Search results for: optimal reaction network

Authors: Hua Chai, Qiangling Duan, Huiqi Cao, Mi Li, Jinhua Sun

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Nitrocellulose (NC), a kind of energetic material, has been widely used in the industrial and military fields. However, this material can also cause serious social disasters due to storage conditions. Thermal hazard of nitrocellulose (NC) was experimentally investigated using the CALVET heat flux calorimeter C80, and three kinds of storage conditions were considered in the experiments: (1) drying time, (2) moisture content, (3) cycles. The results showed that the heat flow curves of NC moved to the low-temperature direction firstly and then slightly moved back by increasing the drying hours. Moisture that was responsible for the appearance of small exothermic peaks was proven to be the unfavorable safety factor yet it could increase the onset temperature of the main peak to some extent. And cycles could both lower the onset temperature and the maximum heat flow but enlarged the peak temperature. Besides, relevant kinetic parameters such as the heat of reaction (ΔH) and the activation energy (Ea) were obtained and compared. It was found that all the three conditions could reduce the values of Ea and most of them produced larger reaction heat. In addition, the critical explosion temperature (Tb) of the NC samples were derived. It was clear that not only the drying time but also the cycles would increase the thermal hazard of the NC. Yet, the right amount of water helped to reduce the thermal hazard.

Keywords: C80, nitrocellulose, storage conditions, the critical explosion temperature, thermal hazard

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Authors: Kazunori Takai, Weng Kaiwei, Sadao Araki, Hideki Yamamoto

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HFC and PFC gases have been commonly and widely used as refrigerant of air conditioner and as etching agent of semiconductor manufacturing process, because of their higher heat of vaporization and chemical stability. On the other hand, HFCs and PFCs gases have the high global warming effect on the earth. Therefore, we have to be decomposed these gases emitted from chemical apparatus like as refrigerator. Until now, disposal of these gases were carried out by using combustion method like as Rotary kiln treatment mainly. However, this treatment needs extremely high temperature over 1000 °C. In the recent year, in order to reduce the energy consumption, a hydrolytic decomposition method using catalyst and plasma decomposition treatment have been attracted much attention as a new disposal treatment. However, the decomposition of fluorine-containing gases under the wet condition is not able to avoid the generation of hydrofluoric acid. Hydrofluoric acid is corrosive gas and it deteriorates catalysts in the decomposition process. Moreover, an additional process for the neutralization of hydrofluoric acid is also indispensable. In this study, the decomposition of C2F6 using zeolite and zeolite/CaO mixture as reactant was evaluated in the dry condition at 923 K. The effect of the chemical structure of zeolite on the decomposition reaction was confirmed by using H-Y, H-Beta, H-MOR and H-ZSM-5. The formation of CaF2 in zeolite/CaO mixtures after the decomposition reaction was confirmed by XRD measurements. The decomposition of C2F6 using zeolite as reactant showed the closely similar behaviors regardless the type of zeolite (MOR, Y, ZSM-5, Beta type). There was no difference of XRD patterns of each zeolite before and after reaction. On the other hand, the difference in the C2F6 decomposition for each zeolite/CaO mixtures was observed. These results suggested that the rate-determining process for the C2F6 decomposition on zeolite alone is the removal of fluorine from reactive site. In other words, the C2F6 decomposition for the zeolite/CaO improved compared with that for the zeolite alone by the removal of the fluorite from reactive site. HMOR/CaO showed 100% of the decomposition for 3.5 h and significantly improved from zeolite alone. On the other hand, Y type zeolite showed no improvement, that is, the almost same value of Y type zeolite alone. The descending order of C2F6 decomposition was MOR, ZSM-5, beta and Y type zeolite. This order is similar to the acid strength characterized by NH3-TPD. Hence, it is considered that the C-F bond cleavage is closely related to the acid strength.

Keywords: hexafluoroethane, zeolite, calcium oxide, decomposition

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Authors: Bhuvanesh Baniya

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Alzheimer disease is illnesses that are responsible for neuronal cell death and resulting in lifelong cognitive problems. Due to their unclear mechanism, there are no effective drugs available for the treatment. For a long time, herbal drugs have been used as a role model in the field of the drug discovery process. Holy basil in the Indian medicinal system (Ayurveda) is used for several neuronal disorders like insomnia and memory loss for decades. This study aims to identify active components of holy basil as potential inhibitors for the treatment of Alzheimer disease. To fulfill this objective, the Network pharmacology approach, gene ontology, pharmacokinetics analysis, molecular docking, and molecular dynamics simulation (MDS) studies were performed. A total of 7 active components in holy basil, 12 predicted neurodegenerative targets of holy basil, and 8063 Alzheimer-related targets were identified from different databases. The network analysis showed that the top ten targets APP, EGFR, MAPK1, ESR1, HSPA4, PRKCD, MAPK3, ABL1, JUN, and GSK3B were found as significant target related to Alzheimer disease. On the basis of gene ontology and topology analysis results, APP was found as a significant target related to Alzheimer’s disease pathways. Further, the molecular docking results to found that various compounds showed the best binding affinities. Further, MDS top results suggested could be used as potential inhibitors against APP protein and could be useful for the treatment of Alzheimer’s disease.

Keywords: holy basil, network pharmacology, neurodegeneration, active phytochemicals, molecular docking and simulation

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Authors: Vanaja Rao

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In the recent times, the need for the integration of Renewable Energy Sources (RES) in a Smart Grid is on the rise. As a result of this, associated energy storage systems are known to play important roles in sustaining the efficient operation of such RES like wind power and solar power. This paper investigates the importance of location selection of Energy Storage Systems (ESSs) in a Smart Grid. Three scenarios of ESS location is studied and analyzed in a Smart Grid, which are – 1. Near the generation/source, 2. In the middle of the Grid and, 3. Near the demand/consumption. This is explained with the aim of assisting any Distribution Network Operator (DNO) in deploying the ESSs in a power network, which will significantly help reduce the costs and time of planning and avoid any damages incurred as a result of installing them at an incorrect location of a Smart Grid. To do this, the outlined scenarios mentioned above are modelled and analyzed with the National Grid’s datasets of energy generation and consumption in the UK power network. As a result, the outcome of this analysis aims to provide a better overview for the location selection of the ESSs in a Smart Grid. This ensures power system stability and security along with the optimum usage of the ESSs.

Keywords: distribution networks, energy storage system, energy security, location planning, power stability, smart grid

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Authors: Augustus K. Lebechi, Kenneth I. Ozoemena

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Rechargeable zinc-air batteries (RZABs) have been described as one of the most viable next-generation ‘beyond-the-lithium-ion’ battery technologies with great potential for renewable energy storage. It is safe, with a high specific energy density (1086 Wh/kg), environmentally benign, and low-cost, especially in resource-limited African countries. For widespread commercialization, the sluggish oxygen reaction kinetics pose a major challenge that impedes the reversibility of the system. Hence, there is a need for low-cost and highly active bifunctional electrocatalysts. Manganese oxide catalysts on carbon conducting additives remain the best couple for the realization of such low-cost RZABs. In this work, hausmannite Mn₃O₄ nanoparticles were synthesized through the annealing method from commercial electrolytic manganese dioxide (EMD), multi-walled carbon nanotubes (MWCNTs) were synthesized via the chemical vapor deposition (CVD) method and carbon nanofibers (CNFs) were synthesized via the electrospinning process with subsequent carbonization. Both Mn₃O₄ catalysts and the carbon conducting additives (MWCNT and CNF) were thoroughly characterized using X-ray powder diffraction spectroscopy (XRD), scanning electron microscopy (SEM), thermogravimetry analysis (TGA) and X-ray photoelectron spectroscopy (XPS). Composite electrocatalysts (Mn₃O₄/CNT and Mn₃O₄/CNF) were investigated for oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) in an alkaline medium. Using the established electrocatalytic modalities for evaluating the electrocatalytic performance of materials (including double layer, electrochemical active surface area, roughness factor, specific current density, and catalytic stability), CNFs proved to be the most efficient conducting additive material for the Mn₃O₄ catalyst. From the DFT calculations, the higher performance of the CNFs over the MWCNTs is related to the ability of the CNFs to allow for a more favorable distribution of the d-electrons of the manganese (Mn) and enhanced synergistic effect with Mn₃O₄ for weaker adsorption energies of the oxygen intermediates (O*, OH* and OOH*). In a proof-of-concept, Mn₃O₄/CNF was investigated as the air cathode for rechargeable zinc-air battery (RZAB) in a micro-3D-printed cell configuration. The RZAB showed good performance in terms of open circuit voltage (1.77 V), maximum power density (177.5 mW cm-2), areal-discharge energy and cycling stability comparable to Pt/C (20 wt%) + IrO2. The findings here provide fresh physicochemical perspectives on the future design and utility of CNFs for developing manganese-based RZABs.

Keywords: bifunctional electrocatalyst, oxygen evolution reaction, oxygen reduction reactions, rechargeable zinc-air batteries.

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Authors: Fatiha Benghanem, Mokhtar Berarma, Saida Keraghel, Ali Ourari

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Corrosion is the result of the reaction between a material and its environment. Steel in concrete is protected from corrosion by a passive film promoted by concrete alkalinity. For the initiation of corrosion, this protective film must be destroyed and this can be mainly done in two ways: by the attack of chlorides on the steel or by carbonation of the cover concrete due the reaction with carbon dioxide, which causes reduction in the alkalinity of concrete. The literature reports several ways to decrease or to prevent reinforcement corrosion. Among them, the use of corrosion inhibitors has been an envisaged solution. Two approaches are generally used to evaluate the efficiency of inhibitors for concrete application; one uses simulated pore solution testing , and the other uses actual concrete or mortar specimens. Both methods are some times used in conjunction. The aim of this study is to investigate the use of 2,3-diaminopyridine as a corrosion inhibitors of steel in alkaline media which simulate the electrolyte in the concrete pores. The effectiveness of this compound as corrosion inhibitor was investigated by measuring the corrosion potentials, the polarization curves and the corrosion current densities of steel with and without chlorides. The study of corrosion inhibition by this compound led to the conclusion that he has low rates of inhibition in the absence of aggressive ions and high rates in their presence. This type of organic compounds are promoting for the protection of armatures in concrete.

Keywords: corrosion, inhibitors, mild steel, conjunction

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Authors: Farshad Zeinali, Sajedeh Norouzi, Nader Mokari, Eduard Jorswieck

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The capacity of fifth-generation (5G) vehicle-to-everything (V2X) networks poses significant challenges. To ad- dress this challenge, this paper utilizes New Radio (NR) and New Radio Unlicensed (NR-U) networks to develop a heterogeneous vehicular network (HetNet). We propose a new framework, named joint BS assignment and resource allocation (JBSRA) for mobile V2X users and also consider coexistence schemes based on flexible duty cycle (DC) mechanism for unlicensed bands. Our objective is to maximize the average throughput of vehicles while guaranteeing the WiFi users' throughput. In simulations based on deep reinforcement learning (DRL) algorithms such as deep deterministic policy gradient (DDPG) and deep Q network (DQN), our proposed framework outperforms existing solutions that rely on fixed DC or schemes without consideration of unlicensed bands.

Keywords: vehicle-to-everything (V2X), resource allocation, BS assignment, new radio (NR), new radio unlicensed (NR-U), coexistence NR-U and WiFi, deep deterministic policy gradient (DDPG), deep Q-network (DQN), joint BS assignment and resource allocation (JBSRA), duty cycle mechanism

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Authors: Fengjuan Wang, Azilawati Jamaludin

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Developmental Dyscalculia (DD) is defined as a particular learning difficulty with continuous challenges in learning requisite math skills that cannot be explained by intellectual disability or educational deprivation. Recent studies have increasingly recognized that DD is a heterogeneous, instead of monolithic, learning disorder with not only cognitive and behavioral deficits but so too neural dysfunction. In recent years, neuroimaging studies employed group comparison to explore the neural underpinnings of DD, which contradicted the heterogenous nature of DD and may obfuscate critical individual differences. This research aimed to investigate the neural heterogeneity of DD using case studies with functional near-infrared spectroscopy (fNIRS). A total of 54 aged 6-7 years old of children participated in this study, comprising two comprehensive cognitive assessments, an 8-minute resting state, and an 8-minute one-digit addition task. Nine children met the criteria of DD and scored at or below 85 (i.e., the 16th percentile) on the Mathematics or Math Fluency subtest of the Wechsler Individual Achievement Test, Third Edition (WIAT-III) (both subtest scores were 90 and below). The remaining 45 children formed the typically developing (TD) group. Resting-state data and brain activation in the inferior frontal gyrus (IFG), superior frontal gyrus (SFG), and intraparietal sulcus (IPS) were collected for comparison between each case and the TD group. Graph theory was used to analyze the brain network under the resting state. This theory represents the brain network as a set of nodes--brain regions—and edges—pairwise interactions across areas to reveal the architectural organizations of the nervous network. Next, a single-case methodology developed by Crawford et al. in 2010 was used to compare each case’s brain network indicators and brain activation against 45 TD children’s average data. Results showed that three out of the nine DD children displayed significant deviation from TD children’s brain indicators. Case 1 had inefficient nodal network properties. Case 2 showed inefficient brain network properties and weaker activation in the IFG and IPS areas. Case 3 displayed inefficient brain network properties with no differences in activation patterns. As a rise above, the present study was able to distill differences in architectural organizations and brain activation of DD vis-à-vis TD children using fNIRS and single-case methodology. Although DD is regarded as a heterogeneous learning difficulty, it is noted that all three cases showed lower nodal efficiency in the brain network, which may be one of the neural sources of DD. Importantly, although the current “brain norm” established for the 45 children is tentative, the results from this study provide insights not only for future work in “developmental brain norm” with reliable brain indicators but so too the viability of single-case methodology, which could be used to detect differential brain indicators of DD children for early detection and interventions.

Keywords: brain activation, brain network, case study, developmental dyscalculia, functional near-infrared spectroscopy, graph theory, neural heterogeneity

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Authors: Juliza Hidayati, Yesie M. Sinuhaji, Sawarni Hasibuan

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A company producing drinking water through many incompatibility issues that affect supply chain performance. The study was conducted to determine the factors that affect the performance of the supply chain and improve it. To obtain the dominant factors affecting the performance of the supply chain used Analytic Network Process, while to improve performance is done by using Kaizen. Factors affecting the performance of the supply chain to be a reference to identify the cause of the non-conformance. Results weighting using ANP indicates that the dominant factor affecting the level of performance is the precision of the number of shipments (15%), the ability of the fulfillment of the booking amount (12%), and the number of rejected products when signing (12%). Incompatibility of the factors that affect the performance of the supply chain are identified, so that found the root cause of the problem is most dominant. Based on the weight of Risk Priority Number (RPN) gained the most dominant root cause of the problem, namely the poorly maintained engine, the engine worked for three shifts, machine parts that are not contained in the plant. Improvements then performed using the Kaizen method of systematic and sustainable.

Keywords: analytic network process, booking amount, risk priority number, supply chain performance

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Authors: Yin Yuanling

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A Deep Feedback Recurrent Neural Network (DFRNN) and Bidirectional Long Short-Term Memory (BiLSTM) are designed to address the problem of low accuracy of traditional relationship extraction models. This method combines a deep feedback-based recurrent neural network (DFRNN) with a bi-directional long short-term memory (BiLSTM) approach. The method combines DFRNN, which extracts local features of text based on deep feedback recurrent mechanism, BiLSTM, which better extracts global features of text, and Self-Attention, which extracts semantic information. Experiments show that the method achieves an F1 value of 76.69% on the CEC dataset, which is 0.0652 better than the BiLSTM+Self-ATT model, thus optimizing the performance of the deep learning method in the event relationship extraction task.

Keywords: event relations, deep learning, DFRNN models, bi-directional long and short-term memory networks

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Authors: Victor Chinecherem Ejeke, Raphael Eze Nnam

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The main objective of this research is to produce biodiesel from waste vegetable oil using activated eggshell waste as solid catalysts. A transesterification reaction was performed for the conversion to biodiesel. Waste eggshells were calcined at 700°C, 800°C and 900°C for a time period of 3hrs for the preparation of the renewable catalyst. The calcined waste eggshell catalyst was characterized using X-Ray Florescence (XRF) Spectroscopy, which revealed CaO as the major constituent (90.86%); this was further confirmed by X-Ray Diffraction (XRD) and Fourier Transform Infrared (FTIR) analyses. The prepared catalyst was used for transesterification reaction and the effects of calcination temperature (700 to 900°C), Deep Eutectic Solvent DES loading (3 to 18 wt. %), Waste Egg Shell (WES) catalyst loading (6 to 14 wt. %) on the conversion to biodiesel were studied. The yield of biodiesel using a waste eggshell catalyst (91%) is comparable to conventional catalyst like sodium hydroxide with a yield of 80-90%. The maximum biodiesel production yield was obtained at a specific oil-to methanol molar ratio of 1:10, a temperature of 65°C and a catalyst loading of 14g-wt%. The biodiesel produced was characterized as being composed of methyl Tetradecanoate (C₁₄H₂₈O₂) 30.92% using the Gas Chromatographic (GC-MS) analysis. The fuel properties of the biodiesel (Flashpoint 138ᵒC) were comparable to commercial diesel, and hence it can be used in compression-ignition engines. The results indicated that the catalysts derived from waste eggshell had high potential to be used as biodiesel production catalysts in transesterification of waste vegetable oil with the advantage of reusability and also not requiring water washing steps.

Keywords: waste vegetable oil, catalyst , biodiesel , waste egg shell

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Authors: Anurak Khrueakham, Tassanee Chanphuthin

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Indigo is a well-known natural blue dye that is used hither to even though synthetic ones are commercially available. The removal of indigo from effluents is difficult due to its resistance towards biodegradation which causes an aquatic environment effect. Fenton process is a reaction between hydrogen peroxide H2O2 and Fe2+ to generate •OH (highly reactive oxidant (E◦= 2.8 V)). Additionally, •OH is non-selective oxidant which is capable of destroying wide range of organic pollutants in water and wastewater. The aims of this research were to investigate the effect of H2O2, Fe2+ and pH on indigo wastewater oxidation by Fenton process. A liter reactor was operated in all experiments. The batch reactor was prepared by filling 1 liter of indigo wastewater. The pH was adjusted to the desired value; then, FeSO4 at predetermined amount was added. Finally, H2O2 was immediately added to start the Fenton’s reaction. The Fenton oxidation of indigo wastewater was operated for 60 minutes. Residual H2O2 was analyzed using titanium oxalate method. The Fe2+ concentration was determined by phenanthroline method. COD was determined using closed-reflux titrimetric method to indicate the removal efficiency. The results showed that at pH 2 increasing the initial ferrous concentration from 0.1 mM to 1 mM enhanced the indigo removal from 36% to 59%. Fenton reaction was rapidly due to the high generation rate of •OH. The degradation of indigo increased with increasing pH up to pH 3. This can be explained that the scavenging effect of the •OH by H+ in the condition of low pH is severe to form an oxonium ion, resulting in decrease the production of •OH and lower the decolorization efficiency of indigo. Increasing the initial H2O2 concentration from 5 mM to 20 mM could enhance the decolorization. The COD removal was increased from 35% to 65% with increasing H2O2 concentration from 5 mM to 20 mM. The generations of •OH were promoted by the increase of initial H2O2 concentration. However, the higher concentration of H2O2 resulted in the reduction of COD removal efficiency. The initial ferrous concentrations were studied in the range of 0.05-15.0 mM. The results found that the COD removals increased with increasing ferrous concentrations. The COD removals were increased from 32% to 65% when increase the ferrous concentration from 0.5 mM to 10.0 mM. However, the COD removal did not significantly change at higher 10.0 mM. This is because •OH yielding was lower level of oxidation, therefore, the COD removals were not improved. According to the studies, the Fenton’s reagents were important factors for COD removal by Fenton process. The optimum condition for COD removal of indigo dye wastewater was 10.0 mM of ferrous, 20 mM of H2O2 and at pH 3.

Keywords: indigo dye, fenton oxidation, wastewater treatment, advanced oxidation processes

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Authors: Mohamad Mahdi Namdari

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In today's rapidly changing and evolving business competition, companies and organizations require advanced and efficient tools to manage their markets and sales networks. Big data analysis, quick response in competitive markets, process and operations optimization, and forecasting customer behavior are among the concerns of executive managers. Artificial intelligence, as one of the emerging technologies, has provided extensive capabilities in this regard. The use of artificial intelligence in market and sales network management can lead to improved efficiency, increased decision-making accuracy, and enhanced customer satisfaction. Specifically, AI algorithms can analyze vast amounts of data, identify complex patterns, and offer strategic suggestions to improve sales performance. However, many companies are still distant from effectively leveraging this technology, and those that do face challenges in fully exploiting AI's potential in market and sales network management. It appears that the general public's and even the managerial and academic communities' lack of knowledge of this technology has caused the managerial structure to lag behind the progress and development of artificial intelligence. Additionally, high costs, fear of change and employee resistance, lack of quality data production processes, the need for updating structures and processes, implementation issues, the need for specialized skills and technical equipment, and ethical and privacy concerns are among the factors preventing widespread use of this technology in organizations. Clarifying and explaining this technology, especially to the academic, managerial, and elite communities, can pave the way for a transformative beginning. The aim of this research is to elucidate the capacities of artificial intelligence in market and sales network management, identify its opportunities and benefits, and examine the existing challenges and obstacles. This research aims to leverage AI capabilities to provide a framework for enhancing market and sales network performance for managers. The results of this research can help managers and decision-makers adopt more effective strategies for business growth and development by better understanding the capabilities and limitations of artificial intelligence.

Keywords: artificial intelligence, market management, sales network, big data analysis, decision-making, digital marketing

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Authors: Sinan Alsaadi, Mustafa Merdan

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Communication and access systems can be made with many methods in today’s world. These systems are such standards as Wifi, Wimax, Bluetooth, GPS and GPRS. Devices which use these standards also use system resources excessively in direct proportion to their transmission speed. However, large-scale data communication is not always needed. In such cases, a technology which will use system resources as little as possible and support smart network topologies has been needed in order to enable the transmissions of such small packet data and provide the control for this kind of devices. IEEE issued 802.15.4 standard upon this necessity and enabled the production of Zigbee protocol which takes these standards as its basis and devices which support this protocol. In our project, this communication protocol was preferred. The aim of this study is to provide the immediate data transmission of our robot from the field within the scope of the project. In addition, making the communication with the robot through Zigbee Protocol has also been aimed. While sitting on the computer, obtaining the desired data from the region where the robot is located has been taken as the basis. Arduino Uno R3 microcontroller which provides the control mechanism, 1298 shield as the motor driver.

Keywords: ZigBee, wireless network, remote monitoring, smart home, agricultural industry

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Authors: P. Neeraj, Anurag Sharma, Harsukhpreet Singh

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In current years, Brain-Computer Interface (BCI) scheme based on steady-state Visual Evoked Potential (SSVEP) have earned much consideration. This study tries to evolve an SSVEP based BCI scheme that can regulate any gadget mock-up in two unique positions ON and OFF. In this paper, two distinctive gleam frequencies in low-frequency part were utilized to evoke the SSVEPs and were shown on a Liquid Crystal Display (LCD) screen utilizing Lab View. Two stimuli shading, Yellow, and Blue were utilized to prepare the system in SSVEPs. The Electroencephalogram (EEG) signals recorded from the occipital part. Elements of the brain were separated by utilizing discrete wavelet Transform. A prominent system for multilayer system diverse Neural Network Algorithm (NNA), is utilized to characterize SSVEP signals. During training of the network with diverse calculation Regression plot results demonstrated that when Levenberg-Marquardt preparing calculation was utilized the exactness turns out to be 93.9%, which is superior to another training algorithm.

Keywords: brain computer interface, electroencephalography, steady-state visual evoked potential, wavelet transform, neural network

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Authors: Andrey A. Chernousov, Ben Y. B. Chan

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The thermal behavior of a large-scale, phase change material (PCM) enhanced building envelope system was studied in regard to the need for pre-fabricated construction in subtropical regions. The proposed large-scale envelope consists of a reinforced aluminum skin, insulation core, phase change material and reinforced gypsum board. The PCM impact on an energy efficiency of an enveloped room was resolved by validation of the Energy Plus numerical scheme and optimization of a smart material location in the core. The PCM location was optimized by a minimization method of a cooling energy demand. It has been shown that there is good agreement between the test and simulation results. The optimal location of the PCM layer in Hong Kong summer conditions has been then recomputed for core thicknesses of 40, 60 and 80 mm. A non-dimensional value of the optimal PCM location was obtained to be same for all the studied cases and the considered external and internal conditions.

Keywords: thermal performance, phase change material, energy efficiency, PCM optimization

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Authors: Chen Chih-Cheng, Chen I-Cheng, Lee Yung-Tan, Kuo Yen-Whea, Yu Chin-Hung

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The purpose of this study is to construct a model for best spike player selection in a top volleyball tournament of the world. Data consisted of the records of 2013 World Grand Prix declared by International Volleyball Federation (FIVB). Simple Multiple-Attribute Rating Technique (SMART) was used for optimal decision-making model on the best spike player selection. The research results showed that the best spike player ranking by SMART is different than the ranking by FIVB. The results demonstrated the effectiveness and feasibility of the proposed model.

Keywords: simple multiple-attribute rating technique, World Grand Prix, best spike player, International Volleyball Federation

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Authors: A. M. Iordache, S. M. Iordache, V. Barna, M. Elisa, I. C. Vasiliu, C. R. Stefan, I. Chilibon, I. Stamatin, S. Caramizoiu, C. E. A. Grigorescu

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The general objective of the presentation is the development of an e-tongue like sensor based on modified screen printed electrode (SPE) structures with a receptor part made of porphyrins/metalloporphyrins chemically bound to graphene (the sensitive assembly) to act as antennas and “capture” the histamine molecules. Using a single, ultra-sensitive electrochemical sensor, we measured the concentration of histamine, a compound which is strongly connected to the level of freshness in foods (the caution level of histamine is 50 ppm, whereas the maximum accepted levels range from 200 ppm to 500 ppm). Our approach for the chemical immobilization of the porphyrins onto the surface of the graphenes was via substitution reaction: a solution of graphene in SOCl2 was heated to 800C for 6 hours. Upon cooling, the metallo-porphyrins were added and ultrasonicated for 4 hours. The solution was then allowed to cool to room temperature and then centrifuged in order to separate the deposit. The sensitive assembly was drop casted onto the carbon SPE and cyclic voltammetry was performed in the presence of histamine. The reaction is quasi-reversible and the sensor showed an oxidation potential for histamine at 600 mV. The results indicate a linear dependence of concentration of histamine as function of intensity. The results are reproducible; however the chemical stability of the sensitive assembly is low.

Keywords: histamine, cyclic voltammetry, metallo-porphyrin, food freshness

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Authors: Eduardo F. Mello, Peter H. Bauer

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The primary focus of this paper is the generation of energy-optimal speed trajectories for heterogeneous electric vehicle platoons in urban driving conditions. Optimal speed trajectories are generated for individual vehicles and for an entire platoon under the assumption that they can be executed without errors, as would be the case for self-driving vehicles. It is then shown that the optimization for the “average vehicle in the platoon” generates similar transportation energy savings to optimizing speed trajectories for each vehicle individually. The introduced approach only requires the lead vehicle to run the optimization software while the remaining vehicles are only required to have adaptive cruise control capability. The achieved energy savings are typically between 30% and 50% for stop-to-stop segments in cities. The prime motivation of urban platooning comes from the fact that urban platoons efficiently utilize the available space and the minimization of transportation energy in cities is important for many reasons, i.e., for environmental, power, and range considerations.

Keywords: electric vehicles, energy efficiency, optimization, platooning, self-driving vehicles, urban traffic

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Authors: Zheng Zhihao

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Recently, the Knowledge Graph Framework Network (KGCN) has developed powerful capabilities in knowledge representation and reasoning tasks. However, traditional KGCN often uses a fixed weight mechanism when aggregating information, failing to make full use of rich structural information, resulting in a certain expression ability of node representation and easily causing over-smoothing problems. In order to solve these challenges, the paper proposes an distinct graph neural network model called KGCN-STAR (Knowledge Graph Convolutional Network with Structural Adaptive Receptive Fields). This model dynamically adjusts the perception of each node by introducing a structural adaptive receptive field. Wild range and a subgraph aggregator is designed to capture local structural information more effectively. Experimental results show that KGCN-STAR shows significant performance improvement on multiple knowledge graph data sets, especially showing considerable capabilities in the task of representation learning of complex structures.

Keywords: knowledge graph(KG), graph neural networks (GNN), structural adaptive receptive fields, information aggregation

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Authors: Joseph George, Anne Kotteswara Roa

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Skin disease is one of the most common and popular kinds of health issues faced by people nowadays. Skin cancer (SC) is one among them, and its detection relies on the skin biopsy outputs and the expertise of the doctors, but it consumes more time and some inaccurate results. At the early stage, skin cancer detection is a challenging task, and it easily spreads to the whole body and leads to an increase in the mortality rate. Skin cancer is curable when it is detected at an early stage. In order to classify correct and accurate skin cancer, the critical task is skin cancer identification and classification, and it is more based on the cancer disease features such as shape, size, color, symmetry and etc. More similar characteristics are present in many skin diseases; hence it makes it a challenging issue to select important features from a skin cancer dataset images. Hence, the skin cancer diagnostic accuracy is improved by requiring an automated skin cancer detection and classification framework; thereby, the human expert’s scarcity is handled. Recently, the deep learning techniques like Convolutional neural network (CNN), Deep belief neural network (DBN), Artificial neural network (ANN), Recurrent neural network (RNN), and Long and short term memory (LSTM) have been widely used for the identification and classification of skin cancers. This survey reviews different DL techniques for skin cancer identification and classification. The performance metrics such as precision, recall, accuracy, sensitivity, specificity, and F-measures are used to evaluate the effectiveness of SC identification using DL techniques. By using these DL techniques, the classification accuracy increases along with the mitigation of computational complexities and time consumption.

Keywords: skin cancer, deep learning, performance measures, accuracy, datasets

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Authors: Ahmed Abdolkhalig, Rastko Zivanovic

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This paper evaluates the Total Vector Error of an estimated Phasor as define in IEEE C37.118 standard within different medium access in Local Area Networks (LAN). Three different LAN models (CSMA/CD, CSMA/AMP, and Switched Ethernet) are evaluated. The Total Vector Error of the estimated Phasor has been evaluated for the effect of Nodes Number under the standardized network Band-width values defined in IEC 61850-9-2 communication standard (i.e. 0.1, 1, and 10 Gbps).

Keywords: phasor, local area network, total vector error, IEEE C37.118, IEC 61850

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Authors: Mona Heydari, Ehsan Motamedian, Seyed Abbas Shojaosadati

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Prediction of perturbations after genetic manipulation (especially gene knockout) is one of the important challenges in systems biology. In this paper, a new algorithm is introduced that integrates microarray data into the metabolic model. The algorithm was used to study the change in the cell phenotype after knockout of Gss gene in Escherichia coli BW25113. Algorithm implementation indicated that gene deletion resulted in more activation of the metabolic network. Growth yield was more and less regulating gene were identified for mutant in comparison with the wild-type strain.

Keywords: metabolic network, gene knockout, flux balance analysis, microarray data, integration

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Authors: Chaozhi Yang, Xinzhong Chen, Guoqing Huang

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Inerter is a two-terminal inertial element that can produce apparent mass far larger than its physical mass. A double tuned inertial mass damper (DTIMD) is developed by combining a spring with an inerter and a dashpot in series to replace the viscous damper of a tuned mass damper (TMD), and its performance is investigated. Firstly, the DTIMD is optimized numerically with H∞ and H2 methods considering the system’s damping based on the single-degree-of-freedom (SDOF)-DTIMD system, and the optimal structural parameters are obtained. Then, compared with a TMD, the control effect of the DTIMD with the optimal structural parameters on wind-induced vibration of a wind turbine in downwind direction under the shutdown condition is studied. The results demonstrate that the vibration suppression of the DTIMD is superior than that of a TMD at the same mass ratio. And at the identical vibration suppression, the tuned mass of the DTIMD can be reduced by up to 40% compared with a TMD.

Keywords: wind-induced vibration, vibration control, inerter, tuned mass damper, damped system

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Authors: Sakshi Dhumale, Madhushree C., Amba Shetty

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The effect of station density on the geostatistical prediction of groundwater levels is of critical importance to ensure accurate and reliable predictions. Monitoring station density directly impacts the accuracy and reliability of geostatistical predictions by influencing the model's ability to capture localized variations and small-scale features in groundwater levels. This is particularly crucial in regions with complex hydrogeological conditions and significant spatial heterogeneity. Insufficient station density can result in larger prediction uncertainties, as the model may struggle to adequately represent the spatial variability and correlation patterns of the data. On the other hand, an optimal distribution of monitoring stations enables effective coverage of the study area and captures the spatial variability of groundwater levels more comprehensively. In this study, we investigate the effect of station density on the predictive performance of groundwater levels using the geostatistical technique of Ordinary Kriging. The research utilizes groundwater level data collected from 121 observation wells within the semi-arid Berambadi watershed, gathered over a six-year period (2010-2015) from the Indian Institute of Science (IISc), Bengaluru. The dataset is partitioned into seven subsets representing varying sampling densities, ranging from 15% (12 wells) to 100% (121 wells) of the total well network. The results obtained from different monitoring networks are compared against the existing groundwater monitoring network established by the Central Ground Water Board (CGWB). The findings of this study demonstrate that higher station densities significantly enhance the accuracy of geostatistical predictions for groundwater levels. The increased number of monitoring stations enables improved interpolation accuracy and captures finer-scale variations in groundwater levels. These results shed light on the relationship between station density and the geostatistical prediction of groundwater levels, emphasizing the importance of appropriate station densities to ensure accurate and reliable predictions. The insights gained from this study have practical implications for designing and optimizing monitoring networks, facilitating effective groundwater level assessments, and enabling sustainable management of groundwater resources.

Keywords: station density, geostatistical prediction, groundwater levels, monitoring networks, interpolation accuracy, spatial variability

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Authors: Chien-Ho Lin, Ho-Ching Yang, Barbara Knowlton, Shin-Leh Huang, Ming-Chang Chiang

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Practicing motor tasks arranged in an interleaved order (interleaved practice, or IP) generally leads to better learning than practicing tasks in a repetitive order (repetitive practice, or RP), an example of how desirable difficulty during practice benefits learning. Greater difficulty during practice, e.g. IP, is associated with greater brain activity measured by higher blood-oxygen-level dependent (BOLD) signal in functional magnetic resonance imaging (fMRI) in the sensorimotor areas of the brain. In this study resting-state fMRI was applied to investigate whether increase in resting-state brain connectivity immediately after practice predicts the benefit of desirable difficulty to motor learning. 26 healthy adults (11M/15F, age = 23.3±1.3 years) practiced two sets of three sequences arranged in a repetitive or an interleaved order over 2 days, followed by a retention test on Day 5 to evaluate learning. On each practice day, fMRI data were acquired in a resting state after practice. The resting-state fMRI data was decomposed using a group-level spatial independent component analysis (ICA), yielding 9 independent components (IC) matched to the precuneus network, primary visual networks (two ICs, denoted by I and II respectively), sensorimotor networks (two ICs, denoted by I and II respectively), the right and the left frontoparietal networks, occipito-temporal network, and the frontal network. A weighted resting-state functional connectivity (wRSFC) was then defined to incorporate information from within- and between-network brain connectivity. The within-network functional connectivity between a voxel and an IC was gauged by a z-score derived from the Fisher transformation of the IC map. The between-network connectivity was derived from the cross-correlation of time courses across all possible pairs of ICs, leading to a symmetric nc x nc matrix of cross-correlation coefficients, denoted by C = (pᵢⱼ). Here pᵢⱼ is the extremum of cross-correlation between ICs i and j; nc = 9 is the number of ICs. This component-wise cross-correlation matrix C was then projected to the voxel space, with the weights for each voxel set to the z-score that represents the above within-network functional connectivity. The wRSFC map incorporates the global characteristics of brain networks measured by the between-network connectivity, and the spatial information contained in the IC maps measured by the within-network connectivity. Pearson correlation analysis revealed that greater IP-minus-RP difference in wRSFC was positively correlated with the RP-minus-IP difference in the response time on Day 5, particularly in brain regions crucial for motor learning, such as the right dorsolateral prefrontal cortex (DLPFC), and the right premotor and supplementary motor cortices. This indicates that enhanced resting brain connectivity during the early phase of memory consolidation is associated with enhanced learning following interleaved practice, and as such wRSFC could be applied as a biomarker that measures the beneficial effects of desirable difficulty on motor sequence learning.

Keywords: desirable difficulty, functional magnetic resonance imaging, independent component analysis, resting-state networks

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Authors: Alicja Starczewska, Aleksander Nawrat, Krzysztof Daniec, Jarosław Homa, Kacper Hołda

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Border Gateway Protocol is the main routing protocol that enables routing establishment between all autonomous systems, which are the basic administrative units of the internet. Due to the poor protection of BGP, it is important to use additional BGP security systems. Many solutions to this problem have been proposed over the years, but none of them have been implemented on a global scale. This article describes a system capable of building images of real-time BGP network topology in order to detect BGP anomalies. Our proposal performs a detailed analysis of BGP messages that come into local network cards supplemented by information collected by remote collectors in different localizations.

Keywords: BGP, BGP hijacking, cybersecurity, detection

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Authors: P. R. Prabhu, S. M. Kulkarni, S. S. Sharma, K. Jagannath, Achutha Kini U.

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In the present study, response surface methodology has been used to optimize turn-assisted deep cold rolling process of AISI 4140 steel. A regression model is developed to predict surface hardness and surface roughness using response surface methodology and central composite design. In the development of predictive model, deep cold rolling force, ball diameter, initial roughness of the workpiece, and number of tool passes are considered as model variables. The rolling force and the ball diameter are the significant factors on the surface hardness and ball diameter and numbers of tool passes are found to be significant for surface roughness. The predicted surface hardness and surface roughness values and the subsequent verification experiments under the optimal operating conditions confirmed the validity of the predicted model. The absolute average error between the experimental and predicted values at the optimal combination of parameter settings for surface hardness and surface roughness is calculated as 0.16% and 1.58% respectively. Using the optimal processing parameters, the hardness is improved from 225 to 306 HV, which resulted in an increase in the near surface hardness by about 36% and the surface roughness is improved from 4.84µm to 0.252 µm, which resulted in decrease in the surface roughness by about 95%. The depth of compression is found to be more than 300µm from the microstructure analysis and this is in correlation with the results obtained from the microhardness measurements. Taylor Hobson Talysurf tester, micro Vickers hardness tester, optical microscopy and X-ray diffractometer are used to characterize the modified surface layer.

Keywords: hardness, response surface methodology, microstructure, central composite design, deep cold rolling, surface roughness

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Authors: Rehin Sulay, Anandhu Krishna, Jintumol Mathew, Vibin Ipe Thomas

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N-Nitrosodimethyl amine, the simplest member of the N-Nitrosoamine family, is a carcinogenic and mutagenic agent that has gained considerable research interest owing to its toxic nature. Ozonation of industrially important hydrazines such as unsymmetrical dimethylhydrazine (UDMH) or monomethylhydrazine (MMH) has been associated with NDMA formation and accumulation in the environment. UDMH/MMH - ozonation also leads to several other transformation products such as acetaldehyde dimethyl hydrazone (ADMH), tetramethyl tetra azene (TMT), diazomethane, methyl diazene, etc, which can be either precursors or competitors for NDMA formation.In this work, we explored the formation mechanism of ADMH and TMT from UDMH-ozonation and their further oxidation to NDMA using the second-order Moller Plesset perturbation theory employing the 6-311G(d) basis set. We have also investigated how MMH selectively forms methyl diazene and diazomethane under normal conditions and NDMA in the presence of excess ozone. Our calculations indicate that the reactions proceed via an initial H abstraction from the hydrazine –NH2 group followed by the oxidation of the generated N-radical species. The formation of ADMH from the UDMH-ozone reaction involves an acetaldehyde intermediate, which then reacts with a second UDMH molecule to generate ADMH. The preferable attack of ozone molecule on N=C bond of ADMH generates DMAN intermediate, which subsequently undergoes oxidation to form NDMA. Unlike other transformation products, TMT formation occurs via the dimerization of DMAN. Though there exist a N=N bonds in the TMT, which are preferable attacking sites for ozone, experimental studies show the lower yields of NDMA formation, which corroborates with the high activation barrier required for the process(42kcal/mol).Overall, our calculated results agree well with the experimental observations and rate constants. Computational calculations bring insights into the electronic nature and kinetics of the elementary reactions of this pathway, enabled by computed energies of structures that are not possible to access experimentally.

Keywords: reaction mechanism, ozonation, substituted hydrazine, transition state

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Authors: Harminder Kaur, Manpreet Kaur, Akanksha Kapila, Reenu

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Schiff base with general formula H₂L was derived from condensation of o-vanillin and 3-nitro-o-phenylenediamine. This Schiff base was used for the synthesis of organotin(IV) complexes with general formula R₂SnL [R=Phenyl or n-octyl] using equimolar quantities. Elemental analysis UV-Vis, FTIR, and multinuclear spectroscopic techniques (¹H, ¹³C, and ¹¹⁹Sn) NMR were carried out for the characterization of the synthesized complexes. These complexes were coloured and soluble in polar solvents. Computational studies have been performed to obtain the details of the geometry and electronic structures of ligand as well as complexes. Geometry of the ligands and complexes have been optimized at the level of Density Functional Theory with B3LYP/6-311G (d,p) and B3LYP/MPW1PW91 respectively followed by vibrational frequency analysis using Gaussian 09. Observed ¹¹⁹Sn NMR chemical shifts of one of the synthesized complexes showed tetrahedral geometry around Tin atom which is also confirmed by DFT. HOMO-LUMO energy distribution was calculated. FTIR, ¹HNMR and ¹³CNMR spectra were also obtained theoretically using DFT. Further IRC calculations were employed to determine the transition state for the reaction and to get the theoretical information about the reaction pathway. Moreover, molecular docking studies can be explored to ensure the anticancer activity of the newly synthesized organotin(IV) complexes.

Keywords: DFT, molecular docking, organotin(IV) complexes, o-vanillin, 3-nitro-o-phenylenediamine

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