Search Results (78)

Advances in pharmaceutical technology have positioned 3D printing and bioprinting as promising tools for developing personalized drug therapies. These innovations may redefine compounding practices by enabling precise, patient-specific drug formulations. Evaluating pharmacists’ readiness to adopt such technologies is therefore becoming increasingly important. Aim: The aim of this study is to investigate pharmacists’ knowledge, attitudes, and perceived barriers regarding the application of 3D printing and bioprinting technologies, as well as their perspectives on the regulation and implementation of these technologies in the context of personalized pharmacy. Materials and Methods: A custom-designed questionnaire was developed for the purposes of this pilot study, based on a review of the existing literature and informed by expert consultation to ensure conceptual relevance and clarity. The survey was conducted between September and December 2024. The data collection instrument comprises three main sections: (1) sociodemographic and professional characteristics, (2) knowledge regarding the applications of 3D printing and bioprinting in pharmacy, and (3) attitudes toward the regulatory framework and implementation of these technologies. Results: A total of 353 respondents participated, and 65.5% of them (n = 231) correctly distinguished between the concepts of “3D printing” and “bioprinting.” More than 25% (n = 88) were uncertain, and 8.5% (n = 30) were unable to differentiate between the two. Regarding the perceived benefits of personalized pharmacy, 83% (n = 293) of participants identified “the creation of personalized medications tailored to individual needs” as the main advantage, while 66% (n = 233) highlighted the “optimization of drug concentration to enhance therapeutic efficacy and minimize toxicity and adverse effects.” Approximately 60% (n = 210) of the pharmacists surveyed believed that the introduction of 3D-bioprinted pharmaceuticals would have a positive impact on the on-site preparation of customized drug formulations in community and hospital pharmacies. Lack of regulatory guidance and unresolved ethical concerns were identified as primary barriers. Notably, over 40% (n = 142) of respondents expressed concern that patients could be subjected to treatment approaches resembling “laboratory experimentation.” Nearly 90% (n = 317) of participants recognized the need for specialized training and expressed a willingness to engage in such educational initiatives. Conclusions: Three-dimensional printing and bioprinting technologies are considered cutting-edge instruments that may contribute to the advancement of pharmaceutical practice and industry, particularly in the field of personalized medicine. However, respondents’ views suggest that successful integration may require improved pharmacist awareness and targeted educational initiatives, along with the development and adaptation of appropriate regulatory frameworks to accommodate these novel technologies in drug design and compounding. Full article

Fully immersive engine room simulators are increasingly recognised as prominent tools in advancing maritime education and training. However, end-users’ acceptance of these innovative technologies remains insufficiently explored. To address this research gap, this case-specific pilot study applied the Technology Acceptance Model (TAM) to explore maritime engineering students’ intentions to adopt the newly introduced head-mounted display (HMD) virtual reality (VR) engine room simulator as a training tool. Sampling (N = 84) was conducted at the Faculty of Maritime Studies, University of Rijeka, during the initial simulator trials. Structural Equation Modelling (SEM) revealed that perceived usefulness was the primary determinant of students’ behavioural intention to accept the simulator as a tool for training purposes, acting both as a direct predictor and as a mediating variable, transmitting the positive effect of perceived ease of use onto the intention. By providing preliminary empirical evidence on the key factors influencing maritime engineering students’ intentions to adopt HMD-VR simulation technologies within existing training programmes, this study’s findings might offer valuable insights to software developers and educators in shaping future simulator design and enhancing pedagogical practices in alignment with maritime education and training (MET) standards. Full article

With the rapid advancement of the civil aviation sector and the concurrent expansion of pilot training programs, a pressing need arises for more efficient assessment methodologies during the pilot training process. Traditional written evaluations conducted by flight instructors are often marred by subjectivity and inefficiency, rendering them inadequate to satisfy the stringent demands of Competency-Based Training and Assessment (CBTA) frameworks. To address this challenge, this study presents a novel multi-label classification model that seamlessly integrates RoBERTa, a robust language model, with Graph Convolutional Networks (GCNs). By simultaneously modeling text features and label interdependencies, this model enables the automated, multi-dimensional classification of instructor evaluations. It incorporates a dynamic weight fusion strategy, which intelligently adjusts the output weights of RoBERTa and GCNs based on label correlations. Additionally, it introduces a label co-occurrence graph convolution layer, designed to capture intricate higher-order dependencies among labels. This study is based on a real-world dataset comprising 1078 evaluations and 158 labels, covering six major dimensions, including operational capabilities and communication skills. To provide context for the improvement, the proposed RoBERTa + GCN model is compared with key baseline models, such as BERT and LSTM. The results show that the RoBERTa + GCN model achieves an F1 score of 0.9737, representing an average improvement of 4.73% over these traditional methods. This approach enhances the consistency and efficiency of flight training assessments and provides new insights into integrating natural language processing and graph neural networks, demonstrating broad application prospects. Full article

This paper presents the innovative key knowledge breakthroughs achieved as one of the results of the BUILD2050 Erasmus+ project, focused on its contribution to advancing climate-resilient building engineering education and practice. In a recent work, the new methodologies applied in the BUILD2050 initiative were presented. This work discusses the incorporation of new knowledge in the courses and dissemination for the sustainability of the trans-European training experience. The challenge faced by the European Union for 2050 is achieving climate neutrality and decarbonization across all economic sectors, including the significantly impactful construction sector. To achieve this objective, it is necessary to develop technologies in an integrated way, following a holistic approach appropriately adapted to climatic conditions, cultural contexts, and natural resource availability through circular economy methodologies. To this end, it is necessary to develop innovative training methods with multidisciplinary content, incorporating a transnational perspective and scope, enabling continuous updating through learning cycles. These study cycles could be shorter and more complementary, allowing greater flexibility in knowledge acquisition while also enabling the creation of specialized training programs similar to those currently available. The BUILD2050 project has developed a transformative educational framework comprising eight comprehensive “Pilot Training” courses to address the critical challenge of integrating sustainability and circularity concepts into educational curricula at all levels, building engineering training and professional development. Addressing this gap is essential for transforming the construction sector and achieving global climate goals. The results of the BUILD2050 project demonstrate the potential of structured, trans-European training experiences to enhance professional competencies and support the transition to climate-neutral construction. Moving forward, widespread adoption and continuous dissemination of these educational advancements will be vital in ensuring a sustainable built environment by 2050. Full article

Background: Prolonged manual wheelchair usage often leads to musculoskeletal disorders in the upper body of individuals with spinal cord injury (SCI) due to repetitive, unidirectional movements. To mitigate these issues, targeted exercise of the back muscles—particularly those involving pulling movements of the arms and shoulders—is recommended. Therefore, this study aimed to develop a detachable, two-way propulsion system for manual wheelchairs, enabling propulsion through both pushing forward and pulling backward on the wheelchair pushrims. Methods: The propulsion system was engineered using a planetary gear train to facilitate dual-direction propulsion. Specifically, the planetary gear reverses the rotational direction, allowing the wheelchair to advance forward even when users pull the pushrims backward. Thus, the wheelchair can move forward through either pushing forward or pulling backward actions. Results: A prototype of the proposed system was fabricated using 3D printing technology and its functionality was verified. The prototype successfully demonstrated the two-way propulsion capability and the operation of the attachment mechanism. Additionally, the pilot test confirmed that an individual with SCI was able to propel a manual wheelchair equipped with the two-way propulsion system using both propulsion methods and switch between the methods independently while maintaining stability and safety throughout the test. Conclusion: The developed detachable two-way propulsion system shows significant promise as both a mobility aid and an exercise device, potentially reducing musculoskeletal complications among individuals with SCI who regularly utilize manual wheelchairs. Full article

Background: Establishing safe and efficient oral feeds for preterm infants is one of the last milestones to be achieved prior to discharge home. However, this process commonly elicits stress and anxiety in both care providers, such as nurses and the entire healthcare team in the Neonatal Intensive Care Unit (NICU), as well as parents. These feelings of uncertainty are exacerbated by the non-linear progression of oral feeding development and the absence of a systematized approach to initiate and advance feedings. Methods: In this 48-bed tertiary perinatal centre, staff surveys and a needs assessment showed dissatisfaction and increasing stress and anxiety due to the inconsistencies in initiating and advancing oral feeds. This paper describes the formation of a multidisciplinary feeding committee which reviewed various oral feeding training materials and the ultimate creation of two innovative oral feeding algorithms and their corresponding education materials. Results: The Sunnybrook Feeding Committee has developed two evidence-based algorithms, one for initiating oral feeds and another for monitoring progress with objective decision-making points during common oral feeding challenges. To complement and support these algorithms, educational materials and a comprehensive documentation process were also created. These resources included detailed instructions, visual aids, and step-by-step guides to help staff understand and apply the algorithms effectively. Additionally, the educational materials aimed to standardize training and ensure consistency across the NICU, further promoting a systematic approach to preterm oral feeding. Implementation of these algorithms also aimed to provide evidence-based, expert-guided guidelines for assessing readiness, initiating feeds, monitoring progress, and making necessary adjustments. Conclusions: This structured approach lays the foundation for a unit-wide language and systematic process for oral feeding. The next steps in this quality improvement project involve educating and piloting the implementation of the developed oral feeding algorithms, gathering staff feedback, and refining the tools accordingly. The goal is to enhance overall care quality, reduce stress for both care providers and parents, and ensure the best possible start for vulnerable preterm infants, ultimately supporting a smooth and successful transition to home. Full article

Background: Patients with advanced breast cancer (BC) may be treated with stereotactic ablative radiotherapy (SABR) for tumor control. Variable treatment responses are a clinical challenge and there is a need to predict tumor radiosensitivity a priori. There is evidence showing that tumor infiltrating lymphocytes (TILs) are markers for chemotherapy response; however, this association has not yet been validated in breast radiation therapy. This pilot study investigates the computational analysis of TILs to predict SABR response in patients with inoperable BC. Methods: Patients with inoperable breast cancer (n = 22) were included for analysis and classified into partial response (n = 12) and stable disease (n = 10) groups. Pre-treatment tumor biopsies (n = 104) were prepared, digitally imaged, and underwent computational analysis. Whole slide images (WSIs) were pre-processed, and then a pre-trained convolutional neural network model (CNN) was employed to identify the regions of interest. The TILs were annotated, and spatial graph features were extracted. The clinical and spatial features were collected and analyzed using machine learning (ML) classifiers, including K-nearest neighbor (KNN), support vector machines (SVMs), and Gaussian Naïve Bayes (GNB), to predict the SABR response. The models were evaluated using receiver operator characteristics (ROCs) and area under the curve (AUC) analysis. Results: The KNN, SVM, and GNB models were implemented using clinical and graph features. Among the generated prediction models, the graph features showed higher predictive performances compared to the models containing clinical features alone. The highest-performing model, using computationally derived graph features, showed an AUC of 0.92, while the highest clinical model showed an AUC of 0.62 within unseen test sets. Conclusions: Spatial TIL models demonstrate strong potential for predicting SABR response in inoperable breast cancer. TILs indicate a higher independent predictive performance than clinical-level features alone. Full article

This study investigates the effectiveness of immersive audiovisual simulations in eliciting emotional responses and replicating the psychological and cognitive demands of high-risk operational environments, particularly in firefighting scenarios. Conducted in two successive phases, the research first employed a pilot study involving 90 participants (45 firefighters and 45 students) who were exposed to a controlled audiovisual simulation. Emotional responses were assessed using the Differential Emotion Scale (DES), the Emotion Regulation Questionnaire (ERQ), and the Perceived Stress Scale (PSS). The second phase involved an immersive room experiment with 36 firefighters, where the same audiovisual stimulus was presented in a fully immersive environment, integrating interactive decision-making tasks to enhance ecological validity. The findings indicate that both methods effectively elicited the targeted emotional responses, including stress, fear, anger, and serenity, with firefighters exhibiting greater emotional regulation and adaptive coping strategies compared to students. The immersive room environment significantly amplified emotional engagement, resulting in stronger emotional responses from the first scene onward. These results underscore the potential of immersive training tools in preparing emergency responders for high-stress situations by strengthening psychological resilience, improving emotional regulation, and optimizing decision-making under pressure. The study contributes to advancing evidence-based training methodologies in emergency response, public safety, and crisis management, emphasizing the importance of integrating immersive technologies into professional training programs. Full article

(1) Background: The process of epiretinal membrane peeling (MP) requires precise intraoperative visualization to achieve optimal surgical outcomes. This study investigates the integration of preoperative Optical Coherence Tomography (OCT) images into real-time surgical video feeds, providing a dynamic overlay that enhances the decision-making process during surgery. (2) Methods: Five MP surgeries were analyzed, where preoperative OCT images were first manually aligned with the initial frame of the surgical video by selecting five pairs of corresponding points. A homography transformation was then computed to overlay the OCT onto that first frame. Subsequently, for consecutive frames, feature point extraction (the Shi–Tomasi method) and optical flow computation (the Lucas–Kanade algorithm) were used to calculate frame-by-frame transformations, which were applied to the OCT image to maintain alignment in near real time. (3) Results: The method achieved a 92.7% success rate in optical flow detection and maintained an average processing speed of 7.56 frames per second (FPS), demonstrating the feasibility of near real-time application. (4) Conclusions: The developed approach facilitates enhanced intraoperative visualization, providing surgeons with easier retinal structure identification which results in more comprehensive data-driven decisions. By improving surgical precision while potentially reducing complications, this technique benefits both surgeons and patients. Furthermore, the integration of OCT overlays holds promise for advancing robot-assisted surgery and surgical training protocols. This pilot study establishes the feasibility of real-time OCT integration in MP and opens avenues for broader applications in vitreoretinal procedures. Full article

The pharmaceutical industry is undergoing a significant transition from batch to continuous manufacturing, driven by increasing regulatory requirements and sustainability pressures. Digital twins (DTs) play a pivotal role in facilitating this transition by enabling real-time data visualisation, process optimisation, and predictive analytics. While substantial progress has been made in the development and application of DTs, particularly in industries such as energy and automotive, there remains a critical need for further research and development focused on creating sustainability-oriented digital twins tailored to pharmaceutical processes. In the pharmaceutical sector, DTs are being progressively utilised not only for real-time monitoring and analysis but also as dynamic training platforms for engineers and operators, enhancing both operational efficiency and workforce competency. This paper examines the University of Sheffield’s Diamond Pilot Plant (DiPP), a facility showcasing the future of pharmaceutical manufacturing through the integration of Industry 4.0 technologies and advanced sensors. This paper focuses on developing a data-driven model to predict energy consumption in a twin-screw granulator (TSG) within the DiPP. The model, based on second-degree polynomial regression, demonstrates strong predictive accuracy with R-squared values exceeding 0.8. By optimising energy performance indicators, this work aims to improve the sustainability of pharmaceutical manufacturing processes. This research contributes to the field of pharmaceutical manufacturing by providing a foundation for creating energy models and advancing the development of comprehensive DT. Full article

The United States Environmental Protection Agency (US EPA) recently finalized the enforceable maximum contaminant levels for some per- and polyfluoroalkyl substances (PFAS) in drinking water which intends to substantially decrease their level in it. Conventional processes in full-scale drinking water treatment plants (DWTPs) are usually inefficient in PFAS removal from source water (i.e., groundwater and surface water). There is an increasing interest in investigating/evaluating advanced treatment technologies for PFAS removal from PFAS-contaminated water to help generate a number of potential solutions to this water engineering design challenge/problem. While numerous excellent research studies have been carried out and reported in the literature on the efficiency of several treatment processes in removing PFAS from PFAS-contaminated water, mostly at lab- and pilot-scales, full-scale DWTP investigations still need further attention. This study reviews the US EPA’s PFAS water quality guidelines/regulations, remediation technologies for PFAS in water, and PFAS removal studies on full-scale DWTPs. Then, it discusses some configurations of DWTP for PFAS removal from source water (i.e., groundwater and surface water) as well as suggesting future directions. Further research on the effect of environmental factors (e.g., organic matter) on PFAS removal, the effective elimination of short-chain PFAS from real PFAS-contaminated source water using cost-effective and industrially applicable remediation technologies, the efficiency/performance of full-scale treatment trains including innovative advanced technologies in long-term for PFAS removal from source water to produce drinking water and the associated costs, as well as cost reduction/minimization via process optimization is still of interest. Full article

Developing effective cognitive training tools for older adults, specifically addressing executive functions such as planning, is a challenging task. It is of paramount importance to ensure the implementation of engaging activities that must be tailored to the specific needs and expectations of older adults. Furthermore, it is essential to provide the appropriate level of complexity for the planning task. A human-centred approach was used to address the issues identified in the design of the tool. Two pilot studies were conducted with older adults to fine-tune the training task and optimize its suitability for them. This also led to an enhancement of the underlying planning engine, transitioning from a simple fast-forward planner (PDDL4J) to an advanced heuristic search planner (ENHSP). The results show that user studies enabled the development of a cognitive training system that gradually increased the proposed difficulty levels of the planning task while maintaining usability and satisfaction among older adults. This highlights the importance of conducting user studies when implementing cognitive training tools for older adults. Full article

As cities evolve into smarter and more connected environments, there is a growing need for innovative solutions to improve urban mobility. This study examines the potential of integrating blockchain technology into passenger transportation systems within smart cities, with a particular emphasis on a blockchain-enabled Mobility-as-a-Service (MaaS) solution. In contrast to traditional technologies, blockchain’s decentralized structure improves data security and guarantees transaction transparency, thus reducing the risk of fraud and errors. The proposed MaaS framework enables seamless collaboration between key transportation stakeholders, promoting more efficient utilization of services like buses, trains, bike-sharing, and ride-hailing. By improving integrated payment and ticketing systems, the solution aims to create a smoother user experience while advancing the urban goals of efficiency, environmental sustainability, and secure data handling. This research evaluates the feasibility of a Hyperledger Fabric-based solution, demonstrating its performance under various load conditions and proposing scalability adjustments based on pilot results. The conclusions indicate that blockchain-enabled MaaS systems have the potential to transform urban mobility. Further exploration into pilot projects and the expansion to freight transportation are needed for an integrated approach to city-wide transport solutions. Full article

This study develops and validates a Reduced Order Model (ROM) integrated with Digital Twin technology for real-time temperature control in the Main Control Room (MCR) of a nuclear power plant. Utilizing Computational Fluid Dynamics (CFD) simulations, we obtained detailed three-dimensional thermal flow distributions under various operating conditions. A ROM was generated using machine learning techniques based on 94 CFD cases, achieving a mean temperature error of 0.35%. The ROM was further validated against two excluded CFD cases, demonstrating high correlation coefficients (R > 0.84) and low error metrics, confirming its accuracy and reliability. Integrating the ROM with the Heating, Ventilating, and Air Conditioning (HVAC) system, we conducted a two-month simulation, showing effective maintenance of MCR temperature within predefined criteria through adaptive HVAC control. This integration significantly enhances operational efficiency and safety by enabling real-time monitoring and control while reducing computational costs and time associated with full-scale CFD analyses. Despite promising results, the study acknowledges limitations related to ROM’s dependency on training data quality and the need for more comprehensive validation under diverse and unforeseen conditions. Future research will focus on expanding the ROM’s applicability, incorporating advanced machine learning methods, and conducting pilot tests in actual nuclear plant environments to further optimize the Digital Twin-based control system. Full article

Red-light running at signalised intersections poses a significant safety risk, necessitating advanced predictive technologies to predict red-light violation behaviour, especially for advanced red-light warning (ARLW) systems. This research leverages Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to forecast the red-light running and stopping behaviours of drivers in connected vehicles. We utilised data from the Ipswich Connected Vehicle Pilot (ICVP) in Queensland, Australia, which gathered naturalistic driving data from 355 connected vehicles at 29 signalised intersections. These vehicles broadcast Cooperative Awareness Messages (CAM) within the Cooperative Intelligent Transport Systems (C-ITS), providing kinematic inputs such as vehicle speed, speed limits, longitudinal and lateral accelerations, and yaw rate. These variables were monitored at 100-millisecond intervals for durations from 1 to 4 s before reaching various distances from the stop line. Our results indicate that the LSTM model outperforms the GRU in predicting both red-light running and stopping behaviours with high accuracy. However, the pre-trained GRU model performs better in predicting red-light running specifically, making it valuable in applications requiring early violation prediction. Implementing these models can enhance red-light violation countermeasures, such as dynamic all-red extension (DARE), decreasing the likelihood of severe collisions and enhancing road users’ safety. Full article