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Brain tumors are the most destructive disease, leading to a very short life expectancy in their highest grade. The misdiagnosis of brain tumors will result in wrong medical intercession and reduce chance of survival of patients. The accurate diagnosis of brain tumor is a key point to make a proper treatment planning to cure and improve the existence of patients with brain tumors disease. The computer-aided tumor detection systems and convolutional neural networks provided success stories and have made important strides in the field of machine learning. The deep convolutional layers extract important and robust features automatically from the input space as compared to traditional predecessor neural network layers. In the proposed framework, we conduct three studies using three architectures of convolutional neural networks (AlexNet, GoogLeNet, and VGGNet) to classify brain tumors such as meningioma, glioma, and pituitary. Each study then explores the transfer learning techniques, i.e., fine-tune and freeze using MRI slices of brain tumor dataset—Figshare. The data augmentation techniques are applied to the MRI slices for generalization of results, increasing the dataset samples and reducing the chance of over-fitting. In the proposed studies, the fine-tune VGG16 architecture attained highest accuracy up to 98.69 in terms of classification and detection.

A bstract We consider the double copy of massive Yang-Mills theory in four dimensions, whose decoupling limit is a nonlinear sigma model. The latter may be regarded as the leading terms in the low energy effective theory of a heavy Higgs model, in which the Higgs has been integrated out. The obtained double copy effective field theory contains a massive spin-2, massive spin-1 and a massive spin-0 field, and we construct explicitly its interacting Lagrangian up to fourth order in fields. We find that up to this order, the spin-2 self interactions match those of the dRGT massive gravity theory, and that all the interactions are consistent with a Λ 3 = ( m 2 M Pl ) 1 / 3 cutoff. We construct explicitly the Λ 3 decoupling limit of this theory and show that it is equivalent to a bi-Galileon extension of the standard Λ 3 massive gravity decoupling limit theory. Although it is known that the double copy of a nonlinear sigma model is a special Galileon, the decoupling limit of massive Yang-Mills theory is a more general Galileon theory. This demonstrates that the decoupling limit and double copy procedures do not commute and we clarify why this is the case in terms of the scaling of their kinematic factors.

Cognitive Reserve (CR) is the brain's inherent capacity to withstand neurological changes and maintain cognitive functionality despite pathological or age related changes. It also provides valuable insight into one's resilience to Alzheimer's disease pathology. Epidemiological research indicates, attainment of higher cognitive reserve reduces the risk of developing Alzheimer's disease by maintaining better brain functionality. Here, we propose a novel mobile application, Cognitive Reserve Measurement, designed for comprehensive assessment, longitudinal monitoring and targeted augmentation of CR to alleviate the risk of Alzheimer's disease. To meticulously track behaviours germane to cognitive well-being, our application employs interfaces like a home screen, journal data, and game screen to encourage user engagement and regular participation in cognition-boosting activities. The established Cognitive Reserve Index Questionnaire (CRIq) is seamlessly integrated to provide a robust quantitative evaluation. App was developed using Flutter and Dart for cross-platform functionality, the application's backend relies on Firebase for secure and real-time storage of user data, such as journal entries and CRI scores. Ensuring data security is a top priority given the sensitive nature of cognitive health. Upon initial setup, user's attain their baseline based score on Cognitive Reserve Index Questionnaire (CRIq) score. The Journal section prompts users daily to reflect on activities from the previous day and the Journal Data section is for tracking activities that influence cognitive health shall purposefully track user habits stimulus for CR (e.g. regular physical activity, social engagement, sleep regimen, stress management etc.) and progress over time through sophisticated data visualization. Users can also engage in brain training games designed to enhance memory and reasoning skills. Currently the app is in the testing phase with a prototype being evaluated for usability and engagement to promote cognitive resilience and support healthy aging. Aiming to address the escalating global need for effective cognitive health intervention within an increasing geriatric population, the application leverages the accessibility of mobile technology and provides a tool for precise measurement and optimization of CR. The foundational rationale for this application underscores the pivotal role of CR in preserving healthy brain functionality across lifespan, eventually preventing or delaying Alzheimer's disease onset.

The integration of robots in nursing homes marks a transformative shift in elderly care, serving as both functional aids and sources of engagement and entertainment. Amid challenges tied to aging populations and limited resources, robots contribute to residents' well-being by facilitating social interactions, providing cognitive stimulation, and offering recreational activities in nursing homes, representing a promising frontier in the evolution of care for aging populations. In this study, a diverse group of residents aged 65 and older across multiple nursing homes engaged with a humanoid robot specially programmed for diverse activities, including joke-telling, singing, dancing, playing games, and aiding with daily tasks. Utilizing a pre-post design, baseline assessments were conducted before the robot's introduction, followed by regular post-implementation evaluations using the Brief Introspection Mood Scale (BMIS), Montreal Cognitive Assessment (MOCA), and electrodermal activity (EDA) recorded through wearable sensors. The study involved thorough training for nursing home staff on the robot's functionalities, and residents were gradually introduced to the robot through supervised interactive sessions, ensuring a smooth integration into the nursing home environment. Quantitative data from BMIS and MOCA underwent statistical analyses to discern patterns and changes over time, while EDA data were scrutinized for correlations with mood and cognitive assessments. Qualitative insights derived from resident and staff interviews, using thematic analysis, captured nuanced experiences. The study confirmed the robot's effectiveness in engaging and entertaining residents, showcasing overwhelmingly positive outcomes. Residents consistently enjoyed enhanced mood and emotional well-being, as indicated by substantial increases in positive affect according to BMIS scores. Quantitative analysis of MOCA scores revealed positive trends in cognitive functionality. Wearable sensors measuring EDA demonstrated heightened physiological arousal and positive emotional responses during residents' interactive sessions with the robot. Staff reported improved resident morale and observed the robot's effectiveness in creating a lively and interactive atmosphere within the nursing home. The results of this study affirm the multifunctional entertainment robot's positive impact on residents in nursing homes. From bolstering emotional well-being to enhancing cognitive functions, the robot emerges as a promising tool for enriching the lives of elderly individuals in care settings.

The integration of robots in nursing homes marks a transformative shift in elderly care, serving as both functional aids and sources of engagement and entertainment. Amid challenges tied to aging populations and limited resources, robots contribute to residents' well-being by facilitating social interactions, providing cognitive stimulation, and offering recreational activities in nursing homes, representing a promising frontier in the evolution of care for aging populations. In this study, a diverse group of residents aged 65 and older across multiple nursing homes engaged with a humanoid robot specially programmed for diverse activities, including joke-telling, singing, dancing, playing games, and aiding with daily tasks. Utilizing a pre-post design, baseline assessments were conducted before the robot's introduction, followed by regular post-implementation evaluations using the Brief Introspection Mood Scale (BMIS), Montreal Cognitive Assessment (MOCA), and electrodermal activity (EDA) recorded through wearable sensors. The study involved thorough training for nursing home staff on the robot's functionalities, and residents were gradually introduced to the robot through supervised interactive sessions, ensuring a smooth integration into the nursing home environment. Quantitative data from BMIS and MOCA underwent statistical analyses to discern patterns and changes over time, while EDA data were scrutinized for correlations with mood and cognitive assessments. Qualitative insights derived from resident and staff interviews, using thematic analysis, captured nuanced experiences. The study confirmed the robot's effectiveness in engaging and entertaining residents, showcasing overwhelmingly positive outcomes. Residents consistently enjoyed enhanced mood and emotional well-being, as indicated by substantial increases in positive affect according to BMIS scores. Quantitative analysis of MOCA scores revealed positive trends in cognitive functionality. Wearable sensors measuring EDA demonstrated heightened physiological arousal and positive emotional responses during residents' interactive sessions with the robot. Staff reported improved resident morale and observed the robot's effectiveness in creating a lively and interactive atmosphere within the nursing home. The results of this study affirm the multifunctional entertainment robot's positive impact on residents in nursing homes. From bolstering emotional well-being to enhancing cognitive functions, the robot emerges as a promising tool for enriching the lives of elderly individuals in care settings.

While often perceived negatively, gossip can act as a powerful social catalyst, reigniting interest in familiar activities among individuals affected by dementia. Studies indicate that 20-40% of community-dwelling older adults experience symptoms of apathy, while approximately one-third of individuals aged 85 and older have some form of dementia. Apathy affects 50-70% of people with dementia, contributing to disengagement and social withdrawal. Social robots with conversational capabilities may leverage gossip to stimulate memory recall and encourage participation in meaningful activities. We developed a socially interactive robot programmed to deliver positive, familiar, and activity-based gossip to individuals with dementia. A pilot study was conducted in a memory care facility, where the robot engaged participants, twice a week in conversations designed to rekindle interest in previously abandoned activities. A two-tailed paired t-test was performed on pre- and post-intervention assessment scores to evaluate the effectiveness of the approach. Participants (n = 8) showed increased engagement in activities that they had previously discontinued, with improvements in mood and social interaction. Cognitive function, assessed using the MoCA, improved for 6 participants (p = 0.0087). Apathy levels, measured by the Apathy Evaluation Scale, decreased for all participants (p = 0.00024). Quality of life, assessed through the QoL-AD, improved for all participants (p = 0.0076). Mood, evaluated using the Geriatric Depression Scale (GDS), improved for four participants (p = 0.025). Social robots delivering gossip-based interactions offer a novel, non-pharmacological approach to re-engaging individuals with dementia in meaningful activities. This intervention has the potential to enhance cognitive function, reduce apathy, and improve overall well-being in dementia care settings.

A bstract Recent explorations on how to construct a double copy of massive gauge fields have shown that, while any amplitude can be written in a form consistent with colour-kinematics duality, the double copy is generically unphysical. In this paper, we explore a new direction in which we can obtain a sensible double copy of massive gauge fields due to the special kinematics in three-dimensional spacetimes. To avoid the appearance of spurious poles at 5-points, we only require that the scattering amplitudes satisfy one BCJ relation. We show that the amplitudes of Topologically Massive Yang-Mills satisfy this relation and that their double copy at three, four, and five-points is Topologically Massive Gravity.

The tendency of skin diseases to manifest in a unique and yet similar appearance, absence of enough competent dermatologists, and urgency of diagnosis and classification on time and accurately, makes the need of machine aided diagnosis blatant. This study is conducted with the purpose of broadening the research in skin disease diagnosis with computer by traversing the capabilities of deep Learning algorithms to classify two skin diseases noticeably close in appearance, Psoriasis and Lichen Planus. The resemblance between these two skin diseases is striking, often resulting in their classification within the same category. Despite this, there is a dearth of research focusing specifically on these diseases. A customized 50 layers ResNet-50 architecture of convolutional neural network is used and the results are validated through fivefold cross-validation, threefold cross-validation, and random split. By utilizing advanced data augmentation and class balancing techniques, the diversity of the dataset has increased, and the dataset imbalance has been minimized. ResNet-50 has achieved an accuracy of 89.07%, sensitivity of 86.46%, and specificity of 86.02%. With their promising results, these algorithms make the potential of machine aided diagnosis clear. Deep Learning algorithms could provide assistance to physicians and dermatologists by classification of skin diseases, with similar appearance, in real-time.

Low-grade gliomas (LGG) constitute a heterogeneous group of neoplasms arising from the supporting glial cells of the central nervous system (CNS). Patients typically undergo radiotherapy, chemotherapy, and surgical resection as part of the first-line therapy. However, the tumor cells associated with LGG often exhibit poor treatment outcomes due to frequent resistance. One of the main agents exhibiting high resistance is temozolomide. This study employs an integrated approach utilizing Weighted Gene Co-Expression Network Analysis (WGCNA) and network analysis, revealing 5 hub genes: CFAP126, TEKT1, TEKT2, C1orf194, and C9orf116, which are implicated in TMZ resistance. Survival analysis identifies three significant genes, including CFAP126, TEKT1, and TEKT2. Lower expression levels of TEKT1 and TEKT2 correlate with higher survival probabilities, while higher expression of CFAP126 is associated with improved overall survival. Enrichment analysis indicates that many of identified module genes are involved in cilia development and related processes, microtubule assembly and regulation in contributing to TMZ resistance in LGG. Although these pathways are associated, the exact mechanisms by which contributing genes cause TMZ resistance are still unclear. This study was entirely done in silico, and further laboratory investigations are warranted to validate the involvement of these genes in TMZ resistance. Nevertheless, these findings enhance our understanding of the processes leading to TMZ resistance in LGG, which may yield significant benefits for prognosis, diagnosis, and treatment.

A bstract The consistency of the EFT of two interacting spin-2 fields is checked by applying forward limit positivity bounds on the scattering amplitudes to exclude the region of parameter space devoid of a standard UV completion. We focus on two classes of theories that have the highest possible EFT cutoff, namely those theories modelled on ghost-free interacting theories of a single massive spin-2 field. We find that the very existence of interactions between the spin-2 fields implies more stringent bounds on all the parameters of the EFT, even on the spin-2 self-interactions. This arises for two reasons. First, with every new field included in the low-energy EFT, comes the ‘knowledge’ of an extra pole to be subtracted, hence strengthening the positivity bounds. Second, while adding new fields increases the number of free parameters from the new interactions, this is rapidly overcome by the increased number of positivity bounds for different possible scattering processes. We also discuss how positivity bounds appear to favour relations between operators that effectively raise the cutoff of the EFT.