Abstract: In March 2020, in Spain, a sanitary and unexpected crisis caused by COVID-19 was declared. All of a sudden, all degrees, classes and evaluation tests and projects had to be transformed into online activities. However, the chaotic situation generated by a complex operation like that, executed without any well-established procedure, led to very different experiences and, finally, results. In this paper, we are describing three experiences in two different Universities in Madrid. On the one hand, the Technical University of Madrid, a public university with little experience in online education was considered. On the other hand, Alfonso X el Sabio University, a private university with more than five years of experience in online teaching was involved. All analyzed subjects were related to computer engineering. Professors and students answered a survey and personal interviews were also carried out. Besides, the professors’ workload and the students’ academic results were also compared. From the comparative analysis of all these experiences, we are extracting the most successful strategies, methodologies, and activities. The recommendations in this paper will be useful for courses during the next months when the sanitary situation is still affecting an educational organization. While, at the same time, they will be considered as input for the upcoming digitalization process of higher education.
Abstract: The construction industry plays an important role in environmental and carbon emissions as it consumes a huge amount of natural resources and energy. Sustainable engineering involves the process of planning, design, procurement, construction and delivery in which the whole building and construction process resulting from building and construction can be effectively and sustainability managed to achieve the use of natural resources. Implementation of sustainable technology development and innovation, adoption of the advanced construction process and facilitate the facilities management to implement the energy and waste control more accurately and effectively. Study and research in the relationship of BIM and environment assessment tools lack a clear discussion. In this paper, we will focus on the synergy of BIM technology and sustainable engineering in the AEC industry and outline the key factors which enhance the use of advanced innovation, technology and method and define the role of stakeholders to achieve zero-carbon emission toward the Paris Agreement to limit global warming to well below 2°C above pre-industrial levels. A case study of the adoption of Building Information Modeling (BIM) and environmental assessment tools in Hong Kong will be discussed in this paper.
Abstract: Bayesian approach can be used for parameter identification and extraction in state space models and its ability for analyzing sequence of data in dynamical system is proved in different literatures. In this paper, adaptive Kalman filter with Bayesian approach for identification of variances in measurement parameter noise is developed. Next, it is applied for estimation of the dynamical state and measurement data in discrete linear dynamical system. This algorithm at each step time estimates noise variance in measurement noise and state of system with Kalman filter. Next, approximation is designed at each step separately and consequently sufficient statistics of the state and noise variances are computed with a fixed-point iteration of an adaptive Kalman filter. Different simulations are applied for showing the influence of noise variance in measurement data on algorithm. Firstly, the effect of noise variance and its distribution on detection and identification performance is simulated in Kalman filter without Bayesian formulation. Then, simulation is applied to adaptive Kalman filter with the ability of noise variance tracking in measurement data. In these simulations, the influence of noise distribution of measurement data in each step is estimated, and true variance of data is obtained by algorithm and is compared in different scenarios. Afterwards, one typical modeling of nonlinear state space model with inducing noise measurement is simulated by this approach. Finally, the performance and the important limitations of this algorithm in these simulations are explained.
Abstract: There has been a rising trend in using impulse current generators to investigate the lightning strike protection of materials including aluminum and composites in structures such as wind turbine blade and aircraft body. The focus of this research is to present an impulse current generator built in the High Voltage Lab at Mississippi State University. The generator is capable of producing component A and D of the natural lightning discharges in accordance with the Society of Automotive Engineers (SAE) standard, which is widely used in the aerospace industry. The generator can supply lightning impulse energy up to 400 kJ with the capability of producing impulse currents with magnitudes greater than 200 kA. The electrical circuit and physical components of an improved impulse current generator are described and several lightning strike waveforms with different amplitudes is presented for comparing with the standard waveform. The results of this study contribute to the fundamental understanding the functionality of the impulse current generators and present an impulse current generator developed at the High Voltage Lab of Mississippi State University.
Abstract: The purpose of the comparative study of the affricate consonants in Chinese and Slovak is to increase the awareness of the main distinguishing features between these two languages taking into consideration this particular group of consonants. We determine the main difficulties of the Slovak learners in the process of acquiring correct pronunciation of affricate initial consonants in Chinese based on the understanding of the distinguishing features of Chinese and Slovak affricates in combination with the experimental measuring of voice onset time (VOT) values. The software tool Praat is used for the analysis of the recorded language samples. The language samples contain recordings of a Chinese native speaker and Slovak students of Chinese with different language proficiency levels. Based on the results of the analysis in Praat, we identify erroneous pronunciation and provide clarification of its cause.
Abstract: One of the most significant issues as attended a lot in recent years is that of recognizing the sentiments and emotions in social media texts. The analysis of sentiments and emotions is intended to recognize the conceptual information such as the opinions, feelings, attitudes and emotions of people towards the products, services, organizations, people, topics, events and features in the written text. These indicate the greatness of the problem space. In the real world, businesses and organizations are always looking for tools to gather ideas, emotions, and directions of people about their products, services, or events related to their own. This article uses the Twitter social network, one of the most popular social networks with about 420 million active users, to extract data. Using this social network, users can share their information and opinions about personal issues, policies, products, events, etc. It can be used with appropriate classification of emotional states due to the availability of its data. In this study, supervised learning and deep neural network algorithms are used to classify the emotional states of Twitter users. The use of deep learning methods to increase the learning capacity of the model is an advantage due to the large amount of available data. Tweets collected on various topics are classified into four classes using a combination of two Bidirectional Long Short Term Memory network and a Convolutional network. The results obtained from this study with an average accuracy of 93%, show good results extracted from the proposed framework and improved accuracy compared to previous work.
Abstract: Since the introduction of ServiceNow, the UK’s Science Museum Group’s (SMG) ICT service desk portal, there has not been an analysis of the tools available to SMG staff for Just-in-time knowledge acquisition (Knowledge Management Systems) and reporting ICT incidents with a focus on an aspect of professional identity namely, gender. Therefore, it is important for SMG to investigate the apparent disparities so that solutions can be derived to minimize this digital divide if one exists. This study is conducted in the milieu of UK museums, galleries, arts, academic, charitable, and cultural heritage sector. It is acknowledged at SMG that there are challenges with keeping up with an ever-changing digital landscape. Subsequently, this entails the rapid upskilling of staff and developing an infrastructure that supports just-in-time technological knowledge acquisition and reporting technology related issues. This problem was addressed by analysing ServiceNow ICT incident reports and reports from knowledge articles from a six-month period from February to July. This study found a statistically significant relationship between gender and reporting an ICT incident. There is also a significant relationship between gender and the priority level of ICT incident. Interestingly, there is no statistically significant relationship between gender and reading knowledge articles. Additionally, there is no statistically significant relationship between gender and reporting an ICT incident related to the knowledge article that was read by staff. The knowledge acquired from this study is useful to service desk management practice as it will help to inform the creation of future knowledge articles and ICT incident reporting processes.
Abstract: Identification of plant diseases has been performed using machine learning and deep learning models on the datasets containing images of healthy and diseased plant leaves. The current study carries out an evaluation of some of the deep learning models based on convolutional neural network architectures for identification of plant diseases. For this purpose, the publicly available New Plant Diseases Dataset, an augmented version of PlantVillage dataset, available on Kaggle platform, containing 87,900 images has been used. The dataset contained images of 26 diseases of 14 different plants and images of 12 healthy plants. The CNN models selected for the study presented in this paper are AlexNet, ZFNet, VGGNet (four models), GoogLeNet, and ResNet (three models). The selected models are trained using PyTorch, an open-source machine learning library, on Google Colaboratory. A comparative study has been carried out to analyze the high degree of accuracy achieved using these models. The highest test accuracy and F1-score of 99.59% and 0.996, respectively, were achieved by using GoogLeNet with Mini-batch momentum based gradient descent learning algorithm.
Abstract: The purpose of this study is to explore the characteristics of developing a machine learning application using synthetic data. The study is structured to develop the application for the purpose of deploying the computer vision model. The findings discuss the realities of attempting to develop a computer vision model for practical purpose, and detail the processes, tools and techniques that were used to meet accuracy requirements. The research reveals that synthetic data represent another variable that can be adjusted to improve the performance of a computer vision model. Further, a suite of tools and tuning recommendations are provided.
Abstract: Developing new medicine and health solutions and improving patient health currently rely on the successful execution of clinical trials, which generate relevant safety and efficacy data. For their success, recruitment and retention of participants are some of the most challenging aspects of protocol adherence. Main barriers include: i) lack of awareness of clinical trials; ii) long distance from the clinical site; iii) the burden on participants, including the duration and number of clinical visits, and iv) high dropout rate. Most of these aspects could be addressed with a new paradigm, namely the Remote Decentralized Clinical Trials (RDCTs). Furthermore, the COVID-19 pandemic has highlighted additional advantages and challenges for RDCTs in practice, allowing participants to join trials from home and not depending on site visits, etc. Nevertheless, RDCTs should follow the process and the quality assurance of conventional clinical trials, which involve several processes. For each part of the trial, the Building Blocks, existing software and technologies were assessed through a systematic search. The technology needed to perform RDCTs is widely available and validated but is yet segmented and developed in silos, as different software solutions address different parts of the trial and at various levels. The current paper is analyzing the availability of technology to perform RDCTs, identifying gaps and providing an overview of Basic Building Blocks and functionalities that need to be covered to support the described processes.
Abstract: Lubrication, cooling and chip removal are the desired functions of any cutting fluid. Conventional or flood lubrication requires high volume flow rate and cost associated with this is higher. In addition, flood lubrication possesses health risks to machine operator. To avoid these consequences, dry machining and minimum quantity are two alternatives. Dry machining cannot be a suited alternative as it can generate greater heat and poor surface finish. Here, turning work is carried out on a Lathe machine using EN3 Mild steel. Variable cutting speeds and depth of cuts are provided and corresponding temperatures and surface roughness values were recorded. Experimental results are analyzed by Minitab software. Regression analysis, main effect plot, and interaction plot conclusion are drawn by using ANOVA. There is a 95.83% reduction in the use of cutting fluid. MQL gives a 9.88% reduction in tool temperature, this will improve tool life. MQL produced a 17.64% improved surface finish. MQL appears to be an economical and environmentally compatible lubrication technique for sustainable manufacturing.
Abstract: Marine shipping has now become one of the major worldwide contributors to pollution and greenhouse gas emissions. Hybrid ships technology based on multiple energy sources has taken a great scope of research to get rid of ship emissions and cut down fuel expenses. Insufficiency between power generated and the demand load to withstand the transient behavior on ships during severe climate conditions will lead to a blackout. Thus, an efficient energy management system (EMS) is a mandatory scope for achieving higher system efficiency while enhancing the lifetime of the onboard storage systems is another salient EMS scope. Considering energy storage system conditions, both the battery state of charge (SOC) and temperature represent important parameters to prevent any malfunction of the storage system that eventually degrades the whole system. In this paper, a two battery packs ratio fuzzy logic control model is proposed. The overall aim is to control the charging/discharging current while including both the battery SOC and temperature in the energy management system. The full designs of the proposed controllers are described and simulated using Matlab. The results prove the successfulness of the proposed controller in stabilizing the system voltage during both loading and unloading while keeping the energy storage system in a healthy condition.
Abstract: This paper presents a software product quality evaluation model based on the ISO/IEC 25010 quality model. The evaluation characteristics and sub characteristics were identified from the ISO/IEC 25010 quality model. The multidimensional structure of the quality model is based on characteristics such as functional suitability, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability, and associated sub characteristics. Random numbers are generated to establish the decision maker’s importance weights for each sub characteristics. Also, random numbers are generated to establish the decision matrix of the decision maker’s final scores for each software product against each sub characteristics. Thus, objective criteria importance weights and index scores for datasets were obtained from the random numbers. In the proposed model, five different software product quality evaluation datasets under three different weight vectors were applied to multiple criteria decision analysis method, preference analysis for reference ideal solution (PARIS) for comparison, and sensitivity analysis procedure. This study contributes to provide a better understanding of the application of MCDMA methods and ISO/IEC 25010 quality model guidelines in software product quality evaluation process.
Abstract: Continuous improvement activities are becoming a key organizational success factor; those improvement activities include but are not limited to kaizen, six sigma, lean production, and continuous improvement projects. Kaizen is a Japanese philosophy of continuous improvement by making small incremental changes to improve an organization’s performance, reduce costs, reduce delay time, reduce waste in production, etc. This research aims at proposing a measuring system for kaizen activities from a sustainable balanced scorecard perspective. A survey was developed and disseminated among kaizen experts in both Egypt and Japan with the purpose of allocating key performance indicators for both kaizen process (critical success factors) and result (kaizen benefits) into the five sustainable balanced scorecard perspectives. This research contributes to the extant literature by presenting a kaizen measurement of both kaizen process and results that will illuminate the benefits of using kaizen. Also, the presented measurement can help in the sustainability of kaizen implementation across various sectors and industries. Thus, grasping the full benefits of kaizen implementation will contribute to the spread of kaizen understanding and practice. Also, this research provides insights on the social and cultural differences that would influence the kaizen success. Determining the combination of the proper kaizen measures could be used by any industry, whether service or manufacturing for better kaizen activities measurement. The comparison between Japanese implementation of kaizen, as the pioneers of continuous improvement, and Egyptian implementation will help recommending better practices of kaizen in Egypt and contributing to the 2030 sustainable development goals. The study results reveal that there is no significant difference in allocating kaizen benefits between Egypt and Japan. However, with regard to the critical success factors some differences appeared reflecting the social differences and understanding between both countries, a single integrated measurement was reached between the Egyptian and Japanese allocation highlighting the Japanese experts’ opinion as the ultimate criterion for selection.
Abstract: Additive Friction Stir Manufacturing, or AFSM, is a new industrial process that follows the emergence of friction-based processes. The AFSM process is a solid-state additive process using the energy produced by the friction at the interface between a rotating non-consumable tool and a substrate. Friction depends on various parameters like axial force, rotation speed or friction coefficient. The feeder material is a metallic rod that flows through a hole in the tool. There is still a lack in understanding of the physical phenomena taking place during the process. This research aims at a better AFSM process understanding and implementation, thanks to numerical simulation and experimental validation performed on a prototype effector. Such an approach is considered a promising way for studying the influence of the process parameters and to finally identify a process window that seems relevant. The deposition of material through the AFSM process takes place in several phases. In chronological order these phases are the docking phase, the dwell time phase, the deposition phase, and the removal phase. The present work focuses on the dwell time phase that enables the temperature rise of the system due to pure friction. An analytic modeling of heat generation based on friction considers as main parameters the rotational speed and the contact pressure. Another parameter considered influential is the friction coefficient assumed to be variable, due to the self-lubrication of the system with the rise in temperature or the materials in contact roughness smoothing over time. This study proposes through a numerical modeling followed by an experimental validation to question the influence of the various input parameters on the dwell time phase. Rotation speed, temperature, spindle torque and axial force are the main monitored parameters during experimentations and serve as reference data for the calibration of the numerical model. This research shows that the geometry of the tool as well as fluctuations of the input parameters like axial force and rotational speed are very influential on the temperature reached and/or the time required to reach the targeted temperature. The main outcome is the prediction of a process window which is a key result for a more efficient process implementation.
Abstract: Various factors such as the method of installation, the pile type, the pile material and the pile shape, can affect the final bearing capacity of a pile executed in the soil; among them, the method of installation is of special importance. The physical modeling is among the best options in the laboratory study of the piles behavior. Therefore, the current paper first presents and reviews the frustum confining vessel (FCV) as a suitable tool for physical modeling of deep foundations. Then, by describing the loading tests of two open-ended and closed-end steel piles, each of which has been performed in two methods, “with displacement" and "without displacement", the effect of end conditions and installation method on the final bearing capacity of the pile is investigated. The soil used in the current paper is silty sand of Firuzkuh, Iran. The results of the experiments show that in general the without displacement installation method has a larger bearing capacity in both piles, and in a specific method of installation the closed ended pile shows a slightly higher bearing capacity.
Abstract: The purpose of this study is to evaluate the incorporation of hemp fibers (HF) in concrete. Hemp fiber reinforced concrete (HFRC) is becoming more popular as an alternative for regular mix designs. This study was done to evaluate the compressive strength of HFRC regarding mix procedure. HF were obtained from the manufacturer and hand processed to ensure uniformity in width and length. The fibers were added to concrete as both wet and dry mix to investigate and optimize the mix design process. Results indicated that the dry mix had a compressive strength of 1157 psi compared to the wet mix of 985 psi. This dry mix compressive strength was within range of the standard mix compressive strength of 1533 psi. The statistical analysis revealed that the mix design process needs further optimization and uniformity concerning the addition of HF. Regression analysis revealed that the standard mix design had a coefficient of 0.9 as compared to the dry mix of 0.375 indicating a variation in the mixing process. While completing the dry mix, the addition of plain HF caused them to intertwine creating lumps and inconsistency. However, during the wet mixing process, combining water and HF before incorporation allows the fibers to uniformly disperse within the mix hence the regression analysis indicated a better coefficient of 0.55. This study concludes that HRFC is a viable alternative to regular mixes however more research surrounding its characteristics needs to be conducted.
Abstract: The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluates the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.
Abstract: One of the most relevant phenomena in manufacturing is the residual stress state development through the manufacturing chain. In most cases, the residual stresses have their origin in the heterogenous plastification produced by the processes. Although a few manufacturing processes have been successfully approached by numerical modeling, there is still lack of understanding on how these processes' interactions will affect the final stress state. The objective of this work is to analyze the effect of the grinding procedure on the residual stress state generated by a quasi-static indentation. The model consists in a simplified approach of shot peening, modeling four cases with variations in indenter size and force. This model was validated through topography, measured by optical 3D focus-variation. The indentation model configured with two loads was then exposed to two grinding procedures and the result was analyzed. It was observed that the grinding procedure will have a significant effect on the stress state.
Abstract: This paper represents the conception that complex problems do not necessary need similar complex solutions in order to cope with the complexity. Furthermore, a simple solution based on established methods can provide a sufficient way dealing with the complexity. To verify this conception, the presented paper focuses on the field of change management as a part of new product development process in automotive sector. In the field of complexity management, dealing with increasing complexity is essential, while, only non-flexible rigid processes that are not designed to handle complexity are available. The basic methodology of this paper can be divided in four main sections: 1) analyzing the complexity of the change management, 2) literature review in order to identify potential solutions and methods, 3) capturing and implementing expertise of experts from change management filed of an automobile manufacturing company and 4) systematical comparison of the identified methods from literature and connecting these with defined requirements of the complexity of the change management in order to develop a solution. As a practical outcome, this paper provides a method to capture the complexity of engineering changes (EC) and includes it within the EC evaluation process, following case-related process guidance to cope with the complexity. Furthermore, this approach supports the conception that dealing with complexity is possible while utilizing rather simple and established methods by combining them in to a powerful tool.