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Methylene blue (MB) is classified as a cationic dye which is widely used as chemical indicator, coloring agent and biological stain. The discharge of this dye to the water streams is harmful to the human beings. For this reason, this study investigated the removal of MB from aqueous solution by hydrogel nanocomposite.

In experimental part, at first, ultraviolet (UV)-curable hydrogel/chitosan nanocomposite, which improves its elasticity by urethane acrylate, was synthesized and characterized by FTIR and SEM analysis. Afterward, the synthesized hydrogel nanocomposite was applied for the removal of MB and the influence of operational condition including nanocomposite loading, dye concentration, contact time and pH of solution was specified. Moreover, isotherm studies as well as kinetics survey were performed.

Langmuir, Freundlich, Brunauer, Emmett and Teller and Tempkin adsorption isotherms were assessed for the analysis of experimental data indicating the Freundlich isotherm was the best fitted one. The adsorption kinetics data was examined indicating the adsorption kinetics appropriate to pseudo-second-order kinetics model.

The predominant water absorption property of the UV-curable hydrogel/chitosan nanocomposite to 8.5 steps and outstanding adsorption capacity for the elimination of MB on hydrogel nanocomposite subscribed that the synthesized hydrogel could be a favorable adsorbent for simultaneous absorption of water and removal of cationic dyes.

The purpose of this paper is to design a synergetic controller to precisely and quickly track reference signals in robot manipulators. Having smooth control signal this controller enables the nonlinear robot system to track desired references in presence of disturbances in a finite time.

A new synergetic manifold is introduced here, followed by adding a nonlinear exponential term to it have a precise tracking within a finite time of the desired references with disturbances. Previously the nonlinear term was inserted in the main synergetic equation which makes it complicated due to its hard mathematical approach. Using Lyapunov function, the stability of the system in the presence of disturbances is proved. The validity of the resulted system is confirmed by simulating it in Simulink.

Using a terminal synergetic controller with new manifold proposed in this work enables system’s state variables to track desired reference signal in the presence of disturbances from any initial condition with proper precision and rate. Simulation results show that compared to similar methods it provides a more proper speed and a finite time convergence with high precision and speed.

Providing fast and precise convergence, the proposed controller can be used in robot manipulator systems which need fast response and also have a precise performance such as in printing 3D objects and any industrial process.

The purpose of this paper is to evaluate the possible relationship of dietary glycemic index (GI) and glycemic load (GL) with ulcerative colitis (UC) risks because there is insufficient evidence on the association of GI and GL with UC incidences.

In this case–control study, dietary GI and GL of 62 patients with histology-confirmed UC and 124 controls were assessed using a validated semi-quantitative food frequency questionnaire. The primary outcome measure was the association between dietary GI and GL and UC risk.

In age-adjusted and unadjusted models, there was no statistically significant association between dietary GI and GL and UC risk. However, after adjusting for all known covariates, the odds ratio of UC in third tertile of GI was 2.86 folds higher than first tertile (95% CI: 1.02–8.00, p for trend = 0.04). In the case of GL, when the confounders were adjusted, although subjects in the third tertile were at 2.70 times higher risk of having UC than those in the first tertile (95% CI: 0.72–10.15, p for trend = 0.15), the association was not statistically significant.

In conclusion, the findings indicate a positive relationship between dietary GI and risk of UC. Further prospective studies are needed to elucidate if the findings are consistent, in addition to experimental studies to explore the potential mechanisms of this association.

This paper aims to present a hierarchal and operational model of coding knowledge towards facilitating the transformation of knowledge in organizations.

The methodology used, is based on collaborative participation in knowledge coding that is widely used in large settings such as Wikipedia.

Knowledge coding means the transfer of knowledge into a model which can be stored and shared. According to this definition, Knowledge can be accepted as a set of facts and the relationships among them. Through the suggested hierarchical model, primarily facts and initial entities are determined and knowledge record begins with the start of recording routines. In general, each routine is made up of simpler routines and facts. Thus the final model, which is a set of compound and complicated routines, can encode different levels of knowledge with different complexities. The suggested model in the explained processes is a conceptual model and a descriptive model in explaining facts.

Due to high-level programming expertise, in this paper, only the method of implementation of the proposed operational model has been explained. The proposed method maintains various advantages such as applicability, comprehensibility by different people in an organization, the possibility of knowledge coding at different levels, supporting abstract concepts besides operational ones and finally the possibility of implementing it by existing tools.

The suggested model can be used for a variety of needs. For instance, in this paper, the first modelling example referred to a software concept, while the other referred to the implementation of an organizational process. Due to a hierarchy in describing knowledge, the suggested model can be used by any user with any level of knowledge (either user or registrar).

This method can create a change in social media and make it possible for anyone in any society with any level of information to share their knowledge at their own level and use the knowledge of others at the same level.

The strength of the proposed model stems from its hierarchical nature which is considered for knowledge coding at different levels and includes advantages such as comprehensibility for different people in an organization, the possibility of knowledge coding at different levels and supporting abstract concepts in addition to operational ones.

In many countries over the world, women have waged peace to challenge systemic oppressions and build societies that are reflective of women’s voices, and in fact, all voices. Moved by the desire for change, and often even willing to put themselves at risk, these women have paved the way for societal change focused on peace, justice, and freedom. With the assistance of narratives from the Women’s PeaceMakers program at the University of San Diego (San Diego, California), we can come to know some of these women and understand their stories. This chapter shares the findings from a pilot study that helps to understand the work of these Women PeaceMakers through the lens of the Integral Perspective of Peace Leadership (McIntyre Miller & Green, 2015). It also offers recommendations for others engaging in the leadership and followership work of creating, sustaining, and actualizing a movement with particular attention paid to the modern United States-based Me Too and Time’s Up™ movements.

Recommender systems are techniques that allow companies to develop sales and marketing and as a result, attract more customers. There are several different types of recommender systems which collaborative filtering (CF) method is more popular and is used in various fields. However, similar to other recommender systems, this system has its own limitations. Nowadays, recommender systems are combined with other systems to enhance the quality and precision. The purpose of this paper is to present a new method to increase the accuracy and quality of recommendations associated with filtering systems.

First, the recency, frequency, and monetary (RFM) variables of the clients are extracted and variables’ weights are calculated. Then, using weighted RFM and expectation maximization clustering algorithms and their combination with the closest K-neighbors, recommendations for each cluster is independently extracted. Finally, the results are compared with the outcome of conventional CF techniques. Remarkably, sale transactions of a big distribution and sale of goods centers are used in this study.

The results indicated that the proposed method has higher accuracy compared to the conventional CF method. Likewise, the clusters which have higher values were received more accurate recommendations. Another point was that the proposed method was faster on obtaining the results than the conventional method as the recommendations were performed with respect to the customers of the same cluster, while all clients were assessed in the conventional method and as a result, the calculation speed is reduced as the number of customers increases in this method.

The results indicated that the proposed method has higher accuracy compared to the conventional CF method. Likewise, the clusters which have higher values were received more accurate recommendations. This is very important for businesses and trade centers as more than 80 percent of their profits come from valued customers and hence, recommendations with higher accuracy to these valued customers lead to more profits to sales centers. Since the valued customers were calculated in the proposed method and the value of each customer was distinguished for sales representatives, the accomplished recommendations can be coordinated with sales’ strategies to make it more targeted.

Nowadays, communications, products, services and costs are customized through the internet technology. The main theory to continue competitiveness in the organizations is customer relationship management (CRM). CRM enables organizations to efficiently interact with customers and gather, store and examine their data for providing a complete view of them. On the other hand, the subject of cloud computing has increasingly become the bridge for the success of the CRM implementation. Therefore, this study aims to investigate the impact of cloud computing (new cloud facility, knowledge of information technology (IT), cloud security and cost) on the success of CRM systems.

The model and the questioners-based data are analyzed using the Smart PLS 3.0. The data were gathered based on 80 employees of three main agricultural companies in Iran.

The obtained results have indicated that all of the considered factors, new cloud facilities, knowledge of IT, cloud security and cost, play an important role in CRM systems’ success. Also, the evaluation and examination of the consistency and validity of the model are performed through the structural equation model.

First, the authors have conducted a study in a single region. It cannot be guaranteed that the results can be generalized to other regions. Second, for this cross-sectional study, the research design was conducted that showed constant relationships between variables. The research done for this study is cross-sectional. Third, because of time and financial restrictions, the authors have gathered data using a sample from a single location.

Proposing a new model for investigating of the impact of cloud computing (new cloud facility, knowledge of Information Technology (IT), cloud security and cost) on the success of CRM systems is the main originality of this paper.

Introduction: Human resource management (HRM) is going through a transformation phase due to the pandemic. The COVID-19 crisis compelled the employees to work virtually. To mitigate the effects of COVID-19, several organisations heavily invested in artificial intelligence (AI) in the realm of HRM.

Purpose: With limited studies on the paradigm shift in HRM post-pandemic and the role of AI, the study investigates and proposes a conceptual framework for the paradigm shift in HRM practices post-COVID-19 pandemic and the significance of AI. Furthermore, the study investigates the outcomes of the use of AI in HRM for organisations and employees.

Methodology: A comprehensive review of the literature based on the guidelines of Tranfield, Denyer, and Smart (2003) and Crossan and Apaydin (2010) has been followed. A systematic literature review assisted in critically analysing, synthesising, and mapping the extant literature by identifying the broad themes involved.

Findings: COVID-19-related economic disruption has led to a paradigm shift in HRM practices. AI-enabled HRM practices are now centred around remote and contingent workforce management, mindfulness, social capital, increasing employee engagement, reskilling and upskilling towards new competencies, etc. AI is making remote work seamless through smooth recruitment and selection process, onboarding, career and development, tracking and managing the performance, facilitating learning, and talent management. Post-pandemic, AI-powered tools based on data mining (DM), predictive analytics, big data analytics, natural language processing (NLP), intelligent robots, machine learning (ML), virtual (VR)/augmented reality (AR), etc., have paved the way for managing the HRM practices effectively, thereby leading to enhanced organisational performance, employee well-being, automation, and reduced cost.

The study aims to determine the relationship between emotional maturity and domestic violence in infertile women.

The cross-sectional study was conducted on 184 infertile women. Demographic questionnaire, Emotional Maturity Scale and domestic violence questionnaire were used to collect data. The Chi-square test and multivariate logistic regression were used to analyze the data.

More than 50% of the women in the study experienced domestic violence, and about the same percentage of them had unstable emotional maturity. The total score of domestic violence and its types were significantly related to the women’s emotional maturity (P < 0.001). In multivariate analysis, the significant relationship was found between domestic violence with Spouse's education level (primary/secondary) [OR = 0.25 (0.09–0.66)] and emotional maturity (unstable) [OR = 3.59 (1.83–7.02)].

The overall prevalence of infertility in Iran was 7% in 2021. Furthermore, although infertility is a problem among couples, most of its social burden lies with women. In a number of developing countries, as well as in Iran, childbearing is perceived as a social value for married women. Therefore, infertile women are at risk for depression, anxiety, low self-esteem, dissatisfaction and reduced quality of life.

This study found that half of the surveyed women experienced domestic violence. Women’s emotional immaturity, as well as their spouses’ lower education level and type of occupation, were highly correlated with women’s experience of domestic violence. To help prevent domestic violence among couples, there should be couple training classes to improve women’s emotional maturity.”