Search

Hydrocephalus is a central nervous system disorder which most commonly affects infants and toddlers. It starts as an abnormal build-up of cerebrospinal fluid in the ventricular system of the brain. Hence, early diagnosis becomes vital, which may be performed by Computed Tomography (CT), one of the most effective diagnostic methods for diagnosing Hydrocephalus (CT), where the enlarged ventricular system becomes apparent. However, most disease progression assessments rely on the radiologist's evaluation and physical measures, which are subjective, time-consuming, and inaccurate. This paper develops an automatic prediction utilizing the H-detect framework for enhanced accurate hydrocephalus prediction. This paper uses a pre-processing step to normalize the input image and remove unwanted noises, which can help extract valuable features easily. The feature extraction is done by segmenting the image based on edge detection using triangular fuzzy rules. Thereby, the exact information on the nature of CSF inside the brain is highlighted. These segmented images are saved and again given to the CatBoost algorithm. The Categorical feature processing allows for quicker training. When necessary, the overfitting detector will stop model training and thus efficiently predicts Hydrocephalus. The outcomes demonstrate that the new H-detect strategy outperforms the traditional approaches.

This study focused on the synthesis of anhydrous sol-gel synthesis of TiO2 nanoparticles using a hydrothermal route. The chemical properties of the synthesized TiO2 nanoparticles were confirmed through Fourier Transform Infrared Spectroscopy (FT-IR) and UV–Vis spectroscopy, highlighting their optical features. Additionally, Energy Dispersive X-ray Spectrometer (EDX) analysis revealed the high purity of the nanomaterial, while the Transmission Electron Microscope (TEM) displayed spherical particles ranging in size from ≥ 80 nm. Furthermore, X-ray diffraction analysis (XRD), Raman and HRTEM analyses indicated that the nanomaterial exhibits an anatase and rutile structure. When, Hen Egg-White Lysozyme (HEWL) incubated with TiO2 led to notable changes: reduced fluorescence indicating protein unfolding, increased light scattering showing protein aggregation, the presence of amyloid fibrils and hydrophobic regions, and reduced enzyme activity. FTIR spectroscopy revealed amyloid structure formation, while Dynamic Light Scattering confirmed the presence of larger protein oligomers. These findings highlight TiO2's adverse impact on HEWL structure and function, emphasizing the need for further research on nanoparticle toxicity in biological systems, particularly in biomedical applications.

There are two sorts of energy resources: sustainable power resources and non-sustainable power resources. Due to some negative ecological effects including air pollution, climate change, and resource rot, people are concentrating on using sustainable energy resources to produce electricity. Solar energy, usually referred to as sun-oriented energy, is one of the most frequently researched environmentally beneficial power resources. In order to fulfill the growing demand for energy and increase energy efficiency, new developments and advancements in the field of solar energy are required. There are two sorts of energy resources: sustainable power resources and non-sustainable power resources. Due to some negative ecological effects including air pollution, climate change, and resource rot, people are concentrating on using sustainable energy resources to produce electricity. Solar energy, usually referred to as sun-oriented energy, is one of the most frequently researched environmentally beneficial power resources. In order to fulfill the growing demand for energy and increase energy efficiency, new developments and advancements in the field of solar energy are required. The traditional solar energy cell's inability to create power in the evening is a horrible flaw. This investigation focuses mostly on solar-powered energy and discusses its evolution, improvements, and future perspectives.

As per the latest survey produced by the Forest Survey, the forest cover is 19.27% of the geographic area. According to this report every country can meet the human needs of 16% of the world’s population from the 1% of the world’s forest resource. The Forest Survey said that 90% of the forest fires created by humans. They pose a threat not only to the forest wealth but also this leads to the main threat to biodiversity, a change in the ecosystem. The environment gets dry and twinges, which leads to produce flames in the forest. There are two types of forest fire i) Surface Fire and ii) Crown Fire iii) Ground Fire. Surface Fire: The forest fire starts its flame primarily as a surface fire, spreading along the ground with the help of dry grasses and so on. Crown Fire: It starts flame on the crown of the shrubs, bushes and trees and sustained on the surface. This type of fire is very dangerous because resinous material given off burning logs burn furiously. If there is a slope with fire then the fire spread from the top of the slope to the down. Ground fire occurs in the humus and peaty layers beneath the litter of under composed portion of forest floor with intense heat but practically no flame. Such fires are relatively rare and have been recorded occasionally at high altitudes in Himalayan fir and spruce forests. In Remote sensing field, the knowledge of surface temperature plays a vital role. By using brightness and emissivity feature, temperature mapping and analysis can be done. The surface temperature values are measured to detect the forest fire from the ASTER image. ASTER stands for Advanced Space borne Thermal Emission and Reflection Radiometer. ASTER image contains 5 thermal bands (wave length ranges from 8.125 μm to 11.65 μm) and these are used in comparison. To convert digital numbers to exoatmospheric radiance, ASTER thermal bands are used. The converted exoatmospheric radiance is then mapped into surface radiance using the Emissivity Normalization method.

In order to achieve sustainability goals, biomass is a renewable energy source that lowers emissions of greenhouse gases and other hazardous gases. Biochemical and thermochemical methods are both used to produce bioenergy from biomass. Pyrolysis is an effective thermochemical conversion technique used for the conversion of biomass into energy-rich bio-oil. In this study, the pyrolysis characteristics and bio-oil obtained from the residues of Ricinus communis were investigated. The experimental run was designed to analyze the impact of bed temperature on product yield by varying the process temperature from 350°C to 750°C. In this study, a maximum of 46.5 wt% of bio-oil was produced at 500°C. The maximum conversion was recorded at temperatures ranging from 450°C to 550°C. The bio-oil obtained at maximum yield conditions was analyzed using different analytical techniques. The Fourier transform infrared spectroscopy (FT-IR) and gas chromatography and mass spectroscopy (GC-MS) analyses of the bio-oil revealed that the oil has a significant amount of phenol derivatives, oxygenated chemicals, acids, and esters. The physical properties of the bio-oil showed that it is viscous and has a medium heating value compared with commercial fossil fuel.

Increased population growth, industrialization, and modern culture create a variety of consequences, including environmental pollution, heavy metal accumulation, and decreasing energy resources. This perilous position necessitates the development of long-term energy resources and strategies to address environmental threats and power shortages. In this study, an investigation into the use of castor seed oil cake and waste tyres as a feed material for the copyrolysis process for yielding maximum oil production was performed. The copyrolysis experiments were performed by changing the mass percentage of waste tyres with oil cake to make different ratios of 100 : 0, 75 : 25, 50 : 50, 25 : 75, and 0 : 100. At 50 : 50 ratio, the maximum positive synergy on oil production was obtained. At that condition, a maximum of 59.8 wt% oil was produced and characterized to analyze its physiochemical properties. The coprocessing of the selected two feed materials enables the stabilization of the oil, as the produced oil has a lower oxygen content with a maximum heating value of 38.72 MJ/kg. The Fourier transform infrared spectroscopy (FTIR) and gas chromatography-mass spectrometry (GC-MS) analysis of the oil showed the existence of aromatic hydrocarbons and phenolic elements. Adding waste tyres to the biomass improved the quality of the oil by increasing carbon content with reduced oxygen content.

In this research work, an attempt was made to weld AA7075 alloy using the friction stir welding (FSW) technique. The experimental runs were designed using the Taguchi L18 orthogonal array and welds were obtained by varying tilt angle, tool rotation speed, tool feed rate, and axial load, whereas weld quality was accessed in terms of tensile strength and microhardness. The microstructure was examined using an optical microscope. The studies revealed that the tool angle was the most influential factor followed by the tool feed rate as both the parameters impacted the intensity of heat developed. It was observed that the tool tilt decreased the microhardness of the welds. The UTS values and macrostructure imply that the weld should be subjected to higher tool torque conditions. The material flow was not periodic nor coordinated, as seen by the tool-tilted weld's macrostructure. With a tool tilt, the weld pressure is lowered, and the lower pressure could not be enough to prevent volumetric defects. The reduced pressure at quicker welding rates may have had an effect on the development of flaws.

Nanocomposites are being studied for their mechanical, thermal, and water absorption capabilities. Polylactic acid/chitosan blends have been studied extensively for their physical, mechanical, and morphological properties. Although the three materials have been blended, no research has been done on the mechanical or morphological properties of PLA/CS/TiC NPs. PLA/CS bonding is quite deprived, and thus researchers are trying to improve it by introducing TiC NPs; this would improve the composites’ overall quality (mechanical and thermal characteristics as well as water absorption) by increasing the strength of the bind between the two materials. The impacts of TiC NPs on PLA/CS properties are studied using FTIR and XRD and thermal (TGA) and mechanical investigations. Titanium carbide nanoparticles in the polylactic acid/chitosan matrix increase the mechanical characteristics of the materials. As an outcome, the TiC content in the sample rises to 4 wt % even though adding TiC NPs increased the mechanical properties by up to 2%. The findings of this study might be applied to the development of environmentally friendly casings.

In this study, the impacts of co-pyrolyzing wood-based biomass from Ficus benghalensis with PET on liquid oil output, reactivity, and heating values were investigated. The effects of temperature on the product distribution of individual pyrolysis and the biomass-plastic ratio on co-pyrolysis were investigated. For individual pyrolysis, a maximum amount of 40.8 wt (%) liquid oil was obtained from biomass at 450°C. On the other hand, a maximum of 59.5 wt (%) liquid oil was obtained from PET at 500°C. The co-pyrolysis experiments were conducted by blending PET with biomass at different percentages, such as 20%, 40%, 60%, and 80%. At 60% addition of PET, a more positive synergistic effect was identified due to radical secondary reactions. In addition, the physical and chemical characterization studies conducted on pyrolysis oil showed that biomass and plastic materials could be used to make valuable chemicals.

The choice of the optimal orientation of the solar panels is by far one of the most important issues in the practical application of solar installations. The use of phase changing materials (PCMs) is an efficient approach of storing solar thermal energy. Because PCMs are isothermal in nature, they provide better density energy storage and the capacity to function across a wide temperature range. Unfortunately, this feature is very rare on various solar power panels; however, ignoring it can reduce the performance of the panels to unacceptable levels. The fact is that the angle of incidence of rays on the surface greatly affects the reflection coefficient and, consequently, the role of unacceptable solar energy. In this paper, a smart energy harvesting model was proposed. In the case of glass, when the angle of incidence varies vertically from its surface to 30, the reflection coefficient is practically unchanged and slightly less than 5%, i.e., more than 95% of the radiation goes inwards. Furthermore, the reflection increase is noticeable, and the area of the reflected radiation by 60 doubles to almost 10%. At an angle of incidence of 70, it reflects 20% of the radiation, and at 80, 40%. For most other objects, the dependence of the reflection magnitude on the angle of incidence is approximately the same.

In friction stir welding, tool shoulder diameter and its rotational speed are the major influencing parameters than others. A simple novel correlation is proposed to select the optimum range of tool shoulder diameter with respect to the chosen rotational speed and vice versa. The conditions to apply derived correlation were defined through process heat index number as the joint efficiency in the friction stir welding depends on the effective heat supply to the volume of material deformed in the stir zone. Weld speed is the key parameter through which generated heat can be regulated towards optimum heat supply to attain defect-free weld in the stir zone. Effective heat input also has obvious effect on grain growth and corresponding property eradication in the heat affected zone. The experimental study was carried out on AA2024-T3 plates to understand the effect of process heat index on the prescribed optimum range of tool shoulder and rotational speed defined in the correlation. Eventually, a novel relationship was attained between the first order process influencing parameters to deliver maximum joint efficiency.

Solar energy is the kind of alternative energy that a photovoltaic (PV) system may generate. The PV system depends on the environmental conditions present at the moment, such as the humidity level, surface temperature of the PV system, and wind speed. These are the most important characteristics to consider when calculating the amount of electricity generated by the PV system. Therefore, the calculation of power production is not a simple task. In the meanwhile, we are working on developing the machine learning algorithm that will be used to estimate electricity production using the fundamental characteristics that are now accessible. Now, we begin the process of machine learning by developing a time series model since the essential parameters will change over time. We get preliminary information from four distinct locations around the country. We arrived at this conclusion by using the ANN and regression modeling approaches for best to create the most significant overall performance across all cities.