Statistical Techniques in Ecology: Descriptive Statistics and Normal Distribution | Chapter 4| Mathematics and Computer Science: Research Updates Vol. 9

 

Ecological science relies on robust estimates of the abundance, diversity, and spatial distribution of individuals and species, but these quantities are notoriously difficult to observe directly. Statistics may be considered as the science and technique of collecting, analysing, and making inferences from data, and these references are stated as probabilities. The study aims to explore and apply quantitative and statistical methods in ecology to understand the relationships between populations and their environment, assess the effects of environmental hazards on animal and plant populations, and evaluate overall ecological balance. Fundamental statistical concepts, including descriptive statistics, probability distribution, regression and correlations, and the chi-square distribution, are demonstrated to show their function in analysing ecological data. On the other hand, specialised methods, such as species-abundance relations and species-diversity measures, provide insights into community structure and ecosystem stability. The study recommends the use of logarithmic distributions to accurately fit species-abundance data and enhance the reliability of ecological analyses.

 

 

Author(s) Details

B. K. Singh
Department of Mathematics, School of Sciences, IFTM University, Moradabad-244102, Uttar Pradesh, India.

 

Rajan Singh
Department of Mathematics, School of Sciences, IFTM University, Moradabad-244102, Uttar Pradesh, India.

 

Anshul Dubey
Department of Mathematics, School of Sciences, IFTM University, Moradabad-244102, Uttar Pradesh, India.

 

Nidhi Tiwari
Department of Mathematics, School of Sciences, IFTM University, Moradabad-244102, Uttar Pradesh, India.

 

Nidhi Prabhaka
Department of Mathematics, School of Sciences, IFTM University, Moradabad-244102, Uttar Pradesh, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/mcsru/v9/7057

 

Asymptotic and Bootstrap Implementation of the D'Agostino-Berlanger-D'Agostino K2 Normality Test in R | Chapter 3 | Mathematics and Computer Science: Research Updates Vol. 9

 

The K² test could be one of the best tests for assessing normality, yet its use is limited, likely because it is not commonly included in standard statistical software, despite being implementable in R through the moments package. Moreover, its asymptotic approximation has been questioned for small samples, and no bootstrap version currently exists, even though it is feasible in R. This simulation study aimed to: (1) verify the linear independence and nonlinear relationship between √b₁ and b₂; (2) develop an R script for the K² test in both asymptotic and bootstrap versions; (3) assess the fit of the bootstrap distribution of the K² statistic to a chi-square distribution with two degrees of freedom; (4) compare the power of both implementations against non-normal distributions; and (5) contrast the bootstrap version of K² with the Shapiro–Wilk test in small samples. A Monte Carlo simulation with 10,000 replications was conducted, using 16 non-normal distributions as alternative hypotheses and sample sizes (n) ranging from 20 to 2,000 in increments of 20. Linear independence and a parabolic relationship between √b₁ and b₂ were confirmed, and the R script was verified to be functional. The script is available for download as a Word document from a GitHub repository. The bootstrap distribution of K² converged to a chi-square distribution for n ≥ 120. The asymptotic version of K² and the Shapiro–Wilk test showed greater power than the bootstrap version, except for mesokurtic asymmetric distributions. Bootstrap implementation is recommended in these cases for n < 120, while the asymptotic version is generally more powerful and appropriate for n ≥ 20. The developed R script is highly useful for assessing the normality assumption required by many parametric tests, such as t-tests and F-tests for comparing means and variances, as well as for characterising the distribution of a sample of quantitative data; therefore, its use is recommended for these purposes.

 

Author(s) Details

José Moral de la Rubia
School of Psychology, UANL, Mexico.

 

Please see the book here :- https://doi.org/10.9734/bpi/mcsru/v9/6969

 

Certainty-Independent Aspects in Fluid Mechanics: Fundamental Laws and Universal Behaviours | Chapter 2 | Mathematics and Computer Science: Research Updates Vol. 9

 

This chapter explores certainty-independent parts of fluid mechanics, focusing on fundamental concepts that are true regardless of specific initial conditions or parameter uncertainty. It examines how robust frameworks for comprehending fluid behaviour are supplied by conservation laws, dimensional analysis, similarity solutions, and stability theory without requiring exact knowledge of every system variable. The research demonstrates that many fluid processes have universal characteristics that transcend specific experimental configurations, offering reliable forecasting capabilities in a range of applications. Through theoretical analysis and case examples, the importance of these certainty-independent components in both fundamental research and practical engineering applications is highlighted.

 

Author(s) Details

K. Chinnadurai
Department of Mathematics, AMET University, Kanathur, Chennai, India.

 

Salahuddin
Department of Mathematics, AMET University, Kanathur, Chennai, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/mcsru/v9/6897

Deep Learning-Driven Chatbots for Crop Health Monitoring and Agricultural Decision Support | Chapter 1 | Mathematics and Computer Science: Research Updates Vol. 9

 

Numerous problems in agriculture, including unpredictable crop yields, disease susceptibility, and the consequences of weather variability, put nutrition and farmer livelihoods at risk. In order to increase agricultural yields, detect diseases early, and provide valuable insights on the Crop Yield Prediction Dataset and Plant Village Dataset, this research provides an AI-powered solution to these issues by integrating deep learning, sophisticated machine learning algorithms, and instantaneous data analysis. The system employs a sophisticated methodology that forecasts temperature, humidity, and conditions for the next five days using the PyOWM API; detects crop diseases using data augmentation and deep learning models such as CNN (accuracy 99.14%), DenseNet-201 (accuracy 99.04%), and Visual Geometry Group-VGG19 (accuracy 97%); and predicts crop yield using models such as Multi-Layer Perceptron-MLP (R2 Score: 0.8242), MLP + Regressor, and Random Forest Regressor achieves the highest R2 Score (0.1789). An AI chatbot that provides farmers with recommendations, disease control methods, and personalised suggestions is part of the technology's real-time help. In order to provide an AI-driven system for weather forecasting, disease detection, yield prediction, and real-time assistance via a chatbot, this project integrates models with high accuracy rates. The user-friendly Streamlit UI is available in Telugu, Hindi, and English, and SQLite handles the secure login and registration procedure.

 

 

Author(s) Details

 

Anantha Kranthi Suravarapu
Department of CSE (Artificial Intelligence & Machine Learning), Ramachandra College of Engineering, Eluru, Andhra Pradesh, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/mcsru/v9/6825

Development and Validation of a Simple and Reliable UV Spectrophotometric Method for the Simultaneous Estimation of Metformin Hydrochloride and Pravastatin Sodium | Chapter 9 | Pharmaceutical Science: New Insights and Developments Vol. 10

 

Background: Metformin hydrochloride is a drug used in the treatment of type 2 diabetes. It exhibits high aqueous solubility, limited solubility in ethanol, and negligible solubility in organic solvents such as acetone, ether, and chloroform.

 

Aim: A straightforward and reliable ultraviolet (UV) spectrophotometric method was developed and validated for the simultaneous estimation of Metformin Hydrochloride (MH) and Pravastatin Sodium (PS) in their pure forms.

 

Methodology: The proposed method employs an absorbance subtraction approach using UV spectrophotometry. Quantification was carried out by measuring absorbance at two selected wavelengths, 232 nm for Metformin Hydrochloride and 238 nm for Pravastatin Sodium. Method validation was performed in accordance with ICH guidelines, including accuracy studies conducted at three concentration levels (75%, 100%, and 125%), and percentage recovery was calculated for both drugs.

 

Results: The method demonstrated acceptable sensitivity, with limits of detection and quantification determined as 0.481 μg/mL and 0.670 μg/mL for MH, and 1.15 μg/mL and 1.68 μg/mL for PS, respectively. Statistical evaluation of validation parameters confirmed that the method exhibited satisfactory precision, accuracy, and selectivity within the specified limits.

 

Conclusion: The validated UV spectrophotometric method is simple, precise, and accurate, making it suitable for the simultaneous estimation of Metformin Hydrochloride and Pravastatin Sodium. The method can be effectively applied for routine analysis of these drugs in pure form and pharmaceutical dosage formulations.

 

 

Author(s) Details

Ankita Sharma
Shiva Institute of Pharmacy, Bilaspur, H.P., India.

 

Kapil Kumar Verma
Minerva College of Pharmacy, Indora, Kangra, H.P., India.

 

Inder Kumar
Minerva College of Pharmacy, Indora, Kangra, H.P., India.

 

Anju Bala
Chandigarh Group of Colleges Landran, Kharar, Greater Mohali, Punjab, India.

 

Bhumika Thakur
Shiva Institute of Pharmacy, Bilaspur, H.P., India.

 

Vandana Thakur
Abhilashi College of Pharmacy, Nerchowk, Mandi, H.P., India.

 

Please see the book here :- https://doi.org/10.9734/bpi/psnid/v10/7061

Dipeptidyl Peptidase-4 Inhibitors: An Overview of Their Combination with Oral Hypoglycemic Agents | Chapter 8 | Pharmaceutical Science: New Insights and Developments Vol. 10

 

Type 2 diabetes mellitus (T2DM), the most prevalent form, is characterised by insulin insensitivity as a result of insulin resistance, declining insulin production, and eventual pancreatic beta-cell failure. Dipeptidyl peptidase 4 (DPP-4) inhibitors are a new class of Oral Hypoglycemic Drugs (OHD) that can control T2DM. This chapter aims to provide a critical and systematic update of the specialised literature on the therapeutic effects and safety profile of hypoglycemic drugs, used in combination with DPP-4 inhibitors, in the management of type 2 diabetes mellitus. Given the rapid advances in the field of medicine and the pharmaceutical industry, therapeutic strategies for this pathology have undergone significant changes, oriented not only towards effective glycemic control but also towards reducing cardiovascular risks and mortality associated with the disease. Numerous studies have investigated the impact of different therapeutic combinations on the evolution of patients with type 2 diabetes mellitus, paying particular attention to the associations between metformin and other classes of hypoglycemic drugs. An important part of the research has focused on comparing the classic metformin–sulfonylurea combination with more recent therapeutic regimens, such as metformin associated with DPP-4 inhibitors. The data suggest that the use of sulfonylureas combined with metformin is associated with a significantly increased risk of severe hypoglycemia compared with metformin–DPP-4 inhibitors. Another relevant observation is related to the use of insulin in combination with metformin. According to the analysed data, this therapeutic combination was associated with a higher risk of all-cause mortality, compared with treatment based on DPP-4 inhibitors combined with metformin. In conclusion, the current evidence supports the use of DPP-4 inhibitors in combination with metformin as a safer therapeutic alternative to sulfonylureas or insulin, especially in terms of reducing the risk of severe hypoglycemia, cardiovascular events and mortality. These results highlight the need to integrate recent clinical data into medical practice guidelines and to individualise the treatment for patients with type 2 diabetes.

 

 

Author(s) Details

Nina Filip
Department of Morpho Functional Sciences II, Biochemistry, Faculty of Medicine, Grigore T. Popa University of Medicine and Pharmacy Iasi, Romania.

 

Cristina Elena Iancu
Department of Biochemistry, Faculty of Pharmacy, Grigore T. Popa University of Medicine and Pharmacy Iasi, Romania.

 

Diana Zamosteanu
Department of Morpho Functional Sciences I, Pathology, Faculty of Medicine, Grigore T. Popa University of Medicine and Pharmacy Iasi, Romania.

 

Cristiana Filip
Department of Morpho Functional Sciences II, Biochemistry, Faculty of Medicine, Grigore T. Popa University of Medicine and Pharmacy Iasi, Romania.

 

Magdalena Birsan
Department of Drug Industry and Pharmaceutical Biotechnology, Faculty of Pharmacy, Grigore T. Popa University of Medicine and Pharmacy Iasi, Romania.

 

Madalina Mocanu
Department of Dermatology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Romania.

 

Please see the book here :- https://doi.org/10.9734/bpi/psnid/v10/7009

Vaccine and Adjuvant-Induced Autoimmune Responses: Mechanisms and Evidence | Chapter 7 | Pharmaceutical Science: New Insights and Developments Vol. 10

 

Vaccinations are one of the most important preventive tools against infectious diseases. The efficacy of a vaccine depends not only on the antigen components but also on adjuvants that are often used in order to stimulate the immune system in a more effective way. Human beings, in a normal immune homeostatic state, immune cells like macrophages, natural killer cells, iNKT, MAIT, g delta T cells and conventional B as well as conventional T cells, in one way or other recognise the host body components as self via the immune surveillance mechanisms. Though when there was a shift in immune homeostasis due to chronic induction by environmental stimulus, interplay of predisposing genetic elements, family history, bystander pathologic inflammatory system, innate and adaptive immune dysregulation, change in proteomic signature, as well as microbial interactions in a unified collective theme “Unified autoimmunity theme”. Immune cells become prone to recognise the self or self as a non-self with subsequent induction of autoimmune diseases. Vaccines and adjuvants associated with autoimmunity are currently being reported all over the world. The present chapter was aimed at vaccine and adjuvant-mediated autoimmune diseases. Different human-approved vaccines induce different autoimmune diseases; more than one vaccine may induce the same autoimmune disease. Shoenfeld Syndrome encompasses adjuvant-induced autoimmune/inflammatory syndrome, including Postvaccination reactions with an adjuvanted vaccine, macrophagic myofasciitis, sick building disease condition, Gulf War disease condition and siliconosis. A protocol for the practical evaluation of these diseases was suggested. Understanding the unified autoimmune theme and Shoenfeld Syndrome is crucial for producing vaccines with a safer side effect profile. Clinicians and researchers can use this knowledge to monitor, prevent, and manage vaccine-related autoimmune reactions more effectively.

 

 

Author(s) Details

 

Ibrahim M. S. Shnawa
Department of Medical Biotechnology, College of Biotechnology, University of Qasim, Babylon, 5001, Iraq and Department of Prosthodontics, College of Health and Medical and Medical Technologies, University of Hilla, Babylon, 5001, Iraq.

 

Please see the book here :- https://doi.org/10.9734/bpi/psnid/v10/7004