Search results for: decision weights

Authors: Mamadou Sileye Niang

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The study is a contribution to the development of a feed for juvenile tilapia Oreochromis niloticus, from local raw materials in order to reduce the cost of feeding farmed tilapia in Senegal. Three feeds were formulated from local raw materials. The basic composition of the tested feeds is as follows: A1 (peanut meal, rice bran, millet bran, maize meal and no fish meal); A2 (peanut meal, rice bran, millet bran, maize meal and 10% fish meal) and A3 (peanut meal, rice bran, millet bran, maize meal and 25% fish meal). All feeds contain 31% protein. The trial compared three batches, in 2 replicates, with different diets. The initial weight of the juveniles was 0.37± 0.5g. The daily ration was distributed at 9 am and 4 pm. After 90 days of the experiment, the final mean weights were 2.45 ± 0.5g; 2.75±0.5g; and 4.67 ± 0.5g for A1, A2, and A3, respectively. A performance test, of which the objective was to compare growth parameters, was conducted. The results of the growth parameters of juveniles fed A3 were significantly higher (p < 0.05) than those fed A1 and A2. The weight growth study shows similar growth during the first month. However, from this date onwards, juveniles fed A3 show a faster growth, which is maintained throughout the experiment. On the other hand, the Protein Efficiency Coefficient and the Survival Rate showed no significant difference. The zootechnical parameters are not significantly different (p > 0.05) between the two tanks for the same feed treatment.

Keywords: nutrition, feed, fingerlings, Oreochromis, local raw materials, feed cost

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Authors: Arpit Sachan, Nilanshu Kumar

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This paper tries to understand the nuances of feminisation of agriculture in the Indian context and how that is associated with better intrahousehold bargaining power for women. The economic survey of India indicates a constant increase in the share of the female workforce in Indian agriculture in the past few decades. This can be accounted for by many factors like the migration of male workers to urban areas and, therefore, the complete burden of agriculture shifting on the female counterparts. Therefore this study is an attempt to study that how this increase in the female workforce corresponds to a better decision-making ability for women in rural farm households. This paper is an attempt to carefully evaluate this aspect of the feminisation of Indian agriculture. The paper tries to study how various factors that improve the status of women in agriculture change with things like resource ownership. This paper uses both the macro-level and micro-level data to study the dynamics of the proportion of the workforce in agriculture across different states in India and how that has translated into better indicators for women in rural areas. The fall in India’s rank in the global gender wage gap index is alarming in such a context, and this creates a puzzle with increasing female workforce participation. The paper will consider if the condition of women improved over time with the increased share of employment or not? Using field survey data, this paper tries to understand if there exists any digression for some of the indicators both at the macro and micro level. The paper also tries to integrate the economic understanding of gender aspects of the workforce and the sociological stance prevailing in the existing literature. Therefore, this paper takes a mixed-method approach to better understand the role that social structure plays in the improved status of women within and across various households. Therefore, this paper will finally help us understanding if at all there is a feminisation of Indian agriculture or it's just exploitation of a different kind. This study intends to create a distinction between the gendered labour force in Indian agriculture and the complete democratization of Indian agriculture. The study is primarily focused on areas where the exodus of male migrants pushes women to work on agricultural farms. The question posits is whether it is the willingness of women to work in agriculture or is it urbanisation and development-induced conditions that make women work in agriculture as farm labourers? The motive is to understand if factors like resource ownership and the ability to autonomous decision-making are interlinked with an increased proportion of the female workforce or not? Based on this framework, we finally provide a brief comment on policy implications of government intervention in improving Indian agriculture and the gender aspects associated with it.

Keywords: feminisation, intrahousehold bargaining, farm households, migration, agriculture, decision-making

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Authors: Mehrdad Hosseinishakib, Mozhgan Chakani, Gholamreza Babaei

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This study aims to investigate the relationship between the bilateral communication channels, communication technologies with effective communications and communication technologies, employee participation in motivated decision-making of employees using the EFQM excellence model in Education Organization of Area 4 in Karaj. This research is an applied research in terms of the purpose and is a descriptive survey research in terms of nature and method and assesses the current situation using field studies. The statistical population consists of all employees and managers of Education Organization of Area 4 in Karaj including 5442 persons and random sampling was used and sample size is 359 using Cochran formula. Measurement tool is a researcher-made questionnaire with 20 questions including two categories of expertise and general questions. The first category includes general questions about respondents' personal characteristics such as gender and level of education, work experience and courses of study. The second category includes expertise questions of the questionnaire that have been designed to test research hypotheses that its reliability was approved by Cronbach's alpha coefficient 0.916 and its validity was approved according to the vies of teachers and some senior managers of Education Organization of Area 4 in Karaj. The results of the analysis of the findings show that there is a significant relationship between mutual communication channels, communication technologies with effective communication between employees and management. There is also a significant relationship between communication technologies and employee motivation and employee participation in their motivated decision-making in Education Organization of Area 4 in Karaj.

Keywords: communication channels, effective communication, EFQM model, ANOVA

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Authors: H. Naseh, M. Mirshams, M. Mirdamadian, H. R. Fazeley

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This paper presents an extension of fuzzy-genetic algorithm multi-objective optimization methodology that could effectively be used to find the overall satisfaction of objective functions (selecting the design variables) in the early stages of design process. The coupling of objective functions due to design variables in an engineering design process will result in difficulties in design optimization problems. In many cases, decision making on design variables conflicts with more than one discipline in system design. In space launch system conceptual design, decision making on some design variable (e.g. oxidizer to fuel mass flow rate O/F) in early stages of the design process is related to objective of liquid propellant engine (specific impulse) and Tanks (structure weight). Then, the primary application of this methodology is the design of a liquid propellant engine with the maximum specific impulse and cylindrical stiffened tank with the minimum weight. To this end, the design problem is established the fuzzy rule set based on designer's expert knowledge with a holistic approach. The independent design variables in this model are oxidizer to fuel mass flow rate, thickness of stringers, thickness of rings, shell thickness. To handle the mentioned problems, a fuzzy-genetic algorithm multi-objective optimization methodology is developed based on Pareto optimal set. Consequently, this methodology is modeled with the one stage of space launch system to illustrate accuracy and efficiency of proposed methodology.

Keywords: cylindrical stiffened tanks, multi-objective, genetic algorithm, fuzzy approach

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Authors: Sara Iqbal

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Introduction:Renal transplant is increasingly one of the most popular transplants within the UK; with an aging population along with obesity epidemic we are witnessing increasing rates of diabetes – one of the commonest indications for renal transplant. However, the demand is far greater than supply. Many donors are provided by women of child-bearing age; however the long-term effects are still uncertain. Aim:Determine pregnancy outcomes and complications of women of child-bearing age following renal donation. Methods: A review of the current available literature was preformed using MEDLINE and EMBASE up to 2014. Search criteria included key terms such as pregnancy outcome post-renal donor, pregnancy outcomes and complications. Relevant articles were selected based on pure methodological medical research, after careful analysis, they were recorded within this review. Results: Out of 1141 women involved in transplant studies, 574 pregnancies reported having donated a single-renal donor prior to pregnancy. Of which a staggering miscarriage rate 32.4% (n=186) was reported, amongst this other complications included gestational hypertension of 10% (n=59) and gestational diabetes 2.3% (n=13). Other significantly noted complications included chronic hypertension, low-birth weights, and pregnancy-related death. Conclusions: After unilateral renal donor transplant, haemodynamics change along with pregnancy, predisposing women to developing several complications compared to pregnancies with no history any renal-donor transplant. Despite this, further investigation is required in order to accurately determine the safety of renal-donors in women of child-bearing age.

Keywords: renal transplant, pregnancy, complications, medical and health sciences

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Authors: V. R. Sadasivam, M. Vipin, P. Vineeth, M. Sajith, G. Sathiskumar, R. Manikandan, N. Vijayarangan

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Global positioning system technology is what leads to such things as navigation systems, GPS tracking devices, GPS surveying and GPS mapping. All that GPS does is provide a set of coordinates which represent the location of GPS units with respect to its latitude, longitude and elevation on planet Earth. It also provides time, which is accurate. The tracking devices themselves come in different flavors. They will contain a GPS receiver, and GPS software, along with some way of transmitting the resulting coordinates. GPS in mobile tend to use radio waves to transmit their location to another GPS device. The purpose of this prototype “Mobi Navi Tour for Rescue Operation” timely communication, and lightning fast decision-making with a group of people located in different places with a common goal. Timely communication and tracking the people are a critical issue in many situations, environments. Expedited can find missing person by sending the location and other related information to them through mobile. Information must be drawn from the caller and entered into the system by the administrator or a group leader and transferred to the group leader. This system will locate the closest available person, a group of people working in an organization/company or vehicle to determine availability and their position to track them. Misinformation cannot lead to the wrong decision in the rapidly paced environment in a normal and an abnormal situation. In “Mobi Navi Tour for Rescue Operation” we use Google Cloud Messaging for android (GCM) which is a service that helps developers send data from servers to their android applications on android devices. The service provides a simple, lightweight mechanism that servers can use to tell mobile applications to contact the server directly, to fetch updated application or user data.

Keywords: android, gps, tour, communication, service

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Authors: Xuelian Qin

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Urban "organic renewal" based on the development of existing resources in high-density urban areas has become the mainstream of urban development in the new era. As an important stock resource of public space in high-density urban areas, promoting its value remodeling is an effective way to alleviate the shortage of public space resources. However, due to the lack of evaluation links in the process of underpass space renewal, a large number of underpass space resources have been left idle, facing the problems of low space conversion efficiency, lack of accuracy in development decision-making, and low adaptability of functional positioning to citizens' needs. Therefore, it is of great practical significance to construct the evaluation system of under-bridge space renewal potential and explore the renewal mode. In this paper, some of the under-bridge spaces in the main urban area of Chongqing are selected as the research object. Through the questionnaire interviews with the users of the built excellent space under the bridge, three types of six levels and twenty-two potential evaluation indexes of "objective demand factor, construction feasibility factor and construction suitability factor" are selected, including six levels of land resources, infrastructure, accessibility, safety, space quality and ecological environment. The analytical hierarchy process and expert scoring method are used to determine the index weight, construct the potential evaluation system of the space under the bridge in high-density urban areas of Chongqing, and explore the direction of renewal and utilization of its suitability. To provide feasible theoretical basis and scientific decision support for the use of under bridge space in the future.

Keywords: high density urban area, potential evaluation, space under bridge, updated using

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Authors: F. Ghazalnaz Sharifonnasabi, Iman Makhdoom

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Breast cancer is a serious health concern that affects many people around the world. According to a study published in the Breast journal, the global burden of breast cancer is expected to increase significantly over the next few decades. The number of deaths from breast cancer has been increasing over the years, but the age-standardized mortality rate has decreased in some countries. It’s important to be aware of the risk factors for breast cancer and to get regular check- ups to catch it early if it does occur. Machin learning techniques have been used to aid in the early detection and diagnosis of breast cancer. These techniques, that have been shown to be effective in predicting and diagnosing the disease, have become a research hotspot. In this study, we consider two deep learning approaches including: Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). We also considered the five-machine learning algorithm titled: Decision Tree (C4.5), Naïve Bayesian (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) Algorithm and XGBoost (eXtreme Gradient Boosting) on the Breast Cancer Wisconsin Diagnostic dataset. We have carried out the process of evaluating and comparing classifiers involving selecting appropriate metrics to evaluate classifier performance and selecting an appropriate tool to quantify this performance. The main purpose of the study is predicting and diagnosis breast cancer, applying the mentioned algorithms and also discovering of the most effective with respect to confusion matrix, accuracy and precision. It is realized that CNN outperformed all other classifiers and achieved the highest accuracy (0.982456). The work is implemented in the Anaconda environment based on Python programing language.

Keywords: breast cancer, multi-layer perceptron, Naïve Bayesian, SVM, decision tree, convolutional neural network, XGBoost, KNN

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Authors: Ki-Yeo Kim

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Trends in genetics are transforming in order to identify differential coexpressions of correlated gene expression rather than the significant individual gene. Moreover, it is known that a combined biomarker pattern improves the discrimination of a specific cancer. The identification of the combined biomarker is also necessary for the early detection of invasive oral squamous cell carcinoma (OSCC). To identify the combined biomarker that could improve the discrimination of OSCC, we explored an appropriate number of genes in a combined gene set in order to attain the highest level of accuracy. After detecting a significant gene set, including the pre-defined number of genes, a combined expression was identified using the weights of genes in a gene set. We used the Principal Component Analysis (PCA) for the weight calculation. In this process, we used three public microarray datasets. One dataset was used for identifying the combined biomarker, and the other two datasets were used for validation. The discrimination accuracy was measured by the out-of-bag (OOB) error. There was no relation between the significance and the discrimination accuracy in each individual gene. The identified gene set included both significant and insignificant genes. One of the most significant gene sets in the classification of normal and OSCC included MMP1, SOCS3 and ACOX1. Furthermore, in the case of oral dysplasia and OSCC discrimination, two combined biomarkers were identified. The combined genomic expression achieved better performance in the discrimination of different conditions than in a single significant gene. Therefore, it could be expected that accurate diagnosis for cancer could be possible with a combined biomarker.

Keywords: oral squamous cell carcinoma, combined biomarker, microarray dataset, correlated genes

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Authors: Oliveira M., Gonçalves L., Francisco I., Caramelo F., Vale F., Sanz D., Domingues M., Lopes M., Moreia D., Lopes T., Santos T., Cardoso H.

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The ARTHUR project consists of a platform that allows the virtual performance of maxillofacial surgeries, offering, in a photorealistic concept, the possibility for the patient to have an idea of the surgical changes before they are performed on their face. For this, the system brings together several image formats, dicoms and objs that, after loading, will generate the bone volume, soft tissues and hard tissues. The system also incorporates the patient's stereophotogrammetry, in addition to their data and clinical history. After loading and inserting data, the clinician can virtually perform the surgical operation and present the final result to the patient, generating a new facial surface that contemplates the changes made in the bone and tissues of the maxillary area. This tool acts in different situations that require facial reconstruction, however this project focuses specifically on two types of use cases: bone congenital disfigurement and acquired disfiguration such as oral cancer with bone attainment. Being developed a cloud based solution, with mobile support, the tool aims to reduce the decision time window of patient. Because the current simulations are not realistic or, if realistic, need time due to the need of building plaster models, patient rates on decision, rely on a long time window (1,2 months), because they don’t identify themselves with the presented surgical outcome. On the other hand, this planning was performed time based on average estimated values of the position of the maxilla and mandible. The team was based on averages of the facial measurements of the population, without specifying racial variability, so the proposed solution was not adjusted to the real individual physiognomic needs.

Keywords: 3D computing, image processing, image registry, image reconstruction

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Authors: Jinan Fiaidhi, Sabah Mohammed, Petros Zezos

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Capsule endoscopy (CE) is an established noninvasive diagnostic modality in investigating small bowel disease. CE has a pivotal role in assessing patients with suspected bleeding or identifying evidence of active Crohn's disease in the small bowel. However, CE produces lengthy videos with at least eighty thousand frames, with a frequency rate of 2 frames per second. Gastroenterologists cannot dedicate 8 to 15 hours to reading the CE video frames to arrive at a diagnosis. This is why the issue of analyzing CE videos based on modern artificial intelligence techniques becomes a necessity. However, machine learning, including deep learning, has failed to report robust results because of the lack of large samples to train its neural nets. In this paper, we are describing a thick data approach that learns from a few anchor images. We are using sound datasets like KVASIR and CrohnIPI to filter candidate frames that include interesting anomalies in any CE video. We are identifying candidate frames based on feature extraction to provide representative measures of the anomaly, like the size of the anomaly and the color contrast compared to the image background, and later feed these features to a decision tree that can classify the candidate frames as having a condition like the Crohn's Disease. Our thick data approach reported accuracy of detecting Crohn's Disease based on the availability of ulcer areas at the candidate frames for KVASIR was 89.9% and for the CrohnIPI was 83.3%. We are continuing our research to fine-tune our approach by adding more thick data methods for enhancing diagnosis accuracy.

Keywords: thick data analytics, capsule endoscopy, Crohn’s disease, siamese neural network, decision tree

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Authors: Wiseman Emmanuel Nkhomah, Chiyembekezo Kachimanga, Julia Huggins, Fabien Munyaneza

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Background: Partners In Health – Malawi introduced one of Operational Researches called Primary Health Care (PHC) Surveys in 2020, which seeks to assess progress of delivery of care in the district. The study consists of 5 long surveys, namely; Facility assessment, General Patient, Provider, Sick Child, Antenatal Care (ANC), primarily conducted in 4 health facilities in Neno district. These facilities include Neno district hospital, Dambe health centre, Chifunga and Matope. Usually, these annual surveys are conducted from January, and the target is to present final report by June. Once data is collected and analyzed, there are a series of reviews that take place before reaching final report. In the first place, the manual process took over 9 months to present final report. Initial findings reported about 76.9% of the data that added up when cross-checked with paper-based sources. Purpose: The aim of this approach is to run away from manually pulling the data, do fresh analysis, and reporting often associated not only with delays in reporting inconsistencies but also with poor quality of data if not done carefully. This automation approach was meant to utilize features of new technologies to create visualizations, reports, and dashboards in Power BI that are directly fished from the data source – CommCare hence only require a single click of a ‘refresh’ button to have the updated information populated in visualizations, reports, and dashboards at once. Methodology: We transformed paper-based questionnaires into electronic using CommCare mobile application. We further connected CommCare Mobile App directly to Power BI using Application Program Interface (API) connection as data pipeline. This provided chance to create visualizations, reports, and dashboards in Power BI. Contrary to the process of manually collecting data in paper-based questionnaires, entering them in ordinary spreadsheets, and conducting analysis every time when preparing for reporting, the team utilized CommCare and Microsoft Power BI technologies. We utilized validations and logics in CommCare to capture data with less errors. We utilized Power BI features to host the reports online by publishing them as cloud-computing process. We switched from sharing ordinary report files to sharing the link to potential recipients hence giving them freedom to dig deep into extra findings within Power BI dashboards and also freedom to export to any formats of their choice. Results: This data automation approach reduced research timelines from the initial 9 months’ duration to 5. It also improved the quality of the data findings from the original 76.9% to 98.9%. This brought confidence to draw conclusions from the findings that help in decision-making and gave opportunities for further researches. Conclusion: These results suggest that automating the research data process has the potential of reducing overall amount of time spent and improving the quality of the data. On this basis, the concept of data automation should be taken into serious consideration when conducting operational research for efficiency and decision-making.

Keywords: reporting, decision-making, power BI, commcare, data automation, visualizations, dashboards

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Authors: Shuang Qiu, Lingfeng Wang, Zhipeng Chen, Shijian Ge

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Macroalgae are considered a promising approach for wastewater treatment as well as an alternative animal feed in addition to a biofuel feedstock. Their large size and/or tendency to grow as dense floating mats or substrate-attached turfs lead to lower separation and drying costs than microalgae. In this study, the macroalgae species Ulva lactuca (U. lactuca) were used to investigate their capacity for treating municipal wastewaters, and the feasibility of using the harvested biomass as an alternative food source for the fruitfly Drosophila melanogaster, an animal model for biological research. Results suggested that U. lactuca could successfully grow on three types of wastewaters studied with biomass productivities of 8.12-64.3 g DW (dry weight)/(m²∙d). The secondary wastewater (SW) was demonstrated as the most effective wastewater medium for U. lactuca growth. However, both high nitrogen (92.5-98.9%) and phosphorus (64.5-88.6%) removal efficiencies were observed in all wastewaters, particularly in primary wastewater (PW) and SW, however, in central wastewater (CW), the highest removal rates were obtained (N 24.7 ± 0.97 and P 0.69 ± 0.01 mg/(g DW·d)). Additionally, the inclusion of 20% washed U. lactuca with 80% standard fruitfly food (w/w) resulted in a longer lifespan and more stable body weights in flies. On the other hand, similar results were not obtained for the food treatment with the addition of 20 % unwashed U. lactuca. This study suggests a promising method for the macroalgae-based treatment of municipal wastewater and the biomass for animal feed.

Keywords: animal feed, flies, macroalgae, nutrient recovery, Ulva lactuca, wastewater

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Authors: Pilly Joseph Kagosi

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The study aims at assessing challenges observed during implementation of participatory irrigation management (PIM) approach for food security in semi-arid areas of Tanzania. Data were collected through questionnaire, PRA tools, key informants discussion, Focus Group Discussion (FGD), participant observation and literature review. Data collected from questionnaire was analyzed using SPSS while PRA data was analyzed with the help of local communities during PRA exercise. Data from other methods were analyzed using content analysis. The study revealed that PIM approach has contribution in improved food security at household level due to involvement of communities in water management activities and decision making which enhanced availability of water for irrigation and increased crop production. However there were challenges observed during implementation of the approach including; minimum participation of beneficiaries in decision making during planning and designing stages, meaning inadequate devolution of power among scheme owners; Inadequate and lack of transparency on income expenditure in Water Utilization Associations’ (WUAs), water conflict among WUAs members, conflict between farmers and livestock keepers and conflict between WUAs leaders and village government regarding training opportunities and status; WUAs rules and regulation are not legally recognized by the National court and few farmers involved in planting trees around water sources. However it was realized that some of the mentioned challenges were rectified by farmers themselves facilitated by government officials. The study recommends that, the identified challenges need to be rectified for farmers to realize impotence of PIM approach as it was realized by other Asian countries.

Keywords: potentials of implementing participatory approach, challenges of participatory approach, irrigation management, Tanzania

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Authors: Djeffal Asma, Zemmouri Noureddine

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In order for building designer to be able to balance environmental concerns with other performance requirements, they need clear and concise information. For certain decisions during the design process, qualitative guidance, such as design checklists or guidelines information may not be sufficient for evaluating the environmental benefits between different building materials, products and designs. In this case, quantitative information, such as that generated through a life cycle assessment, provides the most value. LCA provides a systematic approach to evaluating the environmental impacts of a product or system over its entire life. In the case of buildings life cycle includes the extraction of raw materials, manufacturing, transporting and installing building components or products, operating and maintaining the building. By integrating LCA into building design process, designers can evaluate the life cycle impacts of building design, materials, components and systems and choose the combinations that reduce the building life cycle environmental impact. This article attempts to give an overview of the integration of LCA methodology in the context of building design, and focuses on the use of this methodology for environmental considerations concerning process design and optimization. A multiple case study was conducted in order to assess the benefits of the LCA as a decision making aid tool during the first stages of the building design under specific climate conditions of the North East region of Algeria. It is clear that the LCA methodology can help to assess and reduce the impact of a building design and components on the environment even if the process implementation is rather long and complicated and lacks of global approach including human factors. It is also demonstrated that using LCA as a multi objective optimization of building process will certainly facilitates the improvement in design and decision making for both new design and retrofit projects.

Keywords: life cycle assessment, buildings, sustainability, elementary schools, environmental impacts

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Authors: Alireza Javed, Keivan Jamshidi

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Reactive amyloidosis is a condition that complicates a long list of chronic inflammation, chronic infectious, malignant, and hereditary disorders. In the present study the potential effects of two amyloidogenic substances: ie. AgNO3 and Freund's Adjuvant (AF) with that of vitamin free casein, on spatio-temporal pattern of experimental amyloidosis in mice, were compared. For this purpose, a total of 40 male Swees mice, obtained from Pasteur Institute Tehran, after being weighted were randomly divided into 4 groups including 2 treatments, 1 control (vitamin free casein) and 1 positive control (normal saline). At the end of 3rd, 5th and 7th weeks of experiment 3 mice were randomly selected and euthnised. Spleen sample of each animal obtained and preserved in 10% neutral buffer formalin. Sample were then processed through different stages of dehydration, clearing and impregnation and finally embedded in paraffin blocks. Sections of 5µm thickness then cut and stained by alkaline Congo red techniques. Spleen weights and the data obtained from the microscopic quantitative analysis did show no significant differences between groups A and B, A and C, and B and C. However, significant differences were observed between groups A and D, B and D, and C and D respectively. It is concluded that two compounds ie; AgNO3 and Freund's Adjuvant have the same potential, as does vitamin free casein have, in spatio – temporal pattern of experimental amyloidosis in mice.

Keywords: amyloidosis, mice, AgNO3, Freund's Adjuvant

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Authors: Adesuyi Ayodeji Steve, Zahn Munch

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This study investigates the use of MODIS NDVI to identify agricultural land cover change areas on an annual time step (2007 - 2012) and characterize the trend in the study area. An ISODATA classification was performed on the MODIS imagery to select only the agricultural class producing 3 class groups namely: agriculture, agriculture/semi-natural, and semi-natural. NDVI signatures were created for the time series to identify areas dominated by cereals and vineyards with the aid of ancillary, pictometry and field sample data. The NDVI signature curve and training samples aided in creating a decision tree model in WEKA 3.6.9. From the training samples two classification models were built in WEKA using decision tree classifier (J48) algorithm; Model 1 included ISODATA classification and Model 2 without, both having accuracies of 90.7% and 88.3% respectively. The two models were used to classify the whole study area, thus producing two land cover maps with Model 1 and 2 having classification accuracies of 77% and 80% respectively. Model 2 was used to create change detection maps for all the other years. Subtle changes and areas of consistency (unchanged) were observed in the agricultural classes and crop practices over the years as predicted by the land cover classification. 41% of the catchment comprises of cereals with 35% possibly following a crop rotation system. Vineyard largely remained constant over the years, with some conversion to vineyard (1%) from other land cover classes. Some of the changes might be as a result of misclassification and crop rotation system.

Keywords: change detection, land cover, modis, NDVI

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Authors: M. Mokhber, J. Hedayati

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From the managerial perspective, it is important to devise an operational plan based on top priorities due to limited resources, diversity of measures and high costs needed to improve safety in infrastructure. Dealing with the high-risk corridors across Iran, this study prioritized the corridors according to statistical data on accidents involving fatalities, injury or damage over three consecutive years. In collaboration with the Iranian Police Department, data were collected and modified. Then, the prioritization criteria were specified based on the expertise opinions and international standards. In this study, the prioritization criteria included accident severity and accident density. Finally, the criteria were standardized and weighted (equal weights) to score each high-risk corridor. The prioritization phase involved the scoring and weighting procedure. The high-risk corridors were divided into twelve groups out of 50. The results of data analysis for a three-year span suggested that the first three groups (150 corridors) along with a quarter of Iranian road network length account for nearly 60% of traffic accidents. In the next step, according to variables including weather conditions particular roads for the purpose of winter safety measures were extracted from the abovementioned categories. According to the results ranking, ‎‏9‏‎ roads with the overall ‎length of about ‎‎‏1000‏‎ Km of high-risk corridors are considered as preferences of ‎safety measures‎.

Keywords: high-risk corridors, HRCs, road safety rating, road scoring, winter safety measures

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Authors: Sahin Emrah Amrahov, Fatih Aybar, Serhat Dogan

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In this paper we consider the rule reduct generation problem. Rule Reduct Generation (RG) and Modified Rule Generation (MRG) algorithms, that are used to solve this problem, are well-known. Alternative to these algorithms, we develop Pruning Rule Generation (PRG) algorithm. We compare the PRG algorithm with RG and MRG.

Keywords: rough sets, decision rules, rule induction, classification

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Authors: Hritik Bhattarai, Vivek Dumre, Ananya Neupane, Poonam Koirala, Anjali Singh

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The availability of clean and reliable groundwater is essential for the sustainment of human and environmental health. Groundwater is a crucial resource that contributes significantly to the total annual supply. However, over-exploitation has depleted groundwater availability considerably and led to some land subsidence. Determining the potential zone of groundwater is vital for protecting water quality and managing groundwater systems. Groundwater potential zones are marked with the assistance of Geographic Information System techniques. During the study, a standard methodology was proposed to determine groundwater potential using an integration of GIS and AHP techniques. When choosing the prospective groundwater zone, accurate information was generated to get parameters such as geology, slope, soil, temperature, rainfall, drainage density, and lineament density. However, identifying and mapping potential groundwater zones remains challenging due to aquifer systems' complex and dynamic nature. Then, ArcGIS was incorporated with a weighted overlay, and appropriate ranks were assigned to each parameter group. Through data analysis, MCDA was applied to weigh and prioritize the different parameters based on their relative impact on groundwater potential. There were three probable groundwater zones: low potential, moderate potential, and high potential. Our analysis showed that the central and lower parts of the Bagmati River Basin have the highest potential, i.e., 7.20% of the total area. In contrast, the northern and eastern parts have lower potential. The identified potential zones can be used to guide future groundwater exploration and management strategies in the region.

Keywords: groundwater, geographic information system, analytic hierarchy processes, multi-criteria decision analysis, Bagmati

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Authors: Rishi Agarwal

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Digital twins are emerging as a strong technology used to imitate and monitor physical objects digitally in real time across sectors. It is not only dealing with the digital space, but it is also actuating responses in the physical space in response to the digital space processing like storage, modeling, learning, simulation, and prediction. This paper explores the application of digital twins for enhancing physical security in retail stores. The retail sector still relies on outdated physical security practices like manual monitoring and metal detectors, which are insufficient for modern needs. There is a lack of real-time data and system integration, leading to ineffective emergency response and preventative measures. As retail automation increases, new digital frameworks must control safety without human intervention. To address this, the paper proposes implementing an intelligent digital twin framework. This collects diverse data streams from in-store sensors, surveillance, external sources, and customer devices and then Advanced analytics and simulations enable real-time monitoring, incident prediction, automated emergency procedures, and stakeholder coordination. Overall, the digital twin improves physical security through automation, adaptability, and comprehensive data sharing. The paper also analyzes the pros and cons of implementation of this technology through an Emerging Technology Analysis Canvas that analyzes different aspects of this technology through both narrow and wide lenses to help decision makers in their decision of implementing this technology. On a broader scale, this showcases the value of digital twins in transforming legacy systems across sectors and how data sharing can create a safer world for both retail store customers and owners.

Keywords: digital twin, retail store safety, digital twin in retail, digital twin for physical safety

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Authors: A. Buruzs, M. F. Hatwágner, A. Torma, L. T. Kóczy

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The development, operation and maintenance of Integrated Waste Management Systems (IWMS) affects essentially the sustainable concern of every region. The features of such systems have great influence on all of the components of sustainability. In order to reach the optimal way of processes, a comprehensive mapping of the variables affecting the future efficiency of the system is needed such as analysis of the interconnections among the components and modelling of their interactions. The planning of a IWMS is based fundamentally on technical and economical opportunities and the legal framework. Modelling the sustainability and operation effectiveness of a certain IWMS is not in the scope of the present research. The complexity of the systems and the large number of the variables require the utilization of a complex approach to model the outcomes and future risks. This complex method should be able to evaluate the logical framework of the factors composing the system and the interconnections between them. The authors of this paper studied the usability of the Fuzzy Cognitive Map (FCM) approach modelling the future operation of IWMS’s. The approach requires two input data set. One is the connection matrix containing all the factors affecting the system in focus with all the interconnections. The other input data set is the time series, a retrospective reconstruction of the weights and roles of the factors. This paper introduces a novel method to develop time series by content analysis.

Keywords: content analysis, factors, integrated waste management system, time series

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Authors: Elizabeth Chinyere Amadi

Abstract:

Yeast (sacchoromyces cerevisiae) was isolated from the fermenting sap of raffia palm (Raphia hookeri) wine. Different concerntrations of the yeast isolate were used to produce bread samples – B, C, D, E, F containing (2, 3, 4, 5, 6) g of yeast isolate respectively, other ingredients were kept constant. Sample A, containing 2g of commercial baker yeast served as control. The proof heights, weights, volumes and specific volume of the dough and bread samples were determined. The bread samples were also subjected to sensory evaluation using a 9–point hedonic scale. Results showed that proof height increased with increased concentration of the yeast isolate; that is direct proportion. Sample B with the least concentration of the yeast isolate had the least loaf height and volume of 2.80c m and 200 cm³ respectively but exhibited the highest loaf weight of 205.50g. However, Sample A, (commercial bakers’ yeast) had the highest loaf height and volume of 5.00 cm and 400 cm³ respectively. The sensory evaluation results showed sample D compared favorably with sample A in all the organoleptic attributes-(appearance, taste, crumb texture, crust colour and overall acceptability) tested for (P< 0.05). It was recommended that 4g compressed yeast isolate per 100g flour could be used to proof dough as a substitute for commercial bakers’ yeast and produce acceptable bread loaves.

Keywords: isolation of yeast, performance evaluation of yeast, Raffia palm wine, used at different concentrations, proofing of bread dough

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Authors: Y. Yılmaz, C. Ünal, A. Dursun

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Distribution channels play significant role for hotels. Direct and indirect selling options of hotel rooms have been increased especially with the help of new technologies, i.e. hotel’s own web sites and online booking sites. Although these options emerged as tools for diversifying the distribution channels, vast number of hotels -mostly resort hotels- is still heavily dependent upon international tour operators when selling their products. On the other hand, hotel sector is so vulnerable against crises. Economic, political or any other crisis can affect hotels very badly and so it is critical to have the right balance of distribution channel to avoid the adverse impacts of a crisis. In this study, it is aimed to search the impacts of a general crisis on the selling prices of hotels which have different weights of distribution channels. The study was done in Turkey where various crises occurred in 2015 and 2016 which had great negative impacts on Turkish tourism and led enormous occupancy rate and selling price reductions. 112 upscale resort hotel in Antalya, which is the most popular tourism destination of Turkey, joined to the research. According to the results, hotels with high dependency to international tour operators are more forced to reduce their room prices in crisis time compared to the ones which use their own web sites more. It was also found that the decline in room prices is limited for hotels which are working with national tour operators and travel agencies in crisis time.

Keywords: marketing channels, crisis, hotel, international tour operators, online travel agencies

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Authors: Farshid Hajati, Federico Girosi, Shima Ghassempour

Abstract:

We present a Deep Belief Network (DBN) method for clustering health trajectories. Deep Belief Network (DBN) is a deep architecture that consists of a stack of Restricted Boltzmann Machines (RBM). In a deep architecture, each layer learns more complex features than the past layers. The proposed method depends on DBN in clustering without using back propagation learning algorithm. The proposed DBN has a better a performance compared to the deep neural network due the initialization of the connecting weights. We use Contrastive Divergence (CD) method for training the RBMs which increases the performance of the network. The performance of the proposed method is evaluated extensively on the Health and Retirement Study (HRS) database. The University of Michigan Health and Retirement Study (HRS) is a nationally representative longitudinal study that has surveyed more than 27,000 elderly and near-elderly Americans since its inception in 1992. Participants are interviewed every two years and they collect data on physical and mental health, insurance coverage, financial status, family support systems, labor market status, and retirement planning. The dataset is publicly available and we use the RAND HRS version L, which is easy to use and cleaned up version of the data. The size of sample data set is 268 and the length of the trajectories is equal to 10. The trajectories do not stop when the patient dies and represent 10 different interviews of live patients. Compared to the state-of-the-art benchmarks, the experimental results show the effectiveness and superiority of the proposed method in clustering health trajectories.

Keywords: health trajectory, clustering, deep learning, DBN

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Authors: Saied Farham-Nia, Alireza Ghaffari-Hadigheh

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Game theory is a mathematical tool to study the behaviors of a rational and strategic decision-makers, that analyze existing equilibrium in interest conflict situation and provides an appropriate mechanisms for cooperation between two or more player. Game theory is applicable for any strategic and interest conflict situation in politics, management and economics, sociology and etc. Real worlds’ decisions are usually made in the state of indeterminacy and the players often are lack of the information about the other players’ payoffs or even his own, which leads to the games in uncertain environments. When historical data for decision parameters distribution estimation is unavailable, we may have no choice but to use expertise belief degree, which represents the strength with that we believe the event will happen. To deal with belief degrees, we have use uncertainty theory which is introduced and developed by Liu based on normality, duality, subadditivity and product axioms to modeling personal belief degree. As we know, the personal belief degree heavily depends on the personal knowledge concerning the event and when personal knowledge changes, cause changes in the belief degree too. Uncertainty theory not only theoretically is self-consistent but also is the best among other theories for modeling belief degree on practical problem. In this attempt, we primarily reintroduced Expected Utility Function in uncertainty environment according to uncertainty theory axioms to extract payoffs. Then, we employed Nash Equilibrium to investigate the solutions. For more practical issues, Stackelberg leader-follower Game and Bertrand Game, as a benchmark models are discussed. Compared to existing articles in the similar topics, the game models and solution concepts introduced in this article can be a framework for problems in an uncertain competitive situation based on experienced expert’s belief degree.

Keywords: game theory, uncertainty theory, belief degree, uncertain expected value, Nash equilibrium

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Authors: Tayyibah Shah Alam, Joe Thomas, Nayantara Shenoy

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Presenting a case that we would like to share, the answer is straight forward but the path taken to get to the diagnosis is where it gets interesting. A 31-year-old lady presented to the Rheumatology Outpatient department with left-sided chest pain associated with left-sided elbow joint pain intensifying over the last 2 days. She had been having a prolonged history of chest pain with minimal intensity since 2016. The pain is intermittent in nature. Aggravated while exerting, lifting heavy weights and lying down. Relieved while sitting. Her physical examination and laboratory tests were within normal limits. An electrocardiogram (ECG) showed normal sinus rhythm and a chest X-ray with no significant abnormality was noted. The primary suspicion was recurrent costochondritis. Cardiac blood inflammatory markers and Echo were normal, ruling out ACS. CT chest and MRI Thorax contrast showed small ill-defined STIR hyperintensity with thin peripheral enhancement in the anterior mediastinum in the left side posterior to the 5th costal cartilage and anterior to the pericardium suggestive of changes in the fat-focal panniculitis. Confirming the diagnosis as Epicardial fat necrosis. She was started on Colchicine and Nonsteroidal anti-inflammatory drugs for 2-3 weeks, following which a repeat CT showed resolution of the lesion and improvement in her. It is often under-recognized or misdiagnosed. CT scan was collectively used to establish the diagnosis. Making the correct diagnosis prospectively alleviates unnecessary testing in favor of conservative management.

Keywords: EFN, panniculitis, unknown etiology, recurrent chest pain

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Authors: Nsimbe Allan

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The study examines the impact of Building Information Modeling (BIM) on the cost management of engineering projects, focusing specifically on the Mombasa Port Area Development Project. The objective of this research venture is to determine the mechanisms through which Building Information Modeling (BIM) facilitates stakeholder collaboration, reduces construction-related expenses, and enhances the precision of cost estimation. Furthermore, the study investigates barriers to execution, assesses the impact on the project's transparency, and suggests approaches to maximize resource utilization. The study, selected for its practical significance and intricate nature, conducted a Systematic Literature Review (SLR) using credible databases, including ScienceDirect and IEEE Xplore. To constitute the diverse sample, 69 individuals, including project managers, cost estimators, and BIM administrators, were selected via stratified random sampling. The data were obtained using a mixed-methods approach, which prioritized ethical considerations. SPSS and Microsoft Excel were applied to the analysis. The research emphasizes the crucial role that project managers, architects, and engineers play in the decision-making process (47% of respondents). Furthermore, a significant improvement in cost estimation accuracy was reported by 70% of the participants. It was found that the implementation of BIM resulted in enhanced project visibility, which in turn optimized resource allocation and facilitated the process of budgeting. In brief, the study highlights the positive impacts of Building Information Modeling (BIM) on collaborative decision-making and cost estimation, addresses challenges related to implementation, and provides solutions for the efficient assimilation and understanding of BIM principles.

Keywords: cost management, resource utilization, stakeholder collaboration, project transparency

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Authors: Abiodun O. Ibude

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Several organizations have devised the strategy of engaging the services of staff not directly employed by them in their production and service delivery. Some organizations also engage on contracting another organization to carry out a part of service or production process on their behalf. Outsourcing is becoming an important alternative employment option for most organizations. This paper attempts an exposition on the rights of workers within the more specific context of outsourcing as a human resource management phenomenon. Outsourced employees and their rights are treated conceptually and analytically in a generic sense as a mere subset of the larger whole, that is, labor. Outsourced employees derive their rights, like all workers, from their job context as well as the legal environment (municipal and global) in which they operate. The dynamics of globalization and the implications of this development for labor practices receive considerable attention in this exposition. In this regard, a guarded proposition is made, to examine the practice and effect of engaging outsourcing as an economic decision designed primarily to cut down on operational costs rather than a Human Resources Management decision to improve worker welfare. The population of the study was selected from purposive and simple random sampling techniques. Data obtained were analyzed through a simple percentage, Pearson product-moment correlation, and cross-tabulation. From the research conducted, it was discovered that, although outsourcing possesses opportunities for organizations, there are drawbacks arising from its implementation of job securities. It was also discovered that some employees are being exploited through this strategy. This gives rise to lower motivation and thereby decline in performance. In conclusion, there is need for examination of Human Resource Managers’ strategies that can serve as management policy tools for the protection of the rights of outsourced employees.

Keywords: legal environment, operational cost, outsourcing, protection

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Authors: Vishnu Nagendra, Marten Van Der Veen, Stefania Giodini

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Effective humanitarian logistics operations are a cornerstone in the success of disaster relief operations. However, for effectiveness, they need to be demand driven and supported by adequate data for prioritization. Without this data operations are carried out in an ad hoc manner and eventually become chaotic. The current availability of geospatial data helps in creating models for predictive damage and vulnerability assessment, which can be of great advantage to logisticians to gain an understanding on the nature and extent of the disaster damage. This translates into actionable information on the demand for relief goods, the state of the transport infrastructure and subsequently the priority areas for relief delivery. However, due to the unpredictable nature of disasters, the accuracy in the models need improvement which can be done using remote sensing data from UAVs (Unmanned Aerial Vehicles) or satellite imagery, which again come with certain limitations. This research addresses the need for a framework to combine data from different sources to support humanitarian logistic operations and prediction models. The focus is on developing a workflow to combine data from satellites and UAVs post a disaster strike. A three-step approach is followed: first, the data requirements for logistics activities are made explicit, which is done by carrying out semi-structured interviews with on field logistics workers. Second, the limitations in current data collection tools are analyzed to develop workaround solutions by following a systems design approach. Third, the data requirements and the developed workaround solutions are fit together towards a coherent workflow. The outcome of this research will provide a new method for logisticians to have immediately accurate and reliable data to support data-driven decision making.

Keywords: unmanned aerial vehicles, damage prediction models, remote sensing, data driven decision making

Procedia PDF Downloads 373