Carbon dioxide emission from brickfields around Bangladesh

The study was undertaken at six divisions of Bangladesh to investigate the CO2 emission from brickfields. to explore the rate of carbon emission over the last 10 years, based on existing technology for brick production. The finding reveals that there were more than 45,000 Brick kilns in Bangladesh which together account for about 95% of operating kilns including Bull's Trench Kiln, Fixed Chimney Kiln, Zigzag Kiln and Hoffman Kiln. These kilns were the most carbon emitting source but it varies on fuel type, kiln type and also for location. It has been found that, maximum carbon emission area was Chittagong, which was 93.150 with percentage of last 10 years and 9.310 per cent per year. Whereas Sylhet was lower carbon emission area indicating percentage 17.172 of last 10 years and 4.218 percent per year. It has been found that total annual amount of CO2 emission for 4 types brick kilns from Dhaka, Chittagong, Rajshahi, Khulana, Sylhet and Barisal were 8.862 Mt yr-1, 10.048 Mt yr-1, 12.783 Mt yr-1, 15.250 Mt yr-1, in the year of 2002, 2005, 2007 and 2010 respectively. In Mymensingh district, the maximum CO2 emission and coal consumption was obtained in Chamak brick field, which was 1882 tons and 950 tons, respectively and minimum was obtained in Zhalak brick field, which was 1039.5 tons and 525.0 tons, respectively during the year of 2013. The percentage in last 10 years of CO2 emission was 72.784 and per cent per year 7.970, which is very alarming for us. The estimates obtained from surveys and on-site investigations indicate that these kilns consume an average of 240 tons of coal to produce 1 million bricks. This type of coal has a measured calorific value of 6,400 KJ, heating value of coal is 20.93 GJ t-1 and it produces 94.61 TJ t-1 and 56.1 TJ t-1 CO2 from coal and natural gas, respectively. DOI: http://dx.doi.org/10.3329/ijarit.v4i2.22653 Int. J. Agril. Res. Innov. & Tech. 4 (2): 70-75, December, 201

Frequency planning for clustered jointly processed cellular multiple access channel

Owing to limited resources, it is hard to guarantee minimum service levels to all users in conventional cellular systems. Although global cooperation of access points (APs) is considered promising, practical means of enhancing efficiency of cellular systems is by considering distributed or clustered jointly processed APs. The authors present a novel `quality of service (QoS) balancing scheme' to maximise sum rate as well as achieve cell-based fairness for clustered jointly processed cellular multiple access channel (referred to as CC-CMAC). Closed-form cell level QoS balancing function is derived. Maximisation of this function is proved as an NP hard problem. Hence, using power-frequency granularity, a modified genetic algorithm (GA) is proposed. For inter site distance (ISD) <; 500 m, results show that with no fairness considered, the upper bound of the capacity region is achievable. Applying hard fairness restraints on users transmitting in moderately dense AP system, 20% reduction in sum rate contribution increases fairness by upto 10%. The flexible QoS can be applied on a GA-based centralised dynamic frequency planner architecture

Information theoretic capacity of the cellular uplink - average path loss approximation

In this paper we investigate the information theoretic capacity of the uplink of a cellular system where all base station receivers jointly decode the received signals (“hyper-receiver”). Considering a distance depended power-law path loss and a more realistic Rician fading environment, we model a variable cell density network with geographically distributed user terminals. Multiple tiers of interference are considered and using an average path loss approximation model the analytical result for the per cell sum-rate capacity is found. We examine the various parameters that are affecting the capacity of the system. Especially the effect of the user distribution across the cells and the density of the cells in the cellular system is investigated. We validate the numerical solutions with Monte Carlo simulations for random fading realizations and we interpret the results for the real-world systems

Cloud Empowered Cognitive Inter-cell Interference Coordination for Small Cellular Networks

In this article, we present a Cloud empowered Cognitive Inter-Cell Interference Coordination (C2-ICIC) scheme for small cellular networks. The scheme leverages a recently proposed cloud radio access network (C-RAN) architecture for enabling intra-tier coordination and relaxes the need for inter-tier coordination by adopting the phantom cell architecture. Employing tools from stochastic geometry, we characterize the downlink success probability for a Mobile User (MU) scheduled under the proposed coordination scheme. It is shown that, compared to un-coordinated scheduling, significant performance gains can be realized in ultra dense small cell deployment scenarios under the proposed C2-ICIC scheme. This is attributed to the robust interference protection provisioned by the scheme. It is demonstrated that the gains are particularly large for the users experiencing a weak received signal strength. Indeed, for these users, the received signal-to-interference ratio (SIR) can only be improved by reducing the experienced aggregate co-channel interference. The closed-form expression derived for the downlink success probability is employed to quantify the link level throughput under the proposed scheme. Finally, we briefly explore the design space of the C2-ICIC scheme in terms of interference protection cap which determines both the downlink throughput of the MU scheduled in the coordination mode and the transmission opportunity for the co-channel small cells

A Distributed SON-Based User-Centric Backhaul Provisioning Scheme

5G definition and standardization projects are well underway, and governing characteristics and major challenges have been identified. A critical network element impacting the potential performance of 5G networks is the backhaul, which is expected to expand in length and breadth to cater to the exponential growth of small cells while offering high throughput in the order of gigabit per second and less than 1 ms latency with high resilience and energy efficiency. Such performance may only be possible with direct optical fiber connections that are often not available country-wide and are cumbersome and expensive to deploy. On the other hand, a prime 5G characteristic is diversity, which describes the radio access network, the backhaul, and also the types of user applications and devices. Thus, we propose a novel, distributed, self-optimized, end-to-end user-cell-backhaul association scheme that intelligently associates users with candidate cells based on corresponding dynamic radio and backhaul conditions while abiding by users' requirements. Radio cells broadcast multiple bias factors, each reflecting a dynamic performance indicator (DPI) of the end-to-end network performance such as capacity, latency, resilience, energy consumption, and so on. A given user would employ these factors to derive a user-centric cell ranking that motivates it to select the cell with radio and backhaul performance that conforms to the user requirements. Reinforcement learning is used at the radio cells to optimise the bias factors for each DPI in a way that maximise the system throughput while minimising the gap between the users' achievable and required end-to-end quality of experience (QoE). Preliminary results show considerable improvement in users' QoE and cumulative system throughput when compared with the state-of-the-art user-cell association schemes

Extrinsic information modification in the turbo decoder by exploiting source redundancies for HEVC video transmitted over a mobile channel

An iterative turbo decoder-based cross layer error recovery scheme for compressed video is presented in this paper. The soft information exchanged between two convolutional decoders is reinforced both by channel coded parity and video compression syntactical information. An algorithm to identify the video frame boundaries in corrupted compressed sequences is formulated. This paper continues to propose algorithms to deduce the correct values for selected fields in the compressed stream. Modifying the turbo extrinsic information using these corrections acts as reinforcements in the turbo decoding iterative process. The optimal number of turbo iterations suitable for the proposed system model is derived using EXIT charts. Simulation results reveal that a transmission power saving of 2.28% can be achieved using the proposed methodology. Contrary to typical joint cross layer decoding schemes, the additional resource requirement is minimal, since the proposed decoding cycle does not involve the decompression function

Energy Efficiency-Spectral Efficiency Trade-Off of Transmit Antenna Selection

We investigate the energy efficiency-spectral efficiency (EE-SE) trade-off of transmit antenna selection/maximum ratio combining (TAS) scheme. A realistic power consumption model (PCM) is considered, and it is shown that using TAS can provide significant energy savings when compared to multiple-input multiple-output (MIMO) in the low to medium SE region, regardless the number of antennas, as well as outperform transmit beamforming scheme (MRT) for the entire SE range. For a fixed number of receive antennas, our results also show that the EE gain of TAS over MIMO becomes even greater as the number of transmit antennas increases. The optimal value of SE that maximizes the EE is obtained analytically, and confirmed by numerical results. Moreover, the influence of receiver correlation is also evaluated and it is shown that considering a non-realistic PCM can lead to mistakes when comparing TAS and MIMO

Molecular estimation of alteration in intestinal microbial composition in Hashimoto's thyroiditis patients

The gut microbiota has a crucial effect on human health and physiology. Hypothyroid Hashimoto’s thyroiditis (HT) is an autoimmune disorder manifested with environmental and genetic factors. However, it is hypothesized that intestinal microbes might play a vital role in the pathogenesis of HT. The aim of current was to investigate and characterize the gut microbial composition of HT patients both quantitatively and qualitatively. The fecal samples from 29 HT patients and 12 healthy individuals were collected. The PCR-DGGE targeted V3 site of 16S rRNA gene and real time PCR for Bifidobacterium Lactobacillus, Bacteroides vulgatus and Clostridium leptum were performed. Pyrosequencing of 16S rRNA gene with V4 location was performed on 20 randomly selected samples. The comparative analysis of diversity and richness indices revealed diversification of gut microbiota in HT as compared to control. The statistical data elucidate the alterations in phyla of HT patients which was also affirmed at the family level. We observed the declined abundance of Prevotella_9 and Dialister, while elevated genera of the diseased group included Escherichia-Shigella and Parasutterella. The alteration in gut microbial configuration was also monitored at the species level, which showed an increased abundance of E. coli in HT. Therefore, the current study is in agreement with the hypothesis that HT patients have intestinal microbial dysbiosis. The taxa statistics at species-level along with each gut microbial community were modified in HT. Thus, the current study may offer the new insights into the treatment of HT patients, disease pathway, and mechanism

Blockchain-based data privacy management with Nudge theory in open banking

Open banking brings both opportunities and challenges to banks all over the world especially in data management. A blockchain as a continuously growing list of records managed by a peer-to-peer network is widely used in various application scenarios; and it is commonly agreed that the blockchain technology can improve the protection of financial data privacy. However, current blockchain technology still poses some challenges in fully meeting the needs of financial data privacy protection. In order to address the existing problems, this paper proposes a new data privacy management framework based on the blockchain technology for the financial sector. The framework consists of three components: (1) a data privacy classification method according to the characteristics of financial data; (2) a new collaborative-filtering-based model; and (3) a data disclosure confirmation scheme for customer strategies based on the Nudge Theory. We implement a prototype and propose a set of algorithms for this framework. The framework is validated through field experiments and laboratory experiments. © 2019 Elsevier B.V