Factors Explaining the Risk Attitude towards Entrepreneurship in Pakistan: An Exploratory Analysis

This study empirically identifies factors which explain the attitude of individuals towards entrepreneurship, and how attitudes toward risk influence the likelihood of a person turning entrepreneur. The variable ‗fear of failing‘ serves as a proxy variable reflecting risk aversion, as contained in the dataset compiled by the Global Entrepreneurship Monitor (GEM), through interviews of a sample of 2,007 respondents from Pakistan, in 2010. Given that the dependent variable is of binary nature, the probit model is used to empirically determine as to how various demographic, and perceptual factors influence risk aversion among the country‘s citizens, particularly in the context of starting their own businesses. The results suggest that personally knowing other entrepreneurs, who have launched a business in the past two years is the most significant variable affecting risk attitudes among Pakistanis; specifically, those who personally know entrepreneurs are more likely to have a fear of failure, with marginal effects as high as 8 percent. Meanwhile, individuals who feel that society generally approves of entrepreneurship as a career choice are around 5 percent less likely to fear failure, though this is a weak correlation. A number of other variables—which are reported in the literature to have significant correlation with risk attitudes in a global context—are not found to be correlated at traditional significance level for Pakistan. In addition, the study does not reveal systematic differences in the risk attitude of individuals hailing from urban and rural areas, or at provincial level. We suggest some preliminary implications based on the findings, and also identify a potential avenue for follow-up research. JEL Classification: L26; M13; O53 Keywords: Entrepreneurship; Emerging Economy; Risk Aversio

Optimizing Brain Tumor Classification: A Comprehensive Study on Transfer Learning and Imbalance Handling in Deep Learning Models

Deep learning has emerged as a prominent field in recent literature, showcasing the introduction of models that utilize transfer learning to achieve remarkable accuracies in the classification of brain tumor MRI images. However, the majority of these proposals primarily focus on balanced datasets, neglecting the inherent data imbalance present in real-world scenarios. Consequently, there is a pressing need for approaches that not only address the data imbalance but also prioritize precise classification of brain cancer. In this work, we present a novel deep learning-based approach, called Transfer Learning-CNN, for brain tumor classification using MRI data. The proposed model leverages the predictive capabilities of existing publicly available models by utilizing their pre-trained weights and transferring those weights to the CNN. By leveraging a publicly available Brain MRI dataset, the experiment evaluated various transfer learning models for classifying different tumor types, including meningioma, glioma, and pituitary tumors. We investigate the impact of different loss functions, including focal loss, and oversampling methods, such as SMOTE and ADASYN, in addressing the data imbalance issue. Notably, the proposed strategy, which combines VGG-16 and CNN, achieved an impressive accuracy rate of 96%, surpassing alternative approaches significantly.Comment: Our code is available at https://github.com/Razaimam45/AI701-Project-Transfer-Learning-approach-for-imbalance-classification-of-Brain-Tumor-MRI

Human factors issues in telerobotic decommissioning of legacy nuclear facilities

This thesis investigates the problems of enabling human workers to control remote robots, to achieve decommissioning of contaminated nuclear facilities, which are hazardous for human workers to enter. The mainstream robotics literature predominantly reports novel mechanisms and novel control algorithms. In contrast, this thesis proposes experimental methodologies for objectively evaluating the performance of both a robot and its remote human operator, when challenged with carrying out industrially relevant remote manipulation tasks. Initial experiments use a variety of metrics to evaluate the performance of human test-subjects. Results show that: conventional telemanipulation is extremely slow and difficult; metrics for usability of such technology can be conflicting and hard to interpret; aptitude for telemanipulation varies significantly between individuals; however such aptitude may be rendered predictable by using simple spatial awareness tests. Additional experiments suggest that autonomous robotics methods (e.g. vision-guided grasping) can significantly assist the operator. A novel approach to telemanipulation is proposed, in which an ``orbital camera`` enables the human operator to select arbitrary views of the scene, with the robot's motions transformed into the orbital view coordinate frame. This approach is useful for overcoming the severe depth perception problems of conventional fixed camera views. Finally, a novel computer vision algorithm is proposed for target tracking. Such an algorithm could be used to enable an unmanned aerial vehicle (UAV) to fixate on part of the workspace, e.g. a manipulated object, to provide the proposed orbital camera view

An investigation on teaching and learning activities that suit different intelligences in an english classroom

One of the measures taken by the Ministry of Education to help students in learning the English subject is by introducing Multiple Intelligences (MI) in all primary and secondary schools in February 2000. Multiple Intelligences (MI) was introduced by Dr. Howard Gardner in 1983, which emphasizes on seven different intelligences that should be possessed by normal individuals. Dr. Howard proposed that people use at least seven relatively autonomous intellectual capacities; each with its own distinctive mode of thinking to approach problems and come out with solutions. These capacities include verbal- linguistic, musical-rhythmic, logical-mathematical, visual-spatial, bodily-kinesthetic, interpersonal and intrapersonal intelligences. This research attempts to identify the dominant intelligence/s of MI among the participants and find out types of teaching-learning activities that would be able to enhance the participants' speaking and writing skills by relying on the MI framework. By understanding participants' dominant intelligence/s, the researcher hopes that this study will benefit educators as well as learners in creating meaningful teaching-learning environment in the English classroom

Design, Simulation and Modeling of a Micromachined U-shaped Cantilever Device for Application in Magnetic Field Detection

The research explores the potential applicability of the Lorentz force actuation of a MEMS based U-shaped cantilever which is made entirely of aluminum. The main objective of the study is to design, simulate and derive mathematical models for the behavior of the cantilever. The design is based on CMOS fabrication technology and bulk micromachining implemented in CoventorWare simulation environment using a Si substrate and SiO2 insulating layer supporting the Al U-shaped cantilever. Analytical models describing 3-D vibration modes (mode 1, 2 and 3) of the cantilever and their verification by simulation are discussed based on the direction of the current through the cantilever and the direction of the orthogonal external magnetic field. The response of the cantilever is discussed in two situations: static and dynamic. The static motion is obtained w hen a constant force, representing the Lorentz force due to a direct current through the cantilever placed in a static magnetic field, is applied. On the other hand, the dynamic vibration is realized when a periodic force is applied representing the Lorentz force due to a static external magnetic field acting on an alternating current through the cantilever. Results show that the displacement of the cantilever is significantly large indicating that high sensitivity can be achieved when it is driven at its resonant frequency. Three resonant frequencies were obtained for the three modes of vibration of 3, 8 and 86.6 kHz for mode 1, 2 and 3 respectively when the thickness is 5 µm, width is 20 µm, length of the base is 760 µm and length of the arm is 1000 µm. Results show the resonant frequency and sensitivity of mode 1 depend on the thickness and length of the arms only, mode 2 depends on the length of the base, length of the arms and thickness. While the resonant frequency and sensitivity of mode 3 are depend on the length of the base, length of the arms and width. The displacement as a function of the applied force is shown to be perfectly linear. The quality factors (Q-factor) of the system for the three modes were determined to be the same at the same damping coefficient. The systems response is found to decrease exponentially with increasing damping. Finally polysilicon piezoresistors in Wheatstone’s bridge configuration is used to convert the response of the cantilever to electrical measurements at various voltages for different dimensions of the cantilever. The highest sensitivity of about 64 V/T without amplification is obtained for a thin beam of 0.6 m polysilicon embedded in 2 µm thick silicon cantilever beam

Development of intelligent drone battery charging system based on wireless power transmission using hill climbing algorithm.

In this work, an advanced drone battery charging system is developed. The system is composed of a drone charging station with multiple power transmitters and a receiver to charge the battery of a drone. A resonance inductive coupling-based wireless power transmission technique is used. With limits of wireless power transmission in inductive coupling, it is necessary that the coupling between a transmitter and receiver be strong for efficient power transmission; however, for a drone, it is normally hard to land it properly on a charging station or a charging device to get maximum coupling for efficient wireless power transmission. Normally, some physical sensors such as ultrasonic sensors and infrared sensors are used to align the transmitter and receiver for proper coupling and wireless power transmission; however, in this system, a novel method based on the hill climbing algorithm is proposed to control the coupling between the transmitter and a receiver without using any physical sensor. The feasibility of the proposed algorithm was checked using MATLAB. A practical test bench was developed for the system and several experiments were conducted under different scenarios. The system is fully automatic and gives 98.8% accuracy (achieved under different test scenarios) for mitigating the poor landing effect. Also, the efficiency η of 85% is achieved for wireless power transmission. The test results show that the proposed drone battery charging system is efficient enough to mitigate the coupling effect caused by the poor landing of the drone, with the possibility to land freely on the charging station without the worry of power transmission loss

Flexural Performance of PVA Reinforced ECC Beams: Numerical and Parametric Studies

Engineered cementitious composite (ECC) refers to the group of cementitious mixtures possessing the strainhardening and crack control abilities. In this research, the mechanical performance of ECC beams will be investigated with respect to the effect of aggregate size and amount, by employing nonlinear finite element method. The validity of the models were verified with the experimental results of the ECC beams under monotonic loading. Based on the numerical analysis method,nonlinear parametric study was then conducted to evaluate the influences of various parameters on the flexural stress and flexural deflection of ECC beams. A new models that accounts for the ECC aggregate content (AC), ECC compressive strength (fECC), and maximum aggregate size (Dmax) parameters are proposed. The analytical results obtained from the proposed models were compared with experimental results obtained from 57 ECC beam tests previously published. The simulation results indicated that when increase the aggregate size and content no definite trend in flexural strength is observed and the ductility of ECC is negatively influenced by the increase of aggregate size and content. Also, the ECC beams revealed enhancement in terms of flexural stress, strain, and midspan deflection when compared with the reference beam (microsilica MSC), where, the average improvement percentage of the specimens were 45%, 1242%, and 1427.15%,respectively. These results are quite similar to that of the experimental results, which provides that the finite element model is in accordance with the desirable flexural behaviour of the ECC beams. Furthermore, the proposed models can be used to predict the flexural behaviour of ECC beams with great accuracy

Assessment of genetic diversity using DNA markers among Brassica rapa var. yellow sarson germplasm lines collected from Eastern Uttar Pradesh and Uttarakhand hills

The genetic diversity and the relatedness among thirty-one germplasm lines of yellow sarson collected from eastern UP were evaluated using morphological characters and Random Amplified Polymorphic DNA (RAPD) markers. Molecular parameters, viz. A total number of bands, average polymorphic band, average percent polymorphism, average polymorphic information content (PIC), Jaccard’s similarity coefficient, Principal Coordinate Analysis (PCA) and dendrogram generated using RAPD markers. A total of 148 different polymorphic amplification products were obtained using 10 selected decamer primers. The Jaccard similarity coefficient ranged from 0.557-0.899. Maximum polymorphism detected was 100 %.The range of amplification was from 190bp to 9 kb. Some unique bands were also reported with different primers that can be used for the identification of particular accession. PYSC-11-11 and PYSC-11-36 genotypes showed a maximum number of unique loci of different size. 31 germplasm lines grouped into two major clusters I and II based on RAPD profiling. Morphological characterization was done on the basis of leaf, petal and beak characteristics. The similarity value among the germplasm lines ranged from 0.222 to 1.000 using morphological descriptors. The dendrogram generated grouped the germplasm accession into two major groups at 44% similarity value. The cluster analysis was comparable up to some extent with Principal Coordinate Analysis (PCA) of two and three-dimensional plots. The variability revealed by morphological and molecular profile were found to be non-comparable. This study indicated the presence of high genetic diversity among collected yellow sarson germplasm, which could be used for developing for breeding and germplasm management purposes

Scientific trends on research on denture stomatitis based on Scopus database : a bibliometric analysis

Denture stomatitis is a clinical condition that affects people who wear removable maxillary dentures. It causes redness, soreness, and erythema and ultimately affects the general condition of the patient. The objective of this study was to analyze the le

First Step with R for Life Sciences: Learning Basics of this Tool for NGS Data Analysis

Background: R is one of the renowned programming language which is an open source software developed by the scientific community to compute, analyze and visualize big data of any field including biomedical research for bioinformatics applications.Methods: Here, we outlined R allied packages and affiliated bioinformatics infrastructures e.g. Bioconductor and CRAN. Moreover, basic concepts of factor, vector, data matrix and whole transcriptome RNA-Seq data was analyzed and discussed. Particularly, differential expression workflow on simulated prostate cancer RNA-Seq data was performed through experimental design, data normalization, hypothesis testing and downstream investigations using EdgeR package. A few genes with ectopic expression were retrieved and knowhow to gene enrichment pathway analysis is highlighted using available online tools.Results: Data matrix of (4×3) was constructed, and a complex data matrix of Golub et al., was analyzed through χ2 statistics by generating a frequency table of 15 true positive, 4 false positive, 15 true negative and 4 false negative on gene expression cut-off values, and a test statistics value of 10.52 with 1 df and p= 0.001 was obtained, which reject the null hypothesis and supported the alternative hypothesis of “predicted state of a person by gene expression cut-off values is dependent on the disease state of patient” in our data. Similarly, sequence data of human Zyxingene was selected and a null hypothesis of equal frequencies was rejected.Conclusion: Machine-learning approaches using R statistical package is a supportive tool which can provide systematic prediction of putative causes, present state, future consequences and possible remedies of any problem of modern biology.Keywords: NGS data; R language; Zyxin gen