rate in the overall and middle-income groups, while in low- and high-income countries, it decreases. According to the second model, foreign globalization and the foreign output gap boost overall low-income, middle-income, and upper-middle-income groups. While import reduces the inflation level globally, while in low-income, middle-income, and upper-middle-income groups, it increases inflation. Finally, the unemployment level boosts the global inflation level and as well as in low income, and it impedes inflation rate in upper-middle-income group. Despite this, there is considerable variation in countries' effect, perhaps due to differences in political institutions' quality, central bank independence, exchange rate systems, financial development, and legal traditions.

Foreign direct investment plays a vital role in promoting economic growth, especially for developing economies. It causes improvement in the different sectors such as education, healthcare, manufacturing industries, and creates more jobs. The speed of FDI inflows has been increasing in Pakistan each year. In order to attract more FDI, many countries try to reframe their tax policies by introducing different tax incentives such as tax holidays, investment allowances, exemptions, deductions etc. The purpose of the present paper is to find the implication of taxation in the decision of FDI inflows in Pakistan. Time series data is used spanning over 1985 to 2020. The data was obtained from two sources: the “World Development Indicator” (WDI) and “Economic Survey of Pakistan”. “Auto-Regressive Distributed Lag” (ARDL) and “Error Correction Model” (ECM) techniques are used for empirical analysis. The study concludes that low taxes motivate foreign investors' investment contribution and the long-run relationship between taxes and FDI in Pakistan. Other control variables, including GDP growth, trade openness and exchange rate, positively impact FDI. It is suggested that decision-makers should direct policies to reduce the taxes to welcome FDI in Pakistan. In this regard, the government needs to reconsider its priorities while making policies favouring FDI.

Opportunistic networks utilize pocket switching for routing where each node forwards its messages to a suitable next node. The selection of the forwarder node is crucial for the efficient performance of a routing protocol. In any opportunistic network, some nodes have a paramount role in the routing process and these nodes could be identified with the assistance of the existing centrality measures available in network theory. However, the central nodes tend to suffer from congestion because a large number of nodes that are relatively less central attempt to forward their payload to the central nodes to increase the probability of the message delivery. This paper evaluates mechanisms to transform the social encounters into congestion aware metrics so that high-ranking central nodes are downgraded when they encounter congestion. The network transformations are aimed at aggregating the connectivity patterns of the nodes to implicitly accumulate the network information to be utilized by centrality measures for routing purposes. We have analyzed the performance of the metrics’ computed centrality measures using routing simulation on three real-world network traces. The results revealed that betweenness centrality along with the congestion aware network metrics holds the potential to deliver a competitive number of messages. Additionally, the proposed congestion aware metrics significantly balance the routing load among the central nodes.

Employing general-purpose graphics processing units (GPGPU) with the help of OpenCL has resulted in greatly reducing the execution time of data-parallel applications by taking advantage of the massive available parallelism. However, when a small data size application is executed on GPU there is a wastage of GPU resources as the application cannot fully utilize GPU compute-cores. There is no mechanism to share a GPU between two kernels due to the lack of operating system support on GPU. In this paper, we propose the provision of a GPU sharing mechanism between two kernels that will lead to increasing GPU occupancy, and as a result, reduce execution time of a job pool. However, if a pair of the kernel is competing for the same set of resources (i.e., both applications are compute-intensive or memory-intensive), kernel fusion may also result in a significant increase in execution time of fused kernels. Therefore, it is pertinent to select an optimal pair of kernels for fusion that will result in significant speedup over their serial execution. This research presents FusionCL, a machine learning-based GPU sharing mechanism between a pair of OpenCL kernels. FusionCL identifies each pair of kernels (from the job pool), which are suitable candidates for fusion using a machine learning-based fusion suitability classifier. Thereafter, from all the candidates, it selects a pair of candidate kernels that will produce maximum speedup after fusion over their serial execution using a fusion speedup predictor. The experimental evaluation shows that the proposed kernel fusion mechanism reduces execution time by 2.83× when compared to a baseline scheduling scheme. When compared to state-of-the-art, the reduction in execution time is up to 8%.

Due to the expeditious inclination of online services usage, the incidents of ransomware proliferation being reported are on the rise. Ransomware is a more hazardous threat than other malware as the victim of ransomware cannot regain access to the hijacked device until some form of compensation is paid. In the literature, several dynamic analysis techniques have been employed for the detection of malware including ransomware; however, to the best of our knowledge, hardware execution profile for ransomware analysis has not been investigated for this purpose, as of today. In this study, we show that the true execution picture obtained via a hardware execution profile is beneficial to identify the obfuscated ransomware too. We evaluate the features obtained from hardware performance counters to classify malicious applications into ransomware and non-ransomware categories using several machine learning algorithms such as Random Forest, Decision Tree, Gradient Boosting, and Extreme Gradient Boosting. The employed data set comprises 80 ransomware and 80 non-ransomware applications, which are collected using the VirusShare platform. The results revealed that extracted hardware features play a substantial part in the identification and detection of ransomware with F-measure score of 0.97 achieved by Random Forest and Extreme Gradient Boosting.

In the heterogeneous computing environment, programmers map the applications either on CPUs or GPUs. However, this default mapping process does not produce improved results, particularly on the heterogeneous clusters. If one resource of the cluster is more compute capable, then most of the scheduling schemes favor that powerful device. In this scenario, the scheduling schemes overload the powerful resources while making all other compute resources remain under utilized. This load imbalance problem results in higher energy consumption and increased execution time. In this research, a novel Resource-Aware Load Balancer for the Heterogeneous Cluster (RALB-HC) is proposed that distributes workload based on resources computing capabilities and applications computing needs. The RALB-HC uses supervised machine learning approach to classify applications using the static code-features. The RALB-HC framework comprises of two phases: (1) job mapping based on the availability of the resources and (2) the resource-aware load balancing to achieve the higher resource utilization ratio. The experimental results on a large set of real-world and synthetic workloads show that the RALB-HC reduces execution time by 31.61%, increased resource utilization ratio by 67.8% and improved throughout 147.35% as compared to baseline scheduling schemes.

COVID-19 has infected 223 countries and caused 2.8 million deaths worldwide (at the time of writing this article), and the death rate is increasing continuously. Early diagnosis of COVID patients is a critical challenge for medical practitioners, governments, organizations, and countries to overcome the rapid spread of the deadly virus in any geographical area. In this situation, the previous epidemic evidence on Machine Learning (ML) and Deep Learning (DL) techniques encouraged the researchers to play a significant role in detecting COVID-19. Similarly, the rising scope of ML/DL methodologies in the medical domain also advocates its significant role in COVID-19 detection. This systematic review presents ML and DL techniques practiced in this era to predict, diagnose, classify, and detect the coronavirus. In this study, the data was retrieved from three prevalent full-text archives, i.e., Science Direct, Web of Science, and PubMed, using the search code strategy on 16 March 2021. Using professional assessment, among 961 articles retrieved by an initial query, only 40 articles focusing on ML/DL-based COVID-19 detection schemes were selected. Findings have been presented as a country-wise distribution of publications, article frequency, various data collection, analyzed datasets, sample sizes, and applied ML/DL techniques. Precisely, this study reveals that ML/DL technique accuracy lay between 80% to 100% when detecting COVID-19. The RT-PCR-based model with Support Vector Machine (SVM) exhibited the lowest accuracy (80%), whereas the X-ray-based model achieved the highest accuracy (99.7%) using a deep convolutional neural network. However, current studies have shown that an anal swab test is super accurate to detect the virus. Moreover, this review addresses the limitations of COVID-19 detection along with the detailed discussion of the prevailing challenges and future research directions, which eventually highlight outstanding issues

Sustainable development goals developed to reduce the level of climate transformation and its effects, this study explores the causal association between aggregate energy consumption resources, trade liberalization, CO2 emissions, and modern agriculture in selected ASEAN nations from 2000 to 2018 with the help of panel FMOLS (fully modified ordinary least square). Empirical findings have shown that the value addition level of agricultural products minimizes CO2 emissions in those countries where pollution is high. And found a positive relationship between energy consumption and CO2 emissions. Trade liberalization has made it possible to reduce CO2 emissions in economies where environmental pollution is getting lower. While the use of fossil fuels has intensified CO2 emissions, renewable energy consumption has confirmed positive effects on the pollution of the environment. While Climate-smart agriculture preferred institutions to raise income and productivity, adjusting to climate change sustainably leads to decreased greenhouse gas emissions. For example, new energy resources, renewable energy help keep the environment clean and healthy. It avoids excessive dependency on fossil fuel energy for the determinants of the agriculture sector. On the other hand, Trade policy can motivate the flow of investment opportunities and technology for a specialty in economies of scale and production. Therefore, examining strategies that encourage the agricultural sector's productivity and creating active markets for international trade in ASEAN countries will improve living standards and keep the environment clean and healthy.

Every day there are a lot of things that happen around the world. There are various ways to record every event that is occurring around the world, such as news, blogs, and articles. Over the past few years, there are multiple news available on every event that has occurred. It adds to the size of information that is available for human beings to consume. People are, moving from paper-based newspapers to digital newspapers to get their daily feed of news and digitization has a role to play in this behaviour. These days every person is preoccupied with a lot of work, online and offline, as mentioned earlier the amount of information is being increased with every passing day. For this reason, people are only interested in news that match their interests. A large amount of data in the form of text is available online, hence its classification based on its hidden features can lead to the better recommendation of news to individuals. In this research work, we have used focus area and temporal features to classify news using a Convolutional Neural Network (CNN). The results of the proposed methodology in the form of precision, accuracy, recall, and F1-Score show that these features indeed can be used for recommender systems.

Almost every state or society particularly a pluralistic one is composed of various nationalities or sub-national groups whereas every segment has its own distinct identity being a source of pride and strength for its survival. In relatively homogenous societies, the process of nation-building though not easy is at least less cumbersome whereas in pluralistic societies this process demands a lot of hard work on the part of the elite who must exhibit vision, wisdom, foresight and patience; because such societies are always fraught with the danger that any of the nationalities or sub-national group may dominate the others. The societies having a considerable level of social, cultural and linguistic heterogeneity hence horizontally/vertically divided and cross-divided in the pursuit of their distinct preferences and priorities can hardly be united under a single administrative structure nevertheless they can be persuaded to form a union under some common government.  For this purpose, a federal arrangement may be suitable option because it provides for unity in diversity rather than unity in its absolute terms. The federation of Pakistan, since its vey inception, is struggling to create the said unity particularly with reference to the situation in Balochistan which is facing a long and multi-faceted unrest posing a substantial challenge for the federation. The present study empirically explores the nature of grievances of the people of Balochistan with respect to the federation of Pakistan by conducting a field survey and establishes that majority of the people of Balochistan do not believe that the federal design in Pakistan is functioning in its true letter and spirit with reference to Balochistan.

Recently, the tourism industry has a significant role in economic growth worldwide and attain the attention of recent researchers. Therefore, the ongoing study aims to examine the role of tourism IT adoption, TR management orientation, and tourism agility on the sustainable tourism supply chain performance (STSCP) of Maldives’ tourism industry. The investigation of mediating role of tourism supply chain resilience among the nexus of tourism IT adoption, TR management orientation, tourism agility, and sustainable tourism supply chain performance of Maldives’ tourism industry are also included in the goal of the ongoing study. The data were obtained using questionnaires from the tourism industry employees related to the supply chain process, and AMOS has been utilized for analysis purposes. The results indicated that tourism IT adoption, TR management orientation, and tourism agility positively associate with the sustainable tourism supply chain performance of the Maldives’ tourism industry. The findings also exposed that tourism supply chain resilience has played a positive mediating role among the nexus of tourism IT adoption, TR management orientation, tourism agility, and STSCP of the Maldives’ tourism industry. These findings are guided to the policy developers and implementers that they should increase their focus on the tourism industry that could enhance the country's economic growth.

Climate prediction on subseasonal to decadal time scales is a rapidly advancing field that is synthesizing improvements in climate process understanding and modeling to improve and expand operational services worldwide. Weather and climate variations on subseasonal to decadal timescales can have enormous social, economic and environmental impacts, making skillful predictions on these timescales a valuable tool for decision makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) timescales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) timescales, while the focus broadly remains similar, (e.g., on precipitation, surface and upper ocean temperatures and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal and externally-forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere-ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these area