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This article aims to explore coronary care unit (CCU) extubation structures, processes and outcomes. There were 13 unplanned-extubation cases (UE) among 251 intubated patients (5.2 per cent) in a cardiologist-led CCU in 2008. Seven did not require re-intubation, implying possible earlier extubation. A quality improvement project was undertaken with a goal to eliminate CCU UE within 12 months.

Using the clinical practice improvement (CPI) method, the most significant root causes were missing sedation/analgesia protocol, no ventilator weaning protocol and absent respiratory therapist during the CCU morning rounds. Non-physician directed sedation/analgesia and ventilation weaning protocols were created and put on trial in Plan-Do-Study-Act cycles before formal implementation. Arrangements were made to allocate a respiratory therapist to the CCU daily for morning rounds.

For 12 months after fully implementing the interventions, UE incidence dropped from 5.2 per cent to 0.9 per cent (p=0.006). There were no adverse outcomes, re-intubation and/or readmission to CCU within 48 hours.

Through a multi-disciplinary CPI approach, adopting non-physician directed protocols has successfully streamlined and improved airway management in mechanically ventilated patients in a cardiologist-led CCU.

There is little published data on improving intubated patient care in cardiologist-led CCUs. Previous studies centered on intensive care units managed by critical care specialists.

This paper examines the moderated-moderation model of reciprocity belief and fear of negative evaluation between supervisors' abusive reactions and subordinates' distress.

The authors obtained 412 valid responses from Egyptian hotel employees and analyzed them using PROCESS model 3.

The three-way interaction findings proved that when employees have high reciprocity beliefs and low fear of negative evaluations, the abusive supervision-psychological distress relationship is dampened.

Organizations have the opportunity to implement human resource development (HRD) strategies focused on cultivating reduced apprehension toward negative evaluation and fostering a robust sense of positive reciprocity. To achieve this, HRD and HRM initiatives can encompass elements such as bolstering organizational and coworker support, promoting cultural assimilation and redefining work practices.

This paper adopts a comprehensive approach that recognizes the intricate interrelationships within the workplace by identifying subtle dynamics of abusive supervision and its impacts. It also explores the complex nature of such relationships rather than taking a purely causal perspective.

The primary intent of this study was to assess the influence of novelty-seeking on the satisfaction of consumers. The investigation concentrated on Malaysian consumers who have experience using online food delivery (OFD) applications.

To perform the research, non-probability and convenience sampling methods were enforced to gather the required data. An online questionnaire in the form of a Google Survey was conducted in Kuala Lumpur, Malaysia. Upon completion of the survey, the results were analyzed using SPSS software. Both the Service Quality (SERVQUAL) model and expectation disconfirmation theory (EDT) were exploited to shed light on the impact of consumer satisfaction.

Analysis of responses from the 250 participants unveiled that novelty-seeking positively influences consumer satisfaction. The finding depicted that reliability and responsiveness have the most positive impact on consumer satisfaction whereas tangibility has no effect on the satisfaction of consumers by using OFD applications.

This study had three main limitations: first, the limitations on access to the participants during the pandemic; second, combining quantitative and qualitative methods to obtain more accurate results; third, the study was limited to the context of Kuala Lumpur, Malaysia.

The conclusions brought to the fore that OFD marketers should provide appropriate service quality while concentrating on novelty and well-designed apps to surge consumer satisfaction.

OFD apps have facilitated customers' access to various meals and helped food vendors survive in the competitive marketplace. A new aspect, novelty-seeking, is added to the SERVQUAL dimensions (i.e. empathy, tangibility, reliability, assurance and responsiveness) identified in the literature review.

The focus of this study lies in understanding the extrinsic vs intrinsic motivators which drive the m-coupon sharing behaviour in social networking sites (SNSs). A consumer can make promotional tool (in our case m-coupons) viral if the cues trigger an apt motivation. This study fills the need gap by identifying which motivations must be focused to make a promotional tool viral by the consumer especially in an emerging economy like India.

We designed conceptual framework based on extensive literature review and employed hierarchal regression methodology to investigate the motivation to share m-coupon.

Sense of self-worth, Socializing and Reciprocity emerge as strong reasons for a consumer to share m-coupons amongst friends and peers in SNS. Results have shown that intrinsic motivation works very effectively when a consumer shares m-coupons in SNSs.

This study has certain limitations. First, the impact of age, gender and education can also influence the results as perception evolves with age and education. Second, in our study, we have not classified m-coupons in different categories. Different types of m-coupons may have a different impact on consumers.

The paper presents findings, which are useful for marketers to develop a customer-centric viral promotional strategy.

This study is one of the few studies in integrating types of motivation with coupon proneness and coupon sharing in social media. This study has specifically targeted the emerging economy where m-coupons usage has seen a surge. Study has shown that it is the intrinsic motivation which is very crucial for encouraging consumer for participating in SNSs and share e-word of mouth amongst friends and peers.

Although food product value and food safety is widely acknowledged as a critical concern by consumers, little empirical evidence exists regarding how food product value is created and how product risk decreases as a result of service-brand equity. The purpose of this paper is to explore whether food service-brand equity (i.e. brand awareness and brand image) affects consumer-perceived food value, food physical risk, and brand preference.

In this study, data were collected from steakhouse consumers using a convenience sample (n=386). Structural equation modelling was used to analyse the survey data.

The results reveal that service-brand awareness and brand image produce considerably dissimilar effects on consumer-perceived food value and risk. Brand awareness positively affects consumer-perceived food value but does not influence perceived physical risk. By contrast, brand image negatively influences perceived physical risk and positively affects brand preference, but it does not add perceived value to the food product.

This study is the first to address these concerns, which are essential for understanding the role of service-brand equity in developing food-risk and value perceptions, and brand preference.

Although the impact of carbon emissions regulations is evident to upstream automakers, their influence on downstream B2C car-sharing platforms remains unclear. This article reveals the influence of carbon emission regulations on the performance of supply chain members. In particular, we focused on the decision of B2C car-sharing platforms.

We develop a three-stage dynamic game model consisting of an automaker, a B2C car-sharing platform and consumers.

The carbon emission cap has a critical threshold. Above this threshold, the regulation is ineffective for the platform’s operating model. Below it, the regulation affects the platform, moderated by customers' green awareness. The threshold initially decreases (weakly) and then increases in awareness. Effective caps reduce profits for the manufacturer, B2C car-sharing platform and supply chain, while ineffective caps see higher profits with increased awareness.

Firstly, this paper explores the impact of carbon emission caps on the operational strategies of B2C car-sharing platforms within the sharing economy, complementing existing research. Secondly, it identifies conditions where stricter caps prompt B2C car-sharing platforms to adjust their operational models and offers fresh insights for managers and departments responsible for carbon emission policy formulation. Thirdly, the study uncovers how carbon emission caps affect the performance of supply chain members, providing crucial managerial insights for sustainable operations.

This study proposes a collaborative federated learning (CFL) framework to address personal data transmission and retention issues for artificial intelligence (AI)-enabled video surveillance in public spaces.

This study examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Based on the requirements, this study proposes a CFL framework to gradually adapt AI models’ knowledge while reducing personal data transmission and retention. The framework uses three different federated learning strategies to rapidly learn from different new data sources while minimizing personal data transmission and retention to a central machine.

The findings confirm that the proposed CFL framework can help minimize the use of personal data without compromising the AI model's performance. The gradual learning strategies help develop AI-enabled video surveillance that continuously adapts for long-term deployment in public spaces.

This study makes two specific contributions to advance the development of AI-enabled video surveillance in public spaces. First, it examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Second, it proposes a CFL framework to minimize data transmission and retention for AI-enabled video surveillance. The study provides comprehensive experimental results to evaluate the effectiveness of the proposed framework in the context of facial expression recognition (FER) which involves large-scale datasets.

With the development of intelligent technology, deep learning has made significant progress and has been widely used in various fields. Deep learning is data-driven, and its training process requires a large amount of data to improve model performance. However, labeled data is expensive and not readily available.

To address the above problem, researchers have integrated semi-supervised and deep learning, using a limited number of labeled data and many unlabeled data to train models. In this paper, Generative Adversarial Networks (GANs) are analyzed as an entry point. Firstly, we discuss the current research on GANs in image super-resolution applications, including supervised, unsupervised, and semi-supervised learning approaches. Secondly, based on semi-supervised learning, different optimization methods are introduced as an example of image classification. Eventually, experimental comparisons and analyses of existing semi-supervised optimization methods based on GANs will be performed.

Following the analysis of the selected studies, we summarize the problems that existed during the research process and propose future research directions.

This paper reviews and analyzes research on generative adversarial networks for image super-resolution and classification from various learning approaches. The comparative analysis of experimental results on current semi-supervised GAN optimizations is performed to provide a reference for further research.

Current multi-source image fusion methods frequently overlook the issue of detailed features when employing deep learning technology, resulting in inadequate target feature information. In real-world mission scenarios, such as military information acquisition or medical image enhancement, the prominence of target feature information is of paramount importance. To address these challenges, this paper introduces a novel infrared-visible light fusion model.

Leveraging the foundational architecture of the traditional DenseFuse model, this paper optimizes the backbone network structure and incorporates a Unique Feature Encoder (UFE) to meticulously extract the distinctive features inherent in the two images. Furthermore, it integrates the Convolutional Block Attention Module (CBAM) and the Squeeze and Excitation Network (SE) to enhance and replace the original spatial and channel attention mechanisms.

Compared to other methods such as IFCNN, NestFuse, DenseFuse, etc., the values of entropy, standard deviation, and mutual information index of the method presented in this paper can reach 6.9985, 82.6652, and 13.6022, respectively, which are significantly improved compared with other methods.

This paper presents a UFEFusion framework that synergizes with the CBAM attention mechanism to markedly augment the extraction of detailed features relative to other methods. Moreover, the framework adeptly extracts and amplifies unique features from disparate images, thereby elevating the overall feature representation capability.

The purpose of this study is to present actions and initiatives, developed within the scope of the Empowering Teachers to Trigger Cybersecurity at Schools (ETTCS) CyberTeach Erasmus Project, to enhance cybersecurity literacy through innovative digital content and contemporary Learning Management System (LMS) platform.

The concept of cybersecurity literacy has become increasingly crucial in recent years, as the digitization of most human activities is being completed within the framework of the 4th Industrial Revolution. Almost all devices, vehicles and services in the near future will be interconnected to the internet and operate on advanced computing platforms. The benefits of these technological advancements are evident, as are the potential risks. To this end, organized cybercriminals, black hat hackers and state-sponsored actors may attempt, through various cyberattacks, to steal personal data, cause road accidents in connected autonomous vehicles and, in general, disrupt critical infrastructures. Cybersecurity is a growing concern when it comes to digitalization and cloudification. This way, digital assets must be conveniently protected to avoid any concern about their confidentiality, integrity and authentication. Therefore, the ability of every citizen to use the internet and smart devices wisely and securely is one of the most fundamental skills they should possess. In this work, the authors present actions and initiatives, developed within the scope of the ETTCS CyberTeach Erasmus Project, to enhance cybersecurity literacy through innovative digital content and contemporary LMS platform. A new approach to teach cybersecurity, based on innovative teaching methods, is presented to prepare future citizens and their teachers to keep up with cybersecurity issues in an efficient manner. To this end, the authors propose ways to reach cybersecurity literacy, giving use case examples and proposing the necessary digital skills.

A new approach to teach cybersecurity, based on innovative teaching methods, is presented to prepare future citizens and their teachers to keep up with cybersecurity issues in an efficient manner. To this end, the authors propose ways to reach cybersecurity literacy, giving use case examples and proposing the necessary digital skills.

This work demonstrates a new methodology to infuse cybersecurity awareness into teachers so that they can train and prepare their students accordingly. The authors identify the teacher as the critical link between a young generation of digital natives, who consider the internet as a utility, and the most experienced information technology security experts striving to enforce good cybersecurity practices among users.