Search Results (20)

The aim of our study was to provide a retrospective single-center experience of the additional workload associated with routine PGT, including embryologist training and suggested staffing levels. A total of 4945 IVF cycles were retrospectively considered, of which 1680 were PGT cycles with a total of 5258 biopsied blastocysts. An exponential increase in the proportion of PGTs over OPUs was observed, from 0.2% in 2015 to 72.9% in 2024. The number of viable embryos for biopsy was significantly increased by the systematic adoption of an extended embryo culture and the concomitant transition from a day 2 Double Embryo Transfer (DET) to a day 5 Single Blastocyst Transfer (SET) policy in 2020. In order to cope with the increasing workload, a concomitant increase in the number of embryologists involved in blastocyst biopsy was adopted, with a second embryologist in 2020, a third in 2021, and a fourth in 2022, with a trend comparable to that observed for the proportion of PGT cycles over IVF cycles performed during the study period. The appropriate number of staff required for the IVF laboratory was calculated using the Staffing Model for ART (smART) calculator, based on 12 routine IVF procedures. An optimal balance between operational procedures and staffing levels was achieved when the difference (Δ) was ≤10%, ensuring the efficient maintenance of PGT in the IVF laboratory. Full article

Wheelchair-mounted assistive robotic manipulators can provide reach and grasp functions for power wheelchair users. This in-lab study evaluated a vision-guided shared control (VGS) system with twelve users completing two multi-step kitchen tasks: a drinking task and a popcorn making task. Using a mixed methods approach participants compared VGS and manual joystick control, providing performance metrics, qualitative insights, and lessons learned. Data collection included demographic questionnaires, the System Usability Scale (SUS), NASA Task Load Index (NASA-TLX), and exit interviews. No significant SUS differences were found between control modes, but NASA-TLX scores revealed VGS control significantly reduced workload during the drinking task and the popcorn task. VGS control reduced operation time and improved task success but was not universally preferred. Six participants preferred VGS, five preferred manual, and one had no preference. In addition, participants expressed interest in robotic arms for daily tasks and described two main operation challenges: distinguishing wrist orientation from rotation modes and managing depth perception. They also shared perspectives on how a personal robotic arm could complement caregiver support in their home. Full article

Magnetic Resonance Angiography (MRA) is widely used for cerebrovascular assessment, with Time-of-Flight (TOF) MRA being a common non-contrast imaging technique. However, maximum intensity projection (MIP) images generated from TOF-MRA often include non-essential vascular structures such as external carotid branches, requiring manual editing for accurate visualization of intracranial arteries. This study proposes a deep learning-based semantic segmentation approach to automate the removal of these structures, enhancing MIP image clarity while reducing manual workload. Using DeepLab v3+, a convolutional neural network model optimized for segmentation accuracy, the method achieved an average Dice Similarity Coefficient (DSC) of 0.9615 and an Intersection over Union (IoU) of 0.9261 across five-fold cross-validation. The developed system processed MRA datasets at an average speed of 16.61 frames per second, demonstrating real-time feasibility. A dedicated software tool was implemented to apply the segmentation model directly to DICOM images, enabling fully automated MIP image generation. While the model effectively removed most external carotid structures, further refinement is needed to improve venous structure suppression. These results indicate that deep learning can provide an efficient and reliable approach for automated cerebrovascular image processing, with potential applications in clinical workflows and neurovascular disease diagnosis. Full article

In the field of passive Brain–computer Interfaces (BCI), the need to develop systems that require rapid setup, suitable for use outside of laboratories is a fundamental challenge, especially now, that the market is flooded with novel EEG headsets with a good quality. However, the lack of control in operational conditions can compromise the performance of the machine learning model behind the BCI system. First, this study focuses on evaluating the performance loss of the BCI system, induced by a different positioning of the EEG headset (and of course sensors), so generating a variation in the control features used to calibrate the machine learning algorithm. This phenomenon is called covariate shift. Detecting covariate shift occurrences in advance allows for preventive measures, such as informing the user to adjust the position of the headset or applying specific corrections in new coming data. We used in this study an unsupervised Machine Learning model, the Isolation Forest, to detect covariate shift occurrence in new coming data. We tested the method on two different datasets, one in a controlled setting (9 participants), and the other in a more realistic setting (10 participants). In the controlled dataset, we simulated the movement of the EEG cap using different channel and reference configurations. For each test configuration, we selected a set of electrodes near the control electrodes. Regarding the realistic dataset, we aimed to simulate the use of the cap outside the laboratory, mimicking the removal and repositioning of the cap by a non-expert user. In both datasets, we recorded multiple test sessions for each configuration while executing a set of Workload tasks. The results obtained using the Isolation Forest model allowed the identification of covariate shift in the data, even with a 15-s recording sample. Moreover, the results showed a strong and significant negative correlation between the percentage of covariate shift detected by the method, and the accuracy of the passive BCI system (p-value < 0.01). This novel approach opens new perspectives for developing more robust and flexible BCI systems, with the potential to move these technologies towards out-of-the-lab use, without the need for supervision for use by a non-expert user. Full article

Advancements in artificial intelligence (AI) have rapidly transformed various sectors, and the field of echocardiography is no exception. AI-driven technologies hold immense potential to revolutionize echo labs’ diagnostic capabilities and improve patient care. This paper explores the importance for echo labs to embrace AI and stay ahead of the curve in harnessing its power. Our manuscript provides an overview of the growing impact of AI on medical imaging, specifically echocardiography. It highlights how AI-driven algorithms can enhance image quality, automate measurements, and accurately diagnose cardiovascular diseases. Additionally, we emphasize the importance of training echo lab professionals in AI implementation to optimize its integration into routine clinical practice. By embracing AI, echo labs can overcome challenges such as workload burden and diagnostic accuracy variability, improving efficiency and patient outcomes. This paper highlights the need for collaboration between echocardiography laboratory experts, AI researchers, and industry stakeholders to drive innovation and establish standardized protocols for implementing AI in echocardiography. In conclusion, this article emphasizes the importance of AI adoption in echocardiography labs, urging practitioners to proactively integrate AI technologies into their workflow and take advantage of their present opportunities. Embracing AI is not just a choice but an imperative for echo labs to maintain their leadership and excel in delivering state-of-the-art cardiac care in the era of advanced medical technologies. Full article

The use of molecular testing to direct diagnosis and treatment options in ovarian tumours has rapidly expanded in recent years, in particular with regard to the recommendation for routine homologous recombination deficiency (HRD) testing in all patients with high-grade ovarian epithelial tumours. The implications of this increased level of testing upon the pathologist is significant in terms of increased workload, the provision of adequate tumour samples for molecular testing, and the interpretation of complex molecular pathology reports. In order to optimise the quality of reports generated, it is important to establish clear pathways of communication on both a local and national level between clinicians, pathology lab staff, and medical scientists. On a national level, in the United Kingdom, Genomic Laboratory Hubs (GLHs) have been established to provide a uniform high-quality molecular diagnostics service to all patients with ovarian tumours within the National Health services in the country. On a local level, there are a number of small steps that can be taken to improve the quality of tissues available for testing and to streamline the processes involved in generating requests for molecular testing. This article discusses these factors from the perspective of the clinical histopathologist. Full article

Load balancing is a very important concept in cloud computing. In this work, studies are conducted on workload traces at Los Alamos National Lab (LANL). The jobs in this trace are asymmetric in nature as most of them have small time limit. Two hybrid algorithms, a Genetic Algorithm combined with First Come First Serve (GA_FCFS) and Genetic Algorithm combined with Round Robin (GA_RR), are proposed here. The results obtained are compared with the existing First Come First Serve (FCFS), Round Robin (RR) and Genetic Algorithm (GA). Makespan and Resource Utilization are used for the comparison of results. In terms of Makespan, it is observed that GA_RR outperforms the other methods for all the batch sizes. Although the performance of GA_FCFS is much better than that of the other three well-established algorithms FCFS, RR and GA, it is still worse than that of the GA_RR algorithm for all the cases. GA_RR performs best in terms of Resource Utilization also and GA_FCFS is a close competitor. Overall, GA_RR outperforms all the other algorithms. Full article

The COVID-19 pandemic changed higher education radically and challenged faculties to adapt their teaching to the new circumstances. The aim of this study is to highlight changes, in particular, the advantages and disadvantages associated with them, and to find out what conclusions were drawn for the future in the three experimental natural sciences of biology, chemistry, and physics at the University of Konstanz (Germany). In a guided interview, the majority of the university teachers in the bachelor’s programs were interviewed, and their statements were subsequently categorized. While lectures and tutorials in distance learning were held asynchronously or synchronously online, laboratory courses used a variety of formats. The number of disadvantages cited, as well as the number of university faculty citing the same disadvantage, is greater than for advantages. The most commonly cited drawbacks fall into the areas of workload, communication, feedback, and active student participation. Physical presence and a return to the original learning objectives in the lab courses is wanted by the majority. The results point to commonalities between the science subjects and should encourage science departments to work together on similar problems in similar formats in the future. Furthermore, there is an urgent and ongoing need for the training of natural science teachers in competence-oriented digital teaching. Full article

To meet the increasing demand for its services, a cloud system should make optimum use of its available resources. Additionally, the high and low oscillations in cloud workload are another significant symmetrical issue that necessitates consideration. A suggested particle swarm optimization (PSO)-based ensemble meta-learning workload forecasting approach uses base models and the PSO-optimized weights of their network inputs. The proposed model employs a blended ensemble learning strategy to merge three recurrent neural networks (RNNs), followed by a dense neural network layer. The CPU utilization of GWA-T-12 and PlanetLab traces is used to assess the method’s efficacy. In terms of RMSE, the approach is compared to the LSTM, GRU, and BiLSTM sub-models. Full article

The growing use of power converters connected to the grid motivates their study in power electronics courses and the prototype development in the degree final project (DFP). However, the practical realization of using state-of-the-art components and conversion techniques is complex due to the numerous multidisciplinary aspects that students must consider in its design and development and the workload associated with the DFP. An example of this is that, unlike a conventional power factor correction (PFC) design, the individual dedication of students to complete the design and validation of modern bridgeless PFC stages exceeds the number of credits of the DFP. The reason for this is that it includes system modeling, becoming familiar with the devices used, discrete selection, circuit design, control development, and programming, to build the converter and verify the operation of the complete system. To reinforce the individual skills needed for the DFP and reduce this time, a novel strategy is proposed. It allows the student to focus their efforts on integrating the individual skills achieved in the degree at the appropriate competence level during the modeling and construction of the power converter while carrying out part of the tasks out of the lab, if necessary, as was the case during the pandemic restrictions. For this, the rapid prototyping technique is introduced to speed up the overall design and speed up the tuning of digital controllers. This manuscript presents a teaching experience in which students build digitally controlled power converters using Texas Instruments microcontroller boards and PLECS^®. The example of a bridgeless totem-pole power factor corrector is shown. Although it began to develop and was motivated due to the restrictions during the COVID-19 pandemic, the experience has been verified and is maintained over time, successfully consolidating. Full article

External human-machine interaction (eHMI) road projections are a new feature for automotive lighting to improve vehicle communication with other road users. These modalities are used to draw users’ attention and awareness to specific situations. However, such advanced capabilities are still being debated to be used on the road in the context of whether or not such road projections can provide a clear and understandable message to road users in a specific scenario or lead to anticipation and change in the driving behavior. Therefore, it is necessary to investigate human factors aspects, such as the feeling of safety, useability, understanding, acceptability, and driver behavior. This study investigates the change in distance and luminance contrast and its effect on human driving behavior and acceptability in blind spot detection scenarios on the highway. A lab experiment with 12 participants is performed to analyze: understanding, satisfaction, usability, visibility, safety, workload, and driving behavior towards eHMI projection while varying projecting distance and luminance contrast. Video recordings and a designed questionnaire were used during the whole process. Results show that ego vehicle drivers prefer a projection distance between 5 to 10 m. However, a distance of 5 m is preferred by overtaking vehicle drivers in terms of visibility and safety. Luminance contrasts have no significant effect on the symbol’s visibility in 5 m and 10 m projection distances. In contrast, participants in overtaking vehicles feel difficult to understand the situation for 15 m condition, which increases their overall workload significantly (p < 0.019). No significant effect is recorded in terms of change in driving behavior. Full article

To alleviate the workload in prevailing expert-based onsite inspection, a vision-based method using state-of-the-art deep learning architectures is proposed to automatically detect ceiling damage in large-span structures. The dataset consists of 914 images collected by the Kawaguchi Lab since 1995 with over 7000 learnable damages in the ceilings and is categorized into four typical damage forms (peelings, cracks, distortions, and fall-offs). Twelve detection models are established, trained, and compared by variable hyperparameter analysis. The best performing model reaches a mean average precision (mAP) of 75.28%, which is considerably high for object detection. A comparative study indicates that the model is generally robust to the challenges in ceiling damage detection, including partial occlusion by visual obstructions, the extremely varied aspect ratios, small object detection, and multi-object detection. Another comparative study in the F1 score performance, which combines the precision and recall in to one single metric, shows that the model outperforms the CNN (convolutional neural networks) model using the Saliency-MAP method in our previous research to a remarkable extent. In the case of a large-area ratio with a non-ceiling region, the F1 score of these two models are 0.83 and 0.28, respectively. The findings of this study push automatic ceiling damage detection in large-span structures one step further. Full article

Since 2011, the organisational and management process of the Italian Prison Administration has started to change. The Open section and Dynamic supervision measures introduced into the Italian penitentiary system, requires that all prison workers participate in the observation and treatment of the prisoners’ activities, carried out within a multidisciplinary perspective. This may imply a significant increase, in both the workload and possible sources of stress for prison workers and, therefore, hinder the organizational change. To enable the process of change, while monitoring the employees’ wellbeing, monthly multidisciplinary meetings have been planned, involving the ward staff of each prison. This study aims to both understand the impact of the organisational change on the employees of a prison in northern Italy and to explore the sustainability of the ward staff tool. Ten multidisciplinary meetings were analysed over a year, focusing on topics discussed within the group and relational positions assumed by the members. Content analysis has been performed through the T-LAB software, whereas the analysis of the interactive modalities has been carried out through the application of the Interaction Process Analysis grid. Results showed the group’s tendency to focus on the task, neglecting the relational dimension and moments of shared reflection related to the process. The study allows us to reflect on those aspects that may undermine the organisational and employee wellbeing and to assess the sustainability of a new organizational tool. Full article

The main goal of this study was to improve students’ outcomes and perception in Mathematics. For this, 12 out of 34 voluntary students were involved in an international contest: European Space Agency (ESA) Mission Space Lab. The experience was organized as STEM, under a guided PjBL. Students identified an environmental problem, executed a way to monitor it from the International Space Station (ISS) and interpreted the data received. Students’ final report was awarded by ESA. Additionally, participants increased their performance in their math final exams compared to the control group. Furthermore, the perception of students and their families about the usefulness of mathematics was very positive. The only drawback detected was the increase of workload. Thus, Green STEM, using direct instruction and guide in PjBL, may be a good tool to improve students’ grades and opinion about the importance of mathematics. Full article

Unmanned Aerial Vehicles (UAVs) used for humanitarian applications require simple, accessible and reliable components. For example, a communication system between UAV and the Ground Control Station (GCS) is essential in order to monitor UAV status; various communication protocols are available in the industry. Such systems must be simple for non-technical personnel (e.g., healthcare workers) to operate. In this study, a novel link failure detection and switching algorithm was proposed for a dissimilar redundant UAV communication system designed for long-range vaccine delivery in rural areas. The algorithm would ease the workload of the operators and address a research gap in the design of such algorithms. A two-layer design is proposed: A baseline layer using the heartbeat method, and optimisations to speed up local failure detection. To dynamically tune the heartbeat timeout for the algorithm’s baseline without intervention from ground operators, the modified Jacobson’s algorithm was used. Lab simulations found that the algorithm was generally accurate in converging to an optimal value, but has less satisfactory performance at poor or unpredictable connectivity, or when link switches get triggered frequently. Improvements have been suggested for the algorithm. This study contributes to ongoing research on ensuring reliable UAV communication for humanitarian purposes. Full article