Specific Spots Research Articles

Accuracy and Limitations of Measuring Corrosion Rates and Depth of Penetration on Corroded Steel Plates Using Dual-Element Ultrasonic Sensors

Monitoring corrosion is a vital step to prevent catastrophic structural failure. With accurate corrosion rate data, the risk of failure can be mitigated by performing routine maintenance on components that have become compromised. When corrosion occurs in an inaccessible area such as the inside of a pipe or back side of an underground storage tank, however, conventional monitoring techniques such as visual inspection are inadequate or impossible. Non-destructive evaluation (NDE) techniques must be used to determine the condition of the structure to maintain its integrity. Ultrasonic (UT) Thickness Sensors were investigated for corrosion monitoring use. The chosen sensors had a resolution of a limit of 25.4μm. A system was designed to utilize Electrochemical Machining (ECM) to simulate a corrosion at various rates in a localized region (approximately 1 cm in diameter) while its thickness was monitored by a permanently attached UT sensor. Multiple techniques such as a 3D profilometer, and depth-gauge were used to validate the measurement of the permanently attached UT sensor. Multiple ECM experiments were conducted using NaCl electrolyte solution at 1, 10, and 100 g/L concentrations. Occasionally, variations in pit formation were observed and likely caused by the orientation of the counter electrode to the steel surface. The smallest and most consistent surface roughness was obtained using the 10 g/L concentration. Larger variations in surface roughness were observed at 1 and 100 g/L NaCl concentrations leading to the selection of 10 g/L solution for experiments. The ECM system rendered reproducible machining rates showing that the UT sensors were capable of monitoring very low corrosion rates averaged to 3μm per day. Discrepancies up to a few mm were sometimes measured between the UT sensor, 3D profilometer and depth-gage values. The discrepancies were attributed to non-uniform corrosion in the vicinity of the permanently attached UT sensor. The sensor therefore provided readings of a specific localized spot of the corroded region, or the profile of the corroded region interfered with the UT signals. The accuracy of UT sensor readings was also studied for various geometrical pit profiles. Certain defect geometries made it impossible for the UT sensor to record accurate measurements. This was also observed on naturally corroded steel plates. Therefore, UT sensors may sometimes provide inaccurate or no reading over certain corrosion profiles and should therefore be used with prudence.

Axes-aligned non-linear optimized PnP algorithm

Pose estimation is an important component of many real-world computer vision systems. Most existing pose estimation algorithms need a large number of point correspondences to accurately determine the pose of an object. Since the number of point correspondences depends on the object’s appearance, lighting and other external conditions, detecting many points may not be feasible. In many real-world applications, the movement of objects is limited, e.g. due to gravity. Hence, detecting objects with only three degrees of freedom is usually sufficient. This allows us to improve the accuracy of pose estimation by changing the underlying equations of the perspective-n-point problem to three variables instead of six. By using the simplified equations, our algorithm is more robust against detection errors with limited point correspondences. In this article, we study three scenarios where such constraints apply. The first one is about parking a vehicle on a specific spot. Here, a stationary camera is detecting the vehicle to assist the driver. The second scenario describes the perspective of a moving camera detecting objects in its environment. This scenario is common for driver assistance systems, autonomous cars or mobile robots. Third, we describe a camera observing objects from a birds-eye view, which occurs in industrial applications. In all three scenarios, observed objects can only move in the ground plane and rotate around the vertical axis. Hence, three degrees of freedom are sufficient to estimate the pose. Experiments with synthetic data and real-world photographs have shown that our algorithm outperforms state-of-the-art pose estimation algorithms. Depending on the scenario, our algorithm is able to achieve 50% better accuracy, while being equally fast.

Immunogenicity and safety of different dose levels of Ad26.RSV.preF/RSV preF protein vaccine in adults aged 60 years and older: A randomized, double-blind, placebo-controlled, phase 2a study

BackgroundRespiratory syncytial virus (RSV) can cause severe illness in older adults. A combination vaccine containing Ad26.RSV.preF and purified recombinant RSV preF protein has previously demonstrated efficacy and tolerability in older adults. We report results of a dose-ranging study to determine immunogenicity and safety of different doses of the Ad26.RSV.preF component in the combined Ad26.RSV.preF/RSV preF protein vaccine to support Ad26.RSV.preF drug product release and stability specifications. MethodsIn this randomized, double-blind, placebo-controlled, phase 2a study, adults aged ≥60 years in good or stable health were randomly assigned within 1 of 3 cohorts to receive either placebo or Ad26.RSV.preF/RSV preF protein, composed of different doses of Ad26.RSV.preF with a fixed dose of RSV preF protein (150 μg). Ad26.RSV.preF doses in Cohort 1 (4 dose-down groups) ranged from 3.7 × 109 to 1.0 × 1011 viral particles (vp). Doses in Cohorts 2 and 3 (2 dose-up groups, each) ranged from 1.0 to 1.6 × 1011 vp. Primary endpoints were immunogenicity (RSV preF protein antibody titers) for Cohort 1 and safety (solicited local and systemic adverse events [AEs] and unsolicited AEs) for Cohorts 2 and 3. Immunogenicity analyses (RSV preF protein antibody titers, RSV A2 neutralizing antibodies, and RSV-F–specific interferon-γ enzyme-linked immunosorbent spot) were performed on the day of vaccination and 14 days, 3 months, and 6 months postvaccination. Safety was monitored from vaccination until study end. ResultsOverall, 454 participants were enrolled and received 1 dose of study vaccine or placebo (Cohort 1, n = 226; Cohort 2, n = 124; Cohort 3, n = 104). No substantial differences in measured immune responses were observed between lower or higher Ad26.RSV.preF doses compared with Ad26.RSV.preF 1.0 × 1011 vp across all postvaccination time points. All Ad26.RSV.preF doses between 3.7 × 109 vp and 1.6 × 1011 vp were well tolerated, with no safety issues identified. ConclusionsResults of this dose-ranging study may be used to inform the refinement of Ad26.RSV.preF drug product release and stability specifications.Clinical Trial Registration:ClinicalTrials.gov Identifier: NCT04453202

Biological synthesis of silver nanoparticles using Senna auriculata flower extract for antibacterial activities.

In this study, biological synthesis of silver nanoparticles (AgNPs) using Senna auriculata flower extract for antibacterial activities was reported. The silver spectra compared to the plant extract show a rightward shift in AgNP peaks, indicating successful nanoparticle formation. The absorption band at 302 nm and the disappearance or shift of other peaks further confirm the synthesis. X-ray diffraction (XRD) analysis reveals that the AgNPs synthesized with S. auriculata extract have an average crystallite size of 25 nm. Transmission electron microscopy (TEM) results exhibited a polydispersed, spherical shape with sizes ranging from 70 nm, in clear contrast to the electron microscope image that showed their spherical shape. When examining the selected area electron diffraction (SAED) image, a specific set of lattice planes was correlated with a specific spot. A histogram of AgNP particle size distribution can be seen. AgNPs were tested against four different strains of bacteria for their antibacterial effectiveness, including gram negative bacteria (Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus), as well as gram positive bacteria (S. aureus, d. Bacillus subtilis), at various concentrations of AgNP. The results of in vitro experiments indicate that AgNPs containing S. auriculata flowers inhibit amylase well. At two concentrations, ~16.03% and ~70.99%, AgNPs inhibit the reaction at low and high concentrations, respectively.

Comprehensive blueprint of Salmonella genomic plasticity identifies hotspots for pathogenicity genes.

Understanding the dynamic evolution of Salmonella is vital for effective bacterial infection management. This study explores the role of the flexible genome, organised in regions of genomic plasticity (RGP), in shaping the pathogenicity of Salmonella lineages. Through comprehensive genomic analysis of 12,244 Salmonella spp. genomes covering 2 species, 6 subspecies, and 46 serovars, we uncover distinct integration patterns of pathogenicity-related gene clusters into RGP, challenging traditional views of gene distribution. These RGP exhibit distinct preferences for specific genomic spots, and the presence or absence of such spots across Salmonella lineages profoundly shapes strain pathogenicity. RGP preferences are guided by conserved flanking genes surrounding integration spots, implicating their involvement in regulatory networks and functional synergies with integrated gene clusters. Additionally, we emphasise the multifaceted contributions of plasmids and prophages to the pathogenicity of diverse Salmonella lineages. Overall, this study provides a comprehensive blueprint of the pathogenicity potential of Salmonella. This unique insight identifies genomic spots in nonpathogenic lineages that hold the potential for harbouring pathogenicity genes, providing a foundation for predicting future adaptations and developing targeted strategies against emerging human pathogenic strains.

Steerable droplet precise bouncing on a superhydrophobic surface with superhydrophilic stripes

The precise rebound of a droplet upon hitting a solid surface has garnered significant attention in recent years due to its critical applications in self-cleaning, printing industries, and the design of heat exchanger surfaces, among others. This study introduces an innovative approach that combines femtosecond laser processing with a high-temperature stearic acid modification to create surfaces that feature superhydrophilic (SHL) stripes on a superhydrophobic substrate. By controlling the offset distance between the droplet's impact point and the SHL stripe, we achieved a directional and precise rebound of the droplets. Our findings indicate that the lateral displacement of the droplet increases with the offset distance and always tilts toward the direction of the SHL stripe. This study also incorporates numerical simulations to validate the findings, shedding light on the energy conversion mechanisms at the liquid–solid interface during the impact, particularly during the retraction phase. This discovery is significant for more accurately predicting the specific landing spots of rebounding droplets.

Description in the Biography "Snake Sand: Biography of Ketziot Anṣār Detainee 3" by Al-Mutawakkil Ṭāhā

This study aims to uncover the technique of description in the biography of Al-Mutawakkil Ṭāhā " Snake Sand: The Biography of Ketziot Anṣār Detainee 3," which served as a vivid example of creativity born from the crucible of suffering in the depths of prisons, the darkness of Israeli occupation cells. It examines how the description evolves from one scene to another based on the psychological state endured by the author and the detainees. Research Problem: The biography was written under difficult conditions that differ from those of other literary works, conditions experienced by the prisoner in the darkness of cells, which imbue it with unique psychological dimensions, harsh challenges, and aesthetic illuminations. All of this is accomplished through the use of descriptive techniques to establish time and place settings, present characters, and capture actions, visions, dreams, and marvels. The research used a descriptive-analytical methodology to study the description and its manifestations in Al-Mutawakkil's biography. It yields several key findings, including: The author focused on describing the oppressions of the occupier in the setting represented by their confinement in desert prisons, turning these places into hostile territories. The prison became a place that shackled the author, physically and psychologically, confining them to a specific spot within it, devoid of the freedom to move even between different sections of the prison. The author emphasized describing antagonistic places, such as detention centers and prisons, while also highlighting attempts to adapt and humanize them.

The Impact of Specific High-Pathogenic Tp53 Mutations on Survival in Head and Neck Cancers

Head and neck cancers (HNC) constitute about 30-40 % of all cancers in India. tp53 tumor suppressor gene is the most common gene mutated in HNC but has not been demonstrated to have any diagnostic or prognostic significance. therefore, we hypothesize that the survival rate and prognosis of head and neck cancer patients are linked with certain specific high-pathogenic tp53 hot spot mutations. To evaluate the hypothesis, we conducted a retrospective study on a cohort of 100 HNC patients between April 2013 and April 2018. targeted deep sequencing was performed to identify specific somatic mutations. Kaplan–Meier analysis and log-rank tests were performed. Tp53 mutations were identified in 47 of the 100 cases, with dual mutations in 4 patients. Functional characteristics displayed loss of function in 54.9% and gain of function in 27.5 % of cases. the majority of cases displayed missense aberration (74.5%), followed by nonsense mutations (11.76%). Of these pathogenic mutations, 33.3% fell into 6 hot spot residues while mini hot spot residues constituted 20.5% of the mutations. There was no statistically significant difference in the median survival rate between patients with and without the out tp53 mutation. however, the median survival rate was linked with poor prognosis in patients with specific pathogenic hot spot mutations in 247, 248, 245, and 175 residues (p value= 0.028). The majority of hot spot mutations (66%) showed a gain of function which may have been contributory to the pathogenicity of the disease. Results provide evidence that the existence of high-pathogenicity hot spot residues appears to be the only mutation that warrants further research to identify the therapeutic and prognostic significance of the tp53 gene in the treatment of HNC. further systemic studies are recommended to validate this hypothesis.

A Deep Learning Image Corrosion Classification Method for Marine Vessels Using an Eigen Tree Hierarchy Module

This paper involves the automation of a visual characterisation technique for corrosion in marine vessels, as it appears in the hull preventive coatings of marine vessels and their surfaces. We propose a module that maximizes the utilisation of features learned by a deep convolutional neural network to identify areas of corrosion and segment pixels in regions of inspection interest for corrosion detection. Our segmentation module is based on Eigen tree decomposition and information-based decision criteria in order to produce specific corroded spots—regions of interest. To assess performance and compare it with our method, we utilize several state-of-the-art deep learning architectures.The results indicate that our method achieves higher accuracy and precision while maintaining the significance score across the entire dataset. To the best of our knowledge, this is the first Eigen tree-based module in the literature in the context of trained neural network predictors for classifying corrosion in marine vessel images.

A Brief Study on Nanogel: A Review

Strong nanoparticles called nanogels may be utilized in controlled drug delivery applications to distribute active pharmaceutical ingredients. Because of their chemical makeup and formulations that are unsuited for other formulations, nanogels provide a safer and more effective drug delivery mechanism for both hydrophilic and hydrophobic medicines. Functionalized nanoparticles, which serve as drug carriers and can be loaded with medications and other active materials to be released in a regulated way at a specific spot, can now be larger thanks to nanogel technology. The objective of this paper is to present a broad overview of nanogels, their innovative applications across several domains, and their latest synthesis process.

Solid-State Anion Exchange Enabled by Pluggable vdW Assembly for In Situ Halide Manipulation in Perovskite Monocrystalline Film.

The fabrication of perovskite single crystal-based optoelectronics with improved performance is largely hindered by limited processing techniques. Particularly, the local halide composition manipulation, which dominates the bandgap and thus the formation of heterostructures and emission of multiple-wavelength light, is realized via prevalent liquid- or gas-phase anion exchange with the utilization of lithography, while the monocrystalline nature is sacrificed due to polycrystalline transition in exchange with massive defects emerging, impeding carrier separation and transportation. Thus, a damage-free and lithography-free solid-state anion exchange strategy, aiming at in situ halide manipulation in perovskite monocrystalline film, is developed. Typically, CsPbCl3 working as medium to deliver halide is van der Waals (vdW) assembled to specific spots of CsPbBr3, followed by the removal of CsPbCl3 after anion exchange, with the halide composition in contact area modulated and monocrystalline nature of CsPbBr3 preserved. CsPbBr3-CsPbBrxCl3-x monocrystalline heterostructure has been achieved without lithography. Device based on the heterostructure shows apparent rectification behavior and improved photo-response rate. Heterostructure arrays can also be constructed with customized medium crystal. Furthermore, the halide composition can be accurately tuned to enable full coverage of visible spectra. The solid-state exchange enriches the toolbox for processing vulnerable perovskite and paves the way for the integration of monocrystalline perovskite optoelectronics.

Screening and Optimization of IAA Production by PGPR isolated from Rhizosphere of a Pterocarpus marsupium Roxb. and their Effect on Plant Growth

Indole Acetic Acid (IAA) production is important attribute of PGPR that promote plant growth and development. The rhizosphere is hotspot in the soil that harbors PGPR. The present study was aimed with isolation and screening of IAA producing bacteria from the rhizosphere of Pterocarpus marsupium Roxb. Optimum culture conditions (pH, temperature, incubation period and L-tryptophan concentration for IAA production were studied for selected isolates and their effect on wheat growth and root development was evaluated. Among twenty four IAA producing isolates five isolates (Et1, Rp1, Rp5, Rp6, and Rp9) produced maximum IAA in range of 50-70 μg/mL and was used in optimization studies. Maximum IAA was produced in 96 hours of incubation, at pH 7 and with 0.1mg/mL of L-tryptophan by all five isolates. 30oC is the most suitable temperature for Et1, Rp1, Rp5, Rp9; whereas Rp6 produced nearly same amount of IAA at wide range of temperature 30-35oC (77-84.12 μg/mL) and at pH 7-8 ( 73-74μg/mL). Out of the five isolates, Rp6 exhibits the highest potential, having a maximum IAA of 84.12 μg/mL at 35°C and pH 7. Although tryptophan influences IAA synthesis but at higher concentration of tryptophan inhibits IAA synthesis. To validate the production of IAA, crude extracts were analyzed using thin layer chromatography (TLC). A spot of standard IAA with the same Rf value (0.91) was found to match a specific spot from the crude IAA.

Cloud Broker: Customizing Services for Cloud Market Requirements

Cloud providers offer various purchasing options to enable users to tailor their costs according to their specific requirements, including on-demand, reserved instances, and spot instances. On-demand and spot instances satisfy short-term workloads, whereas reserved instances fulfill long-term instances. However, there are workloads that fall outside of either long-term or short-term categories. Consequently, there is a notable absence of services specifically tailored for medium-term workloads. On-demand services, while offering flexibility, often come with high costs. Spot instances, though cost-effective, carry the risk of termination. Reserved instances, while stable and less expensive, may have a remaining period that extends beyond the duration of users’ tasks. This gap underscores the need for solutions that address the unique requirements and challenges associated with medium-term workloads in the cloud computing landscape. This paper introduces a new cloud broker that introduces IaaS services for medium-term workloads. On one hand, this broker strategically reserves resources from providers, and on the other hand, it interacts with users. Its interaction with users is twofold. It collects users’ preferences regarding commitment term for medium-term workloads and then transforms the leased resources based on commitment term, aligning with the requirements of most users. To ensure profitability, the broker sells these services utilizing an auction algorithm. Hence, in this paper, an auction algorithm is introduced and developed, which treats cloud services as virtual assets and integrates the depreciation over time. The findings affirm the lack of services that fulfill medium workloads while ensuring the financial viabilty and profitability of the broker, given that the estimated return on investment (ROI) is acceptable.

Understanding Forearm Muscle Activity during Everyday Common Grasps: Insights for Rehabilitation, Prosthetic Control, and Human–Machine Interaction

The specific role of forearm muscles in the development of activities of daily living (ADL) remains unknown. Consequently, studying forearm muscle activity during the most commonly used grasps in ADL would yield valuable insights for hand function evaluation, rehabilitation, and advancements in prosthetic control. In this study, forearm muscle activity was analyzed in 22 healthy subjects, examining seven representative forearm areas during the performance of seven types of grasps at 50% of maximum effort. A Scheirer–Ray–Hare test revealed significant differences for grasp, spot, and their interaction (α < 0.05), but not for repetition (and its interactions). Specific significant differences between grasps were found in specific spots by means of Bonferroni post hoc analyses, ensuring the possibility to discriminate between grasps, which is key to identifying the person’s intention to perform a particular grasp. The median values ranged from 4.4% to 32.8%, depending on the spot and grasp, with small 95% confidence intervals (0.5% to 5.5%). Cylindrical grasp requires the highest muscle activity among all spots, while lateral pinch demands the least. The findings elucidate the contribution, coordination, and function of each muscle in relation to each grasp, with implications for rehabilitation, prosthetics, and telerobotic and teleoperation systems.

Photochemically Induced Propulsion of a 4D Printed Liquid Crystal Elastomer Biomimetic Swimmer.

Underwater organisms exhibit sophisticated propulsion mechanisms, enabling them to navigate fluid environments with exceptional dexterity. Recently, substantial efforts have focused on integrating these movements into soft robots using smart shape-changing materials, particularly by using light for their propulsion and control. Nonetheless, challenges persist, including slow response times and the need of powerful light beams to actuate the robot. This last can result in unintended sample heating and potentially necessitate tracking specific actuation spots on the swimmer. To tackle these challenges, new azobenzene-containing photopolymerizable inks are introduced, which can be processed by extrusion printing into liquid crystalline elastomer (LCE) elements of precise shape and morphology. These LCEs exhibit rapid and significant photomechanical response underwater, driven by moderate-intensity ultraviolet (UV) and green light, being the actuation mechanism predominantly photochemical. Inspired by nature, a biomimetic four-lapped ephyra-like LCE swimmer is printed. The periodically illumination of the entire swimmer with moderate-intensity UV and green light, induces synchronous lappet bending toward the light source and swimmer propulsion away from the light. The platform eliminates the need of localized laser beams and tracking systems to monitor the swimmer's motion through the fluid, making it a versatile tool for creating light-fueled robotic LCE free-swimmers.

The main results of monitoring the power of the active layer at CALM sites of the Nadymsky object

Monitoring of the thickness of the active layer within the lake-marsh type of terrain in the northern taiga was carried out at three sites organized in 1997, 2013 and 2022. In these areas, two types of tracts are distinguished: frozen peat bogs and swampy runoff hollows with a lowered roof of permafrost (the cross-section of the sites is the same type, the only difference is in the thickness of the peat). The work methodology complied with the unified protocol of the international CALM program. Additionally, the surface settlement was assessed (based on leveling from a local benchmark), geophysical studies were carried out and the temperature of the active layer rocks was measured at different gullies. In swampy runoff hollows, hollows with open water and on specific mineral spots within peat bogs, the depth of the permafrost roof is 3–10 m. At the boundaries of peat bogs and in swampy runoff hollows, the thawing depth exceeds 2 m, and on peat bogs it varies from 0.8 to 1.6 m depending on the thickness of the organic layer and the condition of the ground cover. On peatlands with an organic layer more than 1 m thick and a continuous ground cover, the depth of the seasonal thawing layer in some places does not exceed 0.4 m. These peatlands respond poorly to climate change. The depth of the permafrost roof within thin peat bogs (less than 0.5 m of peat) with oppressed vegetation cover is currently more than 2 m, marking the beginning of the replacement of the seasonally thawed layer with seasonally frozen and the formation of areas with a lowered permafrost roof. Monitoring of soil temperatures in such areas has showed that a random combination of climatic factors in individual years can lead to short-term (two years) new formation of permafrost or the merging of seasonal permafrost with permafrost. Thus, the formation of non-merging permafrost is of a reciprocating nature.

Smart Car Parking System using PLC

This project involves the development of a system tailored for multilevel parking facilities. The system aims to streamline the parking process by providing real-time information to drivers upon entry. Outside the parking area, a LED indicator is used which displays the available parking spots along with their corresponding numbers. Additionally, the system keeps track of the total number of parking spots available, updating the LED indicator accordingly. By presenting drivers with specific vacant spot numbers upon arrival, the system minimizes the time spent searching for a parking space, ultimately enhancing efficiency and convenience for drivers

Deep Learning for Automatic Diagnosis of Skin Cancer Using Dermoscopic Images

Over the past few years, the healthcare industry has seen a dramatic increase in the use of intelligent automation enabled by AI technology. These developments are made to better the standard of medical decision making and the standard of treatment given to patients. Fuzzy boundaries, shifting sizes, and aberrations like hair or ruler lines all provide difficulties for automatic detection of skin lesions in dermoscopic images, slowing down the otherwise efficient process of diagnosing skin cancer. However, these difficulties may be conquered by employing image processing software. To address these issues, the authors of this paper provide a novel IMLT-DL model for intelligent dermoscopic image processing. Multi-level thresholding and deep learning are brought together in this model. Top hat filtering and inpainting have been included into IMLT-DL for use in image processing. In addition, Mayfly Optimization has been used in tandem with multilayer Kapur's thresholding to identify specific trouble spots. For further investigation, it uses an Inception v3-based feature extractor, and for data classification, it makes use of gradient boosting trees (GBTs). On the ISIC dataset, this model was shown to outperform state-of-the-art alternatives by a margin of 0.992% over the duration of trial iterations. These advances are a major step forwards in the quest for faster and more accurate skin lesion detection.

Description of two new species of Dinometa from East Africa with remarks on D. maputuana (Lepidoptera, Lasiocampidae, Lasiocampinae).

The genus Dinometa Aurivillius, 1927 (type species Gastroplakaeis maputuana Wichgraf, 1906) is reviewed, with two new species described: Dinometa ethani sp. n. from Tanzania and Dinometa abigailae sp. n. from Kenya and Tanzania. All Dinometa species showed no significant differences in male genitalia, but D. ethani sp. n. and D. abigailae sp. n. are allopatric with D. maputuana. D. abigailae sp. n. has specific reddish spots on hindwings that distinguishes it from closely distributed D. ethani sp. n. Two D. maputuana specimens collected at the same night and locality in the Republic of South Africa have an intraspecific variation of 1.52% in cytochrome c oxidase subunit I, while all barcoded D. maputuana are 2.432.74% distant from D. ethani sp. n. Adults, male genitalia and distribution maps of all three species are illustrated.

An optimized capsule neural networks for tomato leaf disease classification

Plant diseases have a significant impact on leaves, with each disease exhibiting specific spots characterized by unique colors and locations. Therefore, it is crucial to develop a method for detecting these diseases based on spot shape, color, and location within the leaves. While Convolutional Neural Networks (CNNs) have been widely used in deep learning applications, they suffer from limitations in capturing relative spatial and orientation relationships. This paper presents a computer vision methodology that utilizes an optimized capsule neural network (CapsNet) to detect and classify ten tomato leaf diseases using standard dataset images. To mitigate overfitting, data augmentation, and preprocessing techniques were employed during the training phase. CapsNet was chosen over CNNs due to its superior ability to capture spatial positioning within the image. The proposed CapsNet approach achieved an accuracy of 96.39% with minimal loss, relying on a 0.00001 Adam optimizer. By comparing the results with existing state-of-the-art approaches, the study demonstrates the effectiveness of CapsNet in accurately identifying and classifying tomato leaf diseases based on spot shape, color, and location. The findings highlight the potential of CapsNet as an alternative to CNNs for improving disease detection and classification in plant pathology research.