Search results for: data reduction

Authors: M. Graus, K. Westhoff, X. Xu

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The climate change causes a change in all aspects of society. While the expansion of renewable energies proceeds, industry could not be convinced based on general studies about the potential of demand side management to reinforce smart grid considerations in their operational business. In this article, a procedure model for a case-specific data-driven decision support for industrial energy management based on a holistic data analytics approach is presented. The model is executed on the example of the strategic decision problem, to integrate the aspect of renewable energies into industrial energy management. This question is induced due to considerations of changing the electricity contract model from a standard rate to volatile energy prices corresponding to the energy spot market which is increasingly more affected by renewable energies. The procedure model corresponds to a data analytics process consisting on a data model, analysis, simulation and optimization step. This procedure will help to quantify the potentials of sustainable production concepts based on the data from a factory. The model is validated with data from a printer in analogy to a simple production machine. The overall goal is to establish smart grid principles for industry via the transformation from knowledge-driven to data-driven decisions within manufacturing companies.

Keywords: data analytics, green production, industrial energy management, optimization, renewable energies, simulation

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Authors: K. Umbleja, M. Ichino, H. Yaguchi

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In this paper, we propose a coloring method for multivariate data visualization by using parallel coordinates based on dissimilarity and tree structure information gathered during hierarchical clustering. The proposed method is an extension for proximity-based coloring that suffers from a few undesired side effects if hierarchical tree structure is not balanced tree. We describe the algorithm by assigning colors based on dissimilarity information, show the application of proposed method on three commonly used datasets, and compare the results with proximity-based coloring. We found our proposed method to be especially beneficial for symbolic data visualization where many individual objects have already been aggregated into a single symbolic object.

Keywords: data visualization, dissimilarity-based coloring, proximity-based coloring, symbolic data

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Authors: Gaurangi Saxena, Ravindra Saxena

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Lately, cloud computing is used to enhance the ability to attain corporate goals more effectively and efficiently at lower cost. This new computing paradigm “The Cloud Computing” has emerged as a powerful tool for optimum utilization of resources and gaining competitiveness through cost reduction and achieving business goals with greater flexibility. Realizing the importance of this new technique, most of the well known companies in computer industry like Microsoft, IBM, Google and Apple are spending millions of dollars in researching cloud computing and investigating the possibility of producing interface hardware for cloud computing systems. It is believed that by using the right middleware, a cloud computing system can execute all the programs a normal computer could run. Potentially, everything from most simple generic word processing software to highly specialized and customized programs designed for specific company could work successfully on a cloud computing system. A Cloud is a pool of virtualized computer resources. Clouds are not limited to grid environments, but also support “interactive user-facing applications” such as web applications and three-tier architectures. Cloud Computing is not a fundamentally new paradigm. It draws on existing technologies and approaches, such as utility Computing, Software-as-a-service, distributed computing, and centralized data centers. Some companies rent physical space to store servers and databases because they don’t have it available on site. Cloud computing gives these companies the option of storing data on someone else’s hardware, removing the need for physical space on the front end. Prominent service providers like Amazon, Google, SUN, IBM, Oracle, Salesforce etc. are extending computing infrastructures and platforms as a core for providing top-level services for computation, storage, database and applications. Application services could be email, office applications, finance, video, audio and data processing. By using cloud computing system a company can improve its customer relationship management. A CRM cloud computing system may be highly useful in delivering a sales team a blend of unique functionalities to improve agent/customer interactions. This paper attempts to first define the cloud computing as a tool for running business activities more effectively and efficiently at a lower cost; and then it distinguishes cloud computing with grid computing. Based on exhaustive literature review, authors discuss application of cloud computing in different disciplines of management especially in the field of marketing with special reference to use of cloud computing in CRM. Study concludes that CRM cloud computing platform helps a company track any data, such as orders, discounts, references, competitors and many more. By using CRM cloud computing, companies can improve its customer interactions and by serving them more efficiently that too at a lower cost can help gaining competitive advantage.

Keywords: cloud computing, competitive advantage, customer relationship management, grid computing

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Authors: Roberto Machado

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We conducted a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology, analyzing 2,996 studies and synthesizing 41 of them to explore the evolution of data science and its integration into geography. By employing optimization algorithms, we accelerated the review process, significantly enhancing the efficiency and precision of literature selection. Our findings indicate that data science has developed over five decades, facing challenges such as the diversified integration of data and the need for advanced statistical and computational skills. In geography, the integration of data science underscores the importance of interdisciplinary collaboration and methodological innovation. Techniques like large-scale spatial data analysis and predictive algorithms show promise in natural disaster management and transportation route optimization, enabling faster and more effective responses. These advancements highlight the transformative potential of data science in geography, providing tools and methodologies to address complex spatial problems. The relevance of this study lies in the use of optimization algorithms in systematic reviews and the demonstrated need for deeper integration of data science into geography. Key contributions include identifying specific challenges in combining diverse spatial data and the necessity for advanced computational skills. Examples of connections between these two fields encompass significant improvements in natural disaster management and transportation efficiency, promoting more effective and sustainable environmental solutions with a positive societal impact.

Keywords: data science, geography, systematic review, optimization algorithms, supervised learning

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Authors: Hina Kausher, Sangita Srivastava

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In India, there is a lack of standard, systematic sizing approach for producing readymade garments. Garments manufacturing companies use their own created size tables by modifying international sizing charts of ready-made garments. The purpose of this study is to tabulate the anthropometric data which covers the variety of figure proportions in both height and girth. 3,000 data has been collected by an anthropometric survey undertaken over females between the ages of 16 to 80 years from some states of India to produce the sizing system suitable for clothing manufacture and retailing. This data is used for the statistical analysis of body measurements, the formulation of sizing systems and body measurements tables. Factor analysis technique is used to filter the control body dimensions from a large number of variables. Decision tree-based data mining is used to cluster the data. The standard and structured sizing system can facilitate pattern grading and garment production. Moreover, it can exceed buying ratios and upgrade size allocations to retail segments.

Keywords: anthropometric data, data mining, decision tree, garments manufacturing, sizing systems, ready-made garments

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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

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Authors: Bukola Tola Aluko, Omotade Ibidun Oloyede

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Ocimum americanum L. (Lamiaceae) is an annual herb that is native to tropical Africa. The in vitro and in vivo antioxidant activity of its aqueous extract was carefully investigated by assessing the DPPH radical scavenging activity, ABTS radical scavenging activity and hydrogen peroxide radical scavenging activity. The reducing power, total phenol, total flavonoids and flavonols content of the extract were also evaluated. The data obtained revealed that the extract is rich in polyphenolic compounds and scavenged the radicals in a concentration-dependent manner. This was done in comparison with the standard antioxidants such as BHT and Vitamin C. Also, the induction of oxidative damage with paracetamol (2000 mg/kg) resulted in the elevation of lipid peroxides and significant (P < 0.05) decrease in activities of superoxide dismutase, glutathione peroxidase, glutathione reductase and catalase in the liver and kidney of rats. However, the pretreatment of rats with aqueous extract of O. americanum leaves (200 and 400 mg/kg), and silymarin (100 mg/kg) caused a significant (P < 0.05) reduction in the values of lipid peroxides and restored the levels of antioxidant parameters in these organs. These findings suggest that the leaves of O. americanum have potent antioxidant properties which may be responsible for its acclaimed folkloric uses.

Keywords: antioxidants, free radicals, ocimum americanum, scavenging activity

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Authors: Sungwon Jung

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The percentage of 20 years or older apartment houses in South Korea is approximately 20% (1.55 million houses), and the explosive increase of aged houses is expected around the first planned new towns. Accordingly, we should prepare for social issues such as difficulty of housing lease and degradation of housing performance. The improvement of performance of aged houses is essential for achieving the national energy and carbon reduction goals, and we should develop techniques to respond to the changing construction environment. Furthermore, we should develop a performance evaluation system that is appropriate for the demands of residents such as the improvement of remodeling floor plan by performance improvement in line with the residence type of the housing vulnerable groups such as low-income group and elderly people living alone. For this purpose, remodeling techniques and business models optimized for the target complexes must be spread through the development of various business models. In addition, it is necessary to improve the remodeling business by improving the laws and systems related to the improvement of the residential performance and to prepare techniques to respond to the increasing business demands. In other words, performance improvement and evaluation and knowledge systems need to be researched as new issues related to remodeling that has not been addressed in the existing research.

Keywords: remodelling, performance evaluation, web-based system, big data

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Authors: Amany Haridy, Ahmed Elseragy, Fahd Omar

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The issue of temperature increase in the urban microclimate has been at the center of attention recently, especially in dense urban areas, such as the City of Alexandria in Egypt, where building surfaces have become the dominant element (more than green areas and streets). Temperatures have been rising during daytime as well as nighttime, however, the research focused on the rise of air temperature at night, a phenomenon known as the urban heat island. This phenomenon has many effects on ecological life, as well as human health. This study provided evidence of the possibility of reducing the urban heat island by using a green building envelope (green wall and green roof) in Alexandria, Egypt. This City has witnessed a boom in growth in its urban fabric and population. A simulation analysis using the Envi-met software to find the ratio of air temperature reduction was performed. The simulation depended on the orientation of the green areas and their density, which was defined through a process of climatic analysis made by the Diva plugin using the Grasshopper software. Results showed that the reduction in air temperature varies from 0.8–2.0 °C, increasing with the increasing density of green areas. Many systems of green wall and green roof can be found in the local market. However, treating an existing building requires a careful choice of system to fit the building construction load and the surrounding nature. Among the systems of choice, there was the ‘geometric system’ of vertical greening that can be fixed on a light aluminum structure for walls and the extensive green system for roofs. Finally, native plants were the best choice in the long term because they fare well in the local climate.

Keywords: envi-met, green building envelope, urban heat island, urban microclimate

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Authors: Wong Ka Fai

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Introduction: Many students are worried before an examination; that is a common picture worldwide. Health problems from stress before examination were insomnia, tiredness, isolation, stomach upset, and anxiety. Nursing students experienced high stress from the examination. Guided imagery is a healing process of applying imagination to help the body heal, survive, or live well. It can bring about significant physiological and biochemical changes, which can trigger the recovery process. A study of nursing students improving their anxiety before examination with guided imagery was proposed. Aim: The aim of this study was to explore the outcome of guided imagery on nursing students’ anxiety before examination in Hong Kong. Method: The qualitative study method was used. 16 first-year students studying nursing programme were invited to practice guided imagery to improve their anxiety before the examination period. One week before the examination, the semi-structured interviews with these students were carried out by the researcher. Result: From the content analysis of interview data, these nursing students showed considerable similarities in their anxiety perception. Nursing students’ perceived improved anxiety was evidenced by a reduction of stressful feelings, improved physical health, satisfaction with daily activities, and enhanced skills for solving problems and upcoming situations. Conclusion: This study indicated that guided imagery can be used as an alternative measure to improve students’ anxiety and psychological problems.

Keywords: nursing students, perception, anxiety, guided imagery

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Authors: Nikola Tomasevic, Marko Batic, Sanja Vranes

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Emerging Semantic Web technologies can be seen as the next step in evolution of the intelligent facility management systems. Particularly, this considers increased usage of open source and/or standardized concepts for data classification and semantic interpretation. To deliver such facility management systems, providing the comprehensive integration and interoperability platform in from of the facility data model is a prerequisite. In this paper, one of the possible modelling approaches to provide such integrative facility data model which was based on the ontology modelling concept was presented. Complete ontology development process, starting from the input data acquisition, ontology concepts definition and finally ontology concepts population, was described. At the beginning, the core facility ontology was developed representing the generic facility infrastructure comprised of the common facility concepts relevant from the facility management perspective. To develop the data model of a specific facility infrastructure, first extension and then population of the core facility ontology was performed. For the development of the full-blown facility data models, Malpensa and Fiumicino airports in Italy, two major European air-traffic hubs, were chosen as a test-bed platform. Furthermore, the way how these ontology models supported the integration and interoperability of the overall airport energy management system was analyzed as well.

Keywords: airport ontology, energy management, facility data model, ontology modeling

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Authors: G. Kaur, A. P. Kulkarni, S. Giddey

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Production of fuels and chemicals using renewable energy is a promising way for large-scale energy storage and export. Solid oxide electrolysers (SOEs) integrated with renewable source of energy can produce 'Syngas' H₂/CO from H₂O/CO₂ in the desired ratio for further conversion to liquid fuels. As only a waste CO₂ from industrial and power generation processes is utilized in these processes, this approach is CO₂ neutral compared to using fossil fuel feedstock. In addition, the waste heat from industrial processes or heat from solar thermal concentrators can be effectively utilised in SOEs to further reduce the electrical requirements by up to 30% which boosts overall energy efficiency of the process. In this paper, the electrochemical performance of various novel steam/CO₂ reduction electrodes (cathode) would be presented. The efficiency and lifetime degradation data for single cells and a stack would be presented along with the response of cells to variable electrical load input mimicking the intermittent nature of the renewable energy sources. With such optimisation, newly developed electrodes have been tested for 500+ hrs with Faraday efficiency (electricity to fuel conversion efficiency) up to 95%, and thermal efficiency in excess of 70% based upon energy content of the syngas produced.

Keywords: carbon dioxide, steam conversion, electrochemical system, energy storage, fuel production, renewable energy

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Authors: Badr Alsulami, Ahmed S. Elamary

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This paper summarizes and presents main results of an in-depth numerical analysis dealing with the shear buckling resistance of aluminum plate girders. The studies conducted have permitted the development of a simple design expression to determine the critical shear buckling stress in aluminum web panels. This expression takes into account the effects of reduction of strength in aluminum alloys due to the welding process. Ultimate shear resistance (USR) of plate girders can be obtained theoretically using Cardiff theory or Hӧglund’s theory. USR of aluminum alloy plate girders predicted theoretically using BS8118 appear inconsistent when compared with test data. Theoretical predictions based on Hӧglund’s theory, are more realistic. Cardiff theory proposed to predict the USR of steel plate girders only. Welded aluminum alloy plate girders studied experimentally by others; the USR resulted from tests are reviewed. Comparison between the test results with the values obtained from Hӧglund’s theory, BS8118 design method, and Cardiff theory performed theoretically. Finally, a new equation based on Cardiff tension-field theory proposed to predict theoretically the USR of aluminum plate girders.

Keywords: shear resistance, aluminum, Cardiff theory, Hӧglund's theory, plate girder

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Authors: Abdulsalam A. Al-Tamimi, Chris Peach, Paulo Rui Fernandes, Paulo J. Bartolo

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Bone fixation implants currently used to treat traumatic fractured bones and to promote fracture healing are built with biocompatible metallic materials such as stainless steel, cobalt chromium and titanium and its alloys (e.g., CoCrMo and Ti6Al4V). The noticeable stiffness mismatch between current metallic implants and host bone associates with negative outcomes such as stress shielding which causes bone loss and implant loosening leading to deficient fracture treatment. This paper, part of a major research program to design the next generation of bone fixation implants, describes the combined use of three-dimensional (3D) topology optimization (TO) and additive manufacturing powder bed technology (Electron Beam Melting) to redesign and fabricate the plates based on the current standard one (i.e., locking compression plate). Topology optimization is applied with an objective function to maximize the stiffness and constraint by volume reductions (i.e., 25-75%) in order to obtain optimized implant designs with reduced stress shielding phenomenon, under different boundary conditions (i.e., tension, bending, torsion and combined loads). The stiffness of the original and optimised plates are assessed through a finite-element study. The TO results showed actual reduction in the stiffness for most of the plates due to the critical values of volume reduction. Additionally, the optimized plates fabricated using powder bed techniques proved that the integration between the TO and additive manufacturing presents the capability of producing stiff reduced plates with acceptable tolerances.

Keywords: additive manufacturing, locking compression plate, finite element, topology optimization

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Authors: Mohamed Ali Abdennadher

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Digital rock physics, an emerging field leveraging advanced imaging and numerical techniques, offers a promising approach to investigating the mechanical properties of granular materials without extensive physical experiments. This study focuses on using X-Ray Computed Tomography (CT) to capture the three-dimensional (3D) structure of coarse-grained soil at the particle level, combined with finite element analysis (FEA) to simulate the soil's behavior under compression. The primary goal is to establish a reliable virtual testing framework that can replicate laboratory results and offer deeper insights into soil mechanics. The methodology involves acquiring high-resolution CT scans of coarse-grained soil samples to visualize internal particle morphology. These CT images undergo processing through noise reduction, thresholding, and watershed segmentation techniques to isolate individual particles, preparing the data for subsequent analysis. A custom Python script is employed to extract particle shapes and conduct a statistical analysis of particle size distribution. The processed particle data then serves as the basis for creating a finite element model comprising approximately 500 particles subjected to one-dimensional compression. The FEA simulations explore the effects of mesh refinement and friction coefficient on stress distribution at grain contacts. A multi-layer meshing strategy is applied, featuring finer meshes at inter-particle contacts to accurately capture mechanical interactions and coarser meshes within particle interiors to optimize computational efficiency. Despite the known challenges in parallelizing FEA to high core counts, this study demonstrates that an appropriate domain-level parallelization strategy can achieve significant scalability, allowing simulations to extend to very high core counts. The results show a strong correlation between the finite element simulations and laboratory compression test data, validating the effectiveness of the virtual experiment approach. Detailed stress distribution patterns reveal that soil compression behavior is significantly influenced by frictional interactions, with frictional sliding, rotation, and rolling at inter-particle contacts being the primary deformation modes under low to intermediate confining pressures. These findings highlight that CT data analysis combined with numerical simulations offers a robust method for approximating soil behavior, potentially reducing the need for physical laboratory experiments.

Keywords: X-Ray computed tomography, finite element analysis, soil compression behavior, particle morphology

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Authors: Jma Hannan, Prawej Ansari, Yasser H. A. Abdel-Wahab, Peter R. Flatt

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Spirulina platensis (SP, Blue-green algae) have been accepted as a supplement for the treatment of pre and post-diabetes. The present study investigated the effects of butanol fraction from ethanol extract of S. platensis on insulin release from BRIN BD11 cells, isolated mouse islets, and perfused rat pancreas, as well as glucose homeostasis in type 2 diabetic rats and their molecular pathways. In a dose-dependent manner, S. platensis increased insulin release from mouse islets and pancreatic β-cells. The extract also elevated insulin release in perfused rat pancreas. Glucose, isobutylmethylxanthine, tolbutamide, and a depolarizing concentration of KCl significantly potentiated insulin release from BRIN BD11 cells. The effect of diazoxide and verapamil, as well as the absence of extracellular Ca2+ showed a reduction in insulin secretion. When administered orally together with glucose (2.5g/kg bw), S. platensis extract improved fasting and postprandial blood glucose in type 2 diabetes. These data suggest that the anti-diabetic activity of S. platensis is partly mediated by insulin secretion via the KATP channel-dependent pathway/the intracellular cAMP pathway.

Keywords: Insulin, glucose, S. platensis, type 2 diabetes, medicinal plants

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Authors: Douglas F. Prawitt, Nathan Y. Sharp, David A. Wood

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Consistent with prior experimental and survey studies, we find that IAFs that spend more time directly assisting the external auditor is associated with lower external audit fees. Interestingly, we do not find evidence that external auditors reduce fees based on work previously performed by the IAF. We also find that the time spent assisting the external auditor has a greater negative effect on external audit fees than the time spent performing tasks upon which the auditor may rely but that are not performed as direct assistance to the external audit. Our results also show that previous proxies used to measure this relation is either not associated with or are negatively associated with our direct measures of how the IAF can contribute to the external audit and are highly positively associated with the size and the complexity of the organization. Thus, we conclude the disparate experimental and archival results may be attributable to issues surrounding the construct validity of measures used in previous archival studies and that when measures similar to those used in experimental studies are employed in archival tests, the archival results are consistent with experimental findings. Our research makes four primary contributions to the literature. First, we provide evidence that internal auditing contributes to a reduction in external audit fees. Second, we replicate and provide an explanation for why previous archival studies find that internal auditing has either no association with external audit fees or is associated with an increase in those fees: prior studies generally use proxies of internal audit contribution that do not adequately capture the intended construct. Third, our research expands on survey-based research (e.g., Oil Libya sh.co.) by separately examining the impact on the audit fee of the internal auditors’ work, indirectly assisting external auditors and internal auditors’ prior work upon which external auditors can rely. Finally, we extend prior research by using a new, independent data source to validate and extend prior studies. This data set also allows for a sample of examining the impact of internal auditing on the external audit fee and the use of a more comprehensive external audit fee model that better controls for determinants of the external audit fee.

Keywords: internal audit, contribution, external audit, function

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Authors: Amirreza Razzaghipour Sorkhab

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Hearing loss is one of the greatest common chronic health situations of older people. Hearing aids are the common treatment, and they recover the quality of life in older adults. Even so, comparatively few older adults with simple, mild to moderate, adult-onset, sensorineural hearing loss use hearing aids. It shouldn’t be expected that more expensive hearing aids always produce better outcomes. Given the importance of quality pledge, approaches of quantifying hearing aid fitting achievement are needed. Studies showed an important reduction in handicap following 3 weeks of hearing aid use, signifying the feasibility of using the Hearing Hindrance Inventory for the Elderly as an outcome measure for hearing aid success after a brief interval of hearing aid use. The results showed important development of the quality of life after three months of using a hearing aid in all members and improvement of their most important problems, i.e., the communication and exchange of data. Hearing loss can impair the conversation of information and so decreases the quality of life. Hearing aids have progressivemeaningfully over the past decade, chiefly due to the growing of digital technology. The next decade should see an even greater number of innovations to hearing aid technology. Development in digital hearing aids will be driven by investigate advances in the next fields such as wireless technology, hearing science, and cognitive scienceMoreover, emerging trends such as connectivity and individuation will also drive new technology. We hope that the advancement of technology will be enough to meet the needs of people with hearing aids.

Keywords: hearing loss, hearing aid, hearing aid technology, health

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Authors: Amadou Wurry Jallow, Adama N. S. Bah, Karamo Bah, Shih-Ye Wang, Kuo-Chung Chu, Chien-Yeh Hsu

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Chronic kidney disease (CKD) is a major public health challenge with high prevalence, rising incidence, and serious adverse consequences. Developing effective risk prediction models is a cost-effective approach to predicting and preventing complications of chronic kidney disease (CKD). This study aimed to develop an accurate machine learning model that can dynamically identify individuals at risk of CKD using various kinds of diagnostic data, with or without laboratory data, at different follow-up points. Creatinine is a key component used to predict CKD. These models will enable affordable and effective screening for CKD even with incomplete patient data, such as the absence of creatinine testing. This retrospective cohort study included data on 19,429 adults provided by a private research institute and screening laboratory in Taiwan, gathered between 2001 and 2015. Univariate Cox proportional hazard regression analyses were performed to determine the variables with high prognostic values for predicting CKD. We then identified interacting variables and grouped them according to diagnostic data categories. Our models used three types of data gathered at three points in time: non-laboratory, laboratory, and metabolic indices data. Next, we used subgroups of variables within each category to train two machine learning models (Random Forest and XGBoost). Our machine learning models can dynamically discriminate individuals at risk for developing CKD. All the models performed well using all three kinds of data, with or without laboratory data. Using only non-laboratory-based data (such as age, sex, body mass index (BMI), and waist circumference), both models predict chronic kidney disease as accurately as models using laboratory and metabolic indices data. Our machine learning models have demonstrated the use of different categories of diagnostic data for CKD prediction, with or without laboratory data. The machine learning models are simple to use and flexible because they work even with incomplete data and can be applied in any clinical setting, including settings where laboratory data is difficult to obtain.

Keywords: chronic kidney disease, glomerular filtration rate, creatinine, novel metabolic indices, machine learning, risk prediction

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Authors: Barış Ozluoymak, Emin Guzel, Ahmet İnce

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Since chemical fertilizers are used for meeting the deficiency of plant nutrients, its many harmful effects are not taken into consideration for the structure of the earth. These fertilizers are hampering the work of the organisms in the soil immediately after thrown to the ground. This interference is first started with a change of the soil pH and micro organismic balance is disrupted by reaction in the soil. Since there can be no fragmentation of plant residues, organic matter in the soil will be increasingly impoverished in the absence of micro organismic living. Biological activity reduction brings about a deterioration of the soil structure. If the chemical fertilization continues intensively, soils will get worse every year; plant growth will slow down and stop due to the intensity of chemical fertilizers, yield decline will be experienced and farmer will not receive an adequate return on his investment. In this research, a prototype of automatic farm manure spreading machine for orange orchards that not just manufactured in Turkey was designed, constructed, tested and eliminate the human drudgery involved in spreading of farm manure in the field. The machine comprised several components as a 5 m3 volume hopper, automatic controlled hydraulically driven chain conveyor device and side delivery conveyor belts. To spread the solid farm manure automatically, the machine was equipped with an electronic control system. The hopper and side delivery conveyor designs fitted between orange orchard tree row spacing. Test results showed that the control system has significant effects on reduction in the amount of unnecessary solid farm manure use and avoiding inefficient manual labor.

Keywords: automatic control system, conveyor belt application, orchard, solid farm manure

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Authors: Usha Lokala, Srinivas Nowduri, Prabhakar K. Sharma

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Useful information has been extracted from the road accident data in United Kingdom (UK), using data analytics method, for avoiding possible accidents in rural and urban areas. This analysis make use of several methodologies such as data integration, support vector machines (SVM), correlation machines and multinomial goodness. The entire datasets have been imported from the traffic department of UK with due permission. The information extracted from these huge datasets forms a basis for several predictions, which in turn avoid unnecessary memory lapses. Since data is expected to grow continuously over a period of time, this work primarily proposes a new framework model which can be trained and adapt itself to new data and make accurate predictions. This work also throws some light on use of SVM’s methodology for text classifiers from the obtained traffic data. Finally, it emphasizes the uniqueness and adaptability of SVMs methodology appropriate for this kind of research work.

Keywords: support vector mechanism (SVM), machine learning (ML), support vector machines (SVM), department of transportation (DFT)

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Authors: Raffaele Danilo Esposito, Domingo Planes Meseguer, Maria Del Pilar Dorado Rodriguez

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As far as we know, it is still unavailable a commercial solution which would allow to manage, openly and configurable up to user needs, the huge amount of data generated in a modern Radiation Oncology Department. Currently, available information management systems are mainly focused on Record & Verify and clinical data, and only to a small extent on physical data. Thus, results in a partial and limited use of the actually available information. In the present work we describe the implementation at our department of a centralized information management system based on a web server. Our system manages both information generated during patient planning and treatment, and information of general interest for the whole department (i.e. treatment protocols, quality assurance protocols etc.). Our objective it to be able to analyze in a simple and efficient way all the available data and thus to obtain quantitative evaluations of our treatments. This would allow us to improve our work flow and protocols. To this end we have implemented a relational data base which would allow us to use in a practical and efficient way all the available information. As always we only use license free software.

Keywords: information management system, radiation oncology, medical physics, free software

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Authors: Komol Phaisarn, Natcha Wattanaprapa

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This research is a survey research with an objective to study the safety of data storage devices of graduate students of academic year 2013, Suan Sunandha Rajabhat University. Data were collected by questionnaire on the safety of data storage devices according to CIA principle. A sample size of 81 was drawn from population by purposive sampling method. The results show that most of the graduate students of academic year 2013 at Suan Sunandha Rajabhat University use handy drive to store their data and the safety level of the devices is at good level.

Keywords: security, safety, storage devices, graduate students

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Authors: Kaddi Mohammed, A. Benatiallah, D. Benatiallah

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Data grid is a technology that has full emergence of new challenges, such as the heterogeneity and availability of various resources and geographically distributed, fast data access, minimizing latency and fault tolerance. Researchers interested in this technology address the problems of the various systems related to the industry such as task scheduling, load balancing and replication. The latter is an effective solution to achieve good performance in terms of data access and grid resources and better availability of data cost. In a system with duplication, a coherence protocol is used to impose some degree of synchronization between the various copies and impose some order on updates. In this project, we present an approach for placing replicas to minimize the cost of response of requests to read or write, and we implement our model in a simulation environment. The placement techniques are based on a cost model which depends on several factors, such as bandwidth, data size and storage nodes.

Keywords: response time, query, consistency, bandwidth, storage capacity, CERN

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Authors: B. Siva Kumar Reddy, B. Lakshmi

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WiMAX is a high speed broadband wireless access technology that adopted OFDM/OFDMA techniques to supply higher data rates with high spectral efficiency. However, OFDM suffers in view of high Peak to Average Power Ratio (PAPR) and high affect to synchronization errors. In this paper, the high PAPR problem is solved by using phase modulation to get Constant Envelop Orthogonal Frequency Division Multiplexing (CE-OFDM). The synchronization failures are brought down by employing a frequency lock loop, Poly phase clock synchronizer, Costas loop and blind equalizers such as Constant Modulus Algorithm (CMA) equalizer and Sign Kurtosis Maximization Adaptive Algorithm (SKMAA) equalizers. The WiMAX physical layer is executed on Software Defined Radio (SDR) prototype by utilizing USRP N210 as hardware and GNU Radio as software plat-forms. A SNR estimation is performed on the signal received through USRP N210. To empathize wireless propagation in specific environments, a sliding correlator wireless channel sounding system is designed by using SDR testbed.

Keywords: BER, CMA equalizer, Kurtosis equalizer, GNU Radio, OFDM/OFDMA, USRP N210

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Authors: Jaehong Yu, Seoung Bum Kim

Abstract:

Clustering analysis can facilitate the extraction of intrinsic patterns in a dataset and reveal its natural groupings without requiring class information. For effective clustering analysis in high dimensional datasets, unsupervised dimensionality reduction is an important task. Unsupervised dimensionality reduction can generally be achieved by feature extraction or feature selection. In many situations, feature selection methods are more appropriate than feature extraction methods because of their clear interpretation with respect to the original features. The unsupervised feature selection can be categorized as feature subset selection and feature ranking method, and we focused on unsupervised feature ranking methods which evaluate the features based on their importance scores. Recently, several unsupervised feature ranking methods were developed based on ensemble approaches to achieve their higher accuracy and stability. However, most of the ensemble-based feature ranking methods require the true number of clusters. Furthermore, these algorithms evaluate the feature importance depending on the ensemble clustering solution, and they produce undesirable evaluation results if the clustering solutions are inaccurate. To address these limitations, we proposed an ensemble-based feature ranking method with random subspace and multiple-k ensemble (FRRM). The proposed FRRM algorithm evaluates the importance of each feature with the random subspace ensemble, and all evaluation results are combined with the ensemble importance scores. Moreover, FRRM does not require the determination of the true number of clusters in advance through the use of the multiple-k ensemble idea. Experiments on various benchmark datasets were conducted to examine the properties of the proposed FRRM algorithm and to compare its performance with that of existing feature ranking methods. The experimental results demonstrated that the proposed FRRM outperformed the competitors.

Keywords: clustering analysis, multiple-k ensemble, random subspace-based feature evaluation, unsupervised feature ranking

Procedia PDF Downloads 341

Authors: Lu Song Ma Li Zhi

Abstract:

With the rapid advancement of artificial intelligence technology, large language models (LLMs) have become a milestone in the field of natural language processing, demonstrating remarkable capabilities in semantic understanding, intelligent question answering, and text generation. These models are gradually penetrating various industries, particularly showcasing significant application potential in the data analysis domain. However, retraining or fine-tuning these models requires substantial computational resources and ample downstream task datasets, which poses a significant challenge for many enterprises and research institutions. Without modifying the internal parameters of the large models, prompt engineering techniques can rapidly adapt these models to new domains. This paper proposes a prompt design strategy aimed at leveraging the capabilities of large language models to automate the generation of data analysis code. By carefully designing prompts, data analysis requirements can be described in natural language, which the large language model can then understand and convert into executable data analysis code, thereby greatly enhancing the efficiency and convenience of data analysis. This strategy not only lowers the threshold for using large models but also significantly improves the accuracy and efficiency of data analysis. Our approach includes requirements for the precision of natural language descriptions, coverage of diverse data analysis needs, and mechanisms for immediate feedback and adjustment. Experimental results show that with this prompt design strategy, large language models perform exceptionally well in multiple data analysis tasks, generating high-quality code and significantly shortening the data analysis cycle. This method provides an efficient and convenient tool for the data analysis field and demonstrates the enormous potential of large language models in practical applications.

Keywords: large language models, prompt design, data analysis, code generation

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Authors: Mohammad Farooq Wani

Abstract:

Tribological studies on nano-sized ß-silicon nitride+5% BN were carried out in dry air at high temperatures to clarify the lack of consensus in the bibliographic data concerning the Tribological behavior of Si3N4 ceramics and effect of doped hexagonal boron nitride on coefficient of friction and wear coefficient at different loads and elevated temperatures. The composites were prepared via high energy mechanical milling and subsequent spark plasma sintering using Y2O3 and Al2O3 as sintering additives. After sintering, the average crystalline size of Si3N4 was observed to be 50 nm. Tribological tests were performed with temperature and Friction coefficients 0.16 to 1.183 and 0.54 to 0.71 were observed for Nano-sized ß-silicon nitride+5% BN composite under normal load of 10N-70 N and over high temperature range of 350 ºC-550 ºC respectively. Specific wear coefficients from 1.33x 10-4 mm3N-1m-1 to 4.42x 10-4 mm3N-1m-1 were observed for Nano-sized Si3N4 + 5% BN composite against Si3N4 ball as tribo-pair counterpart over high temperature range of 350 ºC-550 ºC while as under normal load of 10N to70N Specific wear coefficients of 6.91x 10-4 mm3N-1m-1 to 1.70x 10-4 were observed. The addition of BN to the Si3N4 composite resulted in a slight reduction of the friction coefficient and lower values of wear coefficient.

Keywords: ceramics, tribology, friction and wear, solid lubrication

Procedia PDF Downloads 381

Authors: Abbas Ali Taghipoor Bafghi, Hosein Bakhoda, Fateme Khodaei Chegeni

Abstract:

An experimental investigation is carried out to establish the performance characteristics of a compression ignition engine while using cerium oxide nano particles as additive in neat diesel and diesel-bio diesel blends. In the first phase of the experiments, stability of neat diesel and diesel-bio diesel fuel blends with the addition of cerium oxide nano particles are analyzed. After series of experiments, it is found that the blends subjected to high speed blending followed by ultrasonic bath stabilization improves the stability.In the second phase, performance characteristics are studied using the stable fuel blends in a single cylinder four stroke engine coupled with an electrical dynamo meter and a data acquisition system. The cerium oxide acts as an oxygen donating catalyst and provides oxygen for combustion. The activation energy of cerium oxide acts to burn off carbon deposits within the engine cylinder at the wall temperature and prevents the deposition of non-polar compounds on the cylinder wall results reduction in HC emissions. The tests revealed that cerium oxide nano particles can be used as additive in diesel and diesel-bio diesel blends to improve complete combustion of the fuel significantly.

Keywords: engine, cerium oxide, biodiesel, deposit

Procedia PDF Downloads 349

Authors: N. Samarinas, C. Evangelides, C. Vrekos

Abstract:

The aim of this paper is the comparison of three different methods, in order to produce fuzzy tolerance relations for rainfall data classification. More specifically, the three methods are correlation coefficient, cosine amplitude and max-min method. The data were obtained from seven rainfall stations in the region of central Greece and refers to 20-year time series of monthly rainfall height average. Three methods were used to express these data as a fuzzy relation. This specific fuzzy tolerance relation is reformed into an equivalence relation with max-min composition for all three methods. From the equivalence relation, the rainfall stations were categorized and classified according to the degree of confidence. The classification shows the similarities among the rainfall stations. Stations with high similarity can be utilized in water resource management scenarios interchangeably or to augment data from one to another. Due to the complexity of calculations, it is important to find out which of the methods is computationally simpler and needs fewer compositions in order to give reliable results.

Keywords: classification, fuzzy logic, tolerance relations, rainfall data

Procedia PDF Downloads 316