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Several chaotic system-based encryption techniques have been presented in recent years to protect digital images using cryptography. The challenges of key distribution and administration make symmetric encryption difficult. The purpose of this paper is to address these concerns, the novel hybrid partial differential elliptical Rubik’s cube algorithm is developed in this study as an asymmetric image encryption approach. This novel algorithm generates a random weighted matrix, and uses the masking method on image pixels with Rubik’s cube principle. Security analysis has been conducted, it enhances and increases the reliability of the proposed algorithm against a variety of attacks including statistical and differential attacks.

In this light, a differential elliptical model is designed with two phases for image encryption and decryption. A modified image is achieved by rotating and mixing intensities of rows and columns with a masking matrix derived from the key generation technique using a unique approach based on the elliptic curve and Rubik’s cube principle.

To evaluate the security level, the proposed algorithm is tested with statistical and differential attacks on a different set of test images with peak signal-to-noise ratio, unified average changed intensity and number of pixel change rate performance metrics. These results proved that the proposed image encryption method is completely reliable and enhances image security during transmission.

The elliptic curve–based encryption is hard to break by hackers and adding a Rubik’s cube principle makes it even more complex and nearly impossible to decode. The proposed method provides reduced key size.

The aging and deterioration of engineering building structures present significant risks to both life and property. Fiber Bragg grating (FBG) sensors, acclaimed for their outstanding reusability, compact form factor, lightweight construction, heightened sensitivity, immunity to electromagnetic interference and exceptional precision, are increasingly being adopted for structural health monitoring in engineering buildings. This research paper aims to evaluate the current challenges faced by FBG sensors in the engineering building industry. It also anticipates future advancements and trends in their development within this field.

This study centers on five pivotal sectors within the field of structural engineering: bridges, tunnels, pipelines, highways and housing construction. The research delves into the challenges encountered and synthesizes the prospective advancements in each of these areas.

The exceptional performance of FBG sensors provides an ideal solution for comprehensive monitoring of potential structural damages, deformations and settlements in engineering buildings. However, FBG sensors are challenged by issues such as limited monitoring accuracy, underdeveloped packaging techniques, intricate and time-intensive embedding processes, low survival rates and an indeterminate lifespan.

This introduces an entirely novel perspective. Addressing the current limitations of FBG sensors, this paper envisions their future evolution. FBG sensors are anticipated to advance into sophisticated multi-layer fiber optic sensing networks, each layer encompassing numerous channels. Data integration technologies will consolidate the acquired information, while big data analytics will identify intricate correlations within the datasets. Concurrently, the combination of finite element modeling and neural networks will enable a comprehensive simulation of the adaptability and longevity of FBG sensors in their operational environments.

The purpose of this paper is to examine curiosity as a distal predictor of knowledge sharing facilitated by informal learning. It also probes the boundary conditions imposed by ebullient supervision in the influence of curiosity on knowledge sharing.

The authors collected data through a two-wave survey of 349 knowledge workers in the IT sector and used structural equation modeling to analyze the data.

The results indicated a positive relationship between curiosity and knowledge sharing. In particular, informal learning was found to mediate the relationship between curiosity and knowledge sharing and ebullient supervision was identified as a significant condition that strengthens the effect of curiosity on informal learning.

Organizations can promote knowledge sharing by harnessing curiosity as an intrinsic motivator for employees to engage in informal learning. Moreover, the findings identified ebullient supervision as an extrinsic motivator within the work environment, suggesting its potential to enhance the impact of curiosity on knowledge sharing.

This paper broadens the limited literature on ebullient leadership by revealing how it strengthens the effects of curiosity and informal learning on knowledge sharing.

Understanding and mitigating stock price crash risk is vital for investors and regulators to ensure financial market stability. This study aims to unveil significant research trends and opportunities.

This study adopts the bibliometric and systematic review approach to analyse 485 Scopus-indexed articles through citation, keyword co-occurrence, bibliographic coupling, and publication analyses and delve into the depth of crash risk literature.

This bibliometric review reveals not only a surge in crash risk publications over the last decade but also delineates several emerging thematic threads within this domain. We identify seven distinct themes that have gained prominence in recent literature: bad news hoarding, board characteristics, capital market factors, corporate policies, ownership impact, corporate governance, and external environmental influences on crash risk. This thematic analysis provides a comprehensive overview of the evolving landscape of crash risk research and underscores the multifaceted nature of factors contributing to market instability.

This study makes a substantial contribution by furnishing a thorough examination of existing studies, pinpointing areas where knowledge is lacking, and shedding light on emerging trends and debates within the crash risk literature.

This study identifies current research trajectories and propels future exploration into agency perspectives, audit quality, and corporate disclosures within crash risk literature.

Artificial intelligence (AI) carries the risk of widening gender inequalities due to the digital divide, while simultaneously promising to equalise the situation for women through the gender digital dividend. The conflicting findings from previous studies justify the need to investigate the gendered aspects of Artificial Intelligence (AI) diffusion. Specifically, the aim of this chapter is to understand the relationship between female entrepreneurship and the adoption of AI technologies within business contexts at the macroeconomic level. To achieve this, cluster analyses are conducted for the European Union (EU) countries. The results indicate an inverted U-shaped pattern in the relationship between the level of female entrepreneurship and the diffusion of AI technology in business. In the EU countries belonging to clusters with the highest level of AI diffusion, female entrepreneurship is at a moderate level, while in the EU countries with the lowest level of intelligent transformation, both extremes are observed: the highest and the lowest levels of female entrepreneurship. The variety of patterns in female entrepreneurship and AI technology spread in the EU countries implies the complex and multidimensional nature of the interrelationship, and, thus, it indicates the need for diverse, country-specific policies and practices to reach the intelligent transformation with respect to more equal society.

India has a serious poverty issue that needs to be addressed immediately. The elimination of poverty should be one of the primary goals of economic policy. The elimination of poverty is one of the greatest obstacles to economic development as it is now envisioned. It is difficult to provide a blanket recommendation for achieving economic growth and reducing poverty because each state's experience has been distinct. The states of Punjab and Haryana are two examples of how a strong emphasis on agricultural expansion can help alleviate poverty. Human resource development has been successful in Kerala; thus, it has been replicated in other states. While India's social safety programmes have helped to alleviate poverty, they aren't perfect. COVID-19 reversed the progress made and harmed migrant workers and others in the informal economy. There are now millions more people living in extreme poverty than before. To tackle unemployment, governments and underprivileged communities are appealing to programmes like MGNREGA.

The purpose of this article is to investigate the measures taken by the state to combat poverty.

The retail industry has experienced significant transformations driven by digitalization and the challenges posed by the COVID-19 pandemic. This chapter aims to explore the impact of digitalization on retail business models and understand consumer purchasing habits during emergencies. This chapter will examine the influence of scarcity and competitive arousal on consumer choices, the effects of stock-outs on brand and store preferences, price sensitivity, waiting times and the shift towards online shopping. Additionally, it will address concerns about social exclusion in digital channels and the potential for bridging the gap between offline and online shopping experiences.

The purpose of this paper is to explore how and when employee smart technology, artificial intelligence, robotics and algorithms (STARA) awareness affects job crafting through challenge appraisal and threat appraisal and provides positive stress mindset as a moderator.

The survey data was collected from 319 employees in four Chinese companies. The hypotheses were tested using Mplus 7.0 and regression analysis.

The results indicate that STARA awareness positively prompts approach job crafting via challenge appraisal and also positively predicts avoidance job crafting via threat appraisal. Meanwhile, positive stress mindset enhanced the mediating effect of challenge appraisal and weakened the mediating effect of threat appraisal.

Leaders should prioritize hiring high-positive-stress mindset candidates for jobs, and organizations should also cultivate employees’ positive stress mindset.

Building on the cognitive appraisal theory of stress, this study reveals the underlying mechanism and boundary conditions behind the linkage of STARA awareness and job crafting.

Nurse turnover in critical care units (CCU) significantly affects patient outcomes and health systems worldwide. To safeguard patient care quality, hospitals must address the underlying reasons for turnover and strategize to retain their skilled nursing workforce. The study proposes a prescriptive framework to reduce nurse turnover in CCUs.

In this study, the integrated methodology of Delphi-AHP-Entropy was used for the comparative prioritization of factors and subfactors that influence nursing staff turnover in CCUs.

Study findings reveal that “Organizational factors” and “Individual factors” dictate critical care nurse attrition rate. At the subfactor level, staffing policy, chronic fatigue, and perceived career are the leading concerns for the decision of nurses whether to work or leave.

This study is valuable for both researchers and healthcare professionals. It examines whether actions related to nurse retention align with existing theory and identifies areas requiring further theoretical or applied studies to enhance understanding in this area. This insight can bolster the field’s knowledge base and integrate theoretical and applied knowledge effectively. Additionally, for healthcare professionals, the study provides an overview of key factors conducive to retaining nursing staff in the CCU, offering valuable guidance for implementing effective strategies.

This study uniquely positions itself by presenting a comprehensive and prescriptive framework for critical care nurse retention in the UAE.

Based on the computers as social actors (CASA) theory, this study aims to explore the impact of three characteristics of virtual influencers (conversational tone, autonomy and responsiveness) on social presence, telepresence and customer engagement.

Using the purposive sampling technique, online survey was administered to Chinese Gen-Z consumers engaging with virtual influencers. Subsequently, 357 respondents were sampled. SPSS 29.0 and Smart PLS 4.0 were used to perform the analyses.

The results show that conversational tone and responsiveness have significant positive effects on both social presence and telepresence. Autonomy has a positive effect on telepresence, but not social presence. Social presence and telepresence have a positive impact on customer engagement.

As a burgeoning field, there is still uncertainty among both practitioners and researchers about the methods that virtual influencers engage their users in the context of social media. Limited research has focused on presence (social presence and telepresence) due to virtual influencers. Therefore, the CASA theory offers valuable insights into how virtual influencers’ characteristics contribute to the presence and customer engagement and provides practical guidance for the design of virtual influencers.