Search Results (3,618)

A recurring question in contemporary society is: How can one survive on a single income? Escalating socioeconomic challenges worldwide are pushing many households beyond their financial comfort zones. As a coping strategy, many individuals have adopted side hustles, income-generating activities pursued alongside full-time employment. However, a crucial question arises: do side hustles come at the expense of primary employment, particularly in terms of employee commitment and workplace loyalty? This study examined the moderating role of work–life integration in the relationship between side hustles, employee commitment, and workplace attachment. The unit of analysis consisted of academic and support staff at South African higher education institutions. Primary data were collected through a survey of a stratified random sample of 300 employees, and the data were analysed using structural equation modelling. The findings support that side hustles offer meaningful opportunities to enhance individual livelihoods by mitigating the impact of declining income levels. Moreover, the results indicate that flexible work arrangements are critical for employees engaged in hustle, as such flexibility strengthens organisational commitment and workplace attachment. The study recommends adopting a humanistic management approach that promotes boundaryless jobs, thereby fostering employee commitment and workplace attachment through an inclusive, supportive, and empowering work environment. Full article

Background: South African university students face escalating levels of psychological distress driven by academic overload, financial precarity, and social challenges. Health professions students are particularly vulnerable due to the demanding nature of clinical training and repeated exposure to human suffering. Aim: This study aims to propose an Integrated Student Well-being and Resilience Model tailored to the South African health professions education context. Methods: This conceptual paper draws on empirical evidence from South African studies on student mental health, global campus well-being frameworks, and socio-ecological theory. Bronfenbrenner’s Socio-Ecological Systems Theory and a tiered public health approach were synthesized to develop a multi-level model aimed at addressing the academic, financial, and social determinants of student mental health. Conceptual synthesis: The study unequivocally identified a syndemic of interconnected factors predisposing students to depression, which included the interplay of academic rigour and cognitive burnout, financial vulnerability as a determinant of mental health, the crisis of social connection and psychological safety, and institutional failure and the resilience fallacy. Conclusions: The Integrated Student Well-being and Resilience (ISWR) Model is a systemic architecture designed to coordinate institutional governance with the complex psychosocial needs of health professions students. The model provides a holistic, scalable framework for strengthening student well-being within health professions education. By shifting from reactive counselling to proactive, system-level interventions, the model offers a strategic blueprint for creating resilient, supportive learning environments capable of improving student mental health and fostering a healthier future healthcare workforce. Full article

The increasing use of antibiotics around the globe has contributed to an increase in antimicrobial resistance and become a major risk to both public health and sustainable development. Reliable and fast detection of antibiotic residues in clinical, agricultural, and environmental matrices is required to monitor antimicrobial resistance effectively. The conventional analytical techniques are sensitive, but they are also expensive, complex and lacking in portability. Voltammetry is a recently emerging electrochemical detection technique that is low-cost and rapid. To the best of our knowledge, for the first time, a meta-analysis was conducted on graphene-based electrochemical sensors and biosensors for antibiotic detection over the last decade. This systematic review critically examines the analytical properties of sensors and biosensors, the physicochemical properties of antibiotics, adsorption characteristics, and the use of nanoparticles to improve the selectivity and sensitivity of devices. This review critically examines the cost-effectiveness, scalability, and practicality of point-of-use devices using graphene-based sensors and biosensors. This systematic review also discusses the potential risks to human health from antibiotic contamination and the role of monitoring in contributing to achieving the UN’s Sustainable Development Goals. This systematic review identifies a gap between developing sensors in laboratories versus their deployment as field-deployable devices; it highlights challenges associated with stability, matrix effects and the complexity of manufacturing devices. Finally, it provides recommendations for future research that may help to address this gap to promote the transition of innovative devices from academic to practical applications. Full article

Fully compressible two-phase flow configurations present many challenges for numerical modelling, requiring the development of Real Fluid Models (RFMs) able to simulate flows in subcritical, transcritical and supercritical regimes. Such an RFM has been recently developed at IFPEN based on physical properties lookup tables, mainly for binary and ternary chemical systems. This paper proposes an Artificial Neural Network (ANN) approach to overcome the limitations of lookup tables of thermodynamic properties and to apply RFM to multi-species combustion. A methodology for generating an optimized data set by combining a vapor–liquid equilibrium (VLE) thermodynamic solver and the in situ adaptive tabulation (ISAT) method is developed. It aims to improve the neural network training process for two-phase combustion simulations where many species are present. This ANN methodology has been implemented in the CONVERGE CFD solver and validated using a mixing layer (LOX/GH₂) benchmark from the literature relevant to rocket conditions, and an academic gaseous (H₂/O₂) case relevant to hydrogen combustion. The results show that this ANN approach makes H₂ combustion simulation possible when coupled to the RFM framework and using a 10-species kinetic mechanism. Full article

Graphene-related materials (GRMs) possess exceptional electrical, mechanical, thermal, and surface properties, offering significant potential across broad sectors and applications in electronics, energy storage, composites, and environmental technologies. Despite extensive investment in academic research and translation, large-scale industrial adoption of GRMs remains slower than projected. This review systematically analyzes the global graphene manufacturing landscape using available data from 100 commercial producers, with a focused evaluation of manufacturing technology, types and forms of produced GRMs, raw material sources, product forms, industrial quality control and characterization practices. Graphite-based production routes, particularly graphene oxide (GO) and reduced graphene oxide (rGO), dominate in the market due to their scalability and cost advantages. However, substantial inconsistencies in the quality of produced GRMs, characterization and standardization depth, analytical evidence, and technical data sheets (TDSs) remain widespread. A SWOT (strengths, weaknesses, opportunities and threats) analysis of emerging graphene in the industry highlights technological maturity and expanding market demand but reveals critical weaknesses and challenges in quality, standardization and cost–performance alignment. Overall, quality of manufactured materials, quality control transparency, and standardization rather than material manufacturing limitations emerge as the primary barriers to the widespread commercial realization of graphene. Full article

Student dropout represents a significant challenge for the sustainability of higher education systems, particularly in regional public contexts where academic trajectories are heavily shaped by socioeconomic conditions. While prior research typically examines whether students consider leaving, less attention has been given to how they prioritize the factors that may lead to dropout. This study analyzes students’ perceived factors associated with dropout intention at a regional campus of the Universidad Nacional de Colombia using survey data from 287 valid constant-sum responses. The empirical approach combines descriptive statistics, compositional analysis, and $k-$means clustering on centered log-ratio coordinates. On average, students assigned the greatest weight to socioeconomic factors (42.4 points), followed by personal (32.9) and academic factors (24.6), a pattern reinforced by the compositional center (51.1%, 31.6%, and 17.3%, respectively). Students living in rented housing placed greater emphasis on socioeconomic constraints, while differences across other characteristics were modest. Cluster analysis identified one dominant mixed profile (81.2% of the sample) and several smaller edge profiles, none primarily defined by academic factors alone. These findings indicate that enrolled students perceive potential dropout as a multidimensional set of pressures rather than a single dominant cause, highlighting the need for integrated financial, psychosocial, pedagogical, and academic support strategies to strengthen educational sustainability. Full article

Credit risk prediction is central to financial stability and regulatory compliance, guiding lending decisions and portfolio risk management. While traditional approaches such as logistic regression and tree-based models have long been the industry standard, recent advances in deep learning (DL) have introduced architectures capable of capturing complex nonlinearities, temporal dynamics, and relational dependencies in borrower data. This study provides a comprehensive review of DL methods applied to credit risk prediction, covering multi-layer perceptron, recurrent and convolutional neural networks, transformer, and graph neural networks. We examine benchmark and large-scale datasets, highlight peer-reviewed applications across corporate, consumer, and peer-to-peer lending, and evaluate the benefits of DL relative to classical machine learning. In addition, we critically assess key challenges and identify emerging opportunities. By synthesising methods, applications, and open challenges, this paper offers a roadmap for advancing trustworthy deep learning in credit risk modelling and bridging the gap between academic research and industry deployment. Full article

Smart contracts raise persistent challenges regarding compliance with traditional contract formalities, including writing, signature, notarization, and in certain transactions, registration. These issues are particularly significant in high-value and public-facing transactions such as real estate, where formalities determine legal validity, evidentiary sufficiency and publicity effects. While existing scholarly work has examined these challenges from either doctrinal or technological perspectives, limited attention has been given to how the functional roles of formalities interact with blockchain architecture, practitioner perceptions and institutional legal frameworks. This study addresses this gap through a mixed-methods approach combining doctrinal legal analysis with qualitative socio-legal research based on 27 semi-structured interviews with legal professionals including attorneys, judges, and academic scholars. The analysis is grounded in a civil law framework, with particular reference to the Jordanian legal system, while references to the European Union’s eIDAS Regulation are used illustratively to demonstrate regulatory approaches to digital authentication. The findings demonstrate that blockchain-based systems can effectively support the evidentiary and attribution functions of contractual formalities through cryptographic verification, consensus mechanisms, and automated execution. However, they do not independently satisfy formalities that perform cautionary, constitutive, protective or public order function, namely notarization and registration, which remain dependent on institutional validation and legal recognition. The analysis further shows that practitioner concerns reflect not only doctrinal constraints but also institutional roles and varying levels of technical familiarity. To address these limitations, the study proposes a function-based analytical framework for evaluating smart contract formalities and identifies two complementary pathways for legal adaptation: (i) institutional integration, including registry-linkage systems and hybrid contracts; and (ii) technological adaptation, including digital authentication frameworks and legal oracles that connect on-chain execution to off-chain legal conditions. The study concludes that smart contract formalities’ challenges arise not solely from technological limitations, but from the interaction between legal doctrine, institutional structures, and system design. It advances a functional framework for aligning automation with the evidentiary, protective, and publicity functions of contractual formalities. Full article

Emerging adults who are students (EAS) face challenges balancing academic demands with early career development in a competitive labour market. Career and vocational interventions (CVIs) aim to support this transition by improving employability, academic outcomes, career self-efficacy, and mental health protective factors. This systematic review assesses the effectiveness of CVIs compared with standard approaches in improving employment-related outcomes for EAS, following Cochrane and PRISMA guidelines. Multiple databases were searched for studies published between 2018 and May 2025. Three independent reviewers screened abstracts and full texts in duplicate. Eligible studies were primary research evaluating CVIs for students aged 12–25 yrs and reporting employment-related outcomes. Thirty-one moderate- to low-quality studies examining middle school, high school, and university students were included from an initial 5765 records. CVIs were typically multi-component, combining training, school- or work-based learning, and additional supports. Overall, CVIs were associated with improved career readiness, academic outcomes, employment, resilience, and career adaptability. However, some studies reported lower employment rates and income for women and students with disabilities compared with men and peers without disabilities. These findings suggest that CVIs can support school-to-work transitions but underscore the need for tailored, integrated approaches, and higher-quality longitudinal research to guide policy and practice. Full article

Systemwide Social and Emotional Learning (SEL) promotes a host of academic and behavioral benefits for K-12 students. Yet, many schools face barriers to SEL implementation. Through a research–practice partnership, this study provides insights into facilitators of and challenges to systemwide SEL implementation. We collected 652 field notes of SEL in action across twelve schools in the Northeast and Western regions of the United States between December 2022 and May 2024. All field notes were analyzed with Dedoose using thematic inductive coding. Key facilitators of systemwide SEL include prioritization/support from leadership, professional development for educators, integration into students’ daily experiences, and engagement with parents/caregivers and the community. Key challenges to systemwide SEL involve leadership ambiguity, educator burnout, and student disengagement. Implications for optimizing SEL implementation in educational practice and policy are discussed. Full article

As global digital transformation accelerates, artificial intelligence (AI) and blockchain technologies have evolved from theoretical concepts into practical tools within the field of accounting, particularly in financial reporting. This study conducts a systematic review of 62 sources drawn from major academic databases to develop a comprehensive framework for classifying application scenarios. The findings indicate that the application of artificial intelligence and blockchain technology can help improve the efficiency of financial report generation, enhance the reliability of data, and promote innovation in the auditing process. Nevertheless, persistent challenges remain, including concerns related to data security, technological limitations, and regulatory gaps. The study proposes a structured roadmap for the implementation of these technologies, underscoring their transformative potential in advancing the digital evolution of accounting, while also identifying key directions for future research. Full article

Agriculture in the Pacific is driven primarily by small-scale private farmers, many of whom do not have access to soil testing services or advice, nor the means to interpret analytical results into soil management and agronomic recommendations. Soil degradation through the process of acidification poses a significant risk to food and income security as it directly threatens crop productivity. The nutritional quality of food crops may also be affected through sub-optimal nutrient uptake by plants and nutrient imbalances. The dataset reported here provides a useful platform for the development of a decision-support tool (DST) that will assist Fiji farmers in understanding and managing soil pH and soil acidity. The DST will enable making informed decisions about liming to help correct soil pH. To support this development, historical soil pH data available from the Pacific Soils Portal were combined with updated analyses of agricultural soils from 17 locations in Viti Levu Island (Fiji) collected during a field campaign undertaken in August 2025. The soils were sampled at two depth intervals (0–15 and 15–30 cm) and analyzed for pH using a variety of methods. These methods included direct field measurements using a portable pH-meter as well as traditional laboratory determinations. Of the soils sampled, it was found that most soils exhibited pH levels below 7, which were observed for both depth intervals. Across all samples taken in 2025, it was found that 54.3% of them had soil pH < 5, 38.6% had soil pH between 5 and 6, and 7.1% had pH > 6 (based on soil pH_1:5 soil-to-water method). Depending upon specific land uses, climate and cropping intensity, it was recommended that routine liming be built into soil fertility management programs to help farmers overcome soil acidity-related constraints to production. Liming frequency, timing of application and application rate will need to be determined for specific soil and cropping situations; however, it was suggested that soil pH was not changed by more than 1 unit each time lime was applied. Such an approach should reduce the risk of soil organic matter loss through accelerated mineralization, which would be challenging to restore in that environment if soils remained under continuous cropping. The analytical information contained in this article expanded and updated the datasets available in the Pacific Soils Portal. Furthermore, this work provided an opportunity to build analytical expertise in aspects of soil chemistry at local organizations to support academic and extension activities as well as the ongoing development of the Pacific Soils Portal. Full article

Information security compliance has become critical for organizations worldwide, with the ISO/IEC 27000 family serving as the most widely adopted framework for establishing information security management systems. Despite their global acceptance, these standards present significant interpretation challenges due to their formal language, abstract structure, and extensive cross-referencing across 97 documents. Traditional retrieval-augmented generation (RAG) systems, which rely on independent text chunking and dense vector retrieval, prove inadequate for such highly interconnected regulatory materials, often fragmenting contextual relationships and reducing accuracy. This study introduces a privacy-preserving RAG framework that integrates LightRAG, a knowledge graph-based retrieval system, with locally hosted open-source language models. Unlike chunk-based RAG systems that treat document segments independently, the system in this study constructs a semantic knowledge graph that explicitly models relationships between clauses through typed edges representing cross-references, semantic similarity, and hierarchical dependencies. To enable rigorous evaluation, we developed a curated benchmark dataset of 222 multiple-choice questions with authoritative ground-truth answers, systematically constructed from official ISO standards, certification preparation materials, and academic sources. Through systematic evaluation on this benchmark, we show that knowledge graph-based retrieval achieves higher accuracy than chunk-based RAG and non-retrieval LLM baselines within the evaluated setup. The analysis indicates that embedding model quality is strongly associated with system performance, that hybrid retrieval modes combining local and global graph traversal tend to yield better accuracy, and that mid-sized open-source models paired with strong retrievers can approach the performance of larger proprietary systems. The best configuration achieves 90.54% accuracy, demonstrating the promising effectiveness of graph-structured retrieval for multiple-choice regulatory questions. Full article

Flexible acceleration sensors demonstrate revolutionary potential in healthcare, structural vibration monitoring, and consumer electronics owing to their unique conformal adhesion capability and mechanical adaptability. However, current academic research presents two distinct paradigms for realizing flexibility: one is the hybridly flexible sensor, which incorporates traditional micro-electro-mechanical System (MEMS) acceleration sensor chips with flexible packaging/substrates; the other is the intrinsically flexible sensor, whose sensing unit and substrate are entirely composed of flexible materials enabled by microstructural design. This review first analyzes the fundamental differences and design challenges between these two flexible architectures. It then systematically elucidates five core sensing mechanisms—capacitive, piezoresistive, triboelectric, piezoelectric, and electromagnetic—comparing their working principles, material systems, structural designs, and performance metrics. Among these, piezoelectric and triboelectric types exhibit distinctive advantages in self-powering capability, whereas resistive and capacitive approaches offer greater ease of integration. Furthermore, the applications of intrinsically flexible acceleration sensors in structural health monitoring, wearable devices, automotive safety, and other fields are discussed, with particular emphasis on their unique strengths in real-time vibration monitoring. Finally, the review summarizes existing challenges, such as the trade-off between sensitivity and flexibility, and provides theoretical insights to guide future innovations in intrinsically flexible acceleration sensor technology. Full article

Background: In 2021, an Australian Hospital Nursing Research Hub sponsored 13 healthcare staff to complete the Graduate Certificate (Clinical Redesign), to build capability in health service improvement though work-integrated learning (WIL). Healthcare professionals undertaking workplace-based WIL likely experience significant challenges including balancing professional and student roles and aligning work with academic requirement. These pressures were likely intensified during the Coronavirus disease 2019 (COVID-19) pandemic. This study aimed to explore and understand the experiences of hospital healthcare staff completing WIL redesign projects, including the impacts of COVID-19. Methods: A qualitative descriptive inquiry approach was used to explore individual student experiences. Thirteen staff, mostly nurses, who enrolled in the 2021 course were invited to participate. Online semi-structured interviews were conducted. Data were analyzed using a general inductive thematic analysis approach. Results: Four participants (36%) took part; all were female and working full-time. Five main themes were identified that centered around: COVID-19, Support, Motivation, Alignment and Relevance, and Success. Conclusions: Novel insights include the need to reconceptualize “success” to improve student experience, the critical role of organizational–university–student alignment in enabling WIL studies, and the unique pressures of completing WIL during crisis conditions that direct impact the health sector, such as COVID-19. Although not generalizable, these findings are likely to be important considerations more broadly to strengthen WIL design, support and student experiences, ultimately enhancing health service staff capability to lead quality improvement in the workplace. Full article