Search results for: data infrastructure

Authors: Harsimran Kaur Chadha, Preeti Onkar

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Tourism has grown into one of the world’s largest industries in the last two decades all over the world. It is an important sector of Indian economy as it contributes substantially to the foreign exchange earnings of the country. The tourism policies of India aim to position tourism as a major engine of economic growth. These policies work towards utilizing tourism’s direct and multiplier effect on employment and poverty eradication in a sustainable manner. India is blessed with a great ancient and living civilization that gave rise to four of the world’s great religions and philosophies. Diverse religions, castes, languages, culture of India build a tremendous potential for religious tourism in India. Religious Tourism facilitates development of basic infrastructural facilities, generates income for the local community as well as the government, balances regional development, and fosters peace and socio-cultural harmony. However, tourism development needs to be regulated to prevent the negative impacts. The main challenge towards Sustainable Tourism development is to balance limits and usage of natural resources. The uncontrollable growth of tourism should not lead to resource degradation. Since tourism growth is inevitable, the challenge is to manage it sustainably within environmental, social and economic constraints. This paper tries to explore both the benefits and costs of Religious Tourism Development, using the example of Simhasth Kumbh Mahaparv at Ujjain. Finally it concludes by putting forth the notion that heavy investments for temporary infrastructure development incurred during these large spiritual gatherings need to be sustainable in the long run.

Keywords: challenges, religious, sustainable, tourism

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Authors: Averalda van Graan, Joelaine Chetty, Malory Links, Agness Mwangwela, Sitilitha Masangwi, Dalitso Chimwala, Shiban Ghosh, Elizabeth Marino-Costello

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Food composition data is becoming increasingly important as dealing with food insecurity and malnutrition in its persistent form of under-nutrition is now coupled with increasing over-nutrition and its related ailments in the developing world, of which Malawi is not spared. In the absence of a food composition database (FCDB) inherent to our dietary patterns, efforts were made to develop a country-specific FCDB for nutrition practice, research, and programming. The main objective was to develop a multi-user, country-specific food composition database, and table from existing published and unpublished scientific literature. A multi-phased approach guided by the project framework was employed. Phase 1 comprised a scoping mission to assess the nutrition landscape for compilation activities. Phase 2 involved training of a compiler and data collection from various sources, primarily; institutional libraries, online databases, and food industry nutrient data. Phase 3 subsumed evaluation and compilation of data using FAO and IN FOODS standards and guidelines. Phase 4 concluded the process with quality assurance. 316 Malawian food items categorized into eight food groups for 42 components were captured. The majority were from the baby food group (27%), followed by a staple (22%) and animal (22%) food group. Fats and oils consisted the least number of food items (2%), followed by fruits (6%). Proximate values are well represented; however, the percent missing data is huge for some components, including Se 68%, I 75%, Vitamin A 42%, and lipid profile; saturated fat 53%, mono-saturated fat 59%, poly-saturated fat 59% and cholesterol 56%. A multi-phased approach following the project framework led to the development of the first Malawian FCDB and table. The table reflects inherent Malawian dietary patterns and nutritional concerns. The FCDB can be used by various professionals in nutrition and health. Rising over-nutrition, NCD, and changing diets challenge us for nutrient profiles of processed foods and complete lipid profiles.

Keywords: analytical data, dietary pattern, food composition data, multi-phased approach

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Authors: Sultan Ghaleb Aldaihani

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- The environment in which educational leadership takes place is becoming increasingly complex due to factors like globalization and rapid technological change. - This is creating a "leadership gap" where the complexity of the environment outpaces the ability of leaders to effectively respond. - Educational leadership involves guiding teachers and the broader school system towards improved student learning and achievement. 2. Implications of Artificial Intelligence (AI) in Educational Leadership: - AI has great potential to enhance education, such as through intelligent tutoring systems and automating routine tasks to free up teachers. - AI can also have significant implications for educational leadership by providing better information and data-driven decision-making capabilities. - Computer-adaptive testing can provide detailed, individualized data on student learning that leaders can use for instructional decisions and accountability. 3. Enhancing Decision-Making Processes: - Statistical models and data mining techniques can help identify at-risk students earlier, allowing for targeted interventions. - Probability-based models can diagnose students likely to drop out, enabling proactive support. - These data-driven approaches can make resource allocation and decision-making more effective. 4. Improving Efficiency and Productivity: - AI systems can automate tasks and change processes to improve the efficiency of educational leadership and administration. - Integrating AI can free up leaders to focus more on their role's human, interactive elements.

Keywords: Education, Leadership, Technology, Artificial Intelligence

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Authors: Riktesh Srivastava

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Online Shopping have established an astonishing evolution in the last few years. And it is now apparent that B2C architecture is becoming progressively imperative channel for even traditional brick and mortar type traders as well. In this completion knowing customers and predicting behavior are extremely important. More important, when any customer logs onto the B2C architecture, the traces of their buying patterns can be stored and used for future predictions. Such a prediction is called Customer Lifetime Value (CLV). Earlier, we used Net Present Value to do so, however, it ignores two important aspects of B2C architecture, “market risks” and “big amount of customer data”. Now, we use RFM- Recency, Frequency and Monetary Value to estimate the CLV, and as the term exemplifies, market risks, is well sheltered. Big Data Analysis is also roofed in RFM, which gives real exploration of the Big Data and lead to a better estimation for future cash flow from customers. In the present paper, 6 factors (collected from varied sources) are used to determine as to what attracts the customers to the B2C architecture. For these 6 factors, RFM is computed for 3 years (2013, 2014 and 2015) respectively. CLV and Revenue are the two parameters defined using RFM analysis, which gives the clear picture of the future predictions.

Keywords: CLV, RFM, revenue, recency, frequency, monetary value

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Authors: Mohammed Kachtali, Imad Fenjiro, Jamal Alkarkouri

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Wind erosion is a serious environmental problem in arid and semi-arid regions. Indeed, wind erosion easily removes the finest particles of the soil surface, which also contribute to losing soil fertility. The siltation of infrastructures and cultivated areas and the negative impact on health are additional consequences of wind erosion. In Morocco, wind erosion constitutes the main factor of silting up in coast and Sahara. The aim of our study is to use an equation of wind erosion in order to estimate the soil loses by wind erosion in the coast of Gharb (North of Morocco). The used equation in our model includes the geographic data, climatic data of 30 years and edaphic data collected from area study which contained 11 crossing of 4 stations. Our results have shown that the values of wind erosion are higher and very different between some crossings (p < 0.001). This difference is explained by topography, soil texture, and climate. In conclusion, wind erosion is higher in Gharb coast and varies from station to another; this problem required several methods of control and mitigation.

Keywords: Gharb coast, modeling, silting, wind erosion

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Authors: I. Alhajri, G. Zahedi, R. Alazmi, A. Akbari

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In this paper, an Artificial Neural Network (ANN) was developed to predict Asphaltene Precipitation (AP) during the injection of carbon dioxide into crude oil reservoirs. In this study, the experimental data from six different oil fields were collected. Seventy percent of the data was used to develop the ANN model, and different ANN architectures were examined. A network with the Trainlm training algorithm was found to be the best network to estimate the AP. To check the validity of the proposed model, the model was used to predict the AP for the thirty percent of the data that was unevaluated. The Mean Square Error (MSE) of the prediction was 0.0018, which confirms the excellent prediction capability of the proposed model. In the second part of this study, the ANN model predictions were compared with modified Hirschberg model predictions. The ANN was found to provide more accurate estimates compared to the modified Hirschberg model. Finally, the proposed model was employed to examine the effect of different operating parameters during gas injection on the AP. It was found that the AP is mostly sensitive to the reservoir temperature. Furthermore, the carbon dioxide concentration in liquid phase increases the AP.

Keywords: artificial neural network, asphaltene, CO2 injection, Hirschberg model, oil reservoirs

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Authors: Zainuddin Zakaria, Sun Woh Lye

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Despite the continuous advancements in automated conflict resolution tools, there is still a low rate of adoption of automation from Air Traffic Control Officers (ATCOs). Trust or acceptance in these tools and conformance to the individual ATCO preferences in strategy execution for conflict resolution are two key factors that impact their use. This paper proposes a methodology to unearth and classify ATCO conflict resolution strategies from simulator data of trained and qualified ATCOs. The methodology involves the extraction of ATCO executive control actions and the establishment of a system of strategy resolution classification based on ATCO radar commands and prevailing flight parameters in deconflicting a pair of aircraft. Six main strategies used to handle various categories of conflict were identified and discussed. It was found that ATCOs were about twice more likely to choose only vertical maneuvers in conflict resolution compared to horizontal maneuvers or a combination of both vertical and horizontal maneuvers.

Keywords: air traffic control strategies, conflict resolution, simulator data, strategy classification system

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Authors: Emberga Terhemb Opara Alexander, Selemo Alexader, Onyekwuru Samuel

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The aeromagnetic data have been used to re-evaluate parts of the Upper Benue Trough Nigeria using spectral analysis technique in order to appraise the mineral accumulation potential of the area. The regional field was separated with a first order polynomial using polyfit program. The residual data was subdivided into 24 spectral blocks using OASIS MONTAJ software program. Two prominent magnetic depth source layers were identified. The deeper source depth values obtained ranges from 1.56km to 2.92km with an average depth of 2.37km as the magnetic basement depth while for the shallower sources, the depth values ranges from -1.17km to 0.98km with an average depth of 0.55km. The shallow depth source is attributed to the volcanic rocks that intruded the sedimentary formation and this could possibly be responsible for the mineralization found in parts of the study area.

Keywords: spectral analysis, Upper Benue Trough, magnetic basement depth, aeromagnetic

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Authors: Fathi M. O. Hamed, Salma F. Elkofhaifee

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The aim of this work is to detect geometrical shape objects in an image. In this paper, the object is considered to be as a circle shape. The identification requires find three characteristics, which are number, size, and location of the object. To achieve the goal of this work, this paper presents an algorithm that combines from some of statistical approaches and image analysis techniques. This algorithm has been implemented to arrive at the major objectives in this paper. The algorithm has been evaluated by using simulated data, and yields good results, and then it has been applied to real data.

Keywords: image processing, median filter, projection, scale-space, segmentation, threshold

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Authors: Mohamed Ahmed Kenawi Abdallah

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Chemical composition, cooking loss, shrinkage value, texture coefficient indices, Feder value, microbial examination, and sensory evaluation were done in order to examine the effect of adding 15% germinated quinoa seeds flour as extender to chicken wings meat to produce low fat chicken burger, packaged in two different packing materials and stored frozen for nine months. The data indicated reduction in the moisture content, crude either extract, and increase in the ash content, pH value, and total acidity for the samples extended by quinoa flour compared with the control one. The data showed that the extended samples with quinoa flour had the lowest values of TBA, cooking loss, and shrinkage value compared with the control ones. The data also revealed that, the sample contained quinoa flour had total bacterial count and psychrophilic bacterial count lower than the control sample. In addition, it has higher evaluation values for overall acceptability than the control one.

Keywords: chicken wings, low fat chicken burger, quinoa flour, vacuum packaging.

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Authors: Jinyong Yao, Hongzhi Li, Chao Du, Jiao Li

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Reliability of long-term storage products is related to the availability of the whole system, and the evaluation of storage life is of great necessity. These products are usually highly reliable and little failure information can be collected. In this paper, an analytical method based on data from accelerated storage life test is proposed to evaluate the reliability index of the long-term storage products. Firstly, singularities are eliminated by data normalization and residual analysis. Secondly, with the pre-processed data, the degradation path model is built to obtain the pseudo life values. Then by life distribution hypothesis, we can get the estimator of parameters in high stress levels and verify failure mechanisms consistency. Finally, the life distribution under the normal stress level is extrapolated via the acceleration model and evaluation of the true average life available. An application example with the camera stabilization device is provided to illustrate the methodology we proposed.

Keywords: accelerated storage life test, failure mechanisms consistency, life distribution, reliability

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Authors: Sergio Paez Moncaleano, Alvaro Mauricio Montenegro

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This paper presents a novel statistical methodology for measuring and founding constructs in Latent Regression Analysis. This approach uses the qualities of Factor Analysis in binary data with interpretations on Item Response Theory (IRT). In addition, based on the fundamentals of submodel theory and with a convergence of many ideas of IRT, we propose an algorithm not just to solve the dimensionality problem (nowadays an open discussion) but a new research field that promises more fear and realistic qualifications for examiners and a revolution on IRT and educational research. In the end, the methodology is applied to a set of real data set presenting impressive results for the coherence, speed and precision. Acknowledgments: This research was financed by Colciencias through the project: 'Multidimensional Item Response Theory Models for Practical Application in Large Test Designed to Measure Multiple Constructs' and both authors belong to SICS Research Group from Universidad Nacional de Colombia.

Keywords: item response theory, dimensionality, submodel theory, factorial analysis

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Authors: Anthony Oppong Kyekyeku, Sussana Antwi-Boasiako, Emmanuel De-Graft Johnson Owusu Ansah

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In this observational study, the performance of a known conventional storage system was tested and evaluated for fitness for its intended purpose. The system has a scope extended for the storage of dry cocoa beans. System sensitivity, reproducibility and uncertainties are not known in details. This study discusses the system performance in the context of existing literature on factors that influence the quality of cocoa beans during storage. Controlled conditions were defined precisely for the system to give reliable base line within specific established procedures. Minitab® 17 and R statistical software (R-3.3.1) were used for the statistical analyses. The approach to the storage system testing was to observe and compare through laboratory test methods the quality of the cocoa beans samples before and after storage. The samples were kept in Kilner jars and the temperature of the storage environment controlled and monitored over a period of 408 days. Standard test methods use in international trade of cocoa such as the cut test analysis, moisture determination with Aqua boy KAM III model and bean count determination were used for quality assessment. The data analysis assumed the entire population as a sample in order to establish a reliable baseline to the data collected. The study concluded a statistically significant mean value at 95% Confidence Interval (CI) for the performance data analysed before and after storage for all variables observed. Correlational graphs showed a strong positive correlation for all variables investigated with the exception of All Other Defect (AOD). The weak relationship between the before and after data for AOD had an explained variability of 51.8% with the unexplained variability attributable to the uncontrolled condition of hidden infestation before storage. The current study concluded with a high-performance criterion for the storage system.

Keywords: benchmarking performance data, cocoa beans, hidden infestation, storage system validation

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Authors: Gurjeet Singh, Rabindra K. Panda

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Soil moisture is a key hydrologic state variable and is intrinsically linked to the Earth's water, climate and carbon cycles. On ecological point of view, the soil moisture is a fundamental natural resource providing the transpirable water for plants. Soil moisture varies both temporally and spatially due to spatiotemporal variation in rainfall, vegetation cover, soil properties and topography. Satellite derived soil moisture provides spatio-temporal extensive data. However, the spatial resolution of a typical satellite (L-band radiometry) is of the order of tens of kilometers, which is not good enough for developing efficient agricultural water management schemes at the field scale. In the present study, the soil moisture from radiometer data has been disaggregated using blending approach to achieve higher resolution soil moisture data. The radiometer estimates of soil moisture at a 40 km resolution have been disaggregated to 10 km, 5 km and 1 km resolutions. The disaggregated soil moisture was compared with the observed data, consisting of continuous sensor based soil moisture profile measurements, at three monitoring sites and extensive spatial near-surface soil moisture measurements, concurrent with satellite monitoring in the 500 km2 study watershed in the Eastern India. The estimated soil moisture status at different spatial scales can help in developing efficient agricultural water management schemes to increase the crop production and water use efficiency.

Keywords: disaggregation, eastern India, radiometers, soil moisture, water use efficiency

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Authors: D. Subedi, D. Sharma

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Current transformers (CTs) are used to transform large primary currents to a small secondary current. Since most standard equipment’s are not designed to handle large primary currents the CTs have an important part in any electrical system for the purpose of Metering and Protection both of which are integral in Power system. Now a days due to advancement in solid state technology, the operation times of the protective relays have come to a few cycles from few seconds. Thus, in such a scenario it becomes important to study the transient response of the current transformers as it will play a vital role in the operating of the protective devices. This paper shows the steady state and transient behavior of current transformers and how it changes with change in connected burden. The transient and steady state response will be captured using the data acquisition software LabVIEW. Analysis is done on the real time data gathered using LabVIEW. Variation of current transformer characteristics with changes in burden will be discussed.

Keywords: accuracy, accuracy limiting factor, burden, current transformer, instrument security factor

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Authors: Hang Ju, Changmin Zhu

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The development level of the BeiDou Navigation Satellite System (BDS) can strongly reflect national defense strength as an important spatial information infrastructure. BDS can be not only used for military purposes, such as intelligence gathering, nuclear explosion monitoring, emergency communications, but also for location services, transportation, mapping, precision agriculture. In order to ensure the national defense security and the wide application of BDS in civil and military areas, BDS must be autonomous and controllable. As a complex system of knowledge-intensive, knowledge transformation runs through the whole process of research and development, production, operation, and maintenance of BDS. Based on the perspective of knowledge transformation, this paper expounds on the meaning of socialization, externalization, combination, and internalization of knowledge transformation, and the coupling relationship of autonomy and control on the basis of analyzing the status quo and problems of the autonomy and control of BDS. The autonomous and controllable framework of BDS based on knowledge transformation is constructed from six dimensions of management capability, R&D capability, technical capability, manufacturing capability, service support capability, and application capability. It can provide support for the smooth implementation of information security policy, provide a reference for the autonomy and control of the upstream and downstream industrial chains in Beidou, and provide a reference for the autonomous and controllable research of aerospace components, military measurement test equipment, and other related industries.

Keywords: knowledge transformation, BeiDou Navigation Satellite System, autonomy and control, framework

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Authors: Will Brown

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Martin Heidegger is considered to be one of the leading philosophical lights of the 20th century with his lecture/essay 'The Question Concerning Technology' proving to be an invaluable text in the study of technology and the understanding of how technology influences the world it is set upon. However, this text has not as of yet been applied to the rapid rise and proliferation of ‘smart’ cities. This article is premised upon the application of the aforementioned text and the smart city in order to provide a fresh, if not critical analysis and interpretation of this phenomena. The first section below provides a brief literature review of smart urbanism in order to lay the groundwork necessary to apply Heidegger’s work to the smart city, from which a framework is developed to interpret the infusion of digital sensing technologies and the urban milieu. This framework is comprised of four concepts put forward in Heidegger’s text: circumscribing, bringing-forth, challenging, and standing-reserve. A concluding chapter is based upon the notion of enframement, arguing that once the rubric of data collection is placed within the urban system, future systems will require the capability to harvest data, resulting in an ever-renewing smart city.

Keywords: air quality sensing, big data, Martin Heidegger, smart city

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Authors: María José López Rey, Mar Chaves Carrillo, Manuela Caballero Guisado

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The data presented here are part of a broader investigation on active population aging. The work was carried out in November 2020 in Extremadura, a region of southern Spain. This R + D + I project, called "Active aging scenarios in Extremadura: intervention proposals," was carried out by a team of professors, researchers from the University of Extremadura. The project has been financed by the European Regional Development Funds and the Government of Extremadura. Here, we focus on aspects that have to do with the experience of health, especially during the COVID-19 pandemic, and how this has affected the population related to the main sociodemographic variables. In an exercise of methodological triangulation, thus providing robustness to the analysis, primary data, obtained from the survey designed ad hoc, are combined with other secondary data from various sources and studies carried out in Spain (Sociological Research Centre, and National Institute of Statistics). The survey was carried out on a representative sample of the population over 55 years old, coming from Extremadura. Among the findings, we must highlight the practical invariability of perceptions based on the main sociodemographic variables, as well as some differences indicated by the variables sex and age.

Keywords: aging, health, COVID-19, perceptions

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Authors: Ammarah Irum, Muhammad Ali Tahir

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Document-level sentiment analysis in Urdu is a challenging Natural Language Processing (NLP) task due to the difficulty of working with lengthy texts in a language with constrained resources. Deep learning models, which are complex neural network architectures, are well-suited to text-based applications in addition to data formats like audio, image, and video. To investigate the potential of deep learning for Urdu sentiment analysis, we implemented five different deep learning models, including Bidirectional Long Short Term Memory (BiLSTM), Convolutional Neural Network (CNN), Convolutional Neural Network with Bidirectional Long Short Term Memory (CNN-BiLSTM), and Bidirectional Encoder Representation from Transformer (BERT). In this study, we developed a hybrid deep learning model called BiLSTM-Single Layer Multi Filter Convolutional Neural Network (BiLSTM-SLMFCNN) by fusing BiLSTM and CNN architecture. The proposed and baseline techniques are applied on Urdu Customer Support data set and IMDB Urdu movie review data set by using pre-trained Urdu word embedding that are suitable for sentiment analysis at the document level. Results of these techniques are evaluated and our proposed model outperforms all other deep learning techniques for Urdu sentiment analysis. BiLSTM-SLMFCNN outperformed the baseline deep learning models and achieved 83%, 79%, 83% and 94% accuracy on small, medium and large sized IMDB Urdu movie review data set and Urdu Customer Support data set respectively.

Keywords: urdu sentiment analysis, deep learning, natural language processing, opinion mining, low-resource language

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Authors: Kika Ioannou Kazamia

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This paper examines the action research cycle of the second phase of longitudinal research on sustainable interior design practices, between two groups of stakeholders, designers and clients. During this phase of the action research, the second step - the change stage - of Lewin’s change management model has been utilized to change values, approaches, and attitudes toward sustainable design practices among the participants. Affective domain learning theory is utilized to attach new values. Learning with the use of information technology, collaborative learning, and problem-based learning are the learning methods implemented toward the acquisition of the objectives. Learning methods, and aims, require the design of interventions with participants' involvement in activities that would lead to the acknowledgment of the benefits of sustainable practices. Interventions are steered to measure participants’ decisions for the worth and relevance of ideas, and experiences; accept or commit to a particular stance or action. The data collection methods used in this action research are observers’ reports, participants' questionnaires, and interviews. The data analyses use both quantitative and qualitative methods. The main beneficial aspect of the quantitative method was to provide the means to separate many factors that obscured the main qualitative findings. The qualitative method allowed data to be categorized, to adapt the deductive approach, and then examine for commonalities that could reflect relevant categories or themes. The results from the data indicate that during the second phase, designers and clients' participants altered their behaviours.

Keywords: design, change, sustainability, learning, practices

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Authors: Bahadir Aydin

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Today it is difficult to reach accurate knowledge because of mass data. This huge data makes the environment more and more caotic. Data is a main piller of intelligence. There is a close tie between knowledge and intelligence. Information gathered from different sources can be modified, interpreted and classified by using knowledge development process. This process is applied in order to attain intelligence. Within this process the effect of knowledge is crucial. Knowledge is classified as explicit and tacit knowledge. Tacit knowledge can be seen as "only the tip of the iceberg”. This tacit knowledge accounts for much more than we guess in all intelligence cycle. If the concept of intelligence scrutinized, it can be seen that it contains risks, threats as well as success. The main purpose for all organization is to be succesful by eliminating risks and threats. Therefore, there is a need to connect or fuse existing information and the processes which can be used to develop it. By the help of process the decision-maker can be presented with a clear holistic understanding, as early as possible in the decision making process. Planning, execution and assessments are the key functions that connects to information to knowledge. Altering from the current traditional reactive approach to a proactive knowledge development approach would reduce extensive duplication of work in the organization. By new approach to this process, knowledge can be used more effectively.

Keywords: knowledge, intelligence cycle, tacit knowledge, KIDW

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Authors: Jeff Clarine, Chang-Shyh Peng, Daisy Sang

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Bioassay is the measurement of the potency of a chemical substance by its effect on a living animal or plant tissue. Bioassay data and chemical structures from pharmacokinetic and drug metabolism screening are mined from and housed in multiple databases. Bioassay prediction is calculated accordingly to determine further advancement. This paper proposes a four-step preprocessing of datasets for improving the bioassay predictions. The first step is instance selection in which dataset is categorized into training, testing, and validation sets. The second step is discretization that partitions the data in consideration of accuracy vs. precision. The third step is normalization where data are normalized between 0 and 1 for subsequent machine learning processing. The fourth step is feature selection where key chemical properties and attributes are generated. The streamlined results are then analyzed for the prediction of effectiveness by various machine learning algorithms including Pipeline Pilot, R, Weka, and Excel. Experiments and evaluations reveal the effectiveness of various combination of preprocessing steps and machine learning algorithms in more consistent and accurate prediction.

Keywords: bioassay, machine learning, preprocessing, virtual screen

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Authors: Mimi Roy, Sriroop Chaudhuri

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Sustainable water supply is among the basic needs for human development, especially in the rural areas of the developing nations where safe water supply and basic sanitation infrastructure is direly needed. In light of the above, we propose a simple methodology to develop a composite water sustainability index (WSI) to assess the collective performance of the existing rural water supply services (RWSS) in India over time. The WSI will be computed by summarizing the details of all the different varieties of water supply schemes presently available in India comprising of 40 liters per capita per day (lpcd), 55 lpcd, and piped water supply (PWS) per household. The WSI will be computed annually, between 2010 and 2016, to elucidate changes in holistic RWSS performances. Results will be integrated within a robust geospatial framework to identify the ‘hotspots’ (states/districts) which have persistent issues over adequate RWSS coverage and warrant spatially-optimized policy reforms in future to address sustainable human development. Dataset will be obtained from the National Rural Drinking Water Program (NRDWP), operating under the aegis of the Ministry of Drinking Water and Sanitation (MoDWS), at state/district/block levels to offer the authorities a cross-sectional view of RWSS at different levels of administrative hierarchy. Due to simplistic design, complemented by spatio-temporal cartograms, similar approaches can also be adopted in other parts of the world where RWSS need a thorough appraisal.

Keywords: rural water supply services, piped water supply, sustainability, composite index, spatial, drinking water

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Authors: Eid Alharbi, Yaser Miaji, Saeed Alzahrani

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The importance of fruit quality and freshness is potential in today’s life. Most recent studies show and automatic online sorting system according to the internal disorder for fresh apple fruit has developed by using near infrared (NIR) spectroscopic technology. The automatic convener belts system along with sorting mechanism was constructed. To check the internal quality of the apple fruit, apple was exposed to the NIR radiations in the range 650-1300 nm and the data were collected in form of absorption spectra. The collected data were compared to the reference (data of known sample) analyzed and an electronic signal was pass to the sorting system. The sorting system was separate the apple fruit samples according to electronic signal passed to the system. It is found that absorption of NIR radiation in the range 930-950 nm was higher in the internally defected samples as compared to healthy samples. On the base of this high absorption of NIR radiation in 930-950 nm region the online sorting system was constructed.

Keywords: mechatronics design, NIR, fruit quality, spectroscopic technology

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Authors: Anna Dolinayova, Juraj Camaj, Martin Loch

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Presently it is necessary to ensure the sustainable development of passenger and freight transport. Increasing performance of road freight have been a negative impact to environment and society. It is therefore necessary to increase the competitiveness of intermodal transport, which is more environmentally friendly. The study describe the effectiveness of logistical centers realization for companies and society and research how the partial internalization of external costs reflected in the efficient use of these centers and increase the competitiveness of intermodal transport to road freight. In our research, we use the method of comparative analysis and market research to describe the advantages of logistic centers for their users as well as for society as a whole. Method normal costing is used for calculation infrastructure and total costs, method of conversion costing for determine the external costs. We modelling of total society costs for road freight transport and inter modal transport chain (we assumed that most of the traffic is carried by rail) with different loading schemes for condition in the Slovak Republic. Our research has shown that higher utilization of inter modal transport chain do good not only for society, but for companies providing freight services too. Increase in use of inter modal transport chain can bring many benefits to society that do not bring direct immediate financial return. They often bring the multiplier effects, such as greater use of environmentally friendly transport mode and reduce the total society costs.

Keywords: delivery time, economy effectiveness, logistical centers, ecological efficiency, optimization, society

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Authors: Lavanya Madhuri Bollipo, K. V. Kadambari

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Objective: To discuss key issues in the diagnosis of Parkinson disease (PD), To discuss features influencing PD progression, To discuss importance of brain SPECT data in PD diagnosis, and To discuss the essentiality of machine learning techniques in early diagnosis of PD. An accurate and early diagnosis of PD is nowadays a challenge as clinical symptoms in PD arise only when there is more than 60% loss of dopaminergic neurons. So far there are no laboratory tests for the diagnosis of PD, causing a high rate of misdiagnosis especially when the disease is in the early stages. Recent neuroimaging studies with brain SPECT using 123I-Ioflupane (DaTSCAN) as radiotracer shown to be widely used to assist the diagnosis of PD even in its early stages. Machine learning techniques can be used in combination with image analysis procedures to develop computer-aided diagnosis (CAD) systems for PD. This paper addressed recent studies involving diagnosis of PD in its early stages using brain SPECT data with Machine Learning Techniques.

Keywords: Parkinson disease (PD), dopamine transporter, single-photon emission computed tomography (SPECT), support vector machine (SVM)

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Authors: Tao Feng, Xiaomei Ma, Xian Guo, Jing Wang

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Named Data Network (NDN) is one of the future Internet architecture, all nodes (i.e., hosts, routers) are allowed to have a local cache, used to satisfy incoming requests for content. However, depending on caching allows an adversary to perform attacks that are very effective and relatively easy to implement, such as content pollution attack. In this paper, we use a method of secure network coding based on homomorphic signature system to solve this problem. Firstly ,we use a dynamic public key technique, our scheme for each generation authentication without updating the initial secret key used. Secondly, employing the homomorphism of hash function, intermediate node and destination node verify the signature of the received message. In addition, when the network topology of NDN is simple and fixed, the code coefficients in our scheme are generated in a pseudorandom number generator in each node, so the distribution of the coefficients is also avoided. In short, our scheme not only can efficiently prevent against Intra/Inter-GPAs, but also can against the content poisoning attack in NDN.

Keywords: named data networking, content polloution attack, network coding signature, internet architecture

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Authors: Hantian Wu, Bo Huang, Yuan Zeng

Abstract:

Divisions between wealthy and poor, private and public landscapes are propagated by the increasing economic inequality of cities. While these are the spatial reflections of larger social issues and problems, urban design can at least employ spatial techniques that promote more inclusive rather than exclusive, overlapping rather than segregated, interlinked rather than disconnected landscapes. Indeed, the type of edge or border between urban landscapes plays a critical role in the way the environment is perceived. China experiences rapid urbanization, which poses unpredictable environmental challenges. The urban green cover and water body are under changes, which highly relevant to resident wealth and happiness. However, very limited knowledge and data on their rapid changes are available. In this regard, enhancing the monitoring of urban landscape with high-frequency method, evaluating and estimating the impacts of the urban landscape changes, and understating the driving forces of urban landscape changes can be a significant contribution for urban planning and studying. High-resolution remote sensing data has been widely applied to urban management in China. The map of urban land use map for the entire China of 2018 with 10 meters resolution has been published. However, this research focuses on the large-scale and high-resolution remote sensing land use but does not precisely focus on the seasonal change of urban covers. High-resolution remote sensing data has a long-operation cycle (e.g., Landsat 8 required 16 days for the same location), which is unable to satisfy the requirement of monitoring urban-landscape changes. On the other hand, aerial-remote or unmanned aerial vehicle (UAV) sensing are limited by the aviation-regulation and cost was hardly widely applied in the mega-cities. Moreover, those data are limited by the climate and weather conditions (e.g., cloud, fog), and those problems make capturing spatial and temporal dynamics is always a challenge for the remote sensing community. Particularly, during the rainy season, no data are available even for Sentinel Satellite data with 5 days interval. Many natural events and/or human activities drive the changes of urban covers. In this case, enhancing the monitoring of urban landscape with high-frequency method, evaluating and estimating the impacts of the urban landscape changes, and understanding the mechanism of urban landscape changes can be a significant contribution for urban planning and studying. This project aims to use the high spatiotemporal fusion of remote sensing data to create short-cycle, high-resolution remote sensing data sets for exploring the high-frequently urban cover changes. This research will enhance the long-term monitoring applicability of high spatiotemporal fusion of remote sensing data for the urban landscape for optimizing the urban management of landscape border to promoting the inclusive of the urban landscape to all communities.

Keywords: urban land cover changes, remote sensing, high spatiotemporal fusion, urban management

Procedia PDF Downloads 107

Authors: Saurabh Gupta, Vanshdeep Parmar, Sri Harsha Reddy Yelly, Michele Baker, Elizabeth Bigler, Kunhee Choi

Abstract:

It is known that the adoption of solar powered LED highway lighting systems or sensory LED highway lighting systems can dramatically reduce energy consumption by 55 percent when compared to conventional on-grid High Pressure Sodium (HPS) lamps that are widely applied to most highways. However, an initial high installation cost for building the infrastructure of solar photovoltaic devices hampers a wider adoption of such technologies. This research aims to examine currently available state-of-the-art solar photovoltaic and sensory technologies, identify major obstacles, and analyze each technology to create a benchmarking metrics from the benefit-cost analysis perspective. The on-grid HPS lighting systems will serve as the baseline for this study to compare it with other lighting alternatives such as solar and sensory LED lighting systems. This research will test the validity of the research hypothesis that alternative LED lighting systems produce more favorable benefit-cost ratios and the added initial investment costs are recouped by the savings in the operation and maintenance cost. The payback period of the excess investment and projected savings over the life-cycle of the selected lighting systems will be analyzed by utilizing the concept of Net Present Value (NPV). Researchers believe that if this study validates the research hypothesis, it can promote a wider adoption of alternative lighting systems that will eventually save millions of taxpayer dollars in the long-run.

Keywords: lighting systems, sensory and solar PV, benefit cost analysis, net present value

Procedia PDF Downloads 336

Authors: Akinbo Razak Yinka, Adesanya Kehinde Kazeem, Oladokun Oluwagbenga Peter

Abstract:

Time series data are sequences of observations collected over a period of time. Such data can be used to predict health outcomes, such as disease progression, mortality, hospitalization, etc. The Statistical approach is based on mathematical models that capture the patterns and trends of the data, such as autocorrelation, seasonality, and noise, while Neural methods are based on artificial neural networks, which are computational models that mimic the structure and function of biological neurons. This paper compared both parametric and non-parametric time series models of patients treated for malaria in Maternal and Child Health Centres in Lagos State, Nigeria. The forecast methods considered linear regression, Integrated Moving Average, ARIMA and SARIMA Modeling for the parametric approach, while Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Network were used for the non-parametric model. The performance of each method is evaluated using the Mean Absolute Error (MAE), R-squared (R2) and Root Mean Square Error (RMSE) as criteria to determine the accuracy of each model. The study revealed that the best performance in terms of error was found in MLP, followed by the LSTM and ARIMA models. In addition, the Bootstrap Aggregating technique was used to make robust forecasts when there are uncertainties in the data.

Keywords: ARIMA, bootstrap aggregation, MLP, LSTM, SARIMA, time-series analysis

Procedia PDF Downloads 61