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Green Synthesis of Titanium Dioxide Nanoparticles Using Melia azedarach Leaf Extract and Evaluation
No full textThe present study reports the green synthesis and characterization of titanium dioxide (TiO₂) nanoparticles. The nanoparticles were synthesized via the sol-gel method using an aqueous extract of Melia azedarach leaves. The synthesized TiO₂ nanoparticles were characterized using various analytical techniques, including Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), X-ray Diffraction (XRD), UV–Visible Spectroscopy (UV–Vis), Fourier Transform Infrared Spectroscopy (FTIR), and Energy-Dispersive X-ray Analysis (EDAX). XRD analysis was conducted to determine the crystalline nature of the TiO₂ nanoparticles. SEM and TEM were used to study the shape and size of the nanoparticles. SEM images revealed spherical nanoparticles with sizes ranging from 25 nm to 87 nm, while TEM analysis showed sizes in the range of 15–45 nm. The XRD patterns displayed peaks at 25.36°, 26.54°, 37.05°, 37.78°, 38.54°, 48.12°, 54.02°, and 55.04°, corresponding to the (101), (110), (004), (103), and other crystal planes of anatase and rutile phases of TiO₂.UV–Vis spectra of the green-synthesized TiO₂ nanoparticles were recorded using a Shimadzu UV-2450 PC dual-beam spectrophotometer in the wavelength range of 200–900 nm. EDAX analysis was used to determine the chemical composition and confirm the crystalline nature of the synthesized TiO₂ nanoparticles. It is generally known that TiO₂ exhibits characteristic optical absorption peaks in the range of 4.3–5.3 keV, attributed to surface plasmon resonance
Artificial Intelligence in Remote Sensing: Advancements, Challenges, and Future Directions for Sustainable Applications
No full textThis chapter explores how artificial intelligence (AI) can be incorporated into remote sensing, emphasizing how it can revolutionize a number of fields, such as agriculture, urban planning, disaster relief, and environmental monitoring. It gives a summary of how artificial intelligence (AI) methods, especially machine learning and deep learning, improve the handling, interpretation, and use of data from remote sensing. The chapter also explores the main obstacles preventing AI from being widely used in remote sensing, including issues with data accessibility, model interpretability, training complexity, and ethical considerations. It also offers a number of real-world examples that show how AI can be used to provide useful insights and facilitate data-driven decision-making. In addition to outlining future directions for research, development, and responsible implementation, this chapter provides a balanced perspective on the changing role of AI in remote sensing by addressing both the opportunities and limitations
Empowering Future Educators: Understanding Pre-service Teachers’ Self-efficacy in Instruction and Classroom Management
No full textIntroduction: Classroom management decision difficulty is consistently reported among the top reasons given by both novice and experienced teachers in the teaching profession. To achieve positive learning, we must understand the issues related to teacher self-efficacy that may influence classroom management decisions.
Aim: This study investigated pre-service teachers\u27 self-efficacy in making classroom management decisions as they progressed through the professional development sequence.
Methodology: Quantitative descriptive research methodology was employed. The Proportional stratified sampling technique was used to select 225 students across colleges and programs. Questionnaires were administered for data collection in this study. Means and standard deviations were used to analyse the data.
Findings: Findings indicated that pre-service teachers have higher levels of classroom management decision self-efficacy and instructional self-efficacy.
Conclusion: The practical implication is that this study highlights the importance of building and maintaining self-efficacy through teacher preparation programs and ongoing professional development in Ghana\u27s educational institutions, thereby enhancing the quality of teaching and ultimately benefiting students\u27 academic experiences
Evaluation of Anthurium (Anthurium andreanum) Varieties under Net House Conditions of Bihar
No full textTwelve anthurium varieties were evaluated under Bihar conditions during the year 2024-25 to identify its suitability for commercial flower production. This experiment was laid out in Completely Randomized Design (CRD) with three replications. Significant differences were recorded among the evaluated anthurium varieties for various growth and flowering attributes with the exception of parameters like days taken to first flower bud initiation and flower longevity. The variety Missouri recorded maximum plant spread (38.36 cm), whereas Rainbow Champion yielded maximum number leaves per plant (10.66). Maximum leaf length were recorded in Utah (20.33 cm), whereas Sempre attained maximum plant height at harvesting stage (39.33 cm), leaf width (15.11 cm), number of suckers per plant (4.00), petiole length (35.09 cm), spathe length (8.55 cm), spathe width (8.82 cm), spadix length (6.93 cm) and flower longevity (32.33 days) with minimum days taken to first flower bud initiation (114.00 days) and days taken to first flowering (132.50 days). Variety Orange Champion reported maximum flowering duration (85.33 days), whereas Sierra obtained maximum inflorescence stalk length (32.95 cm). Variety Serino produced maximum number of inflorescence per plant (5.33), while varieties Livium, Elido and Rainbow Champion recorded maximum vase life (21.33 days). The findings of the study would provide valuable insights for anthurium breeders, farmers and stakeholders for cultivation of anthurium and further development of superior anthurium varieties for commercial cultivation
Fintech Lending and MSME: A Pathway to Economic Development
No full textThis review article examines how fintech lending platforms open access to finance for MSMEs, measures the impact of this access on MSME performance and economic outcomes, and identifies the key factors for sustainability and scalability of fintech solutions in the MSME sector. Keywords like “fintech lending”, “MSME” and “economic development” were used for a systematic search across Scopus, Web of Science, JSTOR, ScienceDirect, and Google Scholar. Empirical evidence or conceptual discussions between 2015 and 2024, linking fintech lending to economic development outcomes were included if these were the studies published. A structured framework was used to extract data, and these data were subject to thematic analysis in order to discover recurring patterns. The finding of this study revealed that digital lending platforms also do a great job in overcoming some of the existing barriers to access to MSME finance by using alternative information for credit assessment. Furthermore, fintech lending is beneficial in improving the MSME performance, creating jobs and stimulating entrepreneurial activity. Variety of strategic partnership between fintech providers and traditional banks is important together with supportive regulatory framework for scaling fintech solutions. However, challenges like cybersecurity and digital literacy gaps are persistent and technological advancements and policy are continually needed. The review finds that the fintech lending is a key driver of inclusive economic growth for the massive MSMEs and enhance the financial system resiliency and dynamism
Enhancing Asphalt Performance with Plant Fibers: A Comprehensive Review
No full textConventional asphalt pavements face persistent distresses (rutting, cracking, moisture damage) under extreme environments and heavy traffic. Plant fibers (bamboo, coconut, sisal) emerge as sustainable modifiers, reducing life cycle carbon emissions by 30%-45% versus synthetic fibers while valorizing agricultural waste. Pretreatments (alkali, silane, acetylation) mitigate hydrophilicity, achieving up to 71.3% hemicellulose removal and enhancing fiber-asphalt interfaces. Performance evaluations demonstrate significant improvements: bamboo fiber enhances moisture stability by 20% and substitutes lignin in Stone Mastic Asphalt; coconut fiber (6% dosage) boosts complex modulus by 7.3 times in Trinidad Lake Asphalt and improves low-temperature cracking resistance; sisal fiber (0.3% dosage) enhances tensile strength by 15%-25% and fatigue life via ultra-high tensile strength (363 MPa-700 MPa). Future research should optimize fiber selection, develop eco-friendly treatments, and establish standardized durability guidelines for industrial adoption
Learning to Livelihood: Youth Training in Food Processing Technologies
No full textThis research paper is about the impact of food processing skill development training programmes conducted by Krishi Vigyan Kendra (KVK), Kota, aiming at empowering rural youth in the Hadoti region of Rajasthan. A total of 250 trainees participated in 15-days programmes held between 2021 to 2025, covering practical and theoretical knowledge in food preservation, processing, packaging, FSSAI registration and regulatory compliance. Results showed that 80% of participants rated the training as very good, and 76% felt well equipped to start their own enterprises. This training programmes enhances confidence, improved technical skills, and created awareness about government schemes such as MSME, PMKY, PM-FME, NABARD and entrepreneurial opportunities. This study highlights the importance of context-based, hands-on training in generating rural employment, reducing post-harvest losses, and promoting sustainable livelihoods
Estimation of Variability in Body Morphometric Measurements Using Principal Component Analysis in Mehsana Goats
No full textAims: The study was conducted on Mehsana breed of goats which were selected randomly from their breeding tract in Gujarat State, India with an aim to estimate the variability in body morphometric measurements using Principal Component analysis.
Study Design: The principal component analysis (PCA) was used to characterize the most important component of variables from morphometric traits.
Place and Duration of Study: Department of Animal Genetics and Breeding, College of Veterinary Science and Animal Husbandry, Kamdhenu University erstwhile S.D. Agricultural University, Sardarkrushinagar, Gujarat, India-385 5063 during 2019-20.
Methodology: A total of 118 Mehsana goats were randomly selected for this study from 13 villages of 3 districts. The data on body weight (BW) as well as measures of different body parts (in centimetre) namely, Heart girth (HG), Height at Withers (HW), Height at Rump (HR), Shoulder width (SW), Body length (BL), Tail length (TL) and Paunch girth (PG) were collected.
Results: The accuracy (R2) was estimated up to 70% (Heart girth) when body weight was predicted using single variable. Increase in coefficient of determination (R2) was observed as the number of independent variables in the equation increases. Inclusion of all the independent variables in prediction equation fetched accuracy up to76%. Out of eight principal components, the first two components explained cumulative percentage of variance of 70.967%. The first principal component (PC1) contributed 57.875% of total variation whereas; second component (PC2) explained 13.092% of the total variance. Varimax rotation was used for rotation of principal factors through the transformation of the factors to approximate a simple structure and inferred that HW (0.883), HR (0.848), BL (0.807) and HG (0.761) had significantly higher loadings in the first PC1 and the PC2 mainly contain the significantly higher loadings for TL (0.794).
Conclusion: The variance of about 70.967% was explained by the two major components, out of total eight principal components extracted which indicated that PCA can be used effectively for multivariate analysis in Mehsana goats based body measurers and body morphometric traits
Antihyperlipidemic and Hypolipidemic Effects of a Cocoa Liquor with High Antioxidant Properties on Wistar Rats (Ratus norvegicus)
No full textPrevious studies on cocoa liquor have demonstrated that a blend of raw (30%) and fermented/roasted (70%) cocoa beans yield a cocoa liquor with the desired sensory attributes for chocolate production, as well as high antioxidant potential. The main objective of the present study was to evaluate the in vivo hypolipidemic and anti-hyperlipidemic effects of this cocoa liquor on male Wistar rats fed with a high-fat diet. To achieve this, the cocoa liquor was characterized and incorporated into the rats\u27 diet at a concentration of 50%. The experimentation was conducted with 30 animals aged 10 weeks and weighing between 160-185 g, divided in five groups of six rats each. Two groups for antihyperlipidemic test, two groups for hypolipidemic test, and a control group. After one month of experimentation, the animals were sacrificed, blood was collected for serum extraction, and biochemical analyses were performed. The results show that the cocoa liquor has a high content of polyphenols, flavonoids, iron and zinc, indicating a high antioxidant potential. The hypolipidemic test showed that consumption of this cocoa liquor led to a significant decrease in total cholesterol levels by 7.48%, LDL-C by 20.68%, triglycerides by 3.94%, and a substantial increase in HDL-C by 48.62% compared to the control group receiving no cocoa liquor, with an improvement in the atherogenic index of 37.73%. Additionally, the presence of cocoa liquor in the diet led to a decrease in ALT by 49.39% and AST by 43.87% compared to the untreated control group. Similarly, an improvement in urea and creatinine levels was observed following consumption of the cocoa liquor. Furthermore, the results of the anti-hyperlipidemic effects of the cocoa liquor demonstrated its ability to prevent the onset of hyperlipidemia. We can conclude that consumption of cocoa liquor made from raw and fermented/roasted cocoa beans (30/70) has hypolipidemic and anti-hyperlipidemic effects while protecting vital organs such as the heart, liver and kidney of Wistar rats. Therefore, it can be recommended for use in the formulation of chocolate products for overweight or obese individuals
Deepfake Detection Using Deep Learning: A Review
No full textOf late, social media has amplified rapidly, and deepfake information has cropped in. The Artificial Intelligence (AI) and data analytics have distorted enhancing program performance, which distracted dynamic, real-time insights and intrude risk assessments. But deepfake AI have altered social media that truncate the instantaneous decision-making processes, and mislead the strategic planning. The present review focuses on insights blended approaches, highlighting real-time analytics and improved decision-making by differentiating between real-time data from fake interpretations.
The proliferation of deepfakes across social media has raised significant concerns about misinformation, identity fraud, and public trust. Using advanced AI techniques such as Generative Adversarial Networks (GANs) and diffusion models, they often mimic human likenesses with high precision, making manual detection increasingly difficult. This review explores recent advancements in automated deepfake detection using deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders, GANs and Diffusion Models. Each model\u27s role in identifying manipulated visual and audio content is critically analysed. Detection methodologies span across spatial, temporal, and physiological signal analyses, employing hybrid frameworks for enhanced accuracy. The review also evaluates the effectiveness of publicly available datasets and real-time detection tools such as Sentinel, Sensity, and Fake-catcher. With the continuous evolution of generative models, this study underscores the need for interpretable detection systems to safeguard digital authenticity