Search results for: data reduction

Authors: Teresa McGrath, Ryan Johnson, Rebecca Stamm, Cassidy Clarity, Wei Yung Lui

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Building and construction is the second largest plastic user behind packaging, accounting for 16% of plastic production. Building and construction is also by far the largest user of one of the most impactful plastics, polyvinyl chloride (aka vinyl or PVC), accounting for 69% of PVC production. Building materials also have an outsized contribution to plastic pollution, including microplastic pollution. Yet building materials are often overlooked in plastic waste and pollution reduction efforts. Habitable will present a plastics and petrochemical analysis of over 250 common building material types and demonstrate how changes to building material selection towards safer, renewable, and lower carbon materials can reduce global consumption of plastics and associated pollution.

Keywords: building materials, fenceline communities, microplastics, safer alternatives, embodied carbon, life cycle analysis, petrochemicals, green chemistry

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Authors: Miguel Angel Calvo Salve

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Building enclosures and façades not only have an aesthetic component they must also ensure thermal comfort and improve the acoustics and air quality in buildings. The role of facades design, its assemblies, and construction are key in developing a greener future in architecture. This research and study focus on the design of a multi-leaves building envelope, with a self-supporting wall of granite slabs. The study will demonstrate the advantages of its use in compare with the hanging stone veneer in a vented cladding system. Using the Design of the School of Music and Theatre of the Atlantic Area in Spain as a case study where the multi-leaves enclosure system consists in a self-supported outer leaf of large granite slabs of 15cm. of thickness, a vent cavity with thermal isolation, a brick wall, and a series of internal layers. The methodology used were simulations and data collected in building. The advantages of the self-supporting wall of granite slabs in the outer leaf (15cm). compared with a hanging stone veneer in a vented cladding system can summarize the goals as follows: Using the stone in more natural way, by compression. The weight of the stone slabs goes directly to a strip-footing and don't overload the reinforced concrete structure of the building. The weight of the stone slabs provides an external aerial soundproofing, preventing the sound transmission to the structure. The thickness of the stone slabs is enough to provide the external waterproofing of the building envelope. The self-supporting system with minimum anchorages allows having a continuous and external thermal isolation without thermal bridges. The thickness of ashlars masonry provides a thermal inertia that balances the temperatures between day and night in the external thermal insulation layer. The absence of open joints gives the quality of a continuous envelope transmitting the sensations of the stone, the heaviness in the facade, the rhythm of the music and the sequence of the theatre. The main cost of stone due his bigger thickness is more than compensated with the reduction in assembly costs. Don´t need any substructure systems for hanging stone veneers.

Keywords: self-supporting wall, stone cladding systems, hanging veneer cladding systems, sustainability of facade systems

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Authors: Nithipattara Balsiri

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The purposes of this research was to develop instructional model by using research-based learning enhancing critical thinking, cognitive skills, and culture and local wisdom knowledge of undergraduate students. The sample consisted of 307 undergraduate students. Critical thinking and cognitive skills test were employed for data collection. Second-order confirmatory factor analysis, t-test, and one-way analysis of variance were employed for data analysis using SPSS and LISREL programs. The major research results were as follows; 1) the instructional model by using research-based learning enhancing critical thinking, cognitive skills, and culture and local wisdom knowledge should be consists of 6 sequential steps, namely (1) the setting research problem (2) the setting research hypothesis (3) the data collection (4) the data analysis (5) the research result conclusion (6) the application for problem solving, and 2) after the treatment undergraduate students possessed a higher scores in critical thinking and cognitive skills than before treatment at the 0.05 level of significance.

Keywords: critical thinking, cognitive skills, culture and local wisdom knowledge

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Authors: H. Mahdavinezhad, M. Labbaf, H. R. Tavakoli

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In recent decades, the study and control of the destructive effects of explosive vibration in construction projects has received more attention, and several experimental equations in the field of vibration prediction as well as allowable vibration limit for various structures are presented. Researchers have developed a number of experimental equations to estimate the peak particle velocity (PPV), in which the experimental constants must be obtained at the site of the explosion by fitting the data from experimental explosions. In this study, the most important of these equations was evaluated for strong massive conglomerates around Dez Dam by collecting data on explosions, including 30 particle velocities, 27 displacements, 27 vibration frequencies and 27 acceleration of earth vibration at different distances; they were recorded in the form of two types of detonation systems, NUNEL and electric. Analysis showed that the data from the explosion had the best correlation with the cube root of the explosive, R2=0.8636, but overall the correlation coefficients are not much different. To estimate the vibration in this project, data regression was performed in the other formats, which resulted in the presentation of new equation with R2=0.904 correlation coefficient. Finally according to the importance of the studied structures in order to ensure maximum non damage to adjacent structures for each diagram, a range of application was defined so that for distances 0 to 70 meters from blast site, exponent n=0.33 and for distances more than 70 m, n =0.66 was suggested.

Keywords: blasting, blast-induced vibration, empirical equations, PPV, tunnel

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Authors: Lincy Stephen, N. Yogesh, G. Vasantharajan, V. Subramanian

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This paper presents the design and simulation of a five band metamaterial absorber at microwave frequencies. The absorber unit cell consists of squares and strips arranged as the top layer and a metallic ground plane as the bottom layer on a dielectric substrate. Simulation results show five near perfect absorption bands at 3.15 GHz, 7.15 GHz, 11.12 GHz, 13.87 GHz, and 16.85 GHz with absorption magnitudes 99.68%, 99.05%, 96.98%, 98.36% and 99.44% respectively. Further, the proposed absorber exhibits polarization insensitivity and wide angle receptivity. The surface current analysis is presented to explain the mechanism of absorption in the structure. With these preferable features, the proposed absorber can be excellent choice for potential applications such as electromagnetic interference (EMI) shielding, radar cross section reduction.

Keywords: electromagnetic absorber, metamaterial, multi- band, polarization insensitive, wide angle receptive

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Authors: Dinmukhamed T., Vassiliy S.

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This article suggests the idea of fitting clothes and accessories based on augmented reality. A logical data model has been developed, taking into account the decision-making module (colors, style, type, material, popularity, etc.) based on personal data (age, gender, weight, height, leg size, hoist length, geolocation, photogrammetry, number of purchases of certain types of clothing, etc.) and statistical data of the purchase history (number of items, price, size, color, style, etc.). Also, in order to provide information to the user, it is planned to develop an augmented reality system using a QR code. This system of selection and fitting of clothing and accessories based on augmented reality will be used in stores to reduce the time for the buyer to make a decision on the choice of clothes.

Keywords: augmented reality, online store, decision-making module, like QR code, clothing store, queue

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Authors: Genady Grabarnik, Serge Yaskolko

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Generative Artificial Intelligence (AI) has significantly expanded its applicability with the incorporation of Large Language Models (LLMs) and become a technology with promise to automate some areas that were very difficult to automate before. The paper describes the introduction of generative Artificial Intelligence into Introductory Computer and Data Science courses and analysis of effect of such introduction. The generative Artificial Intelligence is incorporated in the educational process two-fold: For the instructors, we create templates of prompts for generation of tasks, and grading of the students work, including feedback on the submitted assignments. For the students, we introduce them to basic prompt engineering, which in turn will be used for generation of test cases based on description of the problems, generating code snippets for the single block complexity programming, and partitioning into such blocks of an average size complexity programming. The above-mentioned classes are run using Large Language Models, and feedback from instructors and students and courses’ outcomes are collected. The analysis shows statistically significant positive effect and preference of both stakeholders.

Keywords: introductory computer and data science education, generative AI, large language models, application of LLMS to computer and data science education

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Authors: Dorith Tavor, Adi Wolfson

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In the past two decades a variety of green solvents have been proposed, including water, ionic liquids, fluorous solvents, and supercritical fluids. However, their implementation in industrial processes is still limited due to their tedious and non-sustainable synthesis, lack of experimental data and familiarity, as well as operational restrictions and high cost. Several years ago we presented, for the first time, the use of glycerol-based solvents as alternative sustainable reaction mediums in both catalytic and non-catalytic organic synthesis. Glycerol is the main by-product from the conversion of oils and fats in oleochemical production. Moreover, in the past decade, its price has substantially decreased due to an increase in supply from the production and use of fatty acid derivatives in the food, cosmetics, and drugs industries and in biofuel synthesis, i.e., biodiesel. The renewable origin, beneficial physicochemical properties and reusability of glycerol-based solvents, enabled improved product yield and selectivity as well as easy product separation and catalyst recycling. Furthermore, their high boiling point and polarity make them perfect candidates for non-conventional heating and mixing techniques such as ultrasound- and microwave-assisted reactions. Finally, in some reactions, such as catalytic transfer-hydrogenation or transesterification, they can also be used simultaneously as both solvent and reactant. In our ongoing efforts to design a viable protocol that will facilitate the acceptance of glycerol and its derivatives as sustainable solvents, pure glycerol and glycerol triacetate (triacetin) as well as various glycerol-triacetin mixtures were tested as sustainable solvents in several representative organic reactions, such as nucleophilic substitution of benzyl chloride to benzyl acetate, Suzuki-Miyaura cross-coupling of iodobenzene and phenylboronic acid, baker’s yeast reduction of ketones, and transfer hydrogenation of olefins. It was found that reaction performance was affected by the glycerol to triacetin ratio, as the solubility of the substrates in the solvent determined product yield. Thereby, employing optimal glycerol to triacetin ratio resulted in maximum product yield. In addition, using glycerol-based solvents enabled easy and successful separation of the products and recycling of the catalysts.

Keywords: glycerol, green chemistry, sustainability, catalysis

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Authors: Yasuyuki Takahashi, Hirotaka Shimada, Takao Kanzaki

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A Dual-detector SPECT system is widely by use of myocardial SPECT studies. With 180-degree (180°) acquisition, reconstructed images are distorted in the posterior wall of myocardium due to the lack of sufficient data of posterior projection. We hypothesized that quality of myocardial SPECT images can be improved by the addition of data acquisition of only a few posterior projections to ordinary 180° acquisition. The proposed acquisition method (180° plus acquisition methods) uses the dual-detector SPECT system with a pair of detector arranged in 90° perpendicular. Sampling angle was 5°, and the acquisition range was 180° from 45° right anterior oblique to 45° left posterior oblique. After the acquisition of 180°, the detector moved to additional acquisition position of reverse side once for 2 projections, twice for 4 projections, or 3 times for 6 projections. Since these acquisition methods cannot be done in the present system, actual data acquisition was done by 360° with a sampling angle of 5°, and projection data corresponding to above acquisition position were extracted for reconstruction. We underwent the phantom studies and a clinical study. SPECT images were compared by profile curve analysis and also quantitatively by contrast ratio. The distortion was improved by 180° plus method. Profile curve analysis showed increased of cardiac cavity. Analysis with contrast ratio revealed that SPECT images of the phantoms and the clinical study were improved from 180° acquisition by the present methods. The difference in the contrast was not clearly recognized between 180° plus 2 projections, 180° plus 4 projections, and 180° plus 6 projections. 180° plus 2 projections method may be feasible for myocardial SPECT because distortion of the image and the contrast were improved.

Keywords: 180° plus acquisition method, a few posterior projections, dual-detector SPECT system, myocardial SPECT

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Authors: Besma Khalfoun

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In many current Human Activity Recognition (HAR) applications, users' data is frequently shared and centrally stored by third parties, posing a significant privacy risk. This practice makes these entities attractive targets for extracting sensitive information about users, including their identity, health status, and location, thereby directly violating users' privacy. To tackle the issue of centralized data storage, a relatively recent paradigm known as federated learning has emerged. In this approach, users' raw data remains on their smartphones, where they train the HAR model locally. However, users still share updates of their local models originating from raw data. These updates are vulnerable to several attacks designed to extract sensitive information, such as determining whether a data sample is used in the training process, recovering the training data with inversion attacks, or inferring a specific attribute or property from the training data. In this paper, we first introduce PUR-Attack, a parameter-based user re-identification attack developed for HAR applications within a federated learning setting. It involves associating anonymous model updates (i.e., local models' weights or parameters) with the originating user's identity using background knowledge. PUR-Attack relies on a simple yet effective machine learning classifier and produces promising results. Specifically, we have found that by considering the weights of a given layer in a HAR model, we can uniquely re-identify users with an attack success rate of almost 100%. This result holds when considering a small attack training set and various data splitting strategies in the HAR model training. Thus, it is crucial to investigate protection methods to mitigate this privacy threat. Along this path, we propose SAFER, a privacy-preserving mechanism based on adaptive local differential privacy. Before sharing the model updates with the FL server, SAFER adds the optimal noise based on the re-identification risk assessment. Our approach can achieve a promising tradeoff between privacy, in terms of reducing re-identification risk, and utility, in terms of maintaining acceptable accuracy for the HAR model.

Keywords: federated learning, privacy risk assessment, re-identification risk, privacy preserving mechanisms, local differential privacy, human activity recognition

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Authors: Dermot O'Brien, Vasileios Christaras, Georgios Fontaras, Igor Nai Fovino, Ioannis Kounelis

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With the rise of the Internet of Things (IoT), 5G, and blockchain (BC) technologies, vehicles are becoming ever increasingly connected and are already transmitting substantial amounts of data to the original equipment manufacturers (OEMs) servers. This data could be used to help detect mileage fraud and enable more accurate vehicle emissions monitoring. This would not only help regulators but could enable applications such as permitting efficient drivers to pay less tax, geofencing for air quality improvement, as well as pollution tolling and trading platforms for transport-related businesses and EU citizens. Other applications could include traffic management and shared mobility systems. BC enables the transmission of data with additional security and removes single points of failure while maintaining data provenance, identity ownership, and the possibility to retain varying levels of privacy depending on the requirements of the applied use case. This research performs simulations of vehicles interacting with European member state authorities and European Commission BC nodes that are running hyperleger fabric and explores whether the technology is currently feasible for transport applications such as the emission monitoring use-case.

Keywords: future transportation systems, technological innovations, policy approaches for transportation future, economic and regulatory trends, blockchain

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Authors: Santiago Macho, Miquel Montaner, Raivo Ruusalepp, Ferran Candela, Xavier Tarres, Rando Rostok

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During our lives, we generate a lot of personal information such as photos, music, text documents and videos that link us with our past. This data that used to be tangible is now digital information stored in our computers, which implies a software dependence to make them accessible in the future. Technology, however, constantly evolves and goes through regular shifts, quickly rendering various file formats obsolete. The need for accessing data in the future affects not only personal users but also organizations. In a digital environment, a reliable preservation plan and the ability to adapt to fast changing technology are essential for maintaining data collections in the long term. We present in this paper the European FP7 project called DURAFILE that provides the technology to preserve media files for personal users and organizations while maintaining their quality.

Keywords: artificial intelligence, digital preservation, social search, digital preservation plans

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Authors: Zhidi Lei, Xixi Cai, Jue Ding, Peifen Weng, Xiaowei Li

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In the process of preparing thin films by chemical solution method, the uniformity of gel coating has a great influence on the subsequent film thickness. Based on a coating device, the research tracks the interface development of gas-liquid flow by volume of fluid method (VOF). The effects of fluid viscosity and wall wetting property for the shape and position of the coating window are discussed in the process of slot die coating. The result shows that downstream contact lines gets closer to the corner with the increase of fluid viscosity. When the viscosity increases from 0.2Pa∙s to 0.3Pa∙s, 18.2% of the vortex region area will be reduced. With the static contact angle of upper die head surface (θ_sd) increasing, X_u decreased gradually which cause the instability changes of upstream surface. Also, θ_sd increasing brings the reduction of vortex region.

Keywords: film growth, vortex, VOF, slot die coating

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Authors: Adie, Julius Undiukeye

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This research investigated the practice of academic leadership succession planning in Nigerian higher education institutions, drawing on the lived experiences of the academic staff of the case study institutions. It is multi-case study research that adopts a qualitative research method. Ten participants (mainly academic staff) were used as the study sample. The study was guided by four research questions. Semi-structured interviews and archival information from official documents formed the sources of data. The data collected was analyzed using the Constant Comparative Technique (CCT) to generate empirical insights and facts on the subject of this paper. The following findings emerged from the data analysis: firstly, there was no formalized leadership succession plan in place in the institutions that were sampled for this study; secondly, despite the absence of a formal succession plan, the data indicates that academics believe that succession planning is very significant for institutional survival; thirdly, existing practices of succession planning in the sampled institutions, takes the forms of job seniority ranking, political process and executive fiat, ad-hoc arrangement, and external hiring; and finally, data revealed that there are some barriers to the practice of succession planning, such as traditional higher education institutions’ characteristics (e.g. external talent search, shared governance, diversity, and equality in leadership appointment) and the lack of interest in leadership positions. Based on the research findings, some far-reaching recommendations were made, including the urgent need for the ‘formalization’ of leadership succession planning by the higher education institutions concerned, through the design of an official policy framework.

Keywords: academic leadership, succession, planning, higher education

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Authors: Yasmeen Bassas, Sandra Kuebler, Allen Riddell

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Native language identification is one of the growing subfields in natural language processing (NLP). The task of native language identification (NLI) is mainly concerned with predicting the native language of an author’s writing in a second language. In this paper, we investigate the performance of two types of features; content-based features vs. content independent features, when they are evaluated on a different corpus (using social media data “Reddit”). In this NLI task, the predefined models are trained on one corpus (TOEFL), and then the trained models are evaluated on different data using an external corpus (Reddit). Three classifiers are used in this task; the baseline, linear SVM, and logistic regression. Results show that content-based features are more accurate and robust than content independent ones when tested within the corpus and across corpus.

Keywords: NLI, NLP, content-based features, content independent features, social media corpus, ML

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Authors: Young Cheol Choi, Eun-Jin Moon, Sung-Won Yoo, Sang-Hwa Jung, In-Hwan Yang

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Stainless steel slag (STS) is a by-product generated from the stainless steel refining process. The recycling of STS produced in Korea for construction applications is limited due to its poor hydraulic properties. On the other hand, STS has high carbonation reactivity to CO2 as it contains gamma-C2S content. This material is ideal for mineral carbonation which is one of the techniques proposed for carbon emission reduction. The objective of this study is to investigate the feasibility of developing a zero-cement STS binder activated by carbonation as alternative cementitious material. The quantitative analyses for CO2 uptake of STS powder and STS blended cement were investigated using thermogravimetric analysis (TGA), X-ray diffraction (XRD). In addition, the compressive strength and microstructure of STS pastes after CO2 curing were evaluated. Test results showed that STS can be activated by carbonation to gain a sufficient strength as alternative cementitious material.

Keywords: gamma-C2S, CO2 uptake, carbonation, stainless steel slag

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Authors: B. Madhevan, R. Sakkaravarthi, R. Diya

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The number and variety of mobile robot applications are increasing day by day, both in an industry and in our daily lives. First developed as a tool, nowadays mobile robots can be integrated as an entity in Internet-accessible resources. The present work is organized around four potential resources such as cloud computing, Internet of things, Big data analysis and Co-simulation. Further, the focus relies on integrating, analyzing and discussing the need for integrating Internet-accessible resources and the challenges deriving from such integration, and how these issues have been tackled. Hence, the research work investigates the concepts of the Internet-accessible resources from the aspect of the autonomous mobile robots with an overview of the performances of the currently available database systems. IaR is a world-wide network of interconnected objects, can be considered an evolutionary process in mobile robots. IaR constitutes an integral part of future Internet with data analysis, consisting of both physical and virtual things.

Keywords: internet-accessible resources, cloud computing, big data analysis, internet of things, mobile robot

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Authors: Nishaben Dholakiya, Anirban Roy, Ranjan Dey

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The unprecedented rise in global temperatures has triggered complex changes in marine ecosystems, necessitating a deeper understanding of seawater's thermophysical properties by experimentally measuring ultrasonic velocity and density at varying temperatures and salinity. This study investigates the critical relationship between temperature variations and molecular-level interactions in Arabian Sea surface waters, specifically focusing on internal pressure (π) and cohesion energy density (CED) as key indicators of ecosystem disruption. Our experimental findings reveal that elevated temperatures significantly reduce internal pressure, weakening the intermolecular forces that maintain seawater's structural integrity. This reduction in π correlates directly with decreased habitat stability for marine organisms, particularly affecting pressure-sensitive species and their physiological processes. Similarly, the observed decline in cohesion energy density at higher temperatures indicates a fundamental shift in water molecule organization, impacting the dissolution and distribution of vital nutrients and gases. These molecular-level changes cascade through the ecosystem, affecting everything from planktonic organisms to complex food webs. By employing advanced machine learning techniques, including Stacked Ensemble Machine Learning (SEML) and AdaBoost (AB), we developed highly accurate predictive models (>99% accuracy) for these thermophysical parameters. The results provide crucial insights into the mechanistic relationship between climate warming and marine ecosystem degradation, offering valuable data for environmental policymaking and conservation strategies. The novelty of this research serves as no such thermodynamic investigation has been conducted before in literature, whereas this research establishes a quantitative framework for understanding how molecular-level changes in seawater properties directly influence marine ecosystem stability, emphasizing the urgent need for climate change mitigation efforts.

Keywords: thermophysical properties, Arabian Sea, internal pressure, cohesion energy density, machine learning

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Authors: Susan Evans, Janna Gordon-Elliott, Katarzyna Wyka, Virginia Mutch

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During the rise of the COVID-19 pandemic, healthcare workers needed an intervention that could address their profound acute stress. Mindfulness-based stress reduction (MBSR) is a program that has long established effectiveness for mental and physical health outcomes. In recent years, MBSR has been modified such that the duration of both class time and number of sessions has been abbreviated, and its delivery has been adapted for online dissemination, thus increasing the likelihood that individuals who could most benefit from the program would do so. We sought to investigate whether a brief, online version of MBSR could be feasible and acceptable for health care workers (HCW) in acute stress in response to the COVID-19 pandemic. Participants were recruited via an email sent to all hospital employees, which spans residents, physicians, nurses, housekeeping, lab technicians, administrators, and others. Participating HCW were asked about their previous experience with mindfulness and asked to commit to a minimum of 3 sessions. They were then provided with four weekly 1-hour sessions online that included the major mindfulness exercises taught during traditional MBSR programs (i.e., body scan, sitting meditation, mindful eating, and yoga). Participants were provided with supporting slides, videos, demonstrations and asked to track their practice. Hospital staff enrolled in the program; by the end of the first day of recruitment, 40 had applied; by the start date, about 100 were enrolled, and n attended a minimum of 3 sessions, supporting feasibility. Hospital staff also participated and practiced the mindfulness exercises (n=42), thus supporting acceptability. Participants reported that the program was logical, successful, and worth recommending both before starting the program and after completing it (M= 22.02 and M=21.76, respectively, possible range 0-27). There was a slight decline in the belief in improvement in health and well-being due to the program (ES=.37, p=.021). Secondary hypotheses regarding participants’ self-reported stress and levels of mindfulness were also supported, such that participants reported improvements in perceived stress (ES=.45, p=.006), compassion satisfaction, burnout, and secondary traumatic stress (ES=.41, ES=.31, ES=.35, respectively, p<.05). Participants reported significant improvements in the describing facet of mindfulness (ES=.49, p=.004), while all other facets (observing, acting with awareness, nonjudging of inner experience, nonreactivity to inner experience) remained unchanged pre- to post-program. Results from this study suggest that an abridged, online version of MBSR is feasible and accessible to health care workers in acute stress and provides benefits expected from traditional MBSR programs. The lack of a randomized control group limits generalizability. We intend to provide a structure, framework, and lessons learned to hospital administrators and clinical staff seeking to support their employees in acute stress.

Keywords: acute stress, health care workers, mindfulness, online interventions

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Authors: Mohammad A. Alolayan, John S. Evans, James K. Hammitt

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The value of statistical life has been estimated for a middle eastern country with high economical subsidization system. In this study, in-person interviews were conducted on a stratified random sample to estimate the value of mortality risk. Double-bounded dichotomous choice questions followed by open-ended question were used in the interview to investigate the willingness to pay of the respondent for mortality risk reduction. High willingness to pay was found to be associated with high income and education. Also, females were found to have lower willingness to pay than males. The estimated value of statistical life is larger than the ones estimated for western countries where taxation system exists. This estimate provides a baseline for monetizing the health benefits for proposed policy or program to the decision makers in an eastern country. Also, the value of statistical life for a country in the region can be extrapolated from this this estimate by using the benefit transfer method.

Keywords: mortality, risk, VSL, willingness-to-pay

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Authors: Nur Aishah Binti Awi, Zulfiqar Khan

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Lean-kaizen has always been applied in manufacturing sector since many years ago. What about education sector? This paper discuss on how lean-kaizen can also be applied in education sector, specifically in academic area of Malaysian’s higher education sector. The purpose of this paper is to describe the application of lean kaizen in course plan and delivery. Lean-kaizen techniques have been used to identify waste in the course plan and delivery. A field study has been conducted to obtain the data. This study used both quantitative and qualitative data. The researcher had interviewed the chosen lecturers regarding to the problems of course plan and delivery that they encountered. Secondary data of students’ feedback at the end of semester also has been used to improve course plan and delivery. The result empirically shows that lean-kaizen helps to improve the course plan and delivery by reducing the wastes. Thus, this study demonstrates that lean-kaizen can also help education sector to improve their services as achieved by manufacturing sector.

Keywords: course delivery, education, Kaizen, lean

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Authors: Ali Cheraghian, Farshid Hajati, Soheila Gheisari, Yongsheng Gao

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In this paper, we present a novel 2.5D face recognition method based on Gabor Discrete Cosine Transform (GDCT). In the proposed method, the Gabor filter is applied to extract feature vectors from the texture and the depth information. Then, Discrete Cosine Transform (DCT) is used for dimensionality and redundancy reduction to improve computational efficiency. The system is combined texture and depth information in the decision level, which presents higher performance compared to methods, which use texture and depth information, separately. The proposed algorithm is examined on publically available Bosphorus database including models with pose variation. The experimental results show that the proposed method has a higher performance compared to the benchmark.

Keywords: Gabor filter, discrete cosine transform, 2.5d face recognition, pose

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Authors: Reza Rezaeipour Honarmandzad

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In this paper we propose an ANN for detection and localization of fatigue damage in aircraft structures. We used network of piezoelectric transducers for Lamb-wave measurements in order to calculate damage indices. Data gathered by the sensors was given to neural network classifier. A set of neural network electors of different architecture cooperates to achieve consensus concerning the state of each monitored path. Sensed signal variations in the ROI, detected by the networks at each path, were used to assess the state of the structure as well as to localize detected damage and to filter out ambient changes. The classifier has been extensively tested on large data sets acquired in the tests of specimens with artificially introduced notches as well as the results of numerous fatigue experiments. Effect of the classifier structure and test data used for training on the results was evaluated.

Keywords: ANN, fatigue damage, aircraft structures, piezoelectric transducers, lamb-wave measurements

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Authors: Joaquin Ticzon, Christian Demition, Jaime Honra

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Additive manufacturing (AM) or 3D printing has been one of the emerging trends present in various industries, particularly in prototyping. This review focuses on the impact of additive manufacturing on a motor vehicle's performance aiming to investigate potential advancements to further revolutionize the way parts are manufactured. One of the most common problems faced in the automotive industry is carbon footprint emissions from motor vehicles, which was stated to be remedied by lightweight; additively manufactured parts helped reduce these emissions due to weight reduction provided by additively manufactured parts. Composed of various techniques for AM as well as materials utilized during the manufacturing process, which differ in terms of the quality and performance it provides during its application on the final product. Given this, the generative design will not be discussed in such a detailed manner because the focus will revolve around the effects on the performance of a vehicle due to additively manufactured parts.

Keywords: additive manufacturing (AM), automotive, computer aided design (CAD), generative design

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Authors: Natalie Malone

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This study explores the role of a public library in the creation of social spaces for vulnerable populations. The data stems from a longitudinal ethnographic study of the Anderson Library community, which included field notes, artifacts, and interview data. Thematic analysis revealed multiple meanings and thematic relationships within and among the data sources -interviews, field notes, and artifacts. Initial analysis suggests the Anderson Library serves as a space for vulnerable populations, with the sub-themes of fostering interpersonal communication to create a social space for children and fostering interpersonal communication to create a social space for parents and adults. These findings are important as they illustrate the potential of public libraries to serve as community empowering institutions.

Keywords: capital, immigrant families, public libraries, space, vulnerable

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Authors: Mamiza Haq

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

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

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Authors: Bengan Simbarashe Manwere

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The entire African continent, comprising 55 countries, holds a 2% share of the global carbon market. The World Bank attributes the continent’s insignificant share and participation in the carbon market to the limited access to electricity. Approximately 800 million people spread across 47 African countries generate as much power as Spain, with a population of 45million. Only South Africa and North Africa have carbon-reduction investment opportunities on the continent and dominate the 2% market share of the global carbon market. On the back of the 2015 Paris Agreement, South Africa signed into law the Carbon Tax Act 15 of 2019 and the Customs and Excise Amendment Act 13 of 2019 (Gazette No. 4280) on 1 June 2019. By these laws, South Africa was ushered into the league of active global carbon market players. By increasing the cost of production by the rate of R120/tCO2e, the tax intentionally compels the internalization of pollution as a cost of production and, relatedly, stimulate investment in clean technologies. The first phase covered the 1 June 2019 – 31 December 2022 period during which the tax was meant to escalate at CPI + 2% for Scope 1 emitters. However, in the second phase, which stretches from 2023 to 2030, the tax will escalate at the inflation rate only as measured by the consumer price index (CPI). The Carbon Tax Act provides for carbon allowances as mitigation strategies to limit agents’ carbon tax liability by up to 95% for fugitive and process emissions. Although the June 2019 Carbon Tax Act explicitly makes provision for a carbon trading scheme (CTS), the carbon trading regulations thereof were only finalised in December 2020. This points to a delay in the establishment of a carbon trading scheme (CTS). Relatedly, emitters in South Africa are not able to benefit from the 95% reduction in effective carbon tax rate from R120/tCO2e to R6/tCO2e as the Johannesburg Stock Exchange (JSE) has not yet finalized the establishment of the market for trading carbon credits. Whereas most carbon trading schemes have been designed and constructed from the beginning as new tailor-made systems in countries the likes of France, Australia, Romania which treat carbon as a financial product, South Africa intends, on the contrary, to leverage existing trading infrastructure of the Johannesburg Stock Exchange (JSE) and the Clearing and Settlement platforms of Strate, among others, in the interest of the Paris Agreement timelines. Therefore the carbon trading scheme will not be constructed from scratch. At the same time, carbon will be treated as a commodity in order to align with the existing institutional and infrastructural capacity. This explains why the Carbon Tax Act is silent about the involvement of the Financial Sector Conduct Authority (FSCA).For South Africa, there is need to establish they equilibrium stability of the CTS. This is important as South Africa is an innovator in carbon trading and the successful trading of carbon credits on the JSE will lead to imitation by early adopters first, followed by the middle majority thereafter.

Keywords: carbon trading scheme (CTS), Johannesburg stock exchange (JSE), carbon tax act 15 of 2019, South Africa

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Authors: Nguyen-Thanh Son

Abstract:

Flood is among the most frequent and costliest natural hazards. The flood disasters especially affect the poor people in rural areas, who are heavily dependent on agriculture and have lower incomes. Cambodia is identified as one of the most climate-vulnerable countries in the world, ranked 13th out of 181 countries most affected by the impacts of climate change. Flood monitoring is thus a strategic priority at national and regional levels because policymakers need reliable spatial and temporal information on flood-prone areas to form successful monitoring programs to reduce possible impacts on the country’s economy and people’s likelihood. This study aims to develop methods for flood mapping and assessment from MODIS data in Cambodia. We processed the data for the period from 2000 to 2017, following three main steps: (1) data pre-processing to construct smooth time-series vegetation and water surface indices, (2) delineation of flood-prone areas, and (3) accuracy assessment. The results of flood mapping were verified with the ground reference data, indicating the overall accuracy of 88.7% and a Kappa coefficient of 0.77, respectively. These results were reaffirmed by close agreement between the flood-mapping area and ground reference data, with the correlation coefficient of determination (R²) of 0.94. The seasonally flooded areas observed for 2010, 2015, and 2016 were remarkably smaller than other years, mainly attributed to the El Niño weather phenomenon exacerbated by impacts of climate change. Eventually, although several sources potentially lowered the mapping accuracy of flood-prone areas, including image cloud contamination, mixed-pixel issues, and low-resolution bias between the mapping results and ground reference data, our methods indicated the satisfactory results for delineating spatiotemporal evolutions of floods. The results in the form of quantitative information on spatiotemporal flood distributions could be beneficial to policymakers in evaluating their management strategies for mitigating the negative effects of floods on agriculture and people’s likelihood in the country.

Keywords: MODIS, flood, mapping, Cambodia

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Authors: Omotayo Sarafadeen Amuda, Kazeem Kolapo Salam, Oyediran Olarike Favour

Abstract:

This study carried out the physicochemical analysis of the Textile WasteWater (TWW) before and after the adsorption and photocatalytic processes. The adsorbents and catalysts that were used for this study were prepared from C. albidum seed shell activated with steam and then loaded with Titanium Dioxide Nanoparticles (TiO2NPs) and Copper Nanoparticles (Cu NPs), which were synthesized from green tea leaf extract and Citrus limon fruits extract, respectively. The photocatalytic activity was carried out under sunlight irradiation, and the effect of various parameters, such as catalyst dose, pH, contact time, and initial dye concentration, on the removal efficiency, were studied. The reusability of the catalyst was also observed to determine its stability and long-term efficacy. Ultra-violet visible spectroscopy (UV-Vis spectroscopy) was used to determine the dye concentration after each experiment. The adsorbents, nanoparticles, and photocatalysts were appropriately characterized for morphological, functional group, structural, and surface area using Scanning Electron Microscopy (SEM), Fourier-Transform Infrared Spectroscopy (FTIR), X-ray diffraction (XRD) analysis, and Brunauer–Emmett–Teller (BET) analysis respectively. Batch adsorption studies were carried out on the wastewater, using the composite adsorbents, to determine the effects of pH, adsorbent dose, initial dye concentration, and contact time. The batch adsorption studies were conducted based on the runs generated from the Definitive Screen Design (DSD) of the Response Surface Methodology (RSM). The obtained data were subjected to the pseudo-first-order, pseudo-second-order, and intra-particle diffusion kinetic models, the Langmuir and Freundlich isotherm models, and thermodynamic parameters. The findings of this study contribute to the existing knowledge by providing more insights into the identification of efficient, low-cost, and environmentally-friendly approach to textile wastewater treatment. This approach enhances the reduction of potential toxicity from the discharged textile wastewater into the environment and, thus, conforms to Sustainable Development Goal 6 (SDG 6), which ensures the sustainability of the water resources, wastewater, and ecosystems.

Keywords: adsorption, photocatalytic, textile wastewater, green synthesized sequesters, degradation

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Authors: Samuel Nii Tackie, Oyebade K. Oyedotun, Ebenezer O. Olaniyi, Adnan Khashman

Abstract:

Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task; and the performances obtained from these networks evaluated in consideration of achieved recognition rates and training time.

Keywords: artificial neural network, data mining, classification, students’ evaluation

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