Scientific Inquiry and Review

Phytochemistry, Nutritional, and Pharmacological Potential of Citrus Limonum

Wed, 04 Sep 2024 04:35:20 +0000

The current research aimed to provide an overview of the phytochemical configuration, nutritional value, and therapeutic uses of Citrous Limonum (lemon). Its fruit contains a variety of phytochemicals including citric acid, polyphenols, terpenes, limonene, flavonoids, vitamin C, sugar, pectin, citric acid, malic acid, flavonoids, carotenoids, terpineol, fellander, camhenium, citrain, calcium oxalates, and mucilages. Vitamin C is abundant in citrous fruits, as well as macronutrients (dietary fibre and simple sugar) and micronutrients (copper, magnesium, phosphorus, calcium, potassium, pantothenic acid, riboflavin, vit B6 niacin thiamin, and folate). Lemon oil is composed of 70% limonene and 20% monoterpenes, along with significant amounts of aldehydes, such as citral, alcohols (linalool), and esters (coumarin). Lemon peel contains high concentrations of flavonoids, glycosides, coumarins, steroids (beta, gamma, sitosterol), dietary fibers, carbs, and volatile oils, all of which are necessary for good health and appropriate development. However, citrus fruits are low in calories, salts, and cholesterol. They find uses in herbal remedies due to their antioxidant, antifungal, anti-cholesterol, anticancer, antiulcer, antidiabetic, antibacterial, and anti-inflammatory characteristics. It is also important to mention that the excessive use of lemon may also be associated with some risks, such as the lowering of sperm count.

Synthesis and Applications of Nanocomposites in Food Packaging Industry: A Review

Manzar Zahra, Tayyaba Jabeen, Hammad Arshad, Muhammad Yasin, Jigar Ali — Tue, 22 Oct 2024 05:09:45 +0000

The current review focuses on the applications of polymer nanocomposites in the packing industry. Nanocomposite fabrication may be carried out through several synthetic techniques based on the type of material required. Basically, it is the composite formation of polymeric matrix and a reinforcing nanofiller. The nanoclay used for the modification of nanocomposites acts as a reinforcement or filler. Montmorillonite (MMT) is the most frequently used clay material to obtain the desired properties of nanocomposite. Clay reinforcement enhances the food packing properties of the material because of its properties as flame retardant, tensile features, barrier properties, and biodegradability. Among bottom-up and top-down techniques, sol-gel synthesis, self-assembly, and polymerization are the most common techniques used for the synthesis of nanocomposites. Nanocomposites derived from bio-polymers make the material biodegradable which, in turn, is one of the most desirable features for their future use. Owing to improved characteristics, clay nanocomposites form a superior class of materials for food packaging, yet much finer dispersion of nanofillers and compatibility may be devised.

Predictive ARIMA Model with a Machine Learning (ML) Approach for COVID-19 Data in Pakistan

Muhammad Ilyas, Shaheen Abbas, Faisal Nawaz — Thu, 26 Sep 2024 10:02:28 +0000

This study is based on the application of an ARIMA (p, d, q) based machine learning (ML) approach to evaluate the dynamics of COVID-19 pandemic. The focus is on estimating epidemic trends and performing diagnostic scrutiny with model fitting. The data including all four waves of the pandemic pertaining to Pakistan, covering all four provinces (Sindh, Punjab, Khyber Pakhtunkhwa, Balochistan, as well as Gilgit Baltistan, Azad Jammu Kashmir, and the capital city Islamabad, collected from February 26, 2020, to September 30, 2021, is analyzed. The ML algorithm is used to optimize the results of ADF, unit root test which ensures the minimum of ACF, and PACF graphs intention of the data series. The results employ the fitted ARIMA models (1, 1, 1) and (1, 1, 7) for the 1st to 4th waves, confirming daily infected cases across the entire dataset of Pakistan. The cumulative trained observations are from the 1st wave (February 26, 2020, to October 20, 2020), 2nd wave (October 21, 2020, to March 16, 2021), 3rd wave (March 17, 2021, to July 10, 2021), and 4th wave (July 11, 2021, to September 30, 2021), with a further 14-day forecast (from October 1 to October 14, 2021). The results show a strong correlation between the trained and predicted values, ranging from 0.8789 to 0.99236. To select predictive model parameters, the model that results in the minimum Bayesian Information Criterion (BIC) value and residuals from the datasets obtained after detaching the unnecessary errors and the 95^% CI for the forecasting error ( ) are calculated. These values would help to decide the best fitted predictive model.

Optimized Spectral Clustering Methods For Potentially Divergent Biological Sequences

Fri, 27 Sep 2024 00:00:00 +0000

Various recent researches in bioinformatics demonstrated that clustering is a very efficient technique for sequence analysis. Spectral clustering is particularly efficient for highly divergent sequences and GMMs (Gaussian Mixture Models) are often able to cluster overlapping groups if given an adequately designed embedding. The current study used spectral embedding and Mixture Models for clustering potentially divergent biological sequences. The research approach resulted in a pipeline consisting of the following four steps. The first step consists of aligning the biological sequences. The pairwise affinity of the sequences is computed in the second step. Then the Laplacian Eigenmap embedding of the data is performed in the third step. Finally, the last step consists of a GMM-based clustering. Improving the quality of the generated clustering and the performance of this approach is directly related to the enhancement of each one of these four steps. The main contribution is proposing four GMM-based algorithms for automatically selecting the optimal number of clusters and optimizing the clustering quality. A clustering quality assessment method, based on phylogenetic trees, is also proposed. Moreover, a performance study and analysis have been conducted while testing different clustering methods and GMM implementations. Experimental results demonstrated the superiority of using the BIC (Bayesian Information Criterion) for selecting the optimal GMM configuration. Significant processing speed improvements were also recorded for the implementation of the proposed algorithms.

Comparison of Adams-Bashforth-Moulton and Dormand-Prince Methods in Lengyel-Epstein Reaction Model Forming Zinc Oxide Nanostructures

Kaniz Fatima, Basit Ali, Sarwat Ishaque, Humaira Ahmed — Fri, 27 Sep 2024 00:00:00 +0000

This study adopts a numerical approach in the Lengyel-Epstein reaction model for forming zinc oxide (ZnO) nanostructures. It aims to determine the optimal approximation technique to analyze the growth of ion concentrations in ZnO nanostructures. For this purpose, ordinary differential equations are developed using the Dormand-Prince method in the Lengyel-Epstein reaction model. The results obtained from this technique are compared with the results obtained from the Adams-Bashforth-Moulton (ABM) method. After a comparative analysis of both methods, the results showed that the ABM method performs better than the Dormand-Prince method. The accuracy and stability of the ABM method are higher than those of the Dormand-Prince method. Furthermore, error analysis for both methods confirms that the results obtained from the former are more optimized. Moreover, this method also validates the results obtained from the experimental procedure by using the aqueous chemical growth (ACG) method to form the nanostructures of ZnO.

Computing Zagreb Connection Indices for the Cartesian Product of Path and Cycle Graphs

Aiman Arshad, Aneeta Afzal — Thu, 26 Sep 2024 00:00:00 +0000

Topological indices ( ) are a class of graph-based descriptors widely used in chemiformatics and Quantitative Structure-Activity Relationship (QSAR) studies. TIs capture key structural features of molecules by encoding graph-theoretic information, offering a quantitative representation of molecular topology. Each mathematical network is associated with a specific numerical value determined by a function. Among the Zagreb connection indices have been extensively researched. This study delves into the Cartesian product of cycle graphs with path graphs, elucidating the comprehensive implications of . These indices encompass the first , second , and third . Furthermore, comprehensive results of the modified first , modified second , and modified third are presented, along with the first multiplicative ZCI second multiplicative ZCI (SMZCI), and third multiplicative ZCI Moreover, modified first multiplicative ZCI ), modified second multiplicative ZCI and modified third multiplicative ZCI ( are also calculated. To provide precision, both the graphical and numerical analyses of the computed findings are aligned for the two Cartesian products.

The foundation for mathematically modeling complex networks and chemical structures lies in graph theory. Topological indices ( ) are a class of graph-based descriptors widely used in chemiformatics and quantitative structure-activity relationship (QSAR) studies. TIs capture key structural features of molecules by encoding graph-theoretic information, offering a quantitative representation of molecular topology. Each mathematical network is associated with a specific numerical value determined by a topological index function. Among these the Zagreb connection indices are extensively researched TIs. This article delves into the Cartesian product of cycle graphs with path graphs, elucidating the comprehensive implications of . These indices encompass the first, Second and third. Furthermore, we present the comprehensive results of the modified , modified and modified , and also present the first multiplicative Zagreb connection index , and , along with their modified counterparts like modified first multiplicative Zagreb connection index , and . These analysis encompass two distinct types of graphs: cycle graphs and path graphs. To provide further precision, we align both the graphical and numerical analysis of our computed findings for these two Cartesian products.

The Impact of Various Organic Manures on the Germination, Development, and Growth of Okra (Abelmoschus esculentus L.)

Fri, 27 Sep 2024 00:00:00 +0000

The Department of Horticulture at Sindh Agriculture University, Tandojam conducted the pots experiment during the 2018-19 growing season to evaluate the germination, growth, and development of okra using different organic manures. The study involved two varieties, namely Sabzpuri and Resham. Various plant characteristics, such as leaf count, germination index, fresh root biomass, root length, days until first flowers, and plant height were measured. The results showed significant differences across all metrics when comparing treatments with different organic manures. For okra plants grown with M1 (canal sediment + soil), the results were 39.16% with a germination index of 1.74, 6.98 leaves per plant, plant height of 25.05 cm, 58.16 days to flowering, 0.62 g fresh root biomass, 0.19 g dry root biomass, and root length of 22.67 cm. For okra plants grown with M2 (canal sediment + soil + sheep manure), the results were 75.0% with a germination index of 3.22, 9.83 leaves per plant, plant height of 28.83 cm, 41.83 days to flowering, 1.96 g fresh root biomass, 0.36 g dry root biomass, and root length of 32.24 cm. Okra plants grown with M3 (canal sediment+ soil+ fish meal) yielded 1.49 g of fresh root biomass, 0.27 g of dry root biomass, 16.05 leaves per plant, and attained a height of 30.83 cm. In comparison, okra plants grown with M4 (canal sediment+ soil+ bone meal) had a seed germination rate of 59.16%, yielded 12.77 leaves per plant, attained a height of 30.39 cm, and began flowering in 52.83 days. To conclude, M2 (canal sediment + soil + sheep manure) resulted in the most robust growth of okra, outperforming the Resham variety in terms of growth and development.