Characteristics of ultrafine-ground potassium-bearing shale and the effects of roasting pre-cracking

Potassium-bearing shale is being developed as a potential alternative to potash for use in fertilisers. The first step in this process is to reduce its particle size by crushing. This paper explores whether roasting pre-cracked potassium-bearing shale can improve the quality of the resulting ultrafine product. Analysis of the particle size distribution of the ultrafine product and its fractal dimension found contradictory results: the minimum particle size distribution was obtained by roasting for 2.5 h, while the minimum fractal dimension was obtained by roasting for 1 h. Fuzzy comprehensive evaluation was conducted with three indicators—(1) the weight of the − 10 μm product, (2) the fractal dimension of the particle size distribution, and (3) d97—to obtain a unique combination of indicators that reflects the quality and quantity of the products. The weights of the three indicators were calculated by an analytic hierarchical process to be 0.69, 0.149 and 0.161, respectively. Roasting pre-cracked shale for 2–2.5 h was found to improve the mean values of the fuzzy comprehensive evaluation indicators by about 0.07. However, the cost increased from 2.82 RMB to ≥ 10.08 RMB, which is not feasible for widespread industrial implementation.

Collaboration of Business Intelligence and Cloud Computing and Selecting the Best Cloud Business Intelligence Solution

In this study, business intelligence concept and architecture were explained from data sources to reporting with many advantages provided to institutions in the first part. Then, both cloud computing technology with its service and deployment models and the characteristics of cloud computing experienced clarified in the second part of the study. The relationship between cloud computing and business intelligence and the concept arisen from this collaboration, cloud business intelligence, were represented with its benefits and obstacles experienced by companies using this technology in the third part. Four service providers as alternatives serving cloud business intelligence solutions were selected and the criteria were determined according to the needs of the company, that would like to use a cloud business intelligence software. After all the criteria are prioritized and the alternatives are determined, the best software was chosen by using the Analytic Hierarchical Process software, called Expert Choice.

Factors Influencing Nurses' Intention to Leave Profession in Iran

Background: Several reasons account for nurses' intention to leave the nursing profession. Understanding these factors is a step towards reducing such incidents which pose a threat to the nursing profession. This study aimed to identify and rank the circumstances that influence the intention to leave the nursing profession in Iran. Methods: The present study is a cross-sectional analytical study, which employed four other methods to reach a sound conclusion in 2019.The electronic databases, including the Scientific Information Database, PubMed, Scopus, Science Direct, OVID, Cochrane Library, CINAHL,, were searched for studies published from the year 1998 to December 2018 for scientific studies conducted among factor associated intention to leave in Iran. The Exploratory Factor Analysis (EFA) method was then used to extract hidden factors and determine the hierarchical structure of the review's findings. Using the combination of Analytical Hierarchy Process and Fuzzy Logic, the weight of each one of the factors that affect the intention to leave the nursing profession was then calculated. Finally, the accuracy of the results of the Consistency Ratio and Fuzzy analytic hierarchical process (FAHP), was calculated and evaluated. Results: The electronic search delivered 100 studies, from which we identified and extracted 26 most occurring reasons for the desire to leave the nursing profession. Based on the EFA results, we identified four criteria (personal, managerial, organizational, and side issues) with eigenvalues ​​higher than 1. The first criteria (personal issues) scored the highest (53.39 %), while side issues scored the lowest (13.40 %) as reasons accounting for the intention to leave the nursing profession. Conclusion: Job stress, the lack of freedom and independence in the clinical environment, discrimination, and the probability of catching an illness have higher prominence in influencing nurses' tendency to leave work. These factors can be mitigated by altering managerial techniques within organizations.

Key Challenges to Sustainable Humanitarian Supply Chains: Lessons from the COVID-19 Pandemic

COVID-19 has had a major impact on health, economic, social, and industrial activities. It has disrupted supply chain management and affected the movement of essential supplies to a large extent. This study aims to identify and evaluate the challenges hampering sustainable humanitarian supply chain management (SHSCM). Twenty critical challenges to SHSCM are identified using a comprehensive literature review, and three strategies were developed. The challenges and strategies were verified using expert input. The challenges were evaluated using the neutrosophic analytic hierarchical process (AHP) method. The neutrosophic TODIM (an acronym in Portuguese for interactive multicriteria decision making) method was then used to select the best strategy. The findings reveal that facility location problems, short lead times for emergency supplies, spread of rumors, rapid emergence of new clusters, and doubt concerning the available remedy are five critical challenges in SHSCM during COVID-19. Public–private partnerships are identified as the best strategy in SHSCM. Finally, this paper discusses the implications to sustainable development goals in the post-COVID-19 pandemic era.

Evaluation Model of Remote Sensing Satellites Cooperative Observation Capability

This paper proposed a new remote sensing observation capability evaluation model (RSOCE) based on analytic hierarchy process to quantitatively evaluate the capability of multi-satellite cooperative remote sensing observation. The analytic hierarchical process model is a combination of qualitative and quantitative analysis of systematic decision analysis method. According to the objective of the remote sensing cooperative observation mission, we decompose the complex problem into several levels and a number of factors, compare and calculate various factors in pairs, and obtain the combination weights of different schemes. The model can be used to evaluate the observation capability of resource satellites. Taking the optical remote sensing satellites, such as China’s resource satellite series and GF-4, as examples, this paper verifies and evaluates the model for three typical tasks: point target observation, regional target observation, and moving target continuous observation. The results show that the model can provide quantitative reference and model support for comprehensive evaluation of the collaborative observation capability of remote sensing satellites.

Proposed extended analytic hierarchical process for selecting data science methodologies

Decision making can present a considerable amount of complexity in competitive environments; where methods that support possess great relevance. The article presents an extension of the Hierarchic Analytical Process; complemented with Personal Construct Theory, which purpose is to reduce ambiguity when defining and establishing values for the criteria in a determined problem. In recent years, the scope for decision making based on data has considerably raised, which is why Data Science as a scientific field is rising in popularity; where one of the main activities for data scientists is selecting an adequate methodology to guide a project with this traits. The steps defined in the proposed model guide this task, from establishing and prioritizing criteria based on degrees of compliance, grouping them by levels, completing the hierarchical structure of the problem, performing the correct comparisons through different levels in an ascendant manner, to finally obtaining the definitive priorities of each methodology for each validation case and sorting them by their adequacy percentages. Both disparate cases, one referred to an industrial/commercial field and the other to an academic field, were effective to corroborate the extent of usefulness of the proposed model; for which in both cases MoProPEI obtained the best results.