Over 90% of cancer-related mortality worldwide is due to metastatic disease since the dynamic tumor microenvironment poses huge challenges in preventing the spread of metastatic cancer. Introducing the advent of advance biomaterials and their swift evolution, this review highlights the great potential of innovative biomaterials to proficiently tackle the metastatic tumor environment. Focusing on four distinct categories of biomaterials systems, action mechanism of biomaterials utilized in anti-tumor therapy is explained in detail: 1. Nanoplatforms sensitive to biochemical cues including pH, redox, and enzymes are known as stimuli-responsive nanoplatforms that react according their environment, 2. Smart nanoplatforms changing their morphology to penetrate impermeable physical barriers at tumor site, 3. Ingenious biomaterial participating in tumor normalization, and 4. Nanoplatforms with real-time theranostic capabilities due to innate feedback-loop mechanism. Ingeniously structured biomaterials with extensive evidence in preclinical efficacy encourage their inclusion in metastatic tumor therapy however, their utilization in medical settings is prevented due to various challenges; impractical manufacturing cost, regulatory and safety issues as well as large-scale production are major challenges restraining their widespread use. A concrete framework is proposed in this review to accelerate the biomaterial structure standardization process, following the GMP and other regulatory guidelines with the aim of implementing biomaterial-based tumor diagnostics and therapies. Since incorporating advancing technologies in tumor therapy such AI-driven, autonomous biomaterial structure or patient-specific tumor models would enable confront the proliferating metastatic tumor cases.

This research underscores the importance of enhancing the early detection of diabetic retinopathy and glaucoma, two prominent culprits behind vision loss. Typically, retinal diseases lurk without symptoms until they inflict severe vision impairment, underscoring the critical need for early identification. The research is centered on the potential of leveraging fundus images, which offer invaluable insights by analyzing various attributes of retinal blood vessels, such as their length, width, tortuosity, and branching patterns. The conventional practice of manually segmenting retinal vessels by medical professionals is both intricate and time-consuming, demanding specialized expertise. This approach, reliant on pathologists, grapples with limitations related to scalability and accessibility. To surmount these challenges, the research introduces an automated solution employing computer vision. It conducts an evaluation of diverse retinal vessel segmentation and classification methods, including machine learning, filtering-based, and model-based techniques. Robust performance assessments, involving metrics like the true positive rate, true negative rate, and accuracy, facilitate a comprehensive comparison of these methodologies. The ultimate goal of this research is to create more efficient and accessible diagnostic tools, consequently enhancing the early detection of eye diseases through automated retinal vessel segmentation and classification. This endeavor combines the capabilities of computer vision and deep learning to pioneer new benchmarks in the realm of biomedical imaging, thereby addressing the pressing issues surrounding eye disease diagnosis.

An experiment was conducted to assess the effect of psychoenergetic energy in litchi as positive and negative thoughts using a simple meditation technique at ICAR-NRC on Litchi, Muzaffarpur. The plant produced 24.75 g of fruit given positive energy, while the plant with negative thought energy produced 22.12 g of fruit. The fruit and seed weight increased by 11.88% and 13.63%, respectively, due to positive energy. The number of fruit retentions increased by 23.77% due to positive energy. Anthocyanin content in pericarp was increased by 5.45% in plants given positive energy. Fruit qualities were also significantly affected by psychoenergy. TSS (Brix) was significantly increased by 13.54% in plants given positive energy as compared to negative energy, and titratable acidity was reduced by 25% due to positive energy. Ascorbic acid was also increased by 30% in plant given positive thoughts. Sun burn was reduced by 54.76% and fruit cracking by 63.64% due to energy of thought. Fruit borer infestation was reduced by 70%, and mite infestation was reduced by 90% in plants given positive energy. The psychoenergetic potential is vast, and its ability to improve crop yield and quality cannot be overstated. The hidden power of thought is being practiced by all, but mostly people do not know this power and use it in an improper manner. This is a high time when we need to practice generating powerful thoughts to change present-day agriculture and its dependents.

Heat transfer fluids (HTFs) are critical in numerous industrial processes (e.g., the chemical industry, oil and gas, and renewable energy), enabling efficient heat exchange and precise temperature control. HTF degradation, primarily due to thermal cracking and oxidation, negatively impacts system performance, reduces fluid lifespan, and increases operational costs associated with correcting resulting issues. Regular monitoring and testing of fluid properties can help mitigate these effects and provide insights into the health of both the fluid and the system. To date, there is no extensive literature published on this topic, and the current narrative review was designed to address this gap. This review outlines the typical operating temperature ranges for industrial heat transfer fluids (i.e., steam, organic, synthetic, and molten salts) and then focuses specifically on organic and synthetic fluids used in industrial applications. It also outlines the mechanisms of fluid degradation and the impact of fluid type and condition. Other topics covered include the importance of fluid sampling and analysis, the parameters used to assess the extent of thermal degradation, and the management strategies that can be considered to help sustain fluid and system health. Operating temperature, system design, and fluid health play a significant role in the extent of thermal degradation, and regular monitoring of fluid properties, such as viscosity, acidity, and flash point, is crucial in detecting changes in condition (both early and ongoing) and providing a basis for decisions and interventions needed to mitigate or even reverse these effects. This includes, for example, selecting the right HTF for the specific application and operating temperature. This article concludes that by understanding the mechanisms of thermal degradation and implementing appropriate management strategies, it is possible to sustain the lifespan of thermal fluids and systems, ensure safe operation, and help minimise operational expenditure.

This research introduces a novel framework integrating stochastic finite element analysis (FEA) with advanced circular statistical methods to optimize heat pump efficiency under material uncertainties. By modeling directional variability in thermal conductivity using both uniform and Von Mises distributions, the study highlights the superiority of the Von Mises distribution in providing consistent and efficient thermal performance. The Von Mises distribution, known for its concentration around a mean direction, demonstrates a significant advantage over the uniform distribution, resulting in higher mean efficiency and lower variability. The findings underscore the importance of considering both stochastic effects and directional consistency in thermal systems, paving the way for more robust and reliable design strategies.

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.