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Mathematical Modeling of Concrete Fracture Energy of Notched Specimens Using Experimental Evidence
The tensile stiffness of concrete is an important parameter for crack initiation. The microcrack initiation and propagation regulate the stressstrain behavior and the failure mode of concrete. Therefore, fundamental awareness of the fracture mechanism in terms of fracture energy is a requisite to comprehend concrete behavior. There is research consensus that fracture energy alone does not suffice to characterize the ductility/brittleness and also the size dependency of concrete. Therefore, it is necessary to evaluate the fracture energy and the characteristic length for a realistic assessment of the fracture behavior of concrete. Towards this objective, this study examined the fracture energy of concrete by experimentation, and the fracture energy proposed by various models in the literature. Further, the characteristic length proposed by Hillerborg which depicted both the material influence and the size effect, has been computed. Based on the RILEM50 FM recommendations, 18 specimens with varying grades of concrete and notch depths have been tested and the fracture energy parameters have been evaluated. Also, two regression models with key fracture parameters as variables for two-notch ratios, have been formulated for the concrete fracture energy. The arguments have been supported by experimental evidence. The Author(s), under exclusive licence to Shiraz University 2024. -
Interaction of Generational Differences with Gender and Residential Nature in Attitudes Toward Interfaith Marriages
The present study examined the interaction effects of generations, gender, and residential nature on attitudes toward interfaith marriage in a sample of 1190 Indian participants from iGen, Xennials and Millennials, and Baby Boomers generations. Data were collected using a socio-demographic response sheet and the Attitude Scale, with lower ratings indicating positive attitudes and higher ratings indicating negative attitudes. The results of this study demonstrated that generational differences are significantly associated with gender and residential nature. There was a significant interaction between generation and gender and generation and residential nature on attitudes toward interfaith marriages. 2024 Taylor & Francis Group, LLC. -
The Role of Gratitude as a Moderator of the Relationship Between Belief in a Just World and Forgiveness Among Middle-Aged Adults in India
This research explores the relationship between personal belief in a just world (PBJW), gratitude, and forgiveness within the context of middle-aged adults in India. While prior research has established links between PBJW and forgiveness, this investigation delves deeper, examining how gratitude moderates these relationships. The primary objective is to unveil how gratitude moderates the connection between PBJW and forgiveness, filling a significant research gap within the Indian context. The researchers collected data from 386 middle-aged Indian adults through online and offline surveys. The study reveals a positive but weak correlation between PBJW and forgiveness. Gratitude significantly moderates this relationship, amplifying the impact of PBJW on forgiveness. These discoveries offer fresh insights into the complex dynamics underlying forgiveness processes among middle-aged adults in India, addressing a critical gap in the existing research landscape within this cultural context. Practical implications are drawn for counselors and formators that support efforts to promote forgiveness and enhance interpersonal harmony and psychological health. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Lyrics of longing: Exploring the role of music in the lived experience of homesickness among college students
The study investigates the multifaceted role of music during homesickness among first-year college students in India. As compared to other mental health outcomes, homesickness is a relatively understudied phenomenon, yet noteworthy due to its direct association with depression and anxiety. Although empirical evidence about music highlights its therapeutic potential for managing stress and anxiety, few studies have explored its role in connection with homesickness. The data for this study were collected through semi-structured interviews with 10 students about their perception of using music during homesickness. Through interpretative phenomenological analysis, the emerging themes pointed to a mixed influence, highlighting the bittersweet nature of music during homesickness. While music validates feelings and boosts confidence and motivation, it also triggers restorative nostalgia and serves as an escape from confronting homesickness. Moreover, native songs fostered an appreciation for ones culture and helped students connect with their roots. The study contributes to understanding how music is a versatile tool for students dealing with homesickness, offering solace and potential challenges. It serves as a guide to future intervention studies that could explore musics long-term influences. Recognising the diverse ways students perceive and respond to music provides valuable insights for developing tailored interventions and support systems. The Author(s) 2024. -
Further Results on the 3-Consecutive Vertex Coloring Number of Certain Graphs
A 3-consecutive vertex coloring is an assignment of colors on vertices of a graph G such that for any 3-consecutive vertices a, b and c, the color of b is the same as the color of a or c. ? 3c(G) denotes the maximum number of colors that can be used to 3-consecutive vertex color a graph G. The main aim of this article is to give the value of ? 3c(G) for some particular types of graphs, which includes: necklace graphs; the Cartesian product of two paths, a cycle and a path, and two cycles; the corona product of a path and a clique; Mobius Ladder graphs; the 3rd edge line graph; triangular snake graphs, double triangular snake graphs, triple triangular snake graphs, quadrilateral snake graphs and the alternative versions of them; Hanoi graphs; Sun graphs; Barbel graphs; the n-pan graph. The objective of this article is to explore some important results on ? 3c(G). 2024 The Authors. -
Zeolite Framework-Anchored Carbon-Doped White Graphene as Antipoisoning Cathode Materials for Proton-Exchange Membrane Fuel Cells
Efficient, robust, and highly sustainable platinum (Pt)-free electrocatalysts are pivotal for advancing the fuel cell (FC) performance. This study introduces a facile and green approach for synthesizing a rationally designed Co-based zeolite imidazole framework (ZIF) anchored onto carbon (C)-doped white graphene (C-WG) as an electrocatalyst (Z@-C-WG) for the oxygen reduction reaction (ORR). The synergistic effects between the ZIF and C-WG yield an electrocatalyst with enriched active sites. The intrinsic dual active sites coupled with favorable physicochemical properties promote oxygen adsorption and enhance the mass transfer rate. The hybrid catalyst demonstrates significantly improved activity, stability, and poisoning resistivity compared to Pt/C. The synthesized electrocatalyst exhibits superior ORR activity with an onset potential of Eon ?0.967 V (EonPt/C ?0.94 V) in acidic medium and Eon ?0.931 V (Eon(Pt/C) ?0.919) in alkaline medium. Validation through intrinsic parameters including electrochemical active area (ECSA), active site density (ASD), mass activity (MA), and turnover frequency (TOF) corroborates the catalysts enhanced performance. The stability tested for over 35 h coupled with high methanol tolerance affirms the catalysts robust activity. The Z@-C-WG electrocatalyst surpasses Pt/C in resisting poisoning species (CO and KSCN); also, poststripping analysis strongly confirms the presence of abundant active centers. Overall, this study offers a unique perspective toward the engineering of ORR catalyst architecture for fuel cell cathode applications. 2024 American Chemical Society. -
ASSESSMENT OF WATER QUALITY IMPACTS ON CROP PRODUCTIVITY IN SALINE SOILS: INTEGRATING HYDRO CHEMICAL ANALYSIS AND CROP PERFORMANCE
This study aims to address the effect of water quality on crop productivity on saline soils. Water quality parameters to be studied include salinity level, pH, oxygen concentration, nutrient, and heavy metal level. The study will particularly focus on irrigation water sources that are situated in regions characterised by saline soils. Moreover, there are intended growth experiments for the evaluation of various specific crops such as rice, wheat or maize with different levels of water quality. The study will incorporate sophisticated analytical tools including ion chromatography, atomic absorption spectroscopy (AAS), and electric field mapping (EMI) to shed light on the current water quality and soil conditions of the selected area. The experiments that we plan to conduct will involve studying the growth parameters, yield, water use efficiency and the percentage of uptake of nutrients under several water quality scenarios. The data collected in this work will ascertain the link between the values of water quality parameters, soil salinity extent and productivity of crops and will provide the basis for the creation of innovative saline soil agriculture irrigation management practices. 2024, Scibulcom Ltd.. All rights reserved. -
A Legal Analysis of Cyber-Enabled Wildlife Offences in India: A Qualitative Case Study of Sea Fans (Gorgonia spp.) on YouTube
With the advent of the Internet, offences against threatened species have transitioned online. Such species are directly or indirectly traded on social media despite being protected under Indian wildlife law. A qualitative case study was undertaken to assess the preparedness of national law and policy in prohibiting such offences. Sixty-three YouTube links on sea fans in the Hindi language were accessed over 8 weeks, and the information generated by both content creators and audiences was gathered and categorized for analysis. The legal provisions were then interpreted and applied to assess the extent to which the parties involved could be held liable. Our investigation shows that of these video links, the content creators directly offered specimens for sale in 15.87% of instances, demonstrated physical possession of wild specimens in 23.81% of these posts, and were involved in both activities in 20.63% of the links, which in our analysis is explicitly prohibited under national law. The remaining 39.68% of video links merely disseminated information on the relevance or usage of species in occult or religious practices, for which no express legal provision currently exists. Certain indirect legal provisions were found to be relevant; however, there were challenges associated with their implementation. Even the liability of a social media company was found to be limited if it can be demonstrated that the company exercised due diligence. Therefore, there is a need to explicitly regulate online content that has the potential to drive an unlawful demand for protected species alongside the imposition of enhanced liability on social media companies. Such measures, coupled with community awareness, can reduce cyber-enabled wildlife offences committed through social media channels. 2024 Taylor & Francis Group, LLC. -
Novel HGDBO: A Hybrid Genetic and Dung Beetle Optimization Algorithm for Microarray Gene Selection and Efficient Cancer Classification; [Nuevo HGDBO: Un Algoritmo Hrido de Optimizaci Genica y de Escarabajos Peloteros para la Selecci de Genes en Microrrays y la Clasificaci Eficiente del Ccer]
Introduction: ovarian cancer ranked as the seventh most common cancer and the eighth leading cause of cancer-related mortality among women globally. Early detection was crucial for improving survival rates, emphasizing the need for better screening techniques and increased awareness. Microarray gene data, containing numerous genes across multiple samples, presented both opportunities and challenges in understanding gene functions and disease pathways. This research focused on reducing feature selection time in large gene expression datasets by applying a hybrid bio-inspired method, HGDBO. The goal was to enhance classification accuracy by optimizing gene subsets for improved gene expression analysis. Method: the study introduced a novel hybrid feature selection method called HGDBO, which combined the Dung Beetle Optimization (DBO) algorithm with the Genetic Algorithm (GA) to improve microarray data analysis. The HGDBO method leveraged the exploratory strengths of DBO and the exploitative capabilities of GA to identify relevant genes for disease classification. Experiments conducted on multiple microarray datasets showed that the hybrid approach offered superior classification performance, stability, and computational efficiency compared to traditional methods. Ovarian cancer classification was performed using Nae Bayes (NB) and Random Forest (RF) algorithms. Results and Discussion: the Random Forest model outperformed the Nae Bayes model across all metrics, achieving higher accuracy (0,96 vs. 0,91), precision (0,95 vs. 0,91), recall (0,97 vs. 0,90), F1 score (0,95 vs. 0,91), and specificity (0,97 vs. 0,86). Conclusions: these results demonstrated the effectiveness of the HGDBO method and the Random Forest classifier in improving the analysis and classification of ovarian cancer using microarray gene data. 2024; Los autores. -
An Efficient Technique for One-Dimensional Fractional Diffusion Equation Model for Cancer Tumor
This study intends to examine the analytical solutions to the resulting one-dimensional differential equation of a cancer tumor model in the frame of time-fractional order with the Caputo-fractional operator employing a highly efficient methodology called the q-homotopy analysis transform method. So, the preferred approach effectively found the analytic series solution of the proposed model. The procured outcomes of the present framework demonstrated that this method is authentic for obtaining solutions to a time-fractional-order cancer model. The results achieved graphically specify that the concerned paradigm is dependent on arbitrary order and parameters and also disclose the competence of the proposed algorithm. 2024 The Authors. -
Ergos: redefining storage infrastructure and market access for small farmers in India
Learning outcomes: After completion of the case study, students will be able to analyse the path of the entrepreneurship from idea generation to market development to scaling up business, examine the impact of start-ups like Ergos on Indias agriculture value chain, discuss the challenges faced by tech entrepreneurs in growing a business, identify problems solved by Grain Bank Model and evaluate digitisation of farmings custodial services such as warehousing, market linkages and loans. Case overview/synopsis: The case study discusses how founders of Ergos, India-based leading digital AgriTech start-up, Kishor Kumar Jha and Praveen Kumar, started one of the unique models in the AgriTech landscape in India. After noticing the grim condition of small and marginal farmers in Bihar, India. Kishor and Praveen decided to put their banking and corporate experience to use in the farming sector. Ergos aimed to empower farmers by providing them with a choice on when, how much quantity, and at what price they should sell their farm produce, thus maximising their income. As a result, Ergos launched the grain bank model, which provided farmers with doorstep access of end-to-end post-harvest supply chain solutions by leveraging a robust technology platform to ensure seamless service delivery. Ergos faced many challenges in its journey related to financing, marketing and distribution. Amidst these developments, it remained to be seen how Kishor and Praveen would be able to realise their goal to serve over two million farmers across India by 2025 and create a sustainable income for them through its GrainBank Platform. Complexity academic level: This case study was written for use in teaching graduate and postgraduate management courses in entrepreneurship and business strategy. Supplementary materials: Teaching notes are available for educators only. Subject code: CSS 3: Entrepreneurship 2024, Emerald Publishing Limited. -
Bivariate iterated FarlieGumbelMorgenstern stressstrength reliability model for Rayleigh margins: Properties and estimation
In this paper, we propose bivariate iterated FarlieGumbelMorgenstern (FGM) due to[Huang and Kotz (1984). Correlation structure in iterated Farlie-Gumbel-Morgenstern distributions. Biometrika 71(3), 633636. https://doi.org/10.2307/2336577] with Rayleigh marginals. The dependence stressstrength reliability function is derived with its important reliability characteristics. Estimates of dependence reliability parameters are obtained. We analyse the effects of dependence parameters on the reliability function. We found that the upper bound of the positive correlation coefficient is attaining to 0.41 under a single iteration with Rayleigh marginals. A comprehensive comparison between classical FGM with iterated FGM copulas is graphically examined to assess the over or under estimation of reliability with respect to ? and ?. We propose a two-phase estimation procedure for estimating the reliability parameters. A Monte-Carlo simulation study is conducted to assess the finite sample behaviour of the proposed reliability estimators. Finally, the proposed estimators are examined and validated with real data sets. 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
A stakeholder theory approach to analysing strategies for improving pandemic vaccine supply chain performance
This study aims to formulate strategies that impact the vaccine supply chain (VSC). This study measures the VSC performance using the proposed strategy concerning stakeholders theory. From the literature review and experts consent, the strategies are classified into six broad strategies as-VSC traceability, VSC visibility, VSC velocity, digitalising VSC, localising VSC, and vaccine inventory. A questionnaire is developed for surveying healthcare organisations and hospitals. All six proposed hypotheses got accepted. The developed model satisfies all the model fit parameters. Strategies like VSC traceability, VSC visibility, VSC velocity, digitalising VSC, localising VSC, and vaccine inventory have positively impacted vaccine supply chain performance. This research will be helpful for healthcare professionals and organisations for the faster delivery of the vaccine. This research will also help policymakers in improving the performance of VSC. This study is also the first to use the stakeholder theory approach for measuring VSC performance. Copyright 2024 Inderscience Enterprises Ltd. -
Is carbon neutrality a reality for India?
India, the third-largest carbon dioxide emitter in the world, aims to achieve zero emissions by 2070. India is committed to its Panchamrit and has launched various initiatives such as green bonds, carbon credits, carbon market, investing in green hydrogen, etc. However, given the present scenario with respect to the dependency on coal-based power generation and lack of green financing, the present article assesses the different solutions and their practicality in achieving carbon neutrality. (2024), (Indian Academy of Sciences). All rights reserved. -
EVALUATION OF THE ENVIRONMENTAL EFFECTS OF MEDICAL WASTE AND ITS INCREASE AFTER COVID-19 PANDEMIC
Medical waste is a special course of harmful contaminants. Improper treatment would cause tributary environmental pollution, expressly when countering to communal health tragedies. However, there are quite few explores on the peer group of medical waste, and there is a deficiency of basic considerate of its spatial-temporal heterogeneity. The purpose of this study is to conduct a systematic estimation of the effectiveness of these incongruous discarding procedures in expressions of water eminence and wellbeing. The research is centred on municipal areas characterised by vital medical waste production, which has the probable to taint groundwater and water sources. A complex approach is exploited in the procedure, which comprises of water sample collection, laboratory analysis, field surveys, and GIS-based spatial mapping. Medical waste disposal hotspots, such as healthcare facilities, waste collection points, and disposal sites, will be acknowledged through field surveys. Inspects will be showed on water samples poised from a variability of sources, including lakes, rivers, and groundwater wells, to find pathogens, medical residues, heavy metals, and organic pollutants, which are all gauges of medical waste contamination. The test centre analysis will utilise chic policies to portion the deliberation of pollutants in water samples, thereby gauging the likely hazards they pose to marine ecosystems and human health. Longitudinal visualisation of uncleanness distribution through GIS-based mapping facilitates the credentials of vulnerable areas and potential pathways for pollutant transport. The findings of this research will offer significant helps to our understanding of the extent of environmental deterioration resulting from the inadequate disposal of medical refuse into urban water sources. The results of this study will provide valuable insights for the creation of alertness campaigns, regulatory frameworks, and mitigation strategies that are operative in talking this urgent environmental concern and shielding the truthfulness of water in municipal regions. 2024, Scibulcom Ltd.. All rights reserved. -
Time Efficient Hash Key Generation for Blockchain Enabled Framework
Blockchain, in general, helps organizations to improve the transparency and governance by removing its shortfalls and building better control overall. Blockchain network, public or private, is a competent technology when used in order with an optimized hashing technique. In a blockchain network, one of the common issues is performance while registering any transactions. Blockchain must need to do some preliminary checks to avoid double-spending before registering the transaction. Here, we implement one of the optimization aspects of the hashing technique, which can contribute to the blockchain mining processes and save time. It enables the blockchain to perform efficiently and reliably. In addition, we examine how well different hashing algorithms perform when added to the blockchain network's processes. In this research, we analyze several hashing techniques that are employed in the blockchain and are also applied in the supply chain domain due to their efficacy in mitigating past attacks. Our proposed hashing technique allows a blockchain network to improve security and its overall processes. The proposed hashing technique achieves approx. 10-90% performance gain improvements over other existing technique. Our proposed hashing technique allows a blockchain network to improve security and its overall processes. The study also examines how the supply chain management contributes in increasing of overall lead time where process optimization or technological enhancement plays key roles in minimizing the time of some or all the processes. Lead time is one of the common issues of supply chain which impacts on overall order delivery time. We address on how the conjunction of blockchain with optimized hashing technique can address supply chain lead time issues. 2013 IEEE. -
Influence of non-linear thermal radiation on the dynamics of homogeneous and heterogeneous chemical reactions between the cone and the disk
Purpose: The current work presents a theoretical framework to boost heat transmission in a ternary hybrid nanofluid with homogeneous and heterogeneous reactions in the conical gap between the cone and disk apparatus. Furthermore, the impacts of non-linear thermal radiation on the ternary hybrid nanofluid composed of white graphene, diamond, and titanium dioxide dispersed in water are analyzed. Originality/value: The combination of cone and disk systems is crucial for designing efficient heat exchange devices in the field of biomedical science for various purposes. For instance, in medical devices, the cone-disk apparatus is used to study the flow and heat transfer characteristics for better design and functionality. Hence, a sincere attempt has been made to study the impact of homogeneous and heterogeneous reactions on the nanofluid flow between the cone and disk in the presence of non-linear thermal radiation. Design/methodology/approach: The mathematical model's governing equations are partial differential equations (PDEs) which are then transformed into non-linear ordinary differential equations through appropriate similarity transformations. These transformed resultant equations are approximated by the Runge-Kutta-Fehlberg fourth/fifth order (RKF45) technique. The influence of essential aspects on the flow field, heat, and mass transfer rates was analyzed using a graphical representation. Findings: The interesting part of this research is to discuss the power of parameters in three cases, namely, (1) rotating cone/disk, (2) rotating cone/stationary disk, and (3) stationary cone/rotating disk. Furthermore, the thermal variation of the fluid is analyzed by an artificial neural network with the help of the Levenberg-Marquardt backpropagation algorithm. The regression analysis, mean square error, and error histogram of the neural network are analyzed using this algorithm. From the graph, it is perceived that the flow field climbed up significantly with an increase in the values of radiation parameters in all cases. Also, it is noticed that temperature upsurges significantly by upward values of solid volume fraction of the nanoparticles (?). 2024 the author(s), published by De Gruyter. -
AI-Based Feature Extraction Approaches for Dual Modalities of Autism Spectrum Disorder Neuroimages
High-dimensional data, lower detection accuracy, susceptibility to manual errors, and the requirement of clinical experts are some drawbacks of conventional classification models available for Autism Spectrum Disorder (ASD) detection. To address these challenges and explore the affiliated information from advanced imaging modalities such as Magnetic Resonance Imaging (MRI) in structural MRI (sMRI) and resting state-functional MRI (rs-fMRI), the study applied an Artificial Intelligence (AI) approach. In this context, AI is used to automate the feature extraction process, which is crucial in the interpretation of medical images for diagnosis. The work aims to apply AI-based techniques to extract the features and identify the impact of each feature in the Autism diagnosis. The morphometric features were extracted using sMRI images and rs-fMRI scans were employed to fetch functional connectivity features. Surface-based, region-based, and seed-based analyses are performed for the whole brain, followed by feature selection techniques such as Recursive Feature Elimination (RFE) with correlation, Principal Component Analysis (PCA), Independent Component Analysis (ICA), and graph theory are implemented to extract and distinguish features. The effectiveness of the extracted features was measured as classification accuracy. Support Vector Machine (SVM) with RFE is the best classification model, with 88.67% accuracy for high-dimensional data. SVM is a supervised learning model that outperforms other classification models due to its capability to handle high-dimensional data with a larger feature set. Medical imaging modalities provide detailed insights and visual differences related to various cognitive conditions that must be recognized accurately for efficient diagnosis. The study presented an empirical analysis of various Feature extraction approaches and the significance of the extracted features in high-dimensional data scenarios for Autism classification. 2024 Meenakshi Malviya Chandra J and Nagendra N. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
Unveiling the therapeutic potential of azopyridine derivatives for trypsin inhibition: a DFT and In-Vitro approach
Heterocyclic azo derivatives have emerged as promising scaffolds for drug development. This study focused on the synthesis, computational analysis, and biological evaluation of a series of azopyridine derivatives (1a, 1d, 1 g, 1 h, 1 m, 1p, and 1s) as potential trypsin inhibitors. Density Functional Theory calculations indicated that derivative 1 h exhibited the lowest HOMO-LUMO energy gap 3.167 eV and was characterised as a soft molecule, suggesting strong binding capabilities. Molecular docking studies confirmed that 1 h binds favourably to the active site of trypsin with a glide score of ?6.581 kcal/mol and binding energy of ?29.95 kcal/mol. Along with docking studies, the stability of the trypsin-1 h complex was further analyzed using molecular dynamic simulations at 200 ns. The results showed that the ligand molecule 1 h bound strongly at the active site of trypsin. In-vitro enzyme assays determined the IC50 value of the molecule as 100 M, demonstrating enhanced potency. These results indicate that AzPy derivatives, particularly 1 h, hold considerable promise as therapeutic agents for inflammatory disorders and cancer, paving the way for further exploration in drug development and targeted therapies. Further research is warranted to explore 1hs efficacy, safety, and structure-activity relationships. Highlights: DFT studies were used to classify molecules based on their softness and hardness. Molecular docking, simulation, and in-vitro studies have identified potential anti-trypsin activity of candidate molecules. Experimental and computational calculations were in close agreement. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Saraca asoca (Roxb.) de Wilde, a sacred tree: its nutritional value, elemental composition and anti-nutritional content
The sacred Saraca asoca (Roxb.) de Wilde tree holds significant medicinal value and is utilized in ayurvedic preparations to treat various health conditions. This research investigated the nutritional, elemental and antinutritional properties of S. asoca leaves and flowers. The nutritional qualities of the tree parts were examined using the muffle furnace and micro-Kjeldahl techniques. Titration techniques were used to assess the antinutritional content of plants, whereas EDX (Energy dispersive X-ray) was used to determine the mineral content. Phytochemical analysis revealed the presence of tannins, phenols and flavonoids, along with antioxidant properties that could neutralize free radicals generated by metabolic processes in the body. Nutritional analysis indicated that the floral parts of S. asoca had higher moisture, carbohydrate and crude fat content than the leaves. Conversely, the leaves had elevated ash levels, crude fiber and protein. Leaf samples showed higher concentrations of minerals like calcium, phosphorus, sodium, iodine, iron and manganese compared to the floral samples. In contrast, flower samples exhibited higher potassium, copper, silicon and zinc levels. These findings highlight the rich nutritional profile, abundant phytochemicals and essential minerals in both tree parts, with low anti-nutrient content. This information could be instrumental in developing phytopharmaceuticals and nutritious food products. Additionally, utilizing these tree parts could offer a cost-effective way to enhance nutrient intake and address nutritional deficiencies in humans and animals. Copyright: The Author(s).