Objective: Standardizing image acquisition protocols and image quality across cameras is an important need in imaging, in particular in multi-center clinical trials and the use of image analysis and machine learning algorithms. The objective of this study was to examine the effect of ordered subset expectation maximization (OSEM) reconstruction parameters on the quantitative image quality of cardiac perfusion SPECT images in different typical SPECT cameras and therefore assess the need to change the parameter values across cameras. Methods: The analysis was carried out by comparing the defect contrast-to-noise ratio (CNR) at 12 OSEM subset-iteration combinations. Eight frames were reconstructed using the SIMIND Monte Carlo Simulation package. An activity of 370 MBq (10mCi) and projection acquisition interval of 20 seconds per projection were used. Attenuation (AC) and scatter corrections (SC) were performed in this study for all images. Results: The 16-2 subset-iteration combination yielded the highest CNR and defect contrast values for both cameras. The difference between CNR values for two cameras was found to be close to 5%. Conclusions: Monte Carlo simulations can be useful to investigate how quantitative image quality behaves with respect to reconstruction parameters and correction algorithms in a controlled environment. In this study, the use of different camera brands did not seem to significantly affect the lesion detectability. Further simulations with more extended range of parameters and camera brands may be conducted in the future to quantify further the variability between different brands of cameras.

Retinal disorders, such as diabetic retinopathy, glaucoma, macular edema, and vein occlusions, are significant contributors to global vision impairment. These conditions frequently remain symptomless until patients suffer severe vision deterioration, underscoring the critical importance of early diagnosis. Fundus images serve as a valuable resource for identifying the initial indicators of these ailments, particularly by examining various characteristics of retinal blood vessels, such as their length, width, tortuosity, and branching patterns. Traditionally, healthcare practitioners often rely on manual retinal vessel segmentation, a process that is both time-consuming and intricate, demanding specialized expertise. However, this approach poses a notable challenge since its precision and consistency heavily rely on the availability of highly skilled professionals. To surmount these challenges, there is an urgent demand for an automatic and efficient method for retinal vessel segmentation and classification employing computer vision techniques, which form the foundation of biomedical imaging. Numerous researchers have put forth techniques for blood vessel segmentation, broadly categorized into machine learning, filtering-based, and model-based methods. Machine learning methods categorize pixels as either vessels or non-vessels, employing classifiers trained on hand-annotated images. Subsequently, these techniques extract features using 7D feature vectors and apply neural network classification. Additional post-processing steps are used to bridge gaps and eliminate isolated pixels. On the other hand, filtering-based approaches employ morphological operators within morphological image processing, capitalizing on predefined shapes to filter out objects from the background. However, this technique often treats larger blood vessels as cohesive structures. Model-based methods leverage vessel models to identify retinal blood vessels, but they are sensitive to parameter selection, necessitating careful choices to simultaneously detect thin and large vessels effectively. Our proposed research endeavors to conduct a thorough and empirical evaluation of the effectiveness of automated segmentation and classification techniques for identifying eye-related diseases, particularly diabetic retinopathy and glaucoma. This evaluation will involve various retinal image datasets, including DRIVE, REVIEW, STARE, HRF, and DRION. The methodologies under consideration encompass machine learning, filtering-based, and model-based approaches, with performance assessment based on a range of metrics, including true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), negative predictive value (NPV), false discovery rate (FDR), Matthews's correlation coefficient (MCC), and accuracy (ACC). The primary objective of this research is to scrutinize, assess, and compare the design and performance of different segmentation and classification techniques, encompassing both supervised and unsupervised learning methods. To attain this objective, we will refine existing techniques and develop new ones, ensuring a more streamlined and computationally efficient approach.

In this study, we consider the extended Brinkman's-Darcy model for a triple diffusive convection system which consists of some parameters such as Taylor number (Ta), Solutal Rayleigh numbers (RC1 , RC2 ), and Prandtl number (Pr). To investigate the range of these parameters, a dynamical system of the Ginzburg-Landau equation is developed. The parametric analysis and comparative study of the model for the three Rayleigh numbers which leads to the clear fluid layer, sparsely packed porous layer, and densely packed porous layer is done with the help of bifurcation maps and the Lyapunov exponents. It is found that for a certain range of parameters, the system exhibits a chaotic behaviour.

This contribution aims to appraise, analyze and evaluate the literature relating to the interaction of electromagnetic fields (EMF) with matter and the resulting thermal effects. This relates to the wanted thermal effects via the application of fields as well as those uninvited resulting from exposure to the field. In the paper, the most popular EMF heating technologies are analyzed. This involves on the one hand high frequency induction heating (HFIH) and on the other hand microwave heating (MWH), including microwave ovens and hyperthermia medical treatment. Then, the problem of EMF exposure is examined and the resulting biological thermal effects are illuminated. Thus, the two most common cases of wireless EMF devices, namely digital communication tools and inductive power transfer appliances are analyzed and evaluated. The last part of the paper concerns the determination of the different thermal effects, which are studied and discussed, by considering the governing EMF and heat transfer (or bio heat) equations and their solution methodologies.

Effective small and medium enterprise (SME) leadership demands creative solutions to ensure organisations survive and thrive during the turbulent times that COVID-19 continues to bring. This paper explores how SME leaders (in micro and small organisations) prioritise and access the skills and development needed to provide effective and sustainable leadership to organisations, focusing on the role of resilience and the benefits it provides. Participants were selected through purposive and snowballing sampling. Online surveys and semi-structured interviews were conducted and provided qualitative data that contributes to an understanding of the role of resilience and the view of participants as to what is needed to effectively respond to a dynamic environment. Evidence shows that SME leaders prioritise learning and development opportunities that provide demonstrable benefits throughout the organisation. Building business resilience remains a fuzzy concept; however, viewing resilience as a multi-level construct offers benefits when designing and delivering development opportunities. It has been found that networking, partnerships, and relationship building promote resilience and may offer a solution to how to embed resilience building into development opportunities that SME leaders value and wish to engage with. This article contributes by illustrating and exploring leadership development within SMEs during a period of unexpected and untested uncertainty. The pandemic caused major shock waves within business communities, and SMEs were significantly affected. The research is limited in that it is expected to be a once-in-a lifetime event, and as such conditions may not be replicable, learning opportunities for other ‘shock’ events are possible. The findings of this paper have relevance to practice in that, while the event may be one-off, shocks to the business environment are not.