Ish Kapila

Stem cell-based tissue engineering offers transformative solutions for regenerating damaged tissues, such as bone, cartilage, and neural tissues. Chitosan and cellulose nanoparticles have emerged as promising biomaterials for enhancing stem cell delivery and scaffold performance due to their biocompatibility, biodegradability, and tunable properties. Chitosan, with its antimicrobial and bioadhesive properties, supports stem cell adhesion and differentiation in soft tissue scaffolds. Cellulose nanoparticles, including cellulose nanocrystals (CNCs), nanofibrils (CNFs), and bacterial nanocellulose (BNC), provide mechanical strength for hard tissue regeneration. This review examines their synthesis, properties, and interactions with stem cells, particularly mesenchymal stem cells (MSCs), in tissue engineering applications. A comparative analysis highlights their complementary roles, while challenges like physiological stability and scalability are addressed. Recent advancements, such as 3D bioprinting and growth factor functionalization, enhance their potential. Future research should focus on optimizing stem cell-scaffold interactions and ensuring clinical safety.

The quality of dairy milk depends heavily on microbial contamination and enzyme active measures. Research analysis examined the yeast and mold counts along with Aerobic Plate count (APC) and enzyme activities, including alkaline phosphatase and phosphatase, using 525 pasteurized, 560 raw, and 645 sterilized milk samples. Research measured pH levels and counts of Aerobic Bacillus (AB) and the concentration of Volatile Fatty Acids (VFA). Results indicated that 10.20% of raw milk and 2.45% of pasteurized milk samples exceeded the threshold for APC count. Yeast and mold counts were significantly higher in raw milk (8.56%) compared to pasteurized (5.65%) and sterilized milk (9.30%). AB abundance was highest in raw milk (6.32%) and lowest in sterilized milk (5.65%). Alkaline phosphatase activity (ALP) levels exceeded the critical threshold in raw milk, pasteurized milk and sterilized milk samples. Raw milk had pH of 6.90, slightly higher than pasteurized milk (6.78) but similar to sterilized milk (6.88). VFA concentrations were higher in raw milk (0.40 mM) than in pasteurized (0.28 mM) and sterilized milk (0.35 mM), indicating greater microbial metabolic activity in raw samples. These findings highlight the microbial and enzymatic variations across different milk processing methods, emphasizing need for effective thermal treatments to ensure milk safety. Research established that Bacillus microbes linked positively to ALP measurements in raw milk samples, while pasteurized milk activity levels were related negatively to microbial populations. The analysis demonstrates that dairy milk assessment depends heavily on microbial counts combined with enzymatic activity as well as quality measure indicators.

Several facets of learning, include imaginative writing transformed from digital technologies. Writing creatively fosters students' creativity and critical thinking abilities, an essential part of literacy education. The long-term effects of digital technologies on imaginative writing skills (IWS), despite the increasing usage. The potential of digital tools diversions might constitute the intended benefits and the application of these technologies utilized in educational institutions. The study aims to investigate how digital tool improve writing skills and develop innovative writing abilities by fostering creativity. The research intends to identify which digital tools offer the most significant benefits and how integrated into educational practices to support imaginative writing development. This study included a total of 60 participants, including males (28) and females (32) for imaginative writing skills. Utilizing SPSS software, a statistical analysis of the data was carried out. The data was analyzed using a t-test, ANOVA, and regression analyses. These analyses demonstrated imaginative writing skills. The research findings demonstrated that digital tools greatly enhanced participant’s creative writing skills, vocabulary growth and story structure. The use of digital tools could significantly improve participant’s capacity for imaginative writing skill development.

Introduction: In general, sentiment evaluation has focused on text content analysis. Traditional sentiment analysis (SA) excludes document metadata and focuses on text. The lack of research in this area prevents textual material and metadata from working together to create synergies that could lead to reliable sentiment assessment solutions.Method: We exhibit the potential uses of our integrated methodology across multiple areas and prove its efficacy through testing and evaluation on a different datasets. Data preprocessed using a multiple techniques, such as “stemming, tokenization, and stop word removal”. The “term frequency-inverse document frequency (TF-IDF)”methodology is utilized for feature extraction.Result: The outcomes of our experiments reveal that our suggested method is successful in collecting sensitive sentiment such as” accuracy, recall, precision, and f1-score”. This is a promising development in the field of sentiment assessment exploration.Conclusion: In conclusion, this study proposed a novel approach, the DDO-SVM, which addresses the limitations of conventional SA by incorporating both textual and metadata features. These components integrate well, improving sentiment evaluation across measurements. SA becomes more reliable and effective in actual applications with this breakthrough.

Concentration on solar power as a feasible alternative has increased due to the rapid degradation of traditional energy resources and the urgent requirement for energy options. The optimization of critical parameters, including the maximum power point (MPP), load component, and voltage in the open circuit, is a difficult task, even with the major advances in solar cell technologies. The effective design and performance improvement of solar cells are hampered by the constraints of current optimization approaches, which show limits with regard to converging speed and accuracy. To evaluate important solar cell characteristics, this work presents an innovative hybrid whale-simulated swarm optimization (HWSSO) technique. Comprehensive studies and simulations show that the suggested approach performs better in identifying important solar cell properties. The outcomes demonstrate the suggested strategy works to achieve optimized solar cell design, which advances renewable energy technology. The results point to bright futures for using the suggested method in the field of solar energy growth.

Cardiovascular diseases (CVDs) constitute a major cause of death and disability worldwide and represent a major public health problem. Timely intervention and better patient outcomes depend on early identification of CVD. A novel Sand Cat Swarm-driven AdaBoost (SCS-AB) technique is presented for the diagnosis of CVD. The suggested strategy chooses useful features from intricate cardiovascular (CV) datasets by utilizing the shared knowledge of the SCS optimization technique. The SCS method, which addresses feature selection issues in CVD diagnosis, is motivated by the social behaviour of sand cats. It demonstrates a cooperative and self-organizing decision-making process. The AdaBoost incorporates the chosen features, improving its ability to distinguish between patients. The gathered dataset is used in this study's analysis of the suggested SCS-AB approach. The SCS-AB driven CVD prognosis is tested on the Python platform in this study. When contrasted with other standard approaches, the experimental results show that the suggested methodology is beneficial with regard to accuracy, sensitivity and specificity. The method has demonstrated its potential for practical clinical applications as well, offering a useful instrument for rapid CVD identification and risk evaluation.

This study investigates the use of Multi-Agent Reinforcement Learning (MARL) to enhance the efficiency of power system operation and control. The simulated power system environment is represented as a multi-agent system, where intelligent agents are used to mimic generators and loads. The MARL framework utilizes Q-learning algorithms to allow agents to independently adjust their activities in accordance with changing operating circumstances. The resulting simulated data represents a wide-ranging power grid scenario, including buses with different generator capacity, load needs, and transmission line capacities. The findings indicate a significant improvement in the stability of the system via Multi-Agent Reinforcement Learning (MARL), since the agents’ capacity to learn and adapt enables them to quickly alter the outputs of generators and meet the needs of the load, so ensuring that voltage and frequency levels remain within acceptable limits. The MARL framework significantly improves economic efficiency by enabling actors to optimize their behaviors in order to reduce the total costs of the system. The agility of the MARL-based control method is emphasized by the decrease in response time to dynamic disturbances, as agents demonstrate quick and efficient reactions to unforeseen occurrences. The favorable results highlight the potential of MARL as a decentralized decision-making model in power systems, providing advantages in terms of stability, economic efficiency, and the capacity to respond to disruptions. Although the research uses artificial data in a controlled setting, the observed enhancements indicate the flexibility and efficacy of the MARL framework. Future research should prioritize the integration of more practical situations and tackling computational obstacles to further confirm the suitability and expandability of Multi-Agent Reinforcement Learning (MARL) in actual power systems.