Search Results (711)

Phishing remains a major cybersecurity threat, yet the application of quantum machine learning (QML) to phishing detection is still at an early stage. This study presents a systematic literature review aimed at providing a concise overview of existing QML-based approaches for phishing detection, identifying methodological trends, limitations, and future research directions. A PRISMA-guided review protocol was applied to peer-reviewed journal and conference articles published between 2021 and 2025, retrieved from major scientific databases. Eligible studies were analyzed in terms of QML models, feature encoding strategies, experimental settings, evaluation metrics, and study quality using an adapted Newcastle–Ottawa Scale. The results indicate that current research is limited in volume and largely focuses on hybrid quantum–classical models, particularly quantum support vector machines and variational quantum classifiers. Reported performance is highly dependent on encoding methods, circuit depth, and simulator-based experimentation, with few studies evaluating real quantum hardware. Common challenges include small datasets, lack of external validation, hardware noise, scalability constraints, and the absence of standardized benchmarks. Overall, the review suggests that QML for phishing detection remains exploratory and is not yet competitive with mature classical approaches, but it holds potential as an experimental research direction, provided that future studies address robustness, reproducibility, and practical deployment constraints. Full article

Indoor Environmental Quality (IEQ) plays a vital role in occupant health and productivity. However, current Building Management Systems (BMS) often struggle in sustaining optimal IEQ levels due to limitations in data management and lack of occupant-centric feedback loops. To address these gaps, this research synthesizes the state-of-the-art methods for IEQ monitoring, assessment, and control within Building Automation Systems (BAS), identifying both technological and methodological advancements, as well as highlighting the challenges and potential opportunities for future innovations. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this multi-stage literature review analyzes 176 publications from 1997 to 2024, with a focus on the decade of rapid technological evolution from 2014 to 2024. The review focuses on high-impact journals indexed in Scopus to ensure quality while acknowledging the potential bias inherent in a single-database search. The synthesis reveals a methodological shift in monitoring from sparse, zone-level sensing towards dense, multi-modal systems that incorporate physiological data via wearables and behavioral recognition through computer vision. Assessment techniques are evolving from static models such as the Predicted Mean Vote (PMV) towards adaptive, personalized frameworks supported by Digital Twins and integrated simulations. Furthermore, control logic is transitioning toward Reinforcement Learning and Model Predictive Control to proactively manage occupancy surges and environmental variables. This evolution of monitoring approaches, assessment techniques, and control strategies is represented within the study’s Three-Tiered Developmental Trajectory, providing a novel Body of Knowledge (BOK) for mapping the transition of building systems from reactive tools to autonomous, occupant-centric agents. This study also introduces a Cross-Modal Interaction Matrix to systematically analyze the systemic trade-offs between IEQ domains. Furthermore, by establishing the “Implementation Frontier,” this work identifies the specific technical and ethical bottlenecks, such as “false vacancy” sensing errors, fragmented data silos, and the ethical complexities of high-resolution data collection that prevent academic innovations from becoming industry standards. To bridge these gaps, we conclude that the next generation of “cognitive buildings” must prioritize three pillars: resolving binary sensing limitations, harmonizing data via vendor-neutral APIs, and adopting privacy-preserving architectures to ensure scalable, interoperable, and occupant-centric optimization. Full article

In Australia and around the world, young students are increasingly experiencing declines in wellbeing, engagement, and sense of belonging. These trends are accompanied by rising anxiety and social disconnection that affect learning and development. In response to these concerns, this study investigated whether a 10-week nature play intervention could support wellbeing, engagement, and learning in Year One students. A quasiexperimental mixed-methods design was used with students (N = 50; mean age = 6.87 years) from a metropolitan public primary school in Sydney, Australia. Participants were allocated to an intervention (n = 25) or a waitlist control group (n = 25). Data was collected at three time points: pre-intervention, post-intervention, and four-month follow-up. Quantitative measures included reading and mathematics results, and a student self-report questionnaire assessing play, nature connection, engagement, and wellbeing. Qualitative data comprised teacher and student journals, teacher interviews, parent surveys, and researcher observations. The results indicated significant improvement in mathematics, alongside children’s reported expressions of joy, autonomy, and emerging awareness of human–nature reciprocity. Despite limitations in sample size and context, these outcomes suggest that nature play supports holistic development while promoting a broader view of education that values wellbeing, engagement, and belonging alongside academic learning. Full article

In today’s AI-driven world, nurturing students’ capacity for independent, self-reflective learning is vital. They must build lifelong learning skills and develop personalized strategies through ongoing self-regulation. In this study, we employed a learning journal template to support self-regulated physics learning, highlighting the role of homework assignments designed to target different levels of cognitive domains. Our learning journal-supported approach aims to facilitate students’ preparation for lessons at home. Guided questions help students review the content covered in previous classes and reflect on the effectiveness of the instructional methods applied. The intervention focused specifically on the physics topic of dynamics, examining how students’ conceptual understanding and performance developed within this domain. The efficacy of this approach was tested among 7th- and 9th-grade students. Results indicate that the learning journal-based method, combined with structured homework, had a positive impact on students’ performance within the topic of dynamics. Full article

Background: Unfractionated heparin (UFH) remains the standard anticoagulant for extracorporeal membrane oxygenation (ECMO), despite complications, such as heparin resistance, heparin-induced thrombocytopenia, bleeding and variable pharmacokinetics. This has prompted the search for alternative and novel anticoagulation strategies, including pharmacologic agents, circuit modifications, and monitoring approaches. This scoping review aimed to map the breadth and characteristics of evidence on ECMO anticoagulation strategies beyond UFH. Methods: A comprehensive search of peer-reviewed and gray literature was conducted across PubMed, Cochrane, Clinical Trials, WHO Trials Registry, and conference abstracts through manual searches in key journals. Clinical, pre-clinical, and gray literature studies evaluating pharmacologic agents, anticoagulation-free or heparin-sparing, biocompatible circuits, and monitoring innovations were included. Data were charted and synthesized descriptively to identify trends, gaps, and emerging directions. Results: A total of 269 records were included. Evidence was highly heterogeneous among study designs, populations, ECMO modalities, and outcome definitions. Most clinical studies were retrospective cohorts and adult-centered, with limited multicenter randomized controlled trials and underrepresentation of neonatal and pediatric populations. Direct thrombin inhibitors were frequently studied and clinically implemented alternatives to UFH. Other agents, including nafamostat mesylate, prostaglandin E1, and factor pathway inhibitors remain early in clinical investigation. Anticoagulation-free strategies and biocompatible circuit technologies were mostly supported through pre-clinical and single-center studies. Monitoring and modeling innovations, like TEG, ROTEM, real-time imaging, and machine learning, are quickly emerging. Conclusions: ECMO anticoagulation is transitioning from UFH reliance toward diversified and personalized strategies. Future research should prioritize multicenter randomized controlled trials, standardize protocols, expand to neonatal and pediatric investigation, and integrate strategies. Full article

Three-dimensional (3D) reconstruction using images is one of the most significant topics in computer vision and photogrammetry, with wide-ranging applications in robotics, augmented reality, and mapping. This study investigates methods of 3D reconstruction using video (especially monocular video) data and focuses on techniques such as Structure from Motion (SfM), Multi-View Stereo (MVS), Visual Simultaneous Localization and Mapping (V-SLAM), and videogrammetry. Based on a statistical analysis of SCOPUS records, these methods collectively account for approximately 6863 journal publications up to the end of 2024. Among these, about 80 studies are analyzed in greater detail to identify trends and advancements in the field. The study also shows that the use of video data for real-time 3D reconstruction is commonly addressed through two main approaches: photogrammetry-based methods, which rely on precise geometric principles and offer high accuracy at the cost of greater computational demand; and V-SLAM methods, which emphasize real-time processing and provide higher speed. Furthermore, the application of IMU data and other indicators, such as color quality and keypoint detection, for selecting suitable frames for 3D reconstruction is investigated. Overall, this study compiles and categorizes video-based reconstruction methods, emphasizing the critical step of keyframe extraction. By summarizing and illustrating the general approaches, the study aims to clarify and facilitate the entry path for researchers interested in this area. Finally, the paper offers targeted recommendations for improving keyframe extraction methods to enhance the accuracy and efficiency of real-time video-based 3D reconstruction, while also outlining future research directions in addressing challenges like dynamic scenes, reducing computational costs, and integrating advanced learning-based techniques. Full article

Purpose: This study systematically reviews research on six emerging technologies in external auditing, Big Data, Blockchain, Machine Learning, Deep Learning, Artificial Intelligence (AI), and Robotic Process Automation (RPA), to clarify what is currently known and to identify where the main gaps remain. Rather than treating each technology in isolation, this study brings them together under a single integrative review to provide a consolidated reference point for scholars assessing their impact on external audit practices. Design/Methodology/Approach: Following a structured systematic review protocol, searches were conducted in Scopus, ScienceDirect and SpringerLink (2000–2024) using technology-related keywords combined with “audit”, “auditor” and “auditing”. After applying explicit inclusion and exclusion criteria, 471 records were reduced to 32 ABS-listed journal articles, which were analysed thematically. Findings: The review shows that research on emerging technologies in external auditing is still fragmented, with substantial variation in the depth and maturity of evidence across the six technologies. The strongest empirical base is concentrated in Big Data analytics and ML-based predictive models (including more advanced Deep Learning variants), whereas Blockchain and RPA work remains predominantly conceptual or confined to small-scale design-science implementations. Across technologies, most studies are single-country and either rely on auditors’ self-reported perceptions of adoption and impact or evaluate model performance without tracing effects on audit strategies and engagement outcomes, which limits external validity and construct measurement. Very few articles explicitly integrate the Audit Risk Model or other formal theories, and almost no work examines multi-technology “audit stacks” or generative AI, leaving substantial gaps in understanding how these tools jointly reshape inherent, control and detection risk across the audit cycle. Originality/Value: By integrating six technologies within a single external audit framework, the review offers a technology-specific evidence map and a targeted future research agenda that can guide scholars, audit firms and regulators in designing studies and policies aligned with actual gaps in the current literature. Full article

Graduate employability has become a central concern for higher education institutions as labour markets undergo rapid transformation driven by digitalisation, technological change, and evolving organisational practices. Universities are increasingly expected to equip graduates with a broad range of employability skills and to collaborate with industry to enhance labour market readiness. However, existing research on employability skills development and university-industry collaboration remains fragmented across disciplines, contexts, and stakeholder perspectives. This systematic review synthesises evidence on how universities prepare their graduates for the labour market through employability skills development and university-industry collaboration. Following PRISMA guidelines, 84 journal articles and conference papers published between 2015 and 2025 were identified through a systematic search of the Scopus database and analysed thematically. The findings reveal that graduate employability is conceptualised as a multidimensional and context-dependent construct encompassing discipline-specific, transversal, digital, career management, and professional disposition-related skills. Employability skills development is most strongly supported through pedagogical approaches that emphasise authentic engagement with professional contexts, including work-integrated learning, project- and challenge-based learning, and technology-mediated collaboration. Reported outcomes extend beyond immediate employment metrics to include enhanced confidence, skills acquisition, employability awareness, curriculum relevance, and organisational learning. However, the effectiveness and sustainability of these initiatives are shaped by structural and institutional conditions, including policy frameworks, resourcing, partnership coordination, and equity of access. The review contributes an integrative synthesis that connects employability skills, pedagogical design, and university-industry collaboration, and outlines implications for policy, educational practice, and future research. Full article

Cyber aggression and abuse (CAA) has become a major interdisciplinary research area spanning psychology, communication, public health, and computer science. Existing reviews have largely focused on detection methods and model performance, offering limited insight into how CAA research themes have evolved over time at the field level. This study addresses this gap by, to the best of our knowledge, applying Latent Dirichlet Allocation (LDA) to 2309 Web of Science–indexed publications with English-language abstracts published between 2000 and 2024, providing a large-scale, longitudinal, and multi-level analysis of the literature. The model identifies 29 latent topics, which are organized using the User–Activity–Content (UAC) framework to link psychosocial research, platform-mediated behaviors, and computational detection approaches. Temporal analysis reveals a clear methodological transition: early dominance of survey-based and psychosocial themes gradually declines in relative prominence, while computational topics related to machine learning, deep learning, and pre-trained language models exhibit sustained growth, particularly after 2010. A Hot–Cold topic classification further distinguishes emerging, stable, and declining research directions. Journal-level, disciplinary, and geographic analyses reveal systematic differentiation across venues and regions, with complementary emphases on psychosocial and computational approaches. These findings provide a structured, field-level perspective on the evolution of CAA research and offer practical value for researchers, funding agencies, journal editors, and publishers by identifying dominant, emerging, and declining themes that can inform research prioritization, editorial planning, and strategic investment. Full article

The growing reliance on renewable energy sources, particularly solar photovoltaics (PVs), requires intelligent management strategies to address challenges of intermittency, storage, and efficiency in autonomous microgrids. This review investigates IoT-based solutions for energy optimization, focusing on hardware platforms, communication protocols, and intelligent control strategies that enhance the reliability and autonomy of PV-powered systems. This review follows a structured methodological protocol including predefined research questions, database selection, screening criteria, and systematic categorization of studies of IoT-enabled solar microgrid applications, relying on peer-reviewed journal articles, reputable conference proceedings, and scholarly works published between 2020 and 2025. The focus centers on microcontroller-based platforms (e.g., Arduino, ESP32, NodeMCU, TTGO LoRa32) and Single-Board Computers (SBCs) (e.g., Raspberry Pi), alongside the integration of optimization algorithms with Machine Learning (ML) and Neural Network (NN) approaches. Results highlight that lightweight microcontrollers offer cost-effective monitoring, ESP32 and NodeMCU balance real-time analytics with energy efficiency, Raspberry Pi supports edge-level AI processing, and LoRa enables scalable long-range communication for remote PV systems. Furthermore, optimization algorithms (PSO, WOA-SA) and neural models (ANN, LSTM, CNN–LSTM) are explored as methods to improve forecasting accuracy, fault detection, and demand-side management. Conclusions indicate that IoT-based architectures significantly improve energy efficiency, support predictive maintenance, and enable scalable deployment of autonomous solar microgrids. The study emphasizes the necessity of hybrid IoT architectures, combining edge and cloud intelligence, to balance computational complexity, power constraints, and cybersecurity requirements. These findings provide practical insights into designing robust, cost-effective, and scalable IoT-enabled PV microgrids that contribute to decentralized and sustainable energy transitions. Full article

Background: This cross-sectional designed study aimed to identify the sources of oral health activities (OHA) by introducing a new OHA sources questionnaire (OHAQ-S). Methods: The OHAQ-S was developed from a sample of 658 university students and included measurements from nine sources: scales for parents, Dental medical doctors (DMDs), and primary school sources, as well as single-item measures for other sources. Using QHAQ-S measures, gender differences, determinants of OH activities, and differences between OH types were analysed. Results: Gender differences were observed in five OH sources (university, high school, self-learning, friends, and kindergarten). In both female and male subsamples, primary sources such as parents and DMD predicted overall OH activities, though with different secondary sources. In the female subsample, some differences in OHAQ-S sources appeared between the four OH types. The excellent OH type most notably differed from others by having higher reported incidence of self-learning—dental floss usage and DMD sources—and marginally higher reported use of university and parental sources. In the male subsample, multiple differences in OHAQ-S sources were found among the four OH types. The excellent OH type most distinguished itself by reporting higher levels of DMD, self-learning—dental floss usage, university—acquired OH knowledge, parental, and media and internet—health journal sources. Conclusions: Female students have differently expressed and more-pronounced OHA sources relative to male students and some sources encountered earlier (kindergarten and high school sources), and “independent” learning sources (self-learning versus friends sources). In both subsamples, predictive relationships of OHAQ-S measures with overall OH activities were verified. The findings on the elements of the discriminative and predictive validity of the pilot version of the questionnaire show that the OHAQ-S questionnaire represents a quality basis for constructing a questionnaire on sources of OH activities. Full article

Fog computing extends cloud services to the network edge to support low-latency IoT applications. However, since fog environments are distributed and resource-constrained, intrusion detection systems must be adapted to defend against cyberattacks while keeping computation and communication overhead minimal. This systematic review presents research on intrusion detection systems (IDSs) for fog computing and synthesizes advances and research gaps. The study was guided by the “Preferred-Reporting-Items for-Systematic-Reviews-and-Meta-Analyses” (PRISMA) framework. Scopus and Web of Science were searched in the title field using TITLE/TI = (“intrusion detection” AND “fog computing”) for 2021–2025. The inclusion criteria were (i) 2021–2025 publications, (ii) journal or conference papers, (iii) English language, and (iv) open access availability; duplicates were removed programmatically using a DOI-first key with a title, year, and author alternative. The search identified 8560 records, of which 4905 were unique and included for qualitative grouping and bibliometric synthesis. Metadata (year, venue, authors, affiliations, keywords, and citations) were extracted and analyzed in Python to compute trends and collaboration. Intrusion detection systems in fog networks were categorized into traditional/signature-based, machine learning, deep learning, and hybrid/ensemble. Hybrid and DL approaches reported accuracy ranging from 95 to 99% on benchmark datasets (such as NSL-KDD, UNSW-NB15, CIC-IDS2017, KDD99, BoT-IoT). Notable bottlenecks included computational load relative to real-time latency on resource-constrained nodes, elevated false-positive rates for anomaly detection under concept drift, limited generalization to unseen attacks, privacy risks from centralizing data, and limited real-world validation. Bibliometric analyses highlighted the field’s concentration in fast-turnaround, open-access journals such as IEEE Access and Sensors, as well as a small number of highly collaborative author clusters, alongside dominant terms such as “learning,” “federated,” “ensemble,” “lightweight,” and “explainability.” Emerging directions include federated and distributed training to preserve privacy, as well as online/continual learning adaptation. Future work should consist of real-world evaluation of fog networks, ultra-lightweight yet adaptive hybrid IDS, self-learning, and secure cooperative frameworks. These insights help researchers select appropriate IDS models for fog networks. Full article

The convergence of Robotics and artificial intelligence (AI) has transformed engineering by enabling the design of intelligent systems capable of learning, adapting, and performing complex tasks. These synergies are driving innovation across multiple engineering disciplines, including mechanical, materials, electrical, industrial, civil, and aerospace engineering. This review provides a comprehensive overview of the knowledge structure and emerging research directions of Robotics and AI in engineering, with the aim of identifying research trends, influential authors, leading institutions, and emerging thematic areas. Data were collected from the Web of Science and Scopus databases, covering the period from 2020 to 2025, and analyzed using bibliometric mapping techniques and performance indicators. The results reveal a sustained growth in research on autonomous systems, collaborative robots, and human–robot interaction within engineering contexts, with a strong emphasis on AI-driven optimization. Bibliometric analyses show that deep learning, reinforcement learning, and computer vision constitute the core enabling technologies structuring the field. In addition, the results highlight a high degree of international collaboration and a concentration of scientific output and impact in a limited number of leading countries, institutions, and journals. Full article

Recreational dance offers significant psychological well-being potential. However, traditional instruction emphasizes technique while limiting attention to nervous system development and embodied meaning-making. Despite empirical support for polyvagal theory, motor learning science, somatic education, and phenomenology, their systematic integration into unified structures is not clearly established in recreational dance contexts. This protocol integrates nervous system regulation, motor learning, and creative expression within structured Imperial Society of Teachers of Dancing (ISTD) modern dance syllabus for recreational adults. It presents a 12-week integrated dance-mindfulness intervention addressing this gap through a three-phase structure grounded in neuroscience and embodied pedagogy. The intervention comprises eight standardized components delivered weekly. The randomized controlled trial evaluates intervention effects using the Satisfaction With Life Scale (SWLS), Depression Anxiety Stress Scales-21 (DASS-21), the Mindful Attention Awareness Scale (MAAS), the Subjective Happiness Scale (SHS), and the Leisure Involvement Scale (LIS). Qualitative assessment via semi-structured phenomenological interviews (Weeks 8 and 12) and weekly journaling captures somatic awareness, nervous system resilience, technical confidence, creative expression, relational and social belonging, and embodied meaning-making. Intervention participants are expected to show significantly greater improvements compared to controls. Results will establish evidence-based practice standards for recreational dance and demonstrate neuroscience integration’s efficacy for psychological wellbeing and embodied meaning-making. Full article

Technological innovation plays a crucial role in improving educational quality worldwide. In Ethiopia, however, literacy skills face significant obstacles, worsening the problem of learning poverty. This study aimed to analyze the effects of integrated AI educational tools on students’ literacy development. It also explored how learners perceived the use of these tools in reading and writing instruction. A quasi-experimental single-group time series design, combining both quantitative and qualitative approaches, was used. A total of 46 students from the Information Technology department at Injibara University were selected through a comprehensive census sampling method. For a period of three months, participants received reading and writing lessons supported by AI tools (NoRedInk, Rewordifyv2.1.0, and LanguageTool 9.5.0) to assess their impact on literacy skills. Data collection included pre- and post-tests, focus group discussions, and reflective journals. Quantitative data were analyzed with ANOVA, and qualitative data underwent thematic analysis using thematic techniques. Results revealed that the integration of AI educational tools significantly enhanced students’ literacy skills, including grammar, vocabulary, comprehension, content organization, and writing style. Students also expressed positive perceptions of using these tools in their reading and writing lessons. Therefore, this study encourages scholars, educators, and learners to adopt integrated AI educational tools to improve literacy development. Full article