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This study aims to synthesize Prussian blue {Fe^III₄[Fe^II(CN)₆]₃} pigment by reacting ferric chloride with different ferrocyanides through the same procedure. The influence of the ferrocyanide used on resulting pigment properties is studied.

Prussian blue is commonly synthesized by direct or indirect methods, through iron salt and ferrocyanide/ferricyanide reactions. In this study, the direct, single-step process was pursued by dropwise addition of the ferrocyanide into ferric chloride (both as aqueous solutions). Two batches – (K-PB) and (Na-PB) – were prepared by using potassium ferrocyanide and sodium ferrocyanide, respectively. The development of pigment was confirmed by an identification test and characterized by spectroscopic techniques. Pigment properties were determined, and light fastness was observed for acrylic emulsion films incorporating dispersed pigment.

The two pigments differed mainly in elemental detection owing to the dissimilar ferrocyanide being used; IR spectroscopy where only (Na-PB) showed peaks indicating water molecules; and bleeding tendency where (K-PB) was water soluble whereas (Na-PB) was not. The pigment exhibited remarkable blue colour and good bleeding resistance in several solvents and showed no fading in 24 h of light exposure though oil absorption values were high.

This article is a comparative study of Prussian blue pigment properties obtained using different ferrocyanides. The dissimilarity in the extent of water solubility will influence potential applications as a colourant in paints and inks. K-PB would be advantageous in aqueous formulations to confer a blue colour without any dispersing aid but unfavourable in systems where other coats are water-based due to their bleeding tendency.

The purpose of this research is to further the understanding of how to implement gender-responsive budgeting (GRB) systems. The author explores whether GRB reforms might benefit from integration into a performance-oriented budgetary structure and whether GRB's equity orientation is linked to additional implementation challenges.

This research follows a qualitative case study of South Korea guided by a predetermined research framework and built from extensive documentation, archival records and expert interviews.

GRB’s integration into a performance framework can be burdensome, and the equity orientation of GRB results in additional implementation challenges, such as higher vulnerability to the political context and active resistance from public officials.

This research shows that integrating GRB with performance systems may require overcoming significant administrative obstacles. Also, GRB’s equity orientation is linked to a higher vulnerability to the political cycle and active resistance from civil servants.

Home insurance is widely recognised as a tool for mitigating economic risk associated with natural disasters. This study aims to analyse the influence of homeowners’ home insurance knowledge (both objective and subjective types), perceived benefits (PB) and perceived vulnerability towards disaster loss (PVUL) on their intention to purchase (ITP).

This research makes use of survey data collected from 394 respondents (the homeowners) residing in various parts of India. The structural equation modelling is used to verify 11 hypotheses proposed in the study.

The findings indicate that both objective knowledge (OK) and subjective knowledge (SK) of home insurance have significant influence on homeowners’ benefit perception and PVUL. The homeowners’ PB of home insurance negatively affect PVUL. The OK of home insurance has a stronger influence on homeowners’ ITP home insurance than SK while the homeowners benefit perceptions and PVUL significantly affects homeowners’ ITP home insurance. These findings confirms that if homeowners are knowledgeable about home insurance, they perceive the plans as more beneficial and feel less vulnerable about catastrophic events, resulting in positive intentions towards purchasing them.

To the best of the authors’ knowledge, this is the first comprehensive research that assesses the Indian homeowners’ knowledge, PB and PVUL in influencing their ITP home insurance. The finding of this paper will assist both public and private insurance companies in India and similar markets in designing and implementing effective strategies to sell home insurance policies.

The purpose of this paper is to assess the level of nurses’ competencies while providing care to COVID-19 patients.

A descriptive, correlational design was used to collect data from nurses who were providing care to COVID-19 patients at four public hospitals.

A total of 377 nurses (64.5% females) aged 23–50 consented to participate and completed the survey. The mean score of nurses’ competencies in providing care to COVID-19 patients was 2.5 (SD = 0.81). The results of correlation coefficient tests disclosed a significant positive correlation between reported competence level and sex r_pb (377) = 0.18, p < 0.01; working area r_pb (377) = 0.2, p < 0.01; disaster experience r_pb (377) = 0.16, p < 0.01; disaster education r_pb (377) = 0.25, p < 0.01; and disaster training r_pb (377) = 0.31, p < 0.01.

The COVID-19 pandemic response heavily relied on nurses. However, they had a gap in clinical competencies that indicates an urgent need to incorporate disaster management courses in basic nursing education and to update training in hospitals based on nurses’ needs to improve their capabilities in dealing with COVID-19 pandemic.

To the best of the authors’ knowledge, this is the first study that investigated the perceived level of Jordanian nurses’ competencies in providing care to COVID-19.

This study aims to systematically review the health risk assessment of the concentration of heavy metals in Pistacia species globally.

The authors systematically searched PubMed, Science Direct, Scopus and Google Scholar to identify all articles published between 1 January 2002 and 20 August 2022. A total of 33 studies met the authors’ inclusion criteria, and their data were extracted. Additionally, the potential risk to human health was assessed by calculating the target hazard quotient and hazard index for both child and adult consumers.

The estimated daily intake for heavy metals in the included studies ranged from 9.72 × 10^–9 to 7.35 (mg/day) in the following order: zinc (Zn) > mercury (Hg) > iron (Fe) > lead (Pb) > copper (Cu) > aluminum (Al) > nickel (Ni) > chromium (Cr) > manganese (Mn) > cadmium (Cd) > arsenic (As) > selenium (Se) > cobalt (Co). Among the studies that investigated heavy metals in Pistacia species around the world, the non-carcinogenic risk for all species of Pistacia was determined to be less than 1, except for Pb and Hg in Pistacia lentiscus.

The soil near the industrial area contained excessive amounts of heavy metals, which led to the transfer of heavy metals to plants. Owing to the insufficiency of the number of studies that examined heavy metals in Pistacia species, further monitoring and investigations were recommended.

The purpose of the present work is to study the impacts of rapid cooling and Tb rare-earth additions on the structural, thermal and mechanical behavior of Bi–0.5Ag lead-free solder for high-temperature applications.

Effect of rapid solidification processing on structural, thermal and mechanical properties of Bi-Ag lead-free solder reinforced Tb rare-earth element.

The obtained results indicated that the microstructure consists of rhombohedral Bi-rich phase and Ag99.5Bi0.5 intermetallic compound (IMC). The addition of Tb could effectively reduce the onset and melting point. The elastic modulus of Tb-containing solders was enhanced to about 90% at 0.5 Tb. The higher elastic modulus may be attributed to solid solution strengthening effect, solubility extension, microstructure refinement and precipitation hardening of uniform distribution Ag99.5Bi0.5 IMC particles which can reasonably modify the microstructure, as well as inhibit the segregation and hinder the motion of dislocations.

It is recommended that the lead-free Bi-0.5Ag-0.5Tb solder be a candidate instead of common solder alloy (Sn-37Pb) for high temperature and high performance applications.

The COVID-19 outbreak reached a critical stage when it became imperative for public health systems to act decisively and design potential behavioral operational strategies aimed at containing the pandemic. Isolation through social distancing played a key role in achieving this objective. This research study examines the factors affecting the intention of individuals toward social distancing in India.

A correlation study was conducted on residents from across Indian states (N = 499). Online questionnaires were floated, consisting of health belief model and theory of planned behavior model, with respect to social distancing behavior initially. Finally, structural equation modeling was used to test the hypotheses.

The results show that perceived susceptibility (PS), facilitating conditions (FC) and subjective norms are the major predictors of attitude toward social distancing, with the effect size of 0.277, 0.132 and 0.551, respectively. The result also confirms that the attitude toward social distancing, perceived usefulness of social distancing and subjective norms significantly predict the Intention of individuals to use social distancing with the effect size of 0.355, 0.197 and 0.385, respectively. The nonsignificant association of PS with social distancing intention (IN) (H1b) is rendering the fact that attitude (AT) mediates the relationship between PS and IN; similarly, the nonsignificant association of FC with IN (H5) renders the fact that AT mediates the relationship between FC and IN.

The results of the study are helpful to policymakers to handle operations management of nudges like social distancing.

The research is one of its kind that explores the behavioral aspects of handling social nudges through FC.

Confidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty. Their applications encompass economic forecasting, market research, financial forecasting, econometric analysis, policy analysis, financial reporting, investment decision-making, credit risk assessment and consumer confidence surveys. Signal-to-noise ratio (SNR) finds applications in economics and finance across various domains such as economic forecasting, financial modeling, market analysis and risk assessment. A high SNR indicates a robust and dependable signal, simplifying the process of making well-informed decisions. On the other hand, a low SNR indicates a weak signal that could be obscured by noise, so decision-making procedures need to take this into serious consideration. This research focuses on the development of confidence intervals for functions derived from the SNR and explores their application in the fields of economics and finance.

The construction of the confidence intervals involved the application of various methodologies. For the SNR, confidence intervals were formed using the generalized confidence interval (GCI), large sample and Bayesian approaches. The difference between SNRs was estimated through the GCI, large sample, method of variance estimates recovery (MOVER), parametric bootstrap and Bayesian approaches. Additionally, confidence intervals for the common SNR were constructed using the GCI, adjusted MOVER, computational and Bayesian approaches. The performance of these confidence intervals was assessed using coverage probability and average length, evaluated through Monte Carlo simulation.

The GCI approach demonstrated superior performance over other approaches in terms of both coverage probability and average length for the SNR and the difference between SNRs. Hence, employing the GCI approach is advised for constructing confidence intervals for these parameters. As for the common SNR, the Bayesian approach exhibited the shortest average length. Consequently, the Bayesian approach is recommended for constructing confidence intervals for the common SNR.

This research presents confidence intervals for functions of the SNR to assess SNR estimation in the fields of economics and finance.

This paper explores the adoption of cloud computing for digital preservation, focusing on the case of Preservica, a leading provider of software-as-a-service (SaaS)-based digital preservation solutions. This study aims to analyse Preservica’s approach to long-term active digital preservation (LTADP) based on internationally accepted standards.

The research paper conducts an in-depth analysis of Preservica’s SaaS-based cloud computing solution in relation to ISO 14721:2012 (OAIS) and ISO 9001 standards. It examines the steps followed by Preservica for LTADP and highlights the importance of adherence to relevant standards.

Preservica’s adoption of cloud computing and the SaaS model offers scalable, cost-effective and flexible solutions for digital preservation. By adhering to internationally accepted standards and following a comprehensive set of LTADP steps, including file format identification, compatibility checks, checksum creation, secure storage, preservation status tracking, change monitoring, format migration and periodic testing, Preservica ensures the long-term preservation and accessibility of digital content.

This research contributes to the understanding of how cloud computing can be effectively used for digital preservation by examining Preservica’s approach. It emphasizes the significance of adherence to internationally accepted standards and highlights the benefits of integrating acceptance models to optimize the implementation and user acceptance of Preservica.

This research sought to examine the differential effect of chatbot banking artificial intelligence (AI) on consumer experience in the banking industry. A positivist paradigm was adopted to sample 389 consumers who were previously exposed to chatbot banking in Zimbabwe. A causal research design was employed whilst a quantitative approach was followed. In analysing data, the research study applied the structural equation modelling (SEM) technique. The authors found that chatbot banking significantly improves customer experience (CX) in the banking industry. Reliability and responsiveness of the chatbot need to be enhanced for effective improvements in CX. A need was also identified to enhance CX through the development of an ease-to-use chatbot which is embedded in everyday messaging applications of consumers. A significant association was also found between perceived benefits of chatbot banking and CX. This study informs the development of competitive advantage by banks and other related companies through AI-based CX management strategies. In times of pandemics and beyond, chatbot banking can be very instrumental in improving CX.