Analysis of Casing and Tubing Buckling in Inclined Well

This report presents an analysis on the effect of different angle of well inclination towards the buckling effects on the casing. The buckling effects are including the stress, shear stress, strain and minimum buckling force required for the casing to start buckle. It is essential to analyse the buckling effect on the inlined well as failure in tubing and casing will cause loss of wells, which give a negative impact economically. J. D. Clegg (1971) mentioned in his paper that combination of non- uniform load and hydrostatic external pressure is believed to have caused most of the casing and tubing failures. The interaction between angle of inclination and minimum buckling force required for the casing to start buckle is calculated theoritically, while the effect of different angle of inclination on stress distribution were simulated and observed using ANSYS 14. ANSYS software has proven to be a successful tool in studying and simulating the effect of different angle of inclination towards the stress distribution of on the casing surface. The result obtained from the simulations are succesful. As the angle of the well inclination increases, the stress exerted on casing surface was changed. Besides, increase in the angle of inclination also increased the shear stress on the casing surface. The distribution of the stress also changed as the angle is changed

Contribution of Onion Seed Production to Poverty Reduction: A Case Study of Malakand Division, Pakistan

According to the latest estimates, roughly one-third of the total population of the developing countries live in poverty, majority of which are rural inhabitants (as reported 35 percent of the Pakistani rural mass). In Pakistan, the income distribution has worsened in the rural areas while it has marginally improved in urban areas during the period 1979 through 1996-97 [Pakistan (2001)]. The rural poverty is continuously feeding unemployment through migration of unskilled people to the urban areas. Poverty reduction is a priority area for Pakistan. The government is taking measures for addressing problems of the poor who are the most vulnerable amongst the different socioeconomic groups. Poverty alleviation is the main focus of the government in addition to develop physical infrastructure in rural areas and remove income disparities between income groups and regions. The government of Pakistan has initiated measures to poverty reduction through establishing number of institutions namely Pakistan Poverty Alleviation Fund, Micro-credit Bank (Khushali Bank), Pakistan Baitual Mal, Income Safety Nets, and launching Khushal Pakistan Programme and Food Support Programme. All these programmes are aiming at helping poor and hungry people by providing them food for temporary relief and micro credit for initiating sustainable economic activities. Since the majority of our population is living in rural areas, so the government is diverting more resources to improve the access for rural services and encourage greater participation in economic activities through creating employment opportunities. The programmes in education, health and population sectors have been specifically designed to extend socioeconomic opportunities to rural poor.

How to Generate Store Loyalty? Exploring the Role of Preferential Treatment and Salesperson Trust: Mediating role of Commitment to Salesperson

This article describes how much the importance and impact of preferential treatment by salespersons & customers-salespersons relationship & trust for store loyalty. Providing preferential treatment and customer trust to salesperson can be very useful for managers so that customers can be loyal. This long-term relationship helps retailers gain profits and survive in business. A questionnaire was used to collect data. Most of the data were collected from Faisalabad University students by using convenience sampling, but some of the respondents responsible for different spheres were also included in generalizability. A sufficient number of females have also been included according to the needs of the research. Both preferential treatment and trust in salesperson have a positive impact on the build-up of store loyalty. The results also that trust in the salesperson is more affecting the customer's commitment to the salesperson and thus creates loyal customers.&nbsp

Analysis of Casing and Tubing Buckling in Inclined Well

This report presents an analysis on the effect of different angle of well inclination towards the buckling effects on the casing. The buckling effects are including the stress, shear stress, strain and minimum buckling force required for the casing to start buckle. It is essential to analyse the buckling effect on the inlined well as failure in tubing and casing will cause loss of wells, which give a negative impact economically. J. D. Clegg (1971) mentioned in his paper that combination of non- uniform load and hydrostatic external pressure is believed to have caused most of the casing and tubing failures. The interaction between angle of inclination and minimum buckling force required for the casing to start buckle is calculated theoritically, while the effect of different angle of inclination on stress distribution were simulated and observed using ANSYS 14. ANSYS software has proven to be a successful tool in studying and simulating the effect of different angle of inclination towards the stress distribution of on the casing surface. The result obtained from the simulations are succesful. As the angle of the well inclination increases, the stress exerted on casing surface was changed. Besides, increase in the angle of inclination also increased the shear stress on the casing surface. The distribution of the stress also changed as the angle is changed

A Deep-Unfolded Spatiotemporal RPCA Network For L+S Decomposition

Low-rank and sparse decomposition based methods find their use in many applications involving background modeling such as clutter suppression and object tracking. While Robust Principal Component Analysis (RPCA) has achieved great success in performing this task, it can take hundreds of iterations to converge and its performance decreases in the presence of different phenomena such as occlusion, jitter and fast motion. The recently proposed deep unfolded networks, on the other hand, have demonstrated better accuracy and improved convergence over both their iterative equivalents as well as over other neural network architectures. In this work, we propose a novel deep unfolded spatiotemporal RPCA (DUST-RPCA) network, which explicitly takes advantage of the spatial and temporal continuity in the low-rank component. Our experimental results on the moving MNIST dataset indicate that DUST-RPCA gives better accuracy when compared with the existing state of the art deep unfolded RPCA networks

Feature Selection on Sentinel-2 Multi-spectral Imagery for Efficient Tree Cover Estimation

This paper proposes a multi-spectral random forest classifier with suitable feature selection and masking for tree cover estimation in urban areas. The key feature of the proposed classifier is filtering out the built-up region using spectral indices followed by random forest classification on the remaining mask with carefully selected features. Using Sentinel-2 satellite imagery, we evaluate the performance of the proposed technique on a specified area (approximately 82 acres) of Lahore University of Management Sciences (LUMS) and demonstrate that our method outperforms a conventional random forest classifier as well as state-of-the-art methods such as European Space Agency (ESA) WorldCover 10m 2020 product as well as a DeepLabv3 deep learning architecture.Comment: IEEE IGARSS 202

Contribution of Onion Seed Production to Poverty Reduction: A Case Study of Malakand Division, Pakistan

According to the latest estimates, roughly one-third of the total population of the developing countries live in poverty, majority of which are rural inhabitants (as reported 35 percent of the Pakistani rural mass). In Pakistan, the income distribution has worsened in the rural areas while it has marginally improved in urban areas during the period 1979 through 1996-97 [Pakistan (2001)]. The rural poverty is continuously feeding unemployment through migration of unskilled people to the urban areas. Poverty reduction is a priority area for Pakistan. The government is taking measures for addressing problems of the poor who are the most vulnerable amongst the different socioeconomic groups. Poverty alleviation is the main focus of the government in addition to develop physical infrastructure in rural areas and remove income disparities between income groups and regions

Logistics Hub Location Optimization: A K-Means and P-Median Model Hybrid Approach Using Road Network Distances

Logistic hubs play a pivotal role in the last-mile delivery distance; even a slight increment in distance negatively impacts the business of the e-commerce industry while also increasing its carbon footprint. The growth of this industry, particularly after Covid-19, has further intensified the need for optimized allocation of resources in an urban environment. In this study, we use a hybrid approach to optimize the placement of logistic hubs. The approach sequentially employs different techniques. Initially, delivery points are clustered using K-Means in relation to their spatial locations. The clustering method utilizes road network distances as opposed to Euclidean distances. Non-road network-based approaches have been avoided since they lead to erroneous and misleading results. Finally, hubs are located using the P-Median method. The P-Median method also incorporates the number of deliveries and population as weights. Real-world delivery data from Muller and Phipps (M&P) is used to demonstrate the effectiveness of the approach. Serving deliveries from the optimal hub locations results in the saving of 815 (10%) meters per delivery

Large Rashba splittings in bulk and monolayer of BiAs

Two-dimensional materials with Rashba split bands near the Fermi level are key to developing upcoming next-generation spintronics. They enable generating, detecting, and manipulating spin currents without an external magnetic field. Here, we propose BiAs as a novel layered semiconductor with large Rashba splitting in bulk and monolayer forms. Using first-principles calculations, we determined the lowest energy structure of BiAs and its basic electronic properties. Bulk BiAs has a layered crystal structure with two atoms in a rhombohedral primitive cell, similar to the parent Bi and As elemental phases. It is a semiconductor with a narrow and indirect band gap. The spin-orbit coupling leads to Rashba-Dresselhaus spin splitting and characteristic spin texture around the L-point in the Brillouin zone of the hexagonal conventional unit cell, with Rashba energy and Rashba coupling constant for valence (conduction) band of $E_R$= 137 meV (93 meV) and $\alpha_R$= 6.05 eV\AA~(4.6 eV{\AA}). In monolayer form (i.e., composed of a BiAs bilayer), BiAs has a much larger and direct band gap at $\Gamma$, with a circular spin texture characteristic of a pure Rashba effect. The Rashba energy $E_R$= 18 meV and Rashba coupling constant $\alpha_R$= 1.67 eV{\AA} of monolayer BiAs are quite large compared to other known 2D materials, and these values are shown to increase under tensile biaxial strain.Comment: 15pages,9figure

Emotion classification in poetry text using deep neural network

Emotion classification from online content has received considerable attention from researchers in recent times. Most of the work in this direction has been carried out on classifying emotions from informal text, such as chat, sms, tweets and other social media content. However, less attention is given to emotion classification from formal text, such as poetry. In this work, we propose an emotion classification system from poetry text using a deep neural network model. For this purpose, the BiLSTM model is implemented on a benchmark poetry dataset. This is capable of classifying poetry into different emotion types, such as love, anger, alone, suicide and surprise. The efficiency of the proposed model is compared with different baseline methods, including machine learning and deep learning models