Navrachana University Vadodara

The construction of mechanically stabilized earth walls (MSEW) has become increasingly popular, especially in road and railway infrastructure projects. MSEW provides several advantages over typical retaining walls, including ease of construction, low cost, and low environmental impact. This study has been divided into two parts. The first part focuses on the parametric analysis of a Geogrid Reinforced Earth Wall (GREW), evaluating the influence of various parameters such as height, type of reinforcement, tensile strength of reinforcement, type of backfill, type of foundation soil, and water table fluctuation on the stability of GREW. The second part assesses the use of a rubber tire and sand mixture as a potential backfill material for GREW. This material was chosen because it has been found to outperform other typical materials such as crushed rock, sand, or gravel. The stability of GREW concerning parametric studies and using a sand and shredded rubber tire mix as backfill is analyzed for internal and external stability using the GEO-5 software. Overall, the study provides distinctive insights into the design and analysis of GREW, which might be useful for civil engineers and researchers working in retaining wall applications. The study's findings can assist in improving the safety and durability of retaining walls in various civil engineering applications.

Legumes are one of the most important economic crops cultivated for their nutritional value which includes proteins, dietary fibers, phytosterols, polyphenols, and micronutrients. However, they are challenged by many factors like water scarcity, high salinity, metal toxicity, and biotic factors like microorganisms and parasitic nematodes. To protect themselves against these potential threats, plants produce many signaling molecules like peptide elicitors called plant elicitor peptides. Plant elicitor peptides (PEPs) are a class of elicitors which elicits defense responses by activating defense pathways in terms of elevated expression of defense-related genes, hormone production, and induction of secondary messenger synthesis. They belong to a class of endogenous elicitors which lead to enhanced immunity against various abiotic and biotic stresses. These peptides are perceived by membrane receptors in the plant cells, which bind to the peptide ligands to initiate the signaling cascades. The exploration of PEP’s represents a good alternative with potential in crop protection. This chapter deals with the peptide elicitors used to combat abiotic and biotic stress in leguminous plants.

Dasatinib (DAS) has recently gained significant interest for its anticancer potential. Yet, the lipophilicity inherent in DAS limited its potential use as a chemotherapeutic drug. This study aimed to examine the effectiveness of polyethylene glycol‐polycaprolactone (PEG‐PCL) as a nanocarrier for DAS to increase its anticancer capabilities. The DAS‐loaded PEG‐PCL nanoparticles (termed as DAS@PEG‐PCL NPs) were characterized using Fourier transform infrared (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and dynamic light scattering (DLS). Morphological staining and MTT tests were employed to investigate drug‐loaded nanoparticles' apoptotic and anti‐proliferative effects. The MTT assay demonstrated that incorporating DAS onto PEG‐PCL NPs resulted in a dose‐dependent increase in cytotoxicity in A549 (lung cancer) and HeLa (cervical cancer) cells. The A549 cancer cells were analyzed for their morphology using the acridine orange/ethidium bromide (AO/EB) and DAPI staining techniques. Overall, these findings demonstrate that the polymeric PEG‐PCL nanoparticle systems hold great potential as a novel therapeutic strategy for cancer treatment.

In the current study, a series of unconsolidated undrained triaxial tests, consolidated undrained triaxial tests, bender element tests, and cyclic triaxial tests were performed on micaceous sand specimens at different mica contents (0 to 30%). All the specimens were prepared at 95% maximum dry density of their respective mica-sand combinations to simulate the soil mass conditions in railway/road embankments. The experimental results showed a large reduction in the angle of internal friction as mica content increased from 0 to 30% in unconsolidated undrained tests (total stress). Consolidated undrained triaxial testing reported the decrease in shear strength with the increase in mica content (effective stress). A large decrease in shear modulus was observed with the increase in mica content from 0 to 30% in cyclic triaxial testing. The damping ratio increased with the increase in mica content. The small strain shear modulus obtained from bender element tests showed a significant decrease with the increase in mica content.

Marine macroalgae are emerging candidates for mitigating life style disorders. The vast history of their utilization from South Asian countries provides strong evidence for their selective suitability in routine diet. The ever increasing awareness for alternative and herbal medicine have led to the explorations into these unique marine resources that are enriched with medically valuable bioactives. The types of bioactive pharmaceuticals include peptides as well as unique sulfated carbohydrates in addition to the secondary metabolites such as polyphenols which make these biomass highly valuable commodity compared to the terrestrial plants. The chapter summarises introductory remarks on the ecological role of marine macroalgae followed by their key therapeutic roles of various bioactive metabolites as mentioned above that include metabolic disorders, microbial infections and the recently unraveled role in modulation of gut-microbiome. The chapter also discusses the key limitations in the development of marine based therapeutants such as the possible toxicity, cultivation practices and supply-chain and environmental influences. The chapter is concluded discussing the future prospects in the area.

Cancer remains a significant cause of mortality in the world, with increasing prevalence worldwide. There are numerous treatments ranging from surgery to chemotherapy and radiotherapy, but since cancer is a heterogeneous disease, only few patients possibly respond to treatments. However, it opens a huge space for the advent of targeted therapies such as hormone therapy, immunotherapy, and target-specific drugs. Hormonal therapy using hormone agonists/antagonists or hormone receptor inhibitors—called the next-generation hormonal agents—hits distinct hormonal pathways that are involved in breast, prostate and ovarian cancer. Preliminary results show that through combination of drugs, it is possible that the synergistic effects may actually lead to better survival than with the use of single drugs. With manageable adverse effects, hormonal therapy offers much hope for treatment of this rather challenging malignancy of the hormone-sensitive cancers, especially in combination with other treatments.

Imbalanced class distributions pose a prevalent challenge in numerous classification problems, requiring effective strategies for learning from such skewed data. Traditional machine learning algorithms often struggle with imbalanced datasets, as they tend to bias their classification functions toward the majority class, resulting in suboptimal performance for minority classes. In our research, we propose a novel approach to address this challenge specifically tailored for Support Vector Machines (SVM), a well-established family of learning algorithms. Our method leverages a kernel trick to enhance the SVM’s classification capabilities on imbalanced datasets named KTI. It aims to streamline the classification process by incorporating adaptive data transformations within the algorithm itself, offering a more efficient and integrated solution for handling imbalanced data. Experimental evaluations conducted on diverse real-world datasets demonstrate the superior performance of our proposed strategy compared to existing methods, showcasing its potential for practical applications in classification tasks with skewed class distributions.

Poor dietary habits and a lack of understanding are contributing to the rapid global increase in the number of diabetic people. Therefore, a framework that can accurately forecast a large number of patients based on clinical details is needed. Artificial intelligence (AI) is a rapidly evolving field, and its implementations to diabetes, a worldwide pandemic, have the potential to revolutionize the strategy of diagnosing and forecasting this chronic condition. Algorithms based on artificial intelligence fundamentals have been developed to support predictive models for the risk of developing diabetes or its complications. In this review, we will discuss AI-based diabetes prediction. Thus, AI-based new-onset diabetes prediction has not beaten the statistically based risk stratification models, in traditional risk stratification models. Despite this, it is anticipated that in the near future, a vast quantity of well-organized data and an abundance of processing power will optimize AI's predictive capabilities, greatly enhancing the accuracy of diabetic illness prediction models.

The changing notion of "companion animals" and their increasing global status as family members underscores the dynamic interaction between gut microbiota and host health. This review provides a comprehensive understanding of the intricate microbial ecology within companion animals required to maintain overall health and prevent disease. Exploration of specific diseases and syndromes linked to gut microbiome alterations (dysbiosis), such as inflammatory bowel disease, obesity, and neurological conditions like epilepsy, are highlighted. In addition, this review provides an analysis of the various factors that impact the abundance of the gut microbiome like age, breed, habitual diet, and microbe-targeted interventions, such as probiotics. Detection methods including PCR-based algorithms, fluorescence in situ hybridisation, and 16S rRNA gene sequencing are reviewed , along with their limitations and the need for future advancements. Prospects for longitudinal investigations, functional dynamics exploration, and accurate identification of microbial signatures associated with specific health problems offer promising directions for future research. In summary, it is an attempt to provide a deeper insight into the orchestration of multiple microbial species shaping the health of companion animals and possible species-specific differences.

Objective This qualitative study was conducted to explore the perceptions of dental teachers about utilizing blended learning (BL) in undergraduate teaching following the COVID-19 pandemic. The objectives were to study their willingness to move to BL and to identify the reasons for their willingness or lack thereof for this transition. Materials and methods This descriptive qualitative study is part of doctoral research that was conducted among health sciences teachers from the state of Maharashtra, India. The population for this study included all dental teachers from the 38 dental colleges in the state. To ensure proper representation of teachers, a proportionate stratified sampling method was used. The research tool used in this study was developed and validated, and data collection was conducted online using SurveyMonkey. Results The results showed that 137 (97.16%) teachers were inclined toward utilizing BL. Thematic analysis of the responses received was carried out using NVivo software. Around 10 main emergent themes were identified, which were grouped under four key areas. The majority of the dental teachers were appreciative of the benefits of BL in terms of accessibility, flexibility, and learner engagement, but some preferred traditional teaching methods. Conclusion For the dental teachers to continue with BL in instruction, there is a need to empower them via faculty development programs for successful adaptation to technology-enhanced instruction and to overcome the limitations and challenges associated with technology integration in education. The transformation from traditional face-to-face to technology-enhanced BL seems to be a worthwhile opportunity for future dental education. How to cite this article Lele G, Sikdar M. Perceptions of Dental Teachers about Blended Learning: A Qualitative Analysis. Int J Clin Pediatr Dent 2024;17(5):532-538.

CdTe is compound of II-VI group semiconducting material that is used for photovoltaic energy conversion because of its direct energy band gap, ease of fabrication, stable nature in air, and excellent absorption efficiency. It is possible to expand its application to other areas through an improvement brought about by doping. In this regard, the vacuum sealing approach is utilized for the making of Fe0.05(CdTe)1−xSbx samples for x = 0, 0.03, 0.05, and 0.10 bulk alloys. Different analyses performed include the study of the structural characteristics, bandgap variations, and vibrational characteristics of the bulk alloys. From the X-ray diffraction pattern, the cubic structure of the single-phase CdTe structure is confirmed. The band gap values of the samples decrease from 1.35 eV to 1.27 eV, resulting from the inclusion of Sb in Fe-doped CdTe. The transverse-optical (TO) phonon mode present in pure CdTe splits as a result of the addition of Sb, as observed in the Raman spectra at room temperature.

Many research articles on various helical gearbox stages are accessible in reputable journals. The majorities of earlier research, despite the fact that triple step helical gearboxes have several benefits, were confined on single step and double step helical gears. Lately, most researching experts who work in this field have started paying close attention to the importances of three stage helical gearboxes. Throughout this research, authors seek to carry out an exhaustive evaluation of the current triple step helical transmission articles for examining the most widely used survey designs, approaches, tools & techniques. The above investigation expansively analysed 315 journal articles on helical gearbox various stages and then used content analysis procedure with an inductive method of research for carrying out an organized review of 45 literatures on three step helix transmission box that appeared in different scientific publications over the preceding two decades. Apart from this; the study presents a broad assessment of currently utilized materials, several future possibilities and problems in employing different materials for various parts of gearbox and its casing. This important research finding indicates there’s an enormous potentiality towards dominating the area of research as well as continuing more chances for exploring growth of three stage helical gearboxes, which provides novel career opportunities for both experts and industrial professionals.

The present study investigated the impact of seasonal variation on biomass availability, proximate composition, fatty acid profile, minerals content, pigments, moisture content, and elemental composition of an edible green alga Gayralia brasiliensis collected from Shirgaon estuary Maharashtra, west coast of India. Additionally, the physico-chemical parameters of the Shirgaon estuary were analyzed and correlated with biomass abundance and biochemical composition of G. brasiliensis. The findings of the present study demonstrated a significant variation in the physico-chemical parameters of seawater throughout the seasons. During the summer season, the biomass abundance (258.23 ± 23.06 g m⁻² FW), and the total protein content (11.12 ±1.37 % DW) were found highest while the total lipid content declined considerably. Nonetheless, the contents n-6 and n-3 PUFA increased significantly over the winter season. The concentration of micro-elements was highest during the winter season while the macro-elements found plenty during the monsoon season. The correlogram analysis revealed that biomass, total protein, total carbohydrate, chlorophyll pigments, and tissue C, H, N, and S were positively correlated with each other throughout all three seasons. Based on Pearson corelation analysis it is confirmed that among the environmental parameters, irradiance and temperature were found the most limiting factors for the growth of G. brasiliensis. Further, dissolved inorganic nitrate significantly influence the growth of G. brasiliensis negatively. Overall, the results of this study imply that G. brasiliensis should be harvested for desired metabolites during the respective seasons. The findings of the present study are expected to be extremely valuable for future cultivation strategies and edible applications of G. brasiliensis in India.

Phase change materials (PCMs) can absorb, store, and release substantial latent heat within a specific temperature range during phase transition and have gained huge attention due to environmental concerns and energy crises. However, PCMs have a significant downside in energy storage due to their relatively lower thermal conductivity, leading to inadequate heat transfer (HT) performance. The foremost aim of the research is to synthesize an eco‐friendly coconut shell biochar (CSB) dispersed with organic A46 PCM in the temperature range of 44°C to 46°C to form a green nanocomposite. A two‐step approach is adopted to formulate the nanocomposites with different weight concentrations (0.2% and 0.8%) of green CSB particles. The developed nanocomposite's thermal conductivity and chemical stability were examined using a thermal properties analyzer and a Fourier transforms infrared spectrometer. The developed biochar composites have excellent thermal conductivity (0.39 W/m K) compared with base PCM (0.22 W/m K). Also, the developed nanocomposites were physically mixed together; there were no additional functional groups formed compared to pristine PCM, and the prepared materials were composite. Furthermore, a numerical analysis was performed using two‐dimensional energy modeling software to ascertain the HT rate of A46 composites. These thermally energized green nanocomposites show great promise for thermal energy storage and thermal management applications like battery thermal management, photovoltaic thermal systems, desalination systems, electronic cooling, building applications, and textiles.

The current research investigates individual and combined toxicity effects of nickel (Ni) and imidacloprid (IMI) on earthworm species Eisenia fetida fetida. Employing standardized toxicity parameters, we assessed the impact of environmentally relevant concentrations (ERC) of Ni, IMI, and their mixtures on key biomarkers and reproductive fitness of earthworms. Our findings reveal concentration-dependent responses with discernible adverse effects on physiological parameters. The ERC obtained for Ni was 0.095 ppm, and for imidacloprid was 0.01 ppm. Two concentrations (ERC and 1/5th) of both toxicants (individually and in combinations) were further given for 14 days, and parameters like avoidance behaviour, antioxidants, histology, and metabolomic profile were observed. The behaviour of earthworms was noted, where at 24–48 h, it was found to be in control soil, while later, at 72–96 h, they migrated to toxicants-treated soil. Levels of antioxidants (superoxide dismutase, catalase, reduced glutathione, ascorbic acid), lipid peroxidation, and lactate dehydrogenase were elevated in the testis, spermatheca, ovary, and prostate gland in a high concentration of Ni + IMI. Histological studies showed more vacuolization and disruption of epithelium that was increased in the prostate gland of the Ni + IMI high group, decreased number of spermatids, and damaged cell architecture was noted in testis and spermatheca of the Ni + IMI high group. The highest number of metabolites was found in Ni exposed group (181), followed by IMI (131) and Control (125). Thus, this study sheds light on the ecotoxicological effects of combinational exposure of these contaminants on an essential soil-dwelling organism, where IMI was more toxic than Ni, and both toxicants decreased earthworm reproductive fecundity.

Timely prediction of bearing faults is essential for minimizing unexpected machine downtime and improving industrial equipment’s operational dependability. The Q transform was utilized for preprocessing the sixty-four vibration signals that correspond to the four bearing conditions. Additionally, statistical features, also known as attributes, are extracted from the Histogram of Oriented Gradients (HOG). To assess these features, the Explainable AI (XAI) technique employed the SHAP (Shapely Additive Explanations) method. The effectiveness of the GRU, LSTM, and SVM models in the first stage was evaluated using training and tenfold cross-validation. The SSA optimization algorithm (SSA) was employed in a subsequent phase to optimize the hyperparameters of the algorithms. The findings of the research are rigorously analyzed and assessed in four specific areas: the default configuration of the model, the inclusion of selected features using XAI, the optimization of hyperparameters, and a hybrid technique that combines SSA and XAI-based feature selection. The GRU model has superior performance compared to the other models, achieving an impressive accuracy of 98.2%. This is particularly evident when using SSA and XAI-informed features. The subsequent model is the LSTM, which has an impressive accuracy rate of 96.4%. During tenfold cross-validation, the Support Vector Machine (SVM) achieves a noticeably reduced maximum accuracy of 84.82%, even though the hybrid optimization technique shows improvement. The results of this study usually show that the most effective model for fault prediction is the GRU model, configured with the attributes chosen by XAI, followed by LSTM and SVM.