Search results for: systematic science

Authors: Haya Sammana

Abstract:

A well-defined curricula must be adopted to meet the increasing complexity and diversity in the software applications. In reality, some IT majors such as computer science and computer engineering receive the software engineering education in a single course which is considered as a big challenged for the instructors and universities. Also, it requires students to gain the most of practical experiences that simulate the real work in software companies. Furthermore, we have noticed that there is no consensus on how, when and what to teach in that introductory course to gain the practical experiences that are required by the software companies. Because all of software engineering disciplines will not fit in just one course, so the course needs reasonable choices in selecting its topics. This arises an important question which is an essential one to ask: Is this course has the ability to formulate a true software engineer that meets the needs of industry? This question arises a big challenge in selecting the appropriate topics. So answering this question is very important for the next undergraduate students. During teaching this course in the curricula, the feedbacks from an undergraduate students and the keynotes of the annual meeting for an advisory committee from industrial side provide a probable answer for the proposed question: it is impossible to build a true software engineer who possesses all the essential elements of software engineering education such teamwork, communications skills, project management skills and contemporary industrial practice from one course and it is impossible to have a one course covering all software engineering topics. Besides the used teaching approach, the author proposes an implemented three supportive ways aiming for mitigating the expected risks and increasing the opportunity to build a true software engineer.

Keywords: software engineering course, software engineering education, software experience, supportive approach

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Authors: M. J. Kim, D. B. Lee

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Centrifugal casting is a metal casting method that uses forces make by centripetal acceleration to distribute molten material in mold. Centrifugal cast parts manufactured in industry contain gas pipes and water supply lines, moreover rings, turbocharger, bushings, brake drums. Turbochargers were exposed to exhaust temperatures of 900-1050°C require a material for the corrosion resistance that will withstand such high component temperatures during the entire service life of the vehicle. Hence, the study of corrosion resistance for turbocharger is important for practical application. DIN1.4091 steels were used widely. The DIN1.4091 steels whose compositions were Fe-34.4Cr-14.5Ni-2.5Mo-0.4W-0.4Mn-0.5Si-(0.009 or 0.35)S (wt.%) were centrifugally cast, and oxidized at 900°C for 50-200 h in order to find the effect of sulfur on the high-temperature oxidation of Fe-34.4Cr-14.5Ni-2.5Mo-0.4W-0.4Mn-0.5Si-(0.009 or 0.35)S (wt.%) alloys. These alloys formed oxide scales that consisted primarily of Cr₂O₃ as the major oxide and Cr₂MnO₄ as the minor one through preferential oxidation of Cr and Mn. Cr formed a thin CrOx oxide film on the surface to prevent further oxidation, and when it is added more than 20%, the sulphide decreased corrosion rate. The high affinity of Mn with S, led to the formation of scattered MnS inclusions, particularly in the 0.35S-containing cast alloy. Sulfur was harmful to the oxidation resistance because it deteriorated the scale/alloy adherence so as to accelerate the adherence and compactness of the formed scales. Acknowledgement: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1A2B1013169).

Keywords: centrifugal casting, turbocharger, sulfur, oxidation, Fe-34.4Cr-14.5Ni alloy

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Authors: Naohiro Nakamura

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Analysis methods to accurately estimate the behavior of buildings when earthquakes occur is very important for improving the seismic safety of such buildings. Recently, the use of damping devices has increased significantly and there is a particular need to appropriately evaluate the behavior of buildings with such devices during earthquakes in the design stage. At present, however, the accuracy of the analysis evaluations is not sufficient. One reason is that the accuracy of current analysis methods has not been appropriately verified because there is very limited data on the behavior of actual buildings during earthquakes. Many types of shaking table test of large structures are performed at the '3-Dimensional Full-Scale Earthquake Testing Facility' (nicknamed 'E-Defense') operated by the National Research Institute of Earth Science and Disaster Prevention (NIED). In this study, simulations using 3- dimensional analysis models were conducted on shaking table test of a 5-story steel-frame structure with dampers. The results of the analysis correspond favorably to the test results announced afterward by the committee. However, the suitability of the parameters and models used in the analysis and the influence they had on the responses remain unclear. Hence, we conducted additional analysis and studies on these models and parameters. In this paper, outlines of the test are shown and the utilized analysis model is explained. Next, the analysis results are compared with the test results. Then, the additional analyses, concerning with the hysteresis curve of the dampers and the beam-end stiffness of the frame, are investigated.

Keywords: three-dimensional analysis, E-defense, full-scale experimen, vibration control damper

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Authors: Singh Baljinder, Sharma Navneet

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Foeniculum vulgare Miller is a biennial medicinal and aromatic plant belonging to the family Apiaceae (Umbelliferaceae). It is a hardy, perennial–umbelliferous herb with yellow flowers and feathery leaves. The aim is to study the control of blood glucose in alloxan induced diabetic mice.Method used for extraction was continuous hot percolation method in which Soxhlet apparatus was used.95%ethanol was used as solvent. Male albino mice weighing about 20-25 g obtained from Guru Angad Dev University of Veterinary Science, Ludhiana were used for the study. Diabetes was induced by a single i.p. injection of 125 mg/kg of alloxan monohydrate in sterile saline (11). After 48 h, animals with serum glucose level above 200 mg/dl (diabetic) were selected for the study. Blood samples from mice were collected by retro-orbital puncture (ROP) technique. Serum glucose levels were determined by glucose oxidase and peroxidase method. Single administration (single dose) of aqueous extract of fennel (25, 50, and 100 mg/kg, p.o.) in diabetic Swiss albino mice, showed reduction in serum glucose level after 45 min. Maximum reduction in serum glucose level was seen at doses of 100 mg/kg. Aqueous extract of fennel in all doses except 25 mg/kg did not cause any significant decrease in blood glucose. It may be said that the aqueous extract of fennel decreased the serum glucose level and improved glucose tolerance owing to the presence of aldehyde moiety. The aqueous extract of fennel has antihyperglycemic activity as it lowers serum glucose level in diabetic mice.

Keywords: Foeniculum vulgare Miller, antihyperglycemic, diabetic mice, Umbelliferaceae

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Authors: Rifilwe Victor Modiba, Stefan Hendric Foord

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Alien invasive plants (IAP’s) have considerable negative impacts on freshwater habitats and South Africa has implemented an innovative Work for Water (WfW) programme for the systematic removal of these plants aimed at, amongst other objectives, restoring biodiversity and ecosystem services in these threatened habitats. These restoration processes are expensive and have to be evidence-based. In this study in-stream macroinvertebrate and adult Odonata assemblages were used as indicators of restoration success by quantifying the response of biodiversity metrics for these two groups to the removal of IAP’s in a strategic water resource of South Africa that is extensively invaded by invasive alien plants (IAP’s). The study consisted of a replicated design that included 45 sampling units, viz. 15 invaded, 15 uninvaded and 15 cleared sites stratified across the upper reaches of six sub-catchments of the Luvuvhu river catchment, Limpopo Province. Cleared sites were only considered if they received at least two WfW treatments in the last 3 years. The Benthic macroinvertebrate and adult Odonate assemblages in each of these sampling were surveyed from between November and March, 2013/2014 and 2014/2015 respectively. Generalized Linear Models (GLM) with a log link function and Poisson error distribution were done for metrics (invaded, cleared, and uninvaded) whose residuals were not normally distributed or had unequal variance and for abundance. RDA was done for EPTO genera (Ephemeroptera, Plecoptera, Trichoptera and Odonata) and adult Odonata species abundance. GLM was done to for the abundance of Genera and Odonates that had the association with the RDA environmental factors. Sixty four benthic macroinvertebrate families, 57 EPTO genera, and 45 adult Odonata species were recorded across all 45 sampling units. There was no significant difference between the SASS5 total score, ASPT, and family richness of the three invasion classes. Although clearing only had a weak positive effect on the adult Odonate species richness it had a positive impact on DBI scores. These differences were mainly the result of significantly larger DBI scores in the cleared sites as compared to the invaded sites. Results suggest that water quality is positively impacted by repeated clearing pointing to the importance of follow up procedures after initial clearing. Adult Odonate diversity as measured by richness, endemicity, threat and distribution respond positively to all forms of the clearing. The clearing had a significant impact on Odonate assemblage structure but did not affect EPTO structure. Variation partitioning showed that 21.8% of the variation in EPTO assemblage can be explained by spatial and environmental variables, 16% of the variation in Odonate structure was explained by spatial and environmental variables. The response of the diversity metrics to clearing increased in significance at finer taxonomic resolutions, particularly of adult Odonates whose metrics significantly improved with clearing and whose structure responded to both invasion and clearing. The study recommends the use of DBI for surveying river health when hydraulic biotopes are poor.

Keywords: DBI, evidence-based conservation, EPTO, macroinvetebrates

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Authors: Yi-Fung Lin, Yuan-Mei Liao

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Background: Pain is common among patients with cancer and is also one of the most disturbing symptoms. As this suffering is subjective, if patients proactively participate in their pain self-management, pain could be alleviated effectively. However, not everyone can carry out self-management very well because human behavior is a product of the cognition process. In this process, we can see that "self-efficacy" plays an essential role in affecting human behaviors. Methods: We used the eight steps of concept analysis proposed by Walker and Avant to clarify the concept of “self-efficacy for cancer pain management.” A comprehensive literature review was conducted for relevant publications that were published during the period of 1977 to 2021. We used several keywords, including self-efficacy, self-management, concept analysis, conceptual framework, and cancer pain, to search the following databases: PubMed, CINAHL, Web of Science, and Embase. Results: We identified three defining attributes for the concept of self-efficacy for cancer pain management, including pain management abilities, confidence, and continuous pain monitoring, and recognized six skills related to pain management abilities: problem-solving, decision-making, resource utilization, forming partnerships between medical professionals and patients, planning actions, and self-regulation. Five antecedents for the concept of self-efficacy for cancer pain management were identified: pain experience, existing cancer pain, pain-related knowledge, a belief in pain management, and physical/mental state. Consequences related to self-efficacy for cancer pain management were achievement of pain self-management, well pain control, satisfying quality of life, and containing motivation. Conclusions: This analysis provides researchers with a clearer understanding of the concept of “self-efficacy for cancer pain management.” The findings presented here provide a foundation for future research and nursing interventions to enhance self-efficacy for cancer pain management.

Keywords: cancer pain, concept analysis, self-efficacy, self-management

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Authors: Mehwish Asghar

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Breast Cancer (BC) is a common type of cancer among women. Its screening is usually performed using different imaging modalities such as magnetic resonance imaging, mammogram, X-ray, CT, etc. Among these modalities’ mammogram is considered a powerful tool for diagnosis and screening of breast cancer. Sophisticated machine learning approaches have shown promising results in complementing human diagnosis. Generally, machine learning methods can be divided into two major classes: one is Radiomics analysis (RA), where image features are extracted manually; and the other one is the concept of convolutional neural networks (CNN), in which the computer learns to recognize image features on its own. This research aims to improve the incidence of early detection, thus reducing the mortality rate caused by breast cancer through the latest advancements in computer science, in general, and machine learning, in particular. It has also been aimed to ease the burden of doctors by improving and automating the process of breast cancer detection. This research is related to a relative analysis of different techniques for the implementation of different models for detecting and classifying breast cancer. The main goal of this research is to provide a detailed view of results and performances between different techniques. The purpose of this paper is to explore the potential of a convolutional neural network (CNN) w.r.t feature extractor and as a classifier. Also, in this research, it has been aimed to add the module of Radiomics for comparison of its results with deep learning techniques.

Keywords: breast cancer (BC), machine learning (ML), convolutional neural network (CNN), radionics, magnetic resonance imaging, artificial intelligence

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Authors: Vesna Bosilj Vukšić, Lucija Ivančić, Dalia Suša Vugec

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While struggling to succeed in today’s complex market environment and provide better customer experience and services, enterprises encompass digital transformation as a means for reaching competitiveness and foster value creation. A digital transformation process consists of information technology implementation projects, as well as organizational factors such as top management support, digital transformation strategy, and organizational changes. However, to the best of our knowledge, there is little evidence about digital transformation endeavors in organizations and how they perceive it – is it only about digital technologies adoption or a true organizational shift is needed? In order to address this issue and as the first step in our research project, a literature review is conducted. The analysis included case study papers from Scopus and Web of Science databases. The following attributes are considered for classification and analysis of papers: time component; country of case origin; case industry and; digital transformation concept comprehension, i.e. focus. Research showed that organizations – public, as well as private ones, are aware of change necessity and employ digital transformation projects. Also, the changes concerning digital transformation affect both manufacturing and service-based industries. Furthermore, we discovered that organizations understand that besides technologies implementation, organizational changes must also be adopted. However, with only 29 relevant papers identified, research positioned digital transformation as an unexplored and emerging phenomenon in information systems research. The scarcity of evidence-based papers calls for further examination of this topic on cases from practice.

Keywords: digital strategy, digital technologies, digital transformation, literature review

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Authors: Habes Moh’d Khalifeh Hatamleh, Shukri Refai Ibrahim Marashdh

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The study came with the aim of identifying the most prominent quantitative, qualitative, and technological challenges facing the higher education system in Jordan as a dilemma in light of the technological revolution that had a radical contribution to changing the face of science and knowledge in various fields of higher education in Jordan. Human societies that require the adoption of scientific research and its basics as a clear entrance aimed at serving the community and upgrading it civilly. The number of private and public universities has increased, and many students have been accepted for all levels of study in the bachelor’s, higher diploma, master’s and doctoral programs, and the quantitative growth has been accompanied by many negatives, which requires renewal and development in the field of higher education, which led to the emergence of many challenges, and the qualitative challenge in terms of relevance, quality and goodness constitutes an important requirement for the improvement of teaching, scientific research and services in light of the social demand for higher education, in order to reach the quality. The real challenge facing our country is to enter the civilization of advanced technology, which has become the main factor and the starting point for preparing staff capable of accomplishing this transformation and creating an appropriate educational environment for the student to help him to use the sources of knowledge. This study can provide a set of recommendations and proposals that may contribute to addressing challenges and contributing to improving educational outcomes in light of the requirements of the labor market and the needs of society.

Keywords: quantitative, qualitative, technological, challenges, higher education

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Authors: Habes Moh’d Khalifeh Hatamleh, Shukri Refai Ibrahim Marashdh

Abstract:

The study came with the aim of identifying the most prominent quantitative, qualitative, and technological challenges facing the higher education system in Jordan as a dilemma in light of the technological revolution that had a radical contribution to changing the face of science and knowledge in various fields of higher education in Jordan. Human societies that require the adoption of scientific research and its basics as a clear entrance aimed at serving the community and upgrading it civilly. The number of private and public universities has increased, and many students have been accepted for all levels of study in the bachelor’s, higher diploma, master’s and doctoral programs, and the quantitative growth has been accompanied by many negatives, which requires renewal and development in the field of higher education, which led to the emergence of many challenges, and the qualitative challenge in terms of relevance, quality and goodness constitutes an important requirement for the improvement of teaching, scientific research and services in light of the social demand for higher education, in order to reach the quality. The real challenge facing our country is to enter the civilization of advanced technology, which has become the main factor and the starting point for preparing staff capable of accomplishing this transformation and creating an appropriate educational environment for the student to help him to use the sources of knowledge. This study can provide a set of recommendations and proposals that may contribute to addressing challenges and contributing to improving educational outcomes in light of the requirements of the labor market and the needs of society.

Keywords: quantitative, qualitative, technological, challenges, higher education

Procedia PDF Downloads 66

Authors: Hui-Fang Lee, Meng-Chu Liu, Wen-Chi Lu, Hsuan-Jung Hsieh

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The purpose of the study was to examine the effect of health program on the fitness ability of abnormal BMI students of Ching-Yun University of Science and Technology. In order to achieve this purpose, self-regulation theory and dietary education were applied, and the effect of 10-week sports activities and three-day diet records on pre-test and post-test of fitness activities was analyzed. There were 40 original participants. Then, nine people who were with normal BMI, low attendance or unfinished fitness test were eliminated from this research. The valid samples were 31 (77.5%) participants. The fitness activities included sit-bending, one minute sit-up, standing long jump, and three-minute stage boarding. The averages of three-day diet records were compared, and differences of pre-test and post-test of the four fitness activities were analyzed with paired-samples t test. The results showed that there was a significant difference between pre-test and post of male students’ BMI and one minute sit-up. Females’ sit-bending and one minute sit-up had the same effect. Females had high fat intake in three-day diet records. The research showed that the use of self-regulation theory and dietary education, the implementation of sports activities and three-day diet records could significantly enhance the physical fitness indicators or effects. While in the course of sports, we should guide students to think about the gap between self-behavior and ideal behavior, then realize the main reasons and improving methods, and finally go towards the goal and improve the effect of physical fitness.

Keywords: self-regulation theory, dietary education, three-day diet records, physical fitness

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Authors: S. Zith Dey Babu, S. Kour, S. Verma, C. Verma, V. Pathania, A. Agrawal, V. Chaudhary, A. Manoj Puthur, R. Goyal, A. Pal, T. Danti Dey, A. Kumar, K. Wadhwa, O. Ved

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Background: Skin cancer is now is the buzzing button in the field of medical science. The cyst's pandemic is drastically calibrating the body and well-being of the global village. Methods: The extracted image of the skin tumor cannot be used in one way for diagnosis. The stored image contains anarchies like the center. This approach will locate the forepart of an extracted appearance of skin. Partitioning image models has been presented to sort out the disturbance in the picture. Results: After completing partitioning, feature extraction has been formed by using genetic algorithm and finally, classification can be performed between the trained and test data to evaluate a large scale of an image that helps the doctors for the right prediction. To bring the improvisation of the existing system, we have set our objectives with an analysis. The efficiency of the natural selection process and the enriching histogram is essential in that respect. To reduce the false-positive rate or output, GA is performed with its accuracy. Conclusions: The objective of this task is to bring improvisation of effectiveness. GA is accomplishing its task with perfection to bring down the invalid-positive rate or outcome. The paper's mergeable portion conflicts with the composition of deep learning and medical image processing, which provides superior accuracy. Proportional types of handling create the reusability without any errors.

Keywords: computer-aided system, detection, image segmentation, morphology

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Authors: Baker Ssekitto, Ambrose Mbogo

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The study reveals that indeed agriculture employs over 70% of Uganda’s population, of which majority are youth and women. The study further reveals that over 70% of the farmers are smallholder farmers based in rural areas, whose operations are greatly affected by; climate change, weak digital skills, limited access to productivity knowledge along value chains, limited access to quality farm inputs, weak logistics systems, limited access to quality extension services, weak business intelligence, limited access to quality markets among others. It finds that the emerging 4th industrial revolution powered by artificial intelligence, 5G and data science will provide possibilities of addressing some of these challenges. Furthermore, the study finds that despite rapid development of ICT4Agric Innovation, their uptake is constrained by a number of factors including; limited awareness of these innovations, low internet and smart phone penetration especially in rural areas, lack of appropriate digital skills, inappropriate programmes implementation models which are project and donor driven, limited articulation of value addition to various stakeholders among others. Majority of farmers and other value chain actors lacked knowledge and skills to harness the power of ICTs, especially their application of ICTs in monitoring and evaluation on quality of service in the extension system and farm level processes.

Keywords: artificial intelligence, productivity, ICT4agriculture, value chain, logistics

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Authors: Sara ElElimy, Samir Moustafa

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Mobile network operators start to face many challenges in the digital era, especially with high demands from customers. Since mobile network operators are considered a source of big data, traditional techniques are not effective with new era of big data, Internet of things (IoT) and 5G; as a result, handling effectively different big datasets becomes a vital task for operators with the continuous growth of data and moving from long term evolution (LTE) to 5G. So, there is an urgent need for effective Big data analytics to predict future demands, traffic, and network performance to full fill the requirements of the fifth generation of mobile network technology. In this paper, we introduce data science techniques using machine learning and deep learning algorithms: the autoregressive integrated moving average (ARIMA), Bayesian-based curve fitting, and recurrent neural network (RNN) are employed for a data-driven application to mobile network operators. The main framework included in models are identification parameters of each model, estimation, prediction, and final data-driven application of this prediction from business and network performance applications. These models are applied to Telecom Italia Big Data challenge call detail records (CDRs) datasets. The performance of these models is found out using a specific well-known evaluation criteria shows that ARIMA (machine learning-based model) is more accurate as a predictive model in such a dataset than the RNN (deep learning model).

Keywords: big data analytics, machine learning, CDRs, 5G

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Authors: Yaroslav Pavlov

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In the modern world, ensuring quality has become increasingly important in various fields of human activity. One universal approach to quality management, proven effective in the food industry, is the HACCP (Hazard Analysis and Critical Control Points) concept. Based on principles of preventing potential hazards to consumers at all stages of production, from raw materials to the final product, HACCP offers a systematic approach to identifying, assessing risks, and managing critical control points (CCPs). Initially used primarily for food production, it was later effectively adapted to the food service sector. Implementing HACCP provides organizations with a reliable foundation for improving food safety, covering all links in the food chain from producer to consumer, making it an integral part of modern quality management systems. The main principles of HACCP—hazard identification, CCP determination, effective monitoring procedures, corrective actions, regular checks, and documentation—are universal and can be adapted to other areas. The adaptation of the HACCP concept is relevant for continuing professional education (CPE) with certain reservations. Specifically, it is reasonable to abandon the term ‘hazards’ as deviations in CCPs do not pose dangers, unlike in food production. However, the approach through CCP analysis and the use of HACCP's main principles for educational services are promising. This is primarily because it allows for identifying key CCPs based on the value creation model of a specific educational organization and consequently focusing efforts on specific CCPs to manage the quality of educational services. This methodology can be called the Analysis of Critical Points in Educational Services (ACPES). ACPES offers a similar approach to managing the quality of educational services, focusing on preventing and eliminating potential risks that could negatively impact the educational process, learners' achievement of set educational goals, and ultimately lead to students rejecting the organization's educational services. ACPES adapts proven HACCP principles to educational services, enhancing quality management effectiveness and student satisfaction. ACPES includes identifying potential problems at all stages of the educational process, from initial interest to graduation and career development. In ACPES, the term "hazards" is replaced with "problematic areas," reflecting the specific nature of the educational environment. Special attention is paid to determining CCPs—stages where corrective measures can most effectively prevent or minimize the risk of failing educational goals. The ACPES principles align with HACCP's principles, adjusted for the specificities of CPE. The method of the learner's journey map (variation of Customer Journey Map, CJM) can be used to overcome the complexity of formalizing the production chain in educational services. CJM provides a comprehensive understanding of the learner's experience at each stage, facilitating targeted and effective quality management. Thus, integrating the learner's journey map into ACPES represents a significant extension of the methodology's capabilities, ensuring a comprehensive understanding of the educational process and forming an effective quality management system focused on meeting learners' needs and expectations.

Keywords: quality management, continuing professional education, customer journey map, HACCP

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Authors: Milica Karadzic, Lidija Jevric, Sanja Podunavac-Kuzmanovic, Strahinja Kovacevic, Anamarija Mandic, Katarina Penov Gasi, Marija Sakac, Aleksandar Okljesa, Andrea Nikolic

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The present paper deals with chromatographic lipophilicity prediction of newly synthesized steroid derivatives. The prediction was achieved using in silico generated molecular descriptors and quantitative structure-retention relationship (QSRR) methodology with the artificial neural networks (ANN) approach. Chromatographic lipophilicity of the investigated compounds was expressed as retention factor value logk. For QSRR modeling, a feedforward back-propagation ANN with gradient descent learning algorithm was applied. Using the novel sum of ranking differences (SRD) method generated ANN models were ranked. The aim was to distinguish the most consistent QSRR model that can be found, and similarity or dissimilarity between the models that could be noticed. In this study, SRD was performed with average values of retention factor value logk as reference values. An excellent correlation between experimentally observed retention factor value logk and values predicted by the ANN was obtained with a correlation coefficient higher than 0.9890. Statistical results show that the established ANN models can be applied for required purpose. This article is based upon work from COST Action (TD1305), supported by COST (European Cooperation in Science and Technology).

Keywords: artificial neural networks, liquid chromatography, molecular descriptors, steroids, sum of ranking differences

Procedia PDF Downloads 310

Authors: Evgenia Iakovleva, Juha Koivisto, Pasi Karppinen, J. Inkinen, Mikko Alava

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Preventing the spread of infectious diseases throughout the worldwide is one of the most important tasks of modern health care. Infectious diseases not only account for one fifth of the deaths in the world, but also cause many pathological complications for the human health. Touch surfaces pose an important vector for the spread of infections by varying microorganisms, including antimicrobial resistant organisms. Further, antimicrobial resistance is reply of bacteria to the overused or inappropriate used of antibiotics everywhere. The biggest challenges in bacterial detection by existing methods are non-direct determination, long time of analysis, the sample preparation, use of chemicals and expensive equipment, and availability of qualified specialists. Therefore, a high-performance, rapid, real-time detection is demanded in rapid practical bacterial detection and to control the epidemiological hazard. Among the known methods for determining bacteria on the surfaces, Hyperspectral methods can be used as direct and rapid methods for microorganism detection on different kind of surfaces based on fluorescence without sampling, sample preparation and chemicals. The aim of this study was to assess the relevance of such systems to remote sensing of surfaces for microorganisms detection to prevent a global spread of infectious diseases. Bacillus subtilis and Escherichia coli with different concentrations (from 0 to 10x8 cell/100µL) were detected with hyperspectral camera using different filters as visible visualization of bacteria and background spots on the steel plate. A method of internal standards was applied for monitoring the correctness of the analysis results. Distances from sample to hyperspectral camera and light source are 25 cm and 40 cm, respectively. Each sample is optically imaged from the surface by hyperspectral imaging system, utilizing a JAI CM-140GE-UV camera. Light source is BeamZ FLATPAR DMX Tri-light, 3W tri-colour LEDs (red, blue and green). Light colors are changed through DMX USB Pro interface. The developed system was calibrated following a standard procedure of setting exposure and focused for light with λ=525 nm. The filter is ThorLabs KuriousTM hyperspectral filter controller with wavelengths from 420 to 720 nm. All data collection, pro-processing and multivariate analysis was performed using LabVIEW and Python software. The studied human eye visible and invisible bacterial stains clustered apart from a reference steel material by clustering analysis using different light sources and filter wavelengths. The calculation of random and systematic errors of the analysis results proved the applicability of the method in real conditions. Validation experiments have been carried out with photometry and ATP swab-test. The lower detection limit of developed method is several orders of magnitude lower than for both validation methods. All parameters of the experiments were the same, except for the light. Hyperspectral imaging method allows to separate not only bacteria and surfaces, but also different types of bacteria, such as Gram-negative Escherichia coli and Gram-positive Bacillus subtilis. Developed method allows skipping the sample preparation and the use of chemicals, unlike all other microbiological methods. The time of analysis with novel hyperspectral system is a few seconds, which is innovative in the field of microbiological tests.

Keywords: Escherichia coli, Bacillus subtilis, hyperspectral imaging, microorganisms detection

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Authors: Mostafa Aminzadeh, Min Deng

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Modeling loss data is an important part of actuarial science. Actuaries use models to predict future losses and manage financial risk, which can be beneficial for marketing purposes. In the insurance industry, small claims happen frequently while large claims are rare. Traditional distributions such as Normal, Exponential, and inverse-Gaussian are not suitable for describing insurance data, which often show skewness and fat tails. Several authors have studied classical and Bayesian inference for parameters of composite distributions, such as Exponential-Pareto, Weibull-Pareto, and Inverse Gamma-Pareto. These models separate small to moderate losses from large losses using a threshold parameter. This research introduces a computational approach using a nonlinear regression model for loss data that relies on multiple predictors. Simulation studies were conducted to assess the accuracy of the proposed estimation method. The simulations confirmed that the proposed method provides precise estimates for regression parameters. It's important to note that this approach can be applied to datasets if goodness-of-fit tests confirm that the composite distribution under study fits the data well. To demonstrate the computations, a real data set from the insurance industry is analyzed. A Mathematica code uses the Fisher information algorithm as an iteration method to obtain the maximum likelihood estimation (MLE) of regression parameters.

Keywords: maximum likelihood estimation, fisher scoring method, non-linear regression models, composite distributions

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Authors: Emeka Thaddues Njoku

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Since 2009, the Nigerian Government has established diverse counter-terrorism legislations and practices in response terrorism in North Eastern part of the country. However, these measures have hampered not only the ability of civil society organizations to sustain the autonomous spaces that define/locate them at the intersection between the state and public but also the balance between freedom and security. Hence, this study examines the various elements associated with the interface between the counter terrorism security framework of the government and the capacity of civil society organizations to carry out their mandates in Nigeria. In order to achieve this, the survey research of the ex-post facto type will be adopted using the multi-stage sampling technique. A total of two hundred (200) copies of questionnaire will be administered to members of the civil society organizations and 24 In-Depth Interviews (IDI) will be conducted for officials of security agencies, Ministry of Defence and operators of civil society organizations. Fifty respondents will be drawn from each civil society organisations in the areas of humanitarian assistance, human rights Advocacy, development-oriented, peace-building. Moreover, 24 interviewees drawn from the key members of the security agencies (6), Ministry of Defence (6) and 12 operators of civil society organizations-three respondents each will represent the four civil society organizations mentioned above. Also, secondary data will be used to complement In-depth Interview (IDI) sessions. All collected data will be coded and analysed using descriptive statistics of frequency counts and simple percentage in the Statistical Package for Social Science (SPSS). Content analysis will be used for the In-depth interview and secondary data.

Keywords: counter-terrorism, civil society organizations, freedom, terrorism

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Authors: Vorya Shabrandi

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The study factors of poverty and inequality causes in countries is the subject of many scholars and economists in the last century, theorists in various areas of economic science know different factors as the roots of poverty and inequality in Iran after the 1979 revolution. Economists have emphasized political elements and political scientists on political elements. This research reviews the political economy of poverty and corruption in Iran after the revolution. The findings of this research, based on AcemOgluand Robinson theory, show how the institutional structural dependence of Iran's economy to raw has led to the growth of its non-economic economic institutions and its consequence of the continuity of the release and looting economy and poverty and inequality in Iran's political economy Is. This research was carried out using descriptive-analytical and comparative methods. Many economists try to justify the conditions of the country based on war, sanctions; And the external factors, and ... knows. In this study, we tried to examine the roots of poverty and the looting economy of Iran by implementing Research AcemOgluand Robinson on the institutions and roots of poverty. Looking for a framework for understanding why countries, such as Iran, the reason for the difference in revenue in different countries, as well as the poor or wealth of countries, regardless of the non-effective and non-professional institutions, and why inefficient institutions in some countries, such as Iran, such as Iran It remains and does not have a voluntary political powers to change these institutions. Findings The research shows that institutions are broadly the main reason for the roots of the robust and looting economy (poverty and inequality) in Iran.

Keywords: Iran, plunderable (Loot) economy, raw shopping, poverty and inequality, acem oglu and robinson, non-inclusive institutions

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Authors: Elena Krelja Kurelović, Jasminka Tomljanović, Vlatka Davidović

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The development of web technologies and mobile devices makes creating, accessing, using and sharing information or communicating with each other simpler every day. However, while the amount of information constantly increasing it is becoming harder to effectively organize and find quality information despite the availability of web search engines, filtering and indexing tools. Although digital technologies have overall positive impact on students’ lives, frequent use of these technologies and digital media enriched with dynamic hypertext and hypermedia content, as well as multitasking, distractions caused by notifications, calls or messages; can decrease the attention span, make thinking, memorizing and learning more difficult, which can lead to stress and mental exhaustion. This is referred to as “information overload”, “information glut” or “information anxiety”. Objective of this study is to determine whether students show signs of information overload and to identify the possible predictors. Research was conducted using a questionnaire developed for the purpose of this study. The results show that students frequently use technology (computers, gadgets and digital media), while they show moderate level of information literacy. They have sometimes experienced symptoms of information overload. According to the statistical analysis, higher frequency of technology use and lower level of information literacy are correlated with larger information overload. The multiple regression analysis has confirmed that the combination of these two independent variables has statistically significant predictive capacity for information overload. Therefore, the information science teachers should pay attention to improving the level of students’ information literacy and educate them about the risks of excessive technology use.

Keywords: information overload, computers, mobile devices, digital media, information literacy, students

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Authors: Ramin Rostamkhani, Thurasamy Ramayah

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One of the most important topics of today’s industrial organizations is the challenging issue of supply chain management. In this field, scientists and researchers have published numerous practical articles and models, especially in the last decade. In this research, to our best knowledge, the discussion of data modeling of supply chain management components using well-known statistical distribution functions has been considered. The world of science owns mathematics, and showing the behavior of supply chain data based on the characteristics of statistical distribution functions is innovative research that has not been published anywhere until the moment of doing this research. In an analytical process, describing different aspects of functions including probability density, cumulative distribution, reliability, and failure function can reach the suitable statistical distribution function for each of the components of the supply chain management. It can be applied to predict the behavior data of the relevant component in the future. Providing a model to adapt the best statistical distribution function in the supply chain management components will be a big revolution in the field of the behavior of the supply chain management elements in today's industrial organizations. Demonstrating the final results of the proposed model by introducing the process capability indices before and after implementing it alongside verifying the approach through the relevant assessment as an acceptable verification is a final step. The introduced approach can save the required time and cost to achieve the organizational goals. Moreover, it can increase added value in the organization.

Keywords: analyzing, process capability indices, statistical distribution functions, supply chain management components

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Authors: Anna Piasek, Anna Szymkiewicz, Gabriela Wiktor, Jolanta Pulit-Prociak, Marcin Banach

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Carbon dots (CDs) have great potential for application in many fields of science. They are characterized by fluorescent properties that can be manipulated. The nanomaterial has many advantages in addition to its unique properties. CDs may be obtained easily, and they undergo surface functionalization in a simple way. In addition, there is a wide range of raw materials that can be used for their synthesis. An interesting possibility is the use of numerous waste materials of natural origin. In the research presented here, the synthesis of CDs was carried out according to the principles of Green chemistry. Beet molasses was used as a natural raw material. It has a high sugar content. This makes it an excellent high-carbon precursor for obtaining CDs. To increase the fluorescence effect, we modified the surface of CDs with silver (Ag-CDs) nanoparticles. The process of obtaining CQD was based on the hydrothermal method by applying microwave radiation. Silver nanoparticles were formed via the chemical reduction method. The synthesis plans were performed on the Design of the Experimental method (DoE). Variable process parameters such as concentration of beet molasses, temperature and concentration of nanosilver were used in these syntheses. They affected the obtained properties and particle parameters. The Ag-CDs were analyzed by UV-vis spectroscopy. The fluorescence properties and selection of the appropriate excitation light wavelength were performed by spectrofluorimetry. Particle sizes were checked using the DLS method. The influence of the input parameters on the obtained results was also studied.

Keywords: fluorescence, modification, nanosilver, molasses, Green chemistry, carbon dots

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Authors: Redouane Larbi Boufeniza, Jing-Jia Luo

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This study assesses the suitability of deep learning (DL) for downscaling sea surface temperature (SST) over the Mediterranean Sea in the context of seasonal forecasting. We design a set of experiments that compare different DL configurations and deploy the best-performing architecture to downscale one-month lead forecasts of June–September (JJAS) SST from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0) for the period of 1982–2020. We have also introduced predictors over a larger area to include information about the main large-scale circulations that drive SST over the Mediterranean Sea region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results showed that the convolutional neural network (CNN)-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme SST spatial patterns. Besides, the CNN-based downscaling yields a much more accurate forecast of extreme SST and spell indicators and reduces the significant relevant biases exhibited by the raw model predictions. Moreover, our results show that the CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of the Mediterranean Sea. The results demonstrate the potential usefulness of CNN in downscaling seasonal SST predictions over the Mediterranean Sea, particularly in providing improved forecast products.

Keywords: Mediterranean Sea, sea surface temperature, seasonal forecasting, downscaling, deep learning

Procedia PDF Downloads 66

Authors: Shara Toibayeva, Irbulat Utepbergenov, Lyazzat Issabekova, Aidana Bodesova

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The article discusses the importance of quality assessment as a strategic tool in business and emphasizes the significance of the effectiveness of quality management systems (QMS) for enterprises. The evaluation of these systems takes into account the specificity of quality indicators, the multilevel nature of the system, and the need for optimal selection of the number of indicators and evaluation of the system state, which is critical for making rational management decisions. Methods and models of automated enterprise quality management are proposed, including an intelligent automated quality management system integrated with the Management Information and Control System. These systems make it possible to automate the implementation and support of QMS, increasing the validity, efficiency, and effectiveness of management decisions by automating the functions performed by decision makers and personnel. The paper also emphasizes the use of recurrent neural networks to improve automated quality management. Recurrent neural networks (RNNs) are used to analyze and process sequences of data, which is particularly useful in the context of document quality assessment and non-conformance detection in quality management systems. These networks are able to account for temporal dependencies and complex relationships between different data elements, which improves the accuracy and efficiency of automated decisions. The project was supported by a grant from the Ministry of Education and Science of the Republic of Kazakhstan under the Zhas Galym project No. AR 13268939, dedicated to research and development of digital technologies to ensure consistency of QMS regulatory documents.

Keywords: automated control system, quality management, document structure, formal language

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Authors: Joseph Kimutai Langat

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Tea typology is the science of classifying tea. This study was carried out between November 2023 and July 2024, whose main objective was to investigate the typological classification nomenclature of processed tea in the world, narrowing down to Kenya. Centres of origin, historical background, tea growing region, scientific naming system, market, fermentation levels, processing/ oxidation levels and cultural reasons are used to classify tea at present. Of these, the most common typology is by oxidation, and more specifically, by the production methods within the oxidation categories. While the Asian tea producing countries categorises tea products based on the decreasing oxidation levels during the manufacturing process: black tea, green tea, oolong tea and instant tea, Kenya’s tea typology system is based on the degree of fermentation process, i.e. black tea, purple tea, green tea and white tea. Tea is also classified into five categories: black tea, green tea, white tea, oolong tea, and dark tea. Black tea is the main tea processed and exported in Kenya, manufactured mainly by withering, rolling, or by use of cutting-tearing-curling (CTC) method that ensures efficient conversion of leaf herbage to made tea, oxidizing, and drying before being sorted into different grades. It is from these varied typological methods that this review paper concludes that different regions of the world use different classification nomenclature. Therefore, since tea typology is not standardized, it is recommended that a global tea regulator dealing in tea classification be created to standardize tea typology, with domestic in-country regulatory bodies in tea growing countries accredited to implement the global-wide typological agreements and resolutions.

Keywords: classification, fermentation, oxidation, tea, typology

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Authors: Francis O. Iyoha, Daniel E. Gberevbie, Charles T. Iruonagbe, Matthew E. Egharevba

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Cost of governance in Nigeria has become a challenge to development and concern to practitioners and scholars alike in the field of business and social science research. It has been observed that it takes 70 percent of the nation’s revenue to maintain less than 20 percent of the Nigerian population that are public servants. Furthermore, it has been observed that on a consistent yearly basis, the recurrent expenditure of government from the national budget keeps rising, while capital expenditure meant for development keeps falling. The implication is that development is stagnated in the country. For instance, in the 2010 national budget of NGN4.60tn or USD28.75b, only NGN1.80tn or USD11.15b was set aside for capital expenditure. Also, in the 2013 national budget of NGN4.92tn or USD30.75b, only NGN1.50tn or USD9.38b was set aside for capital expenditure. Therefore, with the analysis of secondary data, this study examined the reasons for the high cost of governance in Nigeria. It observed that the high cost of governance in the country is in the interest of the ruling class, arising from their unethical behaviour – corrupt practices and the poor management of public resources. As a result, the study recommends the need to intensify the war against corruption and mismanagement of public resources by government officials as possible solution to overcome the high cost of governance in Nigeria. This could be achieved by strengthening the constitutional powers of the various anti-corruption agencies in the area of arrest, investigation and prosecution of offenders without the interference of the executive arm of government either at the local, state or federal level.

Keywords: cost of governance, capital expenditure, recurrent expenditure, unethical behavior, Nigeria

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Authors: Abdullah Almowanes

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The speed of adoption of new information and communication technologies is often seen as an indicator of the growth of knowledge- and technological innovation-based regional economies. Indeed, technological progress and scientific inquiry in any society have undergone a particularly profound transformation with the introduction of computer networks. In the spring of 1981, the Bitnet network was launched to link thousands of nodes all over the world. In 1985 and as one of the first adopters of Bitnet, Saudi Arabia launched a Bitnet-based network named Gulfnet that linked computer centers, universities, and libraries of Saudi Arabia and other Gulf countries through high speed communication lines. In this paper, the origins and the deployment of Gulfnet are discussed as well as social, economical, political, and cultural ramifications of the new information reality created by the network. Despite its significance, the social and cultural aspects of Gulfnet have not been investigated in history of science and technology literature to a satisfactory degree before. The presented research is based on an extensive archival research aimed at seeking out and analyzing of primary evidence from archival sources and records. During its decade and a half-long existence, Gulfnet demonstrated that the scope and functionality of public computer networks in Saudi Arabia have to be fine-tuned for compliance with Islamic culture and political system of the country. It also helped lay the groundwork for the subsequent introduction of the Internet. Since 1980s, in just few decades, the proliferation of computer networks has transformed communications world-wide.

Keywords: Bitnet, computer networks, computing and culture, Gulfnet, Saudi Arabia

Procedia PDF Downloads 239

Authors: Rupak Kumar Sarma, Ratul Saikia

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Macrophytes encompass major functional group in eutrophic wetland ecosystems. As a key functional element of freshwater lakes, they play a crucial role in regulating various wetland biogeochemical cycles, as well as maintain the biodiversity at the ecosystem level. The high carbon-rich underground biomass of macrophyte populations may harbour diverse microbial community having significant potential in maintaining different biogeochemical cycles. The present investigation was designed to study the macrophyte-microbe interaction in coupled nitrification and denitrification, considering Deepor Beel Lake (a Ramsar conservation site) of North East India as a model eutrophic system. Highly eutrophic sites of Deepor Beel were selected based on sediment oxygen demand and inorganic phosphorus and nitrogen (P&N) concentration. Sediment redox potential and depth of the lake was chosen as the benchmark for collecting the plant and sediment samples. The average highest depth in winter (January 2016) and summer (July 2016) were recorded as 20ft (6.096m) and 35ft (10.668m) respectively. Both sampling depth and sampling seasons had the distinct effect on variation in macrophyte community composition. Overall, the dominant macrophytic populations in the lake were Nymphaea alba, Hydrilla verticillata, Utricularia flexuosa, Vallisneria spiralis, Najas indica, Monochoria hastaefolia, Trapa bispinosa, Ipomea fistulosa, Hygrorhiza aristata, Polygonum hydropiper, Eichhornia crassipes and Euryale ferox. There was a distinct correlation in the variation of major sediment physicochemical parameters with change in macrophyte community compositions. Quantitative estimation revealed an almost even accumulation of nitrate and nitrite in the sediment samples dominated by the plant species Eichhornia crassipes, Nymphaea alba, Hydrilla verticillata, Vallisneria spiralis, Euryale ferox and Monochoria hastaefolia, which might have signified a stable nitrification and denitrification process in the sites dominated by the selected aquatic plants. This was further examined by a systematic analysis of microbial populations through culture dependent and independent approach. Culture-dependent bacterial community study revealed the higher population of nitrifiers and denitrifiers in the sediment samples dominated by the six macrophyte species. However, culture-independent study with bacterial 16S rDNA V3-V4 metagenome sequencing revealed the overall similar type of bacterial phylum in all the sediment samples collected during the study. Thus, there might be the possibility of uneven distribution of nitrifying and denitrifying molecular markers among the sediment samples collected during the investigation. The diversity and abundance of the nitrifying and denitrifying molecular markers in the sediment samples are under investigation. Thus, the role of different aquatic plant functional types in microorganism mediated nitrogen cycle coupling could be screened out further from the present initial investigation.

Keywords: denitrification, macrophyte, metagenome, microorganism, nitrification

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Authors: Eduwin Eko Franjaya, Andi Gunawan, Wahju Qamara Mugnisjah

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Sustainable agriculture in the form of integrated farming with its LEISA (Low External Input Sustainable Agriculture) concept has brought a positive impact on agriculture development and ambient amelioration. But, most of the small farmers in Indonesia did not know how to put the concept of it and how to combine agricultural commodities on the site effectively and efficiently. This research has an aim to promote integrated farming (agrofisheries, etc) to the farmers by designing the agricultural landscape to become integrated farming landscape as medium of education for the farmers. The method used in this research is closely related with the rule of design in the landscape architecture science. The first step is inventarization for the existing condition on the research site. The second step is analysis. Then, the third step is concept-making that consists of base concept, design concept, and developing concept. The base concept used in this research is sustainable agriculture with LEISA. The concept design is related with activity base on site. The developing concept consists of space concept, circulation, vegetation and commodity, production system, etc. The fourth step as the final step is planning and design. This step produces site plan of integrated farming based on LEISA. The result of this research is site plan of integrated farming with its explanation, including the energy flow of integrated farming system on site and the production calendar of integrated farming commodities for education and agri-tourism opportunity. This research become the right way to promote the integrated farming and also as a medium for the farmers to learn and to develop it.

Keywords: integrated farming, LEISA, planning and design, site plan

Procedia PDF Downloads 499