Abstract
Industry 4.0 uses the network concept to establish an interconnected manufacturing system. Industry 4.0 integrates the more recent digital concepts such as artificial intelligence (AI), the internet of things (IoT), big data, cloud computing, and 3D printing. The next maturity level, Industry 5.0, aims to shift the focus back to human-centric production by creating a sustainable and collaborative environment with humans and machines. Every manufacturer aims to find new ways to increase profits, reduce risks, and improve production efficiency. AI tools can process and interpret vast volumes of data from the production floor to spot patterns, analyze and predict consumer behavior, and detect real-time anomalies in production processes. This work studies the impact of AI in Industries 4.0 and 5.0. In Industry 4.0, AI can help in classic tasks such as predictive maintenance, production optimization, and customer personalization. Industry 5.0 enables sustainable manufacturing development and human-AI interaction. In this work, the authors demonstrate the impact of AI in Industry 4.0 and 5.0. © 2023, IGI Global. All rights reserved.

Abstract
Lung cancer is the 2^nd most diagnosed cancer worldwide. The corresponding histopathological analysis, being both costly and time-consuming, demands the commitment of skilled professionals who, while engrossed in this task, experience constraints on their ability to attend to other crucial responsibilities. Moreover, as it is a human-driven process, mistakes may lead to incorrect diagnosis and treatment. Given the disease frequency and mortality, automated diagnostic systems, using Artificial Intelligence (AI), represent valuable improvements in diagnostic timing and overall performance. Recently, Deep Learning (DL) has been widely used for extracting features from histopathologic images approaching more accurate and expeditious analysis. With this line of research in mind, a brief review of recent technical/scientific works within the scope of lung Digital Pathology (DP) image analysis is provided in this paper, covering different computer vision tasks including classification, segmentation, and detection. Furthermore, available datasets and open-source annotation tools capable of providing support to the aforementioned DP-related tasks are also overviewed. Afterward, a summary table and a discussion around the reviewed approaches is provided, consolidating critical information such as technique/DL architecture, involved datasets, metrics, etc. From this study, it was observed that the ARA-CNN technique achieved the highest Area Under the Curve (AUC) ranging from 0.72 to 0.99 for classification. On the other hand, the multimodal-based approach, with an AUC of 0.95, performed better for the segmentation task. As for the detection task, the BCNN approach stood out, achieving a high AUC of 0.988. This review work aims to provide a comprehensive overview of recent advancements in lung DP image analysis and serves as a foundation for future research in this critical area.

Abstract
Cities are complex and dynamic systems in which a network of actors interact, creating value through different activities. Cities can, therefore, be viewed as service ecosystems. Municipalities take advantage of digitalization to implement a service-dominant logic in urban and mobility planning and management, developing strategies with which citizens, local authorities, and other actors can create value together. While citizens are offered a better service experience, local authorities use citizens' input to improve decision-making processes. This research considers that designing an integrated service supported by an integrated information system can respond to current challenges in decision-making and information access for transport and mobility. Through a multidisciplinary methodological approach, this work proposes some guidelines to design an integrated information system to improve citizens' participation in urban planning and mobility services.

Abstract
Is there an initialization for deep networks that requires no learning? ExplainFix adopts two design principles: the fixed filters principle that all spatial filter weights of convolutional neural networks can be fixed at initialization and never learned, and the nimbleness principle that only few network parameters suffice. We contribute (a) visual model-based explanations, (b) speed and accuracy gains, and (c) novel tools for deep convolutional neural networks. ExplainFix gives key insights that spatially fixed networks should have a steered initialization, that spatial convolution layers tend to prioritize low frequencies, and that most network parameters are not necessary in spatially fixed models. ExplainFix models have up to x100 fewer spatial filter kernels than fully learned models and matching or improved accuracy. Our extensive empirical analysis confirms that ExplainFix guarantees nimbler models (train up to 17% faster with channel pruning), matching or improved predictive performance (spanning 13 distinct baseline models, four architectures and two medical image datasets), improved robustness to larger learning rate, and robustness to varying model size. We are first to demonstrate that all spatial filters in state-of-the-art convolutional deep networks can be fixed at initialization, not learned.This article is categorized under:Technologies > Machine LearningFundamental Concepts of Data and Knowledge > Explainable AIFundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining

Abstract
This article examines the impact of digital influencers ' recommendations, especially Instagrammers, on the pur-chase intention of healthy food. In addition to the direct influence of source credibility on behavioral intention, the study also examines the influence of self-brand congruence and consumers' involvement with healthy food on purchase intention. To test research hypotheses, a quantitative study was conducted with 221 Portuguese con-sumers. High and low involvement with healthy food groups were classified by K-Means Clustering, and the analysis of the structure and the measurement models was performed by using Smart-PLS software. The results confirmed that Instagrammers' credibility drives self-brand congruence and purchase intention for healthy food. It was also confirmed that the involvement with healthy food moderates the influence of self-brand congruity and Instagrammers' credibility on consumers' intention to purchase healthy food, and that brand self-congruence partially mediates the influence of Instagrammers' credibility on purchase intention. Overall, this work offers rel-evant insights for both marketing managers and researchers, as it demonstrates the importance of considering the indirect effects of source credibility on purchase intention of healthy food and of comparing consumers with high and low product involvement to effectively evaluate the impact of digital influencers' in healthy food endorsement.

Abstract
Social Engineering (SE) is a significant problem for enterprises. Cybercriminals continue developing new and sophisticated methods to trick individuals into disclosing confidential information or granting unauthorized access to infrastructure systems. These attacks remain a significant threat to enterprise systems despite significant investments in technical architecture and security measures. User awareness training and other behavioral interventions are critical for improving SE resilience. However, their effectiveness still needs to be determined, as personality traits may turn some individuals more susceptible to SE attacks. This paper aims to provide a comprehensive assessment of the state of knowledge in this field, identifying best practices for improving SE resilience in organizations and supporting the development of new research studies to address this issue. Its goal is to help enterprises of any size develop a framework to reduce the risk of successful SE attacks and create a culture of security awareness.