Search results for: landscape approach

Authors: Wei Zhao

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The pandemic crisis disrupted normal classroom practices so that the constraints of the traditional practice became apparent. This turns out to be new opportunities for technology-based learning and teaching. However, how the technology supports the preschool teachers go through this sudden crisis and how preschool teachers conceived of the use of technology, appropriate and design technological artifacts as a mediator of knowledge construction in order to suit young children’s literacy level are rarely explored. This study addresses these issues by looking at the influence of a web-supported teacher community on changes/shifts in preschool teachers’ epistemological beliefs and practices. This teachers’ professional development community was formulated before the pandemic time and developed virtually throughout the home-based learning caused by Covid-19. It served as a virtual and asynchronous community for those teachers to collaboratively plan for and conduct online lessons using the knowledge-building approach for the purpose of sustaining children’s learning curiosity and opening up new learning opportunities during the lock-down period. The knowledge-building approach helps to increase teachers’ collective responsibility to collaboratively work on shared educational goals in the teacher community and awareness of noticing new ideas or innovations in their classroom. Based on the data collected across five months during and after the lock-down period and the activity theory, results show a dynamic interplay between the evolution of the community culture, the growth of teacher community and teachers’ identity transformation and professional development. Technology is useful in this regard not only because it transforms the geographical distance and new gathering guidelines after the outbreak of pandemic into new ways of communal communication and collaboration. More importantly, while teachers selected, monitored and adapted the technology, it acts as a catalyst for changes in teachers’ old teaching practices and epistemological dispositions.

Keywords: activity theory, changes in epistemology and practice, knowledge building, web-based teachers’ professional development community

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Authors: Mahesh Balan U, Rohith Srinivaas Mohanakrishnan, Venkat Subramanian

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Positive and unlabeled learning (PU) has gained more attention in both academic and industry research literature recently because of its relevance to existing business problems today. Yet, there still seems to be some existing challenges in terms of validating the performance of PU learning, as the actual truth of unlabeled data points is still unknown in contrast to a binary classification where we know the truth. In this study, we propose a novel PU learning technique based on the Pseudo-F measure, where we address this research gap. In this approach, we train the PU model to discriminate the probability distribution of the positive and unlabeled in the validation and spy data. The predicted probabilities of the PU model have a two-fold validation – (a) the predicted probabilities of reliable positives and predicted positives should be from the same distribution; (b) the predicted probabilities of predicted positives and predicted unlabeled should be from a different distribution. We experimented with this approach on a credit marketing case study in one of the world’s biggest fintech platforms and found evidence for benchmarking performance and backtested using historical data. This study contributes to the existing literature on semi-supervised learning.

Keywords: PU learning, semi-supervised learning, pseudo f measure, classification

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Authors: Majid Aldalbahi

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In 2009, over 3.4 billion people in the world resided in urban areas as a result of rapid urban growth. This figure is estimated to increase to 6.5 billion by 2050. This urban growth phenomenon has raised challenges for many countries in both the developing and developed worlds. Urban growth is a complicated process involving the spatiotemporal changes of all socio-economic and physical components at different scales. The socio-economic components of urban growth are related to urban population growth and economic growth, while physical components of urban growth and economic growth are related to spatial expansion, land cover change and land use change which are the focus of this research. The interactions between these components are complex and no-linear. Several factors and forces cause these complex interactions including transportation and communication, internal and international migrations, public policies, high natural growth rates of urban populations and public policies. Urban growth has positive and negative consequences. The positive effects relates to planned and orderly urban growth, while negative effects relate to unplanned and scattered growth, which is called sprawl. Although urban growth is considered as necessary for sustainable urbanization, uncontrolled and rapid growth cause various problems including consumption of precious rural land resources at urban fringe, landscape alteration, traffic congestion, infrastructure pressure, and neighborhood conflicts. Traditional urban planning approaches in fast growing cities cannot accommodate the negative consequences of rapid urban growth. Microsimulation programme, and modelling techniques are effective means to provide new urban development, management and planning methods and approaches. This paper aims to use these techniques to understand and analyse the complex interactions for the case study of Riyadh city, a fast growing city in Saudi Arabia.

Keywords: policy implications, urban planning, traffic congestion, urban growth, Suadi Arabia, Riyadh

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Authors: Badrane Nohayla, Bamousse Zineb

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Financial crises, exchange rate volatility, fluctuations in commodity prices, increased competitive pressures, and environmental issues are all threats that businesses face. In light of these diverse challenges, proactive, agile, and innovative cash management becomes an indispensable financial shield, allowing companies to thrive despite the adverse conditions of the global environment. In the same spirit, uncertainty, turbulence, volatility, and competitiveness continue to disrupt economic environments, compelling companies to swiftly master innovative breakthroughs that provide added value. In such a context, innovation emerges as a catalytic vector for performance, aiming to reduce costs, strengthen growth, and ultimately ensure the sustainability of Moroccan companies in the national arena. Moreover, innovation in treasury management promises to be one of the key pillars of financial stability, enabling companies to navigate the tumultuous waters of a globalized environment. Therefore, the objective of this study is to better understand the impact of innovative treasury management on cost optimization and, by extension, performance improvement. To elucidate this relationship, we conducted an exploratory qualitative study with 20 large Moroccan companies operating in the Casablanca-Settat region. The results highlight that innovation at the heart of treasury management is a guarantee of sustainability against the risks of failure and stands as a true pivot of the performance of Moroccan companies, an important parameter of their financial balance and a catalytic vector of their growth in the national economic landscape. In this regard, the present study aims to explore the extent to which innovation at the core of the treasury function serves as an indispensable tool for boosting performance while optimising costs in large Moroccan companies.

Keywords: innovative cash management, artificial intelligence, financial performance, risk management, cost savings

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Authors: Jiaqi Huang, Lu Jin

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Pervious concrete features with high water permeability rate. However, due to the lack of fine aggregates, the compressive strength is usually lower than other conventional concrete products. Optimization of pervious concrete mix design has long been recognized as an effective mechanism to achieve high compressive strength while maintaining desired permeability rate. In this paper, a Gibbs Sampling based algorithm is proposed to approximate the optimal mix design to achieve a high compressive strength of pervious concrete. We prove that the proposed algorithm efficiently converges to the set of global optimal solutions. The convergence rate and accuracy depend on a control parameter employed in the proposed algorithm. The simulation results show that, by using the proposed approach, the system converges to the optimal solution quickly and the derived optimal mix design achieves the maximum compressive strength while maintaining the desired permeability rate.

Keywords: convergence, Gibbs Sampling, high compressive strength, optimal mix design, pervious concrete

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Authors: Muhammad Saqib Manzoor, Camille Dickson-Deane, Prashan Karunaratne

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Cobb-Douglas production (utility) function is a fundamental function widely used in economics teaching and research. The key reason is the function's characteristics to describe the actual production using inputs like labour and capital. The characteristics of the function like returns to scale, marginal, and diminishing marginal productivities are covered in the introductory units in both microeconomics and macroeconomics with a 2-dimensional static visualisation of the function. However, less insight is provided regarding three-dimensional surface, changes in the curvature properties due to returns to scale, the linkage of the short-run production function with its long-run counterpart and marginal productivities, the level curves, and the constraint optimisation. Since (freshman) learners have diverse prior knowledge and cognitive skills, the existing “one size fits all” approach is not very helpful. The aim of this study is to bridge this gap by introducing technological intervention with interactive animations of the three-dimensional surface and sequential unveiling of the characteristics mentioned above using Python software. A small classroom intervention has helped students enhance their analytical and visualisation skills towards active and authentic learning of this topic. However, to authenticate the strength of our approach, a quasi-Delphi study will be conducted to ask domain-specific experts, “What value to the learning process in economics is there using a 2-dimensional static visualisation compared to using a 3-dimensional dynamic visualisation?’ Here three perspectives of the intervention were reviewed by a panel comprising of novice students, experienced students, novice instructors, and experienced instructors in an effort to determine the learnings from each type of visualisations within a specific domain of knowledge. The value of this approach is key to suggesting different pedagogical methods which can enhance learning outcomes.

Keywords: cobb-douglas production function, quasi-Delphi method, effective teaching and learning, 3D-visualisations

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Authors: Jamshaid ul Rahman, Akhter Ali, Adnan Manzoor

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Learning-based systems have received increasing interest in recent years; recognition structures, including end-to-end speak recognition, are one of the hot topics in this area. A famous work on end-to-end speaker verification by using Angular Softmax Loss gained significant importance and is considered useful to directly trains a discriminative model instead of the traditional adopted i-vector approach. The margin-based strategy in angular softmax is beneficial to learn discriminative speaker embeddings where the random selection of margin values is a big issue in additive angular margin and multiplicative angular margin. As a better solution in this matter, we present an alternative approach by introducing a bit similar form of an additive parameter that was originally introduced for face recognition, and it has a capacity to adjust automatically with the corresponding margin values and is applicable to learn more discriminative features than the Softmax. Experiments are conducted on the part of Fisher dataset, where it observed that the additive parameter with angular softmax to train the front-end and probabilistic linear discriminant analysis (PLDA) in the back-end boosts the performance of the structure.

Keywords: additive parameter, angular softmax, speaker verification, PLDA

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Authors: T. Iakovleva

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Present paper suggests a theoretical model that describes the process of the development of responsible innovations on the firm level in health and welfare sectors. There is a need to develop new firm strategies in these sectors. This paper suggests to look on the concept of responsible innovation that was originally developed on the social level and to apply this new concept to the new area of firm strategy. The rapid global diffusion of information and communication technologies has greatly improved access to knowledge. At the same time, communication is cheap, information is a commodity, and global trade increases technological diffusion. As a result, firms and users, including those outside of industrialized nations, get early exposure to the latest technologies and information. General-purpose technologies such as mobile phones and 3D printers enable individuals to solve local needs and customize products. The combined effect of these changes is having a profound impact on the innovation landscape. Meanwhile, the healthcare sector is facing unprecedented challenges, which are magnified by budgetary constraints, an aging population and the desire to provide care for all. On the other hand, patients themselves are changing. They are savvier about their diseases, they expect their relation with the healthcare professionals to be open and interactive, but above all they want to be part of the decision process. All of this is a reflection of what is already happening in other industries where customers have access to large amount of information and became educated buyers. This article addresses the question of how ICT research and innovation may contribute to developing solutions to grand societal challenges in a responsible way. A broad definition of the concept of responsibility in the context of innovation is adopted in this paper. Responsibility is thus seen as a collective, uncertain and future-oriented activity. This opens the questions of how responsibilities are perceived and distributed and how innovation and science can be governed and stewarded towards socially desirable and acceptable ends. This article addresses a central question confronting politicians, business leaders, and regional planners.

Keywords: responsible innovation, ICT, healthcare, welfare sector

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Authors: Ahmed Kamil Hasan Al-Ali, Bouchra Senadji, Ganesh Naik

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We propose a system to real environmental noise and channel mismatch for forensic speaker verification systems. This method is based on suppressing various types of real environmental noise by using independent component analysis (ICA) algorithm. The enhanced speech signal is applied to mel frequency cepstral coefficients (MFCC) or MFCC feature warping to extract the essential characteristics of the speech signal. Channel effects are reduced using an intermediate vector (i-vector) and probabilistic linear discriminant analysis (PLDA) approach for classification. The proposed algorithm is evaluated by using an Australian forensic voice comparison database, combined with car, street and home noises from QUT-NOISE at a signal to noise ratio (SNR) ranging from -10 dB to 10 dB. Experimental results indicate that the MFCC feature warping-ICA achieves a reduction in equal error rate about (48.22%, 44.66%, and 50.07%) over using MFCC feature warping when the test speech signals are corrupted with random sessions of street, car, and home noises at -10 dB SNR.

Keywords: noisy forensic speaker verification, ICA algorithm, MFCC, MFCC feature warping

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Authors: Fu Lin

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We consider the worst-case load-shedding problem in electric power networks where a number of transmission lines are to be taken out of service. The objective is to identify a prespecified number of line outages that lead to the maximum interruption of power generation and load at the transmission level, subject to the active power-flow model, the load and generation capacity of the buses, and the phase-angle limit across the transmission lines. For this nonlinear model with binary constraints, we show that all decision variables are separable except for the nonlinear power-flow equations. We develop an iterative decomposition algorithm, which converts the worst-case load shedding problem into a sequence of small subproblems. We show that the subproblems are either convex problems that can be solved efficiently or nonconvex problems that have closed-form solutions. Consequently, our approach is scalable for large networks. Furthermore, we prove the convergence of our algorithm to a critical point, and the objective value is guaranteed to decrease throughout the iterations. Numerical experiments with IEEE test cases demonstrate the effectiveness of the developed approach.

Keywords: load shedding, power system, proximal alternating linearization method, vulnerability analysis

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Authors: Seyed Hamid Mirmohammad Sadeghi, Raffaella Sesana, Daniela Maffiodo

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This study presents a procedure for friction calculation of column electric power steering (C-EPS) system which affects handling and comfort in driving. The friction losses estimation is obtained from experimental tests and mathematical calculation. Parts in C-EPS mainly involved in friction losses are bearings and worm gear. In the theoretical approach, the gear geometry and Hertz law were employed to measure the normal load and the sliding velocity and contact areas from the worm gears driving conditions. The viscous friction generated in the worm gear was obtained with a theoretical approach and the result was applied to model the friction in the steering system. Finally, by viscous friction coefficient and Coulomb friction coefficient, values of friction in worm gear were calculated. According to the Bearing Company and the characteristics of each bearing, the friction torques due to load and due to speed were calculated. A MATLAB Simulink model for calculating the friction in bearings and worm gear in C-EPS were done and the total friction value was estimated.

Keywords: friction, worm gear, column electric power steering system, simulink, bearing, EPS

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Authors: Palita Sooksamran, Katcharin Mahawong

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This research aims to offer the development of innovator teachers training curriculum to create instructional innovation according to active learning approach to enhance learning achievement. The research and development process is carried out in 3 steps: Step 1 The study of the needs necessary to develop a training curriculum: the inquiry was conducted by a sample of teachers in private schools in Phayao province that provide basic education at the level of education. Using a questionnaire of 176 people, the sample was defined using a table of random numbers and stratified samples, using the school as a random layer. Step 2 Training curriculum development: the tools used are developed training curriculum and curriculum assessments, with nine experts checking the appropriateness of the draft curriculum. The statistic used in data analysis is the average ( ) and standard deviation (S.D.) Step 3 study on effectiveness of training curriculum: one group pretest/posttest design applied in this study. The sample consisted of 35 teachers from private schools in Phayao province. The participants volunteered to attend on their own. The results of the research showed that: 1.The essential demand index needed with the list of essential needs in descending order is the choice and create of multimedia media, videos, application for learning management at the highest level ,Developed of multimedia, video and applications for learning management and selection of innovative learning management techniques and methods of solve the problem Learning , respectively. 2. The components of the training curriculum include principles, aims, scope of content, training activities, learning materials and resources, supervision evaluation. The scope of the curriculum consists of basic knowledge about learning management innovation, active learning, lesson plan design, learning materials and resources, learning measurement and evaluation, implementation of lesson plans into classroom and supervision and motoring. The results of the evaluation of quality of the draft training curriculum at the highest level. The Experts suggestion is that the purpose of the course should be used words that convey the results. 3. The effectiveness of training curriculum 1) Cognitive outcomes of the teachers in creating innovative learning management was at a high level of relative gain score. 2) The assessment results of learning management ability according to the active learning approach to enhance learning achievement by assessing from 2 education supervisor as a whole were very high , 3) Quality of innovation learning management based on active learning approach to enhance learning achievement of the teachers, 7 instructional Innovations were evaluated as outstanding works and 26 instructional Innovations passed the standard 4) Overall learning achievement of students who learned from 35 the sample teachers was at a high level of relative gain score 5) teachers' satisfaction towards the training curriculum was at the highest level.

Keywords: training curriculum, innovator teachers, active learning approach, learning achievement

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Authors: Hadeer Hegazy, Wael Ewieda, Ranin Soliman, Samah Elway, Asmaa Tawfik, Ragaa Sayed, Sahar Mousa

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The current healthcare system is facing major challenges in terms of cost, quality of care, and access to services. In response, the concept of value-based healthcare has emerged as a new approach to healthcare delivery. This concept puts the focus on patient values rather than on the traditional medical model of care. To achieve this, healthcare organizations must be agile and able to anticipate and respond quickly to changing needs. Agile management is essential for healthcare organizations to achieve value-based care, as it allows them to rapidly adjust their strategies to changing circumstances. Additionally, it is argued that agile management can help healthcare organizations gain a better understanding of the needs of their patients and develop better care delivery models. Besides, it can help healthcare organizations develop new services, innovate, and become more efficient. The authors provide evidence to support their argument, drawing on examples from successful value-based healthcare initiatives at children’s cancer hospital Egypt-57357. The paper offers insight into how agile management can be used to facilitate the shift towards value-based healthcare and how it can be used to maximize value in the healthcare system.

Keywords: value-based healthcare, agility in healthcare, nursing department, patients outcomes

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Authors: Mais Haj Qasem, Maen M. Al Assaf, Ali Rodan

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Parallel hybrid storage systems consist of a hierarchy of different storage devices that vary in terms of data reading speed performance. As we ascend in the hierarchy, data reading speed becomes faster. Thus, migrating the application’ important data that will be accessed in the near future to the uppermost level will reduce the application I/O waiting time; hence, reducing its execution elapsed time. In this research, we implement trace-driven two-levels parallel hybrid storage system prototype that consists of HDDs and SSDs. The prototype uses data mining techniques to classify application’ data in order to determine its near future data accesses in parallel with the its on-demand request. The important data (i.e. the data that the application will access in the near future) are continuously migrated to the uppermost level of the hierarchy. Our simulation results show that our data migration approach integrated with data mining techniques reduces the application execution elapsed time when using variety of traces in at least to 22%.

Keywords: hybrid storage system, data mining, recurrent neural network, support vector machine

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Authors: Franziska Mais, Till Gramberg

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The paper presents the development of a sustainable business model for a platform-based application tailored for sawing companies in small and medium-sized enterprises (SMEs). The focus is on the integration of sustainability principles into the design of the business model to ensure a technologically advanced, legally sound, and economically efficient solution. Easy2IoT is a research project that aims to enable companies in the prefabrication sheet metal and sheet metal processing industry to enter the Industrial Internet of Things (IIoT) with a low-threshold and cost-effective approach. The methodological approach of Easy2IoT includes an in-depth requirements analysis and customer interviews with stakeholders along the value chain. Based on these insights, actions, requirements, and potential solutions for smart services are derived. The structuring of the business ecosystem within the application plays a central role, whereby the roles of the partners, the management of the IT infrastructure and services, as well as the design of a sustainable operator model are considered. The business model is developed using the value proposition canvas, whereby a detailed analysis of the requirements for the business model is carried out, taking sustainability into account. This includes coordination with the business model patterns, according to Gassmann, and integration into a business model canvas for the Easy2IoT product. Potential obstacles and problems are identified and evaluated in order to formulate a comprehensive and sustainable business model. In addition, sustainable payment models and distribution channels are developed. In summary, the article offers a well-founded insight into the systematic development of a sustainable business model for platform-based applications in SME sawmills, with a particular focus on the synergy of ecological responsibility and economic efficiency.

Keywords: business model, sustainable business model, IIoT, IIoT-platform, industrie 4.0, big data

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Authors: Hamid Rostami Jaz, Kamran Ameri Siahooei

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Up to now different methods have been used to forecast the index returns and the index rate. Artificial intelligence and artificial neural networks have been one of the methods of index returns forecasting. This study attempts to carry out a comparative study on the performance of different Radial Base Neural Network and Feed-Forward Perceptron Neural Network to forecast investment returns on the index. To achieve this goal, the return on investment in Tehran Stock Exchange index is evaluated and the performance of Radial Base Neural Network and Feed-Forward Perceptron Neural Network are compared. Neural networks performance test is applied based on the least square error in two approaches of in-sample and out-of-sample. The research results show the superiority of the radial base neural network in the in-sample approach and the superiority of perceptron neural network in the out-of-sample approach.

Keywords: exchange index, forecasting, perceptron neural network, Tehran stock exchange

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Authors: Yu-Ping Chiu, Dung-Ying Lin

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Traditional staff scheduling methods, relying on employee experience, often lead to inefficiencies and resource waste. The challenges of transferring scheduling expertise and adapting to changing labor regulations further complicate this process. Manual approaches become increasingly impractical as companies accumulate complex scheduling rules over time. This study proposes an algorithmic optimization approach to address these issues, aiming to expedite scheduling while ensuring strict compliance with labor regulations and company policies. The method focuses on generating optimal schedules that minimize weighted company objectives within a compressed timeframe. Recognizing the limitations of conventional commercial software in modeling and solving complex real-world scheduling problems efficiently, this research employs Tabu Search with both long-term and short-term memory structures. The study will present numerical results and managerial insights to demonstrate the effectiveness of this approach in achieving intelligent and efficient staff scheduling.

Keywords: intelligent memory structures, mixed integer programming, meta-heuristics, staff scheduling problem, tabu search

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Authors: Rajwanlop Kumpoopong, Sukit Yindeesuk, Pornchai Silarom

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On May 5, 2014, an earthquake of magnitude 6.3 Richter hit the Northern part of Thailand. The epicenter was in Phan District, Chiang Rai Province. This earthquake or the so-called 2014-Chiang Rai Earthquake is the strongest ground shaking that Thailand has ever been experienced in her modern history. The 2014-Chiang Rai Earthquake confirms the geological evidence, which has previously been ignored by most engineers, that earthquakes of considerable magnitudes 6 to 7 Richter can occurr within the country. This promptly stimulates authorized agencies to pay more attention at the safety of their assets and promotes the comprehensive review of seismic resistance design of their building structures. The focus of this paper is to summarize the damages of highway bridges as a result of the 2014-Chiang Rai ground shaking, the remedy actions, and the research needs. The 2014-Chiang Rai Earthquake caused considerable damages to nearby structures such as houses, schools, and temples. The ground shaking, however, caused damage to only one highway bridge, Mae Laos Bridge, located several kilometers away from the epicenter. The damage of Mae Laos Bridge was in the form of concrete spalling caused by pounding of cap beam on the deck structure. The damage occurred only at the end or abutment span. The damage caused by pounding is not a surprise, but the pounding by only one bridge requires further investigation and discussion. Mae Laos Bridge is a river crossing bridge with relatively large approach structure. In as much, the approach structure is confined by strong retaining walls. This results in a rigid-like approach structure which vibrates at the acceleration approximately equal to the ground acceleration during the earthquake and exerts a huge force to the abutment causing the pounding of cap beam on the deck structure. Other bridges nearby have relatively small approach structures, and therefore have no capability to generate pounding. The effect of mass of the approach structure on pounding of cap beam on the deck structure is also evident by the damage of one pedestrian bridge in front of Thanthong Wittaya School located 50 meters from Mae Laos Bridge. The width of the approach stair of this bridge is wider than the typical one to accommodate the stream of students during pre- and post-school times. This results in a relatively large mass of the approach stair which in turn exerts a huge force to the pier causing pounding of cap beam on the deck structure during ground shaking. No sign of pounding was observed for a typical pedestrian bridge located at another end of Mae Laos Bridge. Although pounding of cap beam on the deck structure of the above mentioned bridges does not cause serious damage to bridge structure, this incident promotes the comprehensive review of seismic resistance design of highway bridges in Thailand. Given a proper mass and confinement of the approach structure, the pounding of cap beam on the deck structure can be easily excited even at the low to moderate ground shaking. In as much, if the ground shaking becomes stronger, the pounding is certainly more powerful. This may cause the deck structure to be unseated and fall off in the case of unrestrained bridge. For the bridge with restrainer between cap beam and the deck structure, the restrainer may prevent the deck structure from falling off. However, preventing free movement of the pier by the restrainer may damage the pier itself. Most highway bridges in Thailand have dowel bars embedded connecting cap beam and the deck structure. The purpose of the existence of dowel bars is, however, not intended for any seismic resistance. Their ability to prevent the deck structure from unseating and their effect on the potential damage of the pier should be evaluated. In response to this expected situation, Thailand Department of Highways (DOH) has set up a team to revise the standard practices for the seismic resistance design of highway bridges in Thailand. In addition, DOH has also funded the research project 'Seismic Resistance Evaluation of Pre- and Post-Design Modifications of DOH’s Bridges' with the scope of full-scale tests of single span bridges under reversed cyclic static loadings for both longitudinal and transverse directions and computer simulations to evaluate the seismic performance of the existing bridges and the design modification bridges. The research is expected to start in October, 2015.

Keywords: earthquake, highway bridge, Thailand, damage, pounding, seismic resistance

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Authors: Mujahid S. Alharbi

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The construction of process plants in oil and gas-producing countries, such as Saudi Arabia, necessitates substantial investment in design and building. Each new plant, while unique, includes common building types, suggesting an opportunity for design standardization. This study investigates the adoption of standardized Issue For Construction (IFC) packages for non-process buildings in Saudi Aramco. A SWOT analysis presents the strengths, weaknesses, opportunities, and threats of this approach. The approach's benefits are illustrated using the Hawiyah Unayzah Gas Reservoir Storage Program (HUGRSP) as a case study. Standardization not only offers significant cost savings and operational efficiencies but also expedites project timelines, reduces the potential for change orders, and fosters local economic growth by allocating building tasks to local contractors. Standardization also improves project management by easing interface constraints between different contractors and promoting adaptability to future industry changes. This research underscores the standardization of non-process buildings as a powerful strategy for cost optimization, efficiency enhancement, and local economic development in process plant construction within the oil and gas sector.

Keywords: building, construction, management, project, standardization

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Authors: Rakesh Kumar, Shashi Janeoo, Ankit Dhiman, Siddharth Chopra

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Arynes are versatile reactive intermediates that offer broad opportunities in green organic synthesis. Arynes are potential aryl group surrogates for the transition metal-free environment friendly arylation reactions. Regioselective arylations of quinolines were achieved by the reactions of quinoline N-oxides with aryne intermediates generated in situ from the Kobayashi precursors. Various 2-substituted quinolines provided 3-arylated-2-substituted quinolines under ambient conditions. Acridine N-oxides also reacted well and provided unusual 4-arylacridines. Various fluorine containing 2,3-diarylquinaolines prepared using this approach were evaluated for antibacterial activity and two compounds inhibited the drug-resistant strains of S-aureus with a good selectivity index. Further, the 2,3-diarylquinolines as the potential optoelectronic materials were prepared by the aryne chemistry approach and their optical and electronic properties for such applications are under study. The aryne intermediates provide an effective Green Chemistry tool to achieve versatile arylated heteroarenes for diverse applications.

Keywords: arynes, arylation, quinolines, acridines.

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Authors: Stefano Fantin

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The present analysis outcomes from the research undertaken in the course of the European-funded project EUNITY, which targets the gaps in research and development on cybersecurity and privacy between Europe and Japan. Under these auspices, the research presents a study on the policy approach of Japan, the EU and a number of Member States of the Union with regard to the handling and discovery of software vulnerabilities, with the aim of identifying methodological differences and similarities. This research builds upon a functional comparative analysis of both public policies and legal instruments from the identified jurisdictions. The result of this analysis is based on semi-structured interviews with EUNITY partners, as well as by the participation of the researcher to a recent report from the Center for EU Policy Study on software vulnerability. The European Union presents a rather fragmented legal framework on software vulnerabilities. The presence of a number of different legislations at the EU level (including Network and Information Security Directive, Critical Infrastructure Directive, Directive on the Attacks at Information Systems and the Proposal for a Cybersecurity Act) with no clear focus on such a subject makes it difficult for both national governments and end-users (software owners, researchers and private citizens) to gain a clear understanding of the Union’s approach. Additionally, the current data protection reform package (general data protection regulation), seems to create legal uncertainty around security research. To date, at the member states level, a few efforts towards transparent practices have been made, namely by the Netherlands, France, and Latvia. This research will explain what policy approach such countries have taken. Japan has started implementing a coordinated vulnerability disclosure policy in 2004. To date, two amendments can be registered on the framework (2014 and 2017). The framework is furthermore complemented by a series of instruments allowing researchers to disclose responsibly any new discovery. However, the policy has started to lose its efficiency due to a significant increase in reports made to the authority in charge. To conclude, the research conducted reveals two asymmetric policy approaches, time-wise and content-wise. The analysis therein will, therefore, conclude with a series of policy recommendations based on the lessons learned from both regions, towards a common approach to the security of European and Japanese markets, industries and citizens.

Keywords: cybersecurity, vulnerability, European Union, Japan

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Authors: Ahmed Amrani, Oussama Allali, Amira Ben Hamida, Felix Defrance, Stephanie Morland, Eva Pineau, Thomas Lacroix

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The energy planning process is a very complex task that involves several stakeholders and requires the consideration of several local and global factors and constraints. In order to optimize and simplify this process, we propose a tool-based iterative approach applied to district heating planning. We build our tool with the collaboration of a French territory using actual district data and implementing the European incentives. We set up an iterative process including data visualization and analysis, identification and extraction of information related to the area concerned by the operation, design of sustainable planning scenarios leveraging local renewable and recoverable energy sources, and finally, the evaluation of scenarios. The last step is performed by a dynamic digital twin replica of the city. Territory’s energy experts confirm that the tool provides them with valuable support towards sustainable energy planning.

Keywords: climate change, data management, decision support, digital twin, district heating, energy planning, renewables, smart city

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Authors: Ruiyi Zhang, Wanglin Yan

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Strict land-use plan is issued based on city planning act for controlling urbanization and conserving semi-natural landscape. And the agrarian land resource in the suburbs has indispensable socio-economic value and contributes to the sustainability of the regional environment. However, the agrarian hinterland of metropolitan is witnessing severe farmland conversion and abandonment, while the contribution of land-use planning to farmland conservation remains unclear in those areas. Hypothetically, current land-use plan contributes to farmland loss. So, this research investigated the relationship between farmland loss and land-use planning at municipality level to provide base data for zoning in the metropolitan suburbs, and help to develop a sustainable land-use plan that will conserve the agrarian hinterland. As data and methods, 1) Farmland data of Census of Agriculture and Forestry for 2005 to 2015 and population data of 2015 and 2018 were used to investigate spatial distribution feathers of farmland loss in Tokyo Metropolitan District (TMD) for two periods: 2005-2010;2010-2015. 2) And the samples were divided by four urbanization facts. 3) DID data and zoning data for 2006 to 2018 were used to specify urbanization level of zones for describing land-use plan. 4) Then we conducted multiple regression between farmland loss, both abandonment and conversion amounts, and the described land-use plan in each of the urbanization scenario and in each period. As the results, the study reveals land-use plan has unignorable relation with farmland loss in the metropolitan suburbs at ward-city-town-village level. 1) The urban promotion areas planned larger than necessity and unregulated urbanization promote both farmland conversion and abandonment, and the effect weakens from inner suburbs to outer suburbs. 2) And the effect of land-use plan on farmland abandonment is more obvious than that on farmland conversion. The study advocates that, optimizing land-use plan will hopefully help the farmland conservation in metropolitan suburbs, which contributes to sustainable regional policy making.

Keywords: Agrarian land resource, land-use planning, urbanization level, multiple regression

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Authors: Kouzi Katia

Abstract:

This paper proposes a Direct Torque Control (DTC) algorithm of dual Stator Induction Motor (DSIM) drive using two approach intelligent techniques: Artificial Neural Network (ANN) approach replaces the switching table selector block of conventional DTC and Mamdani Fuzzy Logic controller (FLC) is used for stator resistance estimation. The fuzzy estimation method is based on an online stator resistance correction through the variations of stator current estimation error and its variation. The fuzzy logic controller gives the future stator resistance increment at the output. The main advantage of suggested algorithm control is to reduce the hardware complexity of conventional selectors, to avoid the drive instability that may occur in certain situation and ensure the tracking of the actual of the stator resistance. The effectiveness of the technique and the improvement of the whole system performance are proved by results.

Keywords: artificial neural network, direct torque control, dual stator induction motor, fuzzy logic estimator, switching table

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Authors: Francesca Radice

Abstract:

Domestic and sexual violence provokes, on average in Australia, one female death per week due to intimate violence behaviours. 83% of couples meet online, and intercepting domestic and sexual violence at this level would be beneficial. It has been observed that violent or coercive behaviour has been apparent from initial conversations on dating apps like Tinder. Child pornography, stalking, and coercive control are some criminal offences from dating apps, including women murdered after finding partners through Tinder. Police databases and predictive policing are novel approaches taken to prevent crime before harm is done. This research will investigate how police databases can be used in a privacy-preserving way to characterise users in terms of their potential for violent crime. Using the COPS database of NSW Police, we will explore how the past criminal record can be interpreted to yield a category of potential danger for each dating app user. It is up to the judgement of each subscriber on what degree of the potential danger they are prepared to enter into. Sentiment analysis is an area where research into natural language processing has made great progress over the last decade. This research will investigate how sentiment analysis can be used to interpret interchanges between dating app users to detect manipulative or coercive sentiments. These can be used to alert law enforcement if continued for a defined number of communications. One of the potential problems of this approach is the potential prejudice a categorisation can cause. Another drawback is the possibility of misinterpreting communications and involving law enforcement without reason. The approach will be thoroughly tested with cross-checks by human readers who verify both the level of danger predicted by the interpretation of the criminal record and the sentiment detected from personal messages. Even if only a few violent crimes can be prevented, the approach will have a tangible value for real people.

Keywords: sentiment analysis, data mining, predictive policing, virtual manipulation

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Authors: Prathmesh Vikas Patil, Yashaswini Lomte Patil

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Laminated composite materials have considerable attention in various engineering applications due to their exceptional strength-to-weight ratio and mechanical properties. The analysis of laminated composite plates in three-dimensional (3D) elasticity is a complex problem, as it requires accounting for the orthotropic anisotropic nature of the material and the interactions between multiple layers. Conventional approaches, such as the classical plate theory, provide simplified solutions but are limited in performing exact analysis of the plate. To address such a challenge, the state space method emerges as a powerful numerical technique for modeling the behavior of laminated composites in 3D. The state-space method involves transforming the governing equations of elasticity into a state-space representation, enabling the analysis of complex structural systems in a systematic manner. Here, an effort is made to perform a 3D elasticity analysis of plates with cross-ply and angle-ply laminates using the state space approach. The state space approach is used in this study as it is a mixed formulation technique that gives the displacements and stresses simultaneously with the same level of accuracy.

Keywords: cross ply laminates, angle ply laminates, state space method, three-dimensional elasticity analysis

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Authors: Sakti Mandal

Abstract:

Varying distribution of land surface temperature in an urbanized environment is a globally addressed phenomenon. Usually has been noticed that criticality of surface temperature increases from the periphery to the urban centre. As the centre experiences maximum severity of heat throughout the year, it also represents most critical zone in terms of thermal condition. In this present study, an attempt has been taken to propose a quantitative approach of thermal critical zonation (TCZ) on the basis of seasonal temperature variation. Here the zonation is done by calculating thermal critical value (TCV). From the Landsat 8 thermal digital data of summer and winter seasons for the year 2014, the land surface temperature maps and thermally critical zonation has been prepared, and corresponding dataset has been computed to conduct the overall study of that particular study area. It is shown that TCZ can be clearly identified and analyzed by the help of inter-seasonal temperature range. The results of this study can be utilized effectively in future urban development and planning projects as well as a framework for implementing rules and regulations by the authorities for a sustainable urban development through an environmentally affable approach.

Keywords: thermal critical values (TCV), thermally critical zonation (TCZ), land surface temperature (LST), Landsat 8, Kolkata Municipal Corporation (KMC)

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Authors: N. D'Amico, E. Grossi, B. Colombo, F. Rigiroli, M. Buscema, D. Fazzini, G. Cornalba, S. Papa

Abstract:

Purpose: To characterize, through a radiomic approach, the nature of nodules considered non-specific by expert radiologists, recognized in magnetic resonance mammography (MRm) with T1-weighted (T1w) sequences with paramagnetic contrast. Material and Methods: 47 cases out of 1200 undergoing MRm, in which the MRm assessment gave uncertain classification (non-specific nodules), were admitted to the study. The clinical outcome of the non-specific nodules was later found through follow-up or further exams (biopsy), finding 35 benign and 12 malignant. All MR Images were acquired at 1.5T, a first basal T1w sequence and then four T1w acquisitions after the paramagnetic contrast injection. After a manual segmentation of the lesions, done by a radiologist, and the extraction of 150 radiomic features (30 features per 5 subsequent times) a machine learning (ML) approach was used. An evolutionary algorithm (TWIST system based on KNN algorithm) was used to subdivide the dataset into training and validation test and to select features yielding the maximal amount of information. After this pre-processing, different machine learning systems were applied to develop a predictive model based on a training-testing crossover procedure. 10 cases with a benign nodule (follow-up older than 5 years) and 18 with an evident malignant tumor (clear malignant histological exam) were added to the dataset in order to allow the ML system to better learn from data. Results: NaiveBayes algorithm working on 79 features selected by a TWIST system, resulted to be the best performing ML system with a sensitivity of 96% and a specificity of 78% and a global accuracy of 87% (average values of two training-testing procedures ab-ba). The results showed that in the subset of 47 non-specific nodules, the algorithm predicted the outcome of 45 nodules which an expert radiologist could not identify. Conclusion: In this pilot study we identified a radiomic approach allowing ML systems to perform well in the diagnosis of a non-specific nodule at MR mammography. This algorithm could be a great support for the early diagnosis of malignant breast tumor, in the event the radiologist is not able to identify the kind of lesion and reduces the necessity for long follow-up. Clinical Relevance: This machine learning algorithm could be essential to support the radiologist in early diagnosis of non-specific nodules, in order to avoid strenuous follow-up and painful biopsy for the patient.

Keywords: breast, machine learning, MRI, radiomics

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Authors: Madiha Liaqat, Rehan Ahmed Khan, Shahid Kamal

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Multiple imputation with delta adjustment is a versatile and transparent technique for addressing univariate missing data in the presence of various missing mechanisms. This approach allows for the exploration of sensitivity to the missing-at-random (MAR) assumption. In this review, we outline the delta-adjustment procedure and illustrate its application for assessing the sensitivity to deviations from the MAR assumption. By examining diverse missingness scenarios and conducting sensitivity analyses, we gain valuable insights into the implications of missing data on our analyses, enhancing the reliability of our study's conclusions. In our study, we focused on assessing logPSA, a continuous biomarker in incomplete prostate cancer data, to examine the robustness of conclusions against plausible departures from the MAR assumption. We introduced several approaches for conducting sensitivity analyses, illustrating their application within the pattern mixture model (PMM) under the delta adjustment framework. This proposed approach effectively handles missing data, particularly loss to follow-up.

Keywords: loss to follow-up, incomplete response, multiple imputation, sensitivity analysis, prostate cancer

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Authors: Naman Solanki, Souraj Mondal

Abstract:

With the world moving towards online communication, the video datastore has exploded in the past few years. Consequently, it has become crucial to analyse participant’s engagement levels in online communication videos. Engagement prediction of people in videos can be useful in many domains, like education, client meetings, dating, etc. Video-level or frame-level prediction of engagement for a user involves the development of robust models that can capture facial micro-emotions efficiently. For the development of an engagement prediction model, it is necessary to have a widely-accepted standard dataset for engagement analysis. DAiSEE is one of the datasets which consist of in-the-wild data and has a gold standard annotation for engagement prediction. Earlier research done using the DAiSEE dataset involved training and testing standard models like CNN-based models, but the results were not satisfactory according to industry standards. In this paper, a multi-level classification approach has been introduced to create a more robust model for engagement analysis using the DAiSEE dataset. This approach has recorded testing accuracies of 0.638, 0.7728, 0.8195, and 0.866 for predicting boredom level, engagement level, confusion level, and frustration level, respectively.

Keywords: computer vision, engagement prediction, deep learning, multi-level classification

Procedia PDF Downloads 114