Search results for: conventional neural network

Authors: Mehrshad Khosraviani, Faramarz Abbaspour Leyl Abadi

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The fundamental component of the computer network of modern information society will be considered. These networks are connected to the network of the internet generally. Due to the fact that the primary purpose of the Internet is not designed for, in recent decades, none of these networks in many of the attacks has been very important. Today, for the provision of security, different security tools and systems, including intrusion detection systems are used in the network. A common diagnosis system based on artificial immunity, the designer, the Adhasaz Foundation has been evaluated. The idea of using artificial safety methods in the diagnosis of abnormalities in computer networks it has been stimulated in the direction of their specificity, there are safety systems are similar to the common needs of m, that is non-diagnostic. For example, such methods can be used to detect any abnormalities, a variety of attacks, being memory, learning ability, and Khodtnzimi method of artificial immune algorithm pointed out. Diagnosis of the common system of education offered in this paper using only the normal samples is required for network and any additional data about the type of attacks is not. In the proposed system of positive selection and negative selection processes, selection of samples to create a distinction between the colony of normal attack is used. Copa real data collection on the evaluation of ij indicates the proposed system in the false alarm rate is often low compared to other ir methods and the detection rate is in the variations.

Keywords: artificial immune system, abnormality detection, intrusion detection, computer networks

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Authors: Lamaa Sellami, Bechir Alaya

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Nowadays, video-on-demand (VoD) applications are becoming one of the tendencies driving vehicular network users. In this paper, considering the unpredictable vehicle density, the unexpected acceleration or deceleration of the different cars included in the vehicular traffic load, and the limited radio range of the employed communication scheme, we introduce the “Dynamic Vehicular Clustering” (DVC) algorithm as a new scheme for video streaming systems over VANET. The proposed algorithm takes advantage of the concept of small cells and the introduction of wireless backhauls, inspired by the different features and the performance of the Long Term Evolution (LTE)- Advanced network. The proposed clustering algorithm considers multiple characteristics such as the vehicle’s position and acceleration to reduce latency and packet loss. Therefore, each cluster is counted as a small cell containing vehicular nodes and an access point that is elected regarding some particular specifications.

Keywords: video-on-demand, vehicular ad-hoc network, mobility, vehicular traffic load, small cell, wireless backhaul, LTE-advanced, latency, packet loss

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Authors: Tobias Milz, L. Macpherson, Varvara Vetrova

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The law is a fundamental pillar of human societies as it shapes, controls and governs how humans conduct business, behave and interact with each other. Recent advances in computer-assisted technologies such as NLP, data science and AI are creating opportunities to support the practice, research and study of this pervasive domain. It is therefore not surprising that there has been an increase in investments into supporting technologies for the legal industry (also known as “legal tech” or “law tech”) over the last decade. A sub-discipline of particular appeal is concerned with assisted legal research. Supporting law researchers and practitioners to retrieve information from the vast amount of ever-growing legal documentation is of natural interest to the legal research community. One tool that has been in use for this purpose since the early nineteenth century is legal citation indexing. Among other use cases, they provided an effective means to discover new precedent cases. Nowadays, computer-assisted network analysis tools can allow for new and more efficient ways to reveal the “hidden” information that is conveyed through citation behavior. Unfortunately, access to openly available legal data is still lacking in New Zealand and access to such networks is only commercially available via providers such as LexisNexis. Consequently, there is a need to create, analyze and provide a legal citation network with sufficient data to support legal research tasks. This paper describes the development and analysis of a legal citation Network for New Zealand containing over 300.000 decisions from 125 different courts of all areas of law and jurisdiction. Using python, the authors assembled web crawlers, scrapers and an OCR pipeline to collect and convert court decisions from openly available sources such as NZLII into uniform and machine-readable text. This facilitated the use of regular expressions to identify references to other court decisions from within the decision text. The data was then imported into a graph-based database (Neo4j) with the courts and their respective cases represented as nodes and the extracted citations as links. Furthermore, additional links between courts of connected cases were added to indicate an indirect citation between the courts. Neo4j, as a graph-based database, allows efficient querying and use of network algorithms such as PageRank to reveal the most influential/most cited courts and court decisions over time. This paper shows that the in-degree distribution of the New Zealand legal citation network resembles a power-law distribution, which indicates a possible scale-free behavior of the network. This is in line with findings of the respective citation networks of the U.S. Supreme Court, Austria and Germany. The authors of this paper provide the database as an openly available data source to support further legal research. The decision texts can be exported from the database to be used for NLP-related legal research, while the network can be used for in-depth analysis. For example, users of the database can specify the network algorithms and metrics to only include specific courts to filter the results to the area of law of interest.

Keywords: case citation network, citation analysis, network analysis, Neo4j

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Authors: K. Govender, M. Sinclair

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This paper demonstrates a proof of concept whereby shorter trips and land use densification can be promoted through an alternative approach to planning and implementation of road infrastructure in the South African context. It briefly discusses how the development of the Compact City concept relies on a combination of promoting shorter trips and densification through a change in focus in road infrastructure provision. The methodology developed in this paper uses a traffic model to test the impact of synthesized deterrence functions on congestion locations in the road network through the assignment of traffic on the study network. The results from this study demonstrate that intelligent planning of road infrastructure can indeed promote reduced urban sprawl, increased residential density and mixed-use areas which are supported by an efficient public transport system; and reduced dependence on the freeway network with a fixed road infrastructure budget. The study has resonance for all cities where urban sprawl is seemingly unstoppable.

Keywords: compact cities, densification, road infrastructure planning, transportation modelling

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Authors: Ardavan Zamanpour, Somaieh Yassari

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ZigBee protocol network was developed in industries and MIT laboratory in 1997. ZigBee is a wireless networking technology by alliance ZigBee which is designed to low board and low data rate applications. It is a Protocol which connects between electrical devises with very low energy and cost. The first version of IEEE 802.15.4 which was formed ZigBee was based on 2.4GHZ MHZ 912MHZ 868 frequency band. The name of system is often reminded random directions that bees (BEES) traversing during pollination of products. Such as alloy of the ways in which information packets are traversed within the mesh network. This paper aims to study the performance and effectiveness of this protocol in wireless networks.

Keywords: ZigBee, protocol, wireless, networks

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Authors: Onyedikachi Ulelu, Andrew P. Longstaff, Simon Fletcher, Simon Parkinson

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The widespread interest in 'Industry 4.0' or 'digital manufacturing' has led to significant research requiring the acquisition of data from sensors, instruments, and machine signals. In-depth research then identifies methods of analysis of the massive amounts of data generated before and during manufacture to solve a particular problem. The ultimate goal is for industrial Internet of Things (IIoT) data to be processed automatically to assist with either visualisation or autonomous system decision-making. However, the collection and processing of data in an industrial environment come with a cost. Little research has been undertaken on how to specify optimally what data to capture, transmit, process, and store at various levels of an edge-cloud network. The first step in this specification is to categorise IIoT data for efficient and effective use. This paper proposes the required attributes and classification to take manufacturing digital data from various sources to determine the most suitable location for data processing on the edge-cloud network. The proposed classification framework will minimise overhead in terms of network bandwidth/cost and processing time of machine tool data via efficient decision making on which dataset should be processed at the ‘edge’ and what to send to a remote server (cloud). A fast-and-frugal heuristic method is implemented for this decision-making. The framework is tested using case studies from industrial machine tools for machine productivity and maintenance.

Keywords: data classification, decision making, edge computing, industrial IoT, industry 4.0

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Authors: Ceren Kaya, Okan Erkaymaz, Orhan Ayar, Mahmut Özer

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Diabetes Mellitus (Diabetes) is a disease based on insulin hormone disorders and causes high blood glucose. Clinical findings determine that diabetes can be diagnosed by electrophysiological signals obtained from the vital organs. 'Diabetic Retinopathy' is one of the most common eye diseases resulting on diabetes and it is the leading cause of vision loss due to structural alteration of the retinal layer vessels. In this study, features of horizontal and vertical Video-Oculography (VOG) signals have been used to classify non-proliferative and proliferative diabetic retinopathy disease. Twenty-five features are acquired by using discrete wavelet transform with VOG signals which are taken from 21 subjects. Two models, based on multi-layer perceptron and radial basis function, are recommended in the diagnosis of Diabetic Retinopathy. The proposed models also can detect level of the disease. We show comparative classification performance of the proposed models. Our results show that proposed the RBF model (100%) results in better classification performance than the MLP model (94%).

Keywords: diabetic retinopathy, discrete wavelet transform, multi-layer perceptron, radial basis function, video-oculography (VOG)

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Authors: Foad Nazari, Seyed Mahmood Hosseini, Mohammad Hossein Abolbashari, Mohammad Hassan Abolbashari

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The present study is concerned with the optimal design of functionally graded plates using particle swarm optimization (PSO) algorithm. In this study, meshless local Petrov-Galerkin (MLPG) method is employed to obtain the functionally graded (FG) plate’s natural frequencies. Effects of two parameters including thickness to height ratio and volume fraction index on the natural frequencies and total mass of plate are studied by using the MLPG results. Then the first natural frequency of the plate, for different conditions where MLPG data are not available, is predicted by an artificial neural network (ANN) approach which is trained by back-error propagation (BEP) technique. The ANN results show that the predicted data are in good agreement with the actual one. To maximize the first natural frequency and minimize the mass of FG plate simultaneously, the weighted sum optimization approach and PSO algorithm are used. However, the proposed optimization process of this study can provide the designers of FG plates with useful data.

Keywords: optimal design, natural frequency, FG plate, hybrid meshless method, MLPG method, ANN approach, particle swarm optimization

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Authors: Mamiza Haq

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This paper examines the relevance of bank capital and charter value on bank insolvency and liquidity risks. Using an unbalanced panel of 2,111 unique local banks across 22 countries over 1998-2012, we find that both bank capital and charter value lower insolvency and liquidity risks, but this effect varies among conventional, Islamic, and Islamic-window banks. The risk constraining effect of bank capital becomes more prominent in the post 2007-2008 global financial crisis. Moreover, the relationships vary when conditioned upon other key bank-specific characteristics. For instance, the effect of capital on risk-reduction diminishes in the presence of high charter value for conventional-G7 and Islamic-window banks, during-GFC and pre-GFC period; respectively. Our findings have important policy implications related to bank safety. The results are robust to a range of robustness tests.

Keywords: bank capital, charter value, risk, financial crisis

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Authors: Ahmed Elbeheri, Tarek Zayed

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Steel bridges deterioration has been one of the problems in North America for the last years. Steel bridges deterioration mainly attributed to the difficult weather conditions. Steel bridges suffer fatigue cracks and corrosion, which necessitate immediate inspection. Visual inspection is the most common technique for steel bridges inspection, but it depends on the inspector experience, conditions, and work environment. So many Non-destructive Evaluation (NDE) models have been developed use Non-destructive technologies to be more accurate, reliable and non-human dependent. Non-destructive techniques such as The Eddy Current Method, The Radiographic Method (RT), Ultra-Sonic Method (UT), Infra-red thermography and Laser technology have been used. Digital Image processing will be used for Corrosion detection as an Alternative for visual inspection. Different models had used grey-level and colored digital image for processing. However, color image proved to be better as it uses the color of the rust to distinguish it from the different backgrounds. The detection of the rust is an important process as it’s the first warning for the corrosion and a sign of coating erosion. To decide which is the steel element to be repainted and how urgent it is the percentage of rust should be calculated. In this paper, an image processing approach will be developed to detect corrosion and its severity. Two models were developed 1st to detect rust and 2nd to detect rust percentage.

Keywords: steel bridge, bridge inspection, steel corrosion, image processing

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Authors: Ojin Kwon, Yong-Jin Yoon, Liu Xin, Zhang Hongbao

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Wireless Body Area Network (WBAN) is a short-range wireless communication around human body for various applications such as wearable devices, entertainment, military, and especially medical devices. WBAN attracts the attention of continuous health monitoring system including diagnostic procedure, early detection of abnormal conditions, and prevention of emergency situations. Compared to cellular network, WBAN system is more difficult to control inter- and inner-cell interference due to the limited power, limited calculation capability, mobility of patient, and non-cooperation among WBANs. In this paper, we compare the performance of resource allocation scheme based on several Pseudo Orthogonal Codewords (POCs) to mitigate inter-WBAN interference. Previously, the POCs are widely exploited for a protocol sequence and optical orthogonal code. Each POCs have different properties of auto- and cross-correlation and spectral efficiency according to its construction of POCs. To identify different WBANs, several different pseudo orthogonal patterns based on POCs exploits for resource allocation of WBANs. By simulating these pseudo orthogonal resource allocations of WBANs on MATLAB, we obtain the performance of WBANs according to different POCs and can analyze and evaluate the suitability of POCs for the resource allocation in the WBANs system.

Keywords: wireless body area network, body sensor network, resource allocation without feedback, interference mitigation, pseudo orthogonal pattern

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Authors: Fadhl Alakwaa

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One of the sustainable goals of the system biology is understanding gene-gene interactions. Hence, gene regulatory networks (GRN) need to be constructed for understanding the disease ontology and to reduce the cost of drug development. To construct gene regulatory from gene expression we need to overcome many challenges such as data denoising and dimensionality. In this paper, we develop an integrated system to reduce data dimension and remove the noise. The generated network from our system was validated via available interaction databases and was compared to previous methods. The result revealed the performance of our proposed method.

Keywords: gene regulatory network, biclustering, denoising, system biology

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Authors: Yavuz Unal, Kemal Polat, H. Erdinc Kocer

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In this study, a decision support system comprising three stages has been proposed to detect the disc abnormalities of the lumbar region. In the first stage named the feature extraction, T2-weighted sagittal and axial Magnetic Resonance Images (MRI) were taken from 55 people and then 27 appearance and shape features were acquired from both sagittal and transverse images. In the second stage named the feature weighting process, k-means clustering based feature weighting (KMCBFW) proposed by Gunes et al. Finally, in the third stage named the classification process, the classifier algorithms including multi-layer perceptron (MLP- neural network), support vector machine (SVM), Naïve Bayes, and decision tree have been used to classify whether the subject has lumbar disc or not. In order to test the performance of the proposed method, the classification accuracy (%), sensitivity, specificity, precision, recall, f-measure, kappa value, and computation times have been used. The best hybrid model is the combination of k-means clustering based feature weighting and decision tree in the detecting of lumbar disc disease based on both sagittal and axial MR images.

Keywords: lumbar disc abnormality, lumbar MRI, lumbar spine, hybrid models, hybrid features, k-means clustering based feature weighting

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Authors: Reza Moslemi, Sebastien Perrier

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Water infrastructure deterioration can result in increased operational costs owing to increased repair needs and non-revenue water and consequently cause a reduced level of service and customer service satisfaction. Various water main condition assessment technologies have been introduced to the market in order to evaluate the level of pipe deterioration and to develop appropriate asset management and pipe renewal plans. One of the challenges for any condition assessment and inspection program is to determine the percentage of the water network and the combination of pipe segments to be inspected in order to obtain a meaningful representation of the status of the entire water network with a desirable level of accuracy. Traditionally, condition assessment has been conducted by selecting pipes based on age or location. However, this may not necessarily offer the best approach, and it is believed that by using a smart sampling methodology, a better and more reliable estimate of the condition of a water network can be achieved. This research investigates three different sampling methodologies, including random, stratified, and systematic. It is demonstrated that selecting pipes based on the proposed clustering and sampling scheme can considerably improve the ability of the inspected subset to represent the condition of a wider network. With a smart sampling methodology, a smaller data sample can provide the same insight as a larger sample. This methodology offers increased efficiency and cost savings for condition assessment processes and projects.

Keywords: condition assessment, pipe degradation, sampling, water main

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Authors: Haoqi Gao, Koichi Ogawara

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Retinal vascular segmentation of color fundus is the basis of ophthalmic computer-aided diagnosis and large-scale disease screening systems. Early screening of fundus diseases has great value for clinical medical diagnosis. The traditional methods depend on the experience of the doctor, which is time-consuming, labor-intensive, and inefficient. Furthermore, medical images are scarce and fraught with legal concerns regarding patient privacy. In this paper, we propose a new Generative Adversarial Network based on CycleGAN for retinal fundus images. This method can generate not only synthetic fundus images but also generate corresponding segmentation masks, which has certain application value and challenge in computer vision and computer graphics. In the results, we evaluate our proposed method from both quantitative and qualitative. For generated segmented images, our method achieves dice coefficient of 0.81 and PR of 0.89 on DRIVE dataset. For generated synthetic fundus images, we use ”Toy Experiment” to verify the state-of-the-art performance of our method.

Keywords: retinal vascular segmentations, generative ad-versarial network, cyclegan, fundus images

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Authors: Talal Alshammari, Nasser Alshammari, Mohamed Sedky, Chris Howard

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With the widespread adoption of the Internet-connected devices, and with the prevalence of the Internet of Things (IoT) applications, there is an increased interest in machine learning techniques that can provide useful and interesting services in the smart home domain. The areas that machine learning techniques can help advance are varied and ever-evolving. Classifying smart home inhabitants’ Activities of Daily Living (ADLs), is one prominent example. The ability of machine learning technique to find meaningful spatio-temporal relations of high-dimensional data is an important requirement as well. This paper presents a comparative evaluation of state-of-the-art machine learning techniques to classify ADLs in the smart home domain. Forty-two synthetic datasets and two real-world datasets with multiple inhabitants are used to evaluate and compare the performance of the identified machine learning techniques. Our results show significant performance differences between the evaluated techniques. Such as AdaBoost, Cortical Learning Algorithm (CLA), Decision Trees, Hidden Markov Model (HMM), Multi-layer Perceptron (MLP), Structured Perceptron and Support Vector Machines (SVM). Overall, neural network based techniques have shown superiority over the other tested techniques.

Keywords: activities of daily living, classification, internet of things, machine learning, prediction, smart home

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Authors: Jong-Jy Shyu, Soo-Chang Pei, Yun-Da Huang

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In this paper, design of wide-range variable fractional-delay (WR-VFD) finite impulse response (FIR) digital filters is proposed. With respect to the conventional VFD filter which is designed such that its delay is adjustable within one unit, the proposed VFD FIR filter is designed such that its delay can be tunable within a wider range. By the traces of coefficients of the fractional-delay FIR filter, it is found that the conventional method of polynomial substitution for filter coefficients no longer satisfies the design demand, and the circuits perform the sinc function (sinc converter) are added to overcome this problem. In this paper, least-squares method is adopted to design WR-VFD FIR filter. Throughout this paper, several examples will be proposed to demonstrate the effectiveness of the presented methods.

Keywords: digital filter, FIR filter, variable fractional-delay (VFD) filter, least-squares approximation

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Authors: Li Guo, Jianhong Wang, Dian Huang, Shengzhong Feng

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Computing networks as a new network architecture has shown great promise in boosting the utilization of different resources, such as computing, caching, and communications. To maximise the efficiency of resource orchestration in computing network control systems (CNCSs), this work proposes a dynamic orchestration strategy of a different resource based on task requirements from computing power requestors (CPRs). Specifically, computing power providers (CPPs) in CNCSs could share information with each other through communication channels on the basis of blockchain technology, especially their current idle resources. This dynamic process is modeled as a cooperative game in which CPPs have the same target of maximising long-term rewards by improving the resource utilization ratio. Meanwhile, the task requirements from CPRs, including size, deadline, and calculation, are simultaneously considered in this paper. According to task requirements, the proposed orchestration strategy could schedule the best-fitting resource in CNCSs, achieving the maximum long-term rewards of CPPs and the best quality of experience (QoE) of CRRs at the same time. Based on the EdgeCloudSim simulation platform, the efficiency of the proposed strategy is achieved from both sides of CPRs and CPPs. Besides, experimental results show that the proposed strategy outperforms the other comparisons in all cases.

Keywords: computing network control systems, resource orchestration, dynamic scheduling, blockchain, cooperative game

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Authors: Ahmad E. Aldousaria, Abdulla Al Kafy

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Urbanization is an incessant process that involves the transformation of land use/land cover (LULC), resulting in a reduction of cool land covers and thermal comfort zones (TCZs). This study explores the directional shrinkage of TCZs in Kuwait using Landsat satellite data from 1991 – 2021 to predict the future LULC and TCZ distribution for 2026 and 2031 using cellular automata (CA) and artificial neural network (ANN) algorithms. Analysis revealed a rapid urban expansion (40 %) in SE, NE, and NW directions and TCZ shrinkage in N – NW and SW directions with 25 % of the very uncomfortable area. The predicted result showed an urban area increase from 44 % in 2021 to 47 % and 52 % in 2026 and 2031, respectively, where uncomfortable zones were found to be concentrated around urban areas and bare lands in N – NE and N – NW directions. This study proposes an effective and sustainable framework to control TCZ shrinkage, including zero soil policies, planned landscape design, manmade water bodies, and rooftop gardens. This study will help urban planners and policymakers to make Kuwait an eco–friendly, functional, and sustainable country.

Keywords: land cover change, thermal environment, green cover loss, machine learning, remote sensing

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Authors: Abhishek Jain, Kavish Kejriwal, V. Balaji Rao, Abhigna Chavda

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Increasing environmental and social concerns are forcing companies to take a fresh view of the impact of supply chain operations on environment and society when designing a supply chain. A challenging task in today’s food industry is the distribution of high-quality food items throughout the food supply chain. Improper storage and unwanted transportation are the major hurdles in food supply chain and can be tackled by making dynamic storage facility location decisions with the distribution network. Since food supply chain in India is one of the biggest supply chains in the world, the companies should also consider environmental impact caused by the supply chain. This project proposes a multi-objective optimization model by integrating sustainability in decision-making, on distribution in a food supply chain network (SCN). A Multi-Objective Mixed-Integer Linear Programming (MOMILP) model between overall cost and environmental impact caused by the SCN is formulated for the problem. The goal of MOMILP is to determine the pareto solutions for overall cost and environmental impact caused by the supply chain. This is solved by using GAMS with CPLEX as third party solver. The outcomes of the project are pareto solutions for overall cost and environmental impact, facilities to be operated and the amount to be transferred to each warehouse during the time horizon.

Keywords: multi-objective mixed linear programming, food supply chain network, GAMS, multi-product, multi-period, environment

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Authors: Reema Jain

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Nanofluids are emergent fluids that exhibit thermal properties superior than that of the conventional fluid. Nanofluids are suspensions of nanoparticles in fluids that show significant enhancement of their properties at modest nanoparticle concentrations. Solar collectors are commonly used in areas such as industries, heating, and cooling for domestic purpose, thermal power plants, solar cooker, automobiles, etc. Performance and efficiency of solar collectors depend upon various factors like collector & receiver material, solar radiation intensity, nature of working fluid, etc. The properties of working fluid which flow through the collectors greatly affects its performance. In this research work, a theoretical effort has been made to enhance the efficiency and improve the performance of solar collector by using Nano fluids instead of conventional fluid like water as working fluid.

Keywords: nanofluids, nanoparticles, heat transfer, solar collector

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Authors: Mukesh K. Dwivedi, Rajeshwar N. Srivastava, Amit K. Bhagat, Saloni Raj

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Introduction: Management of Pressure Ulcer (PU) is an ongoing clinical challenge particularly in traumatic paraplegia patients in developing countries where socio economic conditions often dictate treatment modalities. When negative pressure wound therapy (NPWT) was introduced, there were a series of devices (V.A.C., KCI, San Antonio, TX) manufactured. These devices for NPWT are costly and hard to afford by patients in developing countries like India. Considering this limitation, this study was planned to design an RCT to compare NPWT by an indigenized locally constructed NPD and conventional gauze dressing for the treatment of PU. Material and Methods: This RCT (CTRI/2014/09/0050) was conducted in the Department of Orthopaedic Surgery at King George’s Medical University (KGMU), India. Thirty-four (34) subjects of traumatic paraplegia having PU of stage 3 or 4, were enrolled and randomized in two treatment groups (NPWT Group & Conventional dressing group). The outcome measures of this study were surface area and depth of PU, exudates, microorganisms and matrix metalloproteinase-8 (MMP-8) during 0 to 9 weeks follow-ups. Levels of MMP-8 were analyzed in the tissues of PU at week 0, 3, 6 and week 9 by Enzyme Linked Immuno Sorbent Assay (ELISA). Results: Significantly reduced length of PU in NPWT group was observed at week 6 (p=0.04) which further reduced at week 9 (p=0.001) as compared to conventionally treated group. Similarly significant reduction of width and depth of PU was observed in NPWT at week 9 (p<0.05). The exudate became significantly (p=0.001) lower in NPWT group as compared with conventionally treated group from 6th to 9th week. Clearance and conversion of slough into red granulation tissue was significantly higher in NPWT group (p=0.001). At week 9, the wound culture was negative in all the subjects of NPWT group, while it was positive in 10 (41⋅6%) subjects of conventional group. Significantly lower level of MMP-8 was observed in subjects of NPWT group at week 6 (0.006**), and continually more reduction was observed at week 9 (<0.0001**) as compared to the conventional group. Conclusion: NPWT by locally constructed NPD is better wound care procedure for management of PU. Our device gave similar results as commercially available devices. Reduction of level of MMP-8 and increased rate of healing was achieved by negative pressure wound therapy (NPWT) as compared to conventional dressing.

Keywords: NPWT, NPD, MMP8, ELISA

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Authors: Emmanuel Iyamuremye, Jean François Maniriho, Irenee Ndayambaje

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Mathematics is the foundational enabling discipline that underpins science, technology, and engineering disciplines. Science, technology, engineering, and mathematics (STEM) subjects are foreseen as the engine for socio-economic transformation. Rwanda has done reforms in education aiming at empowering and preparing students for the real world job by providing career pathways in science, technology, engineering, and mathematics related fields. While that considered so, the performance in mathematics has remained deplorable in both formative and national examinations. Therefore, this paper aims at exploring the extent to which the engage, explore, explain, elaborate and evaluate (5Es) instructional model contributing towards students’ achievement in mathematics. The present study adopted the pre-test, post-test non-equivalent control group quasi-experimental design. The 5Es instructional model was applied to the experimental group while the control group received instruction with the conventional teaching method for eight weeks. One research-made instrument, mathematics achievement test (MAT), was used for data collection. A pre-test was given to students before the intervention to make sure that both groups have equivalent characteristics. At the end of the experimental period, the two groups have undergone a post-test to ascertain the contribution of the 5Es instructional model. Descriptive statistics and analysis of covariance (ANCOVA) were used for the analysis of the study. For determining the improvement in mathematics, Hakes methods of calculating gain were used to analyze the pre-test and post-test scores. Results showed that students exposed to 5Es instructional model achieved significantly better performance in mathematics than students instructed using the conventional teaching method. It was also found that 5Es instructional model made lessons more interesting, easy and created friendship among students. Thus, 5Es instructional model was recommended to be adopted as a close substitute to the conventional teaching method in teaching mathematics in lower secondary schools in Rwanda.

Keywords: 5Es instructional model, achievement, conventional teaching method, mathematics

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Authors: Liu Yinga, Bai Xingjiab

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The conventional way to exploit coalbed methane is to drop reservoirs pressure through drainage, which means that reducing pressure through water drainage for coalbed methane desorption. However, it has many limitations. In this paper, the recovery by conventional way is low, in order to exploit water-sensitive reservoir, combustion of coal seam is proposed to increase recovery ratio, and then theoretical feasibility is elaborated through four aspects: temperature, pressure, superficial area, competitive adsorption, then given an example of water sensitive reservoir, results can be obtained that recovery is effectively improved through combustion of coal seam. At the same time, the suitability and efficiency of combustion of coal seam determine that it can be widely applied.

Keywords: coalbed methane, drainage decompression, water-sensitive, combustion of coal seams, competitive adsorption

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Authors: Nasim Fallahi, Erasmo Carrera, Alfonso Pagani

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Variable angle tow describes the fibres which are curvilinearly steered in a composite lamina. Significantly, stiffness tailoring freedom of VAT composite laminate can be enlarged and enabled. Composite structures with curvilinear fibres have been shown to improve the buckling load carrying capability in contrast with the straight laminate composites. However, the optimal design and analysis of VAT are faced with high computational efforts due to the increasing number of variables. In this article, an efficient optimum solution has been used in combination with 1D Carrera’s Unified Formulation (CUF) to investigate the optimum fibre orientation angles for buckling analysis. The particular emphasis is on the LE-based CUF models, which provide a Lagrange Expansions to address a layerwise description of the problem unknowns. The first critical buckling load has been considered under simply supported boundary conditions. Special attention is lead to the sensitivity of buckling load corresponding to the fibre orientation angle in comparison with the results which obtain through the Genetic Algorithm (GA) optimization frame and then Artificial Neural Network (ANN) is applied to investigate the accuracy of the optimized model. As a result, numerical CUF approach with an optimal solution demonstrates the robustness and computational efficiency of proposed optimum methodology.

Keywords: beam structures, layerwise, optimization, variable stiffness

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Authors: Chia-Chi Chang

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Most battery-driven wireless sensor devices will enter in sleep mode as soon as possible to extend the overall lifetime of a sensor network. It is necessary to turn off unnecessary radio and peripheral functions, especially the radio unit always consumes more energy than other components during wireless communication. The microcontroller is the most important part of the wireless sensor device. It is responsible for the manipulation of sensing data and communication protocols. The microcontroller always has different sleep modes, each with a different level of energy usage. The deeper the sleep, the lower the energy consumption. Most wireless sensor devices can only enter the sleep mode: the external low-frequency oscillator is still running to wake up the sleeping microcontroller when the sleep timer expires. In this paper, our sensor device can enter the extended sleep mode: none of the oscillator is running and the wireless sensor device has the nanoampere consumption and self-awaking ability. Finally, these wireless sensor devices were deployed in a smart home security network.

Keywords: wireless sensor network, battery-driven, sleep mode, home security

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Authors: S. Farid F. Mojtahedi, Ali Khosravi, Behnaz Naeimian, S. Adel A. Hosseini

Abstract:

Land subsidence is a gradual settling or sudden sinking of the land surface from changes that take place underground. There are different causes of land subsidence; most notably, ground-water overdraft and severe weather conditions. Subsidence of the land surface due to ground water overdraft is caused by an increase in the intergranular pressure in unconsolidated aquifers, which results in a loss of buoyancy of solid particles in the zone dewatered by the falling water table and accordingly compaction of the aquifer. On the other hand, exploitation of underground water may result in significant changes in degree of saturation of soil layers above the water table, increasing the effective stress in these layers, and considerable soil settlements. This study focuses on estimation of soil moisture at surface using different methods. Specifically, different methods for the estimation of moisture content at the soil surface, as an important term to solve Richard’s equation and estimate soil moisture profile are presented, and their results are discussed through comparison with field measurements obtained from Yanco1 station in south-eastern Australia. Surface soil moisture is not easy to measure at the spatial scale of a catchment. Due to the heterogeneity of soil type, land use, and topography, surface soil moisture may change considerably in space and time.

Keywords: artificial neural network, empirical method, remote sensing, surface soil moisture, unsaturated soil

Procedia PDF Downloads 359

Authors: Atef Ibrahim, Hamed Elsimary, Abdullah Aljumah, Fayez Gebali

Abstract:

This paper presents a novel semi-systolic array architecture for an optimized parallel sequence alignment algorithm. This architecture has the advantage that it can be modified to be reused for multiple pass processing in order to increase the number of processing elements that can be packed into a single FPGA and to increase the number of sequences that can be aligned in parallel in a single FPGA. This resolves the potential problem of many FPGA resources left unused for designs that have large values of short read length. When using the previously published conventional hardware design. FPGA implementation results show that, for large values of short read lengths (M>128), the proposed design has a slightly higher speed up and FPGA utilization over the the conventional one.

Keywords: bioinformatics, genome sequence alignment, re-sequencing applications, systolic array

Procedia PDF Downloads 531

Authors: Natela Gerliani, Mohammed Aider

Abstract:

Nowadays, the interest of using sustainable technologies for protein extraction from underutilized oilseeds is growing. Currently, a major disposal problem for the oil industry is by-products of plant food processing such as soybean meal. That is why valorization of soybean meal is important for the oil industry since it contains high-quality proteins and other valuable components. Generally, soybean meal is used in livestock and poultry feed but is rarely used in human feed. Though chemical composition of this meal compensate nutritional deficiency and can be used to balance protein in human food. Regarding the efficiency of soybean meal valorization, extraction is a key process for obtaining enriched protein ingredient, which can be incorporated into the food matrix. However, most of the food components such as proteins extracted from oilseeds by-products imply the utilization of organic and inorganic chemicals (e.g. acids, bases, TCA-acetone) having a significant environmental impact. In a context of sustainable production, the use of an electro-activation technology seems to be a good alternative. Indeed, the electro-activation technology requires only water, food grade salt and electricity as main materials. Moreover, this innovative technology helps to avoid special equipment and trainings for workers safety as well as transport and storage of hazardous materials. Electro-activation is a technology based on applied electrochemistry for the generation of acidic and alkaline solutions on the basis of the oxidation-reduction reactions that occur at the vicinity electrode/solution interfaces. It is an eco-friendly process that can be used to replace the conventional acidic and alkaline extraction. In this research, the electro-activation technology for protein extraction from soybean meal was carried out in the electro-activation reactor. This reactor consists of three compartments separated by cation and anion exchange membranes that allow creating non-contacting acidic and basic solutions. Different current intensities (150 mA, 300 mA and 450 mA) and treatment durations (10 min, 30 min and 50 min) were tested. The results showed that the extracts obtained by the electro-activation method have good quality in comparison to conventional extracts. For instance, extractability obtained with electro-activation method was 55% whereas with the conventional method it was only 36%. Moreover, a maximum protein quantity of 48 % in the extract was obtained with the electro-activation technology comparing to the maximum amount of protein obtained by conventional extraction of 41 %. Hence, the environmentally sustainable electro-activation technology seems to be a promising type of protein extraction that can replace conventional extraction technology.

Keywords: by-products, eco-friendly technology, electro-activation, soybean meal

Procedia PDF Downloads 228

Authors: Shahriar Shahbazpanahi, Alaleh Kamgar

Abstract:

So far, the conventional experimental and theoretical analysis in fracture mechanics have been applied to study concrete flexural- cracked beams, which are strengthened using fiber reinforced polymer (FRP) composite sheets. However, there is still little knowledge about the shear capacity of a side face FRP- strengthened shear-cracked beam. A numerical analysis is herein presented to model the fracture mechanics of a four-point RC beam, with two inclined initial notch on the supports, which is strengthened with side face FRP sheets. In the present study, the shear crack is forced to conduct by using an initial notch in supports. The ABAQUS software is used to model crack propagation by conventional cohesive elements. It is observed that the FRP sheets play important roles in preventing the propagation of shear cracks.

Keywords: crack, FRP, shear, strengthening

Procedia PDF Downloads 550