Abstract
The resource-constrained project scheduling problem (RCPSP) is a well-known scheduling problem that has attracted attention since several decades. Despite the rapid progress of exact and (meta-)heuristic procedures, the problem can still not be solved to optimality for many problem instances of relatively small size. Due to the known complexity, many researchers have proposed fast and efficient meta-heuristic solution procedures that can solve the problem to near optimality. Despite the excellent results obtained in the last decades, little is known why some heuristics perform better than others. However, if researchers better understood why some meta-heuristic procedures generate good solutions for some project instances while still falling short for others, this could lead to insights to improve these meta-heuristics, ultimately leading to stronger algorithms and better overall solution quality. In this study, a new hardness indicator is proposed to measure the difficulty of providing near-optimal solutions for meta-heuristic procedures. The new indicator is based on a new concept that uses the o-distance metric to describe the solution space of the problem instance, and relies on current knowledge for lower and upper bound calculations for problem instances from five known datasets in the literature. This new indicator, which will be called the o -D indicator, will be used not only to measure the hardness of existing project datasets, but also to generate a new benchmark dataset that can be used for future research purposes. The new dataset contains project instances with different values for the o -D indicator, and it will be shown that the value of the o-distance metric actually describes the difficulty of the project instances through two fast and efficient meta-heuristic procedures from the literature.

Abstract
Anomaly detection in the industrial context, identifying defective products and their categorization, is a prevalent task. It is aimed to acknowledge if training and testing multilabel classification models on textures to deploy on an MCU is possible. The focus is deploying lightweight models on MCUs, performing a multilabel classification on textures for industrial usage. For this purpose, a Systematic Literature Review was conducted, which allows knowing the commonly used machine learning models in industrial products anomaly detection and what methods are used to defect detection on textures. Through the Systematic Literature Review, was possible to understand the range of different and combined methods, the methods used in multilabel classification, the most common hyper-parametrizations and popular inferences engines to train machine-learning models to deploy on MCUs, and some techniques applied to overcome the restricted resources of memory and inference time associated with MCUs.

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.

Abstract
The role of digital transformation (DT) in economic development is a vital and recurring point of research. It is particularly relevant if we consider the high percentage of digital transformation initiatives that fail to deliver the expected results, particularly in Small and Medium Enterprises (SMEs). This paper analyzes what is needed to make this transformation successful from an implementation perspective and, simultaneously, from the standpoint of obtaining the company’s expected results. This phenomenon is even more critical to decipher and understand when we look at the small and medium enterprises that face more significant challenges due to the scarcity of resources and needed skills. This work reviews a large variety of models through an extensive systematic literature review (SLR) that assess the readiness and maturity of the digital transformation of enterprises, with a focus on SMEs, with its primary objectives being (1) to review the existing studies and models that assess an organization’s maturity and readiness in the context of digital transformation, focusing on SMEs; (2) to identify if there are gaps considering the importance of the SMEs; and (3) to propose a standardized set of dimensions that should always be considered in a digital transformation assessment. The outcome of this research provides an essential contribution by identifying apparent gaps in the assessment of digital transformation in SMEs and proposing a scalable and standardized set of categories and subcategories that can be used across any future assessment model. These contributions are even more relevant when referencing minimal deep research in the context of SMEs and Digital Transformation.

Abstract
In a competitive business landscape heavily reliant on information and communication technology, organisations must be prepared to address disruptions in their business operations. Business continuity management involves effective planning for the swift reestablishment of business processes in the short term. However, there are still obstacles to implementing business continuity plans, which can be justified by various factors. The purpose of this study is to present the perspectives and future research paths based on a systematic literature review from the peer-reviewed literature published from 1 January 2000 to 31 December 2021. This systematic literature review adheres to the guidelines established by evidence-based software engineering and leverages the Parsifal online tool. The primary research results identify and establish connections between the common components and activities of business continuity management as defined in international standards and frameworks to identify gaps in the existing knowledge. These findings will contribute to the development of a framework that provides a practical approach applicable to organisations of all sizes, taking into account each aspect of business continuity management, with a particular emphasis on information and communication technology systems. This paper's contribution lies in offering insights from a systematic literature review regarding the strategic principles for designing and implementing a business continuity plan, along with a comprehensive overview of related research. Furthermore, it presents a path forward to guide future research efforts aimed at addressing the gaps in the literature concerning continuity planning.

Abstract
The global overview of the challenges faced in trying to minimise the risks of organisations in the face of cyber-attacks is arduous for any organisation. Defining an appropriate risk management model that proactively minimises cybersecurity incidents is a critical challenge. Many malicious attacks occur daily, and there is only sometimes an adequate response. There is a significant investment in research to identify the main factors that may cause such incidents, always trying to have the most appropriate response and, consequently, potentiating the response capacity and success. At the same time, several different methodologies evaluate risk management and the maturity level of organisations. Due to the lack of predictive models based on data (evidence), there is a significant investment in research to identify the main factors that may cause such incidents, starting to design models based on AI-Artificial Intelligence. This research will go in the direction of developing a user-friendly model supporting the assessment of the methodological aspects of an organisation. © 2023, IGI Global.