Search Results (173)

Fenbendazole is an important anti-parasitic medicine widely used in the veterinary field and has recently been considered as a possible anti-cancer agent in humans by some researchers. Fenbendazole encounters challenges in its usage due to its limited aqueous solubility, which consequently impacts its therapeutic efficacy. In this work, an in vitro mechanistic investigation was conducted to evaluate the compatibility, amorphization behaviour and dissolution profile of fenbendazole dispersed in Soluplus^® using the solid dispersion approach via hot-melt extrusion. Three different fenbendazole/Soluplus^® ratios were formulated and characterised through systematic experimentation. Powder X-Ray Diffraction (PXRD), Differential Scanning Calorimetry (DSC), Scanning Electron Microscopy (SEM), Energy Dispersive X-Ray (EDX) and Fourier Transform Infrared Spectroscopy (FTIR) were employed for thermal, physical, chemical and morphological analyses. The solubility of the drug formulation during a dissolution test was investigated using Ultraviolet–Visible (UV–Vis) spectrophotometric measurements. In vitro dissolution testing in acidic and neutral media was employed as a controlled environment to compare dissolution behaviour among different loadings. The extrudates demonstrated markedly enhanced apparent solubility compared to neat fenbendazole, with the 5% formulation showing the highest dissolution rate (approximately 85% after 48 h). This improvement can be attributed to better wetting properties and drug dispersion within the Soluplus^® matrix. This innovative strategy holds promise in surmounting fenbendazole’s solubility limitations, presenting a comprehensive solution to enhance its therapeutic effectiveness. Full article

The growth of generative artificial intelligence (GAI), remarkably, Large Language Models (LLMs) such as ChatGPT, converts the educational environment by empowering intelligent, data-driven education and curriculum design innovation. This study aimed to assess the integration of LLMs into higher education to foster curriculum design, learning outcomes, and innovative work behaviour (IWB). Specifically, this study investigated how LLMs’ perceived usefulness (PU) and perceived ease of use (PEOU) can support educators to be engaged in IWB—idea generation (IG), idea promotion (IP), opportunity exploration (OE), and reflection (Relf)—employing a web-based survey and targeting faculty members. A total of 493 replies were obtained and found to be valid to be analysed with partial least squares structural equation modelling (PLS-SEM). The results indicated that PU and PEOU have a significant positive impact on the four dimensions of IWB in the context of LLMs for curriculum development. The evaluated model can assist in bridging the gap between AI technology acceptance and educational strategy by offering some practical evidence and implications for university leaders and policymakers. Additionally, this study offered a data-driven pathway to advance higher education IWB through the adoption of LLMs. Full article

Extended stiffened end-plate bolted connections represent one of the most utilised steel connection types in seismic-prone regions, and several studies have been dedicated to the improvement of their performance. Recently, a new stiffened joint configuration, with a non-symmetric octagonal bolt arrangement, was proposed, highlighting its excellent performance in seismic scenarios. Therefore, two new design procedures according to both the European and North American codes were developed. Within this framework, the present work aims to investigate the performance of this innovative joint under column loss scenarios. A total of sixteen beam-to-column steel assemblies, defined by varying the beam depth and the design procedure, were numerically investigated using advanced FE models validated against experimental results. The numerical results show that the innovative joints exhibit a ductile behaviour, even better than traditional joints designed according to the current versions of EU and US codes. Indeed, the new bolt arrangement allows us to reduce the damage in the connection thanks to a better stress distribution among the bolts. Full article

Background: Dementia poses a significant health concern among Australia’s First Nations peoples, who experience higher prevalence and earlier onset compared to non-First Nations populations. Despite growing research attention, the overall scope and characteristics of existing literature on dementia in these communities remain unclear. Objective: This scoping review aimed to map and synthesise existing evidence on the burden of dementia among First Nations peoples, focusing on associated risk factors and culturally responsive approaches to prevention, intervention, and care. Methods: Following the PRISMA Extension for Scoping Reviews guidelines, a comprehensive search was conducted across Scopus, EMBASE, PubMed, PsycINFO, CINAHL, the Indigenous Studies Portal, and Google Scholar for English-language studies published between 2004 and 2025. Search terms combined dementia and cognitive impairment with First Nations, Indigenous peoples, and related concepts, alongside terms for risk factors, intervention, prevention, care strategies, and health disparities. Two reviewers independently screened studies and extracted data using a standardised template. Of the 620 records identified, 324 were screened, 130 were assessed in full, and 75 met the inclusion criteria. Data were narratively synthesised to identify key themes and evidence gaps. Results: The review revealed a disproportionate burden of dementia among First Nations peoples, characterised by earlier onset and higher prevalence than in non-First Nations populations. Major modifiable risk factors included social determinants of health, lifestyle behaviours, and inequitable access to healthcare. Studies emphasised the importance of culturally safe, community-led, and multidisciplinary approaches; however, many interventions remain poorly adapted to the diverse cultural contexts of First Nations communities. The review also identified gaps in diagnostic tools, culturally appropriate care pathways, and the integration of traditional knowledge and digital innovations in dementia management. Conclusions: Addressing dementia inequities among First Nations Australians demands transformative, community-driven action that extends beyond descriptive research. Future work should prioritise co-designed, culturally grounded interventions that embed First Nations knowledge systems, strengthen healthcare capacity, and foster long-term community empowerment. Embedding cultural safety within policy and clinical frameworks, and shifting toward preventive, strengths-based approaches, will advance equity in dementia care and provide valuable insights for First Nations health systems globally. Full article

Mental health disorders affect large numbers of people worldwide and are a major cause of long-term disability. Digital health technologies such as mobile apps and wearable devices now generate rich behavioural data that could support earlier detection and more personalised care. However, these data are highly sensitive and distributed across devices and platforms, which makes privacy protection and scalable analysis challenging; federated learning offers a way to train models across devices while keeping raw data local. When combined with edge, fog, or cloud computing, federated learning offers a way to support near-real-time mental health analysis while keeping raw data local. This review screened 1104 records, assessed 31 full-text articles using a five-question quality checklist, and retained 17 empirical studies that achieved a score of at least 7/10 for synthesis. The included studies were compared in terms of their FL and edge/cloud architectures, data sources, privacy and security techniques, and evidence for operation in real-world settings. The synthesis highlights innovative but fragmented progress, with limited work on comorbidity modelling, deployment evaluation, and common benchmarks, and identifies priorities for the development of scalable, practical, and ethically robust FL systems for digital mental health. Full article

A system dynamics approach is described to explore the path of Carbon Capture diffusion. The proposed model, in principle, follows the Bass diffusion of innovation theory and includes all major influencing factors. The primary contribution of this paper is the modification of Bass’s model to reflect parameters affecting the adoption of Carbon capture and storage technology. Consequently, it differs from other extensions to Bass’s model. The underpinning of this work is the system dynamics (SD) approach, which can open a pathway for further research into CCS acceptance. The proposed model’s behaviour is illustrated for various transition pathways of the technology, for different regimes. By modifying the proposed model, the paper also allows consideration of various capturing technologies on their merit. The proposed framework enables the examination of the impact of intervention policies on the adoption of CCS by individual investors. The purpose is to identify the parameters of these policies to support the under-resourced CCS technology and reduce the need for government participation. It is worth noting that the SD is primarily a descriptive method used for scenario analysis to illustrate what the future would look like. Full article

Digital transformation is reshaping work and management, yet evidence on how technological innovation interacts with workplace well-being, leadership, organizational culture, and human-centered management remains fragmented. This study aims to integrate these strands of research by examining how innovation and digitalization affect employee well-being and motivation in organizational contexts. A systematic literature review was conducted in accordance with PRISMA 2020 guidelines, with a protocol registered on INPLASY. The search was performed in the Scopus database and identified 287 eligible studies (1989–February 2025). Bibliometric keyword co-occurrence analysis using VOSviewer (1.6.20), combined with qualitative content and thematic analysis, led to five clusters: (1) innovation and well-being; (2) leadership pathways to workplace well-being; (3) work motivation and job satisfaction; (4) human-centered management in technological progress; and (5) organizational culture. The results show that organizations reconciling innovation and people’s well-being tend to adopt leadership styles and cultures grounded in ethical values, inclusion, psychological safety, and balanced work demands and resources, operationalized through human-centered management practices. These findings offer an integrated framework that goes beyond an instrumental view of technology and provide guidance for leaders, HR professionals, and policymakers designing digital transformation strategies that foster responsible innovation and promote sustainable, health-promoting work environments. Full article

The adoption of artificial intelligence (AI) is reshaping organizational practices, yet workers’ attitudes remain crucial for its successful integration. This study examines how perceived organizational ethical culture, organizational innovativeness, and job performance influence workers’ attitudes towards AI. A survey was administered to 356 workers across diverse sectors, with analyses focusing on 154 participants who reported prior AI use. Measures included the Attitudes Towards Artificial Intelligence at Work (AAAW), Corporate Ethical Virtues (CEV), Inventory of Organizational Innovativeness (IOI), and an adapted version of the In-Role Behaviour Scale. Hierarchical regression analyses revealed that ethical culture dimensions, particularly Clarity and Feasibility, significantly predicted attitudes towards AI, such as anxiety and job insecurity, with Feasibility also associated with the attribution of human-like traits to AI. Supportability, reflecting a cooperative work environment, was linked to lower perceptions of AI human-likeness and adaptability. Among innovation dimensions, only Raising Projects, the active encouragement of employees’ ideas, was positively related to perceptions of AI adaptability, highlighting the importance of participatory innovation practices over abstract signals. Most importantly, perceived job performance improvements through AI predicted more positive attitudes, including greater perceived quality, utility, and reduced anxiety. Overall, this study contributes to the growing literature on AI in organizations by offering an exploratory yet integrative framework that captures the multifaceted nature of AI acceptance in the workplace. Full article

Background: Obesity is a multifactorial disease with significant physical, psychological, and economic impacts on individuals and society. Workers are particularly vulnerable, as obesity is associated with reduced productivity, absenteeism, and premature mortality. Lifestyle interventions combining dietary, physical activity, and behavioural strategies have been investigated as therapeutic approaches in this population. Objective: We aimed to conduct an integrative review assessing the effectiveness of workplace-based obesity treatment models involving dietary interventions, physical activity, and behavioural change. Methods: A search was conducted in PubMed for studies published between 2006 and 2024, with no language restrictions. Eligible studies included experimental or quasi-experimental longitudinal designs involving adult workers. After screening 95 articles, 18 were evaluated in full, and 8 met all inclusion criteria. Data extraction covered study design, intervention type, comparators, outcomes, and methodological quality, assessed using the Newcastle–Ottawa Scale. Results: Half of the included studies reported no significant reduction in body mass index after 6 or 12 months, while the others showed only modest decreases. Nevertheless, all interventions demonstrated improvements in dietary habits (reduced sugar-sweetened beverage intake, increased fruit, vegetable, and fibre consumption), physical activity (increased walking, reduced sedentary behaviour), and behavioural domains (adherence to healthy routines, self-monitoring, and family or employer support). Conclusions: Lifestyle-based workplace interventions for obesity show limited long-term effectiveness in weight reduction but promote healthier lifestyle habits, cardiometabolic health, and more supportive work environments. Future research should include diverse socioeconomic settings, particularly in developing countries, and apply robust designs, longer follow-ups, and innovative strategies to enhance adherence and outcomes. Full article

Background/objectives: The global incidence of skin cancer is rising, creating a need to strengthen prevention strategies. In this review, we examine the contributions of public health, dermatology, behavioural science, and emerging technologies such as artificial intelligence and bioinformatics, which have collectively shaped prevention in recent decades. Methods: Using a narrative scoping review approach guided by the PRISMA-ScR framework, we synthesised research across these disciplines to highlight their roles in enhancing skin cancer prevention. Results: Initial efforts focused on increasing public knowledge through sun protection campaigns and symptom recognition. Dermatologists enhanced early detection through refined techniques and clinical guidelines. Initiatives such as Euromelanoma enabled broader collaboration and population-level screening. As more disciplines joined, advances in risk stratification, digital imaging, artificial intelligence, molecular and genetic diagnostics and bioinformatics became possible. Beyond skin cancer prevention, these tools may have additional applications for systemic health issues. However, a number of challenges remain, particularly regarding data privacy concerns, cost-effectiveness, equitable access, and the validation of artificial intelligence tools in diverse populations. Conclusions: The prevention of skin cancer brings together knowledge spanning the fields of public health and dermatology to behavioural research and digital innovation. Working together, these disciplines have improved early detection and awareness. However, fragmented collaboration across regions throughout the world continue to limit their impact. Improved equity alongside stronger, more coordinated partnerships will be essential for the next phase of progress. Full article

In service-intensive, compliance-driven settings such as banking, identifying how internal corporate social responsibility (ICSR) fosters employees’ vitality and learning is crucial for sustainable organisational performance. Amid growing interest in employee thriving, this study explores how perceived internal corporate social responsibility (PICSR) and moral meaningfulness (MM) shape thriving at work (TaW) through organisational embeddedness (OE). Rooted in self-determination theory, the findings reveal OE as a key mediator between PICSR and TaW, shedding light on how ICSR initiatives influence employee dynamics. The study also reveals that MM alone does not significantly predict TaW directly, but does so indirectly through OE, highlighting the importance of contextual mechanisms. Additionally, it identifies a surprising negative moderating effect of risk-taking, one dimension of intrapreneurial behaviour (IB), on the relationship between OE and TaW, while innovativeness, another dimension, shows no such effect. Theoretical and practical implications underscore the importance of aligning ICSR practices with employees’ psychological needs, supporting moral alignment, and tailoring support for intrapreneurs. Organisations must achieve a balance between autonomy and security to sustain engagement and innovation, advancing human and organisational sustainability, ultimately leading to thriving. Full article

In retail enterprises, employee well-being is recognized as a key factor influencing service quality and operational sustainability. While prior research has extensively investigated enterprise-centric approaches to improving employee well-being, little scholarly attention has been devoted to understanding the effects of customer participation and service scene innovation on employee well-being. Employing a case study methodology, this research investigates a Chinese exemplary retail enterprise “Pang Donglai”, exploring how retail enterprises can leverage service scene innovation to improve employee well-being from the perspective of scene innovation. The findings reveal that service scene innovation in retail enterprises can be categorized into three types: empowerment-oriented scene innovation, autonomy-oriented shopping scene innovation, and thematic display scene innovation. These innovations facilitate empathetic interactions between employees and customers, effectively enhancing employee well-being and creating a virtuous cycle of value co-creation among the enterprise, employees, and customers. Therefore, retail enterprises can continuously improve the working situation of employees, the display of products, and the shopping environment of customers, in order to enhance employee well-being and thus improve their voluntary behaviour and its sustainability. This study provides empirical insights into how retail enterprises can enhance employee well-being through service scene innovation, thereby contributing to the improvement of business performance. Full article

Background and Objective: The intricate connection between daily behaviours and health necessitates robust monitoring, particularly with the advent of Internet of Things (IoT) systems. This study introduces an innovative approach that exploits the synergy of information from various IoT sources to assess the alignment of behavioural routines with health guidelines. The goal is to improve the readability of behaviour models and provide actionable insights for healthcare professionals. Method: We integrate data from ambient sensors, smartphones, and wearable devices to acquire daily behavioural routines by employing process mining (PM) techniques to generate interpretable behaviour models. These routines are grouped according to compliance with health guidelines, and a clustering method is used to identify similarities in behaviours and key characteristics within each cluster. Results: Applied to an elderly care case study, our approach categorised days into three physical activity levels (Insufficient, Sufficient, Desirable) based on daily step thresholds. The integration of multi-source data revealed behavioural variations not detectable through single-source monitoring. We demonstrated that the proposed visualisations in calendar and timeline views aid health experts in understanding patient behaviours, enabling longitudinal monitoring and clearer interpretation of behavioural trends and precise interventions. Notably, the approach facilitates early detection of behaviour changes during contextual events (e.g., COVID-19 lockdown and Ramadan), which are available in our dataset. Conclusions: By enhancing interpretability and linking behaviour to health guidelines, this work signifies a promising path for behavioural analysis and discovering variations to empower smart healthcare, offering insights into patient health, personalised interventions, and healthier routines through continuous monitoring with IoT-driven data analysis. Full article

The real-time simulation of atmospheric clouds for the visualisation of outdoor scenarios has been a computer graphics research challenge since the emergence of the natural phenomena rendering field in the 1980s. In this work, we present an innovative method for real-time cumuli movement and transition based on a Recurrent Neural Network (RNN). Specifically, an LSTM, a GRU and an Elman RNN network are trained on time-series data generated by a parallel Navier–Stokes fluid solver. The training process optimizes the network to predict the velocity of cloud particles for the subsequent time step, allowing the model to act as a computationally efficient surrogate for the full physics simulation. In the experiments, we obtained natural-looking behaviour for cumuli evolution and dissipation with excellent performance by the RNN fluid algorithm compared with that of classical finite-element computational solvers. These experiments prove the suitability of our ontogenetic computational model in terms of achieving an optimum balance between natural-looking realism and performance in opposition to computationally expensive hyper-realistic fluid dynamics simulations which are usually in non-real time. Therefore, the core contributions of our research to the state of the art in cloud dynamics are the following: a progressively improved real-time step of the RNN-LSTM fluid algorithm compared to the previous literature to date by outperforming the inference times during the runtime cumuli animation in the analysed hardware, the absence of spatial grid bounds and the replacement of fluid dynamics equation solving with the RNN. As a consequence, this method is applicable in flight simulation systems, climate awareness educational tools, atmospheric simulations, nature-based video games and architectural software. Full article

The transition to a decarbonised energy system in Ireland presents significant socio-technical challenges. This paper, focused on the work of the SMARTLAB project at the Citizen Innovation Lab in Limerick city, investigated the potential of a localised living lab approach to address these challenges. Engaging across 70 buildings and their inhabitants, the project captured the evolution of attitudes and intentions towards the clean energy transition in ways directly relevant to future policy implementation across grid redevelopment, smart service design, and national retrofit. Project methodology was framed by a living lab approach, with wireless energy and indoor environment sensors installed in participant buildings and participant journeys developed by harnessing the Citizen Innovation Lab ecosystem. The results indicate behaviour changes among participants, particularly focusing on indoor environmental conditions. The study concludes that embedded, localised living labs offer a methodological framework which can capture diverse datasets and encompass complex contemporary contexts towards transition goals. Full article