Search results for: family/personal problems

Authors: Sibawu Witness Siyepu

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This study explores the effects of instructional design using blended learning in the learning of radian measures among Engineering students. Blended learning is an education programme that combines online digital media with traditional classroom methods. It requires the physical presence of both lecturer and student in a mathematics computer laboratory. Blended learning provides element of class control over time, place, path or pace. The focus was on the use of Khan Academy to supplement traditional classroom interactions. Khan Academy is a non-profit educational organisation created by educator Salman Khan with a goal of creating an accessible place for students to learn through watching videos in a computer assisted computer. The researcher who is an also lecturer in mathematics support programme collected data through instructing students to watch Khan Academy videos on radian measures, and by supplying students with traditional classroom activities. Classroom activities entails radian measure activities extracted from the Internet. Students were given an opportunity to engage in class discussions, social interactions and collaborations. These activities necessitated students to write formative assessments tests. The purpose of formative assessments tests was to find out about the students’ understanding of radian measures, including errors and misconceptions they displayed in their calculations. Identification of errors and misconceptions serve as pointers of students’ weaknesses and strengths in their learning of radian measures. At the end of data collection, semi-structure interviews were administered to a purposefully sampled group to explore their perceptions and feedback regarding the use of blended learning approach in teaching and learning of radian measures. The study employed Algebraic Insight Framework to analyse data collected. Algebraic Insight Framework is a subset of symbol sense which allows a student to correctly enter expressions into a computer assisted systems efficiently. This study offers students opportunities to enter topics and subtopics on radian measures into a computer through the lens of Khan Academy. Khan academy demonstrates procedures followed to reach solutions of mathematical problems. The researcher performed the task of explaining mathematical concepts and facilitated the process of reinvention of rules and formulae in the learning of radian measures. Lastly, activities that reinforce students’ understanding of radian were distributed. Results showed that this study enthused the students in their learning of radian measures. Learning through videos prompted the students to ask questions which brought about clarity and sense making to the classroom discussions. Data revealed that sense making through reinvention of rules and formulae assisted the students in enhancing their learning of radian measures. This study recommends the use of Khan Academy in blended learning to be introduced as a socialisation programme to all first year students. This will prepare students that are computer illiterate to become conversant with the use of Khan Academy as a powerful tool in the learning of mathematics. Khan Academy is a key technological tool that is pivotal for the development of students’ autonomy in the learning of mathematics and that promotes collaboration with lecturers and peers.

Keywords: algebraic insight framework, blended learning, Khan Academy, radian measures

Procedia PDF Downloads 282

Authors: Gyu-Dong Kim, Wuk-Jae Yoo, Sang-Youl Lee

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Composite structures offer numerous advantages over conventional structural systems in the form of higher specific stiffness and strength, lower life-cycle costs, and benefits such as easy installation and improved safety. Recently, there has been a considerable increase in the use of composites in engineering applications and as wraps for seismic upgrading and repairs. However, these composites deteriorate with time because of outdated materials, excessive use, repetitive loading, climatic conditions, manufacturing errors, and deficiencies in inspection methods. In particular, damaged fibers in a composite result in significant degradation of structural performance. In order to reduce the failure probability of composites in service, techniques to assess the condition of the composites to prevent continual growth of fiber damage are required. Condition assessment technology and nondestructive evaluation (NDE) techniques have provided various solutions for the safety of structures by means of detecting damage or defects from static or dynamic responses induced by external loading. A variety of techniques based on detecting the changes in static or dynamic behavior of isotropic structures has been developed in the last two decades. These methods, based on analytical approaches, are limited in their capabilities in dealing with complex systems, primarily because of their limitations in handling different loading and boundary conditions. Recently, investigators have introduced direct search methods based on metaheuristics techniques and artificial intelligence, such as genetic algorithms (GA), simulated annealing (SA) methods, and neural networks (NN), and have promisingly applied these methods to the field of structural identification. Among them, GAs attract our attention because they do not require a considerable amount of data in advance in dealing with complex problems and can make a global solution search possible as opposed to classical gradient-based optimization techniques. In this study, we propose an alternative damage-detection technique that can determine the degraded stiffness distribution of vibrating laminated composites made of Glass Fiber-reinforced Polymer (GFRP). The proposed method uses a modified form of the bivariate Gaussian distribution function to detect degraded stiffness characteristics. In addition, this study presents a method to detect the fiber property variation of laminated composite plates from the micromechanical point of view. The finite element model is used to study free vibrations of laminated composite plates for fiber stiffness degradation. In order to solve the inverse problem using the combined method, this study uses only first mode shapes in a structure for the measured frequency data. In particular, this study focuses on the effect of the interaction among various parameters, such as fiber angles, layup sequences, and damage distributions, on fiber-stiffness damage detection.

Keywords: stiffness detection, fiber damage, genetic algorithm, layup sequences

Procedia PDF Downloads 238

Authors: William Ho, Agus Wicaksana

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Anecdotal evidence in the last decade shows that the occurrence of disruptive events and uncertainties in the supply chain is increasing. The coupling of these events with the nature of an increasingly complex and interdependent business environment leads to devastating impacts that quickly propagate within and across organizations. For example, the recent COVID-19 pandemic increased the global supply chain disruption frequency by at least 20% in 2020 and is projected to have an accumulative cost of $13.8 trillion by 2024. This crisis raises attention to organizational resilience to weather business uncertainty. However, the concept has been criticized for being vague and lacking a consistent definition, thus reducing the significance of the concept for practice and research. This study is intended to solve that issue by providing a comprehensive review of the conceptualization, measurement, and antecedents of operational resilience that have been discussed in the supply chain risk management literature (SCRM). We performed a Scholarly Network Analysis, combining citation-based and text-based approaches, on 252 articles published from 2000 to 2021 in top-tier journals based on three parameters: AJG ranking and ABS ranking, UT Dallas and FT50 list, and editorial board review. We utilized a hybrid scholarly network analysis by combining citation-based and text-based approaches to understand the conceptualization, measurement, and antecedents of operational resilience in the SCRM literature. Specifically, we employed a Bibliographic Coupling Analysis in the research cluster formation stage and a Co-words Analysis in the research cluster interpretation and analysis stage. Our analysis reveals three major research clusters of resilience research in the SCRM literature, namely (1) supply chain network design and optimization, (2) organizational capabilities, and (3) digital technologies. We portray the research process in the last two decades in terms of the exemplar studies, problems studied, commonly used approaches and theories, and solutions provided in each cluster. We then provide a conceptual framework on the conceptualization and antecedents of resilience based on studies in these clusters and highlight potential areas that need to be studied further. Finally, we leverage the concept of abnormal operating performance to propose a new measurement strategy for resilience. This measurement overcomes the limitation of most current measurements that are event-dependent and focus on the resistance or recovery stage - without capturing the growth stage. In conclusion, this study provides a robust literature review through a scholarly network analysis that increases the completeness and accuracy of research cluster identification and analysis to understand conceptualization, antecedents, and measurement of resilience. It also enables us to perform a comprehensive review of resilience research in SCRM literature by including research articles published during the pandemic and connects this development with a plethora of articles published in the last two decades. From the managerial perspective, this study provides practitioners with clarity on the conceptualization and critical success factors of firm resilience from the SCRM perspective.

Keywords: supply chain risk management, organizational resilience, scholarly network analysis, systematic literature review

Procedia PDF Downloads 49

Authors: Mohsina Aktar, Bishawjit Mallick

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Bangladesh faces the multidimensional manifestations of climate change e.g. flood, cyclone, sea level rise, drainage congestion, salinity, etc. This study aimed at to find out the community’s perception of the perceived impact of climate change on their wetland resource based livelihood, to analyze their present livelihood scenario and to find out required institutional setup to strengthen present livelihood scenario. Therefore, this study required both quantitative analysis like quantification of wetland resources, occupation, etc. and also exploratory information like policy and institutional reform. For quantitative information 200 questionnaire survey and in some cases observation survey and for socially shareable qualitative and quantitative issues case study and focus group discussion were conducted. In-Depth interview was conducted for socially non-shareable qualitative issues. The overall findings of this study have been presented maintaining a sequence- perception about climate change effect, livelihood scenario and required institutional support of the wetland community. Flood has been ranked where cyclone effect is comparatively less disastrous in this area. Heavy rainfall comes after the cyclone. Female members responded almost same about the ranking and effects of frequently occurred and devastating effects of climate change. People are much more aware of the impact of climate change. Training of Care in RVCC project helps to increase their knowledge level. If the level of education can be increased, people can fight against calamity and poverty with more confidence. People seem to overcome the problems of water logging and thus besides involving in Hydroponics (33.3%) as prime occupation in monsoon; they are also engaged in other business related activities. January to May is the low-income season for the farmers. But some people don’t want to change their traditional occupation and their age is above 45. The young earning member wants to utilize their lean income period by alternative occupation. People who do not have own land and performing water transportation or other types of occupation are now interested about Hydroponics. People who give their land on rent are now thinking about renting their land in monsoon as through that they can earn a sound amount rather than get nothing. What they require is just seed, training, and capital. Present marketing system faces the problem of communication. So this sector needed to be developed. Involvement of women in income earning activity is very low (5.1%), and 100% women are housewives. They became inferior due to their educational level and dominance of their husband. Only one NGO named BCAS (Bangladesh Center for Advanced Studies) has been found engage training facilities and advocacy for this purpose. Upazilla agricultural extension office like other GO remains inactive to give support the community for extension and improvement of Hydroponics agriculture. If the community gets proper support and inspiration, they can fight against crisis of low-income and climate change, with the Hydroponics cultivation system successfully.

Keywords: wetland community, hydroponics, climate change adaptation, livelihood

Procedia PDF Downloads 246

Authors: Nynne Nørup, Julie Helles Eriksen, Rikke M. Moalem, Else Skjold

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Despite a growing focus on the high environmental impact of textiles, textile waste is only recently considered as part of the waste field. Consequently, there is a general lack of knowledge and data within this field. Particularly the lack of a common perception of textiles generates several problems e.g., to recognize the full material potential the fraction contains, which is cruel if the textile must enter the circular economy. This study aims to qualify a method to make the resources in textile waste visible in a way that makes it possible to move them as high up in the waste hierarchy as possible. Textiles are complex and cover many different types of products, fibers and combinations of fibers and production methods. In garments alone, there is a great variety, even when narrowing it to only undergarments. However, textile waste is often reduced to one fraction, assessed solely by quantity, and compared to quantities of other waste fractions. Disregarding the complexity and reducing textiles to a single fraction that covers everything made of textiles increase the risk of neglecting the value of the materials, both with regards to their properties and economical. Instead of trying to fit textile waste into the current primarily linear waste system where volume is a key part of the business models, this study focused on integrating textile waste as a resource in the design and production phase. The study combined interdisciplinary methods for determining replacement rates used in Life Cycle Assessments and Mass Flow Analysis methods with the designer’s toolbox to hereby activate the properties of textile waste in a way that can unleash its potential optimally. It was hypothesized that by activating Denmark's tradition for design and high level of craftsmanship, it is possible to find solutions that can be used today and create circular resource models that reduce the use of virgin fibers. Through waste samples, case studies, and testing of various design approaches, this study explored how to functionalize the method so that the product after the end-use is kept as a material and only then processed at fiber level to obtain the best environmental utilization. The study showed that the designers' ability to decode the properties of the materials and understanding of craftsmanship were decisive for how well the materials could be utilized today. The later in the life cycle the textiles appeared as waste, the more demanding the description of the materials to be sufficient, especially if to achieve the best possible use of the resources and thus a higher replacement rate. In addition, it also required adaptation in relation to the current production because the materials often varied more. The study found good indications that part of the solution is to use geodata i.e., where in the life cycle the materials were discarded. An important conclusion is that a fully developed method can help support better utilization of textile resources. However, it stills requires a better understanding of materials by the designers, as well as structural changes in business and society.

Keywords: circular economy, development of sustainable processes, environmental impacts, environmental management of textiles, environmental sustainability through textile recycling, interdisciplinary method development, resource optimization, recycled textile materials and the evaluation of recycling, sustainability and recycling opportunities in the textile and apparel sector

Procedia PDF Downloads 54

Authors: Piotr Kacejko, Mariusz Duk, Miroslaw Wendeker

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This paper presents an adaptive controller to track the maximum power point of a photovoltaic modules (PV) under fast irradiation change on the city-bus roof. Photovoltaic systems have been a prominent option as an additional energy source for vehicles. The Municipal Transport Company (MPK) in Lublin has installed photovoltaic panels on its buses roofs. The solar panels turn solar energy into electric energy and are used to load the buses electric equipment. This decreases the buses alternators load, leading to lower fuel consumption and bringing both economic and ecological profits. A DC–DC boost converter is selected as the power conditioning unit to coordinate the operating point of the system. In addition to the conversion efficiency of a photovoltaic panel, the maximum power point tracking (MPPT) method also plays a main role to harvest most energy out of the sun. The MPPT unit on a moving vehicle must keep tracking accuracy high in order to compensate rapid change of irradiation change due to dynamic motion of the vehicle. Maximum power point track controllers should be used to increase efficiency and power output of solar panels under changing environmental factors. There are several different control algorithms in the literature developed for maximum power point tracking. However, energy performances of MPPT algorithms are not clarified for vehicle applications that cause rapid changes of environmental factors. In this study, an adaptive MPPT algorithm is examined at real ambient conditions. PV modules are mounted on a moving city bus designed to test the solar systems on a moving vehicle. Some problems of a PV system associated with a moving vehicle are addressed. The proposed algorithm uses a scanning technique to determine the maximum power delivering capacity of the panel at a given operating condition and controls the PV panel. The aim of control algorithm was matching the impedance of the PV modules by controlling the duty cycle of the internal switch, regardless of changes of the parameters of the object of control and its outer environment. Presented algorithm was capable of reaching the aim of control. The structure of an adaptive controller was simplified on purpose. Since such a simple controller, armed only with an ability to learn, a more complex structure of an algorithm can only improve the result. The presented adaptive control system of the PV system is a general solution and can be used for other types of PV systems of both high and low power. Experimental results obtained from comparison of algorithms by a motion loop are presented and discussed. Experimental results are presented for fast change in irradiation and partial shading conditions. The results obtained clearly show that the proposed method is simple to implement with minimum tracking time and high tracking efficiency proving superior to the proposed method. This work has been financed by the Polish National Centre for Research and Development, PBS, under Grant Agreement No. PBS 2/A6/16/2013.

Keywords: adaptive control, photovoltaic energy, city bus electric load, DC-DC converter

Procedia PDF Downloads 181

Authors: Alaa Abdellatif, Gabrièle Breda

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Background. Advanced therapy medicinal products (ATMPs) are innovative therapies that mainly target orphan diseases and high unmet medical needs. ATMP includes gene therapy medicinal products (GTMP), somatic cell therapy medicinal products (CTMP), and tissue-engineered therapies (TEP). Since legislation opened the way in 2007, 25 ATMPs have been approved in the EU, which is about the same amount as the U.S. Food and Drug Administration. However, not all of the ATMPs that have been approved have successfully reached the market and retained their approval. Objectives. We aim to understand all the factors limiting the market access to very promising therapies in a systemic approach, to be able to overcome these problems, in the future, with scientific, regulatory and commercial innovations. Further to recent reviews that focus either on specific countries, products, or dimensions, we will address all the challenges faced by ATMP development today. Methodology. We used mixed methods and a multi-level approach for data collection. First, we performed an updated academic literature review on ATMP development and their scientific and market access challenges (papers published between 2018 and April 2023). Second, we analyzed industry feedback from cell and gene therapy webinars and white papers published by providers and pharmaceutical industries. Finally, we established a comparative analysis of the regulatory guidelines published by EMA and the FDA for ATMP approval. Results: The main challenges in bringing these therapies to market are the high development costs. Developing ATMPs is expensive due to the need for specialized manufacturing processes. Furthermore, the regulatory pathways for ATMPs are often complex and can vary between countries, making it challenging to obtain approval and ensure compliance with different regulations. As a result of the high costs associated with ATMPs, challenges in obtaining reimbursement from healthcare payers lead to limited patient access to these treatments. ATMPs are often developed for orphan diseases, which means that the patient population is limited for clinical trials which can make it challenging to demonstrate their safety and efficacy. In addition, the complex manufacturing processes required for ATMPs can make it challenging to scale up production to meet demand, which can limit their availability and increase costs. Finally, ATMPs face safety and efficacy challenges: dangerous adverse events of these therapies like toxicity related to the use of viral vectors or cell therapy, starting material and donor-related aspects. Conclusion. As a result of our mixed method analysis, we found that ATMPs face a number of challenges in their development, regulatory approval, and commercialization and that addressing these challenges requires collaboration between industry, regulators, healthcare providers, and patient groups. This first analysis will help us to address, for each challenge, proper and innovative solution(s) in order to increase the number of ATMPs approved and reach the patients

Keywords: advanced therapy medicinal products (ATMPs), product development, market access, innovation

Procedia PDF Downloads 49

Authors: Zuhal Eltayeb Awad

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Alryad neighborhood is located in Khartoum town– the administrative center of the Capital of Sudan. The neighborhood is one of the high-income residential areas with villa type development of low-density. It was planned and developed in 1972 with large plots (600-875m²), wide crossing roads and balanced environment. Recently the area transformed into more compact urban form of high density, mixed-use integrated development with more intensive use of land; multi-storied apartments. The most important socio-economic process in the neighborhood has been the commercialization and deinitialization of the area in connect with the displacement of the residential function. This transformation affected the quality of the neighborhood and the inter-related features of the built environment. A case study approach was chosen to gather the necessary qualitative and quantitative data. A detailed survey on existing development pattern was carried out over the whole area of Alryad. Data on the built and social environment of the neighborhoods were collected through observations, interviews and secondary data sources. The paper reflected a theoretical and empirical interest in the particular characteristics of compact neighborhood with high density, and mixed land uses and their effect on social wellbeing of the residents all in the context of the sustainable development. The research problem is focused on the challenges of transformation that associated with compact neighborhood that created multiple urban problems, e.g., stress of essential services (water supply, electricity, and drainage), congestion of streets and demand for parking. The main objective of the study is to analyze the transformation of this area from residential use to commercial and administrative use. The study analyzed the current situation of the neighborhood compared to the five principles of sustainable neighborhood prepared by UN Habitat. The study found that the neighborhood is experienced changes that occur to inner-city residential areas and the process of change of the neighborhood was originated by external forces due to the declining economic situation of the whole country. It is evident that non-residential uses have taken place uncontrolled, unregulated and haphazardly that led to damage the residential environment and deficiency in infrastructure. The quality of urban life and in particular on levels of privacy was reduced, the neighborhood changed gradually to be a central business district that provides services to the whole Khartoum town. The change of house type may be attributed to a demand-led housing market and absence of policy. The results showed that Alryad is not fully sustainable and self-contained, street network characteristics and mixed land-uses development are compatible with the principles of sustainability. The area of streets represents 27.4% of the total area of the neighborhood. Residential density is 4,620 people/ km², that is lower than the recommendations, and the limited block land-use specialization is higher than 10% of the blocks. Most inhabitants have a high income so that there is no social mix in the neighborhood. The study recommended revision of the current zoning regulations in order to control and regulate undesirable development in the neighborhood and provide new solutions which allow promoting the neighborhood sustainable development.

Keywords: compact neighborhood, land uses, mixed use, residential area, transformation

Procedia PDF Downloads 102

Authors: Elizaveta S. Prokofyeva, Svetlana V. Maltseva, Roman D. Zaitsev

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Rapidly evolving modern data analysis technologies in healthcare play a large role in understanding the operation of the system and its characteristics. Nowadays, one of the key tasks in urban healthcare is to optimize the resource allocation. Thus, the application of data analysis in medical institutions to solve optimization problems determines the significance of this study. The purpose of this research was to establish the dependence between the indicators of the effectiveness of the medical institution and its resources. Hospital discharges by diagnosis; hospital days of in-patients and in-patient average length of stay were selected as the performance indicators and the demand of the medical facility. The hospital beds by type of care, medical technology (magnetic resonance tomography, gamma cameras, angiographic complexes and lithotripters) and physicians characterized the resource provision of medical institutions for the developed models. The data source for the research was an open database of the statistical service Eurostat. The choice of the source is due to the fact that the databases contain complete and open information necessary for research tasks in the field of public health. In addition, the statistical database has a user-friendly interface that allows you to quickly build analytical reports. The study provides information on 28 European for the period from 2007 to 2016. For all countries included in the study, with the most accurate and complete data for the period under review, predictive models were developed based on historical panel data. An attempt to improve the quality and the interpretation of the models was made by cluster analysis of the investigated set of countries. The main idea was to assess the similarity of the joint behavior of the variables throughout the time period under consideration to identify groups of similar countries and to construct the separate regression models for them. Therefore, the original time series were used as the objects of clustering. The hierarchical agglomerate algorithm k-medoids was used. The sampled objects were used as the centers of the clusters obtained, since determining the centroid when working with time series involves additional difficulties. The number of clusters used the silhouette coefficient. After the cluster analysis it was possible to significantly improve the predictive power of the models: for example, in the one of the clusters, MAPE error was only 0,82%, which makes it possible to conclude that this forecast is highly reliable in the short term. The obtained predicted values of the developed models have a relatively low level of error and can be used to make decisions on the resource provision of the hospital by medical personnel. The research displays the strong dependencies between the demand for the medical services and the modern medical equipment variable, which highlights the importance of the technological component for the successful development of the medical facility. Currently, data analysis has a huge potential, which allows to significantly improving health services. Medical institutions that are the first to introduce these technologies will certainly have a competitive advantage.

Keywords: data analysis, demand modeling, healthcare, medical facilities

Procedia PDF Downloads 113

Authors: I. M. L. Nadeesha Jayaweera, Adao Alex Trindade

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In choosing a candidate model in likelihood-based modeling via an information criterion, the practitioner is often faced with the difficult task of deciding just how far up the ranked list to look. Motivated by this pragmatic necessity, we construct an uncertainty band for a generalized (model selection) information criterion (GIC), defined as a criterion for which the limit in probability is identical to that of the normalized log-likelihood. This includes common special cases such as AIC & BIC. The method starts from the asymptotic normality of the GIC for the joint distribution of the candidate models in an independent and identically distributed (IID) data framework and proceeds by deriving the (asymptotically) exact distribution of the minimum. The calculation of an upper quantile for its distribution then involves the computation of multivariate Gaussian integrals, which is amenable to efficient implementation via the R package "mvtnorm". The performance of the methodology is tested on simulated data by checking the coverage probability of nominal upper quantiles and compared to the bootstrap. Both methods give coverages close to nominal for large samples, but the bootstrap is two orders of magnitude slower. The methodology is subsequently extended to two other commonly used model structures: regression and time series. In the regression case, we derive the corresponding asymptotically exact distribution of the minimum GIC invoking Lindeberg-Feller type conditions for triangular arrays and are thus able to similarly calculate upper quantiles for its distribution via multivariate Gaussian integration. The bootstrap once again provides a default competing procedure, and we find that similar comparison performance metrics hold as for the IID case. The time series case is complicated by far more intricate asymptotic regime for the joint distribution of the model GIC statistics. Under a Gaussian likelihood, the default in most packages, one needs to derive the limiting distribution of a normalized quadratic form for a realization from a stationary series. Under conditions on the process satisfied by ARMA models, a multivariate normal limit is once again achieved. The bootstrap can, however, be employed for its computation, whence we are once again in the multivariate Gaussian integration paradigm for upper quantile evaluation. Comparisons of this bootstrap-aided semi-exact method with the full-blown bootstrap once again reveal a similar performance but faster computation speeds. One of the most difficult problems in contemporary statistical methodological research is to be able to account for the extra variability introduced by model selection uncertainty, the so-called post-model selection inference (PMSI). We explore ways in which the GIC uncertainty band can be inverted to make inferences on the parameters. This is being attempted in the IID case by pivoting the CDF of the asymptotically exact distribution of the minimum GIC. For inference one parameter at a time and a small number of candidate models, this works well, whence the attained PMSI confidence intervals are wider than the MLE-based Wald, as expected.

Keywords: model selection inference, generalized information criteria, post model selection, Asymptotic Theory

Procedia PDF Downloads 62

Authors: A. K. Tekile, I. L. Kim, J. Y. Lee

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Human activities put freshwater quality under risk, mainly due to expansion of agriculture and industries, damming, diversion and discharge of inadequately treated wastewaters. The rapid human population growth and climate change escalated the problem. External controlling actions on point and non-point pollution sources are long-term solution to manage water quality. To have a holistic approach, these mechanisms should be coupled with the in-water control strategies. The available in-lake or river methods are either costly or they have some adverse effect on the ecological system that the search for an alternative and effective solution with a reasonable balance is still going on. This study aimed at the physical and chemical water quality improvement in a stagnant Yeo-cheon River reach (Korea), which has recently shown sign of water quality problems such as scum formation and fish death. The river water quality was monitored, for the duration of three months by operating only water flow generator in the first two weeks and then ultrasonic irradiation device was coupled to the flow unit for the remaining duration of the experiment. In addition to assessing the water quality improvement, the correlation among the parameters was analyzed to explain the contribution of the ultra-sonication. Generally, the combined strategy showed localized improvement of water quality in terms of dissolved oxygen, Chlorophyll-a and dissolved reactive phosphate. At locations under limited influence of the system operation, chlorophyll-a was highly increased, but within 25 m of operation the low initial value was maintained. The inverse correlation coefficient between dissolved oxygen and chlorophyll-a decreased from 0.51 to 0.37 when ultrasonic irradiation unit was used with the flow, showing that ultrasonic treatment reduced chlorophyll-a concentration and it inhibited photosynthesis. The relationship between dissolved oxygen and reactive phosphate also indicated that influence of ultra-sonication was higher than flow on the reactive phosphate concentration. Even though flow increased turbidity by suspending sediments, ultrasonic waves canceled out the effect due to the agglomeration of suspended particles and the follow-up settling out. There has also been variation of interaction in the water column as the decrease of pH and dissolved oxygen from surface to the bottom played a role in phosphorus release into the water column. The variation of nitrogen and dissolved organic carbon concentrations showed mixed trend probably due to the complex chemical reactions subsequent to the operation. Besides, the intensive rainfall and strong wind around the end of the field trial had apparent impact on the result. The combined effect of water flow and ultrasonic irradiation was a cumulative water quality improvement and it maintained the dissolved oxygen and chlorophyll-a requirement of the river for healthy ecological interaction. However, the overall improvement of water quality is not guaranteed as effectiveness of ultrasonic technology requires long-term monitoring of water quality before, during and after treatment. Even though, the short duration of the study conducted here has limited nutrient pattern realization, the use of ultrasound at field scale to improve water quality is promising.

Keywords: stagnant, ultrasonic irradiation, water flow, water quality

Procedia PDF Downloads 173

Authors: Rosemary R. Carlton, Roxane Caron

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Almost three decades ago, bell hooks (1994) identified the classroom as “the most radical space of possibility in the academy”. A feminist scholar, educator, and activist, hooks urged educators to transgress the boundaries of what might be customary or considered acceptable in teaching, thus encouraging the pursuit of new ways of knowing and different strategies for sharing knowledge. This paper reflects upon a particular response to hooks’ still relevant call for transgression in teaching. Specifically, this paper reports on the design, implementation, and preliminary analysis of a social work course integrating active pedagogy and transnational exchange to encourage students’ critical thinking and autonomous learning in their development as social workers in a global context. The bachelor’s level course, Pratiques spécifiques: Projet international, was developed collaboratively across three francophone institutions of higher learning in Belgium, Canada, and France: the Haute École de Namur-Liège-Luxembourg (Hénallux); the Université de Montréal; and, the Institut d’enseignement supérieur et professionnel, l’IRTS Paris Île-de-France. The driving aims of the course are to promote autonomous learning and critical thinking through a lens of transnational understandings of social problems -competencies indispensable to students’ development as social workers. The course is offered to two paired cohorts, one addressing the subject of “migrations” (Canada/France) and the other the subject of “sexual exploitation” (Canada/Belgium). Through the adaptation of a critical pedagogy of problem-based learning, students are called upon to actively engage in acquiring and applying knowledge to respond to “real life” social issues relating to migration or sexual exploitation. At the conclusion of the course, each cohort of students is brought together for a week-long intensive period of transnational exchange either at the Université de Montréal in Canada or at Hénallux in Belgium. Extending the bounds of the classroom across international borders allows students novel opportunities to deepen and expand their understandings of issues relating to predefined social issues and to critically examine associated social work practices. The paper opens with a presentation of the social work course. Specifically, the authors will outline their adaptation of a pedagogy of problem-based learning integrating transnational exchange in the design and implementation of the course. Returning to hooks’ notion of transgression in teaching, the paper offers a preliminary analysis of how and with what effect the course provides opportunities to transgress hierarchical student-teacher relationships; transgress conventional modes of learning to explore diverse sources of knowledge and transgress the walls of the university to engage with and learn from local and global partners. The paper concludes with a consideration of the potential influence of such transgressions in teaching for students’ development of critical thinking in their practice of social work in global context.

Keywords: active learning, critical pedagogy, social work intervention, transnational learning

Procedia PDF Downloads 141

Authors: Tomasz Stenzel, Daria Dziewulska, Ewa Łukaszuk, Joy Custer, Simona Kraberger, Arvind Varsani

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Viral diseases, especially those leading to impairment of the immune system, are among the most important problems in avian pathology. However, there is not much data available on this subject other than commercial poultry bird species. Recently, increasing attention has been paid to racing pigeons, which have been refined for many years in terms of their ability to return to their place of origin. Currently, these birds are used for races at distances from 100 to 1000 km, and winning pigeons are highly valuable. The rearing system of racing pigeons contradicts the principles of biosecurity, as birds originating from various breeding facilities are commonly transported and reared in “One Loft Race” (OLR) facilities. This favors the spread of multiple infections and provides conditions for the development of novel variants of various pathogens through recombination. One of the most significant viruses occurring in this avian species is the pigeon circovirus (PiCV), which is detected in ca. 70% of pigeons. Circoviruses are characterized by vast genetic diversity which is due to, among other things, the recombination phenomenon. It consists of an exchange of fragments of genetic material among various strains of the virus during the infection of one organism. The rate and intensity of the development of PiCV recombinants have not been determined so far. For this reason, an experiment was performed to investigate the frequency of development of novel PiCV recombinants in racing pigeons kept in OLR-type conditions. 15 racing pigeons originating from 5 different breeding facilities, subclinically infected with various PiCV strains, were housed in one room for eight weeks, which was supposed to mimic the conditions of OLR rearing. Blood and swab samples were collected from birds every seven days to recover complete PiCV genomes that were amplified through Rolling Circle Amplification (RCA), cloned, sequenced, and subjected to bioinformatic analyses aimed at determining the genetic diversity and the dynamics of recombination phenomenon among the viruses. In addition, virus shedding rate/level of viremia, expression of the IFN-γ and interferon-related genes, and anti-PiCV antibodies were determined to enable the complete analysis of the course of infection in the flock. Initial results have shown that 336 full PiCV genomes were obtained, exhibiting nucleotide similarity ranging from 86.6 to 100%, and 8 of those were recombinants originating from viruses of different lofts of origin. The first recombinant appeared after seven days of experiment, but most of the recombinants appeared after 14 and 21 days of joint housing. The level of viremia and virus shedding was the highest in the 2nd week of the experiment and gradually decreased to the end of the experiment, which partially corresponded with Mx 1 gene expression and antibody dynamics. The results have shown that the OLR pigeon-rearing system could play a significant role in spreading infectious agents such as circoviruses and contributing to PiCV evolution through recombination. Therefore, it is worth considering whether a popular gambling game such as pigeon racing is sensible from both animal welfare and epidemiological point of view.

Keywords: pigeon circovirus, recombination, evolution, one loft race

Procedia PDF Downloads 48

Authors: Ivy Chen XQ

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This research places a much-needed focus on the conservation of the Blue Economy (BE) by focusing on the design and development of monitoring systems to track critical indicators on the status of the BE. In this process, local experiences provide an insight into important community issues, as well as the necessity to cooperate and collaborate in order to achieve sustainable options. Researchers worldwide and industry initiatives over the last decade show that the exploitation of marine resources has resulted in a significant decrease in the share of total allowable catch (TAC). The result has been strengthening law enforcement, yet the results have shown that problems were related to poor policies, a lack of understanding of over-exploitation, biological uncertainty and political pressures. This reality and other statistics that show a significant negative impact on the attainment of the Sustainable Development Goals (SDGs), warrant an emphasis on the development of national M&E systems, in order to provide evidence-based information, on the nature and scale of especially transnational fisheries crime and under-sea marine resources in the BE. In particular, a need exists to establish a compendium of relevant BE indicators to assess such impact against the SDGs by using selected SDG indicators for this purpose. The research methodology consists of ATLAS.ti qualitative approach and a case study will be developed of Illegal, unregulated and unreported (IUU) poaching and Illegal Wildlife Trade (IWT) as component of the BE as it relates to the case of abalone in southern Africa and Far East. This research project will make an original contribution through the analysis and comparative assessment of available indicators, in the design process of M&E systems and developing indicators and monitoring frameworks in order to track critical trends and tendencies on the status of the BE, to ensure specific objectives to be aligned with the indicators of the SDGs framework. The research will provide a set of recommendations to governments and stakeholders involved in such projects on lessons learned, as well as priorities for future research. The research findings will enable scholars, civil society institutions, donors and public servants, to understand the capability of the M&E systems, the importance of showing multi-level governance, in the coordination of information management, together with knowledge management (KM) and M&E at the international, regional, national and local levels. This coordination should focus on a sustainable development management approach, based on addressing socio-economic challenges to the potential and sustainability of BE, with an emphasis on ecosystem resilience, social equity and resource efficiency. This research and study focus are timely as the opportunities of the post-Covid-19 crisis recovery package will be grasped to set the economy on a path to sustainable development in line with the UN 2030 Agenda. The pandemic raises more awareness for the world to eliminate IUU poaching and illegal wildlife trade (IWT).

Keywords: Blue Economy (BE), transnational governance, Monitoring and Evaluation (M&E), Sustainable Development Goals (SDGs).

Procedia PDF Downloads 152

Authors: Anahita Mortazavi Manesh, Mojtaba Bagherzadeh

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Metalloporphyrins are well known to mimic the activity of monooxygenase enzymes. In this regard, metalloporphyrin complexes have been largely employed as valuable biomimetic catalysts, owing to the critical roles they play in oxygen transfer processes in catalytic oxidation reactions. Investigating in this area is based on different strategies to design selective, stable and high turnover catalytic systems. Immobilization of expensive metalloporphyrin catalysts onto supports appears to be a good way to improve their stability, selectivity and the catalytic performance because of the support environment and other advantages with respect to recovery, reuse. In other words, supporting metalloporphyrins provides a physical separation of active sites, thus minimizing catalyst self-destruction and dimerization of unhindered metalloporphyrins. Furthermore, heterogeneous catalytic oxidations have become an important target since their process are used in industry, helping to minimize the problems of industrial waste treatment. Hence, the immobilization of these biomimetic catalysts is much desired. An attractive approach is the preparation of the heterogeneous catalyst involves immobilization of complexes on silica coated magnetic nano-particles. Fe3O4@SiO2 magnetic nanoparticles have been studied extensively due to their superparamagnetism property, large surface area to volume ratio and easy functionalization. Using heterogenized homogeneous catalysts is an attractive option to facile separation of catalyst, simplified product work-up and continuity of catalytic system. Homogeneous catalysts immobilized on magnetic nanoparticles (MNPs) surface occupy a unique position due to combining the advantages of both homogeneous and heterogeneous catalysts. In addition, superparamagnetic nature of MNPs enable very simple separation of the immobilized catalysts from the reaction mixture using an external magnet. In the present work, an efficient heterogeneous catalyst was prepared by immobilizing manganese porphyrin on functionalized magnetic nanoparticles through the amino propyl linkage. The prepared catalyst was characterized by elemental analysis, FT-IR spectroscopy, X-ray powder diffraction, atomic absorption spectroscopy, UV-Vis spectroscopy, and scanning electron microscopy. Application of immobilized metalloporphyrin in the oxidation of various organic substrates was explored using Gas chromatographic (GC) analyses. The results showed that the supported Mn-porphyrin catalyst (Fe3O4@SiO2-NH2@MnPor) is an efficient and reusable catalyst in oxidation reactions. Our catalytic system exhibits high catalytic activity in terms of turnover number (TON) and reaction conditions. Leaching and recycling experiments revealed that nanocatalyst can be recovered several times without loss of activity and magnetic properties. The most important advantage of this heterogenized catalytic system is the simplicity of the catalyst separation in which the catalyst can be separated from the reaction mixture by applying a magnet. Furthermore, the separation and reuse of the magnetic Fe3O4 nanoparticles were very effective and economical.

Keywords: Fe3O4 nanoparticle, immobilized metalloporphyrin, magnetically separable nanocatalyst, oxidation reactions

Procedia PDF Downloads 274

Authors: Faris Algahtani

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Background: In Saudi Arabia, it is essential to know what practices are employed and considered effective by special education teachers working with preschool children with intellectual disabilities, as a prerequisite for identifying areas for improvement. Preschool provision for these children is expanding through a network of Intellectual Disability Centres while, in primary schools, a policy of inclusion is pursued and, in mainstream preschools, pilots have been aimed at enhancing learning in readiness for primary schooling. This potentially widens the attainment gap between preschool children with and without intellectual disabilities, and influences the scope for improvement. Goal: The aim of the study was to explore special education teachers’ practices and perceived perceptions of those practices for preschool children with intellectual disabilities in Saudi Arabia Method: A qualitative interpretive approach was adopted in order to gain a detailed understanding of how special education teachers in an IDC operate in the classroom. Fifteen semi-structured interviews were conducted with experienced and qualified teachers. Data were analysed using thematic analysis, based on themes identified from the literature review together with new themes emerging from the data. Findings: American methods strongly influenced teaching practices, in particular TEACCH (Treatment and Education of Autistic and Communication related handicapped Children), which emphasises structure, schedules and specific methods of teaching tasks and skills; and ABA (Applied Behaviour Analysis), which aims to improve behaviours and skills by concentrating on detailed breakdown and teaching of task components and rewarding desired behaviours with positive reinforcement. The Islamic concept of education strongly influenced which teaching techniques were used and considered effective, and how they were applied. Tensions were identified between the Islamic approach to disability, which accepts differences between human beings as created by Allah in order for people to learn to help and love each other, and the continuing stigmatisation of disability in many Arabic cultures, which means that parents who bring their children to an IDC often hope and expect that their children will be ‘cured’. Teaching methods were geared to reducing behavioural problems and social deficits rather than to developing the potential of the individual child, with some teachers recognizing the child’s need for greater freedom. Relationships with parents could in many instances be improved. Teachers considered both initial teacher education and professional development to be inadequate for their needs and the needs of the children they teach. This can be partly attributed to the separation of training and development of special education teachers from that of general teachers. Conclusion: Based on the findings, teachers’ practices could be improved by the inclusion of general teaching strategies, parent-teacher relationships and practical teaching experience in both initial teacher education and professional development. Coaching and mentoring support from carefully chosen special education teachers could assist the process, as could the presence of a second teacher or teaching assistant in the classroom.

Keywords: special education, intellectual disabilities, early intervention , early childhood

Procedia PDF Downloads 116

Authors: Seyed Hossein Hosseini, Seyed Majid Hashemzadeh

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Solar energy is one of the remarkable renewable energy sources that have particular characteristics such as unlimited, no environmental pollution, and free access. Generally, solar energy can be used in thermal and photovoltaic (PV) types. The cost of installation of the PV system is very high. Additionally, due to dependence on environmental situations such as solar radiation and ambient temperature, electrical power generation of this system is unpredictable and without power electronics devices, there is no guarantee to maximum power delivery at the output of this system. Maximum power point tracking (MPPT) should be used to achieve the maximum power of a PV string. MPPT is one of the essential parts of the PV system which without this section, it would be impossible to reach the maximum amount of the PV string power and high losses are caused in the PV system. One of the noticeable challenges in the problem of MPPT is the partial shading conditions (PSC). In PSC, the output photocurrent of the PV module under the shadow is less than the PV string current. The difference between the mentioned currents passes from the module's internal parallel resistance and creates a large negative voltage across shaded modules. This significant negative voltage damages the PV module under the shadow. This condition is called hot-spot phenomenon. An anti-paralleled diode is inserted across the PV module to prevent the happening of this phenomenon. This diode is known as the bypass diode. Due to the performance of the bypass diode under PSC, the P-V curve of the PV string has several peaks. One of the P-V curve peaks that makes the maximum available power is the global peak. Model-based Global MPPT (GMPPT) methods can estimate the optimal point with higher speed than other GMPPT approaches. Centralized, modular, and interleaved DC-DC converter topologies are the significant structures that can be used for GMPPT at a PV string. there are some problems in the centralized structure such as current mismatch losses at PV sting, loss of power of the shaded modules because of bypassing by bypass diodes under PSC, needing to series connection of many PV modules to reach the desired voltage level. In the modular structure, each PV module is connected to a DC-DC converter. In this structure, by increasing the amount of demanded power from the PV string, the number of DC-DC converters that are used at the PV system will increase. As a result, the cost of the modular structure is very high. We can implement the model-based GMPPT through the multi-input interleaved boost DC-DC converter to increase the power extraction from the PV string and reduce hot-spot and current mismatch error in a PV string under different environmental condition and variable load circumstances. The interleaved boost DC-DC converter has many privileges than other mentioned structures, such as high reliability and efficiency, better regulation of DC voltage at DC link, overcome the notable errors such as module's current mismatch and hot spot phenomenon, and power switches voltage stress reduction.

Keywords: solar energy, photovoltaic systems, interleaved boost converter, maximum power point tracking, model-based method, partial shading conditions

Procedia PDF Downloads 102

Authors: Carin Chuang, Kuan-Chou Chen

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An Enterprise Resource Planning (ERP) system is an integrated information system that supports the seamless integration of all the business processes of a company. Implementing an ERP system can increase efficiencies and decrease the costs while helping improve productivity. Many organizations including large, medium and small-sized companies have already adopted an ERP system for decades. Although ERP system can bring competitive advantages to organizations, the lack of proper training approach in ERP implementation is still a major concern. Organizations understand the importance of ERP training to adequately prepare managers and users. The low return on investment, however, for the ERP training makes the training difficult for knowledgeable workers to transfer what is learned in training to the jobs at workplace. Inadequate and inefficient ERP training limits the value realization and success of an ERP system. That is the need to call for a profound change and innovation for ERP training in both workplace at industry and the Information Systems (IS) education in academia. The innovated ERP training approach can improve the users’ knowledge in business processes and hands-on skills in mastering ERP system. It also can be instructed as educational material for IS students in universities. The purpose of the study is to examine the use of ERP simulation games via the ERPsim system to train the IS students in learning ERP implementation. The ERPsim is the business simulation game developed by ERPsim Lab at HEC Montréal, and the game is a real-life SAP (Systems Applications and Products) ERP system. The training uses the ERPsim system as the tool for the Internet-based simulation games and is designed as online student competitions during the class. The competitions involve student teams with the facilitation of instructor and put the students’ business skills to the test via intensive simulation games on a real-world SAP ERP system. The teams run the full business cycle of a manufacturing company while interacting with suppliers, vendors, and customers through sending and receiving orders, delivering products and completing the entire cash-to-cash cycle. To learn a range of business skills, student needs to adopt individual business role and make business decisions around the products and business processes. Based on the training experiences learned from rounds of business simulations, the findings show that learners have reduced risk in making mistakes that help learners build self-confidence in problem-solving. In addition, the learners’ reflections from their mistakes can speculate the root causes of the problems and further improve the efficiency of the training. ERP instructors teaching with the innovative approach report significant improvements in student evaluation, learner motivation, attendance, engagement as well as increased learner technology competency. The findings of the study can provide ERP instructors with guidelines to create an effective learning environment and can be transferred to a variety of other educational fields in which trainers are migrating towards a more active learning approach.

Keywords: business simulations, ERP implementation training, ERPsim, game-based learning, instructional strategy, training innovation

Procedia PDF Downloads 110

Authors: Davit Pipoyan, Meline Beglaryan, Nicolò Merendino

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Mining industry is one of the priority sectors of Armenian economy. Along with the solution of some socio-economic development, it brings about numerous environmental problems, especially toxic element pollution, which largely influences the safety of agricultural products. In addition, accumulation of toxic elements in agricultural products, mainly in edible parts of plants represents a direct pathway for their penetration into the human food chain. In Armenia, the share of plant origin food in overall diet is significantly high, so estimation of dietary intakes of toxic trace elements via consumption of selected fruits and vegetables are of great importance for observing the underlying health risks. Therefore, the present study was aimed to assess dietary exposure of potentially toxic trace elements through the intake of locally grown fruits and vegetables in Akhtala community (Armenia), where not only mining industry is developed, but also cultivation of fruits and vegetables. Moreover, this investigation represents one of the very first attempts to estimate human dietary exposure of potentially toxic trace elements in the study area. Samples of some commonly grown fruits and vegetables (fig, cornel, raspberry, grape, apple, plum, maize, bean, potato, cucumber, onion, greens) were randomly collected from several home gardens located near mining areas in Akhtala community. The concentration of Cu, Mo, Ni, Cr, Pb, Zn, Hg, As and Cd in samples were determined by using an atomic absorption spectrophotometer (AAS). Precision and accuracy of analyses were guaranteed by repeated analysis of samples against NIST Standard Reference Materials. For a diet study, individual-based approach was used, so the consumption of selected fruits and vegetables was investigated through food frequency questionnaire (FFQ). Combining concentration data with contamination data, the estimated daily intakes (EDI) and cumulative daily intakes were assessed and compared with health-based guidance values (HBGVs). According to the determined concentrations of the studied trace elements in fruits and vegetables, it can be stressed that some trace elements (Cu, Ni, Pb, Zn) among the majority of samples exceeded maximum allowable limits set by international organizations. Meanwhile, others (Cr, Hg, As, Cd, Mo) either did not exceed these limits or still do not have established allowable limits. The obtained results indicated that only for Cu the EDI values exceeded dietary reference intake (0.01 mg/kg/Bw/day) for some investigated fruits and vegetables in decreasing order of potato > grape > bean > raspberry > fig > greens. In contrast to this, for combined consumption of selected fruits and vegetables estimated cumulative daily intakes exceeded reference doses in the following sequence: Zn > Cu > Ni > Mo > Pb. It may be concluded that habitual and combined consumption of the above mentioned fruits and vegetables can pose a health risk to the local population. Hence, further detailed studies are needed for the overall assessment of potential health implications taking into consideration adverse health effects posed by more than one toxic trace element.

Keywords: daily intake, dietary exposure, fruits, trace elements, vegetables

Procedia PDF Downloads 274

Authors: Muhammad Shuaibu Birniwa

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Automobile Independent Workshop Service Technicians (popularly called roadside mechanics) are technical personals that repairs most of the automobile vehicles in Nigeria. Majority of these mechanics acquired their skills through apprenticeship training. Modern vehicle imported into the country posed greater challenges to the present automobile technicians particularly in the area of carrying out maintenance repairs of these latest automobile vehicles (autotronics vehicle) due to their inability to possessed autotronic skills competency. To source for solution to the above mentioned problems, therefore a research is carried out in North – Western region of Nigeria to produce a suitable maintenance training modules that can be used to train the technicians for them to upgrade/acquire the needed competencies for successful maintenance repair of the autotronic vehicles that were running everyday on the nation’s roads. A cluster sampling technique is used to obtain a sample from the population. The population of the study is all autotronic inclined lecturers, instructors and independent workshop service technicians that are within North – Western region of Nigeria. There are seven states (Jigawa, Kaduna, Kano, Katsina, Kebbi, Sokoto and Zamfara) in the study area, these serves as clusters in the population. Five (5) states were randomly selected to serve as the sample size. The five states are Jigawa, Kano, Katsina, Kebbi and Zamfara, the entire population of the five states which serves as clusters is (183), lecturers (44), instructors (49) and autotronic independent workshop service technicians (90), all of them were used in the study because of their manageable size. 183 copies of autotronic maintenance training module questionnaires (AMTMQ) with 174 and 149 question items respectively were administered and collected by the researcher with the help of an assistants, they are administered to 44 Polytechnic lecturers in the department of mechanical engineering, 49 instructors in skills acquisition centres/polytechnics and 90 master craftsmen of an independent workshops that are autotronic inclined. Data collected for answering research questions 1, 3, 4 and 5 were analysed using SPSS software version 22, Grand Mean and standard deviation were used to answer the research questions. Analysis of Variance (ANOVA) was used to test null hypotheses one (1) to three (3) and t-test statistical tool is used to analyzed hypotheses four (4) and five (5) all at 0.05 level of significance. The research conducted revealed that; all the objectives, contents/tasks, facilities, delivery systems and evaluation techniques contained in the questionnaire were required for the development of the autotronic maintenance training modules for independent workshop service technicians in the north – western zone of Nigeria. The skills upgrade training conducted by federal government in collaboration with SURE-P, NAC and SMEDEN was not successful because the educational status of the target population was not considered in drafting the needed training modules. The mode of training used does not also take cognizance of the theoretical aspect of the trainees, especially basic science which rendered the programme ineffective and insufficient for the tasks on ground.

Keywords: autotronics, roadside, mechanics, technicians, independent

Procedia PDF Downloads 47

Authors: V. Mantey, N. Findlay, I. Maddox

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The quality and quantity of global satellite data have been increasing exponentially in recent years as spaceborne systems become more affordable and the sensors themselves become more sophisticated. This is a valuable resource for many applications, including disaster management and relief. However, while more information can be valuable, the volume of data available is impossible to manually examine. Therefore, the question becomes how to extract as much information as possible from the data with limited manpower. Buildings are a key feature of interest in satellite imagery with applications including telecommunications, population models, and disaster relief. Machine learning tools are fast becoming one of the key resources to solve this problem, and models have been developed to detect buildings in optical satellite imagery. However, by and large, most models focus on affluent regions where buildings are generally larger and constructed further apart. This work is focused on the more difficult problem of detection in populated regions. The primary challenge with detecting small buildings in densely populated regions is both the spatial and spectral resolution of the optical sensor. Densely packed buildings with similar construction materials will be difficult to separate due to a similarity in color and because the physical separation between structures is either non-existent or smaller than the spatial resolution. This study finds that training models until they are overfitting the input sample can perform better in these areas than a more robust, generalized model. An overfitted model takes less time to fine-tune from a generalized pre-trained model and requires fewer input data. The model developed for this study has also been fine-tuned using existing, open-source, building vector datasets. This is particularly valuable in the context of disaster relief, where information is required in a very short time span. Leveraging existing datasets means that little to no manpower or time is required to collect data in the region of interest. The training period itself is also shorter for smaller datasets. Requiring less data means that only a few quality areas are necessary, and so any weaknesses or underpopulated regions in the data can be skipped over in favor of areas with higher quality vectors. In this study, a landcover classification model was developed in conjunction with the building detection tool to provide a secondary source to quality check the detected buildings. This has greatly reduced the false positive rate. The proposed methodologies have been implemented and integrated into a configurable production environment and have been employed for a number of large-scale commercial projects, including continent-wide DEM production, where the extracted building footprints are being used to enhance digital elevation models. Overfitted machine learning models are often considered too specific to have any predictive capacity. However, this study demonstrates that, in cases where input data is scarce, overfitted models can be judiciously applied to solve time-sensitive problems.

Keywords: building detection, disaster relief, mask-RCNN, satellite mapping

Procedia PDF Downloads 148

Authors: Aamir Shahzad, Mao-Gang He

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Currently, dusty plasmas motivated the researchers' widespread interest. Since the last two decades, substantial efforts have been made by the scientific and technological community to investigate the transport properties and their nonlinear behavior of three-dimensional and two-dimensional nonideal complex (dusty plasma) liquids (NICDPLs). Different calculations have been made to sustain and utilize strongly coupled NICDPLs because of their remarkable scientific and industrial applications. Understanding of the thermophysical properties of complex liquids under various conditions is of practical interest in the field of science and technology. The determination of thermal conductivity is also a demanding question for thermophysical researchers, due to some reasons; very few results are offered for this significant property. Lack of information of the thermal conductivity of dense and complex liquids at different parameters related to the industrial developments is a major barrier to quantitative knowledge of the heat flux flow from one medium to another medium or surface. The exact numerical investigation of transport properties of complex liquids is a fundamental research task in the field of thermophysics, as various transport data are closely related with the setup and confirmation of equations of state. A reliable knowledge of transport data is also important for an optimized design of processes and apparatus in various engineering and science fields (thermoelectric devices), and, in particular, the provision of precise data for the parameters of heat, mass, and momentum transport is required. One of the promising computational techniques, the homogenous nonequilibrium molecular dynamics (HNEMD) simulation, is over viewed with a special importance on the application to transport problems of complex liquids. This proposed work is particularly motivated by the FIRST TIME to modify the problem of heat conduction equations leads to polynomial velocity and temperature profiles algorithm for the investigation of transport properties with their nonlinear behaviors in the NICDPLs. The aim of proposed work is to implement a NEMDS algorithm (Poiseuille flow) and to delve the understanding of thermal conductivity behaviors in Yukawa liquids. The Yukawa system is equilibrated through the Gaussian thermostat in order to maintain the constant system temperature (canonical ensemble ≡ NVT)). The output steps will be developed between 3.0×105/ωp and 1.5×105/ωp simulation time steps for the computation of λ data. The HNEMD algorithm shows that the thermal conductivity is dependent on plasma parameters and the minimum value of lmin shifts toward higher G with an increase in k, as expected. New investigations give more reliable simulated data for the plasma conductivity than earlier known simulation data and generally the plasma λ0 by 2%-20%, depending on Γ and κ. It has been shown that the obtained results at normalized force field are in satisfactory agreement with various earlier simulation results. This algorithm shows that the new technique provides more accurate results with fast convergence and small size effects over a wide range of plasma states.

Keywords: molecular dynamics simulation, thermal conductivity, nonideal complex plasma, Poiseuille flow

Procedia PDF Downloads 252

Authors: Wanvipa Thongborisute, Punyaphat Sirithanabadeekul, Pichit Suvanprakorn, Anan Jiraviroon

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Background: Acne vulgaris is one of the most common dermatologic problems, and can have a significant psychological and physical effect on patients. Propionibacterium acnes' roles in acne vulgaris involve the activation of toll-like receptor 4 (TLR4), and toll-like receptor 2 (TLR2) pathways. By activating these pathways, inflammatory events of acne lesions, comedogenesis and sebaceous lipogenesis can occur. Currently, there are several topical agents commonly use in treating acne vulgaris that are known to have an effect on TLRs, such as retinoic acid and adapalene, but these drugs still have some irritating effects. At present, there is an alarming increase in rate of bacterial resistance due to irrational used of antibiotics both orally and topically. For this reason, acne treatments should contain bioactive molecules targeting at the site of action for the most effective therapeutic effect with the least side effects. Sparganium stoloniferumis a Chinese aquatic herb containing a compound called Sparstolonin B (SsnB), which has been reported to selectively blocks Toll-like receptor 2 (TLR2) and Toll-like receptor 4 (TLR4)-mediated inflammatory signals. Therefore, this topical TLR2 and TLR4 antagonist, in a form of Sparganium stoloniferum-derived compound containing SsnB, should give a benefit in reducing inflammation of acne vulgaris lesions and providing an alternative treatments for patients with this condition. Materials and Methods: The objectives of this randomized double blinded split faced placebo controlled trial is to study the safety and efficacy of the Sparganium stoloniferum-derived compound. 32 volunteered patients with mild to moderate degree of acne vulgaris according to global acne grading system were included in the study. After being informed and consented the subjects were given 2 topical treatments for acne vulgaris, one being topical 2.40% Sparganium stoloniferum extraction (containing Sparstolonin B) and the other, placebo. The subjects were asked to apply each treatment to either half of the face daily morning and night by randomization for 8 weeks, and come in for a weekly follow up. For each visit, the patients went through a procedure of lesion counting, including comedones, papules, nodules, pustules, and cystic lesions. Results: During 8 weeks of experimentation, the result shows a reduction in total lesions number between the placebo and the treatment side show statistical significance starting at week 4, where the 95% confidence interval begin to no longer overlap, and shows a trend of continuing to be further apart. The decrease in the amount of total lesions between week 0 and week 8 of the placebo side shows no statistical significant at P value >0.05. While the decrease in the amount of total lesions of acne vulgaris of the treatment side comparing between week 0 and week 8 shows statistical significant at P value <0.001. Conclusion: The data demonstrates that 2.40% Sparganium stoloniferum extraction (containing Sparstolonin B) is more effective in treating acne vulgaris comparing to topical placebo in treating acne vulgaris, by showing significant reduction in the total numbers of acne lesions. Therefore, this topical Sparganium stoloniferum extraction could become a potential alternative treatment for acne vulgaris.

Keywords: acne vulgaris, sparganium stoloniferum, sparstolonin B, toll-like receptor 2, toll-like receptor 4

Procedia PDF Downloads 153

Authors: Aditya Purohit, Neha Bansal

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Unplanned urbanization and multidirectional sprawl of the cities have resulted in increased motorization and deteriorating transport conditions like traffic congestion, longer commuting, pollution, increased carbon footprint, and above all increased fatalities. In order to overcome these problems, various practices have been adopted including– promoting and implementing mass transport; traffic junction channelization; smart transport etc. However, these methods are found to be primarily focusing on vehicular mobility rather than people accessibility. With this research gap, this paper tries to resolve the mobility issues for Ahmedabad city in India, which being the economic capital Gujarat state has a huge commuter and visitor inflow. This research aims to resolve the traffic congestion and urban mobility issues focusing on Gujarat State Regional Transport Corporation (GSRTC) for the city of Ahmadabad by analyzing the existing operations and network structure of GSRTC followed by finding possibilities of integrating it with other modes of urban transport. The network restructuring (NR) methodology is used with appropriate variations, based on commuter demand and growth pattern of the city. To do these ‘scenarios’ based on priority issues (using 12 parameters) and their best possible solution, are established after route network analysis for 2700 population sample of 20 traffic junctions/nodes across the city. Approximately 5% sample (of passenger inflow) at each node is considered using random stratified sampling technique two scenarios are – Scenario 1: Resolving mobility issues by use of Special Purpose Vehicle (SPV) in joint venture to GSRTC and Private Operators for establishing feeder service, which shall provide a transfer service for passenger for movement from inner city area to identified peripheral terminals; and Scenario 2: Augmenting existing mass transport services such as BRTS and AMTS for using them as feeder service to the identified peripheral terminals. Each of these has now been analyzed for the best suitability/feasibility in network restructuring. A desire-line diagram is constructed using this analysis which indicated that on an average 62% of designated GSRTC routes are overlapping with mass transportation service routes of BRTS and AMTS in the city. This has resulted in duplication of bus services causing traffic congestion especially in the Central Bus Station (CBS). Terminating GSRTC services on the periphery of the city is found to be the best restructuring network proposal. This limits the GSRTC buses at city fringe area and prevents them from entering into the city core areas. These end-terminals of GSRTC are integrated with BRTS and AMTS services which help in segregating intra-state and inter-state bus services. The research concludes that absence of integrated multimodal transport network resulted in complexity of transport access to the commuters. As a further scope of research comparing and understanding of value of access time in total travel time and its implication on generalized cost on trip and how it varies city wise may be taken up.

Keywords: mass transportation, multi-modal integration, network restructuring, travel behavior, urban transport

Procedia PDF Downloads 175

Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Amarendar Reddy Addula

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Definition of Artificial Intelligence: Artificial intelligence is the simulation of mortal intelligence processes by machines, especially computer systems. Explicit operations of AI comprise expert systems, natural language processing, and speech recognition, and machine vision. Artificial Intelligence (AI) is an original medium for digital business, according to a new report by Gartner. The last 10 times represent an advance period in AI’s development, prodded by the confluence of factors, including the rise of big data, advancements in cipher structure, new machine literacy ways, the materialization of pall computing, and the vibrant open- source ecosystem. Influence of AI to a broader set of use cases and druggies and its gaining fashionability because it improves AI’s versatility, effectiveness, and rigidity. Edge AI will enable digital moments by employing AI for real- time analytics closer to data sources. Gartner predicts that by 2025, further than 50 of all data analysis by deep neural networks will do at the edge, over from lower than 10 in 2021. Responsible AI is a marquee term for making suitable business and ethical choices when espousing AI. It requires considering business and societal value, threat, trust, translucency, fairness, bias mitigation, explainability, responsibility, safety, sequestration, and nonsupervisory compliance. Responsible AI is ever more significant amidst growing nonsupervisory oversight, consumer prospects, and rising sustainability pretensions. Generative AI is the use of AI to induce new vestiges and produce innovative products. To date, generative AI sweats have concentrated on creating media content similar as photorealistic images of people and effects, but it can also be used for law generation, creating synthetic irregular data, and designing medicinals and accoutrements with specific parcels. AI is the subject of a wide- ranging debate in which there's a growing concern about its ethical and legal aspects. Constantly, the two are varied and nonplussed despite being different issues and areas of knowledge. The ethical debate raises two main problems the first, abstract, relates to the idea and content of ethics; the alternate, functional, and concerns its relationship with the law. Both set up models of social geste, but they're different in compass and nature. The juridical analysis is grounded on anon-formalistic scientific methodology. This means that it's essential to consider the nature and characteristics of the AI as a primary step to the description of its legal paradigm. In this regard, there are two main issues the relationship between artificial and mortal intelligence and the question of the unitary or different nature of the AI. From that theoretical and practical base, the study of the legal system is carried out by examining its foundations, the governance model, and the nonsupervisory bases. According to this analysis, throughout the work and in the conclusions, International Law is linked as the top legal frame for the regulation of AI.

Keywords: artificial intelligence, ethics & human rights issues, laws, international laws

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Authors: Ammar Alali, Mahmoud Abughaban

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Cost reduction and drilling optimization is the goal of many drilling operators. Historically, stuck pipe incidents were a major segment of non-productive time (NPT) associated costs. Traditionally, stuck pipe problems are part of the operations and solved post-sticking. However, the real key to savings and success is in predicting the stuck pipe incidents and avoiding the conditions leading to its occurrences. Previous attempts in stuck-pipe predictions have neglected the local geology of the problem. The proposed predictive tool utilizes geophysical data processing techniques and Machine Learning (ML) algorithms to predict drilling activities events in real-time using surface drilling data with minimum computational power. The method combines two types of analysis: (1) real-time prediction, and (2) cause analysis. Real-time prediction aggregates the input data, including historical drilling surface data, geological formation tops, and petrophysical data, from wells within the same field. The input data are then flattened per the geological formation and stacked per stuck-pipe incidents. The algorithm uses two physical methods (stacking and flattening) to filter any noise in the signature and create a robust pre-determined pilot that adheres to the local geology. Once the drilling operation starts, the Wellsite Information Transfer Standard Markup Language (WITSML) live surface data are fed into a matrix and aggregated in a similar frequency as the pre-determined signature. Then, the matrix is correlated with the pre-determined stuck-pipe signature for this field, in real-time. The correlation used is a machine learning Correlation-based Feature Selection (CFS) algorithm, which selects relevant features from the class and identifying redundant features. The correlation output is interpreted as a probability curve of stuck pipe incidents prediction in real-time. Once this probability passes a fixed-threshold defined by the user, the other component, cause analysis, alerts the user of the expected incident based on set pre-determined signatures. A set of recommendations will be provided to reduce the associated risk. The validation process involved feeding of historical drilling data as live-stream, mimicking actual drilling conditions, of an onshore oil field. Pre-determined signatures were created for three problematic geological formations in this field prior. Three wells were processed as case studies, and the stuck-pipe incidents were predicted successfully, with an accuracy of 76%. This accuracy of detection could have resulted in around 50% reduction in NPT, equivalent to 9% cost saving in comparison with offset wells. The prediction of stuck pipe problem requires a method to capture geological, geophysical and drilling data, and recognize the indicators of this issue at a field and geological formation level. This paper illustrates the efficiency and the robustness of the proposed cross-disciplinary approach in its ability to produce such signatures and predicting this NPT event.

Keywords: drilling optimization, hazard prediction, machine learning, stuck pipe

Procedia PDF Downloads 185

Authors: Salima Bijapuri, Shweta Bhatbolan, Sejalben Patel

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Ergonomics concerns the fitting of the environment and the equipment to the worker. Ergonomic principles can be employed in different dimensions of the industrial sector. Participation of all the stakeholders is the key to the formulation of a multifaceted and comprehensive approach to lessen the burden of occupational hazards. Taking responsibility for one’s own work activities by acquiring sufficient knowledge and potential to influence the practices and outcomes is the basis of participatory ergonomics and even hastens the process to identify workplace hazards. The study was aimed to check how participatory ergonomics can be effective in the management of work-related musculoskeletal disorders. Method: A mega kitchen was identified in a twin city of Karnataka, India. Consent was taken, and the screening of workers was done using observation methods. Kitchen work was structured to include different tasks, which included preparation, cooking, distributing, and serving food, packing food to be delivered to schools, dishwashing, cleaning and maintenance of kitchen and equipment, and receiving and storing raw material. Total 100 workers attended the education session on participatory ergonomics and its role in implementing the correct ergonomic practices, thus preventing WRMSDs. Demographic details and baseline data on related musculoskeletal pain and discomfort were collected using the Nordic pain questionnaire and VAS score pre- and post-study. Monthly visits were made, and the education sessions were reiterated on each visit, thus reminding, correcting, and problem-solving of each worker. After 9 months with a total of 4 such education session, the post education data was collected. The software SPSS 20 was used to analyse the collected data. Results: The majority of them (78%), depending on the availability and feasibility, participated in the intervention workshops were arranged four times. The average age of the participants was 39 years. The percentage of female participants was 79.49%, and 20.51% of participants comprised of males. The Nordic Musculoskeletal Questionnaire (NMQ) showed that knee pain was the most commonly reported complaint (62%) from the last 12 months with a mean VAS of 6.27, followed by low back pain. Post intervention, the mean VAS Score was reduced significantly to 2.38. The comparison of pre-post scores was made using Wilcoxon matched pairs test. Upon enquiring, it was found that, the participants learned the importance of applying ergonomics at their workplace which inturn was beneficial for them to handle any problems arising at their workplace on their own with self confidence. Conclusion: The participatory ergonomics proved effective with workers of mega kitchen, and it is a feasible and practical approach. The advantage of the given study area was that it had a sophisticated and ergonomically designed workstation; thus it was the lack of education and practical knowledge to use these stations was of utmost need. There was a significant reduction in VAS scores with the implementation of changes in the working style, and the knowledge of ergonomics helped to decrease physical load and improve musculoskeletal health.

Keywords: ergonomic awareness session, mega kitchen, participatory ergonomics, work related musculoskeletal disorders

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Authors: Igor A. Krichtafovitch

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The evolutionary processes are not linear. Long periods of quiet and slow development turn to rather rapid emergences of new species and even phyla. During Cambrian explosion, 22 new phyla were added to the previously existed 3 phyla. Contrary to the common credence the natural selection or a survival of the fittest cannot be accounted for the dominant evolution vector which is steady and accelerated advent of more complex and more intelligent living organisms. Neither Darwinism nor alternative concepts including panspermia and intelligent design propose a satisfactory solution for these phenomena. The proposed hypothesis offers a logical and plausible explanation of the evolutionary processes in general. It is based on two postulates: a) the Biosphere is a single living organism, all parts of which are interconnected, and b) the Biosphere acts as a giant biological supercomputer, storing and processing the information in digital and analog forms. Such supercomputer surpasses all human-made computers by many orders of magnitude. Living organisms are the product of intelligent creative action of the biosphere supercomputer. The biological evolution is driven by growing amount of information stored in the living organisms and increasing complexity of the biosphere as a single organism. Main evolutionary vector is not a survival of the fittest but an accelerated growth of the computational complexity of the living organisms. The following postulates may summarize the proposed hypothesis: biological evolution as a natural life origin and development is a reality. Evolution is a coordinated and controlled process. One of evolution’s main development vectors is a growing computational complexity of the living organisms and the biosphere’s intelligence. The intelligent matter which conducts and controls global evolution is a gigantic bio-computer combining all living organisms on Earth. The information is acting like a software stored in and controlled by the biosphere. Random mutations trigger this software, as is stipulated by Darwinian Evolution Theories, and it is further stimulated by the growing demand for the Biosphere’s global memory storage and computational complexity. Greater memory volume requires a greater number and more intellectually advanced organisms for storing and handling it. More intricate organisms require the greater computational complexity of biosphere in order to keep control over the living world. This is an endless recursive endeavor with accelerated evolutionary dynamic. New species emerge when two conditions are met: a) crucial environmental changes occur and/or global memory storage volume comes to its limit and b) biosphere computational complexity reaches critical mass capable of producing more advanced creatures. The hypothesis presented here is a naturalistic concept of life creation and evolution. The hypothesis logically resolves many puzzling problems with the current state evolution theory such as speciation, as a result of GM purposeful design, evolution development vector, as a need for growing global intelligence, punctuated equilibrium, happening when two above conditions a) and b) are met, the Cambrian explosion, mass extinctions, happening when more intelligent species should replace outdated creatures.

Keywords: supercomputer, biological evolution, Darwinism, speciation

Procedia PDF Downloads 128

Authors: Milada Krejci, Martin Hill, Vaclav Hosek, Dobroslava Jandova, Jiri Kajzar, Pavel Blaha

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The paper declares effects of exercise intervention of the research project “Basic research of balance changes in seniors”, granted by the Czech Science Foundation. The objective of the presented study is to define predictors, which influence bio-psycho-social consequences and effects of balance ability in senior 65 years old and above. We focused on the Fall-Efficacy Scale changes evaluation in seniors. Comprehensive hypothesis of the project declares, that motion uncertainty (dyskinesia) can negatively affect the well-being of a senior in bio-psycho-social context. In total, random selection and testing of 100 seniors (30 males, 70 females) from Prague and Central Bohemian region was provided. The sample was divided by stratified random selection into experimental and control groups, who underwent input and output testing. For diagnostics the methods of Medical Anamnesis, Functional anthropological examinations, Tinetti Balance Assessment Tool, SF-36 Health Survey, Anamnestic comparative self-assessment scale were used. Intervention method called "Life in Balance" based on yoga techniques was applied in four-week cycle. Results of multivariate regression were verified by repeated measures ANOVA: subject factor, phase of intervention (between-subject factor), body fluid (within-subject factor) and phase of intervention × body fluid interaction). ANOVA was performed with a repetition involving the factors of subjects, experimental/control group, phase of intervention (independent variable), and x phase interaction followed by Bonferroni multiple comparison assays with a test strength of at least 0.8 on the probability level p < 0.05. In the paper results of the first-year investigation of the three years running project are analysed. Results of balance tests confirmed no significant difference between females and males in pre-test. Significant improvements in balance and walking ability were observed in experimental group in females comparing to males (F = 128.4, p < 0.001). In the females control group, there was no significant change in post- test, while in the female experimental group positive changes in posture and spine flexibility in post-tests were found. It seems that females even in senior age react better to incentives of intervention in balance and spine flexibility. On the base of results analyses, we can declare the significant improvement in social balance markers after intervention in the experimental group (F = 10.5, p < 0.001). In average, seniors are used to take four drugs daily. Number of drugs can contribute to allergy symptoms and balance problems. It can be concluded that static balance and walking ability of seniors according Tinetti Balance scale correlate significantly with psychic and social monitored markers.

Keywords: exercises, balance, seniors 65+, health, mental and social balance

Procedia PDF Downloads 101