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Showing papers in "The Journal of Engineering in 2020"


Journal ArticleDOI
TL;DR: In this paper, the authors present a review of the main concepts that lead to determining the strategic approach, creation of strategies, organizational structures, strategy formulation, and strategic evaluation as a guide for the organizational management, taking into account the effects produced by different types of strategies on the performance of organizations.
Abstract: The objective of this work is to review the literature of the main concepts that lead to determining the strategic approach, creation of strategies, organizational structures, strategy formulation, and strategic evaluation as a guide for the organizational management, taking into account the effects produced by the different types of strategies on the performance of organizations. In this article, the systemic literature review method was used to synthesize the result of multiple investigations and scientific literature. The process of reading and analysis of the literature was carried out through digital search engines with keywords in areas related to the strategic management. This research reveals the lack of scientific literature containing important theoretical concepts that serve the strategists as a guide in the creation, formulation, and evaluation of strategies. This review contributes to the existing literature by examining the impact of the strategic management on the organizational performance.

91 citations


Journal ArticleDOI
TL;DR: In this paper, a comprehensive literature survey covering the field of engineering, production, and management was done in multidisciplinary databases: Google Scholar, Science Direct, Scopus, Sage, Taylor & Francis, and Emerald Insight.
Abstract: The 21st century has witnessed precipitous changes spanning from the way of life to the technologies that emerged. We have entered a nascent paradigm shift (industry 4.0) where science fictions have become science facts, and technology fusion is the main driver. Thus, ensuring that any advancement in technology reach and benefit all is the ideal opportunity for everyone. In this study, disruptive technologies of industry 4.0 were explored and quantified in terms of the number of their appearances in published literature. The study aimed at identifying industry 4.0 key technologies which have been ill-defined by previous researchers and to enumerate the required skills of industry 4.0. Comprehensive literature survey covering the field of engineering, production, and management was done in multidisciplinary databases: Google Scholar, Science Direct, Scopus, Sage, Taylor & Francis, and Emerald Insight. From the electronic survey, 35 disruptive technologies were quantified and 13 key technologies: Internet of Things, Big Data, 3D printing, Cloud computing, Autonomous robots, Virtual and Augmented reality, Cyber-physical system, Artificial intelligence, Smart sensors, Simulation, Nanotechnology, Drones, and Biotechnology were identified. Both technical and personal skills to be imparted into the human workforce for industry 4.0 were reported. The review identified the need to investigate the capability and the readiness of developing countries in adapting industry 4.0 in terms of the changes in the education systems and industrial manufacturing settings. This study proposes the need to address the integration of industry 4.0 concepts into the current education system.

83 citations


Journal ArticleDOI
TL;DR: In this paper, a study aimed at identifying industry 4.0 neologisms and illustrating the convergence of 12 disruptive technologies including 3D printing, artificial intelligence, augmented reality, big data, blockchain, cloud computing, drones, Internet of Things, nanotechnology, robotics, simulation, and synthetic biology in agriculture, healthcare, and logistics industries was illustrated.
Abstract: Very well into the dawn of the fourth industrial revolution (industry 4.0), humankind can hardly distinguish between what is artificial and what is natural (e.g., man-made virus and natural virus). Thus, the level of discombobulation among people, companies, or countries is indeed unprecedented. The fact that industry 4.0 is explosively disrupting or retrofitting each and every industrial sector makes industry 4.0 the famous buzzword amongst researchers today. However, the insight of industry 4.0 disruption into the industrial sectors remains ill-defined in both academic and nonacademic literature. The present study aimed at identifying industry 4.0 neologisms, understanding the industry 4.0 disruption and illustrating the disruptive technology convergence in the major industrial sectors. A total of 99 neologisms of industry 4.0 were identified. Industry 4.0 disruption in the education industry (education 4.0), energy industry (energy 4.0), agriculture industry (agriculture 4.0), healthcare industry (healthcare 4.0), and logistics industry (logistics 4.0) was described. The convergence of 12 disruptive technologies including 3D printing, artificial intelligence, augmented reality, big data, blockchain, cloud computing, drones, Internet of Things, nanotechnology, robotics, simulation, and synthetic biology in agriculture, healthcare, and logistics industries was illustrated. The study divulged the need for extensive research to expand the application areas of the disruptive technologies in the industrial sectors.

77 citations


Journal ArticleDOI
TL;DR: In this article, a review of commercially existing microscopic traffic simulation frameworks built to evaluate real-world traffic scenario is presented, where the significant contributions made by 2D models in evaluating the lateral and longitudinal vehicle behaviour simultaneously.
Abstract: The area of traffic flow modelling and analysis that bridges civil engineering, computer science, and mathematics has gained significant momentum in the urban areas due to increasing vehicular population causing traffic congestion and accidents. Notably, the existence of mixed traffic conditions has been proven to be a significant contributor to road accidents and congestion. The interaction of vehicles takes place in both lateral and longitudinal directions, giving rise to a two-dimensional (2D) traffic behaviour. This behaviour contradicts with the traditional car-following (CF) or one-dimensional (1D) lane-based traffic flow. Existing one-dimensional CF models did the inclusion of lane changing and overtaking behaviour of the mixed traffic stream with specific alterations. However, these parameters cannot describe the continuous lateral manoeuvre of mixed traffic flow. This review focuses on all the significant contributions made by 2D models in evaluating the lateral and longitudinal vehicle behaviour simultaneously. The accommodation of vehicle heterogeneity into the car-following models (homogeneous traffic models) is discussed in detail, along with their shortcomings and research gaps. Also, the review of commercially existing microscopic traffic simulation frameworks built to evaluate real-world traffic scenario are presented. This review identified various vehicle parameters adopted by existing CF models and whether the current 2D traffic models developed from CF models effectively captured the vehicle behaviour in mixed traffic conditions. Findings of this study are outlined at the end.

32 citations


Journal ArticleDOI
TL;DR: The result shows that the CNN-LSTM hybrid neural network can effectively improve the accuracy of value prediction and direction prediction compared with the single structure neural network.
Abstract: In this study, aiming at the problem that the price of Bitcoin varies greatly and is difficult to predict, a hybrid neural network model based on convolutional neural network (CNN) and long short-term memory (LSTM) neural network is proposed. The transaction data of Bitcoin itself, as well as external information, such as macroeconomic variables and investor attention, are taken as input. Firstly, CNN is used for feature extraction. Then the feature vectors are input into LSTM for training and forecasting the short-term price of Bitcoin. The result shows that the CNN-LSTM hybrid neural network can effectively improve the accuracy of value prediction and direction prediction compared with the single structure neural network. The finding has important implications for researchers and investors in the digital currencies market.

31 citations


Journal ArticleDOI
TL;DR: Experimental results show that both distributed and centralised approaches can ultimately solve the pursuit-evasion problem in different dimensions, but the learning efficiency and coordination performance of the proposed distributed approach are much better than the traditional centralised approach.
Abstract: As a popular research topic in the area of distributed artificial intelligence, the multi-robot pursuit problem is widely used as a testbed for evaluating coordinated and cooperative strategies in multi-robot systems. This study the problem of multi-robot pursuit game using reinforcement learning (RL) techniques is studied. Unlike most existing studies that apply fully centralised deep RL methods based on the centralised-learning and decentralised-execution scheme, the authors propose a fully decentralised multi-agent deep RL approach by modelling each agent as an individual deep RL agent that has its own individual learning system (i.e. individual action-value function, individual leaning update process, and individual action output). To realise coordination among agents, the limited information of other environmental agents is used as input of the learning process. Experimental results show that both distributed and centralised approaches can ultimately solve the pursuit-evasion problem in different dimensions, but the learning efficiency and coordination performance of the proposed distributed approach are much better than the traditional centralised approach.

27 citations


Journal ArticleDOI
TL;DR: Unlike conventional approaches that require pixel-by-pixel processing, the RBCNN method proposed in this study firstly locates the defects and then classifies them by deep neural networks, which shows superior performance on the dataset.
Abstract: National key RD Specialized Research Fund for Strategic and Prospective Industrial Development of Shenzhen City [ZLZBCXLJZI20160729020003]

27 citations


Journal ArticleDOI
TL;DR: This study suggests that techniques presented for single-cell analysis (SCA) are always precise and hold great scope compared to those for cell mixture, as it not only detects the cells but also helps in identification.
Abstract: Electrical detection of biotic materials and their properties is always crucial in Biomedical Engineering. It emphasizes elaborate analysis of a certain disease diagnosis using electrical means which includes the study of electrical properties followed by detection, identification, and quantification of biotic entities. Proper and early detection of cells, which can be normal or cancerous, holds never-ending demand. This review emphasizes the electrical characterization methods in modeling and classification of cell properties. Moreover, standard methods have been highlighted and discussed to yield performance analysis. This study suggests that techniques presented for single-cell analysis (SCA) are always precise and hold great scope compared to those for cell mixture. SCA plays a promising role in disease diagnosis and treatment planning, as it not only detects the cells but also helps in identification. Distinct electrical-based cell detection techniques are highlighted, along with the pros and cons of various SCA devices. The content, in terms of methodologies and available techniques, of this review paper is useful to attract researchers working in this area.

25 citations


Journal ArticleDOI
TL;DR: The artificial fish swarm algorithm-back propagation (AFSA-BP) neural network structure was designed based on AFSA and BP neural network theory and the related mathematical model was established.
Abstract: The non-linear characteristic of power lithium battery restricts the establishment of accurate battery models. To overcome this problem and estimate the battery state of charge (SOC) more accurately, the artificial fish swarm algorithm-back propagation (AFSA-BP) neural network structure was designed based on AFSA and BP neural network theory. According to the test parameters of power lithium battery, the related mathematical model was established. The flow charts of optimising BP neural network with AFSA algorithm and estimating SOC value by AFSA-BP algorithm are given. The specific implementation steps are elaborated. Using the 48 V, 50 Ah lithium iron phosphate (LiFePO4) power battery as experimental object, through the periodic charging and discharging experiments and software simulation, the correctness, validity and accuracy of the application of AFSA-BP neural network in estimating SOC value of the power lithium battery are verified.

21 citations


Journal ArticleDOI
TL;DR: An understanding of 3D printing procedure, mechanism of action, and its impact on the future of construction and architecture through economical, structural, and environmental parameters is achieved and leads to encourage engineers and contractors to take this subject into account for construction in Jordan.
Abstract: Three-dimensional (3D) printing is a procedure used to create 3D objects in which consecutive layers of a material are computer-controlled produced. Such objects can be constructed in any shape using digital model data. First, this paper presents a state-of-the-art review of the advances in 3D printing processes of construction. Then, the architectural, economical, environmental, and structural features of 3D printing are introduced. Examples of 3D printed structures are presented, and the construction challenges facing Jordan, that encouraged this study, are stated. Finally, a precise description regarding the impact of 3D printing is provided by comparing conventional construction data of Ras Alain Multipurpose Hall in Jordan and the expected data if the same building has been built using 3D printing. The suggested model is generated using Revit software. As a result of this study, an understanding of 3D printing procedure, mechanism of action, and its impact on the future of construction and architecture through economical, structural, and environmental parameters is achieved. This leads to encourage engineers and contractors to take this subject into account for construction in Jordan.

21 citations


Journal ArticleDOI
TL;DR: A survey on different applications of Wi-Fi based sensing systems such as elderly people monitoring, activity classification, gesture recognition, people counting, through the wall sensing, behind the corner sensing, and many other applications is presented.
Abstract: Wi-Fi technology has strong potentials in indoor and outdoor sensing applications; it has several important features which makes it an appealing option compared to other sensing technologies. This study presents a survey on different applications of Wi-Fi-based sensing systems such as elderly people monitoring, activity classification, gesture recognition, people counting, through the wall sensing, behind the corner sensing, and many other applications. The challenges and interesting future directions are also highlighted.

Journal ArticleDOI
TL;DR: An ad hoc TROPHY (TAD-HOC) routing protocol for the VANET network for increasing efficiency and effective resource utilization of the network and comparative analysis of the proposed approach shows that the proposed TAD- HOC exhibited effective performance.
Abstract: Intelligent Transportation System (ITS) is a critical factor for Vehicular Ad hoc Networks (VANET). Even though VANET belongs to the class of Mobile Ad hoc Network (MANET), none of the MANET routing protocol applies to VANET. VANET network is dynamic, due to increased vehicle speed and mobility. Vehicle mobility of VANET affects conventional routing algorithm performance, which deals with the dynamicity of the network node. The evaluation of the existing research stated that Ad hoc On-Demand Distance Vector (AODV) is an effective MANET protocol to adopt network changes for significant resource utilization and also provides effective adaptation in the network change. Due to the effective performance of the AODV protocol, it is considered as an effective routing protocol for VANET. This paper proposed an ad hoc TROPHY (TAD-HOC) routing protocol for the VANET network for increasing efficiency and effective resource utilization of the network. To improve the overall performance, ad hoc network is combined with Trustworthy VANET ROuting with grouP autHentication keYs (TROPHY) protocol. The proposed TAD-HOC protocol transmits data based on time demand in the VANET network with the desired authentication. Results of the proposed approach show the increased performance of the VANET network with packet delay, transmission range, and end-to-end delay. The comparative analysis of the proposed approach with I-AODV, AODV-R, and AODV-L shows that the proposed TAD-HOC exhibited effective performance.

Journal ArticleDOI
TL;DR: In this article, the results of the proposition of a hydraulic model in order to determine the roughness coefficient (Manning's coefficient n) of the Tigris River along 3.5 km within the Maysan Governorate, south of Iraq were submitted.
Abstract: In understanding the hydraulic characteristics of river system flow, the hydraulic simulation models are essential tools. This study submits the results of the proposition of a hydraulic model in order to determine the roughness coefficient (Manning’s coefficient n) of the Tigris River along 3.5 km within the Maysan Governorate, south of Iraq. HEC-RAS software was the simulation tool used in this study. The HEC-RAS model was adopted, calibrated, and validated in adopting two sets of observed water levels. Graphical and statistical approaches were used for model calibration and verification. Results from this investigation showed that a value of Manning’s coefficient of 0.025 gave an acceptable agreement between observed and simulated values of water levels.

Journal ArticleDOI
TL;DR: The architectures of two innovative compact orientation independent chipless radio frequency identification device (RFID) tags for emerging applications such as the internet of things are presented.
Abstract: The architectures of two innovative compact orientation independent chipless radio frequency identification device (RFID) tags for emerging applications such as the internet of things are presented. These tags, when illuminated, generate resonant frequencies in the radar cross-section backscattered spectrum, which are used to encode the data. These are based around L-type resonators, which can be read from front and back using linear polarisation waves as they do not have the ground plane. The first tag design consists of L-resonators in the lower triangular of the substrate, and thereby mutual coupling is increased as a reduction in the size. The second tag design incorporates alternate L-resonators in both halves of the substrate and exhibits reduced mutual coupling and enhanced printing strength but with a reduction in bit density. The proposed concept is demonstrated through prototypes of 8-bit chipless tags on Rogers substrate. These tags require very low bandwidth to encode 8-bit and occupy a small board size of 20 mm × 20 mm, and these are considerable improvements in the development of chipless RFID.

Journal ArticleDOI
TL;DR: The proposed detector can rapidly detect tiny defects and the results of SSD and SSDT are further compared not only in PCB defect dataset but also the object detection public dataset PASCAL VOC2007 where SSDT achieves 81.3% mAP, better than SSD (79.5%).
Abstract: Printed circuit board (PCB) defect detection is one of the primary problems in quality control of the most electronic products. Usually, the industrial PCB imagery has high resolution, but defects take up a small proportion (often only ∼10 pixels in size), which makes it difficult to use traditional machine vision methods. To this end, a novel single shot object detector (SSDT) is proposed for tiny defect detection in PCBs in this study. Specifically, a semantic ascending module, which propagates the semantic property of deep layers to shallow layers, is presented by fusing features of different levels. An attention mechanism is utilised to learn the relationship of the features to be fused across channels and a shuffle module is used to eliminate the aliasing effect after fusion. Moreover, the improved non-maximum suppression is proposed to extenuate the overlap effect for testing the whole PCB image. The proposed detector can rapidly detect tiny defects and the results of SSD and SSDT are further compared not only in PCB defect dataset but also the object detection public dataset PASCAL VOC2007 where SSDT achieves 81.3% mAP, better than SSD (79.5%). In final, the proposed detector is validated to be robust to rotation and blur.

Journal ArticleDOI
TL;DR: In this paper, the effect of replacing all normal-weight aggregate (NWA) by lightweight aggregate (LWA) on the behavior of layered steel fibrous self-compacting reinforced concrete slabs with various volume fractions of steel fiber under uniform area load using fine sand technique was investigated.
Abstract: In this research, an attempt has been made to study the effect of replacing all normal-weight aggregate “NWA” by lightweight aggregate “LWA” (having a volume equal to 60% of the volume of normal-weight aggregate) on the behaviour of layered steel fibrous self-compacting reinforced concrete slabs with various volume fractions of steel fiber under uniform area load using fine sand technique. The experimental work consists of two groups “NWA” and “LWA,” each group consists of three slab specimens (having an aspect ratio equal to the golden ratio, i.e., 1.618), the thickness of each slab is divided into two equal layers, the top layer is free from steel fibers, while the steel fibers exist only in the bottom layer with three volume fractions (0%, 0.4%, and 0.8%). Ultimate uniform load of the slabs decreases with the increase in steel fiber content, while the percentage of decrease in the bulk density remains rather constant. It was also found that the ultimate uniform load of the slabs in each group is significantly improved with increasing steel fiber content, and the percentage of this improvement is higher in lightweight concrete “LWC” than in normal-weight concrete “NWC” Finally, it was noticed that when steel fiber increased, the flexural strength of slabs increased higher than shear strength; therefore, the mode of failure has been changed from bending to shear mode for slabs of both groups “NWC” and “LWC.”

Journal ArticleDOI
TL;DR: G gelatin-based hydrogels could find applications in drug delivery carrier, bioink, transdermal therapy, wound healing, and tissue repair because of its nonimmunogenicity, nontoxicity, low cost, and high availability.
Abstract: Hydrogels are hydrophilic polymer networks that absorb any kind of liquid including biological fluids. Natural polymers and their derivatives along with synthetic polymers are used to form the hydrogels. Networks that constitute the hydrogels are created by the crosslinking of either synthesized polymers starting from monomers or already developed polymers. Crosslinking can be developed either physically if secondary intermolecular forces are involved or chemically in which a covalent bond between polymeric chains is created. Gelatins are natural driven protein polymers. One of the main biopolymers used for producing hydrogels is gelatin. Gelatin has a very wide application other than hydrogels. In this review, hydrogels and their property and synthesis mechanism, as well as their application in biomedical along with gelatin chemistry and application, are reviewed. Due to its nonimmunogenicity, nontoxicity, low cost, and high availability gelatin-based hydrogels could find applications in drug delivery carrier, bioink, transdermal therapy, wound healing, and tissue repair. The beneficiation of gelatin can result in their sustainable conversion into high-value biomaterials on the proviso of the existence or development of cost-effective, sustainable technologies for converting this biopolymer into useful bioproducts.

Journal ArticleDOI
TL;DR: In this paper, a fault-tolerant control strategy for power electronics inverters for the integration of PV systems into power systems is proposed, which is a supervisory mechanism designed to aid PV systems to continue their operation during faults.
Abstract: Solar photovoltaic (PV) is a prominent technology for the generation of electricity and its utility is on the rise. The PV-based generation facilities are susceptible to faults which if mismanaged, can result in an interruption in the supply of load demand and damage to the system. Faults in PV systems are caused by a broad range of reasons and hence, it is crucial to situate a fault-tolerant system for reliable operation. This study proposes a fault-tolerant control strategy for power electronics inverters for the integration of PV systems into power systems. This is a supervisory mechanism designed to aid PV systems to continue their operation during faults. A computer simulation verifies the performance of the proposed control strategy under a series of common fault conditions assuming a wide range of configurations for the PV system and load variations.

Journal ArticleDOI
TL;DR: Long Short-Term Memory as a deep neural network has been used for training the model combined with word embedding as a first hidden layer for features extracting and the results show an accuracy of about 82% is achievable using DL method.
Abstract: Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other languages like English. The proposed model tackles Arabic Sentiment Analysis (ASA) by using a DL approach. ASA is a challenging field where Arabic language has a rich morphological structure more than other languages. In this work, Long Short-Term Memory (LSTM) as a deep neural network has been used for training the model combined with word embedding as a first hidden layer for features extracting. The results show an accuracy of about 82% is achievable using DL method.

Journal ArticleDOI
Zhiyuan Li1, Yi Xiao1, Qi Wu1, Min Jin1, Huaxiang Lu 
TL;DR: A novel method for learning siamese neural network which employ a special structure to predict the similarity between handwritten Chinese characters and template images and has a very promising generalization ability to the new classes that never appear in the training set.
Abstract: Just like its remarkable achievements in many computer vision tasks, the convolutional neural networks provide an end-to-end solution in handwritten Chinese character recognition (HCCR) with great success. However, the process of learning discriminative features for image recognition is difficult in cases where little data is available. In this study, the authors propose a novel method for learning siamese neural network which employs a special structure to predict the similarity between handwritten Chinese characters and template images. The optimisation of siamese neural network can be treated as a simple binary classification problem. When the training process finished, the powerful discriminative features will help to generalise the predictive power not just to new data, but to entirely new classes that never appear in the training set. Experiments performed on the ICDAR-2013 offline HCCR datasets have shown that the proposed method has a very promising generalisation ability for new classes that never appear in the training set.

Journal ArticleDOI
TL;DR: A qualitative approach to identify the best layer for fusion and design steps for feeding in the additional feature sets in convolutional network-based detectors is shown.
Abstract: Object detection in real images is a challenging problem in computer vision. Despite several advancements in detection and recognition techniques, robust and accurate localization of interesting objects in images from real-life scenarios remains unsolved because of the difficulties posed by intraclass and interclass variations, occlusion, lightning, and scale changes at different levels. In this work, we present an object detection framework by learning-based fusion of handcrafted features with deep features. Deep features characterize different regions of interest in a testing image with a rich set of statistical features. Our hypothesis is to reinforce these features with handcrafted features by learning the optimal fusion during network training. Our detection framework is based on the recent version of YOLO object detection architecture. Experimental evaluation on PASCAL-VOC and MS-COCO datasets achieved the detection rate increase of 11.4% and 1.9% on the mAP scale in comparison with the YOLO version-3 detector (Redmon and Farhadi 2018). An important step in the proposed learning-based feature fusion strategy is to correctly identify the layer feeding in new features. The present work shows a qualitative approach to identify the best layer for fusion and design steps for feeding in the additional feature sets in convolutional network-based detectors.

Journal ArticleDOI
TL;DR: An explainable image captioning model is proposed, which provides a visual link between the region of an object in the given image and the particular word (or phrase) in the generated sentence.
Abstract: This article presents an eXplainable AI (XAI) approach to image captioning. Recently, deep learning techniques have been intensively used to this task with relatively good performance. Due to the ‘black-box’ paradigm of deep learning, however, existing approaches are unable to provide clues to explain the reasons why specific words have been selected when generating captions for given images, hence leading to generate absurd captions occasionally. To overcome this problem, this article proposes an explainable image captioning model, which provides a visual link between the region of an object (or a concept) in the given image and the particular word (or phrase) in the generated sentence. The model has been evaluated with two datasets, MSCOCO and Flickr30K, and both quantitative and qualitative results are presented to show the effectiveness of the proposed model.

Journal ArticleDOI
TL;DR: In this paper, the authors discuss the development and achievements of the CFG pile composite foundation, along with possible future research directions, and the remarkable evolution took place in the past to address projects' strict differential and post-construction settlement control requirements including embedding the geosynthetic layer into the load transfer platform and combining it with rigid slabs.
Abstract: Problematic soils exist almost everywhere on the globe. State-of-the-art solutions to make civil engineering infrastructures built on them are still highly sought. The CFG (cement-fly ash-gravel) pile composite foundation system has been widely used in buildings, highways, railways, and bridge transition sections owing to its proven engineering characteristics in soft ground treatment. This paper discusses about the development and achievements of its engineering applications, along with possible future research directions. The remarkable evolution took place in the past to address projects’ strict differential and postconstruction settlement control requirements including embedding the geosynthetic layer into the load transfer platform and combining it with rigid slabs, as seen implemented in few CFG pile-supported embankments. It was also observed that the interaction of the existing CFG pile composite foundation with an adjacent new foundation pit excavation inevitably presents a complex soil-structure interaction mechanism among the fundamental components—the retaining wall, mat, piles, cushion, and soil.

Journal ArticleDOI
TL;DR: The performance analysis of 15 kWp (kW peak) Grid-Tied solar PV system (that considered first of its type) implemented at the Training and Energy Research Center Subsidiary of Iraqi Ministry of Electricity in Baghdad city has been achieved as discussed by the authors.
Abstract: The performance analyses of 15 kWp (kW peak) Grid -Tied solar PV system (that considered first of its type) implemented at the Training and Energy Research Center Subsidiary of Iraqi Ministry of Electricity in Baghdad city has been achieved. The system consists of 72 modules arranged in 6 strings were each string contains 12 modules connected in series to increase the voltage output while these strings connected in parallel to increase the current output. According to the observed duration, the reference daily yields, array daily yields and final daily yields of this system were (5.9, 4.56, 4.4) kWh/kWp/day respectively. The energy yield was 1585 kWh/kWp/year while the annual total solar irradiation received by solar array system was 1986.4kWh/m2. The average power losses per day of array, system losses and overall losses were (1.38, 0.15, 1.53) kWh/kWp/day respectively. The average capacity factor and performance ratio per year were 18.4% and 75.5% respectively. These results highlighted the performance analyses of this PV solar system located in Baghdad city. The performance can be considered as good and significant comparing with other world PV solar stations.

Journal ArticleDOI
TL;DR: In this paper, the authors explored industry 4.0 initiatives through a comprehensive electronic survey of the literature to estimate the extent of their launching in different regions, which revealed that there is a big gap existing between countries in the race for industry 5.0.
Abstract: The war to technology and economic powers has been the driver for industrialization in most developed countries. The first industrial revolution (industry 1.0) earned millions for textile mill owners, while the second industrial revolution (industry 2.0) opened the way for tycoons and captains of industry such as Henry Ford, John D. Rockefeller, and J.P. Morgan. The third industrial revolution (industry 3.0) engendered technology giants such as Apple and Microsoft and made magnates of men such as Bill Gates and Steve Jobs. Now, the race for the fourth industrial revolution (industry 4.0) is on and there is no option, and every country whether developed or developing must participate. Many countries have positively responded to industry 4.0 by developing strategic initiatives to strengthen industry 4.0 implementation. Unlocking the country’s potential to industry 4.0 has been of interest to researchers in the recent past. However, the extent to which industry 4.0 initiatives are being launched globally has never been divulged. Therefore, the present study aimed at exploring industry 4.0 initiatives through a comprehensive electronic survey of the literature to estimate the extent of their launching in different regions. Inferences were drawn from industry 4.0 initiatives in developed nations to be used as the recommendations for the East African Community. Results of the survey revealed that 117 industry 4.0 initiatives have been launched in 56 countries worldwide consisting of five regions: Europe (37%), North America (28%), Asia and Oceania (17%), Latin America and the Caribbean (10%), and Middle East and Africa (8%). The worldwide percentage was estimated as 25%. This revealed that there is a big gap existing between countries in the race for industry 4.0.

Journal ArticleDOI
TL;DR: This paper introduces and summarizes the data types, collection methods, and applications of current building performance databases, including those in the United States, the European Union, Japan, and Australia, as well as China.
Abstract: Research on how the database method can assist building performance diagnosis has become an important direction of current green building studies. Many research institutions have paid great attention to the building performance database, adopting new technologies to integrate indoor environmental quality and occupant satisfaction with building energy consumption data. This paper introduces and summarizes the data types, collection methods, and applications of current building performance databases, including those in the United States, the European Union, Japan, and Australia, as well as China. In view of the current problems of limited coverage, poor quality, and ineffective application of green buildings in China, this paper proposes a three-dimensional framework for green building performance databases. The collection and optimization methods of green building performance data are also discussed.

Journal ArticleDOI
TL;DR: In this article, the authors evaluated the mechanical and water absorption properties of wood sawdust-reinforced polypropylene composites and concluded that the optimal properties were attained at 40% wood saw dust content.
Abstract: Sawdust is a natural composite obtained from natural resources such as shrubs and trees and in a large amount from the wood industry as a waste. This study aimed to evaluate the mechanical and water absorption properties of alkaline-treated wood sawdust-reinforced polypropylene composite. The composites were manufactured by the melt-mixing method followed by compression molding with amounts of wood sawdust ranging from 10, 20, 30, and 40 to 50 wt. % by volume. The produced composites were characterized for their mechanical and water absorption characteristics. The results indicated that increasing the amount of wood sawdust content up to 40% improves the tensile, flexural, impact, and compressive strength of the composites but after the wood sawdust contents reach 40%, the mechanical properties of the composite were decreased. Water absorption rate has increased with an increase in the wood sawdust proportion; this is due to the presence of –OH groups on the surface of wood particles. From the result of this study, it can be also concluded that the optimal mechanical and water absorption properties were attained at 40% wood sawdust content. Therefore, sawdust can be used as filler and reinforcement in the PP matrix, which will reduce cost and give environmental benefits.

Journal ArticleDOI
TL;DR: In this article, the hydraulic characteristics of unsteady flow in Al-Gharraf River in order to define the locations that facing problems and suggesting the necessary treatments are described.
Abstract: One and two-dimensional hydraulic models simulations are important to specify the hydraulic characteristics of unsteady flow in Al-Gharraf River in order to define the locations that facing problems and suggesting the necessary treatments. The reach in the present study is 58200m long and lies between Kut and Hai Cities. Both numerical models were simulated using HEC-RAS software, 5.0.4, with flow rates ranging from 100 to 350 m3/s. Multi-scenarios of gates openings of Hai Regulator were applied. While the openings of Al-Gharraf Head Regulator were ranged between 60cm to fully opened. The suitable manning roughness for the unsteady state was 0.025. The obtained results show that the average velocities for the one-dimensional model were ranged between 0.36 and 0.5 m/s, and the average water surface elevations range between 15.14 m and 17.84 m. While these values ranged between 0.25 and 0.44 m/s and 14.125 and 18.82 m respectively for the two-dimensional model. The simulation results of the two-dimensional model were more accurate than their corresponding one-dimensional model, due to more agreement of these values with measured values, which achieved minimum values of the root mean square error and the determination coefficient.

Journal ArticleDOI
TL;DR: In this study, a new method is presented for the realisation of the fractional order integrator using existing successfully applied approximate methods given in the literature and fractance elements are developed.
Abstract: In this study, a new method is presented for the realisation of the fractional order integrator. First, integer order approximate methods are analysed, then based on this analysis and using existing successfully applied approximate methods given in the literature, the design of fractional order integrators with active circuit elements is studied. In other words, fractance elements are developed. Operational amplifier is preferred in the design of fractional order integrator. Time responses of fractional order integrator circuits are computed by using Multisim software of National Instruments. Then the results are compared and verified with the results obtained from Matlab.

Journal ArticleDOI
TL;DR: The outage prediction model (OPM) is a weather-related machine learning-based power outage model, which has been developed at the University of Connecticut for many years and has recently grown to cover three states and five utility service territories.
Abstract: The outage prediction model (OPM) is a weather-related machine learning-based power outage model, which has been developed at the University of Connecticut for many years and has recently grown to cover three states and five utility service territories. This is a large heterogeneous domain supported by a large dataset of hundreds of storm events. This dataset presents the opportunity to investigate the effect of the spatial organisation and training structure on model performance, identify potential weaknesses in the modelling approach, and evaluate the generalisability of the modelling methodology. By organising and sub-dividing the modelling system informed by clustering analysis, this study experiments with the structure of the model to identify potential sources of error in the modelling system and evaluate generalisability. The clustering analysis identifies regions of specific climatic, topographic, and environmental characteristics, and performance improvements are observed when the model is subdivided and trained separately for each cluster in certain clustering. Models trained on all available data from all five utility service territories consistently have good performance showing that the OPM modelling approach is generalisable across different service territories and power utilities.