Asymmetric J-curve in the Commodity Trade between Pakistan and China

The J-curve is a term used to describe short-run deterioration in the trade balance combined with long-run improvement subsequent to a currency devaluation or depreciation. While the majority of studies have tested the symmetric J-curve concept, the new direction is to test for an asymmetric J-curve. We tested both concepts for each of the 21 two-digit industries that trade between Pakistan and its major partner, China. While we found support for the symmetric J-curve in only six industries, we found support for the asymmetric J-curve in 13 industries. The two largest industries, coded 71 (machinery other than electric with 21.14% trade share) and 72 (electrical machinery, apparatus, and appliances with 16.87% trade share) were found to be in the list.

Design and Fabrication of Low-Cost Thermal Imaging Device Prototype to Detect Heat Energy Loss in Electrical Equipment

Ability to detect heat energy loss from electrical equipment can be proven crucial to avoid overheating and unexpected fire incident. The thermal remote camera was invented to prevent power outages or electrical machinery failures. Thermal imaging cameras are device which converts thermal energy (heat) into infrared spectrum to inspect a specific object or scene. Imagery that reflects the spatial variability of temperature differences in a scene observed by a thermal camera are called thermal images. Due to the complexity of the device, it can be cost and not a solution sought for. This research presents the designing and fabrication of low-cost thermal imaging cameras that plays major role in detecting heat energy losses in various applications. This prototype of thermal imaging cameras is tested to monitor and diagnose the heat health, especially in electrical installations and components in non-contact mode. Based on the test result presented, the prototype able to detect heat energy spectrum up to 80°C. The 8x8 thermal array able to calculate average temperature of the tested items.

Investigating the Relationship Between Export Diversification and River Water Pollution: A Time Series Analysis of the Indian Experience

This article attempts to test the environmental Kuznets curve (EKC) for export diversification and river water pollution (proxied by biochemical oxygen demand) for India during the period from 1986 to 2019. Over the past decade, India’s merchandise exports have been dominated by pollution-intensive industries such as mineral fuels, pharmaceuticals, nuclear reactors, organic chemicals and electrical machinery, iron and steel, and textiles. Additionally, India’s export mix is weakly diversified or a small number of commodities form the merchandise export basket. River water pollution is one of the gravest ecological threats in this country. Although a host of reasons define this ecological devastation, this study attempts to investigate if the weakly diversified, pollution-intensive export basket has any link with biochemical oxygen demand. Dickey–Fuller (ADF) and Philip–Perron (PP) tests are employed to determine the stationary properties of the variables and the autoregressive distributed lag (ARDL) cointegration test, as well as the bounds test to check the short- and long-run cointegration. Findings suggest that (a) export diversification is strongly cointegrated with biochemical oxygen demand both in the short and in the long run, and (b) the conventional inverted U-shaped EKC was not validated. Furthermore, a weakly diversified export basket increases water pollution. Suggested policy initiatives to combat industrial water pollution include the introduction of economic instruments. The water pollution abatement experience of industrial clusters suggests that radical institutional and governance reforms are paramount for successful policy reforms. Finally, there is a need to reduce the export commodity basket concentration not just to insulate the economy against global dynamics but also for achieving the goal of sustainable development. JEL codes: F18, Q56, Q53. Q580

Preface

Thanks for your support to 2021 4th International Conference on Energy and Power Engineering (EPE2021) and make papers contributions related to Energy Systems and Analysis, New Energy Materials and Devices. Electrical Machinery. Reliability and Security, Environmental Restoration and Ecological Engineering and related areas. EPE2021 was rescheduled to be held in virtual form via Tencent Meeting on September 18, 2021 because of current epidemic prevention and travel restriction. EPE2021 virtual meeting mainly include Opening ceremony, Keynote Speech, Oral presentations, Poster presentations and free discussions. In order to ensure this virtual meeting smooth running, EPE2021 organizing committee specially opened a test room before opening for experts and authors to test their participation conditions including video, audio and screen sharing, etc. in advance. List of EFE2021 Organization Committee and this titles are available in this pdf.

Diagnosis Methodology Based on Deep Feature Learning for Fault Identification in Metallic, Hybrid and Ceramic Bearings

Scientific and technological advances in the field of rotatory electrical machinery are leading to an increased efficiency in those processes and systems in which they are involved. In addition, the consideration of advanced materials, such as hybrid or ceramic bearings, are of high interest towards high-performance rotary electromechanical actuators. Therefore, most of the diagnosis approaches for bearing fault detection are highly dependent of the bearing technology, commonly focused on the metallic bearings. Although the mechanical principles remain as the basis to analyze the characteristic patterns and effects related to the fault appearance, the quantitative response of the vibration pattern considering different bearing technology varies. In this regard, in this work a novel data-driven diagnosis methodology is proposed based on deep feature learning applied to the diagnosis and identification of bearing faults for different bearing technologies, such as metallic, hybrid and ceramic bearings, in electromechanical systems. The proposed methodology consists of three main stages: first, a deep learning-based model, supported by stacked autoencoder structures, is designed with the ability of self-adapting to the extraction of characteristic fault-related features from different signals that are processed in different domains. Second, in a feature fusion stage, information from different domains is integrated to increase the posterior discrimination capabilities during the condition assessment. Third, the bearing assessment is achieved by a simple softmax layer to compute the final classification results. The achieved results show that the proposed diagnosis methodology based on deep feature learning can be effectively applied to the diagnosis and identification of bearing faults for different bearing technologies, such as metallic, hybrid and ceramic bearings, in electromechanical systems. The proposed methodology is validated in front of two different electromechanical systems and the obtained results validate the adaptability and performance of the proposed approach to be considered as a part of the condition-monitoring strategies where different bearing technologies are involved.