Bangladesh-China-India-Myanmar (BCIM) Economic Corridor in the Context of the ‘Belt & Road’ Initiative

This study is designed to address the following the question: what are the problems and prospects of the Bangladesh-China-India-Myanmar(BCIM) Economic Corridor? This paper is a timely research effort, because the BCIM economic corridor has special economic and geopolitical implications in the context of China’s global emergence. Various Studies suggest that the economic rise of China is a global reality. Therefore, there are enormous potentials in developing cooperative relations and investment atmosphere for greater regional integration. The successful operation of the corridor might bring about positive socioeconomic changes in regional trade integration, production networks, supply chains, connectivity, and living standards of the people. The future of the BCIM Economic Corridor depends largely on how the regional economic players operate within the system and follow the global norms of behaviors.  China’s ‘One Belt, One Road’ Initiative created opportunities and ground for South Asian as well as South East Asian economic integration Keywords: Belt & Road Initiative(一带一路), BCIM Economic Corridor, China’s Global Rise, Regional Integration, Connectivity

MonArch: Network Slice Monitoring Architecture for Cloud Native 5G Deployments

Automated decision making algorithms are expected to play a key role in management and orchestration of network slices in 5G and beyond networks. State-of-the-art algorithms for automated orchestration and management tend to rely on data-driven methods which require a timely and accurate view of the network. Accurately monitoring an end-to-end (E2E) network slice requires a scalable monitoring architecture that facilitates collection and correlation of data from various network segments comprising the slice. The state-of-the-art on 5G monitoring mostly focuses on scalability, falling short in providing explicit support for network slicing and computing network slice key performance indicators (KPIs). To fill this gap, in this paper, we present MonArch, a scalable monitoring architecture for 5G, which focuses on network slice monitoring, slice KPI computation, and an application programming interface (API) for specifying slice monitoring requests. We validate the proposed architecture by implementing MonArch on a 5G testbed, and demonstrate its capability to compute a network slice KPI (e.g., slice throughput). Our evaluations show that MonArch does not significantly increase data ingestion time when scaling the number of slices and that a 5-second monitoring interval offers a good balance between monitoring overhead and accuracy.Comment: Accepted at IEEE/IFIP NOMS 202

Anomaly Detection and Localization in NFV Systems: an Unsupervised Learning Approach

Due to the scarcity of labeled faulty data, Unsupervised Learning (UL) methods have gained great traction for anomaly detection and localization in Network Functions Virtualization (NFV) systems. In a UL approach, training is performed on only normal data for learning normal data patterns, and deviation from the norm is considered as an anomaly. However, it has been shown that even small percentages of anomalous samples in the training data (referred to as contamination) can significantly degrade the performance of UL methods. To address this issue, we propose an anomaly-detection approach based on the Noisy-Student technique, which was originally introduced for leveraging unlabeled datasets in computer-vision classification problems. Our approach not only provides robustness against training-data contamination, but also can leverage this contamination to improve anomaly-detection accuracy. Moreover, after an anomaly is detected, localization of the anomalous virtualized network functions in an unsupervised manner is a challenging task in the absence of labeled data. For anomaly localization in NFV systems, we propose to exploit existing local AI-explainability methods to achieve a high localization performance and propose our own novel AI-explainability method, specifically designed for the anomaly-localization problem in NFV, to improve the performance further. We perform a comprehensive experimental analysis on two datasets collected on different NFV testbeds and show that our proposed solutions outperform the existing methods by up to 22% in anomaly detection and up to 19% in anomaly localization in terms of F1-score

Construction and characterization of the PGN_0296 mutant of Porphyromonas gingivalis

The periodontal pathogen Porphyromonas gingivalis produces gingipains (Kgp, RgpA, and RgpB), cysteine proteases involved in the organism's virulence, and pigmentation. We previously showed that deletion of the PGN_0297 and PGN_0300 genes reduced the proteolytic activity of gingipains. The role of the PGN_0296 gene, consisting of an operon with the PGN_0297 and PGN_0300 genes, is unclear. Herein, we examined the effect of PGN_0296 gene deletion on the proteolytic activity. Although the proteolytic activity of the gingipains did not decrease in the culture supernatant of a PGN_0296 gene deletion mutant (ΔPGN_0296), the growth was delayed

Two-dimensional Nanolithography Using Atom Interferometry

We propose a novel scheme for the lithography of arbitrary, two-dimensional nanostructures via matter-wave interference. The required quantum control is provided by a pi/2-pi-pi/2 atom interferometer with an integrated atom lens system. The lens system is developed such that it allows simultaneous control over atomic wave-packet spatial extent, trajectory, and phase signature. We demonstrate arbitrary pattern formations with two-dimensional 87Rb wavepackets through numerical simulations of the scheme in a practical parameter space. Prospects for experimental realizations of the lithography scheme are also discussed.Comment: 36 pages, 4 figure

Institutions of the ‘Belt & Road’ Initiative: A Systematic Literature Review

China is the second largest economy in the world. It has experienced tremendous economic growth in the history of development. Recently China is going through a period of slow economic growth. In 2013, it launched a large-scale global project-the Belt and Road Initiative. It is termed as ‘project of the century’. Almost 65 countries are assembled under the Initiative. There have been pertinent questions and much confusions with regard to the aims and structure of the Initiative. This article reviews some high-quality peer-reviewed works to find out the nature and goals of the Belt and Road Initiative. In doing so, we have followed the research methodology of systematic literature review. The institutional dynamics and aspects of the Initiative; such as, the Silk Road Fund, Asian Infrastructure Investment Bank, and the economics corridors are explained in the paper. The data reveal that trade and economic activities in the countries along the ‘Belt and Road’ Initiative are on the increase. The evidence presented in this study would facilitate to advance further research in the newly emerging field of the ‘Belt and Road’ Initiative. Keywords: China, The Belt and Road Initiative, Institutions, Asian Infrastructure Investment Bank, Maritime Silk Road, Silk Road Economic Belt

The Influence of Atopic Dermatitis on Health-Related Quality of Life in Bangladesh

Nagata A, Kazi T, Akter Z, Nody FA, Khan MS, Shahriar ASM, Islam MS, Nakagawa T, Inui S. The Influence of Atopic Dermatitis on Health-Related Quality of Life in Bangladesh. International Journal of Environmental Research and Public Health. 2021, 18(21),11593

Prevalence of seed-borne fungi of different vegetables seeds in Bangladesh

The experiment was carried out to evaluate the prevalence of seed-borne fungi of ten selected vegetables seeds e.g. amaranth, Indian spinach, bottle gourd, sweet gourd, snake gourd, okra, bitter gourd, cucumber, brinjal and country bean. Untreated and unknown grower’s bulk vegetables loose seeds were collected from three different seed sources of Rangpur district, Bangladesh e.g. New Bangla Seeds; Islam Seeds and Rafiq Traders. The high level of infection was found in the seeds of Rafiq Traders and the lowest infection was observed in the seeds of New Bangla Seeds. Islam Seeds provided moderate infection of fungal pathogens. Fungal fruiting bodies, mycelium (white and cottony) and acervuli were found under microscopic observation. The observed physical abnormalities viz. deformed, shrinkage, swelling, spotted and undersized seeds were recorded from all seed sources. Aspergillus flavus, Aspergillus niger, Fusarium sp., Alternaria sp., Chaetomium sp., Rhizopus sp. and unidentified bacteria were observed by blotter method. Aspergillus flavus showed maximum incidence (9.0%) in okra and minimum incidence (5.50%) in brinjal. Aspergillus niger was the most predominant (9.0%) in sweet gourd and minor (5.0%) in bottle gourd. Incidence of Fusarium sp. (5.0%) and Rhizopus sp. (8.75%) were predominant in bottle gourd. The lowest incidence for Fusarium sp. (2.50%) and Rhizopus sp. (3.0%) were found in snake gourd and amaranth respectively. The highest incidence of Alternaria sp. (4.75%) and Chaetomium sp. (9.0%) were found both in sweet gourd and bitter gourd but minimum incidence of Alternaria sp. (2.0%) and Chaetomium (3.75%) were recorded in amaranth and okra respectively. In case of unidentified bacteria, maximum incidence (4.50%) was recorded in bottle gourd

State of Charge Estimation for Electric Vehicle Battery Management Systems Using the Hybrid Recurrent Learning Approach with Explainable Artificial Intelligence

Enhancing the accuracy of the battery state of charge (SOC) estimation is essential in developing more effective, dependable, and convenient electric vehicles. In this paper, a hybrid CNN and gated recurrent unit-long short-term memory (CNN-GRU-LSTM) approach, which is a recurrent neural network (RNN) based model with an explainable artificial intelligence (EAI) was used for the battery SOC estimation, where the cell parameters were explicitly synchronized to the SOC. The complexed link between the monitoring signals related to current, voltage, and temperature, and the battery SOC, was established using the deep learning (DL) technique. A LG 18650HG2 li-ion battery dataset was used for training the model so that the battery was subjected to a dynamic process. Moreover, the data recorded at ambient temperatures of −10 °C, 0 °C, 10 °C, and 25 °C are fed into the method during training. The trained model was subsequently used to estimate the SOC instantaneously on the testing datasets. At first, the training process was carried out with all temperature data to estimate the SOC by the trained model at various ambient temperatures. The proposed approach was capable to encapsulate the relationships on time into the network weights and, as a result, it produced more stable, accurate, and reliable estimations of the SOC, compared to that by some other existing networks. The hybrid model achieved a mean absolute error (MAE) of 0.41% to 1.13% for the −10 °C to 25 °C operating temperatures. The EAI was also utilized to explain the battery SOC model making certain decisions and to find out the significant features responsible for the estimation process