Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
29,427
result(s) for
"Khan, Muhammad"
Sort by:
Research, innovation and entrepreneurship in Saudi Arabia : vision 2030
\"This book provides valuable insights into the Kingdom of Saudi Arabia (KSA) though a thorough examination of Saudi Vision 2030, a 15-year economic plan by the KSA to diversify its economy from a heavy dependence on hydrocarbon to knowledge-based resources. Research, Innovation and Entrepreneurship in Saudi Arabia: Vision 2030 discusses how this initiative will assist the government in achieving its envisioned goals by creating a culture of research, innovation and entrepreneurship. It studies the current state of play as well as new policies and reforms in Saudi Arabia which encompass education systems, ICT infrastructure, and a vibrant innovation landscape that includes academia, the public and private sector, and civil society. The authors present a number of real-life case studies as a model of inspiration for cross-sector development. The book provides a source of inspiration for other developing nations in studying the KSA's determined and ambitious plans as a country in a transitioning journey, from a natural resources-based economy towards a knowledge-based country with considerable diversification in all sectors. This book provides a useful reference for students, researchers, policy and decision makers in understanding Saudi innovation and the economic diversification ecosystem\"-- Provided by publisher.
Book
Impact of foreign direct investment, natural resources, renewable energy consumption, and economic growth on environmental degradation: evidence from BRICS, developing, developed and global countries
This research examined the impact of foreign direct investment, natural resources, renewable energy consumption, and economic growth on environmental degradation in BRICS, developing, developed, and global countries for the time period from 1991 to 2018 by using dynamic fixed effect model, GMM, and system GMM estimators. The examined results indicate that FDI causes environmental degradation in BRICS and developing countries while in developed countries, FDI helps environmental degradation reduction. The empirical results indicate that fuel resources and renewable energy consumption help to reduce the environment degradation in BRICS, developing, developed, and global countries while ore and metal resources cause environment degradation improvement in developed countries. Total natural resources (coal, oil, natural gas, and mineral rents) and economic growth are the main factors that boost the environmental degradation in BRICS, developing, developed, and global countries. Based on the examined results, policies are suggested for BRICS, developing, developed, and global countries. It is suggested that policy makers in these countries not only reply to protect environmental degradation but also support the growth of fuel resources, ore, and metal resource and total natural resources.
Journal Article
Young muslims, pedagogy and Islam : contexts and concepts
\"For most young people religion and religiosity is something latent or private, activated by private events or the passing of years. For Muslim young people it can be activated by an incessant Islamophobic discourse that requires fundamental questions of relationships and belonging to be addressed in the public gaze while being positioned as representatives and 'explainers' of their religion and their communities. This much-needed book discusses the realpolitik of developing services for young Muslims in the post-9/11 context and moves beyond notions of gendered provision and confessional activity to ask what defines a Muslim pedagogy. In doing so it presents a 'theoretical frame for Muslim youth work'.\"--Publisher's website.
Book
The relationship between energy consumption, economic growth and carbon dioxide emissions in Pakistan
Developing countries are facing the problem of environmental degradation. Environmental degradation is caused by the use of non-renewable energy consumptions for economic growth but the consequences of environmental degradation cannot be ignored. This primary purpose of this study is to investigate the nexus between energy consumption, economic growth and CO2 emission in Pakistan by using annual time series data from 1965 to 2015. The estimated results of ARDL indicate that energy consumption and economic growth increase the CO2 emissions in Pakistan both in short run and long run. Based on the estimated results it is recommended that policy maker in Pakistan should adopt and promote such renewable energy sources that will help to meet the increased demand for energy by replacing old traditional energy sources such as coal, gas, and oil. Renewable energy sources are reusable that can reduce the CO2 emissions and also ensure sustainable economic development of Pakistan.
Journal Article
What is wrong with Islamic economics? : analysing the present state and future agenda
What is wrong with Islamic economics? takes an objective look at the state of the art in Islamic economics and finance. It analyses reasons for perceived stagnation and also suggests a way forward. As well as probing various myths, the book presents several innovative ideas and a methodology for developing the subject on new foundations. It also highlights weaknesses in the conventional position on prohibition of interest, which has led Islamic banks devise a series of legal tricks. The author notes how the original aim of devising a new brand of banking has become less prominent whilst.
Book
Soil Moisture Measuring Techniques and Factors Affecting the Moisture Dynamics: A Comprehensive Review
The amount of surface soil moisture (SSM) is a crucial ecohydrological natural resource that regulates important land surface processes. It affects critical land–atmospheric phenomena, including the division of energy and water (infiltration, runoff, and evaporation), that impacts the effectiveness of agricultural output (sensible and latent heat fluxes and surface air temperature). Despite its significance, there are several difficulties in making precise measurements, monitoring, and interpreting SSM at high spatial and temporal resolutions. The current study critically reviews the methods and procedures for calculating SSM and the variables influencing measurement accuracy and applicability under different fields, climates, and operational conditions. For laboratory and field measurements, this study divides SSM estimate strategies into (i) direct and (ii) indirect procedures. The accuracy and applicability of a technique depends on the environment and the resources at hand. Comparative research is geographically restricted, although precise and economical—direct measuring techniques like the gravimetric method are time-consuming and destructive. In contrast, indirect methods are more expensive and do not produce measurements at the spatial scale but produce precise data on a temporal scale. While measuring SSM across more significant regions, ground-penetrating radar and remote sensing methods are susceptible to errors caused by overlapping data and atmospheric factors. On the other hand, soft computing techniques like machine/deep learning are quite handy for estimating SSM without any technical or laborious procedures. We determine that factors, e.g., topography, soil type, vegetation, climate change, groundwater level, depth of soil, etc., primarily influence the SSM measurements. Different techniques have been put into practice for various practical situations, although comparisons between them are not available frequently in publications. Each method offers a unique set of potential advantages and disadvantages. The most accurate way of identifying the best soil moisture technique is the value selection method (VSM). The neutron probe is preferable to the FDR or TDR sensor for measuring soil moisture. Remote sensing techniques have filled the need for large-scale, highly spatiotemporal soil moisture monitoring. Through self-learning capabilities in data-scarce areas, machine/deep learning approaches facilitate soil moisture measurement and prediction.
Journal Article
Relationship between inward FDI and environmental degradation for Pakistan: an exploration of pollution haven hypothesis through ARDL approach
In many developing countries, rising pollution and FDI inflows are positively correlated. This paper explores the existence of pollution haven hypothesis in Pakistan by employing the autoregressive distributed lag (ARDL) bounds test on yearly data from 1971 to 2014 for foreign direct investment inflow and four pollutants, i.e., CO
2
emissions, CO
2
emissions from solid fuels, SO
2
emissions, and GHG emissions. In each case, eight different models are tested by incorporating different explanatory variables with foreign direct investment inflow. The outcome of this study shows that in some of the models a positive long-term relationship exists between FDI inflow and CO
2
emissions, CO
2
emissions from solid fuels, and GHG emissions and a negative long-term relationship between FDI inflow and SO
2
emissions. Overall, we found no conclusive evidence of the existence of the pollution haven hypothesis for Pakistan. As Pakistan is taking active measures to attract more FDI, it is essential to introduce appropriate environmental policies and institutional reforms that do not hinder FDI inflows.
Journal Article
Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning
Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively.
Journal Article