Search results for: thermal network

Authors: Shervin Alaei, Reza Moradi

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Trading in financial markets is subject to risks due to their high volatilities. Here, using an LSTM neural network, and by doing some risk-based feature engineering tasks, we developed a method that can accurately predict trends of the Tehran stock exchange market index from a few days ago. Our test results have shown that the proposed method with an average prediction accuracy of more than 94% is superior to the other common machine learning algorithms. To the best of our knowledge, this is the first work incorporating deep learning and risk factors to accurately predict market trends.

Keywords: deep learning, LSTM, trend prediction, risk management, artificial neural networks

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Authors: Gavin Gengan, Hsein Kew

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Glass from varying recycling processes is considered a material that can be used as aggregate. Waste glass is available from different sources and has been used in the construction industry over the last decades. This current study aims to use recycled glass as a partial replacement for conventional aggregate materials. The experimental programme was designed to optimise the mechanical properties of structural concrete made with recycled glass aggregates (GA). NA (natural aggregates) was partially substituted by GA in a mix design of concrete of 30N/mm2 in proportions of 10%, 20%, and 25% 30%, 40%, and 50%. It was found that with an increasing proportion of GA, there is a decline in compressive strength. The optimum percentage replacement of NA by GA is 25%. The heat of hydration was also investigated with thermocouples placed in the concrete. This revealed an early acceleration of hydration heat in glass concrete, resulting from the thermal properties of glass. The gain in the heat of hydration and the better bonding of glass aggregates together with the pozzolanic activity of the finest glass particles caused the concrete to develop early age and long-term strength higher than that of control concrete

Keywords: concrete, compressive strength, glass aggregates, heat of hydration, pozzolanic

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Authors: Ebuwa Osagie, Vasilije Manovic

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Exergy analysis provides the identification of the location, sources of thermodynamic inefficiencies, and magnitude in a thermal system. Thus, both the qualitative and quantitative assessment can be evaluated with exergy, unlike energy which is based on quantitative assessment only. The main purpose of exergy analysis is to identify where exergy is destroyed. Thus, reduction of the exergy destruction and losses associated with the capture plant systems can improve work potential. Furthermore, thermodynamic analysis of different configurations of the process helps to identify opportunities for reducing the steam requirements for each of the configurations. This paper presents steady-state simulation and exergy analysis of the 2-amino-2-methyl-1-propanol (AMP)-based post-combustion capture (PCC) plant. Exergy analysis performed for the AMP-based plant and the different configurations revealed that the rich split with intercooling configuration gave the highest exergy efficiency of 73.6%, while that of the intercooling and the reference AMP-based plant were 57.3% and 55.8% respectively.

Keywords: 2-amino-2-methyl-1-propanol, modelling, and simulation, post-combustion capture plant, exergy analysis, flowsheeting configurations

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Authors: Furkan Ozkan, M. Selcuk Arslan, Hatice Mercan

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Electric vehicles are becoming increasingly popular asa sustainable alternative to traditional combustion engine vehicles. However, to fully realize the potential of EVs in reducing environmental impact and energy consumption, efficient control strategies are essential. This study explores the application of green wave control using model predictive control for electric vehicles, coupled with energy consumption modeling using neural networks. The use of MPC allows for real-time optimization of the vehicles’ energy consumption while considering dynamic traffic conditions. By leveraging neural networks for energy consumption modeling, the EV's performance can be further enhanced through accurate predictions and adaptive control. The integration of these advanced control and modeling techniques aims to maximize energy efficiency and range while navigating urban traffic scenarios. The findings of this research offer valuable insights into the potential of green wave control for electric vehicles and demonstrate the significance of integrating MPC and neural network modeling for optimizing energy consumption. This work contributes to the advancement of sustainable transportation systems and the widespread adoption of electric vehicles. To evaluate the effectiveness of the green wave control strategy in real-world urban environments, extensive simulations were conducted using a high-fidelity vehicle model and realistic traffic scenarios. The results indicate that the integration of model predictive control and energy consumption modeling with neural networks had a significant impact on the energy efficiency and range of electric vehicles. Through the use of MPC, the electric vehicle was able to adapt its speed and acceleration profile in realtime to optimize energy consumption while maintaining travel time objectives. The neural network-based energy consumption modeling provided accurate predictions, enabling the vehicle to anticipate and respond to variations in traffic flow, further enhancing energy efficiency and range. Furthermore, the study revealed that the green wave control strategy not only reduced energy consumption but also improved the overall driving experience by minimizing abrupt acceleration and deceleration, leading to a smoother and more comfortable ride for passengers. These results demonstrate the potential for green wave control to revolutionize urban transportation by enhancing the performance of electric vehicles and contributing to a more sustainable and efficient mobility ecosystem.

Keywords: electric vehicles, energy efficiency, green wave control, model predictive control, neural networks

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Authors: Tu Shuyang, Zhang Xu, Zhou Xiang

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The objective of this paper is to investigate the effect of short-term heat exchange decay of ground heat exchanger (GHE) on the ground source heat pump (GSHP) energy efficiency and capacity. A resistance-capacitance (RC) model was developed and adopted to simulate the transient characteristics of the ground thermal condition and heat exchange. The capacity change of the GSHP was linked to the inlet and outlet water temperature by polynomial fitting according to measured parameters given by heat pump manufacturers. Thus, the model, which combined the heat exchange decay with the capacity change, reflected the energy efficiency decay of the whole system. A case of GSHP system was analyzed by the model, and the result showed that there was risk that the GSHP might not meet the load demand because of the efficiency decay in a short-term operation. The conclusion would provide some guidances for GSHP system design to overcome the risk.

Keywords: capacity, energy efficiency, GSHP, heat exchange

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Authors: Arlan S. Freitas, Leila M. Carvalho, Adriana L. Soares, Arnoud Neto, Marta S. Madruga, Rafael H. Carvalho, Elza I. Ida, Massami Shimokomaki

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The objective of this work was to evaluate the influence of microclimatic profile of broiler transport trucks and holding time (340) min under commercial conditions over the breast meat quality and DOA (Dead On Arrival) in a tropical Brazilian regions as the NorthEast. In this particular region routinely the season is divided into dry and wet seasons. Three loads of 4,100 forty seven days old broiler were monitored from farm to slaughterhouse in a distance of 273 km (320 min), morning periods of August, September and October 2015 rainy days. Meat qualities were evaluated by determining the occurrence of PSE (pale, soft, exudative) meat and DFD (dark, firm, dry) meat. The percentage of DOA per loaded truck was determined by counting the dead broiler during the hanging step at the slaughtering plant. Results showed the occurrence of 26.30% of PSE and 2.49% of DFD and 0.45% of DOA. By having PSE- and DFD- meat means that the birds were under thermal and cold stress leading as consequence to a relative high DOA index.

Keywords: animal welfare, DFD, microclimatic profile, PSE

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Authors: Somayyeh Kalami, Mojgan Nejad

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Based on the source (softwood, hardwood or annual crop) and isolation method (kraft, organosolv, sulfite or pre-enzymatic treatment), there are significant variations in lignin structure and properties. The first step in using lignin as biobased feedstock is to make sure that specific lignin is suitable for intended application. Complete characterization of lignin and measuring its chemical, physical and thermal properties can help to predict its suitability. To replace 100% phenol portion of phenolic adhesive, lignin should have high reactivity toward formaldehyde. Theoretically, lignins with closer backbone structure to phenol should be better candidate for this application. In this study, a number of different lignins were characterized and used to formulate phenolic adhesive. One of the main findings was that lignin sample with higher percentage of hydroxyl-phenyl units was better candidate than lignin with more syringyl units. This could be explained by the fact that hydroxyl-phenyl lignin units have two available ortho positions for reaction with formaldehyde while in syringyl units all ortho and para positions are occupied, and there is no available site in lignin structure to react with formaldehyde.

Keywords: lignin, phenolic adhesive, biobased, sustainable

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Authors: Fathi Abid, Bilel Kaffel

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The olive tree, the olive harvest during winter season and the production of olive oil better known by professionals under the name of the crushing operation have interested institutional traders such as olive-oil offices and private companies such as food industry refining and extracting pomace olive oil as well as export-import public and private companies specializing in olive oil. The major problem facing producers of olive oil each winter campaign, contrary to what is expected, it is not whether the harvest will be good or not but whether the sale price will allow them to cover production costs and achieve a reasonable margin of profit or not. These questions are entirely legitimate if we judge by the importance of the issue and the heavy complexity of the uncertainty and competition made tougher by a high level of indebtedness and the experience and expertise of speculators and producers whose objectives are sometimes conflicting. The aim of this paper is to study the formation mechanism of olive oil prices in order to learn about speculators’ behavior and expectations in the market, how they contribute by their industry knowledge and their financial alliances and the size the financial challenge that may be involved for them to build private information hoses globally to take advantage. The methodology used in this paper is based on two stages, in the first stage we study econometrically the formation mechanisms of olive oil price in order to understand the market participant behavior by implementing ARMA, SARMA, GARCH and stochastic diffusion processes models, the second stage is devoted to prediction purposes, we use a combined wavelet- ANN approach. Our main findings indicate that olive oil market participants interact with each other in a way that they promote stylized facts formation. The unstable participant’s behaviors create the volatility clustering, non-linearity dependent and cyclicity phenomena. By imitating each other in some periods of the campaign, different participants contribute to the fat tails observed in the olive oil price distribution. The best prediction model for the olive oil price is based on a back propagation artificial neural network approach with input information based on wavelet decomposition and recent past history.

Keywords: olive oil price, stylized facts, ARMA model, SARMA model, GARCH model, combined wavelet-artificial neural network, continuous-time stochastic volatility mode

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Authors: M. Pramila Devi, N. V. N. Indra Kiran

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Control charts, predominantly in the form of X-bar chart, are important tools in statistical process control (SPC). They are useful in determining whether a process is behaving as intended or there are some unnatural causes of variation. A process is out of control if a point falls outside the control limits or a series of point’s exhibit an unnatural pattern. In this paper, a study is carried out on four training algorithms for CCPs recognition. For those algorithms optimal structure is identified and then they are studied for type I and type II errors for generalization without early stopping and with early stopping and the best one is proposed.

Keywords: control chart pattern recognition, neural network, backpropagation, generalization, early stopping

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Authors: Suneeta Kumari, Abanti Sahoo

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Industrial effluents treatment is a major problem in the world. All wastewater treatment methods have some problems in the environment. Due to this reason, today many natural biopolymers are being used in the waste water treatment because those are safe for our environment. In this study, synthesis and characterization of polyethersulfone/chitosan membranes (Thin film composite membrane) are carried out. Fish scales are used as raw materials. Different characterization techniques such as Fourier transform infrared spectroscopy (FTIR), X-ray powder diffraction (XRD), scanning electron microscope (SEM) and Thermal gravimetric analysis (TGA) are analysed for the synthesized membrane. The performance of membranes such as flux, rejection, and pore size are also checked. The synthesized membrane is used for the treatment of steel industry waste water where Biochemical oxygen demand (BOD), Chemical Oxygen Demand (COD), pH, colour, Total dissolved solids (TDS), Total suspended solids (TSS), Electrical conductivity (EC) and Turbidity aspects are analysed.

Keywords: fish scale, membrane synthesis, treatment of industrial effluents, chitosan

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Authors: T. T. Naas, Y. Lasbet, C. Kezrane

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The thermal control in many systems is widely accomplished applying mixed convection process due to its low cost, reliability and easy maintenance. Typical applications include the aircraft electronic equipment, rotating-disc heat exchangers, turbo machinery, and nuclear reactors, etc. Natural convection in an inclined square enclosure heated via wall heater has been studied numerically. Finite volume method is used for solving momentum and energy equations in the form of stream function–vorticity. The right and left walls are kept at a constant temperature, while the other parts are adiabatic. The range of the inclination angle covers a whole revolution. The method is validated for a vertical cavity. A general power law dependence of the Nusselt number with respect to the Rayleigh number with the coefficient and exponent as functions of the inclination angle is presented. For a fixed Rayleigh number, the inclination angle increases or decreases is found.

Keywords: natural convection in enclosure, inclined enclosure, Nusselt number, entropy generation analyze

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Authors: David Gamboa, Bernardo Herrera, Karen Cacua

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Nanomaterials have been explored as alternatives to reduce particulate matter from diesel engines, which is one of the most common pollutants of the air in urban centers. However, the use of nanomaterials as additives for diesel has to overcome the instability of the dispersions to be considered viable for commercial use. In this work, functionalization of carbon nanotubes with amide groups was performed to improve the stability of these nanomaterials in a mix of 90% petroleum diesel and 10% palm oil biodiesel (B10) in concentrations of 50 and 100 ppm. The resulting nano fuel was used as the fuel for a stationary internal combustion engine, where the particulate matter, NOx, and CO were measured. The results showed that the use of amide groups significantly enhances the time for the carbon nanotubes to remain suspended in the fuel, and at the same time, these nanomaterials helped to reduce the particulate matter and NOx emissions. However, the CO emissions with nano fuel were higher than those ones with the combustion of B10. These results suggest that carbon nanotubes have thermal and catalytic effects on the combustion of B10.

Keywords: carbon nanotubes, diesel, internal combustion engine, particulate matter

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Authors: K. N. Seetharamu, Naveen Teggi, Kiranakumar Dhavalagi, Narayana Kamath

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A method of temperature calculations is developed to study the conditions leading to hot spot occurrence on multicore chips. A physical model which has salient features of multicore chips is incorporated for the analysis. The model consists of active and background cell laid out in a checkered pattern, and this pattern repeats itself in each fine grain active cells. The die has three layers i) body ii) buried oxide layer iii) wiring layer, stacked one above the other with heat source placed at the interface between wiring and buried oxide layer. With this model we propose analytical method to calculate the target hotspot temperature, heat flow to top and bottom layers of the die and thermal resistance components at each granularity level, assuming appropriate values of die dimensions and parameters. Finally we attempt to find an easier method for the calculation of the target hotspot temperature using graph.

Keywords: checkered pattern, granularity level, heat conduction, multicore chips, target hotspot temperature

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Authors: Dong Wook Lee

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This paper presents a method to analyze the stiffness of the expansion joint with structural support using a hybrid method combining computational and analytical methods. Many expansion joints found in tubes and ducts of mechanical structures are designed to absorb thermal expansion mismatch between their structural members and deal with misalignments introduced from the assembly/manufacturing processes. One of the important design perspectives is the system’s vibrational characteristics. We calculate the stiffness as a characterization parameter for structural joint systems using a combined Finite Element Analysis (FEA) and an analytical method. We apply the methods to two sample applications: external configurations of aircraft engines and nuclear power plant structures.

Keywords: expansion joint, expansion joint stiffness, finite element analysis, nuclear power plants, aircraft engine external configurations

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Authors: A. Melouah, M. Diaf

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The III-N nitride semiconductors appear to be excellent host materials, in particular, GaN epilayers doped with Erbium ions have shown a highly reduced thermal quenching of the Er luminescence intensity from cryogenic to elevated temperatures. The remarkable stability may be due to the large energy band gap of the material. Two methods are used for doping the Gallium nitride films with Erbium ions; ion implantation in the wafers obtained by (CVDOM) and in-situ incorporation during epitaxial growth of the layers by (MBE). Photoluminescence (PL) spectroscopy has been the main optical technique used to characterize the emission of Er-doped III-N semiconductor materials. This technique involves optical excitation of Er3+ ions and measurement of the spectrum of the light emission as a function of energy (wavelength). Excitation at above band gap energy leads to the creation of Electron-Hole pairs. Some of this pairs may transfer their energy to the Er3+ ions, exciting the 4f-electrons and resulting in optical emission. This corresponds to an indirect excitation of the Er3+ ions by electron-hole pairs. The direct excitation by the optical pumping of the radiation can be obtained.

Keywords: photoluminescence, Erbium, GaN, semiconductor materials

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Authors: Huang Yajing

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Context: Computer-aided language learning (CALL) in China has undergone significant development over the past few decades, with distinct stages marking its evolution. This paper aims to provide a comprehensive review of the practice and research in this field in China, tracing its journey from the early stages of audio-visual education to the current multimedia network integration stage. Research Aim: The study aims to analyze the historical progression of CALL in China, identify key developments in the field, and provide recommendations for enhancing CALL practices in the future. Methodology: The research employs document analysis and literature review to synthesize existing knowledge on CALL in China, drawing on a range of sources to construct a detailed overview of the evolution of CALL practices and research in the country. Findings: The review highlights the significant advancements in CALL in China, showcasing the transition from traditional audio-visual educational approaches to the current integrated multimedia network stage. The study identifies key milestones, technological advancements, and theoretical influences that have shaped CALL practices in China. Theoretical Importance: The evolution of CALL in China reflects not only technological progress but also shifts in educational paradigms and theories. The study underscores the significance of cognitive psychology as a theoretical underpinning for CALL practices, emphasizing the learner's active role in the learning process. Data Collection and Analysis Procedures: Data collection involved extensive review and analysis of documents and literature related to CALL in China. The analysis was carried out systematically to identify trends, developments, and challenges in the field. Questions Addressed: The study addresses the historical development of CALL in China, the impact of technological advancements on teaching practices, the role of cognitive psychology in shaping CALL methodologies, and the future outlook for CALL in the country. Conclusion: The review provides a comprehensive overview of the evolution of CALL in China, highlighting key stages of development and emerging trends. The study concludes by offering recommendations to further enhance CALL practices in the Chinese context.

Keywords: English education, educational technology, computer-aided language teaching, applied linguistics

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Authors: Aleksandr A. Gajour, Vladimir G. Merzlikin, Vladimir I. Veselov

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This paper refers to the analysis of the characteristics of industrial and lifestyle facilities heat- energy objects as a part of the thermal envelope of Earth's surface for inclusion in any database of economic forecasting. The idealized model of the Earth's surface is discussed. This model gives the opportunity to obtain the energy equivalent for each element of terrain and world ocean. Energy efficiency criterion of comfortable human existence is introduced. Dynamics of changes of this criterion offers the possibility to simulate the possible technogenic catastrophes with the spontaneous industrial development of the certain Earth areas. Calculated model with the confirmed forecast of the Gulf Stream freezing in the polar regions in 2011 due to the heat-energy balance disturbance for the oceanic subsurface oil polluted layer is given. Two opposing trends of human development under limited and unlimited amount of heat-energy resources are analyzed.

Keywords: Earth's surface, heat-energy consumption, energy criteria, technogenic catastrophes

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Authors: Jamaludin Sallim, Rozlina Mohamed, Roslina Abdul Hamid

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In this paper, we proposed an Ant Colony Optimization (ACO) algorithm together with Traveling Salesman Problem (TSP) approach to investigate the clustering problem in Protein Interaction Networks (PIN). We named this combination as ACOPIN. The purpose of this work is two-fold. First, to test the efficacy of ACO in clustering PIN and second, to propose the simple generalization of the ACO algorithm that might allow its application in clustering proteins in PIN. We split this paper to three main sections. First, we describe the PIN and clustering proteins in PIN. Second, we discuss the steps involved in each phase of ACO algorithm. Finally, we present some results of the investigation with the clustering patterns.

Keywords: ant colony optimization algorithm, searching algorithm, protein functional module, protein interaction network

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Authors: Pavel Baroch, Jiri Rezek, Michal Prochazka, Tomas Kozak, Jiri Houska

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Transparent semiconducting amorphous IGZO films have attracted great attention due to their excellent electrical properties and possible utilization in thin film transistors or in photovoltaic applications as they show 20-50 times higher mobility than that of amorphous silicon. It is also known that the properties of IGZO films are highly sensitive to process parameters, especially to oxygen partial pressure. In this study we have focused on the comparison of properties of transparent semiconducting amorphous indium gallium zinc oxide (IGZO) thin films prepared by conventional sputtering methods and those prepared by high power impulse magnetron sputtering (HiPIMS) method. Furthermore we tried to optimize electrical and optical properties of the IGZO thin films and to investigate possibility to apply these coatings on thermally sensitive flexible substrates. We employed dc, pulsed dc, mid frequency sine wave and HiPIMS power supplies for magnetron deposition. Magnetrons were equipped with sintered ceramic InGaZnO targets. As oxygen vacancies are considered to be the main source of the carriers in IGZO films, it is expected that with the increase of oxygen partial pressure number of oxygen vacancies decreases which results in the increase of film resistivity. Therefore in all experiments we focused on the effect of oxygen partial pressure, discharge power and pulsed power mode on the electrical, optical and mechanical properties of IGZO thin films and also on the thermal load deposited to the substrate. As expected, we have observed a very fast transition between low- and high-resistivity films depending on oxygen partial pressure when deposition using conventional sputtering methods/power supplies have been utilized. Therefore we established and utilized HiPIMS sputtering system for enlargement of operation window for better control of IGZO thin film properties. It is shown that with this system we are able to effectively eliminate steep transition between low and high resistivity films exhibited by DC mode of sputtering and the electrical resistivity can be effectively controlled in the wide resistivity range of 10-² to 10⁵ Ω.cm. The highest mobility of charge carriers (up to 50 cm2/V.s) was obtained at very low oxygen partial pressures. Utilization of HiPIMS also led to significant decrease in thermal load deposited to the substrate which is beneficial for deposition on the thermally sensitive and flexible polymer substrates. Deposition rate as a function of discharge power and oxygen partial pressure was also systematically investigated and the results from optical, electrical and structure analysis will be discussed in detail. Most important result which we have obtained demonstrates almost linear control of IGZO thin films resistivity with increasing of oxygen partial pressure utilizing HiPIMS mode of sputtering and highly transparent films with low resistivity were prepared already at low pO2. It was also found that utilization of HiPIMS technique resulted in significant improvement of surface smoothness in reactive mode of sputtering (with increasing of oxygen partial pressure).

Keywords: charge carrier mobility, HiPIMS, IGZO, resistivity

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Authors: Marcio Freire Cruz, Naoaki Ono, Shigehiko Kanaya, Carlos Arthur Mattos Teixeira Cavalcante

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The systems that record patient care information, known as Electronic Medical Record (EMR) and those that monitor vital signs of patients, such as heart rate, body temperature, and blood pressure have been extremely valuable for the effectiveness of the patient’s treatment. Several kinds of research have been using data from EMRs and vital signs of patients to predict illnesses. Among them, we highlight those that intend to predict, classify, or, at least identify patterns, of sepsis illness in patients under vital signs monitoring. Sepsis is an organic dysfunction caused by a dysregulated patient's response to an infection that affects millions of people worldwide. Early detection of sepsis is expected to provide a significant improvement in its treatment. Preceding works usually combined medical, statistical, mathematical and computational models to develop detection methods for early prediction, getting higher accuracies, and using the smallest number of variables. Among other techniques, we could find researches using survival analysis, specialist systems, machine learning and deep learning that reached great results. In our research, patients are modeled as points moving each hour in an n-dimensional space where n is the number of vital signs (variables). These points can reach a sepsis target point after some time. For now, the sepsis target point was calculated using the median of all patients’ variables on the sepsis onset. From these points, we calculate for each hour the position vector, the first derivative (velocity vector) and the second derivative (acceleration vector) of the variables to evaluate their behavior. And we construct a prediction model based on a Long Short-Term Memory (LSTM) Network, including these derivatives as explanatory variables. The accuracy of the prediction 6 hours before the time of sepsis, considering only the vital signs reached 83.24% and by including the vectors position, speed, and acceleration, we obtained 94.96%. The data are being collected from Medical Information Mart for Intensive Care (MIMIC) Database, a public database that contains vital signs, laboratory test results, observations, notes, and so on, from more than 60.000 patients.

Keywords: dynamic analysis, long short-term memory, prediction, sepsis

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Authors: Yusra Tariq

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The Grass 37 gene is presently known in tomatoes, which are the source of healthy substances such as ascorbic acid, polyphenols, carotenoids and nutrients. It has a significant impact on the growth and development of humans. The GRASS 37 gene is a plant Transcription factor group assuming significant parts in various reactions of different Abiotic stresses such as (drought, salinity, thermal stresses, temperature, and bright waves) which could highly affect the growth. Tomatoes are very sensitive to temperature, and their growth or production occurs optimally in a temperature range from 21 C to 29.5 C during the daytime and from 18.5 C to 21 C during the night. This protein acts as a positive regulator of salt stress response and abscisic acid signaling. This study summarizes the structure characterized by molecular formula and protein-binding domains by different bioinformatics tools such as Expasy translate tool, Expasy Portparam, Swiss Prot and Inter Pro Scan, Clustal W tool regulatory procedure of GRASS gene components, also their reactions to both biotic and Abiotic stresses.

Keywords: GRAS37, gene, bioinformatics, tool

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Authors: M. Olmedilla, M. R. Martinez-Torres, S. L. Toral

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Electronic Word-Of-Mouth (eWOM) communities represent today an important source of information in which more and more customers base their purchasing decisions. They include thousands of reviews concerning very different products and services posted by many individuals geographically distributed all over the world. Due to their massive audience, eWOM communities can help users to find the product they are looking for even if they are less popular or rare. This is known as the long tail effect, which leads to a larger number of lower-selling niche products. This paper analyzes the long tail effect in a well-known eWOM community and defines a tool for finding niche products unavailable through conventional channels.

Keywords: eWOM, online user reviews, long tail theory, product categorization, social network analysis

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Authors: Oluwafunmibi Omotayo Fasanya, Augustine Kena Adjei

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Diabetic nephropathy (DN) is a major complication of diabetes, with fibrosis and propionate metabolism playing critical roles in its progression. Identifying biomarkers linked to these pathways may provide novel insights into DN diagnosis and treatment. This study aims to identify biomarkers associated with fibrosis and propionate metabolism in DN. Analyze the biological pathways and regulatory mechanisms of these biomarkers. Develop a machine learning model to predict DN-related biomarkers and validate their functional roles. Publicly available transcriptome datasets related to DN (GSE96804 and GSE104948) were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/gds), and 924 propionate metabolism-related genes (PMRGs) and 656 fibrosis-related genes (FRGs) were identified. The analysis began with the extraction of DN-differentially expressed genes (DN-DEGs) and propionate metabolism-related DEGs (PM-DEGs), followed by the intersection of these with fibrosis-related genes to identify key intersected genes. Instead of relying on traditional models, we employed a combination of deep neural networks (DNNs) and ensemble methods such as Gradient Boosting Machines (GBM) and XGBoost to enhance feature selection and biomarker discovery. Recursive feature elimination (RFE) was coupled with these advanced algorithms to refine the selection of the most critical biomarkers. Functional validation was conducted using convolutional neural networks (CNN) for gene set enrichment and immunoinfiltration analysis, revealing seven significant biomarkers—SLC37A4, ACOX2, GPD1, ACE2, SLC9A3, AGT, and PLG. These biomarkers are involved in critical biological processes such as fatty acid metabolism and glomerular development, providing a mechanistic link to DN progression. Furthermore, a TF–miRNA–mRNA regulatory network was constructed using natural language processing models to identify 8 transcription factors and 60 miRNAs that regulate these biomarkers, while a drug–gene interaction network revealed potential therapeutic targets such as UROKINASE–PLG and ATENOLOL–AGT. This integrative approach, leveraging deep learning and ensemble models, not only enhances the accuracy of biomarker discovery but also offers new perspectives on DN diagnosis and treatment, specifically targeting fibrosis and propionate metabolism pathways.

Keywords: diabetic nephropathy, deep neural networks, gradient boosting machines (GBM), XGBoost

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Authors: Shaimaa M. Mohamed, Haitham S. Hamza, Imane A. Saroit

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Many wireless sensor network applications require K-coverage of the monitored area. In this paper, we propose a scalable harmony search based algorithm in terms of execution time, K-Coverage Enhancement Algorithm (KCEA), it attempts to enhance initial coverage, and achieve the required K-coverage degree for a specific application efficiently. Simulation results show that the proposed algorithm achieves coverage improvement of 5.34% compared to K-Coverage Rate Deployment (K-CRD), which achieves 1.31% when deploying one additional sensor. Moreover, the proposed algorithm is more time efficient.

Keywords: Wireless Sensor Networks (WSN), harmony search algorithms, K-Coverage, Mobile WSN

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Authors: Sabyasachi Mukhopadhyay, Joseph Ogutu, Gundula Bartzke, Hans-Peter Piepho

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Rainfall is a critical component of climate governing vegetation growth and production, forage availability and quality for herbivores. However, reliable rainfall measurements are not always available, making it necessary to predict rainfall values for particular locations through time. Predicting rainfall in space and time can be a complex and challenging task, especially where the rain gauge network is sparse and measurements are not recorded consistently for all rain gauges, leading to many missing values. Here, we develop a flexible Bayesian model for predicting rainfall in space and time and apply it to Narok County, situated in southwestern Kenya, using data collected at 23 rain gauges from 1965 to 2015. Narok County encompasses the Maasai Mara ecosystem, the northern-most section of the Mara-Serengeti ecosystem, famous for its diverse and abundant large mammal populations and spectacular migration of enormous herds of wildebeest, zebra and Thomson's gazelle. The model incorporates geographical and meteorological predictor variables, including elevation, distance to Lake Victoria and minimum temperature. We assess the efficiency of the model by comparing it empirically with the established Gaussian process, Kriging, simple linear and Bayesian linear models. We use the model to predict total monthly rainfall and its standard error for all 5 * 5 km grid cells in Narok County. Using the Monte Carlo integration method, we estimate seasonal and annual rainfall and their standard errors for 29 sub-regions in Narok. Finally, we use the predicted rainfall to predict large herbivore biomass in the Maasai Mara ecosystem on a 5 * 5 km grid for both the wet and dry seasons. We show that herbivore biomass increases with rainfall in both seasons. The model can handle data from a sparse network of observations with many missing values and performs at least as well as or better than four established and widely used models, on the Narok data set. The model produces rainfall predictions consistent with expectation and in good agreement with the blended station and satellite rainfall values. The predictions are precise enough for most practical purposes. The model is very general and applicable to other variables besides rainfall.

Keywords: non-stationary covariance function, gaussian process, ungulate biomass, MCMC, maasai mara ecosystem

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Authors: Joo-Hyung Oh

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The number of service users of 4G VoLTE (voice over LTE) using LTE data networks is rapidly growing. VoLTE based on all-IP network enables clearer and higher-quality voice calls than 3G. It does, however, pose new challenges; a voice call through IP networks makes it vulnerable to security threats such as wiretapping and forged or falsified information. And in particular, stealing other users’ phone numbers and forging or falsifying call request messages from outgoing voice calls within VoLTE result in considerable losses that include user billing and voice phishing to acquaintances. This paper focuses on the threats of caller phone number spoofing in the VoLTE and countermeasure technology as safety measures for mobile communication networks.

Keywords: LTE, 4G, VoLTE, phone number spoofing

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Authors: Jae-Kyung Choi, Soon-Yong Kwon, Hyung Duk Yun, Hyun-Sang Chung, Seongho Seo, Kukjin Bae

Abstract:

Recently, there were several attempts to utilize a graphene layer as a water desalination membrane. In order to use a graphene layer as a water desalination membrane, fabrication of crack-free suspension of graphene on a porous membrane, having hydrophobic surface, and generation of a uniform holes on a graphene are very important. In here, we showed a simple chemical vapor deposition (CVD) method to create a patterned graphene membrane on a patterned platinum film. After CVD growth process of patterned graphene layer/patterned Pt on SiO2 substrates, the patterned graphene layer can be successfully transferred onto arbitrary substrates via thermal-assisted transfer method. In this result, the transferred patterned graphene membrane has so hydrophobic surface which will certainly impact on the naturally and speed pass way for fresh water. In addition to this, we observed that overlapping of patterned graphene membranes reported previously by our group may generate different size of holes.

Keywords: chemical vapor deposition (CVD), hydrophobic surface, membrane desalination, porous graphene

Procedia PDF Downloads 471

Authors: Jing Ma, Xindong An

Abstract:

Green infrastructure, the most important aspect of improving the quality of life, has been a crucial element of the liveability measurement. With demanding of more liveable urban environment from increasing population in city area, access to green infrastructure in walking distance should be taken into consideration. This article exemplifies the study on accessibility measurement of green infrastructure in central Auckland (New Zealand), using network analysis tool on the basis of GIS, to verify the accessibility levels of green infrastructure. It analyses the overall situation of green infrastructure and draws some conclusions on the city’s different levels of accessibility according to the categories and facilities distribution, which provides valuable references and guidance for the future facility improvement in planning strategies.

Keywords: quality of life, green infrastructure, GIS, accessibility

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Authors: Paul C. Njoku, Archana Swati Njoku

Abstract:

The study deals with the development of a mathematical model to characterize the oil production in Nigeria. This is calculated by initiating the dynamics of oil production in million barrels revenue plan cost of oil production in million nairas and unit cost of production from 1974-1982 in the contest of the federal Republic of Nigeria. This country export oil to other countries as well as importing specialized crude. The transport network from origin/destination tij to pairs is taking into account simulation runs, optimization have been considered in this study.

Keywords: mathematical oil model development dynamics, Nigeria, characterization barrels, dynamics of oil production

Procedia PDF Downloads 387

Authors: Srinivasa Reddy Mallampati, Chi-Hyeon Lee, Nguyen Thi Thanh Truc, Byeong-Kyu Lee

Abstract:

In the present study, feasibility of the selective surface hydrophilization of polyvinyl chloride (PVC) by microwave treatment was evaluated to facilitate the separation from automotive shredder residue (ASR), by the froth flotation. The combination of 60 sec microwave treatment with PAC, a sharp and significant decrease about 16.5° contact angle of PVC was observed in ASR plastic compared with other plastics. The microwave treatment with the addition of PAC resulted in a synergetic effect for the froth flotation, which may be a result of the 90% selective separation of PVC from ASR plastics, with 82% purity. While, simple mixing with a nanometallic Ca/CaO/PO4 dispersion mixture immobilized 95-100% of heavy metals in ASR soil/residues. The quantity of heavy metals leached from thermal residues after treatment by nanometallic Ca/CaO/PO4 was lower than the Korean standard regulatory limit for hazardous waste landfills. Microwave treatment can be a simple and effective method for PVC separation from ASR plastics.

Keywords: automotive shredder residue, chlorinated plastics, hazardous waste, heavy metals, immobilization, separation

Procedia PDF Downloads 521