Search results for: variable magnetization

Authors: Yemane Hailu Fissuh, Zhongzhan Zhang

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In biomedical researches and randomized clinical trials, the most commonly interested outcomes are time-to-event so-called survival data. The importance of robust models in this context is to compare the effect of randomly controlled experimental groups that have a sense of causality. Causal estimation is the scientific concept of comparing the pragmatic effect of treatments conditional to the given covariates rather than assessing the simple association of response and predictors. Hence, the causal effect based semiparametric transformation model was proposed to estimate the effect of treatment with the presence of possibly time-varying covariates. Due to its high flexibility and robustness, the semiparametric transformation model which shall be applied in this paper has been given much more attention for estimation of a causal effect in modeling left-truncated and right censored survival data. Despite its wide applications and popularity in estimating unknown parameters, the maximum likelihood estimation technique is quite complex and burdensome in estimating unknown parameters and unspecified transformation function in the presence of possibly time-varying covariates. Thus, to ease the complexity we proposed the modified estimating equations. After intuitive estimation procedures, the consistency and asymptotic properties of the estimators were derived and the characteristics of the estimators in the finite sample performance of the proposed model were illustrated via simulation studies and Stanford heart transplant real data example. To sum up the study, the bias of covariates was adjusted via estimating the density function for truncation variable which was also incorporated in the model as a covariate in order to relax the independence assumption of failure time and truncation time. Moreover, the expectation-maximization (EM) algorithm was described for the estimation of iterative unknown parameters and unspecified transformation function. In addition, the causal effect was derived by the ratio of the cumulative hazard function of active and passive experiments after adjusting for bias raised in the model due to the truncation variable.

Keywords: causal estimation, EM algorithm, semiparametric transformation models, time-to-event outcomes, time-varying covariate

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Authors: Muhammad Usman, Munir Ahmed

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The demand of high-rise buildings has grown fast during the past decades. The design of these buildings by using RC core wall have been widespread nowadays in many countries. The RC core wall (RCCW) buildings encompasses central core wall and boundary columns joined through post tension slab at different floor levels. The core wall often provides greater stiffness as compared to the collective stiffness of the boundary columns. Hence, the core wall dominantly resists lateral loading i.e. wind or earthquake load. Non-linear response history analysis (NLRHA) procedure is the finest seismic design procedure of the times for designing high-rise buildings. The modern design tools for nonlinear response history analysis and performance based design has provided more confidence to design these structures for high-rise buildings. NLRHA requires selection and scaling of ground motions to match design spectrum for site specific conditions. Designers use several techniques for scaling ground motion records (time series). Time domain and frequency domain scaling are most commonly used which comprises their own benefits and drawbacks. Due to lengthy process of NLRHA, application of only one technique is conceivable. To the best of author’s knowledge, no consensus on the best procedures for the selection and scaling of the ground motions is available in literature. This research aims to provide the finest ground motion scaling technique specifically for designing 40 story high-rise RCCW buildings. Seismic response of 40 story RCCW building is checked by applying both the frequency domain and time domain scaling. Variable sites are selected in three critical seismic zones of Pakistan. The results indicates that there is extensive variation in seismic response of building for these scaling. There is still a need to build a consensus on the subjected research by investigating variable sites and buildings heights.

Keywords: 40-storied RC core wall building, nonlinear response history analysis, ground motions, time domain scaling, frequency domain scaling

Procedia PDF Downloads 131

Authors: Manal Elsayed Elkheshin, Rasha Saleh Mansour, Mohamed Fawzy Mohamed Eldnasury, Mamdouh Elbadry Mohamed

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This research Aims to identify the main factors affecting the production and the fish consumption in Egypt, through the econometric estimation for various forms functions of fish production and fish consumption during the period (1991-2014), as the aim of this research to forecast the production and the fish consumption in Egypt until 2020, through determine the best standard methods using (ARIMA).This research also aims to the economic feasibility of the production of fish in aquaculture farms study; investment cost and represents the value of land, buildings, equipment and irrigation. Aquaculture requires three types of fish (Tilapia, carp fish, and mullet fish), and the total area of the farm, about an acre. The annual Fish production from this project about 3.5 tons. The annual investment costs of about 50500 pounds, Find conclude that the project can repay the cost of their investments after about 4 years and 5 months, and therefore recommend the implementation of the project, and internal rate of return reached (IRR) of about 22.1%, where it is clear that the rate of large internal rate of return, and achieves pound invested in this project annual return is estimated at 22.1 pounds, more than the opportunity cost, so we recommend the need to implement the project.Recommendations:1. Increasing the fish agriculture to decrease the gap of animal protein. 2.Increasing the number of mechanism fishing boats, and the provision of transport equipped to maintain the quality of fish production. 3.Encourage and attract the local and foreign investments, providing advice to the investor on the aquaculture field. 4. Action newsletters awareness of the importance of these projects where these projects resulted in a net profit after recovery in less than five years, IRR amounted to about 23%, which is much more than the opportunity cost of a bank interest rate is about 7%, helping to create work and graduates opportunities, and contribute to the reduction of imports of the fish, and improve the performance of the food trade balance.

Keywords: equation model, individual share, red meat, consumption, production, endogenous variable, exogenous variable, financial performance evaluates fish culture, feasibility study, fish production, aquaculture

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Authors: Christina Williams, Mehari Weldemariam, Ann M. Farese, Thomas J. MacVittie, Maureen A. Kane

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Radiation exposure results in dose-dependent and time-dependent multi-organ damage. Drug development of medical countermeasures (MCM) for radiation-induced injury occurs under the FDA Animal Rule because human efficacy studies are not ethical or feasible. The FDA Animal Rule requires the representation of both sexes and describes several uses for biomarkers in MCM drug development studies. Currently, MCMs are limited and there is no FDA-approved biomarker for any radiation injury. Sex as a variable is essential to identifying biomarkers and developing effective MCMs for acute radiation exposure (ARS) and delayed effects of acute radiation exposure (DEARE). These studies aim to address the death of information on sex differences that have not been determined by studies that included only male, single-sex cohorts. Studies have reported differences in radiosensitivity according to sex. As such, biomarker discovery for radiation-induced damage must consider sex as a variable. This study evaluated the plasma proteomic profile of Rhesus macaque non-human primates after different exposures and doses, as well as time points after radiation. Exposures and doses included total body irradiation between 5-7.5 Gy and partial body irradiation with 5% bone marrow sparing at 9, 9.5 and 10 Gy. Timepoints after irradiation included days 1, 3, 60, and 180, which encompassed both acute radiation syndromes and delayed effects of acute radiation exposure. Bottom-up proteomic analyses of plasma included equal numbers of males and females. In the control animals, few proteomic differences are observed between the sexes. In the irradiated animals, there are a few sex differences, with changes mostly consisting of proteins upregulated in the female animals. Multiple canonical pathways were upregulated in irradiated animals relative to the control animals when subjected to pathway analysis, but differential responses between the sexes are limited. These data provide critical baseline differences according to sex and establish sex differences in non-human primate models relevant to drug development of MCM under the FDA Animal Rule.

Keywords: ionizing radiation, sex differences, plasma proteomics, biomarker discovery

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Authors: Javier E. García-Gallo, Nelson J. Fonseca-Ruiz, John F. Duitama-Munoz

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Sepsis is a syndrome that occurs with physiological and biochemical abnormalities induced by severe infection and carries a high mortality and morbidity, therefore the severity of its condition must be interpreted quickly. After patient admission in an intensive care unit (ICU), it is necessary to synthesize the large volume of information that is collected from patients in a value that represents the severity of their condition. Traditional severity of illness scores seeks to be applicable to all patient populations, and usually assess in-hospital mortality. However, the use of machine learning techniques and the data of a population that shares a common characteristic could lead to the development of customized mortality prediction scores with better performance. This study presents the development of a score for the one-year mortality prediction of the patients that are admitted to an ICU with a sepsis diagnosis. 5650 ICU admissions extracted from the MIMICIII database were evaluated, divided into two groups: 70% to develop the score and 30% to validate it. Comorbidities, demographics and clinical information of the first 24 hours after the ICU admission were used to develop a mortality prediction score. LASSO (least absolute shrinkage and selection operator) and SGB (Stochastic Gradient Boosting) variable importance methodologies were used to select the set of variables that make up the developed score; each of this variables was dichotomized and a cut-off point that divides the population into two groups with different mean mortalities was found; if the patient is in the group that presents a higher mortality a one is assigned to the particular variable, otherwise a zero is assigned. These binary variables are used in a logistic regression (LR) model, and its coefficients were rounded to the nearest integer. The resulting integers are the point values that make up the score when multiplied with each binary variables and summed. The one-year mortality probability was estimated using the score as the only variable in a LR model. Predictive power of the score, was evaluated using the 1695 admissions of the validation subset obtaining an area under the receiver operating characteristic curve of 0.7528, which outperforms the results obtained with Sequential Organ Failure Assessment (SOFA), Oxford Acute Severity of Illness Score (OASIS) and Simplified Acute Physiology Score II (SAPSII) scores on the same validation subset. Observed and predicted mortality rates within estimated probabilities deciles were compared graphically and found to be similar, indicating that the risk estimate obtained with the score is close to the observed mortality, it is also observed that the number of events (deaths) is indeed increasing as the outcome go from the decile with the lowest probabilities to the decile with the highest probabilities. Sepsis is a syndrome that carries a high mortality, 43.3% for the patients included in this study; therefore, tools that help clinicians to quickly and accurately predict a worse prognosis are needed. This work demonstrates the importance of customization of mortality prediction scores since the developed score provides better performance than traditional scoring systems.

Keywords: intensive care, logistic regression model, mortality prediction, sepsis, severity of illness, stochastic gradient boosting

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Authors: Guettal Djaouida, Ziadi Abdelkader

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In this paper, we study a coupling of the Alienor method with the algorithm of Piyavskii-Shubert. The classical multidimensional global optimization methods involves great difficulties for their implementation to high dimensions. The Alienor method allows to transform a multivariable function into a function of a single variable for which it is possible to use efficient and rapid method for calculating the the global optimum. This simplification is based on the using of a reducing transformation called Alienor.

Keywords: global optimization, reducing transformation, α-dense curves, Alienor method, Piyavskii-Shubert algorithm

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Authors: Janne Engblom, Elias Oikarinen

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The understanding of housing price dynamics is of importance to a great number of agents: to portfolio investors, banks, real estate brokers and construction companies as well as to policy makers and households. A panel dataset is one that follows a given sample of individuals over time, and thus provides multiple observations on each individual in the sample. Panel data models include a variety of fixed and random effects models which form a wide range of linear models. A special case of panel data models is dynamic in nature. A complication regarding a dynamic panel data model that includes the lagged dependent variable is endogeneity bias of estimates. Several approaches have been developed to account for this problem. In this paper, the panel models were estimated using the Common Correlated Effects estimator (CCE) of dynamic panel data which also accounts for cross-sectional dependence which is caused by common structures of the economy. In presence of cross-sectional dependence standard OLS gives biased estimates. In this study, U.S housing price dynamics were examined empirically using the dynamic CCE estimator with first-difference of housing price as the dependent and first-differences of per capita income, interest rate, housing stock and lagged price together with deviation of housing prices from their long-run equilibrium level as independents. These deviations were also estimated from the data. The aim of the analysis was to provide estimates with comparisons of estimates between 1980-1999 and 2000-2012. Based on data of 50 U.S cities over 1980-2012 differences of short-run housing price dynamics estimates were mostly significant when two time periods were compared. Significance tests of differences were provided by the model containing interaction terms of independents and time dummy variable. Residual analysis showed very low cross-sectional correlation of the model residuals compared with the standard OLS approach. This means a good fit of CCE estimator model. Estimates of the dynamic panel data model were in line with the theory of housing price dynamics. Results also suggest that dynamics of a housing market is evolving over time.

Keywords: dynamic model, panel data, cross-sectional dependence, interaction model

Procedia PDF Downloads 251

Authors: Yuanxiaoyue Yang

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Emissions trading, or ‘cap-and-trade', has been long promoted by economists as a more cost-effective pollution control approach than traditional performance standard approaches. While there is a large body of empirical evidence for the overall effectiveness of emissions trading, relatively little attention has been paid to other unintended consequences brought by emissions trading. One important consequence is that cap-and-trade could introduce the risk of creating high-level emission concentrations in areas where emitting facilities purchase a large number of emission allowances, which may cause an unequal distribution of environmental benefits. This study will contribute to the current environmental policy literature by linking trading activity with environmental injustice concerns and empirically analyzing the causal relationship between trading activity and emissions reduction under a cap-and-trade program for the first time. To investigate the potential environmental injustice concern in cap-and-trade, this paper uses a differences-in-differences (DID) with instrumental variable method to identify the causal effect of allowance trading behaviors on emission reduction levels under the clean air interstate rule (CAIR), a cap-and-trade program targeting on the power sector in the eastern US. The major data source is the facility-year level emissions and allowance transaction data collected from US EPA air market databases. While polluting facilities from CAIR are the treatment group under our DID identification, we use non-CAIR facilities from the Acid Rain Program - another NOx control program without a trading scheme – as the control group. To isolate the causal effects of trading behaviors on emissions reduction, we also use eligibility for CAIR participation as the instrumental variable. The DID results indicate that the CAIR program was able to reduce NOx emissions from affected facilities by about 10% more than facilities who did not participate in the CAIR program. Therefore, CAIR achieves excellent overall performance in emissions reduction. The IV regression results also indicate that compared with non-CAIR facilities, purchasing emission permits still decreases a CAIR participating facility’s emissions level significantly. This result implies that even buyers under the cap-and-trade program have achieved a great amount of emissions reduction. Therefore, we conclude little evidence of environmental injustice from the CAIR program.

Keywords: air pollution, cap-and-trade, emissions trading, environmental justice

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Authors: Päärn Paiste, Margit Kõiv, Riho Mõtlep, Kalle Kirsimäe

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For small-scale wastewater management, the treatment wetlands (TWs) as a low cost alternative to conventional treatment facilities, can be used. However, P removal capacity of TW systems is usually problematic. P removal in TWs is mainly dependent on the physico–chemical and hydrological properties of the filter material. Highest P removal efficiency has been shown trough Ca-phosphate precipitation (i.e. active filtration) in Ca-rich alkaline filter materials, e.g. industrial by-products like hydrated oil shale ash (HOSA), metallurgical slags. In this contribution we report preliminary results of a full-scale TW system using HOSA material for P removal for a municipal wastewater at Nõo site, Estonia. The main goals of this ongoing project are to evaluate: a) the long-term P removal efficiency of HOSA using real waste water; b) the effect of high organic loading rate; c) variable P-loading effects on the P removal mechanism (adsorption/direct precipitation); and d) the form and composition of phosphate precipitates. Onsite full-scale experiment with two concurrent filter systems for treatment of municipal wastewater was established in September 2013. System’s pretreatment steps include septic tank (2 m2) and vertical down-flow LECA filters (3 m2 each), followed by horizontal subsurface HOSA filters (effective volume 8 m3 each). Overall organic and hydraulic loading rates of both systems are the same. However, the first system is operated in a stable hydraulic loading regime and the second in variable loading regime that imitates the wastewater production in an average household. Piezometers for water and perforated sample containers for filter material sampling were incorporated inside the filter beds to allow for continuous in-situ monitoring. During the 18 months of operation the median removal efficiency (inflow to outflow) of both systems were over 99% for TP, 93% for COD and 57% for TN. However, we observed significant differences in the samples collected in different points inside the filter systems. In both systems, we observed development of preferred flow paths and zones with high and low loadings. The filters show formation and a gradual advance of a “dead” zone along the flow path (zone with saturated filter material characterized by ineffective removal rates), which develops more rapidly in the system working under variable loading regime. The formation of the “dead” zone is accompanied by the growth of organic substances on the filter material particles that evidently inhibit the P removal. Phase analysis of used filter materials using X-ray diffraction method reveals formation of minor amounts of amorphous Ca-phosphate precipitates. This finding is supported by ATR-FTIR and SEM-EDS measurements, which also reveal Ca-phosphate and authigenic carbonate precipitation. Our first experimental results demonstrate that organic pollution and loading regime significantly affect the performance of hydrated ash filters. The material analyses also show that P is incorporated into a carbonate substituted hydroxyapatite phase.

Keywords: active filtration, apatite, hydrated oil shale ash, organic pollution, phosphorus

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Authors: F. Keller, G. Reinhart

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The efficient and economic allocation of resources is one main goal in the field of production planning and control. Nowadays, a new variable gains in importance throughout the planning process: Energy. Energy-efficiency has already been widely discussed in literature, but with a strong focus on reducing the overall amount of energy used in production. This paper provides a brief systematic approach, how energy-supply-orientation can be used for an energy-cost-efficient production planning and thus combining the idea of energy-efficiency and energy-flexibility.

Keywords: production planning, production control, energy-efficiency, energy-flexibility, energy-supply

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Authors: F. Argurio, F. G. Vaccaro

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In a global environment defined by ever-increasing health issues, in spite of the progress made by modern medicine, this paper seeks to readdress the question of global health in an international relations perspective. The research hypothesis is: the lower the security environment, the higher the spread of communicable diseases. This question will be channeled by re-scaling the connotation of 'global' and 'international' dimension through the theoretical lens of glocalization, a theory by Bauman that starts its analysis from simple systems to get to the most complex ones. Glocalization theory will be operationalized by analyzing health in an armed-conflict context. In this respect, the independent variable 'low security environment' translates into the cases of Syria and Yemen, which provide a clear example of the all-encompassing nature of conflict on national health and the effects on regional development. In fact, Syria and Yemen have been affected by poliomyelitis and cholera outbreaks respectively. The dependent variable will be constructed on said communicable diseases which belong to the families of sanitation-related and vaccine-preventable diseases. The research will be both qualitative and quantitative, based on primary (interviews) and secondary (WHO and other NGO’s reports) sources. The methodology is based on the assessment of the vaccine coverage and case-analysis in time and space using epidemiological data. Moreover, local health facilities’ functioning and efficiency will be studied. The article posits that the intervention and cooperation of international organizations with the local authorities becomes crucial to provide the local populations with their primary health needs. In Yemen, the majority of fatal cholera cases were in the regions controlled by the Houthi rebels, not officially accredited by the International Community. Similarly, the polio outbreak in Syria primarily affected the areas not controlled by the Syrian Arab Republic forces, recognized as the leading interlocutor by the WHO. The jeopardized possibilities to access these countries have been pivotal to the determining the problem in controlling sanitation-related and vaccine preventable diseases. This represents a potential threat to global health.

Keywords: health in conflict-affected areas, cholera, polio, Yemen, Syria, glocalization

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Authors: Faiz Khan Mohammad, Saumya Ray Chaudhari, Raghunathan Rengaswamy, Swagatika Sahoo

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Cholera caused by pathogenic gut bacteria Vibrio Cholerae (VC), is a major health problem in developing countries. Different strains of VC exhibit variable responses subject to different extracellular medium (Nag et al, Infect Immun, 2018). In this study, we present a new approach to model the variable VC responses in mono- and co-cultures, subject to continuously changing growth medium, which is otherwise difficult via simple FBA model. Nine VC strain and seven E. coli (EC) models were assembled and considered. A continuously changing medium is modelled using a new iterative-based controlled medium technique (ITC). The medium is appropriately prefixed with the VC model secretome. As the flux through the bacteria biomass increases secretes certain by-products. These products shall add-on to the medium, either deviating the nutrient potential or block certain metabolic components of the model, effectively forming a controlled feed-back loop. Different VC models were setup as monoculture of VC in glucose enriched medium, and in co-culture with VC strains and EC. Constrained to glucose enriched medium, (i) VC_Classical model resulted in higher flux through acidic secretome suggesting a pH change of the medium, leading to lowering of its biomass. This is in consonance with the literature reports. (ii) When compared for neutral secretome, flux through acetoin exchange was higher in VC_El tor than the classical models, suggesting El tor requires an acidic partner to lower its biomass. (iii) Seven of nine VC models predicted 3-methyl-2-Oxovaleric acid, mysirtic acid, folic acid, and acetate significantly affect corresponding biomass reactions. (iv) V. parhemolyticus and vulnificus were found to be phenotypically similar to VC Classical strain, across the nine VC strains. The work addresses the advantage of the ITC over regular flux balance analysis for modelling varying growth medium. Future expansion to co-cultures, potentiates the identification of novel interacting partners as effective cholera therapeutics.

Keywords: cholera, vibrio cholera El. tor, vibrio cholera classical, acetate

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Authors: Muhammad Asim, Mehvish Amjad

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Background: Less than half of women in South Asia (SA) use any modern contraceptive method which leads to a huge burden of unintended pregnancies, unsafe abortions, maternal deaths, and socioeconomic loss. Women empowerment plays a pivotal role in improving various health seeking behaviours, including contraceptive use. The objective of this study to explore the association between women's empowerment and modern contraceptive, among rich and poor segment of population in SA. Methods: We used the most recent, large-scale, demographic health survey data of five South Asian countries, namely Afghanistan, Pakistan, Bangladesh, India, and Nepal. The outcome variable was the current use of modern contraceptive methods. The main exposure variable was a combination (interaction) of socio-economic status (SES) and women’s level of empowerment (low, medium, and high), where SES was bifurcated into poor and rich; and women empowerment was divided into three categories: decision making, attitude to violence and social independence. Moreover, overall women empowerment indicator was also created by using three dimensions of women empowerment. We applied both descriptive statistics and multivariable logistic regression techniques for data analyses. Results: Most of the women possessed ‘medium’ level of empowerment across South Asian Countries. The lowest attitude to violence empowerment was found in Afghanistan, and the lowest social independence empowerment was observed in Bangladesh across SA. However, Pakistani women have the lowest decision-making empowerment in the region. The lowest modern contraceptive use (22.1%) was found in Afghanistan and the highest (53.2%) in Bangladesh. The multivariate results depict that the overall measure of women empowerment does not affect modern contraceptive use among poor and rich women in most of South Asian countries. However, the decision-making empowerment plays a significant role among both poor and rich women to use modern contraceptive methods across South Asian countries. Conclusions: The effect of women’s empowerment on modern contraceptive use is not consistent across countries, and among poor and rich segment of population. Of the three dimensions of women’s empowerment, the autonomy of decision making in household affairs emerged as a stronger determinant of mCPR as compared with social independence and attitude towards violence against women.

Keywords: women empowerment, modern contraceptive use, South Asia, socio economic status

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Authors: Mohammed Abdullah Alshahrani

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The research focuses on investigating the use of partial least squares (PLS) methodology for addressing challenges associated with high-dimensional correlated data. Recent technological advancements have led to experiments producing data characterized by a large number of variables compared to observations, with substantial inter-variable correlations. Such data patterns are common in chemometrics, where near-infrared (NIR) spectrometer calibrations record chemical absorbance levels across hundreds of wavelengths, and in genomics, where thousands of genomic regions' copy number alterations (CNA) are recorded from cancer patients. PLS serves as a widely used method for analyzing high-dimensional data, functioning as a regression tool in chemometrics and a classification method in genomics. It handles data complexity by creating latent variables (components) from original variables. However, applying PLS can present challenges. The study investigates key areas to address these challenges, including unifying interpretations across three main PLS algorithms and exploring unusual negative shrinkage factors encountered during model fitting. The research presents an alternative approach to addressing the interpretation challenge of predictor weights associated with PLS. Sparse estimation of predictor weights is employed using a penalty function combining a lasso penalty for sparsity and a Cauchy distribution-based penalty to account for variable dependencies. The results demonstrate sparse and grouped weight estimates, aiding interpretation and prediction tasks in genomic data analysis. High-dimensional data scenarios, where predictors outnumber observations, are common in regression analysis applications. Ordinary least squares regression (OLS), the standard method, performs inadequately with high-dimensional and highly correlated data. Copy number alterations (CNA) in key genes have been linked to disease phenotypes, highlighting the importance of accurate classification of gene expression data in bioinformatics and biology using regularized methods like PLS for regression and classification.

Keywords: partial least square regression, genetics data, negative filter factors, high dimensional data, high correlated data

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Authors: Wighens Ngoie Ilunga, Pamela J. Welz, Olewaseun O. Oyekola, Daniel Ikhu-Omoregbe

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Currently, most sludge from the wastewater treatment plants of edible oil factories is disposed to landfills, but landfill sites are finite and potential sources of environmental pollution. Production of biodiesel from wastewater sludge can contribute to energy production and waste minimization. However, conventional biodiesel production is energy and waste intensive. Generally, biodiesel is produced from the transesterification reaction of oils with alcohol (i.e., Methanol, ethanol) in the presence of a catalyst. Homogeneously catalysed transesterification is the conventional approach for large-scale production of biodiesel as reaction times are relatively short. Nevertheless, homogenous catalysis presents several challenges such as high probability of soap. The current study aimed to reuse wastewater sludge from the edible oil industry as a novel feedstock for both monounsaturated fats and bioethanol for the production of biodiesel. Preliminary results have shown that the fatty acid profile of the oilseed wastewater sludge is favourable for biodiesel production with 48% (w/w) monounsaturated fats and that the residue left after the extraction of fats from the sludge contains sufficient fermentable sugars after steam explosion followed by an enzymatic hydrolysis for the successful production of bioethanol [29% (w/w)] using a commercial strain of Saccharomyces cerevisiae. A novel nano-magnetic catalyst was synthesised from mineral processing alkaline tailings, mainly containing dolomite originating from cupriferous ores using a modified sol-gel. The catalyst elemental chemical compositions and structural properties were characterised by X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infra-red (FTIR) and the BET for the surface area with 14.3 m²/g and 34.1 nm average pore diameter. The mass magnetization of the nano-magnetic catalyst was 170 emu/g. Both the catalytic properties and reusability of the catalyst were investigated. A maximum biodiesel yield of 78% was obtained, which dropped to 52% after the fourth transesterification reaction cycle. The proposed approach has the potential to reduce material costs, energy consumption and water usage associated with conventional biodiesel production technologies. It may also mitigate the impact of conventional biodiesel production on food and land security, while simultaneously reducing waste.

Keywords: biodiesel, bioethanol, edible oil wastewater sludge, nano-magnetism

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Authors: Man Ying Kang, Joshua Kin Man Nan

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The prolonged pandemic has posed alarming public health challenges to various parts of the world, and face-to-face mental health treatment is largely discounted for the control of virus transmission, online psychological services and self-help mental health kits have become essential. Online self-help mindfulness-based interventions have proved their effects on fostering mental health for different populations over the globe. This paper was to test the effectiveness of an online short-term audio-based mindfulness (SAM) program in enhancing wellbeing, dispositional mindfulness, and reducing stress and negative affect in community settings in China, and to explore possible mechanisms of how dispositional mindfulness, stress, and negative affect influenced the intervention effects on wellbeing. Community-dwelling adults were recruited via online social networking sites (e.g., QQ, WeChat, and Weibo). Participants (n=100) were randomized into the mindfulness group (n=50) and a waitlist control group (n=50). In the mindfulness group, participants were advised to spend 10–20 minutes listening to the audio content, including mindful-form practices (e.g., eating, sitting, walking, or breathing). Then practice daily mindfulness exercises for 3 weeks (a total of 21 sessions), whereas those in the control group received the same intervention after data collection in the mindfulness group. Participants in the mindfulness group needed to fill in the World Health Organization Five Well-Being Index (WHO), Positive and Negative Affect Schedule (PANAS), Perceived Stress Scale (PSS), and Freiburg Mindfulness Inventory (FMI) four times: at baseline (T0) and at 1 (T1), 2 (T2), and 3 (T3) weeks while those in the waitlist control group only needed to fill in the same scales at pre- and post-interventions. Repeated-measure analysis of variance, paired sample t-test, and independent sample t-test was used to analyze the variable outcomes of the two groups. The parallel process latent growth curve modeling analysis was used to explore the longitudinal moderated mediation effects. The dependent variable was WHO slope from T0 to T3, the independent variable was Group (1=SAM, 2=Control), the mediator was FMI slope from T0 to T3, and the moderator was T0NA and T0PSS separately. The different levels of moderator effects on WHO slope was explored, including low T0NA or T0PSS (Mean-SD), medium T0NA or T0PSS (Mean), and high T0NA or T0PSS (Mean+SD). The results found that SAM significantly improved and predicted higher levels of WHO slope and FMI slope, as well as significantly reduced NA and PSS. FMI slope positively predict WHO slope. FMI slope partially mediated the relationship between SAM and WHO slope. Baseline NA and PSS as the moderators were found to be significant between SAM and WHO slope and between SAM and FMI slope, respectively. The conclusion was that SAM was effective in promoting levels of mental wellbeing, positive affect, and dispositional mindfulness as well as reducing negative affect and stress in community settings in China. SAM improved wellbeing faster through the faster enhancement of dispositional mindfulness. Participants with medium-to-high negative affect and stress buffered the therapy effects of SAM on wellbeing improvement speed.

Keywords: mindfulness, negative affect, stress, wellbeing, randomized control trial

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Authors: Pedro Esteban

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Most installations nowadays are exposed to many power quality problems, and they also face numerous challenges to comply with grid code and energy efficiency requirements. The reason behind this is that they are not designed to support nonlinear, non-balanced, and variable loads and generators that make up a large percentage of modern electric power systems. These problems and challenges become especially critical when designing green buildings and smart cities. These problems and challenges are caused by equipment that can be typically found in these installations like variable speed drives (VSD), transformers, lighting, battery chargers, double-conversion UPS (uninterruptible power supply) systems, highly dynamic loads, single-phase loads, fossil fuel generators and renewable generation sources, to name a few. Moreover, events like capacitor switching (from existing capacitor banks or passive harmonic filters), auto-reclose operations of transmission and distribution lines, or the starting of large motors also contribute to these problems and challenges. Active power filters (APF) are one of the fastest-growing power electronics technologies for solving power quality problems and meeting grid code and energy efficiency requirements for a wide range of segments and applications. They are a high performance, flexible, compact, modular, and cost-effective type of power electronics solutions that provide an instantaneous and effective response in low or high voltage electric power systems. They enable longer equipment lifetime, higher process reliability, improved power system capacity and stability, and reduced energy losses, complying with most demanding power quality and energy efficiency standards and grid codes. There can be found several types of active power filters, including active harmonic filters (AHF), static var generators (SVG), active load balancers (ALB), hybrid var compensators (HVC), and low harmonic drives (LHD) nowadays. All these devices can be used in applications in Smart Cities bringing several technical and economic benefits.

Keywords: power quality improvement, energy efficiency, grid code compliance, green buildings, smart cities

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Authors: Poonam Mishra, Shiv Kumar Sharma, Sanjeev Swami

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Purpose: The Purpose of this paper is to investigate the relationship of percieved organizational politics with three levels of antecedents (i.e., organizational level, work environment level and individual level)and its consequents simultaneously. The study addresses antecedents and consequents of percieved political behavior in the higher education sector of India with specific reference to a central university. Design/ Methodology/ Approach: A conceptual framework and hypotheses were first developed on the basis of review of previous studies on organizational politics. A questionnaire was then developed carrying 66 items related to 8-constructs and demographic characteristics of respondents. Jundegemental sampling was used to select respondents. Primary data is collected through structured questionnaire from 45 faculty members of a central university. The sample constitutes Professors, Associate Professors and Assistant Professors from various departments of the University. To test hypotheses data was analyzed statistically using partial least square-structural equations modeling (PLS-SEM). Findings: Results indicated a strong support for OP’s relationship with three of the four proposed antecedents that are, workforce diversity, relationship conflict and need for power with relationship conflict having the strongest impact. No significant relationship was found between role conflict and perception of organizational politics. The three consequences that is, intention to turnover, job anxiety, and organizational commitment are significantly impacted by perception of organizational politics. Practical Implications– This study will be helpful in motivating future research for improving the quality of higher education in India by reducing the level of antecedents that adds to the level of perception of organizational politics, ultimately resulting in unfavorable outcomes. Originality/value: Although a large number of studies on atecedents and consequents of percieved organizational politics have been reported, little attention has been paid to test all the separate but interdependent relationships simultaneously; in this paper organizational politics will be simultaneously treated as a dependent variable and same will be treated as independent variable in subsequent relationships.

Keywords: organizational politics, workforce diversity, relationship conflict, role conflict, need for power, intention to turnover, job anxiety, organizational commitment

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Authors: Huey-Kuo Chen

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Travel demand forecasting including four travel choices, i.e., trip generation, trip distribution, modal split and traffic assignment constructs the core of transportation planning. In its current application, travel demand forecasting has associated with three important issues, i.e., interface inconsistencies among four travel choices, inefficiency of commonly used solution algorithms, and undesirable multiple path solutions. In this paper, each of the three issues is extensively elaborated. An ideal unified framework for the combined model consisting of the four travel choices and variable demand functions is also suggested. Then, a few remarks are provided in the end of the paper.

Keywords: travel choices, B algorithm, entropy maximization, dynamic traffic assignment

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Authors: Gyoomi Kim, Jiyoung Bae

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The purpose of this study was to propose a framework that verifies the structural relationships among students’ use of digital English learning strategy (DELS), affective domains, and their individual variables. The study developed a hypothetical model based on previous studies on language learning strategy use as well as digital language learning. The participants were 720 Korean high school students and 430 university students. The instrument was a self-response questionnaire that contained 70 question items based on Oxford’s SILL (Strategy Inventory for Language Learning) as well as the previous studies on language learning strategies in digital learning environment in order to measure DELS and affective domains. The collected data were analyzed through structural equation modeling (SEM). This study used quantitative data analysis procedures: Explanatory factor analysis (EFA) and confirmatory factor analysis (CFA). Firstly, the EFA was conducted in order to verify the hypothetical model; the factor analysis was conducted preferentially to identify the underlying relationships between measured variables of DELS and the affective domain in the EFA process. The hypothetical model was established with six indicators of learning strategies (memory, cognitive, compensation, metacognitive, affective, and social strategies) under the latent variable of the use of DELS. In addition, the model included four indicators (self-confidence, interests, self-regulation, and attitude toward digital learning) under the latent variable of learners’ affective domain. Secondly, the CFA was used to determine the suitability of data and research models, so all data from the present study was used to assess model fits. Lastly, the model also included individual learner factors as covariates and five constructs selected were learners’ gender, the level of English proficiency, the duration of English learning, the period of using digital devices, and previous experience of digital English learning. The results verified from SEM analysis proposed a theoretical model that showed the structural relationships between Korean students’ use of DELS and their affective domains. Therefore, the results of this study help ESL/EFL teachers understand how learners use and develop appropriate learning strategies in digital learning contexts. The pedagogical implication and suggestions for the further study will be also presented.

Keywords: Digital English Learning Strategy, DELS, individual variables, learners' affective domains, Structural Equation Modeling, SEM

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Authors: S. Mousavian, D. Ashouri, F. Mousavian, V. Nikkhah Rashidabad, N. Ghazinia

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PH, temperature, and time of extraction of each stage, agitation speed, and delay time between stages effect on efficiency of zinc extraction from concentrate. In this research, efficiency of zinc extraction was predicted as a function of mentioned variable by artificial neural networks (ANN). ANN with different layer was employed and the result show that the networks with 8 neurons in hidden layer has good agreement with experimental data.

Keywords: zinc extraction, efficiency, neural networks, operating condition

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Authors: Ho-Won Jung, Donghun Lee, Hyung-Jin Kim

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Predictive analytics (data analysis) uses a subset of measurements (the features, predictor, or independent variable) to predict another measurement (the outcome, target, or dependent variable) on a single person or unit. It applies empirical methods in statistics, operations research, and machine learning to predict the future, or otherwise unknown events or outcome on a single or person or unit, based on patterns in data. Most analyses of metabolic syndrome are not predictive analytics but statistical explanatory studies that build a proposed model (theory building) and then validate metabolic syndrome predictors hypothesized (theory testing). A proposed theoretical model forms with causal hypotheses that specify how and why certain empirical phenomena occur. Predictive analytics and explanatory modeling have their own territories in analysis. However, predictive analytics can perform vital roles in explanatory studies, i.e., scientific activities such as theory building, theory testing, and relevance assessment. In the context, this study is to demonstrate how to use our predictive analytics to support theory building (i.e., hypothesis generation). For the purpose, this study utilized a big data predictive analytics platform TM based on a co-occurrence graph. The co-occurrence graph is depicted with nodes (e.g., items in a basket) and arcs (direct connections between two nodes), where items in a basket are fully connected. A cluster is a collection of fully connected items, where the specific group of items has co-occurred in several rows in a data set. Clusters can be ranked using importance metrics, such as node size (number of items), frequency, surprise (observed frequency vs. expected), among others. The size of a graph can be represented by the numbers of nodes and arcs. Since the size of a co-occurrence graph does not depend directly on the number of observations (transactions), huge amounts of transactions can be represented and processed efficiently. For a demonstration, a total of 13,254 metabolic syndrome training data is plugged into the analytics platform to generate rules (potential hypotheses). Each observation includes 31 predictors, for example, associated with sociodemographic, habits, and activities. Some are intentionally included to get predictive analytics insights on variable selection such as cancer examination, house type, and vaccination. The platform automatically generates plausible hypotheses (rules) without statistical modeling. Then the rules are validated with an external testing dataset including 4,090 observations. Results as a kind of inductive reasoning show potential hypotheses extracted as a set of association rules. Most statistical models generate just one estimated equation. On the other hand, a set of rules (many estimated equations from a statistical perspective) in this study may imply heterogeneity in a population (i.e., different subpopulations with unique features are aggregated). Next step of theory development, i.e., theory testing, statistically tests whether a proposed theoretical model is a plausible explanation of a phenomenon interested in. If hypotheses generated are tested statistically with several thousand observations, most of the variables will become significant as the p-values approach zero. Thus, theory validation needs statistical methods utilizing a part of observations such as bootstrap resampling with an appropriate sample size.

Keywords: explanatory modeling, metabolic syndrome, predictive analytics, theory building

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Authors: Lucille Alonso, Florent Renard

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The characterizations of urban heat island (UHI) and their interactions with climate change and urban climates are the main research and public health issue, due to the increasing urbanization of the population. These solutions require a better knowledge of the UHI and micro-climate in urban areas, by combining measurements and modelling. This study is part of this topic by evaluating microclimatic conditions in dense urban areas in the Lyon Metropolitan Area (France) using a combination of data traditionally used such as topography, but also from LiDAR (Light Detection And Ranging) data, Landsat 8 satellite observation and Sentinel and ground measurements by bike. These bicycle-dependent weather data collections are used to build the database of the variable to be modelled, the air temperature, over Lyon’s hyper-center. This study aims to model the air temperature, measured during 6 mobile campaigns in Lyon in clear weather, using multiple linear regressions based on 33 explanatory variables. They are of various categories such as meteorological parameters from remote sensing, topographic variables, vegetation indices, the presence of water, humidity, bare soil, buildings, radiation, urban morphology or proximity and density to various land uses (water surfaces, vegetation, bare soil, etc.). The acquisition sources are multiple and come from the Landsat 8 and Sentinel satellites, LiDAR points, and cartographic products downloaded from an open data platform in Greater Lyon. Regarding the presence of low, medium, and high vegetation, the presence of buildings and ground, several buffers close to these factors were tested (5, 10, 20, 25, 50, 100, 200 and 500m). The buffers with the best linear correlations with air temperature for ground are 5m around the measurement points, for low and medium vegetation, and for building 50m and for high vegetation is 100m. The explanatory model of the dependent variable is obtained by multiple linear regression of the remaining explanatory variables (Pearson correlation matrix with a |r| < 0.7 and VIF with < 5) by integrating a stepwise sorting algorithm. Moreover, holdout cross-validation is performed, due to its ability to detect over-fitting of multiple regression, although multiple regression provides internal validation and randomization (80% training, 20% testing). Multiple linear regression explained, on average, 72% of the variance for the study days, with an average RMSE of only 0.20°C. The impact on the model of surface temperature in the estimation of air temperature is the most important variable. Other variables are recurrent such as distance to subway stations, distance to water areas, NDVI, digital elevation model, sky view factor, average vegetation density, or building density. Changing urban morphology influences the city's thermal patterns. The thermal atmosphere in dense urban areas can only be analysed on a microscale to be able to consider the local impact of trees, streets, and buildings. There is currently no network of fixed weather stations sufficiently deployed in central Lyon and most major urban areas. Therefore, it is necessary to use mobile measurements, followed by modelling to characterize the city's multiple thermal environments.

Keywords: air temperature, LIDAR, multiple linear regression, surface temperature, urban heat island

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Authors: S. Haider, B. Bhushan

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Eye Gaze behaviour of individuals with and without intellectual disability are investigated in an eye tracking study in terms of sensitivity to Nonaccidental (NAPs) and Metric (MPs) shape properties. Total fixation time is used as an indirect measure of attention allocation. Studies have found Mean reaction times for non accidental properties (NAPs) to be shorter than for metric (MPs) when the MP and NAP differences were equalized. METHODS: Twenty-five individuals with intellectual disability (mild and moderate level of Mental Retardation) and twenty-seven normal individuals were compared on mean total fixation duration, accuracy level and mean reaction time for mild NAPs, extreme NAPs and metric properties of images. 2D images of cylinders were adapted and made into forced choice match-to-sample tasks. Tobii TX300 Eye Tracker was used to record total fixation duration and data obtained from the Areas of Interest (AOI). Variable trial duration (total reaction time of each participant) and fixed trail duration (data taken at each second from one to fifteen seconds) data were used for analyses. Both groups did not differ in terms of fixation times (fixed as well as variable) across any of the three image manipulations but differed in terms of reaction time and accuracy. Normal individuals had longer reaction time compared to individuals with intellectual disability across all types of images. Both the groups differed significantly on accuracy measure across all image types. Normal individuals performed better across all three types of images. Mild NAPs vs. Metric differences: There was significant difference between mild NAPs and metric properties of images in terms of reaction times. Mild NAPs images had significantly longer reaction time compared to metric for normal individuals but this difference was not found for individuals with intellectual disability. Mild NAPs images had significantly better accuracy level compared to metric for both the groups. In conclusion, type of image manipulations did not result in differences in attention allocation for individuals with and without intellectual disability. Mild Nonaccidental properties facilitate better accuracy level compared to metric in both the groups but this advantage is seen only for normal group in terms of mean reaction time.

Keywords: eye gaze fixations, eye movements, intellectual disability, stimulus properties

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Authors: Nihal Kartaltepe Behram, Göksel Ataman, Dila Okçu

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With the development of foreign direct investments, the social, cultural, political and economic interactions between countries and institutions have become visible and they have become determining factors for the strategic structuring and market goals. In this context the purpose of this study is to investigate the effects of cultural distance and institutions on foreign direct investment choices in terms of location and investment model. For international establishments, the concept of culture, as well as the concept of cultural distance, is taken specifically into consideration, especially in the selection of methods for entering the market. In the researches and empirical studies conducted, a direct relationship between cultural distance and foreign direct investments is set and institutions and effective variable factors are examined at the level of defining the investment types. When the detailed calculation strategies and empirical researches and studies are taken into consideration, the most common methods for determining the direct investment model, considering the cultural distances, are full-ownership enterprises and joint ventures. Also, when all of the factors affecting the investments are taken into consideration, it was seen that the effect of institutions such as Government Intervention, Intellectual Property Rights, Corruption and Contract Enforcements is very important. Furthermore agglomeration is more intense and effective on the investment, compared to other factors. China has been selected as the target country, due to its effectiveness in world economy and its contributions to developing countries, which has commercial relationships with. Qualitative research methods are used for this study conducted, to measure the effects of determinative variable factors in the hypotheses of study, on the direct foreign investors and to evaluate the findings. In this study in-depth interview is used as a data collection method and the data analysis is made through descriptive analysis. Foreign Direct Investments are so reactive to institutions and cultural distance is identified by all interviews and analysis. On the other hand, agglomeration is the most strong determiner factor on foreign direct investors in Chinese Market. The reason of this factors, which comprise the sectorial aggregate, are not the strongest factors as agglomeration that the most important finding. We expect that this study became a beneficial guideline for developed and developing countries and local and national institutions’ strategic plans.

Keywords: China, cultural distance, Foreign Direct Investments, institutions

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Authors: Shashi Sharma, V. K. Katiyar, Uaday Singh

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The ability to manipulate magnetic particles in fluid flows by means of inhomogeneous magnetic fields is used in a wide range of biomedical applications including magnetic drug targeting (MDT). In MDT, magnetic carrier particles bounded with drug molecules are injected into the vascular system up-stream from the malignant tissue and attracted or retained at the specific region in the body with the help of an external magnetic field. Although the concept of MDT has been around for many years, however, wide spread acceptance of the technique is still looming despite the fact that it has shown some promise in both in vivo and clinical studies. This is because traditional MDT has some inherent limitations. Typically, the magnetic force is not very strong and it is also very short ranged. Since the magnetic force must overcome rather large hydrodynamic forces in the body, MDT applications have been limited to sites located close to the surface of the skin. Even in this most favorable situation, studies have shown that it is difficult to collect appreciable amounts of the MDCPs at the target site. To overcome these limitations of the traditional MDT approach, Ritter and co-workers reported the implant assisted magnetic drug targeting (IA-MDT). In IA-MDT, the magnetic implants are placed strategically at the target site to greatly and locally increase the magnetic force on MDCPs and help to attract and retain the MDCPs at the targeted region. In the present work, we develop a mathematical model to study the capturing of magnetic nanoparticles flowing in a fluid in an implant assisted cylindrical channel under the magnetic field. A coil of ferromagnetic SS 430 has been implanted inside the cylindrical channel to enhance the capturing of magnetic nanoparticles under the magnetic field. The dominant magnetic and drag forces, which significantly affect the capturing of nanoparticles, are incorporated in the model. It is observed through model results that capture efficiency increases from 23 to 51 % as we increase the magnetic field from 0.1 to 0.5 T, respectively. The increase in capture efficiency by increase in magnetic field is because as the magnetic field increases, the magnetization force, which is attractive in nature and responsible to attract or capture the magnetic particles, increases and results the capturing of large number of magnetic particles due to high strength of attractive magnetic force.

Keywords: capture efficiency, implant assisted-magnetic drug targeting (IA-MDT), magnetic nanoparticles, modelling

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Authors: Rajendra Kumar Dubey

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Einstein’s field equations with variable cosmological term Λ are considered in the presence of viscous fluid for Bianchi type I space time. Exact solutions of Einstein’s field equations are obtained by assuming cosmological term Λ Proportional to (R is a scale factor and m is constant). We observed that the shear viscosity is found to be responsible for faster removal of initial anisotropy in the universe. The physical significance of the cosmological models has also been discussed.

Keywords: bianchi type, I cosmological model, viscous fluid, cosmological constant Λ

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Authors: Marwa Ahmed Abd El Hamied

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In this paper In this paper, a new technique based on Artificial Immunity System (AIS) technique has been developed to track Maximum Power Point (MPP). AIS system is implemented in a photovoltaic system that is subjected to variable temperature and insulation condition. The proposed novel is simulated using Mat Lab program. The results of simulation have been compared to those who are generated from Observation Controller. The proposed model shows promising results as it provide better accuracy comparing to classical model.

Keywords: component, artificial immunity technique, solar energy, perturbation and observation, power based methods

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Authors: Yousif M. Makeen

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This study presents the results of the sedimentological and petrographical analyses on the cored samples from the Bentiu Formation. The cored intervals consist of thick beds of sandstone, which are sometimes intercalated with beds of fine-grained sandstone and, in a minor case, with a siltstone bed. Detailed sedimentological facies analysis revealed the presence of six facies types, which can be clarified in order of their great percentage occurrences as follows: (i) Massive sandstone, (ii) Planar cross-bedded sandstone, (iii) Trough cross-bedded sandstone, (iv) Fine laminated sandstone (v) Fine laminated siltstone and (vi) Horizontally parted sandstone. The petrographical analyses under the plane polarized microscope and the scanning electron microscope (SEM) for the sandstone lithofacies types that exist within the cored intervals allowed classifying these lithofacies into Kaolinitic Subfeldspathic Arenites. Among the detrital components, quartz grains are the most abundant (mainly monocrystalline quartz), followed by feldspars, micas, detrital and authigenic clays, and carbonaceous debris. However, traces of lithic fragments, iron oxides and heavy minerals were observed in some of the analyzed samples, where they occur in minor amounts. Kaolinite is present mainly as an authigenic component in most of the analyzed samples, while quartz overgrowths occur in variable amounts in most of the investigated samples. Carbonates (calcite & siderite) are present in considerable amounts. The grain roundness in most of the investigated sandstone samples ranges from well-rounded to round, and, in fewer samples, is sub-angular to angular. Most of the sandstone samples are moderately compacted and display point, concavo-convex and long grain contacts, whereas the sutured grain contacts, which reflect a higher degree of compaction, are relatively observed in lesser amounts, while the float grain contact has also been observed in minor quantity. Pore types in the analyzed samples are dominantly primary and secondary interparticle forms. Point-counted porosity values range from 19.6% to 30%. Average pore sizes are highly variable and range from 20 to 350 microns. Pore interconnectivity ranges from good to very good.

Keywords: sandstone, sedimentological facies, porosity, quartz overgrowths

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Authors: Alexander I. Dovgyallo, Evgeniy A. Zinoviev, Svetlana O. Nekrasova

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The most of works applied for the pulse tube converters contain the workflow description implemented through the use of mathematical models on stationary modes. However, the study of the thermoacoustic systems unsteady behavior in the start, stop, and acoustic load changes modes is in the particular interest. The aim of the present study was to develop a mathematical thermal excitation model of acoustic oscillations in pulse tube engine (PTE) as a small-scale scheme of pulse tube engine operating at atmospheric air. Unlike some previous works this standing wave configuration is a fully closed system. The improvements over previous mathematical models are the following: the model allows specifying any values of porosity for regenerator, takes into account the piston weight and the friction in the cylinder and piston unit, and determines the operating frequency. The numerical method is based on the relation equations between the pressure and volume velocity variables at the ends of each element of PTE which is recorded through the appropriate transformation matrix. A solution demonstrates that the PTE operation frequency is the complex value, and it depends on the piston mass and the dynamic friction due to its movement in the cylinder. On the basis of the determined frequency thermoacoustically induced heat transport and generation of acoustic power equations were solved for channel with temperature gradient on its ends. The results of numerical simulation demonstrate the features of the initialization process of oscillation and show that that generated acoustic power more than power on the steady mode in a factor of 3…4. But doesn`t mean the possibility of its further continuous utilizing due to its existence only in transient mode which lasts only for a 30-40 sec. The experiments were carried out on small-scale PTE. The results shows that the value of acoustic power is in the range of 0.7..1.05 W for the defined frequency range f = 13..18 Hz and pressure amplitudes 11..12 kPa. These experimental data are satisfactorily correlated with the numerical modeling results. The mathematical model can be straightforwardly applied for the thermoacoustic devices with variable temperatures of thermal reservoirs and variable transduction loads which are expected to occur in practical implementations of portable thermoacoustic engines.

Keywords: nonlinear processes, pulse tube engine, thermal excitation, standing wave

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