The Trade Effects of a South Asian Customs Union: An Expository Study

The paper estimates the static trade effects of a customs union comprising Bangladesh, India, Nepal. Pakistan and Sri Lanka. Although these effects arc found to vary between countries, for the region as a whole the trade-creation effects appear to be greater than the trade-diversion effects. Despite their smallness, the direct ion of the change indicated by the static results seems encouraging to possible attempts at the formation of a customs union among South Asian countries

Application of Machine Learning Techniques in Aquaculture

ABSTRACT: In this paper we present applications of different machine learning algorithms in aquaculture. Machine learning algorithms learn models from historical data. In aquaculture historical data are obtained from farm practices, yields, and environmental data sources. Associations between these different variables can be obtained by applying machine learning algorithms to historical data. In this paper we present applications of different machine learning algorithms in aquaculture applications

Cloud-Enhanced Robotic System for Smart City Crowd Control

Cloud robotics in smart cities is an emerging paradigm that enables autonomous robotic agents to communicate and collaborate with a cloud computing infrastructure. It complements the Internet of Things (IoT) by creating an expanded network where robots offload data-intensive computation to the ubiquitous cloud to ensure quality of service (QoS). However, offloading for robots is significantly complex due to their unique characteristics of mobility, skill-learning, data collection, and decision-making capabilities. In this paper, a generic cloud robotics framework is proposed to realize smart city vision while taking into consideration its various complexities. Specifically, we present an integrated framework for a crowd control system where cloud-enhanced robots are deployed to perform necessary tasks. The task offloading is formulated as a constrained optimization problem capable of handling any task flow that can be characterized by a Direct Acyclic Graph (DAG).We consider two scenarios of minimizing energy and time, respectively, and develop a genetic algorithm (GA)-based approach to identify the optimal task offloading decisions. The performance comparison with two benchmarks shows that our GA scheme achieves desired energy and time performance. We also show the adaptability of our algorithm by varying the values for bandwidth and movement. The results suggest their impact on offloading. Finally, we present a multi-task flow optimal path sequence problem that highlights how the robot can plan its task completion via movements that expend the minimum energy. This integrates path planning with offloading for robotics. To the best of our knowledge, this is the first attempt to evaluate cloud-based task offloading for a smart city crowd control system

Capital Expenditure, Recurring Expenditure, and Development Planning Comments on Dr. Huda's Address

The purpose of this note is to discuss the nature of the problems created -by the "impact of the capital expenditure on the subsequent liabilities of re¬curring expenditure"1 of the government and to suggest certain remedies for either eliminating or reducing such problems. The problem, as stated below, basically relates to the financial planning of development projects. The installation of a development project involves capital costs. The running of the project, after its completion, involves the costs of operation and maintenance, i.e., the recurring costs. The purpose of * financial planning is to maximize the surplus of returns over the costs of opera¬tion, including the maintenance and replacement costs

Adoption of extranet in SCM : significance and drawbacks: a case study of two companies

In recent times, extranet has been used extensively in SCM by business firms with exchange partners to achieve operating efficiency. And organizational factors play a pivotal role to fully acknowledge the benefits of extranet in SCM to achieve this efficiency. The purpose of our study is to gain a better understanding of extranet adoption in SCM and to find out the benefits and drawbacks of the extranet adoption. Our study showed with empirical data of two company’s (LKAB, Sweden and DHL, Bangladesh unit) that there is a direct relationship between organizational factors and extranet adoption. Organizational factors should be taken into consideration actively to see and measure how these factors are affecting the adoption processes to calibrate organizational practices to gain utmost benefits. The extranet investigated differed from what the literature suggested on many points. One of the features that has been revealed from the study that LKAB did not perceive most of the extranet benefits, and the reason is that LKAB has only few fixed customers and they used their extranet system as a supplementary services to their core businesses. On the other hand, DHL made the extranet system as their core business process: thus acknowledging most of the benefits in a positive way. Extranet has the potential to provide far more benefits to companies than they perceive and it is changing faster than the literature emerges. Technological improvements offset major disadvantages that once researchers considered obstacle to extranet implementation. Our research indicated that all members of the company, from top management personnel to first level employees should have the positive mindsets to adapt to contemporary developments in technology to reap the maximum benefits of extranet.Validerat; 20101217 (root

Partition, integration, economic growth, and interregional trade: a study in the growth of interwing trade in Pakistan

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics and Social Science, 1962Vita.Includes bibliographical references. Includes bibliographical references.by Muhammed Akhlaqur Rahman.Ph. D.Ph. D. Massachusetts Institute of Technology, Department of Economics and Social Scienc

A Study on Sensor System Latency in VR Motion Sickness

One of the most frequent technical factors affecting Virtual Reality (VR) performance and causing motion sickness is system latency. In this paper, we adopted predictive algorithms (i.e., Dead Reckoning, Kalman Filtering, and Deep Learning algorithms) to reduce the system latency. Cubic, quadratic, and linear functions are used to predict and curve fitting for the Dead Reckoning and Kalman Filtering algorithms. We propose a time series-based LSTM (long short-term memory), Bidirectional LSTM, and Convolutional LSTM to predict the head and body motion and reduce the motion to photon latency in VR devices. The error between the predicted data and the actual data is compared for statistical methods and deep learning techniques. The Kalman Filtering method is suitable for predicting since it is quicker to predict; however, the error is relatively high. However, the error property is good for the Dead Reckoning algorithm, even though the curve fitting is not satisfactory compared to Kalman Filtering. To overcome this poor performance, we adopted deep-learning-based LSTM for prediction. The LSTM showed improved performance when compared to the Dead Reckoning and Kalman Filtering algorithm. The simulation results suggest that the deep learning techniques outperformed the statistical methods in terms of error comparison. Overall, Convolutional LSTM outperformed the other deep learning techniques (much better than LSTM and Bidirectional LSTM) in terms of error

Cloud-Empowered Data-Centric Paradigm for Smart Manufacturing

In the manufacturing industry, there are claims about a novel system or paradigm to overcome current data interpretation challenges. Anecdotally, these studies have not been completely practical in real-world applications (e.g., data analytics). This article focuses on smart manufacturing (SM), proposed to address the inconsistencies within manufacturing that are often caused by reasons such as: (i) data realization using a general algorithm, (ii) no accurate methods to overcome the actual inconsistencies using anomaly detection modules, or (iii) real-time availability of insights of the data to change or adapt to the new challenges. A real-world case study on mattress protector manufacturing is used to prove the methods of data mining with the deployment of the isolation forest (IF)-based machine learning (ML) algorithm on a cloud scenario to address the inconsistencies stated above. The novel outcome of these studies was establishing efficient methods to enable efficient data analysis