Identification of Technical Solutions to Improve Primary Education: A Real-Life Application

Quality Function Deployment (QFD) is a meticulous procedure of translating consumers’ needs and demands into appropriate solutions. The application of QFD has been expanded, leaving no definite boundary for its potential use, to almost every field of life. It helps   identify not only the needs and demands of a certain matter but also helps find out the solutions of those matters along with assigning them the priorities. Primary education enjoys the fundamental role and provides the foundation for further upbringing of children with respect to their educational, social, intellectual, cultural, emotional and physical proficiencies. This research article is aimed at exploring the Voices of Parents, (VOPs) (parent’s needs and demands) regarding their children’s educational requirements at primary school level. This objective is achieved by identifying significant VOPs and then converting these into Technical Solution for better and high quality of education.  With the help of QFD methodology, a real-life case study has been conducted to identify VOPS their technical solutions, then the order of these technical solutions is determined and, finally, suggestions are made about which technical solution is the most important and which one is the least. The findings provide a guide line for primary school stakeholder to identify problems and their solutions for better standard and quality of education

Comparison of Caralluma tuberculata with Metformin for Anti-Diabetic Activity: An Animal Study

Background: Caralluma tuberculata, is a wild herb, which grows in the hills of Balochistan and has been known to have medicinal and nutritional properties since antiquity. This experimental research was designed to study the hypoglycemic properties of Caralluma tuberculata and to compare them with Metformin.Material and Methods: This was a laboratory-based animal experimental study. It was conducted in the Pharmacology laboratory of Khyber Medical University, Peshawar from February 2016 to August 2016. Two types of extracts of Caralluma tuberculata [crude extract and carbon tetrachloride (CCl4) extract] were prepared and administered to normal and alloxan treated diabetic rabbits. To study anti-diabetic activity, eighty-four rabbits were divided into two main groups. Group I (Normal/Non-Diabetic Rabbits; n= 21) and Group II (Diabetic/Alloxanized Rabbits; n=63). Each group was further divided into sub-groups (7 rabbits in each). Effect of Caralluma tuberculata, Metformin and 2% gum tragacanth on blood glucose levels were checked at 0, 2, 4, 6, 8, 12 and 24 hours of drug administration. The extracts were given in capsule form and in cooking oil. Data analysis was done using SPSS version 16. For calculation and comparison of the hypoglycemic effects at various doses and different time intervals, analysis of variance (ANOVA) and Tukey’s post hoc test were applied.Results: The crude extract, 200mg/kg body weight of Caralluma tuberculata showed significant decrease (p<0.001) in mean blood glucose levels from 2-hour till12 hours. Whereas, highly significant reduction of blood glucose was seen from 2 hours after treatment till 24 hours, when carbon tetrachloride (CCl4) fraction of Caralluma (100mg/kg body weight) in capsule form was administered. Metformin 500mg/kg body weight was given to compare its effects with plant crude extract and it was found that metformin appeared to be less effective in comparison with Caralluma tuberculata.Conclusion: Caralluma tuberculata lowered the blood glucose level and turned out to be more significant in developing hypoglycemia when taken with cooking oil. More work is essential to provide stronger evidence for the use of this natural agent in the management of Diabetes Mellites

Factors Affecting Intention to Use of Small-scale Renewable Energy Technologies in District Sialkot, Pakistan

Due to existing energy shortage; there is need to adopt renewable energy technologies. Low awareness and acceptance are major barriers to development of renewable energy technologies. The present study was conducted to explore factors affecting intention to use of small-scale renewable energy technologies in Pakistan at household level. This present study was conducted in Sialkot District. and non-probability sampling technique was used to select the respondents. There were 160 respondents participated in this study. The theoretical model in this research was developed based on Technology Acceptance Model, Theory of Reasoned Action, and Innovation Diffusion Theory by Rogers and Theory of Planned Behavior. Major findings of this study highlighted that socio-economic factors, facilitating conditions perceived ease of use, perceived usefulness, knowledge, persuasion, and subjective norms are significant determinants of intention to use small-scale renewable energy technologies. The study recommended that government and other stakeholders should use strategies to create awareness and sensitized the people about benefits of adopting small-scale renewable energy technologies and setting up awareness centers at local and national level for encouraging people to adopt small scale renewable energy technologies

Determinants of Adaptation Strategies to Climate Change by Farmers in District Sargodha, Pakistan

Pakistan is one of the most affected countries vulnerable to climate change. Additionally, being a predominantly agricultural economy, farming community is more at risk and climate change is predicted to decrease crop yields in Pakistan as a result of higher temperature, variability in rainfall and floods. Understanding the perception and adoption strategies to climate is important to preparing farming community for climate change impact. The present study was aimed to explore determinants of adaptation strategies to climate change by farmers. The data were collected through interview schedule. Logit regression model was used to explore the factors influencing the decision of farmer adaptation strategies to climate change. The study explored that education, farming experience, annual farm income, access to television, access to extension services, access to climate change information and membership in community based organization are main factors influencing the decision of farmers to climate change adoption. The study recommends that Government must ensure extension service, climate information and credit schemes to farmers to alter the production strategies in response to climate change

Factors Affecting Intention to Use of Small-scale Renewable Energy Technologies in District Sialkot, Pakistan

Due to existing energy shortage; there is need to adopt renewable energy technologies. Low awareness and acceptance are major barriers to development of renewable energy technologies. The present study was conducted to explore factors affecting intention to use of small-scale renewable energy technologies in Pakistan at household level. This present study was conducted in Sialkot District. and non-probability sampling technique was used to select the respondents. There were 160 respondents participated in this study. The theoretical model in this research was developed based on Technology Acceptance Model, Theory of Reasoned Action, and Innovation Diffusion Theory by Rogers and Theory of Planned Behavior. Major findings of this study highlighted that socio-economic factors, facilitating conditions perceived ease of use, perceived usefulness, knowledge, persuasion, and subjective norms are significant determinants of intention to use small-scale renewable energy technologies. The study recommended that government and other stakeholders should use strategies to create awareness and sensitized the people about benefits of adopting small-scale renewable energy technologies and setting up awareness centers at local and national level for encouraging people to adopt small scale renewable energy technologies

Pecking at Pecking Order Theory: Evidence from Pakistan’s Non-financial Sector

This study tests the Pecking Order Theory for the capital structure of listed firms in Pakistan. As per Pecking Order Theory in capital structure formulation, internally generated resources would have first priority, followed by debt issuance where equity is used as a last resort. In its strong form, the Pecking Order Theory sustains that equity issues would never occur, whereas in its weak form, limited amounts of issues are acceptable. The methodology adopted in this empirical study involves cross-section regressions and the testing of hypotheses stemming from the underlying theory in its strong and weak forms. A sample of capital structure of non-financial firms listed at KSE is considered from 2001 to 2008. A statistical tool of panel data regression analysis is used to test different firms’ data. The value of R2, t-test and F-Stat indicate firms in KSE supporting the weak form of pecking order theory, i.e., the option of using internal equity and debt is more preferred and a limited amount of external equity is used for reinvestment and fund raising purposes

Deep Reinforcement Learning for Control of Microgrids: A Review

A microgrid is widely accepted as a prominent solution to enhance resilience and performance in distributed power systems. Microgrids are flexible for adding distributed energy resources in the ecosystem of the electrical networks. Control techniques are used to synchronize distributed energy resources (DERs) due to their turbulent nature. DERs including alternating current, direct current and hybrid load with storage systems have been used in microgrids quite frequently due to which controlling the flow of energy in microgrids have been complex task with traditional control approaches. Distributed as well central approach to apply control algorithms is well-known methods to regulate frequency and voltage in microgrids. Recently techniques based of artificial intelligence are being applied for the problems that arise in operation and control of latest generation microgrids and smart grids. Such techniques are categorized in machine learning and deep learning in broader terms. The objective of this research is to survey the latest strategies of control in microgrids using the deep reinforcement learning approach (DRL). Other techniques of artificial intelligence had already been reviewed extensively but the use of DRL has increased in the past couple of years. To bridge the gap for the researchers, this survey paper is being presented with a focus on only Microgrids control DRL techniques for voltage control and frequency regulation with distributed, cooperative and multi agent approaches are presented in this research

Comparative Expression Studies of Fiber Related Genes in Cotton Spp.

Cotton fibers are the seed trichomes that are developed around the seed and are used to make clothes and yarn for the textile industry. Expression profiling of cotton fiber genes is very important to estimate the differential gene expression level at different fiber developmental stages. Expression analysis of fiber developing genes are very important to enhance the fiber length of cotton. The expression profiling of three gene families in five stages (0, 5, 10, 15 and 20 DPA) of cotton fiber tissues was carried out through real-time PCR. Expression analysis revealed that transcripts of GA-20 Oxidase, XTH, and PEPc were elevated from 5 to 20 days post-anthesis (DPA) fibers. Total RNA was extracted from various stages of cotton fiber development and was reverse transcribed to cDNA for PCR amplification. For data normalization, 18s rRNA was used as an internal control. The objective of this study was to explore the expression level of fiber developing genes at specific stages of fiber development. The results showed that most of the genes were expressed during the elongation phase in between 5 DPA to 15 DPA. Results obtained from this study may be helpful for the further identification of fiber genes and the improvement of fiber characteristics in cotton. PEPc and XTH genes that are expressed with a high rate during the fiber development may be used in breeding programmes for the improvement of fiber quality and quantity

Burst-aware predictive autoscaling for containerized microservices

Autoscaling methods are used for cloud-hosted applications to dynamically scale the allocated resources for guaranteeing Quality-of-Service (QoS). The public-facing application serves dynamic workloads, which contain bursts and pose challenges for autoscaling methods to ensure application performance. Existing State-of-the-art autoscaling methods are burst-oblivious to determine and provision the appropriate resources. For dynamic workloads, it is hard to detect and handle bursts online for maintaining application performance. In this article, we propose a novel burst-aware autoscaling method which detects burst in dynamic workloads using workload forecasting, resource prediction, and scaling decision making while minimizing response time service-level objectives (SLO) violations. We evaluated our approach through a trace-driven simulation, using multiple synthetic and realistic bursty workloads for containerized microservices, improving performance when comparing against existing state-of-the-art autoscaling methods. Such experiments show an increase of × 1.09 in total processed requests, a reduction of × 5.17 for SLO violations, and an increase of × 0.767 cost as compared to the baseline method.This work was partially supported by the European Research Council (ERC) under the EU Horizon 2020 programme (GA 639595), the Spanish Ministry of Economy, Industry and Competitiveness (TIN2015-65316-P and IJCI2016-27485) and the Generalitat de Catalunya (2014-SGR-1051).Peer ReviewedPostprint (author's final draft

EFFECT OF CROSS FIT EXERCISES ON WEIGHT LOSS OF MALES IN LAHORE

The purpose of this research was to explore the effect of Cross Fit, a latest fitness techniques being used all over the world to get a good physique and health, in reducing weight of male persons ranging from 18 to 25 years. A sample of 8 male students ranging age from 18 to 25 years to measure the change in variables like Body weight, %age Fat ratio, %age of Total Body Water contents, %age of Lean Muscle Mass after applying Cross Fit training program in pre and post analysis. After designing and applying a 28 days Cross Fit plan and diet plans for each individual according to their Basal Metabolic Rate (BMR) and Total Daily Energy Expenditure (TDEE), it was found that there is a positive significant change in these variables mentioned above which showed that Cross Fit training program d