Search results for: indirect costs overtime

Authors: A. Boumedine, K. Benfriha, S. Lecheb

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Manufacturing methods for products and structures made of composite materials reduce the number of parts and integrate technical functions, this advantage of composite materials leads to a lot of innovation but also to a reduction of costs and a gain in quality. A material has attributes: its density, it’s resistance, it’s cost, it’s resistance to corrosion. For the design of a product, a certain profile of these attributes is required: low density, resistance removed, low cost. The problem is then to identify this attribute profile and to compare it with those of the materials, in order to find the one that comes closest. The aim of this work is to demonstrate the feasibility of characterizing a mini turbine made of 3D printed fiber-filled composite material by the process of additive manufacturing, then compare the performance of the alloy turbine with the composite turbine according to the results of the simulation by Abaqus software.

Keywords: additive manufacturing, composite materials, design, 3D printer, turbine

Procedia PDF Downloads 134

Authors: Ek-Uma Imkome, Jintana Yunibhand, Waraporn Chaiyawat

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Background: Persons with schizophrenia patient and misusing methamphetamines suffering from social dysfunction that impact on their quality of life. Knowledge of factors related to social dysfunction will guide the effective intervention. Objectives: To determine the direct effect, indirect effect and total effect of an acerbate Psychotics’ Symptoms, Social Support, Stressful life events, Medication use self-efficacy impact on social dysfunction in Thai schizophrenic patient and methamphetamine misuse. Methods: Data were collected from schizophrenic and methamphetamine misuse patient by self report. A linear structural relationship was used to test the hypothesized path model. Results: The hypothesized model was found to fit the empirical data and explained 54% of the variance of the psychotic symptoms (X² = 114.35, df = 92, p-value = 0.05, X² /df = 1.24, GFI = 0.96, AGFI = 0.92, CFI = 1.00, NFI = 0.99, NNFI = 0.99, RMSEA = 0.02). The highest total effect on social dysfunction was psychotic symptoms (0.67, p<0.05). Medication use self-efficacy had a direct effect on psychotic symptoms (-0.25, p<0.01), and social support had direct effect on medication use self efficacy (0.36, p <0.01). Conclusions: Psychotic symptoms and stressful life events were the significance factors that influenced direct on social dysfunctioning. Therefore, interventions that are designed to manage these factors are crucial in order to enhance social functioning in this population.

Keywords: psychotic symptoms, methamphetamine, schizophrenia, stressful life events, social dysfunction, social support, medication use self efficacy

Procedia PDF Downloads 208

Authors: Jongho Park, Taekyun Kim, Jungbhin You, Sungnam Hong, Sun-Kyu Park

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Due to the aging of bridges, increasing of maintenance costs and decreasing of structural safety is occurred. The steel corrosion of reinforced concrete bridge is the most common problem and this phenomenon is accelerating due to abnormal weather and increasing CO₂ concentration due to climate change. To solve these problems, composite members using textile have been studied. A textile reinforced concrete can reduce carbon emissions by reduced concrete and without steel bars, so a lot of structural behavior studies are needed. Therefore, in this study, textile reinforced concrete beam was made and flexural test was performed. Also, the change of flexural strength according to the prestressing was conducted. As a result, flexural strength of TRC with prestressing was increased compared and flexural behavior was shown as reinforced concrete.

Keywords: AR-glass, flexural strength, prestressing, textile reinforced concrete

Procedia PDF Downloads 331

Authors: Juha Kettunen

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This study analyzes the quality and the size of the strategic network of higher education institutions. The study analyses the concept of fitness for purpose in quality assurance. It also analyses the transaction costs of networking that have consequences on the number of members in the network. Empirical evidence is presented of the Consortium on Applied Research and Professional Education, which is a European strategic network of six higher education institutions. The results of the study support the argument that the number of members in the strategic network should be relatively small to provide high quality results. The practical importance is that networking has been able to promote international research and development projects. The results of this study are important for those who want to design and improve international networks in higher education.

Keywords: balanced scorecard, higher education, social networking, strategic planning

Procedia PDF Downloads 348

Authors: Richard Ren

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Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 127

Authors: M. Ntswane, N. Mbuma, M. Labuschagne, A. Mofokeng, M. Rantso

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Cowpea is an essential legume for the nutrition and health of millions of people in different regions. The production and productivity of the crop are very limited in South Africa due to a lack of adapted and stable genotypes. The improvement of nutritional quality is made possible by manipulating the genes of diverse cowpea genotypes available around the world. Assessing the adaptability and stability of the cowpea mutant genotypes for yield and nutritional quality requires examining them in different environments. The objective of the study was to determine the adaptability and stability of cowpea mutant genotypes under South African conditions and to identify the superior genotypes that combine grain yield components, antioxidants, and nutritional quality. Thirty-one cowpea genotypes were obtained from the Agricultural Research Council grain crops (ARC-GC) and were planted in Glen, Mafikeng, Polokwane, Potchefstroom, Taung, and Vaalharts during the 2021/22 summer cropping season. Significant genotype by location interactions indicated the possibility of genetic improvement of these traits. The genotype plus genotype by environment indicated broad adaptability and stability of mutant genotypes. The principal component analysis identified the association of the genotypes with the traits. Phenotypic correlation analysis showed that Zn and protein content were significant and positively correlated and suggested the possibility of indirect selection of these traits. Results from this study could be used to help plant breeders in making informed decisions and developing nutritionally improved cowpea genotypes with the aim of addressing the challenges of poor nutritional quality.

Keywords: cowpea seeds, adaptability, stability, mineral elements, protein content

Procedia PDF Downloads 111

Authors: Ruba Hamed, Khaled Hussainey, Basiem Al-Shattarat, Wasim Al-Shattarat

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This paper examines how the influence of mandating corporate social responsibility reporting (CSR) on subsequent financial performance through accounting-based measures and market-based measures. We provide evidence about the negative impact of reporting CSR voluntarily on the firm’s future performance due to the increased spending on and costs related to such activities. On the contrary, mandating CSR reporting enhances firms’ future performance by signalling to the market about the firm’s positive stance towards sustainability issues in the UK. Our findings are of interest to regulation setters and stakeholders with respect to mandatory CSR reporting and provide further insight and feedback into accounting and reporting practices.

Keywords: accounting-based performance, mandatory CSR, mandatory regulation, market-based performance

Procedia PDF Downloads 124

Authors: Hong Yu, Ion Matei

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Carbon fiber reinforced composites (CFRP) used as aircraft structure are subject to lightning strike, putting structural integrity under risk. Indirect damage may occur after a lightning strike where the internal structure can be damaged due to excessive heat induced by lightning current, while the surface of the structures remains intact. Three damage modes may be observed after a lightning strike: fiber breakage, inter-ply delamination and intra-ply cracks. The assessment of internal damage states in composite is challenging due to complicated microstructure, inherent uncertainties, and existence of multiple damage modes. In this work, a model based approach is adopted to diagnose faults in carbon composites after lighting strikes. A resistor network model is implemented to relate the overall electrical and thermal conduction behavior under simulated lightning current waveform to the intrinsic temperature dependent material properties, microstructure and degradation of materials. A fault detection and identification (FDI) module utilizes the physics based model and a particle filtering algorithm to identify damage mode as well as calculate the probability of structural failure. Extensive simulation results are provided to substantiate the proposed fault diagnosis methodology with both single fault and multiple faults cases. The approach is also demonstrated on transient resistance data collected from a IM7/Epoxy laminate under simulated lightning strike.

Keywords: carbon composite, fault detection, fault identification, particle filter

Procedia PDF Downloads 195

Authors: Murat Toren, Mehmet Çelebi

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Reducing the weight of transformers while providing good performance, cost reduction and increased efficiency is important. Weight is one of the most significant factors in all electrical machines, and as such, many transformer design parameters are related to weight calculations. This study presents a comparison of the weight of oil type transformers and dry type transformer weight. Oil type transformers are mainly used in industry; however, dry type transformers are becoming more widespread in recent years. MATLAB is typically used for designing transformers and design parameters (rated voltages, core loss, etc.) along with design in ANSYS Maxwell. Similar to other studies, this study presented that the dry type transformer option is limited. Moreover, the commonly-used 50 kVA distribution transformers in the industry are oil type and dry type transformers are designed and considered in terms of weight. Currently, the preference for low-cost oil-type transformers would change if costs for dry-type transformer were more competitive. The aim of this study was to compare the weight of transformers, which is a substantial cost factor, and to provide an evaluation about increasing the use of dry type transformers.

Keywords: weight, optimization, oil-type transformers, dry-type transformers

Procedia PDF Downloads 353

Authors: Shabnam Dadparvar, Laijin Shen

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After a period of conflict between Russia and the West, new signs of confrontation between the United States and China are observed. China, as the most populous country in the world with a high rate of economic growth, neither stands the hegemonic power of the United States nor has the intention of direct confrontation with it. By raising the costs of the United States’ leadership at the international level, China seeks to find a better status without direct confrontation with the US. Meanwhile, the Shanghai Cooperation Organization (SCO), as a soft balancing strategy against the hegemony of the United States is used as a tool to reach this goal. The authors by using a descriptive-analytical method try to explain the policies of China and the United States on Shanghai Cooperation Organization as well as confrontation between these two countries within the framework of 'balance of soft power theory'.

Keywords: balance of soft power, Central Asia, Shanghai cooperation organization, terrorism

Procedia PDF Downloads 371

Authors: Khaled M. Alhawiti

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This paper investigates the future of medicine and the use of Natural language processing. The importance of having correct clinical information available online is remarkable; improving patient care at affordable costs could be achieved using automated applications to use the online clinical information. The major challenge towards the retrieval of such vital information is to have it appropriately coded. Majority of the online patient reports are not found to be coded and not accessible as its recorded in natural language text. The use of Natural Language processing provides a feasible solution by retrieving and organizing clinical information, available in text and transforming clinical data that is available for use. Systems used in NLP are rather complex to construct, as they entail considerable knowledge, however significant development has been made. Newly formed NLP systems have been tested and have established performance that is promising and considered as practical clinical applications.

Keywords: clinical information, information retrieval, natural language processing, automated applications

Procedia PDF Downloads 404

Authors: Rafael Tedrus Bento

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The analysis of proportionality as a test is raised as a basic foundation for the achievement of Fundamental Rights. We used legal dogmatics and empirical analysis to seek the expected results, from the reading of the RV Oakes trial by the Supreme Court of Canada. In cases involving freedom of expression, two tests are used to resolve disputes. The first examines whether, in fact, the case can be characterized as a violation of freedom of expression; the second assesses whether this violation can be justified by the reasonable limit clause. This test was defined in the RV Oakes trial by the Supreme Court of Canada, concluding with the Oakes Test, used worldwide as a proportionality test. Resulting is a proportionality between the effects of the limiting measure and the objective - the more serious the harmful effects of a measure, the more important the objective must be.

Keywords: Oakes, proportionality, fundamental rights, Supreme Court of Canada

Procedia PDF Downloads 146

Authors: M. Ben Slimene, M.-S. Ouali

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Asset management of urban infrastructures faces a multitude of challenges that need to be overcome to obtain a reliable measurement of performances. Predicting the performance of slowly aging systems is one of those challenges, which helps the asset manager to investigate specific failure modes and to undertake the appropriate maintenance and rehabilitation interventions to avoid catastrophic failures as well as to optimize the maintenance costs. This article presents a methodology for modeling the deterioration of slowly degrading assets based on an operating history. It consists of extracting degradation profiles by grouping together assets that exhibit similar degradation sequences using an unsupervised classification technique derived from artificial intelligence. The obtained clusters are used to build the performance prediction models. This methodology is applied to a sample of a stormwater drainage culvert dataset.

Keywords: artificial Intelligence, clustering, culvert, regression model, slow degradation

Procedia PDF Downloads 111

Authors: Burak Erkayman

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The manufacturing and service organizations are in the need of using ERP systems to integrate many functions from purchasing to storage, production planning to calculation of costs. Using ERP systems by the integration in the level of information provides companies remarkable advantages in terms of profitability, productivity and efficiency in processes. Cloud computing is one of the most significant changes in information and communication technology. The developments in Cloud Computing attract business world to take advantage of this field. Cloud Computing means much more storage area, more cost saving and faster data transfer rate. In addition to these, it presents new business models, new field of study and practicable solutions for anyone’s use. These developments make inevitable the implementation of ERP systems to cloud environment. In this study, the performance of ERP systems in cloud environment is analyzed through various performance criteria and a comparison between traditional and cloud-ERP systems is presented. At the end of study the transformation and the future of ERP systems is discussed.

Keywords: cloud-ERP, ERP system performance, information system transformation

Procedia PDF Downloads 529

Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

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Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

Procedia PDF Downloads 119

Authors: Moussa Majed, Omar Yaser

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The main objective of this experiment was to study the impact of Probiotic compound on the specific immunity as the case study of infectious bursal disease. Total of 8000 one-day old Ross 108 broiler were randomly divided into two experimental groups; control group (4500 birds) and experimental group (3500 birds). Birds in two groups were reared under similar environmental conditions. Birds in control group received basal diets without probiotic whereas the birds in experimental one were fed basal diets supplemented with a commercial probiotic mixture) probiotic lacting k, which contains bacteria cells beyond to lactobacillus, Streptococcus and bifidobacterium genus that are isolated from gut microflora in healthy chickens(. The commercial probiotic were used according to the manufacturer instruction. 400 blood samples for each group were collected from wing vein every 5-7 days as interval period till 42 days old. Indirect Enzyme-Linked Immunosorbent Assay (ELISA) test was performed to detect the level of infectious bursal disease virus (IBDV) antibodies. The results clearly showed that the mean of immune titers was significantly (p= 0.03) higher in trail group than control one. The coefficient of variance percentages were 55% and 39% for control and trial groups respectively, this illustrates that homogeneity of immunity titers in the trail group was much better comparing with control group. The values of geometric means of titers in the control group and trial group were reported 3820 and 8133, respectively. The crude mortality rate in the experimental group was two times lower comparing with control group (14% and 28% respectively, p = 0.005

Keywords: probiotic, broiler chicken, infectious bursal disease, immunity, ELISA test

Procedia PDF Downloads 70

Authors: Sherif A. Abu El-Magd, Ahmed M. Sefelnasr, Ahmed M. Masoud

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Groundwater modeling is the way and tool for assessing and managing groundwater resources efficiently. The present work was carried out in the ancient Egyptian archeological site (Abydos) fromDynastyIandII.Theareaislocated about 13km west of the River Nilecourse, Upper Egypt. The main problem in this context is that the ground water level rise threatens and damages fragile carvings and paintings of the ancient buildings. The main objective of the present work is to identify the sources of the groundwater recharge in the site, further more, equally important there is to control the ground water level rise. Numerical modeling combined with field water level measurements was implemented to understand the ground water recharge sources. However, building a conceptual model was an important step in the groundwater modeling to phase to satisfy the modeling objectives. Therefore, boreholes, crosssections, and a high-resolution digital elevation model were used to construct the conceptual model. To understand the hydrological system in the site, the model was run under both steady state and transient conditions. Then, the model was calibrated agains the observation of the water level measurements. Finally, the results based on the modeling indicated that the groundwater recharge is originating from an indirect flow path mainly from the southeast. Besides, there is a hydraulic connection between the surface water and groundwater in the study site. The decision-makers and archeologyists could consider the present work to understand the behavior of groundwater recharge and water table level rise.

Keywords: numerical modeling, archeological site, groundwater recharge, egypt

Procedia PDF Downloads 123

Authors: Wenfen Bi, Zeng Lin

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As a developing country, China is badly in need of capital and talents to develop the socialist country with Chinese characteristics. However, a large proportion of high income people with know-how technique, wealth and management experience have immigrated or plan to immigrate to other countries. Of course, this phenomenon has attracted the attention from both the government and researchers. One explanation might be that these high-income people lack confidence in China’s social development. Based on the data on W city’s comprehensive social situation surveyed by center for the social survey research of Wuhan university (CSSR) in 2014, this paper employed the structural equation model (SEM) to evaluate whether personal income affects social confidence, via the mediating effect of the sense of social equity (sense of right equity and sense of distributive equity). Bootstrap mediation analysis revealed that after controlling Demographic variables, personal income had a significant negative influence on sense of right equity and in turn, sense of rights equity can significantly positively predict social confidence. While personal income had no significant effect on sense of distributive equity, and sense of distributive equity did not significantly affect macro social confidence. Also, the direct effects of personal income on social confidence became not significant. These findings revealed the inner mechanism of the relationship between the personal income and social confidence in contemporary China, which was caused by mediating effect of sense of rights equity. That is, the higher the personal income, the lower the sense of rights equity, the lower the social confidence. Thus, the boost of the social confidence, especially for the rich, does not only depend on the equitable distribution of material wealth, but also on the right equity and making people feel rights equally in common life.

Keywords: personal income, sense of right equity, sense of social equity, social confidence

Procedia PDF Downloads 392

Authors: Suminar Pratapa, Agus Riyanto, Silmi Machmudah, Sri Yani Purwaningsih

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The objective of this study was to investigate the optical properties of polyvinyl alcohol (PVA) and its prospective applications by adding crystalline silica which is usually used as a reinforcing agent. To do this, we synthesized and evaluated PVA-based composites reinforced with silica crystals, namely cristobalite, derived from rice husk. The experimental procedure involved the production of SiO2 particles using rice husk precursors, which were subsequently subjected to calcination at a rate of 10 °C/min for a duration of 3 hours. This process primarily resulted in the formation of SiO2 crystals in the cristobalite phase, according to X-ray diffraction (XRD). Following this, the crystals were incorporated into polyvinyl alcohol (PVA) via a casting technique, resulting in the formation of composite sheets. The SiO2 contents in the composites were 0, 2.5, 5.0, and 10.%. XRD and Fourier-transform infrared spectroscopy (FTIR) techniques provided confirmation of the composites' successful synthesis, i.e., it did not yield any indications of chemical bonding between polyvinyl alcohol (PVA) and silicon dioxide (SiO2), indicating that the interaction was limited to interfacial reactions. The incorporation of SiO2 crystals resulted in a notable enhancement in UV-vis light absorption and a decrease in the optical band gap. Addition of 2.5, 5.0, and 10.% SiO2, for example, decreases the direct optical band gap of the composites form 5.37, 5.19, and 5.02 eV respectively, while the indirect band gaps of the samples were 4.44, 4.84, and 4.48 eV, correspondingly. These findings emphasize the efficacy of rice husk-derived SiO2 crystals as both reinforcement agents and modifiers of optical properties in the polymer composites, showcasing their significant potential to modify the composite's structural and optical characteristics.

Keywords: rice husk, cristaline SiO₂, PVA-based composites, structural characteristics, optical properties.

Procedia PDF Downloads 46

Authors: Shutchapol Chopvitayakun

Abstract:

Internship program is a compulsory course of many undergraduate programs in Thailand. It gives opportunities to a lot of senior students as interns to practice their working skills in the real organizations and also gives chances for interns to face real-world working problems. Interns also learn how to solve those problems by direct and indirect experiences. This program in many schools is a well-structured course with a contract or agreement made with real business organizations. Moreover, this program also offers opportunities for interns to get jobs after completing it from where the internship program takes place. Interns also learn how to work as a team and how to associate with other colleagues, trainers, and superiors of each organization in term of social hierarchy, self-responsibility, and self-disciplinary. This research focuses on senior students of Suan Sunandha Rajabhat University, Thailand whose studying major is information technology program. They practiced their working skills or took internship programs in the real business sector or real operating organizations in 2015-2016. Interns are categorized in to two types: normal program and special program. For special program, students study in weekday evening from Monday to Friday or Weekend and most of them work full-time or part-time job. For normal program, students study in weekday working hours and most of them do not work. The differences of these characters and the outcomes of internship performance were studied and analyzed in this research. This work applied some statistical analytics to find out whether the internship performance of each intern type has different performances statistically or not.

Keywords: internship, intern, senior student, information technology program

Procedia PDF Downloads 263

Authors: Cindy Davis, Samantha Rayner

Abstract:

Secondary traumatic stress (STS) is an indirect form of trauma affecting the psychological well-being of mental health workers; STS is found to be a prevalent risk in mental health occupations. Various factors impact the development of STS within the literature; including the level of trauma individuals are exposed to and their level of empathy. Research is limited on STS in mental health workers in Australia; therefore, this study examined STS and related factors of empathetic behavior and trauma caseload among mental health workers. The research utilized an online survey quantitative research design with a purposive sample of 190 mental health workers (176 females) recruited via professional websites and unofficial social media groups. Participants completed an online questionnaire comprising of demographics, the secondary traumatic stress scale and the empathy scale for social workers. A standard hierarchical regression analysis was conducted to examine the significance of covariates, traumatized clients, traumatic stress within workload and empathy in predicting STS. The current research found 29.5% of participants to meet the criteria for a diagnosis of STS. Age and past trauma within the covariates were significantly associated with STS. Amount of traumatized clients significantly predicted 4.7% of the variance in STS, traumatic stress within workload significantly predicted 4.8% of the variance in STS and empathy significantly predicted 4.9% of the variance in STS. These three independent variables and the covariates accounted for 18.5% of the variance in STS. Practical implications include a focus on developing risk strategies and treatment methods that can diminish the impact of STS.

Keywords: mental health, PTSD, social work, trauma

Procedia PDF Downloads 332

Authors: Zhazira Shemsheyeva, Zhanara Suleimenova, Olga Shemshura, Gulnaz Mombekova, Zhanar Rakhmetova

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Bacteria that colonize plant roots and promote plant growth are referred to as plant growth-promoting rhizobacteria (PGPR). They not only provide nutrients to the plants (direct plant growth promotion) and protect plants against the phytopathogens (indirect plant growth promotion) but also increase the soil fertility. Indirectly PGPRs improve the plant growth by becoming a biocontrol agent for a fungal pathogen. The antifungal activities of the PGPrhizobacteria were assayed against different species of phytopathogenic fungi such as Fusarium tricinctum, Fusarium oxysporum, Sclerotiniasclerotiorum, and Botrytis cinerea. Pseudomonas putidaSM-1, Azotobacter sp., and Bacillus thuringiensis AKS/16 strains have been used in experimental tests on growth inhibition of phytopathogenic fungi infecting Kidney beans. Agar well diffusion method was used in this study. Diameters of the zones of inhibition were measured in millimeters. It was found that Bacillus thuringiensis AKS/16 strain showed the lowest antifungal activity against all fungal pathogens tested. Zones of inhibition were 15-18 mm. In contrast, Pseudomonas putida SM-1 exhibited good antifungal activity against Fusarium oxysporum and Fusarium tricinctum by producing 29-30 mm clear zones of inhibition. The moderate inhibitory effect was shown by Azotobacter sp. against all fungal pathogens tested with zones of inhibition from24 to 26 mm. In summary, Pseudomonas putida SM-1 strain demonstrated the potential of controlling root rot diseases in kidney beans.

Keywords: PGPR, pseudomonas putida, kindey beans, antifungal activity

Procedia PDF Downloads 154

Authors: Z. Soleymani, M. Amirzadeh

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Quality Function Deployment (QFD) was developed in 1960 in Japan and introduced in 1983 in America and Europe. The paper presents a real application of this technique in a way that the method of applying QFD in design and production aero fuel pumps has been considered. While designing a product and in order to apply system engineering process, the first step is identification customer needs then its transition to engineering parameters. Since each change in deign after production process leads to extra human costs and also increase in products quality risk, QFD can make benefits in sale by meeting customer expectations. Since the needs identified as well, the use of QFD tool can lead to increase in communications and less deviation in design and production phases, finally it leads to produce the products with defined technical attributes.

Keywords: customer voice, engineering parameters, gear pump, QFD

Procedia PDF Downloads 249

Authors: Guanghua Yu

Abstract:

This article has examined the rise of the UK, US, and China at different historical times to explain the argument that it is open access in the economic sphere, as well as institutional building related to the protection of property rights, contract enforcement, financial market, the rule of law, and human resource accumulation that determine economic and human development. Both the UK, after the Glorious Revolution in the seventeenth century, and China, after its adoption of the open door policy at the end of the 1970s, follow such a path of development. The difference between the UK and China in moving toward that path is the different coordination of elites. While the coordination of elites in the UK through parliament played important roles in forcing the government to consider the wider encompassing interest in society after the Glorious Revolution, the coordination of elites in China has mainly been achieved by the Communist Party of China such that the Chinese Government has started to pay greater deal of attention to the wider encompassing interest in the country from 1978. The article has also examined the rise of the US following colonial settlement to independence and institutional building thereafter. The US case is similarly consistent with the argument that open access in the economic sphere and institutional building matter the most to economic development. More decentralized methods of the coordination of elites in the US among colonies (states), the federal governments, and other political groups similarly shaped the path towards open access in the economic sphere and institutional building. As such, open access in the political sphere plays an indirect role in development at best. If that is correct, there are possibilities that different political systems are able to achieve coordination of elites so that governments will turn their attention to development.

Keywords: open access, interconnected institutions, democracy, development

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Authors: Ahmad Latif Virk, Naeem Ahmad, Muhammad Ishaq Asif Rehmani

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Amid the present environmental crises, developing environment-resilient and cost-effective conservation agriculture strategies to feed the world's ever-growing population is pertinent. Therefore, a field study was conducted to test the hypothesis that residue retention under no-till (NTR) would enhance energy productivity (EP) and energy use efficiency (EUE) while offsetting the carbon footprints (CF), water footprints (WF) and greenhouse gases emissions (GHGs) in rice (Oryza sativa L.)-wheat (Triticum aestivum L.) double cropping system. Two tillage systems viz., conventional tillage (CT) and conservation tillage (no-till; NT), with or without residue retention, were combined into four treatments as CT0 (puddled rice, conventional wheat - residue); CTR (puddled rice, conventional wheat + residue); NT0 (direct rice seeding, zero-tilled wheat - residue); NTR (direct rice seeding, zero-tilled wheat + residue) were evaluated. Overall, results showed that the NT system had 34.2% lower energy consumption, 1.2 times more EP than CT system. Moreover, NTR had 19.8% higher EUE than CT0. The overall system grain yield ranged from 7.8 to 9.3 Mg ha−1 under NT0 and CTR, respectively. The NTR had 56.6% and 17.9% lesser CF and WF, respectively, than CT0. The net GHGs emissions (CO2-eq kg ha−1) under CT0 were the highest, while NTR had the lowest emissions. The NTR enhanced carbon sequestration in soil that can offset half of the system's CO2 emissions. The findings of this study might help develop a suitable strategy for resource/energy conservation and higher productivity while offsetting GHGs emissions in the Indo-Gangetic Plains.

Keywords: residue, yield, indirect emissions, energy use efficiency, carbon sequestration

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Authors: Charles Mothobiso

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This study attempts to identify the client’s main priority when delivering green projects. The aim is to compare whether clients’ interests are similar when delivering conventional buildings as compared to green buildings. Private clients invest more in green buildings as compared to government and parastatal entities. Private clients prioritize on maximizing a return on investment and they mainly invest in energy-saving buildings that have low life cycle costs. Private clients are perceived to be more knowledgeable about the benefits of green building projects as compared to government and parastatal clients. A shortage of expertise and managerial skill leads to the low adaptation of green buildings in government and parastatal projects. Other factors that seem to prevent the adoption of green buildings are the preparedness of the supply chain within the industry and inappropriate procurement strategies adopted by clients.

Keywords: construction clients, design team, green buildings, procurement

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Authors: Zahra Majd, Mohsen Kalantar

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Power management and voltage regulation is one of the most important issues in microgrid (MG) control and scheduling. This paper proposes a multiobjective scheduling formulation that consists of active power costs, voltage fluctuations summation, and technical constraints of MG. Furthermore, load flow and reserve constraints are considered to achieve proper voltage regulation. A modified Jacobian matrix is presented for calculating voltage variations and Mont Carlo simulation is used for generating and reducing scenarios. To convert the problem to a mixed integer linear program, a linearization procedure for nonlinear equations is presented. The proposed model is applied to a typical low-voltage MG and two different cases are investigated. The results show the effectiveness of the proposed model.

Keywords: microgrid, energy management system, voltage fluctuations, modified Jacobian matrix

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Authors: Alex Maina, Mwenda Makathimo, Adwek George, Charles Opiyo

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This research entails the simulation of three possible power scenarios for Kenya from 2015 to 2035 using the Low Emissions Analysis Platform (LEAP). These scenarios represent the unfolding future electricity generation that will fully satisfy the demand while considering the following: energy security, power generation cost and impacts on the environment. These scenarios are Reference Scenario (RS), Nuclear Scenario (NS) and More Renewable Scenario (MRS). The findings obtained reveals that the most sustainable scenario while comparing the costs was found to be the coal scenario with a Net Present Value (NPV) of $30,052.67 million though it has the highest Green House Gases (GHGs) emissions. However, the More Renewable Scenario (MRS) had the least GHGs emissions but was found to be a most expensive scenario to implement with an NPV of $30,733.07 million.

Keywords: energy security, Kenya, low emissions analysis platform, net-present value, greenhouse gases

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Authors: Benchouala Amira, Bojic Clement, Poupin Pascal, Cossu Leguille-carole

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The objective of this study is to evaluate in vitro the cytotoxic and androgenic potential of several antifungal molecules (amphotericin B, econazole, ketoconazole and miconazole) on MDA-Kb2 cell lines. This biological model is an effective tool for the detection of endocrine disruptors because it responds well to the main agonist of the androgen receptor (testosterone) and also to an antagonist: flutamide. The cytotoxicity of each chemical compound tested was measured using an MTT assay (tetrazolium salt, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) which measures the activity of the reductase function of mitochondrial succinate dehydrogenase enzymes of cultured cells. This complementary cytotoxicity test is essential to ensure that the effects of reduction in luminescence intensity observed during androgenic tests are only attributable to the anti-androgenic action of the compounds tested and not to their possible cytotoxic properties. Tests of the androgenic activity of antifungals show that these compounds do not have the capacity to induce transcription of the luciferase gene. These compounds do not exert an androgenic effect on MDA-Kb2 cells in culture for the environmental concentrations tested. The addition of flutamide for the same tested concentrations of antifungal molecules reduces the luminescence induced by amphotericin B, econazole and miconazole, which is explained by a strong interaction of these molecules with flutamide which may have a greater toxic effect than when tested alone. The cytotoxicity test shows that econazole and ketoconazole can cause cell death at certain concentrations tested. This cell mortality is perhaps induced by a direct or indirect action on deoxyribonucleic acid (DNA), ribonucleic acid (RNA) or proteins necessary for cell division.

Keywords: cytotoxicity, androgenic potential, antifungals, MDA-Kb2

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Authors: Serhat Tüzün, Tufan Demirel

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The objective of the present study is to select the most suitable location for a wind power plant station through Choquet integral method. The problem of selecting the location for a wind power station was considered as a multi-criteria decision-making problem. The essential and sub-criteria were specified and location selection was expressed in a hierarchic structure. Among the main criteria taken into account in this paper are wind potential, technical factors, social factors, transportation, and costs. The problem was solved by using different approaches of Choquet integral and the best location for a wind power station was determined. Then, the priority weights obtained from different Choquet integral approaches are compared and commented on.

Keywords: multi-criteria decision making, choquet integral, fuzzy sets, location of a wind power plant

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