The human brain has been described as a complex system. Its study by means of neurophysiological signals has revealed the presence of linear and nonlinear interactions. In this context, entropy metrics have been used to uncover brain behavior in the presence and absence of neurological disturbances. Entropy mapping is of great interest for the study of progressive neurodegenerative diseases such as Alzheimer’s disease. The aim of this study was to characterize the dynamics of brain oscillations in such disease by means of entropy and amplitude of low frequency oscillations from Bold signals of the default network and the executive control network in Alzheimer’s patients and healthy individuals, using a database extracted from the Open Access Imaging Studies series. The results revealed higher discriminative power of entropy by permutations compared to low-frequency fluctuation amplitude and fractional amplitude of low-frequency fluctuations. Increased entropy by permutations was obtained in regions of the default network and the executive control network in patients. The posterior cingulate cortex and the precuneus showed differential characteristics when assessing entropy by permutations in both groups. There were no findings when correlating metrics with clinical scales. The results demonstrated that entropy by permutations allows characterizing brain function in Alzheimer’s patients, and also reveals information about nonlinear interactions complementary to the characteristics obtained by calculating the amplitude of low frequency oscillations.

Poly(methyl methacrylate) (PMMA) is a versatile and widely used polymer that has gained significant attention in various industries due to its unique combination of properties and ease of processing. PMMA, also known as acrylic or plexiglass, is a transparent thermoplastic with exceptional optical clarity, high-impact resistance, and excellent weatherability. This scholarly article endeavors to offer an exhaustive examination of the composition, characteristics, and broad utilization of poly(methyl methacrylate) (PMMA). This study aims to conduct an in-depth analysis of the molecular composition and chemical attributes inherent to PMMA. Furthermore, it intends to examine the mechanical and physical attributes exhibited by PMMA meticulously. Additionally, an exploration of varied methodologies employed in the processing and fabrication of PMMA will be undertaken. The extensive array of applications of PMMA spanning multiple industries will be underscored, followed by a comprehensive discourse on its merits, constraints, contemporary advancements, and prospective avenues. Understanding the properties and applications of PMMA is crucial for engineers, scientists, and professionals working in fields such as automotive, aerospace, medical, and signage, where PMMA finds extensive use.

Graphene and derivatives have been frequently used to form advanced nanocomposites. A very significant utilization of polymer/graphene nanocomposite was found in the membrane sector. The up-to-date overview essentially highlights the design, features, and advanced functions of graphene nanocomposite membranes towards gas separations. In this concern, pristine thin layer graphene as well as graphene nanocomposites with poly(dimethyl siloxane), polysulfone, poly(methyl methacrylate), polyimide, and other matrices have been perceived as gas separation membranes. In these membranes, the graphene dispersion and interaction with polymers through applying the appropriate processing techniques have led to optimum porosity, pore sizes, and pore distribution, i.e., suitable for selective separation of gaseous molecules. Consequently, the graphene-derived nanocomposites brought about numerous revolutions in high-performance gas separation membranes. The structural diversity of polymer/graphene nanocomposites has facilitated the membrane selective separation, permeation, and barrier processes, especially in the separation of desired gaseous molecules, ions, and contaminants. Future research on the innovative nanoporous graphene-based membrane can overcome design/performance-related challenging factors for technical utilizations.

The integration of Big Earth Data and Artificial Intelligence (AI) has revolutionized geological and mineral mapping by delivering enhanced accuracy, efficiency, and scalability in analyzing large-scale remote sensing datasets. This study appraisals the application of advanced AI techniques, including machine learning and deep learning models such as Convolutional Neural Networks (CNNs), to multispectral and hyperspectral data for the identification and classification of geological formations and mineral deposits. The manuscript provides a critical analysis of AI's capabilities, emphasizing its current significance and potential as demonstrated by organizations like NASA in managing complex geospatial datasets. A detailed examination of selected AI methodologies, criteria for case selection, and ethical and social impacts enriches the discussion, addressing gaps in the responsible application of AI in geosciences. The findings highlight notable improvements in detecting complex spatial patterns and subtle spectral signatures, advancing the generation of precise geological maps. Quantitative analyses compare AI-driven approaches with traditional techniques, underscoring their superiority in performance metrics such as accuracy and computational efficiency. The study also proposes solutions to challenges such as data quality, model transparency, and computational demands. By integrating enhanced visual aids and practical case studies, the research underscores its innovations in algorithmic breakthroughs and geospatial data integration. These contributions advance the growing body of knowledge in Big Earth Data and geosciences, setting a foundation for responsible, equitable, and impactful future applications of AI in geological and mineral mapping.

Eucalyptus is an important source of cellulose and a widely cultivated plant. Biotechnology tools can save time spent in breeding and transcriptomic approaches generate a gene profile that allows the identification of candidates involved in processes of interest. RNA-seq is a commonly used technology for transcript analysis and it provides an overview of regulatory pathways. Here, we selected two contrasting Eucalyptus species for cold acclimatization and focused in responsive genes under cold condition aiming woody properties – lignin and cellulose. The number of differentially expressed genes identified in stem sections were 3.300 in Eucalyptus globulus and 1370 in Eucalyptus urograndis. We listed genes with expression higher than 10 times including NAC, MYB and DUF family members. The GO analysis indicates increased oxidative process for E. urograndis. This data can provide information for more detailed analyses for breeding, especially in perennial plants.

This study explores the critical role of the retail sector in the global economy and the importance of working capital management within retail businesses. Recognizing retail’s influence beyond just income generation, the research examines its impact on economic stability, job creation, and national GDP, and how it links industries such as manufacturing and logistics. Employing a blended-methods approach, the study integrates quantitative analysis using AMOS software with qualitative insights from interviews with financial managers and retail experts. Key focus areas include cash flow management, market demand, and supplier relationship management in the context of working capital management. Findings highlight the necessity of effective working capital management in maintaining financial stability, optimizing shareholder wealth, and ensuring long-term business viability in the retail sector. Strategies for enhancing profitability, such as improving supplier relationships and adapting to market demands, are identified. This research contributes to understanding the economic impact of the retail sector and the intricacies of working capital management. It offers insights for policymakers, retail managers, and academics, emphasizing the need for supportive retail industry measures and effective financial management practices. The study fills a gap in literature and sets a foundation for future research in this critical area of economic studies and retail management.