Minimal Processing Research Articles

Optimizing thermoelectric performance of BiSbPbTe thin films through controlled post-annealing: A comprehensive structural and electronic study

This study investigates the structural and electronic properties of BiSbPbTe thin films, focusing on the impact of post-annealing time and temperature. The X-ray diffraction (XRD) analysis reveals a stable rhombohedral phase formation in all samples, with no secondary phases detected within the detection limit. The high crystallinity of the films is confirmed, and post-annealing leads to an increase in peak intensity, indicating enhanced crystalline structure. Surface morphology analysis using scanning electron microscopy (SEM) demonstrates a transition from inhomogeneous features in as-grown films to homogeneous and dense formations in post-annealed films. The grain size increases with post-annealing time, attributed to diffusion, energy minimization, nucleation, growth, and thermal activation processes. The electronic transport properties, including carrier concentration and conductivity, are influenced by post-annealing temperature and duration. A stable carrier concentration is observed for films post-annealed for up to 2 h, while longer durations decrease due to reduced defect vacancies and grain boundaries. The increase in charge carrier mobility, confirmed by SEM results, contributes to enhanced electrical conductivity. The Seebeck coefficient shows an initial enhancement up to 2 h post-annealing, attributed to defect creation and increased grain boundaries. However, further increases in post-annealing duration result in a decrease in the Seebeck coefficient due to enhanced charge carrier mobility and reduced grain boundaries. The thermoelectric power factor (4.4 μW/cm1K2), determined by the square of the Seebeck coefficient and electrical conductivity, reaches its maximum value in the 2-h post-annealed sample, emphasizing the importance of optimizing post-annealing conditions for improved thermoelectric performance.

Evaluation of Novel Food Additives on Product Acceptability in France

Purpose: The aim of the study was to analyze the evaluation of novel food additives on product acceptability in France. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: This study evaluating novel food additives on product acceptability in France, it was found that natural and organic additives significantly enhanced consumer acceptance. The additives were primarily valued for their perceived health benefits and minimal processing. Taste testing revealed that products with natural flavors and colors were preferred over those with synthetic counterparts. However, there was resistance among certain demographics, particularly older consumers, who showed a preference for traditional flavors. Overall, the study suggests that while novel additives can increase acceptability, consumer education and transparent labeling are crucial for widespread adoption. Unique Contribution to Theory, Practice and Policy: Theory of planned behavior (TPB), diffusion of innovations theory & expectancy-value theory may be used to anchor future studies on analyze the evaluation of novel food additives on product acceptability in France. Implement educational programs that inform consumers about the benefits and safety of novel food additives. Advocate for the development of clearer, more consistent regulatory guidelines for novel food additives to ensure safety and quality across markets.

Module lattice based post quantum secure blockchain empowered vehicle to vehicle communication in the internet of vehicles

The importance of vehicle-to-vehicle (V2V) communication is increasing due to high rate of accidents and advancements in information exchange system. The V2V communication facilitates vehicles to exchange real-time informations about their surrounding, such as nearby vehicle, pedestrian, and road hazard. These type of informations are very helpful to detect potential collision, warn driver of imminent danger, and take advanced measure to avoid accident. Both security and privacy of user participating in the vehicular networking system depends upon reliable data transmission system. The suggested approaches must have minimal processing complexity and be unaffected by latency problems due to the scalability of V2V connection. This paper contains module lattice based quantum safe blockchain empowered authentication framework for autonomous vehicle communication system. This framework uses blockchain that is always contains multiple nodes, then we need an aggregation technique to provide authenticity to the system. The proposed framework provides authenticity to the system based on two assumptions, (1) Module Learning With Error and (2) Module Short Integer Solution. The proposed framework is quantum safe, provides high latency and throughput.

Valorization of waste seashells for neutralization of sunflower oil

In this study, an investigation was conducted to explore the potential of utilizing calcium oxide (CaO) derived from carbonized seashells for the purpose of acid removal. Consequently, the objective of this research is to develop an alternative bio-material to be employed in the neutralization process. The CaO produced was compared to the conventional utilization of caustic (NaOH) based on various parameters related to oil quality. It was observed that the removal rates of free fatty acid content using CaO and NaOH were 61% and 85% respectively. Furthermore, it was determined that the use of CaO did not result any adverse effects on the composition of sterols and fatty acids during the neutralization process. On the contrary, the preservation of total sterol content was superior in comparison to NaOH. The neutralization loss observed with CaO was comparable to that of NaOH. Consequently, CaO demonstrates promise as an alkaline substitute for acid removal in vegetable oils. Additionally, it is well-suited for implementation under minimal process conditions, which have become increasingly popular in recent years.

Novel applications of Convolutional Neural Networks in the age of Transformers.

Convolutional Neural Networks (CNNs) have been central to the Deep Learning revolution and played a key role in initiating the new age of Artificial Intelligence. However, in recent years newer architectures such as Transformers have dominated both research and practical applications. While CNNs still play critical roles in many of the newer developments such as Generative AI, they are far from being thoroughly understood and utilised to their full potential. Here we show that CNNs can recognise patterns in images with scattered pixels and can be used to analyse complex datasets by transforming them into pseudo images with minimal processing for any high dimensional dataset, representing a more general approach to the application of CNNs to datasets such as in molecular biology, text, and speech. We introduce a pipeline called DeepMapper, which allows analysis of very high dimensional datasets without intermediate filtering and dimension reduction, thus preserving the full texture of the data, enabling detection of small variations normally deemed 'noise'. We demonstrate that DeepMapper can identify very small perturbations in large datasets with mostly random variables, and that it is superior in speed and on par in accuracy to prior work in processing large datasets with large numbers of features.

An Efficient Feature Selection and Extraction using Metaheuristic Technique for Diabetic Retinopathy

Diabetic retinopathy (DR) is associated with diabetes, which causes harm to the retina as a result of persistent elevated blood sugar levels, resulting in symptoms such as impaired vision. Regular eye examinations are crucial for the timely identification of any issues. The process of selecting characteristics in DR involves optimizing important features using a combination of Hybridized Weight-Optimized Particle Swarm Optimization and Whale Optimization Algorithm (HWOPSO-WOA). This leads to an enhancement in the accuracy of detection, which is crucial for appropriate diagnosis and therapy. Pre-processing encompasses the utilization of linear filters such as Sobel and Prewitt for image manipulation, mean filters for the reduction of noise, and Gaussian filters for the purpose of smoothing. Feature selection employs an objective function that considers significant performance measure, which utilizes binary encoding and conducts fitness evaluation. The methodology is utilized for the analysis of DR utilizing the Indian Diabetic Retinopathy Image Dataset. The results comprise convergence graphs, and a comparative analysis, which emphasize the greater accuracy and efficiency of the Proposed technique. Visual representations, such as fundus images with selected features, highlight the importance of the chosen features in detecting DR. The proposed HWOPSO-WOA achieves highest accuracy of 97.3% and minimal processing time 98.45seconds that outperforms the state-of-art techniques.

Combined Sous-Vide and High Hydrostatic Pressure Treatment of Pork: Is the Order of Application Decisive When Using Minimal Processing Technologies?

The aim of using minimal food processing technologies is to preserve the raw material or to achieve a special technological goal with the least possible impact. When several technologies are used together, the intensity of each treatment can be reduced according to Leistner’s hurdle principle. Does the order of application of the treatments result in a detectable difference? This research focuses on the effect of the combination of the sous-vide technology and the high hydrostatic pressure (HHP) treatment. The effect of the pressure level (300 and 600 MPa) and the influence of the treatment order was investigated on pork (longissimus thoracis and lumborum muscles (LTL)). Physico-chemical and microbiological measurements were carried out on day 0 and after 21-day storage at 2 °C and 8 °C. Significant differences were found for both the order of treatment and pressure level in weight loss (p < 0.001), CIELab color parameters a* and b* (p < 0.001), and denaturation enthalpy (p < 0.01). The texture (p < 0.001) and lipid oxidation TBARS values (p < 0.05) were only influenced by the pressure level. In the challenge test, the initial count of 105 CFU/g Listeria monocytogenes dropped below detection limit in all cases. Total aerobic and anaerobic viable numbers were below/near the detection limit in all combined-treated samples on day 0 and showed only slight or more notable growth after 21-day storage at 2 °C and 8 °C, respectively. An additional 300 MPa pressure treatment can increase the safety of sous-vide cooked pork samples while having only a minor effect on physicochemical properties. The 600 MPa pressure treatment results in a stable, albeit not shelf-stable product, but it also affects a considerable number of quality parameters such as color, texture, weight loss, and TBARS values.

Heterogeneously integrated InGaP/Si waveguides for nonlinear photonics

The heterogeneous integration of III-V semiconductors with the Silicon platform enables the merging of photon sources with Silicon electronics while allowing the use of Silicon mature processing techniques. However, the inherent sufficient quality of III-Vs’ native oxides made imperative the use of deposited interfacial oxide layers or adhesives to permit the bonding. Here we present a novel approach enabling the heterogeneous integration of structured III-V semiconductors on silicates via molecular bonding at 150 °C, much below the CMOS degradation temperature, is presented. The transfer of 235 nm thick and 2 mm long InGaP waveguides with widths of 4.65, 2.6 and 1.22 μm on 4 μm thick Si thermal oxide, with optional SX AR-N 8200.18 cladding, has been experimentally verified. Post-processing of the 1.20 and 0.60 μm input/output tappers has allowed the implementation of double-inverse tapers. The minimal processing requirements and the compatibility with transferring non-cladded structures of the presented technique are demonstrated. The quality of the transferred waveguides bonding interface and their viability for non-linear optics applications has been tested by means of the surface contribution to the optical non-linearity via modal phase-matched second-harmonic generation.

An investigation on the effect of ultrasonication and microfiltration processing on the quality of king coconut (Cocos nucifera var. aurantiaca) water compared to minimal and thermal processing.

The study aimed to investigate the effect of thermal and non-thermal processing on the physicochemical, microbial, and sensory characteristics of king coconut water. King coconut water samples were subjected to ultrasonication (50 kHz, 30 min at 35 °C), microfiltration (0.5 µm), and thermal treatments (at 90 °C for 10 min) with sodium metabisulfite (0.1 g/L) except the fresh sample (control). Samples were tested for physiochemical, microbial, and sensory parameters. Storage studies were conducted at 4 °C for 28 days. pH, titratable acidity, and total sugar of all treated samples were within the Sri Lankan Standard (SLS) limit (4.6-5.5, 0.07-0.1%, 4.1-6.5%, respectively) during the 28 days of storage. Sodium metabisulfite addition was significant in lowering the browning index. Antioxidant and phenolic contents of microfiltered and ultrasonicated samples varied between 49%-65% and 2.5-2.8 GAE mg/100 mL, respectively, during 4 weeks of storage, which was significantly higher compared to the heat-treated samples. Sensory evaluation scored the lowest attribute values for thermally treated samples. Microbial analyses indicated that microfiltered and ultrasonicated king coconut water remained safe for consumption for up to 4 weeks. Ultrasound and microfiltration, with the integration of sodium metabisulfite, were identified as effective methods for processing king coconut water while preserving its wholesome properties.

Test suite optimization under multi-objective constraints for software fault detection and localization: Hybrid optimization based model

Testing and debugging have been the most significant steps of software development since it is tricky for engineers to create error-free software. Software testing takes place after coding with the goal of finding flaws. If errors are found, debugging would be done to identify the source of the errors so that they may be fixed. Detecting as well as locating defects are thus two essential stages in the creation of software. We have created a unique approach with the following two working phases to generate a minimized test suite that is capable of both detecting and localizing faults. In the initial test suite minimization process, the cases were generated and minimized based on the objectives such as D-score and coverage by the utilization of the proposed Blue Monkey Customized Black Widow (BMCBW) algorithm. After this test suite minimization, the fault validation is done which includes the process of fault detection and localization. For this fault validation, we have utilized an improved Long Short-Term Memory (LSTM). At 90% of the learning rate the accuracy of the presented work is 0.97%, 2.20%, 2.52%, 0.97% and 2.81% is better than the other extant models like AOA, COOT, BES, BMO and BWO methods. The results obtained proved that our Blue Monkey Customized Black Widow Optimization-based fault detection and localization approach can provide superior outcomes.

Use of omic heating technology in the process of processing fruits and vegetables. Overview of the subject field

The modern consumer prefers natural products with minimal processing, which allows maximum preservation of the nutritional value of the product and fresh taste and aroma. The purpose of this article is to review the results of studies concerning the effect of ohmic heating on the product in comparison with heat treatment and considering the combined use of this method with non-thermal technologies in the production of canned fruit and vegetables. The material for this study was 105 scientific publications. A search for scientific literature in English and Russian on the use of the ohmic heating technology in the process of processing fruits and vegetables was carried out in the bibliographic databases Scopus, Web of Science, as well as in other sources. The period 2010–2023 was adopted as the time frame for the review of scientific publications. To review the subject field of the study, an algorithm was used in accordance with the PRISMA protocol. A scientific search and review of scientific publications on the research topic showed a limited number of studies assessing the ohmic heating (OH) technology in the processing of fruits and vegetables. In the process of using this technology, electric current is applied, ensuring uniform and rapid release of heat inside the food matrix. OH has a shorter processing time, which helps preserve the organoleptic quality of the product and its nutritional value. Compared to heat treatment, the OH technology not only inactivates microorganisms and food enzymes, but also prevents overheating, which reduces the negative impact of temperature on the chemical composition of the product. Evaporation/concentration, dehydration/drying of fruit juices or purees using OH resulted in higher energy efficiency and reduced processing time compared to traditional processing methods. Despite many advantages, there are limitations to large-scale industrial application of the OH method, including corrosion of the electrodes, which can have a negative impact on the product, as well as uneven heating of some types of fruit and vegetable raw materials during continuous processing. The results of this review can be used in the process of conducting further scientific research, which is advisable to continue in order to determine specific processing parameters and eliminate uneven heating of food raw materials.

Effect of hydrocavitation treatment on physicochemical and functional properties of soy protein isolate

A hydrocavitation reactor was employed to treat diluted samples of soy protein isolate (SPI) at room temperature for 0, 60, and 90-minute intervals, revealing significant improvements in vitro enzymatic digestion, trypsin inhibition, water holding, oil holding, emulsification, and solubility properties. The secondary protein structure underwent significant modifications, with an increase in surface area, etching phenomena, and structural aggregation. The extent of amino acid digestion for hydrocavitation-treated proteins exhibited an augmentation from 180.9 % ± 1.429 μg/mL to 189.5 % ± 0.556 μg/mL. Additionally, the water holding capacity of the treated proteins increased from 3.51 % ± 0.131 to 3.78 % ± 0.097, while there was a corresponding decrease in oil holding capacity. Solubility showed a noteworthy rise from 24.70 % ± 0.042 to 38.23 % ± 0.031 following the hydrocavitation treatment. These findings underscore the potential of hydrocavitation technology as an appealing avenue for non-thermal, chemical-free food processing, enhancing product quality and digestibility through minimal processing techniques.

Improving carbon and nitrogen removal efficiency in high-strength nitrogen wastewater via two-stage nitritation-anammox process

Centralized systems employ biological treatments to remove nutrients, whereas decentralized systems, especially in rural areas, face challenges in implementing them. These systems are low flow but high carbon and nitrogen concentrations, and low C/N ratios. Source-separated wastewater (urine + flush) is an extreme example, like confined animal-feeding wastewater. High nutrient concentrations necessitate innovative technologies typically not employed for decentralized systems. This research demonstrates a simple two-stage nitritation-anammox system to treat high N concentration wastewater (pure urine + flush water). The systems consisted of a Membrane Aerated Biological Reactor (MABR) which performed carbon oxidation and nitritation and novel anammox reactors, (Pancopia AnammoX (PAXs)). The MABRs achieved OC removal over 108–198 g-C/m3-day with ∼ 97% removal, TAN oxidation rates up to 156 g-N/m3-day and nitrite/AN ratios were near 1 with no external control (e.g. pH, DO, cell wasting) other than urine loading rate. The PAXs achieved maximum AN removal efficiencies of 85–97%. The main limitation to complete TN removal in the PAXs was insufficient NO2- in the influent (nonideal influent NO2-/AN ratio). This system was not optimized for energy efficiency and had low volumetric conversion rates compared with other systems. However, it demonstrates that source-separated wastewater can be effectively treated with near complete N removal using a system with minimal process control requirements, lack of solid production, and elimination of diffuse aeration preventing odor generation. These attributes are attractive for applications where conditions do not allow for more complex high-rate systems such as developing societies and rural areas.

Development of an Antioxidant-Rich Sugar-Free Plantain Candy and Assessment of Its Shelf Life in a Flexible Laminate.

Candy is a popular confection worldwide, and it would be beneficial to society if it were converted into a source of antioxidant molecules to eliminate its adverse health effects. The amount of antioxidants available even in fruit candies is questionable due to the high thermal processing losses they undergo and the presence of various food additives. Plantains (Musa paradisiaca) are less known as good sources of biotherapeutic antioxidants, namely l-tryptophan, serotonin and melatonin, and consumption of this highly nutritious fruit is limited to underdeveloped and developing countries. The objectives of this study are: to develop a functional antioxidant-rich sugar-free plantain-based candy with valuable contents of the mentioned biomolecules in synergy; and to ensure its extended shelf life without compromising its physicochemical properties and functionality by wrapping it with a suitable packaging laminate. To accomplish the first objective, lyophilized plantain powder, sorbitol and mannitol were used as base materials with minimal additives under minimal processing conditions to reduce processing loss. Sensory, proximate, physicochemical and phytochemical properties, including the antioxidant synergy among the mentioned biomolecules of the developed candies were evaluated. For the second objective, the candies were enclosed in two different flexible packaging laminates and the optimal packaging was determined based on the microbiological safety and sensory appeal of the wrapped candies. Subsequently, the above-mentioned properties of the packaged (in the most suitable laminate) candies were evaluated at regular time intervals during storage for assessment of their shelf life. The candy had a characteristic flavour of plantain, uniform dark brown colour, rich mouthfeel, pleasant aroma, moderately hard texture and moderate sweetness, along with high antioxidant activity and considerable content of l-tryptophan, serotonin and melatonin (present as a synergistic consortium). During storage of the packaged candy under ambient conditions, it remained microbiologically safe for up to 56 days, and also maintained sensory attributes, antioxidant activity and synergy compared to the control candy. This newly developed semi-hard sugar-free candy with high antioxidant content, containing three important antioxidants, namely l-tryptophan, serotonin and melatonin, could be a good source of biotherapeutic molecules and a substitute for commercial candies consumed globally.

Matched Three-Dimensional Organoids and Two-Dimensional Cell Lines of Metastatic Melanoma to the Brain Mirrors Response to Targeted Molecular Therapy

INTRODUCTION: Metastatic melanoma to the brain (MMB) represents a significant treatment challenge. Limitations in preclinical models, namely the lack of heterogeneity in cell lines and low throughput of xenograft models, have thwarted novel therapeutic development. Surgically eXplanted Organoids (SXOs) are ex vivo, three-dimensional models that recapitulate genotypic and phenotypic features of parent tumors with minimal extracranial processing. We aim to develop the first known matched patient-derived SXO and cell line of BRAF-mutant MMB to investigate responses to targeted therapy. METHODS: MMB SXOs were created by a novel protocol incorporating techniques for creating glioma and cutaneous melanoma organoids. A BRAFV600K-mutant MMB sample was collected and processed within 30 minutes of resection. SXOs were created using a 1.0 mm biopsy punch. Organoids were cultured in low-adherence plates in an optimized culture medium under continuous orbital rotation without an artificial extracellular scaffold. Organoid media was changed every 48 hours. Concurrently, matched patient-derived cell lines were created and passaged weekly. Drug screens were conducted with the CellTiter-Glo Luminescence Viability Assay (cell line) and ReadyProbe Cell Viability Imaging Kit (SXOs). RESULTS: After an initial shedding period, organoid growth was observed within 3-4 weeks. MMB SXOs retained histological phenotypes of the parent tissue, including pleomorphic epithelioid cells with abundant cytoplasm, large nuclei, focal melanin accumulation, strong Sox10 positivity, and HMB45 positivity. After sufficient growth, organoids could be manually parcellated to increase the number of replicates. Matched SXOs and cell lines demonstrated sensitivity to BRAF-V600 and MEK inhibitors. CONCLUSIONS: Here, we outline the creation of the first known scaffold-free organoid model of MMB. Further study using SXOs will improve the translational relevance of preclinical studies and enable the study of the melanoma tumor microenvironment.

Empowering Manets with Advanced Multimodal Biometric Authentication and Encryption

In a mobile ad hoc network (MANET), nodes communicate wirelessly, facing unique challenges. Traditional MANETs suffer from issues like erroneous transmission and vulnerability to unauthorized nodes joining the network, posing security risks. Authentication within MANETs is a significant security concern, prompting ongoing research for enhancements. Our solution integrates multimodal biometric authentication with RSA and AES encryption, providing robust security for user authentication and data protection in MANETs. This approach effectively addresses risks such as unauthorized access and data tampering, crucial for secure communication in dynamic, resource-limited MANET environments. Our proposed system utilizes a combination of face and fingerprint biometrics for encryption, enhancing network security. Through testing, our system demonstrates a high authentication rate of 92.42% with minimal processing times: 0.042 ms for key generation, 0.019 ms for encryption, and 0.032 ms for decryption, based on a 1024-bit key size. These practical results showcase the resilience and efficiency of our secure system.

RATIONAL USE OF SILICATE-CONTAINING WASTE

As a result of the dismantling and demolition of buildings, a considerable number of types of construction waste are generated. The main types of waste include broken bricks, concrete and reinforced concrete products, wood, glass scrap, etc. Waste from the construction industry is cheaper than natural raw materials. Often, they are almost immediately (after minimal processing) suitable for use as substitutes for natural raw materials. In the article, in the course of research, the compositions of raw mixtures containing Portland cement, finely ground broken glass, concrete scrap, and liquid glass for self-leveling floors were identified. Keywords: silicate-containing waste, concrete scrap, cullet, liquid glass, liquid glass, concrete mixtures.

An efficient tool for Parkinson's disease detection and severity grading based on time-frequency and fuzzy features of cumulative gait signals through improved LSTM networks

Parkinson's disease (PD) is a widespread neurodegenerative condition that affects many individuals annually. Early identification and monitoring of disease progression are crucial to effectively managing symptoms and preventing motor complications. This research proposes an automated PD diagnosis and severity-grading model based on time-frequency and fuzzy features using improved uni-directional and bi-directional long short-term memory networks with sensitive hyperparameters optimization. We utilize vertical ground reaction force signals collected from Physionet's publicly available dataset recorded during regular and dual-task clinical trials of walking measurements. Only the cumulative signal of both feet was then utilized and segmented into 30-s windows without further pre-processing. Subsequently, we extracted only four key time-frequency and fuzzy features from each segment, effectively capturing the signal's inherent uncertainty. Bayesian optimization is employed in both detection and grading approaches to fine-tune the two critical hyperparameters: the initial learning rate and the number of hidden units in the network. The detection phase yields an exceptional accuracy of 99.19%, surpassing state-of-the-art studies with the same dataset. In the grading phase, classification based on the unified PD rating scale values achieves an accuracy of 92.28%. The proposed study delves into the potential of cumulative gait signals as a powerful diagnostic tool for PD, aiming to extract precise and intricate information by implementing straightforward and minimal processing endeavors. This method demonstrates significant efficiency in terms of complexity, cost, and energy consumption by utilizing a single-dimensional signal, eliminating the need for pre-processing steps, and limiting the features used for training.

Optimizing precision agriculture: Bayesian-enhanced papaya (Carica papaya L.) fruit disease classification via cubic SVM and ResNet-101 deep features

With the rise of the fruit processing industry, machine learning and image processing have become necessary for quality control and monitoring of fruits. Recently, strong vision-based solutions have emerged in farming industries that make inspections more accurate at a much lower cost. Advanced deep learning methods play a key role in these solutions. In this study, we built an image-based framework that uses the ResNet-101 CNN model to identify different types of papaya fruit diseases with minimal training data and processing power. A case study to identify commonly encountered papaya fruit diseases during harvesting was used to support the results of the suggested methodology. A total of 983 images of both healthy and defective papaya were considered during the experiment. In this study, we initially used the ResNet-101 CNN model for classification and then combined the deep features drawn out from the activation layer (fc1000) of the ResNet-101 CNN along with a multi-class Support Vector Machine (SVM) to classify papaya fruit defect detection. After comparing the performance of both approaches, it was found that Cubic SVM is the best classifier using the deep feature of ResNet-101 CNN, achieved with an accuracy of 99.5% and an area under the curve (AUC) of 1 without any classification error. The findings of this experiment reveal that the ResNet-101 CNN with the cubic SVM model can categorize good, diseased, and defective papaya pictures. Moreover, the suggested model executed the task in a greater way in terms of the F1- Score (0.99), sensitivity (99.50%), and precision (99.71%). The present work not only assists the end user in determining the type of disease but also makes it possible for them to take corrective measures during farming.

Explicit Runge-Kutta Method for Evaluating Ordinary Differential Equations of type v^vi = f(u, v, v′)

The initiative of this paper is to present the Runge Kutta Type technique for the development of mathematical solutions to the problems concerning to ordinary differential equation of order six of structure v vi = f(u, v, v′ ) denoted as RKSD with initial conditions. The three and four stage Runge-Kutta methods with order conditions up to order seven (RKSD7) have been designed to evaluate global and local truncated errors for the ordinary differential equation of order six. The framework and evaluation of equations with their results are well established to obtain the effectiveness of RK method towards implicit function satisfying the required initial conditions and for obtaining zero-stability of RKSD7 in terms of their accuracy with maximum precision under minimal processing.