Search results for: corporate financial performance

Authors: Bilal Murtaza, Xiaoyu Li, Liming Dong, Muhammad Kashif Saleemi, Gen Li, Bowen Jin, Lili Wang, Yongping Xu

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Fusarium spp. produce numerous mycotoxins, such as zearalenone (ZEN), deoxynivalenol (DON), and its acetylated compounds, 3-acetyl-deoxynivalenol (3-ADON) and 15-acetyl-deoxynivalenol (15-ADON) (15-ADON). In a co-culture system, the soil-derived Escherichia marmotae strain degrades ZEN and DON into 3-keto-DON and DOM-1 via enzymatic deep-oxidation. When pure mycotoxins were subjected to Escherichia marmotae in culture flasks, degradation, and detoxification were also attained. DON and ZEN concentrations, ambient pH, incubation temperatures, bacterium concentrations, and the impact of acid treatment on degradation were all evaluated. The results of the ELISA and high-performance liquid chromatography-electrospray ionization-high resolution mass spectrometry (HPLC-ESI-HRMS) tests demonstrated that the concentration of mycotoxins exposed to Escherichia marmotae was significantly lower than the control. ZEN levels were reduced by 43.9%, while zearalenone sulfate ([M/z 397.1052 C18H21O8S1) was discovered as a derivative of ZEN converted by microbes to a less toxic molecule. Furthermore, Escherichia marmotae appeared to metabolize DON 35.10% into less toxic derivatives (DOM-1 at m/z 281 of [DON - O]+ and 3-keto-DON at m/z 295 of [DON - 2H]+). These results show that Escherichia marmotae can reduce Fusarium mycotoxins production, degrade pure mycotoxins, and convert them to less harmful compounds, opening up new possibilities for study and innovation in mycotoxin detoxification.

Keywords: mycotoxins, zearalenone, deoxynivalenol, bacterial degradation

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Authors: Xin Chen, Xinyong Li, Qidong Zhao, Dong Wang

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A new coupling system was constructed by combining photo-electrochemical cell with electro-fenton cell (PEC-EF). The electrode material in this system was derived from MnyFe₁₋yCo Prussian-Blue-Analog (PBA). Mn₀.₄Fe₀.₆Co₀.₆₇-N@C spin-coated on carbon paper behaved as the gas diffusion cathode and Mn₀.₄Fe₀.₆Co₀.₆₇O₂.₂ spin-coated on fluorine-tin oxide glass (FTO) as anode. The two separated cells could degrade Sulfamethoxazole (SMX) simultaneously and some coupling mechanisms by PEC and EF enhancing the degradation efficiency were investigated. The continuous on-site generation of H₂O₂ at cathode through an oxygen reduction reaction (ORR) was realized over rotating ring-disk electrode (RRDE). The electron transfer number (n) of the ORR with Mn₀.₄Fe₀.₆Co₀.₆₇-N@C was 2.5 in the selected potential and pH range. The photo-electrochemical properties of Mn₀.₄Fe₀.₆Co₀.₆₇O₂.₂ were systematically studied, which displayed good response towards visible light. The photoinduced electrons at anode can transfer to cathode for further use. Efficient photo-electro-catalytic performance was observed in degrading SMX. Almost 100% SMX removal was achieved in 120 min. This work not only provided a highly effective technique for antibiotic treatment but also revealed the synergic effect between PEC and EF.

Keywords: electro-fenton, photo-electrochemical, synergic effect, sulfamethoxazole

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Authors: Niklas Panten, Eberhard Abele

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This paper presents a novel approach for real-time and near-optimal control of industrial smart grids by deep reinforcement learning (DRL). To achieve highly energy-efficient factory systems, the energetic linkage of machines, technical building equipment and the building itself is desirable. However, the increased complexity of the interacting sub-systems, multiple time-variant target values and stochastic influences by the production environment, weather and energy markets make it difficult to efficiently control the energy production, storage and consumption in the hybrid industrial smart grids. The studied deep reinforcement learning approach allows to explore the solution space for proper control policies which minimize a cost function. The deep neural network of the DRL agent is based on a multilayer perceptron (MLP), Long Short-Term Memory (LSTM) and convolutional layers. The agent is trained within multiple Modelica-based factory simulation environments by the Advantage Actor Critic algorithm (A2C). The DRL controller is evaluated by means of the simulation and then compared to a conventional, rule-based approach. Finally, the results indicate that the DRL approach is able to improve the control performance and significantly reduce energy respectively operating costs of industrial smart grids.

Keywords: industrial smart grids, energy efficiency, deep reinforcement learning, optimal control

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Authors: Amenaghawon Clement Osemwinyen

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The Activity, Student-centred, Experiment, Improvisation - Plan, Do, See, Improve (ASEI-PDSI) method championed by the Strengthening Mathematics And Science Education (SMASE) - Nigeria Project is an attempt to improve the quality of mathematics, which has consistently declined over the years in both public primary and secondary schools across the country. The study thus assessed the ASEI-PDSI method on students’ attitudes and achievement in junior secondary schools (JSS) mathematics in FCT-Abuja. A survey research design was adopted, and 100 mathematics teachers using a stratified random sampling method were used for the study. The data were collected using structured questionnaires and analyzed using descriptive statistics. The findings showed that the ASEI-PDSI method had significantly improved the attitudes of students toward mathematics. The study also revealed that the ASEI-PDSI method significantly influenced junior secondary school (JSS) students’ mathematics achievement. Amongst the recommendations were that teachers should be encouraged to adopt the ASEI-PDSI method in teaching and learning mathematics in order to create a mathematically stimulating classroom environment which could advertently influence junior secondary school (JSS) students’ attitude and academic performance in mathematics. Also, regular in-service training programs should be organized by stakeholders (government and other interest groups) so as to improve the teaching strategies of teachers, mostly as they affect the ASEI-PDSI method.

Keywords: achievement, ASEI-PDSI method, attitude, mathematics, SMASE

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Authors: Bin Bian, Liang Wang

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A hydraulic spherical motion mechanism (HSMM) is proposed. Unlike traditional systems using serial or parallel mechanisms for multi-DOF rotations, the HSMM is capable of implementing continuous 2-DOF rotational motions in a single joint without the intermediate transmission mechanisms. It has some advantages of compact structure, low inertia and high stiffness. However, as HSMM is a nonlinear and multivariable system, it is very complicate to realize accuracy control. Therefore, an augmented active disturbance rejection controller (ADRC) is proposed in this paper. Compared with the traditional PD control method, three compensation items, i.e., dynamics compensation term, disturbance compensation term and nonlinear error elimination term, are added into the proposed algorithm to improve the control performance. The ADRC algorithm aims at offsetting the effects of external disturbance and realizing accurate control. Euler angles are applied to describe the orientation of rotor. Lagrange equations are utilized to establish the dynamic model of the HSMM. The stability of this algorithm is validated with detailed derivation. Simulation model is formulated in Matlab/Simulink. The results show that the proposed control algorithm has better competence of trajectory tracking in the presence of uncertainties.

Keywords: hydraulic spherical motion mechanism, dynamic model, active disturbance rejection control, trajectory tracking

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Authors: Aderonke A. Okoya, Oluwaseun A. Somoye, Omotayo S. Amuda, Ifeanyi E. Ofoezie

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This study assessed the efficiency of Hydroxyapatite Powder (HAP) in the removal of dyes in wastewater in comparison with Commercial Activated Carbon (CAC). This was with a view to developing cost effective method that could be more environment friendly. The HAP and CAC were used as adsorbent while Indigo dye was used as the adsorbate. The batch adsorption experiment was carried out by varying initial concentrations of the indigo dye, contact time and adsorbent dosage. Adsorption efficiency was classified by adsorption Isotherms using Langmuir, Freundlich and D-R isotherm models. Physicochemical parameters of a textile industry wastewater were determined before and after treatment with the adsorbents. The results from the batch experiments showed that at initial concentration of 125 mg/L of adsorbate in simulated wastewater, 0.9276 ± 0.004618 mg/g and 3.121 ± 0.006928 mg/g of indigo adsorbed per unit time (qt) of HAP and CAC respectively. The ratio of HAP to CAC required for the removal of indigo dye in simulated wastewater was 2:1. The isotherm model of the simulated wastewater fitted well to Freundlich model, the adsorption intensity (1/n) presented 1.399 and 0.564 for HAP and CAC, respectively. This revealed that the HAP had weaker bond than the electrostatic interactions which were present in CAC. The values of some physicochemical parameters (acidity, COD, Cr, Cd) of textile wastewater when treated with HAP decreased. The study concluded that HAP, an environment-friendly adsorbent, could be effectively used to remove dye from textile industrial wastewater with added advantage of being regenerated.

Keywords: adsorption isotherm, commercial activated carbon, hydroxyapatite powder, indigo dye, textile wastewater

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Authors: Claire Xu, Welton Wang, Manasvi Pinnaka, Anqi Pan, Michael Han

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Hospitalizations account for one-third of the total health care spending in the US. Early risk detection and intervention can reduce this high cost and increase the satisfaction of both patients and physicians. Due to the lack of awareness of the potential arising risks in home environment, the opportunities for patients to seek early actions of clinical visits are dramatically reduced. This research aims to offer a highly personalized remote patients monitoring and risk assessment AI framework to identify the potentially preventable hospitalization for both acute as well as chronic diseases. A hybrid-AI framework is trained with data from clinical setting, patients surveys, as well as online databases. 20+ risk factors are analyzed ranging from activities, biometric info, demographic info, socio-economic info, hospitalization history, medication info, lifestyle info, etc. The AI model yields high performance of 87% accuracy and 88 sensitivity with 20+ features. This hybrid-AI framework is proven to be effective in identifying the potentially preventable hospitalization. Further, the high indicative features are identified by the models which guide us to a healthy lifestyle and early intervention suggestions.

Keywords: hospitalization prevention, machine learning, remote patient monitoring, risk prediction

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Authors: Nagham Ismail, Djamel Ouahrani

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Thermal resilience in the built environment reflects the building's capacity to adapt to extreme climate changes. In hot climates, power outages in office buildings pose risks to the health and productivity of workers. Therefore, it is of interest to quantify the thermal resilience of office buildings by developing a user-friendly simplified model. This simplified model begins with creating an assessment metric of thermal resilience that measures the duration between the power outage and the point at which the thermal habitability condition is compromised, considering different power interruption times (morning, noon, and afternoon). In this context, energy simulations of an office building are conducted for Qatar's summer weather by changing different parameters that are related to the (i) wall characteristics, (ii) glazing characteristics, (iii) load, (iv) orientation and (v) air leakage. The simulation results are processed using SPSS to derive linear regression equations, aiding stakeholders in evaluating the performance of commercial buildings during different power interruption times. The findings reveal the significant influence of glazing characteristics on thermal resilience, with the morning power outage scenario posing the most detrimental impact in terms of the shortest duration before compromising thermal resilience.

Keywords: thermal resilience, thermal envelope, energy modeling, building simulation, thermal comfort, power disruption, extreme weather

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Authors: E. H. Hegazy, M. A. Mosaad, A. A. Tawfik, A. A. Hassan, M. Abbas

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The rapid depletion of petroleum reserves and rising oil prices has led to the search for alternative fuels. A promising alternative fuel Jatropha Methyl Easter, JME, has drawn the attention of researchers in recent times as a high potential substrate for production of biodiesel fuel. In this paper, the combustion, performance and emission characteristics of a single cylinder diesel engine when fuelled with JME, diesel oil and natural gas are evaluated experimentally and theoretically. The experimental results showed that the thermal and volumetric efficiency of diesel engine is higher than Jatropha biodiesel engine. The specific fuel consumption, exhaust gas temperature, HC, CO2 and NO were comparatively higher in Jatropha biodiesel, while CO emission is appreciable decreased. CFD investigation was carried out in the present work to compare diesel fuel oil and JME. The CFD simulation offers a powerful and convenient way to help understanding physical and chemical processes involved internal combustion engines for diesel oil fuel and JME fuel. The CFD concluded that the deviation between diesel fuel pressure and JME not exceeds 3 bar and the trend for compression pressure almost the same, also the temperature deviation between diesel fuel and JME not exceeds 40 k and the trend for temperature almost the same. Finally the maximum heat release rate of JME is lower than that of diesel fuel. The experimental and CFD investigation indicated that the Jatropha biodiesel can be used instead of diesel fuel oil with safe engine operation.

Keywords: dual fuel diesel engine, natural gas, Jatropha Methyl Easter, volumetric efficiency, emissions, CFD

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Authors: Muhammad Afzal Malik

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Traditional Learning & Distance based learning are the two systems prevailing in Pakistan. These systems affect the level of education standard. The purpose of this study was to compare the traditional classroom learning and distance learning in Pakistan: (a) To explore the effectiveness of the traditional to Distance learning in Pakistan; (b) To identify the factors that affect traditional and distance learning. This review found that, on average, students in traditional classroom conditions performed better than those receiving education in and distance learning. The difference between student outcomes for traditional Classroom and distance learning classes —measured as the difference between treatment and control means, divided by the pooled standard deviation— was larger in those studies contrasting conditions that blended elements of online and face-to-face instruction with conditions taught entirely face-to-face. This research was conducted to highlight the impact of distance learning education system on education standard. The education standards were institutional support, course development, learning process, student support, faculty support, evaluation and assessment. A well developed questionnaire was administered and distributed among 26 faculty members of GCET, H-9 and Virtual University of Pakistan from each. Data was analyzed through correlation and regression analysis. Results confirmed that there is a significant relationship and impact of DLE system on education standards. This will also provide baseline for future research. It will add value to the existing body of knowledge.

Keywords: distance learning education, higher education, education standards, student performance

Procedia PDF Downloads 262

Authors: Renata Adriana Labanca, Gabriela Rezende Costa, Nilton de Oliveira Couto e Silva, José Marcos Gontijo Mandarino, Rodrigo Santos Leite, Nilson César Castanheira Guimarães, Roberto Gonçalves Junqueira

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The objective of this study was to evaluate the differences in composition between six brands of conventional soybean and six genetically modified cultivars (GM), all of them from Minas Gerais State, Brazil. We focused on the isoflavones profile and mineral content questioning the substantial equivalence between conventional and GM organisms. The statement of compliance label for conventional grains was verified for the presence of genetic modified genes by real time polymerase chain reaction (PCR). We did not detect the presence of the 35S promoter in commercial samples, indicating the absence of transgene insertion. For mineral analysis, we used the method of inductively coupled plasma-optical emission spectrometry (ICP-OES). Isoflavones quantification was performed by high performance liquid chromatography (HPLC). The results showed no statistical difference between the conventional and transgenic soybean groups concerning isoflavone content and mineral composition. The concentration of potassium, the main mineral component of soy, was the highest in conventional soybeans compared to that in GM soy, while GM samples presented the highest concentrations of iron.

Keywords: glycine max, genetically modified organism, bioactive compounds, ICP-OES, HPLC

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Authors: Seongcheol Kwon, Jeongmin Kim, Kwang Ryel Ryu

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We assume an IoT-based smart-home environment where the on-off status of each of the electrical appliances including the room lights can be recognized in a real time by monitoring and analyzing the smart meter data. At any moment in such an environment, we can recognize what the household or the user is doing by referring to the status data of the appliances. In this paper, we focus on a smart-home service that is to activate a robot vacuum cleaner at right time by recognizing the user situation, which requires a situation-aware model that can distinguish the situations that allow vacuum cleaning (Yes) from those that do not (No). We learn as our candidate models a few classifiers such as naïve Bayes, decision tree, and logistic regression that can map the appliance-status data into Yes and No situations. Our training and test data are obtained from simulations of user behaviors, in which a sequence of user situations such as cooking, eating, dish washing, and so on is generated with the status of the relevant appliances changed in accordance with the situation changes. During the simulation, both the situation transition and the resulting appliance status are determined stochastically. To compare the performances of the aforementioned classifiers we obtain their learning curves for different types of users through simulations. The result of our empirical study reveals that naïve Bayes achieves a slightly better classification accuracy than the other compared classifiers.

Keywords: situation-awareness, smart home, IoT, machine learning, classifier

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Authors: Hummad Ch. Umar

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Most of the developing economies, including Pakistan, are confronted with high level of external debt which is adversely affecting their economic performance. The hypothesis of debt overhang is often used to assess the negative relationship between foreign debt and the economic growth of the indebted country. As first objective of the present study, this hypothesis is tested by using Pooled OLS (POLS), Generalized Method of Moment (GMM), Random Effect (RE), and Fixed effect (FE) techniques. As second objective, the study uses the concept of debt Laffer Curve to determine the eligibility condition of the indebted countries for the relief programs. According to this approach, countries lying on the right side of the Laffer Curve are stated to be trapped in the strong debt overhang making them unable to come out of the vicious circle of low growth and high foreign debt. The empirical analysis confirms that only two countries out of twenty two completely fulfill the conditions of being eligible for the debt relief. All other countries continue to face debt burden of different magnitudes. The study further confirms that the debt relief alone is not sufficient for overcoming the debt problem. Instead, sound economic policies and conducive investment decisions are required to lay the foundations of long-term growth and development. Debt relief should be the option for only those countries that meet a minimum measurable criterion of good governance, economic freedom, and consistency of policies.

Keywords: external debt, debt burden, debt overhang, debt laffer curve, debt relief, investment decisions

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Authors: Zia Amjad, Salem S. Alghamdi

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This experiment was carried out at student educational farm College of Food and Agriculture, KSU, kingdom of Saudi Arabia; in order to characterize 154 V. faba accessions based on UPOV and IBPGR descriptors. 24 agro-morphological characters including 11 quantitative and 13 qualitative were observed for genetic variation. All the results were analyzed using multivariate analysis i.e. principle component analysis (PCA). First six principle components (PC) had Eigen-value greater than one; accounted for 72% of available V. faba genetic diversity. However first three components revealed more than 10% of genetic diversity each i.e. 22.36%, 15.86% and 10.89% respectively. PCA distributed the V. faba accessions into different groups based on their performance for the characters under observation. PC-1 which represented 22.36% of the genetic diversity was positively associated with stipule spot pigmentation, intensity of streaks, pod degree of curvature and to some extent with 100 seed weight. PC-2 covered 15.86 of the genetic diversity and showed positive association for average seed weight per plant, pod length, number of seeds per plant, 100 seed weight, stipule spot pigmentation, intensity of streaks (same as in PC-1) and to some extent for pod degree of curvature and number of pods per plant. PC-3 revealed 10.89% of genetic diversity and expressed positive association for number of pods per plant and number of leaflets per plant.

Keywords: agro morphological characterization, diversity, vicia faba, PCA

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Authors: Jui-Pui Hung, Yu-Sheng Lai, Tzuo-Liang Luo, Kung-Da Wu, Yun-Ji Zhan

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This study presented the investigation of the influence of the tool holder interface stiffness on the dynamic characteristics of a spindle tool system. The interface stiffness was produced by drawbar force on the tool holder, which tends to affect the spindle dynamics. In order to assess the influence of interface stiffness on the vibration characteristic of spindle unit, we first created a three dimensional finite element model of a high speed spindle system integrated with tool holder. The key point for the creation of FEM model is the modeling of the rolling interface within the angular contact bearings and the tool holder interface. The former can be simulated by a introducing a series of spring elements between inner and outer rings. The contact stiffness was calculated according to Hertz contact theory and the preload applied on the bearings. The interface stiffness of the tool holder was identified through the experimental measurement and finite element modal analysis. Current results show that the dynamic stiffness was greatly influenced by the tool holder system. In addition, variations of modal damping, static stiffness and dynamic stiffness of the spindle tool system were greatly determined by the interface stiffness of the tool holder which was in turn dependent on the draw bar force applied on the tool holder. Overall, this study demonstrates that identification of the interface characteristics of spindle tool holder is of very importance for the refinement of the spindle tooling system to achieve the optimum machining performance.

Keywords: dynamic stiffness, spindle-tool holder, interface stiffness, drawbar force

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Authors: Atifa Mushtaq, Tanweer Khaliq, Hafiz Alam Sher, Asia Farid, Anila Kanwal, Maliha Sarfraz

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The study was designed to assess the various pharmacokinetic parameters of a commercially available clarithromycin Tablet (Klaricid® 250 mg Abbot, Pakistan) in plasma sample of healthy adult female volunteers by applying a rapid, sensitive and accurate HPLC-UV analytical method. The human plasma samples were evaluated by using an isocratic High Performance Liquid Chromatography (HPLC) system of Sykam consisted of a pump with a column C18 column (250×4.6mn, 5µm) UV-detector. The mobile phase comprises of potassium dihydrogen phosphate (50 mM, pH 6.8, contained 0.7% triethylamine), methanol and acetonitrile (30:25:45, v/v/v) was delivered with injection volume of 20µL at flow rate of 1 mL/min. The detection was performed at λmax 275 nm. By applying this method, important pharmacokinetic parameters Cmax, Tmax, Area under curve (AUC), half-life (t1/2), , Volume of distribution (Vd) and Clearance (Cl) were measured. The parameters of pharmacokinetics of clarithromycin were calculated by software (APO) pharmacological analysis. Maximum plasma concentrations Cmax 2.78 ±0.33 µg/ml, time to reach maximum concentration tmax 2.82 ± 0.11 h and Area under curve AUC was 20.14 h.µg/ml. The mean ± SD values obtained for the pharmacokinetic parameters showed a significant difference in pharmacokinetic parameters observed in previous literature which emphasizes the need for dose adjustment of clarithromycin in Pakistani population.

Keywords: Pharmacokinetc, Clarothromycin, HPLC, Pakistan

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Authors: Peiyuan Guan, Tao Wan, Dewei Chu

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With the flash memory approaching its physical limit, the emerging resistive random-access memory (RRAM) has been considered as one of the most promising candidates for the next-generation non-volatile memory. One selector-one resistor configuration has shown the most promising way to resolve the crosstalk issue without affecting the scalability and high-density integration of the RRAM array. By comparison with other candidates of selectors (such as diodes and nonlinear devices), threshold switching selectors dominated by formation/spontaneous rupture of fragile conductive filaments have been proved to possess low voltages, high selectivity, and ultra-low current leakage. However, the flexibility and transparency of selectors are barely mentioned. Therefore, it is a matter of urgency to develop a selector with highly flexible and transparent properties to assist the application of RRAM for a diversity of memory devices. In this work, threshold switching selectors were designed using a facilely solution-processed fabrication on AgNWs@PDMS composite films, which show high flexibility, transparency and scratch resistance. As-fabricated threshold switching selectors also have revealed relatively high selectivity (~107), low operating voltages (Vth < 1 V) and good switching performance.

Keywords: flexible and transparent, resistive random-access memory, silver nanowires, threshold switching selector

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Authors: P. K. Singhal, R. Naresh, V. Sharma

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This paper presents the three optimization models, namely New Binary Artificial Bee Colony (NBABC) algorithm, NBABC with Local Search (NBABC-LS), and NBABC with Genetic Crossover (NBABC-GC) for solving the Wind-Thermal Unit Commitment (WTUC) problem. The uncertain nature of the wind power is incorporated using the Weibull probability density function, which is used to calculate the overestimation and underestimation costs associated with the wind power fluctuation. The NBABC algorithm utilizes a mechanism based on the dissimilarity measure between binary strings for generating the binary solutions in WTUC problem. In NBABC algorithm, an intelligent scout bee phase is proposed that replaces the abandoned solution with the global best solution. The local search operator exploits the neighboring region of the current solutions, whereas the integration of genetic crossover with the NBABC algorithm increases the diversity in the search space and thus avoids the problem of local trappings encountered with the NBABC algorithm. These models are then used to decide the units on/off status, whereas the lambda iteration method is used to dispatch the hourly load demand among the committed units. The effectiveness of the proposed models is validated on an IEEE 10-unit thermal system combined with a wind farm over the planning period of 24 hours.

Keywords: artificial bee colony algorithm, economic dispatch, unit commitment, wind power

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Authors: Wikanda Phaphan

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Mixture generalized gamma distribution is a combination of two distributions: generalized gamma distribution and length biased generalized gamma distribution. These two distributions were presented by Suksaengrakcharoen and Bodhisuwan in 2014. The findings showed that probability density function (pdf) had fairly complexities, so it made problems in estimating parameters. The problem occurred in parameter estimation was that we were unable to calculate estimators in the form of critical expression. Thus, we will use numerical estimation to find the estimators. In this study, we presented a new method of the parameter estimation by using the expectation – maximization algorithm (EM), the conjugate gradient method, and the quasi-Newton method. The data was generated by acceptance-rejection method which is used for estimating α, β, λ and p. λ is the scale parameter, p is the weight parameter, α and β are the shape parameters. We will use Monte Carlo technique to find the estimator's performance. Determining the size of sample equals 10, 30, 100; the simulations were repeated 20 times in each case. We evaluated the effectiveness of the estimators which was introduced by considering values of the mean squared errors and the bias. The findings revealed that the EM-algorithm had proximity to the actual values determined. Also, the maximum likelihood estimators via the conjugate gradient and the quasi-Newton method are less precision than the maximum likelihood estimators via the EM-algorithm.

Keywords: conjugate gradient method, quasi-Newton method, EM-algorithm, generalized gamma distribution, length biased generalized gamma distribution, maximum likelihood method

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Authors: Runliang Wang, Jianhua Liu, Duo Jia, Xiaoyu Ding

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The contact between sealing surfaces plays a vital role in guaranteeing the sealing performance of various seals. To date, analyses of sealing structures have rarely considered both structural parameters (macroscale) and surface roughness information (microscale) of sealing surfaces due to the complex modeling process. Meanwhile, most of the contact analyses applied to seals were conducted only under normal loading, which still existssome distance from real loading conditions in engineering. In this paper, a multiscale rough contact model, which took both macrostructural parameters of seals and surface roughness information of sealing surfaces into consideration for the cone-cone seal, was established. By using the finite element method (FEM), the combined normal and tangential loading was applied to the model to simulate the assembly process of the cone-cone seal. The evolution of the contact behaviors during the assembly process, such as the real contact area (RCA), the distribution of contact pressure, and contact status, are studied in detail. The results showed the non-linear relationship between the RCA and the load, which was different from the normal loading cases. In addition, the evolution of the real contact area of cone-cone seals with isotropic and anisotropic rough surfaces are also compared quantitatively.

Keywords: contact mechanics, FEM, randomly rough surface, real contact area, sealing

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Authors: Qiang Zhang, Xiaojian Hu

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In multi-vehicle tracking, based on feature selection, the tracking system efficiently tracks vehicles in a video with minimal error in combination with color attribution, which focuses on presenting a simple and fast, yet accurate and robust solution to the problem such as inaccurately and untimely responses of statistics-based adaptive traffic control system in the intersection scenario. In this study, a real-time tracking system is proposed for multi-vehicle tracking in the intersection scene. Considering the complexity and application feasibility of the algorithm, in the object detection step, the detection result provided by virtual loops were post-processed and then used as the input for the tracker. For the tracker, lightweight methods were designed to extract and select features and incorporate them into the adaptive color tracking (ACT) framework. And the approbatory online feature selection algorithms are integrated on the mature ACT system with good compatibility. The proposed feature selection methods and multi-vehicle tracking method are evaluated on KITTI datasets and show efficient vehicle tracking performance when compared to the other state-of-the-art approaches in the same category. And the system performs excellently on the video sequences recorded at the intersection. Furthermore, the presented vehicle tracking system is suitable for surveillance applications.

Keywords: real-time, multi-vehicle tracking, feature selection, color attribution

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Authors: Emanuele Bonamente, Andrea Aquino, Franco Cotana

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The exploitation of the low-temperature geothermal resource via ground-source heat pumps is often limited by the high investment cost mainly due to borehole drilling. From the monitoring of a prototypal system currently used by a commercial building, it was found that a simple upgrade of the conventional layout, obtained including a thermal storage between the ground-source heat exchangers and the heat pump, can optimize the ground energy exploitation requiring for shorter/fewer boreholes. For typical applications, a reduction of up to 66% with respect to the conventional layout can be easily achieved. Results from the monitoring campaign of the prototype are presented in this paper, and upgrades of the thermal storage using phase change materials (PCMs) are proposed using computational fluid dynamics simulations. The PCM thermal storage guarantees an improvement of the system coefficient of performance both for summer cooling and winter heating (up to 25%). A drastic reduction of the storage volume (approx. 1/10 of the original size) is also achieved, making it possible to easily place it within the technical room, avoiding extra costs for underground displacement. A preliminary optimization of the PCM geometry is finally proposed.

Keywords: computational fluid dynamics (CFD), geothermal energy, ground-source heat pumps, phase change materials (PCM)

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Authors: Lubabatu Sada Sodangi

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The rapid growth in exchange and accessibility of information via the internet makes many organisations acquire data on their own operation. The aim of data mining is to analyse the different behaviour of a dataset using observation. Although, the subset of the dataset being analysed may not display all the behaviours and relationships of the entire data and, therefore, may not represent other parts that exist in the dataset. There is a range of techniques used in data mining to determine the hidden or unknown information in datasets. In this paper, the performance of two algorithms Chi-Square Automatic Interaction Detection (CHAID) and multilayer perceptron (MLP) would be matched using an Adult dataset to find out the percentage of an/the adults that earn > 50k and those that earn <= 50k per year. The two algorithms were studied and compared using IBM SPSS statistics software. The result for CHAID shows that the most important predictors are relationship and education. The algorithm shows that those are married (husband) and have qualification: Bachelor, Masters, Doctorate or Prof-school whose their age is > 41<57 earn > 50k. Also, multilayer perceptron displays marital status and capital gain as the most important predictors of the income. It also shows that individuals that their capital gain is less than 6,849 and are single, separated or widow, earn <= 50K, whereas individuals with their capital gain is > 6,849, work > 35 hrs/wk, and > 27yrs their income will be > 50k. By comparing the two algorithms, it is observed that both algorithms are reliable but there is strong reliability in CHAID which clearly shows that relation and education contribute to the prediction as displayed in the data visualisation.

Keywords: data mining, CHAID, multi-layer perceptron, SPSS, Adult dataset

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Authors: A. Latif Karimi

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Since 2001, construction works, especially the construction of new homes and residential buildings, witnessed a dramatic boom across Afghanistan. More so, the construction industry and house builders are relied upon as important players in the country’s job market, economy and infrastructural development schemes. However, a lack of innovation, quality assurance mechanism, substandard construction and market dominance by traditional methods push all the parties in house building sector to shift for more advanced construction techniques and mass production technologies to meet the rising demands for proper accommodation. Meanwhile, rapid population growth and urbanization are widening the gap between the demand and supply of new and modern houses in urban areas like Kabul, Herat, etc. This paper investigates about current condition of construction practices in house building projects, the associated challenges, and the outcomes of transition to more reasonable and sustainable building methods. It is obvious, the introduction and use of Modern Methods of Construction (MMC) can help construction industry and house builders in Afghanistan to tackle the challenges and meet the desired standards for modern houses. This paper focuses on prefabrication, a popular MMC that is becoming more common, improving in quality and available in a variety of budgets. It is revealed that this method is the way forward to improving house building practices as it has been proven to reduce construction time, minimize waste and improve environmental performance of construction developments.

Keywords: modern houses, traditional construction, modern methods of construction, prefabrication, sustainable building

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Authors: Qiangqiang Xu, Xu Ji, Jingyang Han, Changchun Yang, Ming Li

Abstract:

Replacing of complex solar concentrating unit, this paper designs a solar heat-concentrating medium-temperature steam-generating system. Solar radiation is collected by using a large solar collecting and heat concentrating plate and is converged to the metal evaporating pipe with high efficient heat transfer. In the meantime, the heat loss is reduced by employing a double-glazed cover and other heat insulating structures. Thus, a high temperature is reached in the metal evaporating pipe. The influences of the system's structure parameters on system performance are analyzed. The steam production rate and the steam production under different solar irradiance, solar collecting and heat concentrating plate area, solar collecting and heat concentrating plate temperature and heat loss are obtained. The results show that when solar irradiance is higher than 600 W/m², the effective heat collecting area is 7.6 m² and the double-glazing cover is adopted, the system heat loss amount is lower than the solar irradiance value. The stable steam is produced in the metal evaporating pipe at 100 ℃, 110 ℃, and 120 ℃, respectively. When the average solar irradiance is about 896 W/m², and the steaming cumulative time is about 5 hours, the daily steam production of the system is about 6.174 kg. In a single day, the solar irradiance is larger at noon, thus the steam production rate is large at that time. Before 9:00 and after 16:00, the solar irradiance is smaller, and the steam production rate is almost 0.

Keywords: heat concentrating, heat loss, medium temperature, solar steam production

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Authors: Saurabh Chaudhury, Sanjeet Kumar Sinha

Abstract:

The MOSFET has been the main building block in high performance and low power VLSI chips for the last several decades. Device scaling is fundamental to technological advancements, which allows more devices to be integrated on a single die providing greater functionality per chip. Ultimately, the goal of scaling is to build an individual transistor that is smaller, faster, cheaper, and consumes less power. Scaling continued following Moore's law initially and now we see an exponential growth in today's nano scaled chip. However, device scaling to deep nano meter regime leads to exponential increase in leakage currents and excessive heat generation. Moreover, fabrication process variability causing a limitation to further scaling. Researchers believe that with a mix of chemistry, physics, and engineering, nano electronics may provide a solution to increasing fabrication costs and may allow integrated circuits to be scaled beyond the limits of the modern transistor. Carbon nano tube (CNT) and nano wires (NW) based FETs have been analyzed and characterized in laboratory and also been demonstrated as prototypes. This work presents an extensive simulation based study and analysis of CNTFET and NW-FET devices and comparison of the results with conventional MOSFET. From this study, we can conclude that these devices have got some excellent properties and favorable characteristics which will definitely lead the future semiconductor devices in post silicon era.

Keywords: carbon nanotube, nanowire FET, low power, nanoscaled devices, VLSI

Procedia PDF Downloads 386

Authors: Shaode Yu, Jiajian Meng, Bing Zhu, Hang Yu, Qiurui Sun

Abstract:

Speech emotion recognition (SER) is for recognizing human subjective emotions through audio data in-depth analysis. From speech audios, how to comprehensively extract emotional information and how to effectively fuse extracted features remain challenging. This paper presents a dual-stream SER framework that embraces both full training and transfer learning of different networks for thorough feature encoding. Besides, a plug-and-play cross-attention fusion (CAF) module is implemented for the valid integration of the dual-stream encoder output. The effectiveness of the proposed CAF module is compared to the other three fusion modules (feature summation, feature concatenation, and feature-wise linear modulation) on two databases (RAVDESS and IEMO-CAP) using different dual-stream encoders (full training network, DPCNN or TextRCNN; transfer learning network, HuBERT or Wav2Vec2). Experimental results suggest that the CAF module can effectively reconcile conflicts between features from different encoders and outperform the other three feature fusion modules on the SER task. In the future, the plug-and-play CAF module can be extended for multi-branch feature fusion, and the dual-stream SER framework can be widened for multi-stream data representation to improve the recognition performance and generalization capacity.

Keywords: speech emotion recognition, cross-attention fusion, dual-stream, pre-trained

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Authors: Mohammadhossein Moghimi, Robert Stone, Pia Rotshtein

Abstract:

Recording psychological and physiological correlates of human performance within virtual environments and interpreting their impacts on human engagement, ‘immersion’ and related emotional or ‘effective’ states is both academically and technologically challenging. By exposing participants to an effective, real-time (game-like) virtual environment, designed and evaluated in an earlier study, a psychophysiological database containing the EEG, GSR and Heart Rate of 30 male and female gamers, exposed to 10 games, was constructed. Some 174 features were subsequently identified and extracted from a number of windows, with 28 different timing lengths (e.g. 2, 3, 5, etc. seconds). After reducing the number of features to 30, using a feature selection technique, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) methods were subsequently employed for the classification process. The classifiers categorised the psychophysiological database into four effective clusters (defined based on a 3-dimensional space – valence, arousal and dominance) and eight emotion labels (relaxed, content, happy, excited, angry, afraid, sad, and bored). The KNN and SVM classifiers achieved average cross-validation accuracies of 97.01% (±1.3%) and 92.84% (±3.67%), respectively. However, no significant differences were found in the classification process based on effective clusters or emotion labels.

Keywords: virtual reality, effective computing, effective VR, emotion-based effective physiological database

Procedia PDF Downloads 211

Authors: Gail Joseph

Abstract:

In the United States, to advance the quality and education requirements of PreK teachers, there are concerns regarding barriers for existing early childhood educators to access formal degrees and ongoing professional development. Barriers exist related to affordability and access. Affordability is a key factor that impacts teachers access to degree programs. The lack of financial resources makes it difficult for many qualified candidates to begin, and complete, degree programs. Even if funding was not an issue, accessibility remains a pressing issue in higher education. Some common barriers include geography, long work hours, lack of professional community, childcare, and clear articulation agreements. Greater flexibility is needed to allow all early childhood professionals to pursue college coursework that takes into consideration the many competing demands on their schedules. For these busy professionals, it is particularly important that professional development opportunities are available “on demand” and are seen as relevant to their work. Courses that are available during non-traditional hours make attendance more accessible, and professional development that is relevant to what they need to know and be able to do to be effective in their current positions increase access to and the impact of ongoing professional education. EarlyEdU at the University of Washington provides institutes of higher education and state professional development systems with free comprehensive, competency based college courses based on the latest science of how to optimize child learning and outcomes across developmental domains. The coursework embeds an intentional teaching framework which requires teachers to know what to do in the moment, see effective teaching in themselves and others, enact these practices in the classroom, reflect on what works and what does not, and improve with thoughtful practices. Reinforcing the Intentional Teaching Framework in EarlyEdU courses is the Coaching Companion, an open source, web-based video annotation learning tool that supports coaching in higher education by enabling students to view and refine their teaching practices. The tool is integrated throughout EarlyEdU courses. With the Coaching Companion, students see upload teaching interactions on video and then reflect on the degree to which they incorporate evidence-based practices. Coaching Companion eliminates the traditional separation of theory and practice in college-based teacher preparation. Together, the Intentional Teaching Framework and the Coaching Companion transform the course instructor into a job-embedded coach. The instructor watches student interactions with children on video using the Coaching Companion and looks specifically for interactions defined in course assignments, readings, and lectures. Based on these observations, the instructor offers feedback and proposes next steps. Developed on federal and philanthropic funds, all EarlyEdU courses and the Coaching Companion are available for free to 2= and 4-year colleges and universities with early childhood degrees, as well as to state early learning and education departments to increase access to high quality professional development. We studied the impact of the Coaching Companion in two courses and demonstrated a significant increase in the quality of teacher-child interactions as measured by the PreK CLASS quality teaching assessment. Implications are discussed related to policy and practice.

Keywords: education technology, distance education, early childhood education, professional development

Procedia PDF Downloads 111

Authors: Ankur Dixit, Hiroaki Wagatsuma

Abstract:

The crack detection on a concrete bridge is an important and constant task in civil engineering. Chronically, humans are checking the bridge for inspection of cracks to maintain the quality and reliability of bridge. But this process is very long and costly. To overcome such limitations, we have used a drone with a digital camera, which took some images of bridge deck and these images are processed by morphological component analysis (MCA). MCA technique is a very strong application of sparse coding and it explores the possibility of separation of images. In this paper, MCA has been used to decompose the image into coarse and fine components with the effectiveness of two dictionaries namely anisotropic diffusion and wavelet transform. An anisotropic diffusion is an adaptive smoothing process used to adjust diffusion coefficient by finding gray level and gradient as features. These cracks in image are enhanced by subtracting the diffused coarse image into the original image and the results are treated by Sobel edge detector and binary filtering to exhibit the cracks in a fine way. Our results demonstrated that proposed MCA framework using anisotropic diffusion followed by Sobel operator and binary filtering may contribute to an automation of crack detection even in open field sever conditions such as bridge decks.

Keywords: anisotropic diffusion, coarse component, fine component, MCA, Sobel edge detector and wavelet transform

Procedia PDF Downloads 154