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This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.

The proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.

The proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.

Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.

The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.

In this research, the authors demonstrate the advantage of reinforcement learning (RL) based intrusion detection systems (IDS) to solve very complex problems (e.g. selecting input features, considering scarce resources and constrains) that cannot be solved by classical machine learning. The authors include a comparative study to build intrusion detection based on statistical machine learning and representational learning, using knowledge discovery in databases (KDD) Cup99 and Installation Support Center of Expertise (ISCX) 2012.

The methodology applies a data analytics approach, consisting of data exploration and machine learning model training and evaluation. To build a network-based intrusion detection system, the authors apply dueling double deep Q-networks architecture enabled with costly features, k-nearest neighbors (K-NN), support-vector machines (SVM) and convolution neural networks (CNN).

Machine learning-based intrusion detection are trained on historical datasets which lead to model drift and lack of generalization whereas RL is trained with data collected through interactions. RL is bound to learn from its interactions with a stochastic environment in the absence of a training dataset whereas supervised learning simply learns from collected data and require less computational resources.

All machine learning models have achieved high accuracy values and performance. One potential reason is that both datasets are simulated, and not realistic. It was not clear whether a validation was ever performed to show that data were collected from real network traffics.

The study provides guidelines to implement IDS with classical supervised learning, deep learning and RL.

The research applied the dueling double deep Q-networks architecture enabled with costly features to build network-based intrusion detection from network traffics. This research presents a comparative study of reinforcement-based instruction detection with counterparts built with statistical and representational machine learning.

This paper aims to clarify the predicting and compensating method of aeroplane assembly. It proposes modeling the process of assembly. The paper aims to solve the precision assembly of aeroplane, which includes predicting the assembly variation and compensating the assembly errors.

The paper opted for an exploratory study using the state space theory and small displacement torsor theory. The assembly variation propagation model is established. The experiment data are obtained by a real small aeroplane assembly process.

The paper provides the predicting and compensating method for aeroplane assembly accuracy.

This paper fulfils an identified need to study how the assembly variation propagates in the assembly process.

Extending the theory of planned behaviour and the perspective of available resources, the purpose of this study is to expatiate the influence behavioural antecedents of available resources on electrical installation and maintenance work students’ (EIMW) entrepreneurial intentions (EIs) in technical colleges.

The study adopted a multistage sampling technique to select samples for the study. Data collected were analyzed using structural equation models in the empirical analysis.

The study showed that attitude, subjective norms, perceived behavioural control and perceived efficacy are behavioural antecedents that can be manipulated by exogenous factors to enhance EIMW students’ EIs.

This study stirs the government and other education agencies on the need to strengthen the technical institutions through adequate funding, employment of skilled personnel and provision of up-to-date types of machinery and equipment to help the students to compete globally. Therefore, adequate application of available resources will foster positive behaviours of EIMW students towards establishing their business venture.

The purpose of the work is to provide a comprehensive review of the digital image correlation (DIC) technique for those who are interested in performing the DIC technique in the area of manufacturing.

No methodology was used because the paper is a review article.

no fundings.

Herein, the historical development, main strengths and measurement setup of DIC are introduced. Subsequently, the basic principles of the DIC technique are outlined in detail. The analysis of measurement accuracy associated with experimental factors and correlation algorithms is discussed and some useful recommendations for reducing measurement errors are also offered. Then, the utilization of DIC in different manufacturing fields (e.g. cutting, welding, forming and additive manufacturing) is summarized. Finally, the current challenges and prospects of DIC in intelligent manufacturing are discussed.

The impact of stress on personal and work-related outcomes has been studied in the information systems (IS) literature across several professions. However, the cybersecurity profession has received little attention despite numerous reports suggesting stress is a leading cause of various adverse professional outcomes. Cybersecurity professionals work in a constantly changing adversarial threat landscape, are focused on enforcement rather than compliance, and are required to adhere to ever-changing industry mandates – a work environment that is stressful and has been likened to a war zone. Hence, this literature review aims to reveal gaps and trends in the current extant general workplace and IS-specific stress literature and illuminate potentially fruitful paths for future research focused on stress among cybersecurity professionals.

Using the systematic literature review process (Okoli and Schabram, 2010), the authors examined the current IS research that studies stress in organizations. A disciplinary corpus was generated from IS journals and conferences encompassing 30 years. The authors analyzed 293 articles from 21 journals and six conferences to retain 77 articles and four conference proceedings for literature review.

The findings reveal four key research opportunities. First, the demands experienced by cybersecurity professionals are distinct from the demands experienced by regular information technology (IT) professionals. Second, it is crucial to identify the appraisal process that cybersecurity professionals follow in assessing security demands. Third, there are many stress responses from cybersecurity professionals, not just negative responses. Fourth, future research should focus on stress-related outcomes such as employee productivity, job satisfaction, job turnover, etc., and not only security compliance among cybersecurity professionals.

This study is the first to provide a systematic synthesis of the IS stress literature to reveal gaps, trends and opportunities for future research focused on stress among cybersecurity professionals. The study presents several novel trends and research opportunities. It contends that the demands experienced by cybersecurity professionals are distinct from those experienced by regular IT professionals and scholars should seek to identify the key characteristics of these demands that influence their appraisal process. Also, there are many stress responses, not just negative responses, deserving increased attention and future research should focus on unexplored stress-related outcomes for cybersecurity professionals.

The microseismic monitoring technique has great advantages on identifying the location, extent and the mechanism of damage process occurring in rock mass. This study aims to analyze distribution characteristics and the evolution law of excavation damage zone of surrounding rock based on microseismic monitoring data.

In situ test using microseismic monitoring technique is carried out in the large-span transition tunnel of Badaling Great Wall Station of Beijing-Zhangjiakou high-speed railway. An intelligent microseismic monitoring system is built with symmetry monitoring point layout both on the mountain surface and inside the tunnel to achieve three-dimensional and all-round monitoring results.

Microseismic events can be divided into high density area, medium density area and low density area according to the density distribution of microseismic events. The positions where the cumulative distribution frequencies of microseismic events are 60 and 80% are identified as the boundaries between high and medium density areas and between medium and low density areas, respectively. The high density area of microseismic events is regarded as the high excavation damage zone of surrounding rock, which is affected by the grade of surrounding rock and the span of tunnel. The prediction formulas for the depth of high excavation damage zone of surrounding rock at different tunnel positions are given considering these two parameters. The scale of the average moment magnitude parameters of microseismic events is adopted to describe the damage degree of surrounding rock. The strong positive correlation and multistage characteristics between the depth of excavation damage zone and deformation of surrounding rock are revealed. Based on the depth of high excavation damage zone of surrounding rock, the prestressed anchor cable (rod) is designed, and the safety of anchor cable (rod) design parameters is verified by the deformation results of surrounding rock.

The research provides a new method to predict the surrounding rock damage zone of large-span tunnel and also provides a reference basis for design parameters of prestressed anchor cable (rod).

Improving milling quality is expected to improve the quality of domestic rice and hence the competitiveness of Uganda's rice industry. Therefore, this study aims to assess the determinants of four aspects of milling, namely, choice of milling technology, millers' perceptions of the importance of paddy quality attributes, milling return and milling capacity.

Multinomial logit, semi-nonparametric extended ordered probit, linear regression and additive nonparametric models are applied to cross-sectional data obtained from a sample of 196 rice millers.

Physical, economic, institutional, technological and sociodemographic factors are found to be important determinants of the four aspects of milling. Physical factors include the distance of the mill from major town and availability of storage space at the milling premises, while economic factors include milling charge and backward integration of miller into paddy production. Contracting and use of a single-pass mill are important institutional and technological factors, respectively, and miller's household size, age, gender and education are the key sociodemographic variables.

The study's originality lies in its scope, especially in terms of its breadth. Without compromising the needed analytical rigor, it focuses on four aspects of milling that are critical to improving the marketing of Uganda's rice. In doing so, it provides a holistic understanding of this segment of the value chain and offers specific recommendations for improving the marketing of Uganda's rice.

This study aims to address the noted gap in comprehensive overviews detailing the developmental trajectory of Islamic finance (IF) as an interdisciplinary academic field.

The study introduces a unique approach using the combined methodologies of co-word analysis and main path analysis (MPA) by examining a broad collection of IF research articles.

The investigation identifies dominant themes and foundational works that have influenced the IF discipline. The data reveals prominent areas such as Shariah governance, financial resilience, ethical dimensions and customer-centric frameworks. The MPA offers detailed insights, narrating a journey from the foundational principles of IF to its current challenges and opportunities. This journey covers harmonizing religious beliefs with contemporary financial models, changes in regulatory landscapes and the continuous effort to align with broader socioeconomic aspirations. Emerging areas of interest include using new technologies in IF, standardizing global Islamic banking and assessing its socioeconomic effects on broader populations.

This study represents a pioneering effort to map out and deepen the understanding of the IF field, highlighting its dynamic evolution and suggesting potential avenues for future academic exploration.

Self-regulated learning (SRL) is crucial for successful learning and lifelong learning in today’s rapidly changing world, yet research has shown that many learners need support for SRL. Recently, learning analytics has offered exciting opportunities for better understanding and supporting SRL. However, substantial endeavors are still needed not only to detect learners’ SRL processes but also to incorporate human values, individual needs and goals into the design and development of self-regulated learning analytics (SRLA). This paper aims to examine the challenges that lifelong learners faced in SRL, their needs and desirable features for SRLA.

This study triangulated data collected from three groups of educational stakeholders: focus group discussions with lifelong learners (n = 27); five teacher interviews and four expert evaluations. The groups of two or three learners discussed perceived challenges, support needs and willing-to-share data contextualized in each phase of SRL.

Lifelong learners in professional development programs face challenges in managing their learning time and motivation, and support for time management and motivation can improve their SRL. This paper proposed and evaluated a set of design principles for SRLA.

This paper presents a novel approach for theory-driven participatory design with multistakeholders that involves integrating learners, teachers and experts’ perspectives for designing SRLA. The results of the study will answer the questions of how learners’ voices can be integrated into the design process of SRLA and offer a set the design principles for the future development of SRLA.