Effects Of Main Factors Research Articles

Longitudinal Differences in Electron Temperature on Both Sides of Zero Declination Line in the Mid‐latitude Topside Ionosphere

AbstractIn this study, electron temperature (Te) observations from the Defense Meteorological Satellite Program F17 and F18 spacecraft during 2010–2017 are analyzed to explore the longitudinal differences of Te in the topside ionosphere on both sides of zero geomagnetic declination lines in the northern mid‐latitude under geomagnetically weak activities (Kp3). Te in the North American sector (45ºW–135ºW) has prominent longitudinal differences on sector scale. Te increases from the west side to east side in the morning, but vice versa in the afternoon. The relative value of longitudinal differences in Te peaks in May to August. The East Asian sector (45ºE–135ºE) of similar geomagnetic field configurations is selected for verification. There are similar local time and seasonal dependencies in the two sectors, while the intensity of longitudinal differences of Te weakens in the latter sector. The estimation of contributions to the longitudinal difference of Te from thermosphere winds provided by the empirical horizontal wind model (HWM14), peak height (hmF2) and peak density (NmF2) of the F2 layer indicates that the magnetic declination modulation effect of neutral winds at mid‐latitudes changes electron density (Ne), which indirectly changes the longitudinal difference in Te. Furthermore, the zonal wind at different local times and the seasonal variation of meridional winds may be the main effect factors of dependencies in Te longitudinal differences. The sector longitudinal differences of both geomagnetic declination and horizontal winds are possibly major reasons causing the different intensity of this characteristic in the two sectors.

Implementation of the McMaster Model in Family Therapy: Effects on Family Function in Married Couples.

Objective: Family function is one of the main effective factors on stability of the family. Family therapy can promote family function and performances. This study aimed to assess the effects of family therapy on family function among couples in Yazd province (Iran) based on the McMaster model. Method : The research population was selected from volunteer couples in Yazd in 2017 who were invited by publicity announcement to participate in this research. Finally, 40 couples were selected and randomly divided into 2 intervention and control groups. Participants answered to the demographic and Family Assessment Device (FAD) questionnaires. The acquired information was analyzed using inferential and descriptive statistics and SPSS 21 software. Results: The outcomes showed significant differences between the intervention and control groups in problem-solving (p = 0.01), communication (p < 0.0001), emotional responsiveness (p = 0.01), emotional involvement (p < 0.0001), and general function (p = 0.04). The roles and behavior control domains were improved after the intervention in 2 groups, but the differences were not significant. Conclusion: Family therapy based on McMaster model can promote the skills of problem- solving, family communication, emotional responsiveness, emotional involvement, and general function in couples. Healthy family functioning is a substantial domain of concern for mental health specialists who provide family interventions. Our findings enhance considerably to family professionals’ knowledge about patterns of family function in Iranian families.

The impact of risks in limiting E-commerce

Most E-commerce transactions nowadays are electronically executed via well-known internet websites (Amazon, Alibaba, eBay, and others). Online sales in the Middle East, including Jordan, are estimated to count 2% of the overall retail sales, that is too much lower than the 15% in developed markets (Mehta &amp; Bhandari, n.d.); and online sales in Jordan are still limited (Statista, 2020). Therefore, this study comes to determine the threats limiting E commerce in Jordan. The services sector accounts for about two thirds of the Jordanian economy and the insurance sector is considered an important component of it (Ghazal, 2015). The problem is to what extent threats from risks accompanied with E-commerce limit it from the viewpoint of Jordanian insurance companies’ employees. Five (5) insurance companies out of twenty-five (25) are randomly selected for analysis and a questionnaire is conducted according to a psychometric method for data collection. The results show that perceived ease of use, perceived usefulness, and perceived risk with products/services are the main effective factors for predicting transaction loss, while delay time is significantly affected by perceived ease of use and perceived risk with product/service. Policymakers can rely on the results of this study to avoid the risks facing online shopping in Jordan and enhancing it. This study contributes to the literature by reducing the dearth of previous research regarding the determinants of threats and risks limiting online shopping and E-commerce in emerging markets.

Driving forces analysis of CO2 emissions from China's electric industry based on two-phase LMDI decomposition method

The electric power sector is the largest contributor of CO2 emissions in China. With an increasing concern about environment problems, it is essential to identify key factors that affect CO2 emissions from China's electric power industry so as to help the fossil fuel-based country reduce carbon emissions. For this purpose, the two-phase Logarithmic Mean Division Index (LMDI) decomposition method is presented in this paper. Covering the whole industry chain including power generation, transmission, and consumption, the two-phase LMDI decomposition model is constructed. Then, the influencing aspects are decomposed into ten driving factors, namely, (1) fossil energy power generation structure, (2) fossil energy consumption coefficient, (3) thermal power proportion, (4) power generation and consumption ratio, (5) transmission and distribution loss, (6) industrial power consumption intensity, (7) industrial structure, (8) per capita gross domestic product (GDP), (9) total population, and (10) resident power consumption intensity. Based on data from China statistical yearbook, China energy statistics yearbook, and China power statistics yearbook (2005–2017 edition), the decomposition calculation results show that the power generation and consumption ratio, industrial structure, resident power consumption intensity, per capita GDP, population size, and transmission and distribution loss factors are positive driving factors with contributions of 1.2%, 2.47%, 1.5%, 94.29%, 5.43%, and 4.64%, respectively. However, the fossil energy power generation structure, fossil energy consumption coefficient, thermal power proportion, and industrial power consumption intensity are negative driving factors with contribution rates of −0.34%, −21.72%, −9.85%, and −8.44%, respectively. According to the main effect factors identified, some corresponding measures are proposed to reduce carbon emissions from China's power industry.

Transient heat transfer analysis of phase change material melting in metal foam by experimental study and artificial neural network

Phase change material with porous media (PCM-porous) is an efficient and promising thermal management system for an electronic or photovoltaic system that needs heat dissipation or preservation. Studying the transient heat transfer process of the PCM-porous system is conducive to understand the main effect factors that influence its working process and the differences in its melting process between the pure PCM. The lab-based experiment coupled with the artificial neural network (ANN) method is used to study the transient heat transfer process of the PCM-porous system. The experimental results show that the porosity of metal foam has a greater influence on the melting and heat transfer of PCM than the pore density. The experimental data are used to train the ANN model, which predicts the parameters of the PCM-porous system during the heat transfer process, including the liquid fraction, temperature, and average Nusselt number based on time and the porosity of metal foam. Moreover, a classification ANN model is developed to predict the porosity of metal foam based on the liquid fraction, temperature, average Nusselt number. This study provides a reference to choose the porosity of the PCM-porous system for designers and proposes a novel way to explore the heat transfer process of the PCM-porous system.

Optimal loading of omecamtiv mecarbil by chitosan: A comprehensive and comparative molecular dynamics study

Biodegradable carbohydrate polymers are one of the most efficient material groups applicable in the pharmaceutical industry and medicine. In this research, the effect of chitosan chains, as a natural polymer, on the encapsulation of a myosin activator drug, omecamtiv mecarbil, was investigated by molecular dynamics (MD) simulation. The three main effective factors for loading efficiency including drug concentration (11, 46 and 91%), temperature (298.15 and 318.15 K) and the presence of water molecules (560 molecules) were considered to evaluate the polymer loading efficiency. Based on the results of 12 different systems, since the water molecules provide more effective non-covalent drug interactions than those of chitosan, the presence of molecules decreases the polymer efficiency. To obtain a molecular point of view, all possible mutual radial distribution functions were determined. The interactions between chitosan and omecamtiv mecarbil were reduced at higher temperature and in the presence of water molecules. A comprehensive and comparative insight was also discussed on the role of the three factors in changing the radius of gyration, unbonded energy profile, root-mean-square fluctuations, solvent-accessible surface area and number of hydrogen bonding. Although the same trend was not observed for the effect of drug concentration on all parameters in all systems, the higher temperature and presence of water molecules still decreased the number of loaded drugs on the polymer. Moreover, the aggregation and intra-molecular interaction of chitosan chains were reduced in the presence of water and drug molecules, so that the free active polymer sites were released to form hydrogen bonding interactions among these molecules. As a result, a type of distribution leading to the loading of the drug on polymer was observed.

A hybrid computing model to predict rock strength index properties using support vector regression

The uniaxial compressive strength (UCS) and elasticity modulus (E) are two of the most quoted rock strength parameters in engineering application. Due to approved technical difficulties indirect measurements, the tendency for determining these parameters through predictive models using simpler and cheaper tests in practical oriented applications have widely been highlighted. In this paper, a new hybridized multi-objective support vector regression (MSVR) model integrated with the firefly metaheuristic algorithm (FMA) was developed to touch upon a computational method in rock engineering purposes. The optimum internal parameters were adjusted through parametric investigation using 222 physical and mechanical rock properties corresponding to a variety of quarried stones from all over Iran. The accuracy and robustness of models were evaluated using different error indices, the area under curve for receiver operation characteristics (AUCROC) and F1-score criteria. Comparing to MSVR, the predictability level of UCS and E showed 8.35% and 5.47% improvement in hybrid MSVR-FMA. The superior and more promising results imply that hybrid MSVR-FMA as a flexible alternative can be applied for rock strength prediction in designing of construction projects. Using tow sensitivity analyses, the point load index and P-wave velocity were distinguished as the main effective factors on predicted UCS and E.

Hierarchical cluster analysis to identify the homogeneous desertification management units.

Since in most mapping models geometric mean of different criteria are used to determine the desertification intensity, one of the most important issues in desertification studies is understanding the similar areas, which require similar management after determining the desertification intensity map. Two similar classes of desertification intensity may require different management due to differences in the criteria that affect its desertification severity. Therefore, after determining the geomorphological facies as the working units in Sistan plain, we used hierarchical cluster analysis to identify the homogeneous environmental management units (HEMUs) based on indices of MEDALUS model. According to the MEDALUS model, the studied area was divided into two categories namely medium and high desertification classes. Working units (geomorphological facies) are classified into five clusters according to HEMUs analysis based on climate, soil, vegetation, and wind erosion criteria. The first cluster (C11) include six facies with moderate and severe desertification; in all of these units the main effective factor was wind erosion, so they need the same management decisions controlling wind erosion. Two working units (1 and 4) with the same desertification severity were placed in two different clusters due to the main factors affecting each other. The results of the Mann-Whitney test showed that the value of the test statistics was 79. Also, the value of Asymp.Sig was obtained to be 0.018, which is less than 0.025 (two-tailed test), and it can be concluded that the classification of work units in the two models, clustering and desertification, is not equal (P<0.05). So It seems that using cluster analysis to identify the same units, which need the same management decision after preparing the desertification intensity, is necessary.

Efficacy evaluation of China trauma care training program

Objective To understand the current situation of trauma and evaluate the training effect and influencing factors of China Trauma Care Training (CTCT) by investigating the trainees who participated in the course. Methods A total of 1660 trainees who participated in training from May 2017 to May 2018 were selected as the subjects of this study. Through questionnaires, the general information (gender, occupation, professional title, discipline source, length of time engaged in trauma treatment), the situation of trauma in their hospitals (hospital level, trauma mode, number of severe trauma cases each year), the recommended model of trauma treatment, learning methods of trauma knowledge, and the trauma training interval were obtained. The trainees took tests before and after the training, and the test results were used to evaluate the training effect and analyze the influencing factors. Results (1) There were 1 230 males (74.10%), 1 356 physicians (81.93%), 1 247 with intermediate title or lower levels (75.12%), 756 from emergency department/emergency surgery (45.54%), and 899 who were engaged in trauma for more than five years (54.16%). (2) There were 1 068 trainees from tertiary hospitals (64.34%). The main mode of severe trauma in hospitals was emergency+ consultation+ triage (1 198 trainees, 72.17%). A total of 1014 trainees treated less than 200 severe trauma patients each year (61.08%). A total of 1210 trainees recommended emergency/emergency surgery (72.89%) and 350 trainees recommended trauma surgery/trauma hospital (21.08%) as the main mode of treatment. (3) There were various approaches to acquire trauma knowledge, including 1 029 person-times (61.99%) through department internal guidelines, 924 person-times (55.66%) through case discussion, 879 person-times (52.95%) through self-teaching, 767 person-times (46.20%) through lectures, 382 person-times (23.01%) through trauma courses and 285 person-times through further studies at home and abroad (17.17%). A total of 951 trainees recommended 1-2 years as the training interval (57.3%). (4) The average test score before training was (67.5±14.5)points, which were influenced by the hospital level, discipline source, title of trainees, length of time engaged in trauma treatment, and amount of severe trauma cases each year in their hospitals. The hospital level and discipline source were the main effect factors. (5) The average test score after training was (83.8±11.6)points, which was significantly higher than that before training (P<0.01), and the scores of trainees in different subgroups were all improved. The main factors influencing the test score included hospital level, number of severe trauma cases each year, discipline source, professional title, number of severe trauma cases each year treated in the hospital. The major effect factor was the amount of severe trauma cases each year. Conclusions The main mode of trauma in hospitals involved in this study is multi-disciplinary consultation+ decentralized treatment . Few trainees have received systematic trauma training, and their trauma knowledge level and the test scores before training is influenced by hospital level, the case number of severe trauma treatment, discipline source, and trainees’qualifications. After the standardized and standardized trauma training course CTCT , the scores of students from different backgrounds have been significantly improved, and the gap between students has been significantly narrowed. Key words: Wounds and injuries; Questionaires; Trauma training

Integration of sonic and resistivity conventional logs for identification of fracture parameters in the carbonate reservoirs (A case study, Carbonate Asmari Formation, Zagros Basin, SW Iran)

Between all available methods for fractures evaluation, core and image logs are prime methods. However, both of these data are very expensive and practically available in less than 10% of drilled wells. Therefore, a less expensive and quick method would be very helpful for fracture analysis. The main aim of this study is to identify the fracture parameters in the carbonate reservoirs, i.e. fracture density and fracture aperture, using a combination of sonic, resistivity and other conventional logs which commonly available in all wells. For this aim, fractures and their parameters were precisely determined on the image logs in 6 wells from carbonate Asmari Formation as a typical fractured reservoir in the world. Then, the effect of fracture parameters on the sonic and resistivity logs were used to identify fracture parameters in the wells with no core and image logs. Results indicate that the zones with high fracture density and aperture are easily detectable by conventional logs. On the other hand, the behavior of these logs is a function of fracture parameters in the fractured intervals, especially in the zones with high fracture aperture. Separation between shallow (RLA2) and deep (RLA5) resistivity logs along with their cross plot are very reliable indicators for fractured intervals. Sonic transit time rapidly increases where fractures density or aperture increased as well. In this study, a combination of sonic and neutron porosity logs have also been used to determine the effect of fracture aperture, as the main effective factor on the reservoir properties. Furthermore, full waveforms in some studied wells combined with sonic transit time to improve the results of fractures evaluation. Using sonic and resistivity along with neutron porosity and gamma ray logs provides a reliable method for describing the fracture parameters which shows high correlation with image logs results. Due to the high impact of fracture aperture on both reservoir quality and conventional log responses, determination of this parameter is more crucial than other fracture parameters. This method is also applicable for OBM (Oil Base Mud) image tools which conventionally cannot detect fracture aperture.

The impact of courtyard geometry on its mean radiant temperature

For hot regions, studies have been advocating re-adopting the courtyard pattern for its thermal advantages. Aiming at developing thermally comfortable courtyards, studies have been exploring the impact of courtyards geometry on their shading and natural ventilation, which are the two environmental principles of courtyards. However, there is a lack of studies on the impact of manipulating courtyards geometry on the thermal sensation of occupants. This research investigates the impact of changing the courtyard geometry and the resulted shading on Mean Radiant Temperature (MRT) and Globe temperature (Tg). The latter represents the thermal perception of occupants and the former is the main effective factor on the thermal sensation of people in outdoor and semi-outdoor spaces. The research carried out simulation experiments to test 360 different courtyard configurations. The simulation experiments included using Envi-met and IES-VE simulation tools. The former was used to determine MRT and Tg, and the latter to determine shading levels. Baghdad was selected to represent an example of a hot city in which summer air temperature reaches around 50 °C. The results show that the difference in shading that results from changing the courtyard geometry can lead to a difference in MRT and Tg of up to 15°C.

Energy, exergy, and environmental (3E) assessments of an integrated molten carbonate fuel cell (MCFC), Stirling engine and organic Rankine cycle (ORC) cogeneration system fed by a biomass-fueled gasifier

In this paper, a novel syngas-fed combined cogeneration plant, integrating a biomass gasifier, a molten carbonate fuel cell (MCFC), a heat recovery steam generator (HRSG) unit, a Stirling engine, and an organic Rankine cycle (ORC), is introduced and thermodynamically analyzed to recognize its potentials compared to the previous solo/combined systems. For the proposed system, energetic, exergetic as well as environmental evaluations are performed. Based on the results, the gasifier and the fuel cell have a significant contribution to the exergy destruction of the system. Through a parametric study, the current density and the stack temperature difference are known as the main effective factors on the plant performance. Meanwhile, dividing the whole system into three sub-models, i.e., model (1): power production plant including the gasifier and MCFC without including Stirling engine, HRSG, and ORC unit, model (2): the cogeneration system without ORC unit, and model (3): the whole cogeneration system, an environmental impact assessment is carried out regarding CO2 emission. Considering paper as biomass revealed that maximum value of exergy efficiency is 50.18% with CO2 emissions of 28.9 × 10−2 t.MWh−1 which compared to the solo MCFC system indicates 28.40% increase and 13.3 × 10−2 t.MWh−1 decrease in exergy efficiency and CO2 emission, respectively.

Sensitivity analysis of heat dissipation factors in a hot oil pipeline based on orthogonal experiments.

The study of phase-change heat-transfer characteristics of crude oil has been one of the hot issues in the field of gathering and transportation. The process of phase-change heat transfer of crude oil involves many complicated problems such as natural convection treatment, latent heat treatment, phase-change interface determination and fluid characteristic change. A mathematical model based on the additional capacity heat method is proposed in this article, and the momentum equations of crude oil liquid phase are presented for Newtonian and non-Newtonian fluids. The aim of this study was to investigate the influence of different factors on the heat transfer performance during the shutdown process of an overhead pipe. Experiments were conducted to verify the model and the solution method; the experimental and model results showed good agreement with a maximum relative error of 4.57%. The temperature fields and solidification conditions of crude oil in pipelines under different shutdown conditions were determined, and the sensitivity of the main effect factors was determined through an orthogonal experiment. The results show that the order of influence was oil initial temperature >thickness of insulating layer >air temperature >thickness of wax layer. The results of the study have important guiding significance on the control of shutdown time and the determination of restarting schemes.

Quantifying the Relationship among Impact Factors of Shrub Layer Diversity in Chinese Pine Plantation Forest Ecosystems

Shrub layer diversity is an essential component of the forest ecosystem diversity, that contributes significantly to structuring the community and maintaining diversity, especially in plantation forests. In previous studies, researchers have reported the strong relationship among various factors (i.e., soil composition, mean annual temperature, etc.) and shrub diversity. However, how these factors jointly influence shrub diversity and which factors could be considered the key factors is still unknown. In this study, we attempted to quantify the effect among environmental factors, soil factors and forest stand factors on shrub diversity. Twenty-seven variables were selected from 57 Chinese pine plantation plots in Huanglong Mountain, Yanan City, Shaanxi Province, China. The path models showed that latent variable of soil properties is the main effective factor of latent variable of shrub diversity (directly effect, path coefficient = 0.344) and the latent variable of site conditions is another effective factor of latent variable of shrub diversity (indirectly effect, path coefficient = 0.177); Besides, the latent variable of site conditions and forest properties directly affect the latent variable of soil properties (path coefficient = 0.514 and 0.326, respectively). Among the latent variable of soil properties, soil water content (SWC) has the biggest weight of 0.666, which indicated the most significant contribution of SWC to latent variables of shrub diversity. Total nitrogen, weighted 0.375, and total phosphorus, weighted 0.308, are also important factors and make a similar contribution to latent variable of shrub diversity. Soil organic matter (SOM) has a minimal impact (lowest weight, 0.059); among the objective variables of site condition, altitude contributes the most and is followed by litter thickness, weighted at 0.722 and 0.448, respectively. Furthermore, among all the variables affecting the latent variable of forest properties, forest age is recognized as the maximum impactor of soil property change, which weighted −0.941; and is followed by forest stock volume and diameter at breast height (DBH), weighted 0.795 and 0.788, respectively. The crowding index (C) has the lowest weight (−0.235) and demonstrated that spatial distribution and crowding of trees have minimal impact on the latent variable of Soil properties. diversity Overall, our study provides new insights into quantifying the relationships among different driving factors that potentially play a significant role in determining shrub layer diversity within the plantation forest.

Optimization and Modeling of Tetracycline Removal from Wastewater by Three-Dimensional Electrochemical System: Application of Response Surface Methodology and Least Squares Support Vector Machine

A three-dimensional electrochemical system was employed as an advanced treatment technology for treatment of tetracycline-containing wastewater. An initial tetracycline concentration of 20.17–100.23 mg/L, pH range of 2.43–9.18, and current density of 1.03–15.72 mA/cm2 were implemented for the optimization and modeling of the process within the framework of a three-factor, five-level Box–Wilson central composite design-based response surface methodology and least squares support vector machine. The results of statistics corroborated that three main effective factors and reactor performance were very well described by the second-order polynomial equation (coefficient of determination = 0.94, mean square error = 0.0042, root mean square error = 0.065, average absolute deviation = 2.51, and mean absolute error = 0.037). Under the optimal conditions introduced by the desirability function approach, 90.42 (± 2.3)%, 49.91 ± (8.4)% and 28.80 ± (16.70)% of tetracycline, chemical oxygen demand, and total organic carbon could be removed using the three-dimensional electrochemical process from wastewater. The findings of this study demonstrated that the three-dimensional electrochemical system was as an effective, simple, and economic process compared to other electrochemical systems that have been recently used for antibiotics removal and could be considered as a promising technology for further investigations.

Identifying the Succession Dimensions and Formulating the Health Network Managers' Selection Components

Background: Management transport occurs in organizational hierarchy commonly; so, succession planning is of a great importance. In this regard, identification of the effective methods for succession planning leads to organizational success in health care systems. This study aimed to identify the dimensions of succession and formulate the components of health network managers' selection. Methods: This qualitative research was conducted by applying the content analysis approach in 2018. To glean the information, 42 semi-structured interviews were conducted using the snowball sampling method. Data was analyzed by framework analysis applying Max QDA 10 software. Results: The main effective factors for achieving the health network administrator management rank contained 4 main themes of managerial education and skills, specified education and skills, educational methods, and performance evaluation mechanism. Moreover, we identified the main applied points in monitoring the health network managers' duties. Conclusion: The basic criteria should be defined for selecting appropriate people and improving the quality of services. It also can lead to a framework for succession planning and is helpful in organizational decision making improvement.

Response of Wetland Evapotranspiration to Land Use/Cover Change and Climate Change in Liaohe River Delta, China

This study aims to investigate the effects of land use/cover change (LUCC) and climate change on wetland evapotranspiration (ET), and to identify the importance of the main effect factors in the spatiotemporal dynamics of ET. In the wetland of Liaohe River Delta, China, the ET of eight growing seasons during 1985–2017 was estimated using the surface energy balance algorithm for land (SEBAL) model with Landsat and meteorological data. Results show that the average relative error of regional ET estimated by the SEBAL model is 9.01%, and the correlation coefficient between measured and estimated values is 0.61, which indicates that the estimated values are reliable. This study observed significant spatial and temporal variations in ET across the region of interest. The distribution of the average and relative change rate of daily ET in the study area showed bimodal characteristics, that is, the lowest trough occurred in 2005, whereas crests occurred in 1989 and 2014. Simultaneously, the daily ET varied with the land use/cover area. Regional daily ET displays highly heterogeneous spatial distribution, that is, the ET of different land uses/cover types in descending order is as follows: water body, wetland vegetation, non-wetland vegetation, and non-vegetation (except water area). Therefore, the spatial pattern of ET is relevant to the land use/cover types to some extent. In addition, the temporal variation of wetland ET is closely related to landscape transformation and meteorological factor change. A strong correlation was found between ET and the weighted values of meteorological factors, with a correlation coefficient of 0.69. Meanwhile, the annual fluctuations of daily ET and the weighted values were relatively similar. Therefore, the findings highlight the importance of using cheap and readily available remote sensing data for estimating and mapping the variations in ET in coastal wetland.

Application of non-linear regression and soft computing techniques for modeling process of pollutant adsorption from industrial wastewaters

The process of pollutant adsorption from industrial wastewaters is a multivariate problem. This process is affected by many factors including the contact time (T), pH, adsorbent weight (m), and solution concentration (ppm). The main target of this work is to model and evaluate the process of pollutant adsorption from industrial wastewaters using the non-linear multivariate regression and intelligent computation techniques. In order to achieve this goal, 54 industrial wastewater samples gathered by Institute of Color Science & Technology of Iran (ICSTI) were studied. Based on the laboratory conditions, the data was divided into 4 groups (A-1, A-2, A-3, and A-4). For each group, a non-linear regression model was made. The statistical results obtained showed that two developed equations from the A-3 and A-4 groups were the best models with R2 being 0.84 and 0.74. In these models, the contact time and solution concentration were the main effective factors influencing the adsorption process. The extracted models were validated using the t-test and F-value test. The hybrid PSO-based ANN model (particle swarm optimization and artificial neural network algorithms) was constructed for modelling the pollutant adsorption process under different laboratory conditions. Based on this hybrid modeling, the performance indices were estimated. The hybrid model results showed that the best value belonged to the data group A-4 with R2 of 0.91. Both the non-linear regression and hybrid PSO-ANN models were found to be helpful tools for modeling the process of pollutant adsorption from industrial wastewaters.

Plackett-Burman design and response surface optimization of conditions for culturing Saccharomyces cerevisiae in Agaricus bisporus industrial wastewater.

The Agaricus bisporus industrial wastewater that contains a variety of nutrients which could be used as culture for some beneficial microbiology will be one threat to our environment if the wastewater doesn&rsquo;t be comprehensively utilized. Plackett-Burman is the rapid and concise ways of screening the main effective factors. Box-Behnken response surface method is used to optimize interactions between the three main factors and predict optimal fermentation conditions. This study is aimed to select the main influence fac- tors and optimize the conditions for culturing Saccharomyces cerevisiae in A. bisporus industrial wastewater by Plackett-Burman design and Box-Behnken response surface method. We analyzed the total number of living S. cerevisiae in the fermentation broth using multispectral imaging flow cytometry. Plackett-Burman design was used to screen out three factors from the original six factors of processing wastewater concentration, initial pH, inoculum size, liquid volume, culture temperature, and rotation speed that affected the total number of viable S. cerevisiae. Factors significantly affecting the total number of viable S. cerevisiae, including culturing temperature, processing wastewater concentration, and initial pH were investigated. Result. The results indicated that culture temperature (p = 0.0007) and pH (p = 0.0344) as negative factor and concentration (p = 0.0080) as positive effect were the significant factors affecting the total number of S. cere- visiae, inoculum (p = 0.1237) and shaking speed (p = 0.2112) as positive effect and loaded liquid (p = 0.4811) as negative factor were important fact. nd. The Agaricus bisporus industrial wastewater that contains a variety of nutrients which could be used as culture for some beneficial microbiology will be one threat to our environment if the wastewater doesn&rsquo;t be comprehensively utilized. Plackett-Burman is the rapid and concise ways of screening the main effective factors. Box-Behnken response surface method is used to optimize interactions between the three main factors and predict optimal fermentation conditions. This study is aimed to select the main influence fac- tors and optimize the conditions for culturing Saccharomyces cerevisiae in A. bisporus industrial wastewater by Plackett-Burman design and Box-Behnken response surface method. Material and methods. We analyzed the total number of living S. cerevisiae in the fermentation broth using multispectral imaging flow cytometry. Plackett-Burman design was used to screen out three factors from the original six factors of processing wastewater concentration, initial pH, inoculum size, liquid volume, culture temperature, and rotation speed that affected the total number of viable S. cerevisiae. Factors significantly affecting the total number of viable S. cerevisiae, including culturing temperature, processing wastewater concentration, and initial pH were investigated. Result. The results indicated that culture temperature (p = 0.0007) and pH (p = 0.0344) as negative factor and concentration (p = 0.0080) as positive effect were the significant factors affecting the total number of S. cere- visiae, inoculum (p = 0.1237) and shaking speed (p = 0.2112) as positive effect and loaded liquid (p = 0.4811) as negative factor were important factors. The optimum conditions for S. cerevisiae fermentation in A. bi- sporus wastewater were a rotational speed of 150 rpm, a culture temperature of 25&deg;C, an initial pH of 6.0,&nbsp;&nbsp; a concentration of 8.4%, a inoculation volume of 8%, and a 100 mL liquid volume in a 250 mL flask, a culture time of 48 h. Under these conditions, the concentration of total viable yeast reached 1.04 &plusmn;0.02 &times; 108 Obj/mL which was at the 95% confidence interval of predicted model (0.89&ndash;1.14 &times; 108 Obj/mL). The experimental model is reliable and the experimental results are of good stability. Variance analysis is performed to determine the adequacy and significance of the linear model. Thus, Plackett-Burman.

Effects of mobile phone application combined with or without self-monitoring of blood glucose on glycemic control in patients with diabetes: A randomized controlled trial.

Aims/IntroductionThere is potential for mobile applications to deliver new self‐management interventions for chronic disease, especially in diabetes. The aim of the present study was to evaluate the effects of a mobile phone application (MPA) combined with or without self‐monitoring of blood glucose (SMBG) on glycemic control in patients with diabetes.Materials and MethodsThe study was a 24‐week period, four‐arm parallel group, non‐blinded, randomized trial. A total of 185 patients with mean age of 52 years were randomized to group A (no MPA and no SMBG), group B (SMBG only), group C (MPA only) and group D (both MPA and SMBG were used). Changes in glycated hemoglobin (HbA1c), fasting plasma glucose and 1,5‐anhydroglucitol from baseline to week 24 were analyzed.ResultsAt 24 weeks, the HbA1c levels in patients of all groups decreased significantly from baseline. There were significant differences in the proportions of patients that achieved HbA1c <7% between groups, especially in group C and group D, compared with group A at week 24 (60.4%, 62.2% vs 25.5%, all P < 0.05). 1,5‐Anhydroglucitol changes were obvious in group A and group C at week 24 from baseline (all P < 0.05 within groups). Factorial analysis of anova showed that MPA intervention was the main effective factor for HbA1c change (F = 4.59, P = 0.034), and there was no effect on HbA1c change for SMBG intervention (P = 0.975).ConclusionsImplementation of the MPA, Diabetes‐Carer, is effective in improving the proportion of HbA1c <7% in patients with type 2 diabetes.