Aid Of Experts Research Articles

Maize cob-derived activated carbon as an adsorbent for the removal of nickel (II) cation from aqueous solution: optimization and kinetic studies

Adsorption performance of maize cob-derived activated carbon (MCAC) for the removal of nickel (II) from solution was investigated in the present study. The MCAC was prepared by adsorbent chemical activation method. Afterwards, the MCAC was characterized and utilized as the adsorbent for batch adsorption process. Initial adsorbate concentration, contact time and adsorbent dosage were considered as the independent variables for the adsorption process. The experiments were designed based upon central composite design (CCD) of response surface methodology with the aid of Design Expert. The experimental data were fitted into a model using MATLAB regression analysis, and the developed model was later simulated to obtain the operation parameters that will give the optimum removal efficiency. The analysis of variance (ANOVA) revealed that all the considered independent process variables had significant effect on the adsorption process. The simulated response from MATLAB gave 97.113% removal efficiency of nickel (II) on experimental validation at optimum conditions of 9.75 mg/L initial nickel (II) concentration, 120 min contact time and 0.803 g adsorbent dosage. This result correlates with CCD that predicted 97.6154% nickel (II) removal efficiency. The experimental data were observed to be best fitted to pseudo-second-order kinetic model which showed that the process is chemisorption controlled. Hence, it may be concluded from the present data that MCAC is highly effective for the removal of Ni(II) from aqueous solution and the highly generated agricultural waste (Maize cob) can the easily modified as a adsorbent.

Optimization of extraction conditions for phytochemicals from Senna fistula using cheminformatics

Senna fistula (commonly called golden shower tree) is a tropical plant renowned for its established medicinal properties. Additionally, it serves as a popular ornamental tree for homesteads and roadside plantings. But previous phytochemical studies on the plant adopted the conventional methods of extraction without optimal yield of the plant extract. This study investigated on the phytochemistry of S. fistula leaf, stem bark and flower using ethanol, aqueous and palm wine extracts. Also, optimization of extraction with the aid of design expert was carried out to determine the extraction condition that would give the optimum yield using different solvent. Result of qualitative analysis on phytochemicals showed presence of anthraquinones, flavonoids, saponins, terpenoids, phenols, cardiac glycosides and tannins. An extraction of 10 g of S. fistula for 24 h at 300 rpm, with an orbital shaker time of 30 min, gave an optimal yield of 3.6 g. The bio-active compounds found in S. fistula are subjects of more exploratory research with usable products that can benefit mankind as the focus.

Addressing Business Process Deviations through the Evaluation of Alternative Pattern-Based Models

Business processes (BPs) have become extremely complex with thousands of tasks to be completed. These processes should be modelled to manage their complexity, but deviations from these models can occur during execution. To account for the potential outcomes, models are often over-specified, but in fact, it is impossible to anticipate every scenario. Therefore, the issue of how to model the response to these problems arises. The approach put forward in this study entails the creation of a primary model with the aid of domain experts. If a deviation occurs during execution, the system searches for compatible patterns in a collaborative library of workflows, rather than relying on dedicated solutions for specific problems. Nevertheless, the main challenges lie in selecting the most appropriate set of candidates from a vast number of patterns, as well as identifying the optimal injection points on the primary model to correct the deviation. One main objective is to rapidly eliminate incompatible patterns by employing simple mathematical techniques to narrow down the pool of candidate solutions. This serves to minimize the number of patterns that should be considered where more sophisticated methods can then be utilized to rank and select the best solution from this smaller set.

Journal Digest: CBT With Cardiac Rehab, PMDD Neurochemistry, and More

Journal Digest: CBT With Cardiac Rehab, PMDD Neurochemistry, and More

Validity of Physics Teaching Materials Based on STEM to Improve Climate Literacy of High School Students

This study aimed to develop physics teaching material based on Science, Technology, Engineering, and Mathematics (STEM), which criteria valid. The study employed is development research using the ADDIE development model. The ADDIE which consists of five stages, namely Analyze, Design, Development, Implementation, and Evaluation which is limited to three stages, namely; (1) analysis, (2) design, and (3) development for aspects of validity. The validity of the teaching material was determined with the aid of experts and learning practitioners. The validation results of physics teaching material based on STEM were categorized as very valid with a validity level of 92% and student response with a score of 85%. Based on the results obtained, the climate change physics teaching materials based on STEM are suitable for improving high school students' climate literacy.

Compressive Strength and Optimization of Concrete Produced by Replacing Cement with Coconut Shell Ash (CSA) and Groundnut Shell Ash (GSA)

The compressive strength and optimization of concrete produced by replacing cement with CSA and GSA was conducted in this study. The materials used were cement, water, fine aggregate, coarse aggregate, CSA, and GSA. The CSA and GSA replaced cement at 5, 10, 15, 20, and 25% in combination, with a total of seventy two (72) cubes cast, while the concrete compressive strength was determined after curing by complete immersion in water at 7, 14, 21, and 28 days. The modeling and optimization was done with the aid of design expert (version 13) software, while the Analysis of Variance (ANOVA) was done using SPSS (Statistical Package for Social Sciences) version 23 and adopting the Least Square Difference (LSD) method. Results from the findings showed that the CSA used in the study is a good reactive pozzolana, and the GSA is not a reactive pozzolana. Also, the increase in CSA and GSA content in concrete reduces its workability, and concrete compressive strength. However, cement replaced with 5%CSA-0%GSA gave the optimum 28 days compressive strength which is not statistically significantly from the control concrete and can be used to produce concrete of compressive strength close to the control. Further findings from the regression model showed that CSA-GSA concrete compressive strength significantly fits a linear model and can be used to predict the 7, 14, 21, and 28 days compressive strength of CSA-GSA concrete, while the optimized result showed that CSA and GSA replacement of cement at 3.29% and 4.45% respectively yielded an optimum compressive strength of approximately 22.31N/mm2.

Modeling and Optimization of Concrete Compressive Strength Produced by Replacing Natural Aggregate with Recycled Coarse and Recycled Asphalt Pavement Aggregates

The modeling and optimization of concrete compressive strength produced by replacing natural aggregate (NA) with recycled coarse aggregate (RCA) and recycled asphalt pavement (RAP) aggregates was investigated. The materials used were cement, water, fine aggregate, and coarse aggregate (NA, RCA, and RAP). The RCA and RAP replaced NA at 20, 40, 60, 80, and 100% separately, and in combination, with a total of two hundred and forty (240) cubes cast, while the concrete compressive strength was determined after curing by complete immersion in water at 3, 7, 14, 21, and 28 days. The modeling and optimization was done with the aid of design expert (version 13) software, while the Analysis of Variance (ANOVA) was done using SPSS (Statistical Package for Social Sciences) version 23 and adopting the Least Square Difference (LSD) method. Results from the findings showed that the optimum 28 days concrete compressive strength are concrete produced with 20%RAP, 40%(20%RAP +20%RCA), 60%(30%RAP + 30%RCA), and 80%(40%RAP + 40%RCA) replacements which are not significantly different from the control concrete, however, 40%(20%RAP +20%RCA) and 80%(40%RAP + 40%RCA) concrete had compressive strength higher than the control concrete. Also, the 3, 7, 21, and 28 days compressive strength can be predicted with quadratic model, while the 14 days strength can be predicted with 2-Factor Interaction (2FI) model. The optimization result showed that by replacing natural aggregate with 10.0% RAP and 50.0% RCA, the 28 days compressive strength of the concrete was 33.74N/mm2.

EVALUATION OF DRAW SOLUTIONS FOR FORWARD OSMOSIS USING A SODIUM ALGINATE-BACTERIAL CELLULOSE MEMBRANE FOR WATER RECOVERY

Forward osmosis (FO) is an emerging membrane technology that is comparable with existing industrial membrane separation processes. Several studies have shown the potential of bacterial cellulose-alginate (BCA) as membrane material for FO system. An ideal draw solution (DS) compatible with this material was investigated. The three solutions used in the study include NaCl, MgCl2, and fructose. A simulated dirty water was used as the feed solution (FS) and a BCA as the membrane. An optimization study was conducted using central composite design (CCD) with the aid of Design Expert 7.0.0. The optimization was based on a fitted linear model analyzed through ANOVA with the normalized water flux maximized. The optimal solution was determined to be fructose with operating conditions at an osmotic pressure of 70 bar and a flow rate of 300 mL/min. The normalized water flux given these optimal conditions is predicted to be 1.437 LMH∙mm with a desirability of 0.768.

Response surface optimization of a vortex-assisted dispersive liquid–liquid microextraction method for highly sensitive determination of repaglinide in environmental water by HPLC/UV

A vortex-assisted dispersive liquid–liquid microextraction (DLLME) method, mated to chemometrics and combined with HPLC/UV detection was optimized and validated for enrichment and determination of repaglinide in environmental samples using nateglinide as an internal standard (IS). A phosphate buffer (10 mM, pH 2.5): acetonitrile (45:55, v/v) was used as a mobile phase with a flow rate of 1 mL/min in an isocratic elution mode. Chemometrics-assisted optimization was performed using a quadratic integrated d-optimal design. The developed model assessed the statistical significance of the independent variables and their interactions to attain the optimum conditions revealing that extractant type, extractant volume and pH are the most influential factors. Optimization of the extraction procedures was performed with the aid of Design Expert 8® software, which suggested 58 different experiments. The optimal conditions were 30 µL of 1-octanol as extractant, 100 µL of acetonitrile as a disperser at pH 8. Under the optimized conditions, the method showed linearity over the range of 1–100 ng/mL with a limit of detection of 0.4 ng/mL. The accuracy, the intra- and inter-day precision were assessed, the %recoveries were found to be between 98.48 and 100.81% with %RSD lower than 1.3. Using chemometrics in method optimization helped achieve the maximum possible enrichment with the least effort, time, and reagents while considering all possible interactions between variables.

“Zbor imaat graǵanite”: The First Sociological Study, the Polish Sociological Expert Aid to Macedonia in the Mid-1960s and the Post-Earthquake History of Interethnic Relations in Skopje

On the early morning of 26 July 1963, a calamitous earthquake struck the Macedonian capital of Skopje, taking the lives of 1,070 people and destroying more than two-thirds of the urban fabric. The politically non-aligned Yugoslav government immediately issued a call for help for the earthquake-torn city, which was picked up by more than eighty states across the globe, as well as the United Nations and other international organizations. The domestic authorities, in turn, sought to reimagine post-disaster Skopje as a “City of Solidarity,” a symbol of the trans-bloc cooperation, and an “Open City” – one open to domestic and intra-federal migrations and the epitome of the trans-Yugoslav state-building slogan of “brotherhood and unity.” However, the mounting interethnic tensions in the 1980s, the Yugoslav dissolution, and the 2001 insurgency dramatically shifted the public optics over the post-earthquake urban reconstruction and demographic politics – a narrative which found a particular stronghold in the memory politics of post-2001 Macedonia.construct their own interpretations of the social change. The present paper discusses one overlooked episode from the post-earthquake reconstruction of Skopje: from December 1964 to April 1965, the first ever large-scale sociological survey was conducted among Skopjans as part of the Polish expert aid and the preparations for the UN-sponsored Skopje Urban Project. Although the published study contained an exclusive portrayal of the economic and demographic features of the local households and revealed some of the major interethnic issues in the city, it never received proper treatment by the authorities and – up to the Yugoslav dissolution – in the scholarship. Thus, in order to present the major outcomes of this cross-national endeavor, I reconstruct the prehistory, the fieldwork and the immediate results of the survey by triangulating a set of archival materials, semi-structured interviews with its Polish and Macedonian conveners, and secondary literature on Skopje’s urban reconstruction. Finally, I argue that the survey – its realization, results and aftermath – can be read as a key to a better understanding of the post-earthquake history of Skopje and the interethnic relations in the city.

Implementation of machine learning techniques for disease diagnosis

Recently, data mining and machine learning techniques have found widespread use in the field of healthcare. The objective of this study is to develop an automated method for diagnosing illnesses. A Fuzzy logic-based random forest approach and a thorough examination of the patient's medical records are used to diagnose the disease. Clinical diagnosis is performed with the aid of a doctor's expertise and understanding in traditional healthcare. It is more challenging to provide good healthcare in rural and remote areas because patients are more likely to travel a long distance to visit a specialist. Because the number of medical practitioners and facilities in these areas is limited, providing an expert diagnosis in a fair period of time is challenging. The problem can be solved by using expert systems for disease diagnosis that employ data mining techniques and fuzzy logic. Decision trees are often used in machine learning to predict outcomes. Fuzzy datasets are an excellent choice for describing medical facts and expert opinions. Fuzzy decision trees build simple decision trees using fuzzy input. In this proposed system, an expert system that diagnoses disease using a random forest algorithm and fuzzy decision trees is provided. The fuzzy decision trees increase the accuracy of the diagnostic system. On the UCI repository, the proposed method is assessed and found to be more efficient in sickness prediction than current strategies. Classification accuracy has risen as temporal complexity has decreased.

Mechanical Properties of Granite-Gravel Porous Concrete

The need for porous concrete has become increased due its ability to control surface water, increase the rate of recharging groundwater, and reduce pollution of the ecosystem. Granite is a coarse aggregate that is quite expensive when compared with gravel in Nigeria. Therefore, this research is aimed at optimizing blended granite and gravel in the production of porous concrete. Samples of blended granite-gravel porous concrete of varying mix proportions were produced using cement to aggregate mix ratio of 1:4. The samples were tested for their porosity, workability and compressive strengths. The data collected were analyzed with the aid of Design Expert 10.0. It was observed that the optimal combination for the granite-gravel blended porous concrete is 12% granite, 88% gravel, and a water-cement ratio of 0.66%. This combination gave a porous concrete with a compressive strength of 48.4 N/mm2, percentage porosity of 6% and a compacting factor of 0.91. These values when compared to that of the control specimen revealed that the optimal mix gave a porous concrete with higher porosity, higher workability and a better compressive strength.

Brewers, Booze and Medicine: Industrial Funding of Alcoholic Liver Disease Research in 1980s Britain

Summary This article investigates why Britain’s brewing industry chose to sponsor a particular set of studies on liver disease during the 1980s, expounding on the nature of private-sector funding in clinical research. Through previously unexplored internal records, the article underscores how the drinks industry participated in wider debates on alcohol and public health by seeking the aid of medical expertise. Partly to minimise its own responsibility for the rise of problem drinking, the industry financed studies that looked into factors other than alcohol that contributed to the development of cirrhosis. The identification of the disease’s inborn determinants relating to heredity and sex strengthened the notion that only a minority of drinkers were at risk of developing alcohol-related illnesses, undermining the call for policies that targeted all drinking. Although driven by self-interest, the industry nevertheless supported legitimate lines of inquiry that elucidated the complex, multifactorial causation of cirrhosis.

Modified Neural Architecture Search (NAS) Using the Chromosome Non-Disjunction

This paper proposes a deep neural network structuring methodology through a genetic algorithm (GA) using chromosome non-disjunction. The proposed model includes methods for generating and tuning the neural network architecture without the aid of human experts. Since the original neural architecture search (henceforth, NAS) was announced, NAS techniques, such as NASBot, NASGBO and CoDeepNEAT, have been widely adopted in order to improve cost- and/or time-effectiveness for human experts. In these models, evolutionary algorithms (EAs) are employed to effectively enhance the accuracy of the neural network architecture. In particular, CoDeepNEAT uses a constructive GA starting from minimal architecture. This will only work quickly if the solution architecture is small. On the other hand, the proposed methodology utilizes chromosome non-disjunction as a new genetic operation. Our approach differs from previous methodologies in that it includes a destructive approach as well as a constructive approach, and is similar to pruning methodologies, which realizes tuning of the previous neural network architecture. A case study applied to the sentence word ordering problem and AlexNet for CIFAR-10 illustrates the applicability of the proposed methodology. We show from the simulation studies that the accuracy of the model was improved by 0.7% compared to the conventional model without human expert.

Measuring the effect of automatically authored video aid on assembly time for procedural knowledge transfer among operators in adaptive assembly stations

ABSTRACT Can automatically authored videos of industrial operators help other operators to learn procedural tasks? This question is relevant to the advent of the industrial internet of things (IIoT) and Industry 4.0, where smart machines can help human operators rather than replacing them in order to benefit from the best of humans and machines. This study considers an industrial ecosystem where procedural knowledge (PK) is quickly and effectively transferred from one operator to another. Assembly tasks are procedural in nature and present a certain complexity that still does not allow machines and their sensors to capture all the details of the operations. Especially if the assembly operation is adaptive and not fixed in terms of assembly sequence plan. In order to help the operators, videos of other operators executing the complex procedural tasks can be automatically recorded and authored from machines. This study shows by means of statistical design and analysis of experiments that expert aid can reduce the assembly time of an untrained operator, whereas automatically authored video aids can transfer PK but producing an opposite effect on the assembly time. Therefore, hybrid training methods are still necessary and trade-offs have to be considered. Managerial insights from the results suggest an unneglectable impact of the choice to digitise industrial operations too early. The experimental studies presented can act as guidelines for the correct statistical testing of innovative solutions in industry.

Social Media Engagement and Influenza Vaccination During the COVID-19 Pandemic: Cross-sectional Survey Study.

BackgroundVaccines are one of the most important achievements of modern medicine. However, their acceptance is only partial, with vaccine hesitancy and refusal representing a major health threat. Influenza vaccines have low compliance since repeated, annual vaccination is required. Influenza vaccines stimulate discussions both in the real world and online. Social media is currently a significant source of health and medical information. Elucidating the association between social media engagement and influenza vaccination is important and may be applicable to other vaccines, including ones against COVID-19.ObjectiveThe goal of this study is to characterize profiles of social media engagement regarding the influenza vaccine and their association with knowledge and compliance in order to support improvement of future web-associated vaccination campaigns.MethodsA weblink to an online survey in Hebrew was disseminated over social media and messaging platforms. The survey answers were collected during April 2020. Anonymous and volunteer participants aged 21 years and over answered 30 questions related to sociodemographics; social media usage; influenza- and vaccine-related knowledge and behavior; health-related information searching, its reliability, and its influence; and COVID-19-related information searching. A univariate descriptive data analysis was performed, followed by multivariate analysis via building a decision tree to define the most important attributes associated with vaccination compliance.ResultsA total of 213 subjects responded to the survey, of whom 207 were included in the analysis; the majority of the respondents were female, were aged 21 to 40 years, had 1 to 2 children, lived in central Israel, were secular Israeli natives, had higher education, and had a salary close to the national average. Most respondents (128/207, 61.8%) were not vaccinated against influenza in 2019 and used social media. Participants that used social media were younger, secular, and living in high-density agglomerations and had lower influenza vaccination rates. The perceived influence and reliability of the information on social media about COVID-19 were generally similar to those perceptions about influenza.ConclusionsUsing social media is negatively linked to compliance with seasonal influenza vaccination in this study. A high proportion of noncompliant individuals can lead to increased consumption of health care services and can, therefore, overload these health services. This is particularly crucial with a concomitant outbreak, such as COVID-19. Health care professionals should use improved and targeted health communication campaigns with the aid of experts in social media. Targeted communication, based on sociodemographic factors and personalized social media usage, might increase influenza vaccination rates and compliance with other vaccines as well.

What are the main drivers of Blockchain Adoption within Supply Chain? – an exploratory research

This paper aims to identify the main drivers for Blockchain adoption within Supply Chains, following the Design Science Research (DSR) methodology and focusing on defining, validating, and reducing a myriad of factors with the aid of experts on both fields. Based on the literature review, and through means of questionnaires, factors were rated and refuted according to the relevance given. Two rounds narrowed the results, and upon consensus nine drivers were identified fitting under two categories: Challenges and Incentives. Considering the results are based on a limited number of factors, and the impact of firm-level influences were disregarded, respondent’s perceptions could have slightly varied if the sample of participants differed. Overall, it provides academics with a theoretical framework combining existing literature into a set of drivers. Providing insights to vendors on how their reputation may influence clients’ adoption, whilst fulfilling a literature gap in the supply chain area.

Application of response surface methodology in optimizing the process conditions for the regeneration of used mobil oil using different kinds of acids

Optimization of process parameters for the regeneration of used mobil oil by acid/clay method using sulphuric acid as washing agent was successfully carried out. Used mobil oil was characterized before and after regeneration to determine the changes in the physicochemical properties of the oils. The viscosity, flash point and fire point of the oil increased after regeneration while the specific gravity and sulphur content decreased. The Atomic Absorption Spectrometry (AAS) carried out revealed the presence of eight different metals in the fresh mobil whose concentration increased during use but reduced after regeneration. The FTIR instrumentation analysis revealed that the used mobil oil has high concentration of some ester groups, aromatic materials, glycol and sulphur oxidation products while the proportion of these materials plunged significantly after treatment. Changes in the process conditions like acid concentration, settling time and bleaching temperature affected the effectiveness of the regeneration process by affecting the desired changes in the physicochemical properties. The process parameters were optimized using Central Composite Rotatable Design (CCRD) of Response Surface Methodology (RSM), with the aid of design expert. The design, which use purity as the response of the experiment, considered three independent factors of acid concentration, settling time and bleaching temperature; and the effects of these factors on the response are significant since their P-values are less than 0.05. An optimum theoretical purity of 0.86 was obtained at 6.5mol/lit acid concentration, 5.5hours settling time and 95.5 °C bleaching temperature, which agreed excellently with 0.85 actual purity obtained at the same conditions. Therefore, the regenerated mobil oil at these optimal conditions is capable of meeting commercial expectations.

The relationship between parental source of information and knowledge about measles / measles vaccine and vaccine hesitancy

ObjectivesBased on the hypothesis that sources of information might affect knowledge and vaccine acceptance, our objectives were to study parental characteristics and sources of information regarding measles/measles vaccine, its relationship to correct knowledge and to administration of the measles vaccine. BackgroundAlthough measles eradication is potentially achievable, in 2018–2019 a worldwide resurgence of measles and measles-caused deaths occurred. The main driver was incomplete or no vaccination, designated as vaccine hesitancy (VH). MethodsA cross-sectional survey of 399 individuals dispersed all over the country was conducted. Research assistants interviewed parents with a 20-question survey which was previously validated. The questionnaire included four sections: demographics, major sources of information on measles/measles vaccine, knowledge about measles/measles vaccine, and status of child’s vaccination. Univariate and multivariate analyses explored associations between correct knowledge and VH. ResultsThe majority of respondents were between the ages of 25–39 (62%). Of these, 309 (77%) vaccinated their children against measles on time, 32 (8%) vaccinations were incomplete, and 58 (15%) did not vaccinate, for a total VH of 23%. Parents < 30 years-old and those with a single-child vaccinated less frequently (p < 0.001 and p = 0.002, respectively). Internet and social-media were the major source of information for 32% regarding measles/measles vaccine and for 49% regarding the measles outbreak; both sources were negatively associated with correct knowledge (p < 0.001). In the multivariate analysis, knowledge was independently associated with timely vaccine administration (p < 0.001) and internet or social-media as sources with higher VH (OR 2.52, 95%CI 1.18–5.37 and OR 2.44, 95%CI 1.01–5.91, respectively). ConclusionsSocial-media and internet are a common source of information on measles/measles vaccine (probably on other vaccines as well), and often associated with incorrect knowledge, which relates significantly to VH. Healthcare professionals should be aware of this prevailing behavior and respond accordingly in these platforms, with the aid of experts in social-networking.

Social sustainability assessment framework for managing sustainable construction in residential buildings

Nowadays sustainability concept has gained worldwide recognition in the building industry worldwide. However, despite the recent trends toward consideration of environmental and economic aspects in sustainability assessment framework for buildings, the concept of social sustainability has given less attention. Therefore, social issues were less included in the main categories of sustainability assessment schemes. As a result, such frameworks failed to effectively correspond to social aspects of sustainability and they have created gaps in analytical support necessary for sustainable decision-making. While there are some researches regarding social aspects of sustainability worldwide, investigations are still lacking in developing countries. In order to resolve this issue, this study aims to propose an assessment framework which addresses social sustainability in residential building construction by prioritizing social sustainability criteria based on Iran context. In this research, after exploiting social sustainability indicators, through multi-criteria decision making approach, criteria priorities were obtained with the aid of local experts. Results revealed that among the proposed criteria, local experts considered safety issues as the most important and site considerations and equipment as the least important criterion to be considered in the social sustainability assessment for residential buildings in Iran. After safety issues, performance and flexibility factor was the third-highest important criterion in terms of social sustainability in planning and construction in this country. From the results, it can be concluded that the establishment of social sustainability scheme with a consideration of priorities in a particular country can be a pivotal step forward to provide a responsive sustainability assessment for buildings, especially in developing countries such as in Iran. This research can shed light on the way in which other developing countries customize such social sustainability framework based on their own context to offer decision makers an efficient approach for measuring the social sustainability status of a residential building.