Correct application of the classical factorial F-test depends on normality and homogeneity of variance assumptions. If these assumptions are violated the type I error rate will be inflated and power of the test will be decreased. Therefore nonparametric statistical tests have been proposed to analyze the interaction effects in factorial designs. A simulation was conducted to investigate the effect of non-normality on type I error rate and power of the test of the classical factorial F-test and five nonparametric tests namely rank transformation (FR), Winsorized mean (FW), modifies mean (FM), adjusted rank transform (ART) and adjusted median transform (AMT) using program SAS 9.4 with 1,000 replications. The study used 2×2 factorial design with replications of 3, 4 and 6 making sample sizes of 12, 16, and 24, respectively and 3×3 factorial designs with replication of 3 making a sample size of 27 studied at 0.05 level of significance. As a results, when the normality of assumption is satisfied all six statistical tests have the ability to control type I error in all situations. The ART test cannot control type I error rate for 3×3 factorial design when sample size is 27 when normality assumption is violated. For power of the test, the F-test provided the highest test power when the normality of assumption is met. The ART and AMT tests provided approximately the same test power. The AMT and ART tests can be effectively used to analyse the interaction effect between factors A and B in 2×2 factorial design when the sample size is 12 and 16 or 24 respectively and the normality of assumption is not met. Moreover, the results showed that when sample sizes increased, all six statistical tests tended to increase the power of the test.

This paper proposes a new distribution named the negative binomial-weighted Lindley. The study uses the maximum likelihood estimation to estimate the parameters of the proposed distribution and compares the performance of the new method with other distributions. The study finds that the negative binomial-weighted Lindley distribution, obtained by the mixing the negative binomial distribution with the weighted Lindley distribution is another mixed negative binomial distribution and may provide an appropriate fit for data estimation with overdispersion. Some characteristics of the proposed distribution, such as mean and variance are also derived.

This paper characterizes the routing problems arising in distribution of rice-for-the-poor in a district and presents a generic mathematical formulation of vehicle routing problems (VRP) for solving the problems. The proposed generic model, framed as a mixed integer linear programming, is formulated in such a way to encompass three distinct features; namely multiple depots (MD) establishment, multiple trips (MT) transportation, and split delivery (SD) mechanism. This model is implemented for a real-world problem of rice-for-the-poor distribution in the Ponorogo district of Indonesia, involved for deliveries among 3 depots—8, 17, and 23 villages depended on the distribution period—using a fleet of 5 vehicles of homogeneous capacity. Three types of distribution model are identified as MD-MT-VRP, MD-VRP-SD and MD-MT-VRP-SD.

Data envelopment analysis models play an important role in decision making. In this paper, one-stage and two-stage nonlinear programming problems are investigated in order to evaluate the efficiency of two types of network data envelopment analysis model. The first type of network data envelopment analysis model has a series structure with three stages and a feedback between the last step and the middle step, the second model has a three-stage series structure with two feedback between the final step and the first step and the middle step. By examining the overall efficiency of the models based on the one-stage programming problem, a two-stage programming problem is also applied in order to evaluate the efficiency of each step. In order to solve one-stage nonlinear programming problems and two-stage linear and nonlinear programming problems derived from modeling, a linearization method based on coordinate transformation, and constant assumption and gradual growth of some variables is presented. In the last section, the proposed methods have been discussed using some numerical examples.

This paper presents a new penalty function called logarithmic penalty function (LPF) and examines the convergence of the proposed LPF method. Furthermore, the LaGrange multiplier for equality constrained optimization is derived based on the first-order necessary condition. The proposed LPF belongs to both categories: a classical penalty function and an exact penalty function, depending on the choice of penalty parameter. Moreover, the proposed LPF is capable of dealing with some of the problems with irregular features from Hock-Schittkowski collections of test problems.

This paper proposes a framework for selecting energy storage technology (EST) for small-scale renewable energy systems. The present study was able to achieve this using the results that were generated from a Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) and a fuzzy quality function deployment (FQFD) model. Using the FQFD model, experts' opinions were incorporated into the framework during the evaluation of the techno-economic requirements importance for the selection process. The FQFD model considered system safety, availability, flexibility, reliability and effectiveness as customers’ requirements, while nominal voltage, nominal discharge current, peak measured voltage, operating cost and cost of energy were among the techno-economic requirements. During the framework application, a questionnaire was used to collect information from experts on the techno-economic requirements importance, while information on the techno-economic (eleven) requirements for four ESTs were obtained from literature. The FQFD model results were incorporated into a PROMETHEE and TOPSIS models. The PROMETHEE and the TOPSIS models identified Lithium-Ion EST as the best EST for small-scale renewable energy systems.

Mesoporous materials are substances whose pores maintain diameters between 2 and 50 nm, according to IUPAC nomenclature. This paper presents a comprehensive scientometrics on the existing trend on mesoporous materials in chemistry. The study uses Web of Science database as the primary source of value added articles and performs different methods for detecting highly cited articles, most active countries, etc. The search of articles using Web of Science was accomplished with two keywords “Mesoporous materials” and “Chemistry”. In Web of Science, there were about 800 articles related to these keywords over the period 1900-2019. Then the articles were arranged according to the citation order in non-increasing order, and among them, we found about 200 highly cited articles. According to our survey, green chemistry, water, heterogeneous catalysis and aqueous-solution build a structure on mesoporous materials. Moreover, conversion, guest molecules, triblock, oxidation, heterogeneous catalysts set the corner of other structure of the study. The survey also indicates that there were three clusters associated with mesoporous materials in chemistry. In the first cluster silica appears to be the most important word followed by molecularsieves and MCM-41. Organic-group is the most important word in the second cluster followed by hybrid materials. In cluster 3, nanoparticles appears to be the most important word followed by functionalization. In our study, absorption, nanoparticles and drug-delivery are detected as the emerging keywords and future studies could be concentrated on these subjects.

Lamotrigine (LMT), chemically known as [6-(2,3-dichlorophenyl)-1,2,4-triazine-3,5-diamine], is a broad spectrum antiepileptic drug, used as monotherapy and as an adjunct with other antiepileptic drugs for treatment of partial and generalized toxic-clonic seizures. It is used to treat neurological lesions and as a tranquilizer. A selective electrochemical membrane sensor has been developed and validated for determination of LMT. The membrane constructed using LMT and molybdophosphoric acid in THF and PVC is applicable for the detection of 5 × 10^-4 to 9 × 10^-3 M LMT in the pH range between 4.6 and 5.8 with the Nernstian slope of 57.14±1 mV/decade. The regression coefficient value of 0.9932 showed a good linear correlation between the concentrations of LMT and measured cell potentials. The limits of detection (LOD) and quantification (LOQ) values for the fabricated sensor were 1.3 × 10^-5 and 4 × 10^-5 M LMT, respectively. Various experimental conditions were optimized to reach the effective performance characteristics of the sensor. The effect of various cations, anions and organic species on the performance of sensor was studied by following standard-addition procedure. The results revealed no such variations due to presence of foreign ions or species. The fabricated sensor was subjected to validation to check accuracy, precision, robustness and ruggedness. The mean accuracy for determination of LMT was found to be 99.16%. The developed sensor was successfully used to determine LMT in tablets and in spiked human urine.

Metal–organic frameworks (MOFs) are combinations of metal ions or clusters accommodated to organic ligands to shape different dimensional structures. MOFs are considered as a subclass of coordination polymers, with the possible characteristics that they are normally porous. The metals are considered to offer flexible, co-ordination environment under virtually various topologies. Besides, because of the usual liability of metal complexes, the shape of the coordination bonds between the metal ions and the organic linkers can be reversible and this helps the rearrangement of metal ions and organic linkers through the process of polymerization to give highly ordered framework structures. The study has indicated that MOFs has maintained extensive applications in Biological imaging and sensing, Drug delivery systems, Methane storage, Semiconductors, Bio-mimetic mineralization, Carbon capture, Desalination/ion separation, Water vapor capture and Ferroelectrics and Multiferroics. This paper presents a scientometrics study on 1273 papers published articles, books, patents, etc. indexed in Web of Science database over the period 2001-2019. The study presents the most popular keywords used in the literature, determines the network of scientific scholars and discusses the clusters of keywords used for different surveys. The results indicate that metalorganic frameworks and zeoitic imidazolate frameworks are two keywords considered as motor keywords in MOFs studies.

During the past century, humans have increased atmospheric CO₂ concentration by at least a third, which is considered as the most important long-lived “forcing” of climate change. Scientists all over the world are responsible to do their best on offering practical solutions to reduce the effects of CO₂ on environment. This paper uses data envelopment analysis to measure the effects of researches accomplished by scientists from 30 countries which are representative of producing over 80% of CO₂ in the world. The study uses 10034 articles published in Scopus database from 1959 to March, 2019. The study uses the amount of CO₂ produced by each country as the input and total publications, h-index and I-10 as the output of the DEA model. The results indicate that despite the fact that China was responsible for producing nearly 30% of the CO₂, the scientists of this country contributed the least on carbon dioxide emissions. In addition, United Kingdom was responsible for about one percent of CO₂ emissions but the researchers of UK performed the best in terms of offering good quality studies.