Manual Method Research Articles

SMLS-YOLO: an extremely lightweight pathological myopia instance segmentation method.

Pathological myopia is a major cause of blindness among people under 50 years old and can result in severe vision loss in extreme cases. Currently, its detection primarily relies on manual methods, which are slow and heavily dependent on the expertise of physicians, making them impractical for large-scale screening. To tackle these challenges, we propose SMLS-YOLO, an instance segmentation method based on YOLOv8n-seg. Designed for efficiency in large-scale screenings, SMLS-YOLO employs an extremely lightweight model. First, StarNet is introduced as the backbone of SMLS-YOLO to extract image features. Subsequently, the StarBlock from StarNet is utilized to enhance the C2f, resulting in the creation of the C2f-Star feature extraction module. Furthermore, shared convolution and scale reduction strategies are employed to optimize the segmentation head for a more lightweight design. Lastly, the model incorporates the Multi-Head Self-Attention (MHSA) mechanism following the backbone to further refine the feature extraction process. Experimental results on the pathological myopia dataset demonstrate that SMLS-YOLO outperforms the baseline YOLOv8n-seg by reducing model parameters by 46.9%, increasing Box mAP@0.5 by 2.4%, and enhancing Mask mAP@0.5 by 4%. Furthermore, when compared to other advanced instance segmentation and semantic segmentation algorithms, SMLS-YOLO also maintains a leading position, suggesting that SMLS-YOLO has promising applications in the segmentation of pathological myopia images.

Oocytes Vitrification Using Automated Equipment Based on Microfluidic Chip.

Oocyte vitrification has a wide range of applications in assisted reproduction and fertility preservation. It requires precise cryoprotectant agents (CPAs) loading and removal sequences to alleviate osmotic shock, which requires manual manipulation by an embryologist. In this study, a microfluidic system was developed to facilitate the precise adjustment of the CPA concentration around the oocyte by linear loading and removal of CPA. In addition, the microfluidic-based automated vitrification (MAV) device combines CPA loading/removal process, with vitrification process, thereby achieving automated oocyte vitrification. Oocytes were vitrified by Cryotop/QC manual method and MAV method. The results showed that the survival, cleavage, and blastocyst rates of oocytes were 80.44, 54.17, and 32.95% for the MAV method, which were significantly higher than Cryotop manual method (73.35, 43.73, and 23.67%) (p < 0.05). In MAV, solution injection rate during CPA loading/removal process was designed as a 1-segment, 2-segment, and 4-segment function. Accordingly, three concave loading and convex removal protocols were adopted to vitrify oocytes. Oocytes vitrified using the 4-segment function group exhibited increased survival (86.18%), cleavage (63.29%), and blastocyst (45.58%) rates compared to those vitrified using the 1-segment and 2-segment groups. The oocytes vitrification with the highest concentration of CPA, denoted as VS1-TS1, exhibited the highest survival rate after rewarming (86.18%). In contrast, the VS3-TS3 group, characterized by a CPA concentration half that of VS1-TS1, exhibited lower survival (74.14%) and cleavage (59.31%) rates, but displayed the higher blastocyst rate (50.79%) following oocyte activation. Our study demonstrates potential of the MAV device for oocyte or embryo vitrification.

Diagnostic performance of ClinCheck, Dolphin Imaging, and 3D Slicer software for Bolton discrepancy analysis.

To evaluate the diagnostic performance of ClinCheck, Dolphin Imaging orthodontic software, and 3D Slicer for the analysis of Bolton discrepancy (BD). Fifty-five pairs of early-stage digital models of patients treated with Invisalign were printed to measure the BD by manual method with a digital caliper (gold standard). The discrepancy values calculated by ClinCheck were obtained. In addition, the sample STL files were measured using Dolphin Imaging and 3D Slicer software to obtain BD values. To assess reliability, precision, and accuracy of the methods, intraclass correlation coefficients (ICCs), Dahlberg's formula, paired t-tests, and the Bland-Altman method were used, respectively. Repeated-measures analysis of variance with Bonferroni post hoc test was used to assess the difference between groups. The three methods showed reliable measurements (ICC ≥ 0.7), with the values of anterior Bolton slightly higher than overall Bolton. Measurements for the anterior Bolton showed higher precision (Dahlberg's formula 0.65, 0.70, and 0.55) than those for the overall Bolton. For anterior Bolton, only the measurements obtained by ClinCheck and Dolphin Imaging were accurate (P > .05, no proportion bias), while for overall Bolton, all groups had a significant difference. The Bland-Altman plots demonstrated no consistency for anterior Bolton measurements when 3D Slicer was used and for the overall Bolton. ClinCheck and Dolphin Imaging showed accuracy to quantify anterior BD. For the overall Bolton measurements, ClinCheck showed a statistical difference from the manual assessment but without relevant clinical significance.

Sensor self-diagnosis method for a nickel top-blowing furnace based on graph interactive dynamic fusion

Abstract As modern industry gradually advances towards greater automation and intelligence, the scale of nickel top-blowing furnace smelting systems is continuously expanding, leading to an increasing need for sensor maintenance. Traditional periodic evaluations and manual maintenance methods are no longer sufficient to meet the development needs of intelligent sensors. To address this issue, this paper proposes a sensor self-diagnosis method based on graph interactive dynamic fusion, called DLGCN-GIDF. First, a combination of knowledge-driven and data-driven approaches is introduced. By constructing a dual-layer architecture based on a functional module graph network and a sensor graph network, a sensor correlation graph model for the nickel top-blowing furnace system is established. Next, with the aid of a GIDF module, the relative weights between functional modules and sensors are integrated to perform spatiotemporal correlation-based graph fusion. This enables the prediction of spatiotemporal data for sensors from a system perspective. Finally, the goal of sensor self-diagnosis is achieved using a standardised residual testing algorithm. Taking a nickel top-blowing furnace smelting system as an example, the feasibility and effectiveness of our method of sensor fault self-diagnosis are verified.

Pelabelan Sentimen Berbasis Semi-Supervised Learning menggunakan Algoritma LSTM dan GRU

In the sentiment analysis research process, there are problems when still using manual labeling methods by humans (expert annotation), which are related to subjectivity, long time, and expensive costs. Another way is to use computer assistance (machine annotator). However, the use of machine annotators also has the research problem of not being able to detect sarcastic sentences. Thus, the researcher proposed a sentiment labeling method using Semi-Supervised Learning. Semi-supervised learning is a labeling method that combines human labeling techniques (expert annotation) and machine labeling (machine annotation). This research uses machine annotators in the form of Deep Learning algorithms, namely the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. The word weighting method used in this research is Word2Vec Continuous Bag of Word (CBoW). The results showed that the GRU algorithm tends to have a better accuracy rate than the LSTM algorithm. The average accuracy of the training results of the LSTM and GRU algorithm models is 0.904 and 0.913. In contrast, the average accuracy of labeling by LSTM and GRU is 0.569 and 0.592, respectively.

Research on the Cable-to-Terminal Connection Recognition Based on the YOLOv8-Pose Estimation Model

Substations, as critical nodes for power transmission and distribution, play a pivotal role in ensuring the stability and security of the entire power grid. With the ever-increasing demand for electricity and the growing complexity of grid structures, traditional manual inspection methods for substations can no longer meet the requirements for efficient and safe operation and maintenance. The advent of automated inspection systems has brought revolutionary changes to the power industry. These systems utilize advanced sensor technology, image processing techniques, and artificial intelligence algorithms to achieve real-time monitoring and fault diagnosis of substation equipment. Among these, the recognition of cable-to-terminal connection relationships is a key task for automated inspection systems, and its accuracy directly impacts the system’s diagnostic capabilities and fault prevention levels. However, traditional methods face numerous limitations when dealing with complex power environments, such as inadequate recognition performance under conditions of significant perspective angles and geometric distortions. This paper proposes a cable-to-terminal connection relationship recognition method based on the YOLOv8-pose model. The YOLOv8-pose model combines object detection and pose estimation techniques, significantly improving detection accuracy and real-time performance in environments with small targets and dense occlusions through optimized feature extraction algorithms and enhanced receptive fields. The model achieves an average inference time of 74 milliseconds on the test set, with an accuracy of 92.8%, a recall rate of 91.5%, and an average precision mean of 90.2%. Experimental results demonstrate that the YOLOv8-pose model performs excellently under different angles and complex backgrounds, accurately identifying the connection relationships between terminals and cables, providing reliable technical support for automated substation inspection systems. This research offers an innovative solution for automated substation inspection systems, with significant application prospects.

Deep learning technology in vascular image segmentation and disease diagnosis

AbstractBlood vessel segmentation is a crucial aspect of medical image processing, aiding medical professionals in more accurate disease analysis and diagnosis. Manual blood vessel segmentation methods are time‐consuming and cumbersome, making the development of automatic segmentation methods essential. The rapid advancements in deep learning technology have introduced new tools and methods for vascular image segmentation. In this review, we provide a comprehensive overview of deep learning‐based blood vessel segmentation methods across various fields, including retinal vessel segmentation, cerebrovascular segmentation, and pulmonary vessel segmentation. Several prevalent diseases, such as retinal vascular diseases, cerebrovascular diseases, pulmonary vascular diseases, and tumors, have posed significant health challenges globally. This review also discusses the application of deep learning technology in disease diagnosis within these contexts. Finally, considering the current research landscape, we discuss existing challenges and potential future developments in blood vessel segmentation. We aim to assist researchers in gaining a comprehensive understanding and designing effective blood vessel segmentation models, ultimately offering opportunities for early disease diagnosis and treatment.

Artificial Intelligence-Supported Development of Health Guideline Questions.

Guideline questions are typically proposed by experts. To assess how large language models (LLMs) can support the development of guideline questions, providing insights on approaches and lessons learned. Two approaches for guideline question generation were assessed: 1) identification of questions conveyed by online search queries and 2) direct generation of guideline questions by LLMs. For the former, the researchers retrieved popular queries on allergic rhinitis using Google Trends (GT) and identified those conveying questions using both manual and LLM-based methods. They then manually structured as guideline questions the queries that conveyed relevant questions. For the second approach, they tasked an LLM with proposing guideline questions, assuming the role of either a patient or a clinician. Allergic Rhinitis and its Impact on Asthma (ARIA) 2024 guidelines. None. Frequency of relevant questions generated. The authors retrieved 3975 unique queries using GT. From these, they identified 37 questions, of which 22 had not been previously posed by guideline panel members and 2 were eventually prioritized by the panel. Direct interactions with LLMs resulted in the generation of 22 unique relevant questions (11 not previously suggested by panel members), and 4 were eventually prioritized by the panel. In total, 6 of 39 final questions prioritized for the 2024 ARIA guidelines were not initially thought of by the panel. The researchers provide a set of practical insights on the implementation of their approaches based on the lessons learned. Single case study (ARIA guidelines). Approaches using LLMs can support the development of guideline questions, complementing traditional methods and potentially augmenting questions prioritized by guideline panels. Fraunhofer Cluster of Excellence for Immune-Mediated Diseases.

OAR-UNet: Enhancing Long-Distance Dependencies for Head and Neck OAR Segmentation

Accurate segmentation of organs at risk (OARs) is a crucial step in the precise planning of radiotherapy for head and neck tumors. However, manual segmentation methods using CT images, which are still predominantly applied in clinical settings, are inefficient and expensive. Additionally, existing segmentation methods struggle with small organs and have difficulty managing the complex interdependencies between organs. To address these issues, this study proposed an OAR-UNet segmentation method based on a U-shaped architecture with two key designs. To tackle the challenge of segmenting small organs, a Local Feature Perception Module (LFPM) is developed to enhance the sensitivity of the method to subtle structures. Furthermore, a Cross-shaped Transformer Block (CSTB) with a cross-shaped attention mechanism is introduced to improve the ability of the model to capture and process long-distance dependency information. To accelerate the convergence of the Transformer, we designed a Local Encoding Module (LEM) based on depthwise separable convolutions. In our experimental evaluation, we utilized two publicly available datasets, SegRap2023 and PDDCA, achieving Dice coefficients of 78.22% and 89.42%, respectively. These results demonstrate that our method outperforms both previous classic methods and state-of-the-art (SOTA) methods.

Efficacy of Glycyrrhizin as an Intracanal Medicament on Bacterial Load Reduction in Primary Infected Root Canals: A Randomized Clinical Trial.

This study was done to clinically investigate the efficacy of glycyrrhizin-based intracanal medication (ICM) compared to calcium hydroxide Ca(OH)2 and Ledermix on the bacterial counts inside the root canals of necrotic teeth with chronic periapical periodontitis. Thirty-six patients having single-rooted and single-canaled necrotic teeth with chronic periapical periodontitis were enrolled in this research. Aseptic control measures were taken before clinical steps. Access cavity preparation was done. First bacteriological samples (S1) were collected immediately after access cavity preparation and before cleaning and shaping inside the root canals. Cleaning and shaping were performed on the root canals. Patients were randomly allocated into 3 groups according to the type of intracanal medicament used [Ca(OH)2, Ledermix, Glycyrrhizin]. Second bacteriological samples (S2) were collected after 1 week from placing the ICMs. S1 and S2 were transferred to the lab of microbiology for culturing on blood agar dishes in anaerobic conditions, and the bacteria on the plates were enumerated as colony-forming units (CFUs) by the manual counting method. The anti-bacterial efficacy of the ICM was estimated by the percentage reduction in the bacterial colonies from S1 to S2. All tested ICM documented a significant reduction in the CFUs from S1 to S2 (p < 0.05). Regarding S1, there was no significant difference between the three tested materials (p > 0.05). Regarding S2, Glycyrrhizin and Ledermix showed significantly lower CFUs than Ca(OH)2. There was no significant difference between Glycyrrhizin and Ledermix. Under the conditions of the present study, Glycyrrhizin and Ledermix showed comparable antimicrobial effects that were better than the commonly used Ca(OH)2 ICM. Glycyrrhizin may be a promising ICM. Herbal medicine can be considered as an alternative antimicrobial material for root canal disinfection because it has many benefits, like low toxicity, absence of microbial resistance, and favorable antimicrobial efficacy. This study highlights the clinical efficacy of Glycyrrhizin as a promising ICM. How to cite this article: Eltantawi AR, Abdel-Razik GM, Elhawary YM, et al. Efficacy of Glycyrrhizin as an Intracanal Medicament on Bacterial Load Reduction in Primary Infected Root Canals: A Randomized Clinical Trial. J Contemp Dent Pract 2024;25(6):540-546.

Optimizing Small-Scale Farming: A Review of Manually Operated Vegetable Seed Sowing Machine Performance

Small-scale farming plays a crucial role in global vegetable production, particularly in regions where resources and mechanized equipment are limited. Traditional manual sowing methods are labor-intensive and often result in inconsistent seed placement, leading to suboptimal crop yields. This review paper examines the performance of manually operated vegetable seed sowing machines as a means to optimize small-scale farming practices. By analyzing recent advancements and evaluating key performance metrics—such as field capacity, seed placement accuracy, labor efficiency, and cost-effectiveness—this review highlights how these machines can enhance productivity and reduce labor costs. The paper also discusses the influence of various design features on machine performance, including seed metering mechanisms, furrow openers, and ergonomic considerations. Through a comprehensive analysis of existing literature and case studies, this review aims to provide insights into the effectiveness of these machines and offer recommendations for future improvements to better support small-scale farmers. The findings underscore the potential of manually operated seed sowing machines to contribute significantly to the optimization of vegetable production in resource-constrained settings. Keywords: Small-Scale Farming, Manually Operated Machines, Seed Sowing Performance, Field Capacity, Labor Efficiency, Cost-Effectiveness, Machine Design, Ergonomics, Manual Sowing Techniques, Agricultural Innovation, Performance Metrics

Trajectory planning and automatic docking of LNG five-axis loading arm

PurposeIn response to the challenges posed by the conventional manual flange docking method in the LNG (Liquefied Natural Gas) loading process, such as low positioning accuracy, constraints on production efficiency and safety hazards, this study analyzed the LNG five-axis loading arm’s main functions and structural characteristics.Design/methodology/approachAn automated solution for the joints of the LNG loading arm was designed. The forward kinematic model of the LNG loading arm was established using the Denavit–Hartenberg (D-H) parameter method, and its workspace was analyzed. The Newton–Raphson iteration method was employed to solve the inverse kinematics of the LNG loading arm, facilitating trajectory planning. The relationship between the target position and the joint variables was established to verify the stability of the arm’s motion. Flange center identification was achieved using the Hough transform function. Based on the ROS platform, combined with Gazebo and Rviz, an experimental simulation of automatic docking of the LNG loading arm was conducted.FindingsThe docking errors in the XYZ directions were all less than 0.8 mm, meeting the required docking accuracy. Moreover, the motion performance of the loading arm during docking was smooth and free of abrupt changes, validating its capability to accomplish the automatic docking task.Originality/valueThe proposed trajectory planning and automatic docking scheme can be used for the rapid filling of LNG filling arms and LNG tankers to improve the efficiency of LNG transportation. In guiding the docking, the proposed automatic docking scheme is an accurate and efficient way to improve safety.

Optimasi Pengukuran Dinamis dari Visualisasi Model Ruangan 3D Menggunakan Sensor LiDAR dan Framework RoomPlan

Utilizing limited space to be more functional and aesthetic, such as adding furniture or using it as gardening space, can be achieved through effective room mapping. However, manual mapping methods have limitations such as inaccurate measurements, time efficiency, and also lack of room visualization. In this modern era, robotics technology has been widely applied in everyday life, one of which is the use of LiDAR sensors for room mapping. This paper discusses a room mapping system using a mobile application that utilizes LiDAR sensors. The mapping process is carried out by scanning the area to be mapped and rotating the area 360 degrees. The result of this mapping is a scanned 3D model of the room, which allows measurements from every side of the room. Research shows that the mapping results from the software created are similar in shape to actual field conditions. In addition, the measurement accuracy only has a small difference of 0.2%. It is hoped that this research can make it easier for humans to map rooms and make better use of limited space.

AI-based lumbar central canal stenosis classification on sagittal MR images is comparable to experienced radiologists using axial images

Abstract Objectives The assessment of lumbar central canal stenosis (LCCS) is crucial for diagnosing and planning treatment for patients with low back pain and neurogenic pain. However, manual assessment methods are time-consuming, variable, and require axial MRIs. The aim of this study is to develop and validate an AI-based model that automatically classifies LCCS using sagittal T2-weighted MRIs. Methods A pre-existing 3D AI algorithm was utilized to segment the spinal canal and intervertebral discs (IVDs), enabling quantitative measurements at each IVD level. Four musculoskeletal radiologists graded 683 IVD levels from 186 LCCS patients using the 4-class Lee grading system. A second consensus reading was conducted by readers 1 and 2, which, along with automatic measurements, formed the training dataset for a multiclass (grade 0–3) and binary (grade 0–1 vs. 2–3) random forest classifier with tenfold cross-validation. Results The multiclass model achieved a Cohen’s weighted kappa of 0.86 (95% CI: 0.82–0.90), comparable to readers 3 and 4 with 0.85 (95% CI: 0.80–0.89) and 0.73 (95% CI: 0.68–0.79) respectively. The binary model demonstrated an AUC of 0.98 (95% CI: 0.97–0.99), sensitivity of 93% (95% CI: 91–96%), and specificity of 91% (95% CI: 87–95%). In comparison, readers 3 and 4 achieved a specificity of 98 and 99% and sensitivity of 74 and 54%, respectively. Conclusion Both the multiclass and binary models, while only using sagittal MR images, perform on par with experienced radiologists who also had access to axial sequences. This underscores the potential of this novel algorithm in enhancing diagnostic accuracy and efficiency in medical imaging. Key Points QuestionHow can the classification of lumbar central canal stenosis (LCCS) be made more efficient? FindingsMulticlass and binary AI models, using only sagittal MR images, performed on par with experienced radiologists who also had access to axial sequences. Clinical relevanceOur AI algorithm accurately classifies LCCS from sagittal MRI, matching experienced radiologists. This study offers a promising tool for automated LCCS assessment from sagittal T2 MRI, potentially reducing the reliance on additional axial imaging.

Automated Disease Detection in Silkworms Using Machine Learning Techniques

Silkworm diseases pose a major threat to the sericulture industry, with early detection remaining a challenge due to limited infrastructure. This study focuses on detecting Grasserie disease, which can rapidly spread in silkworm rearing units, leading to significant economic losses. A novel dataset of 668 healthy and 574 Grasserie-affected silkworm images forms the basis of this research. The study applies machine learning techniques, using the Histogram Oriented Gradient (HOG) feature descriptor combined with Kernel Principal Component Analysis (KPCA) and supervised classifiers. The integration of Support Vector Machines (SVM) with HOG and KPCA achieved high accuracy (93.16%), recall (93.38%), and precision (91.94%), offering a faster, more accurate alternative to manual detection methods. This approach holds great potential for developing real-time, IoT-based diagnostic tools that enable farmers to quickly identify infected silkworms, reducing disease spread and economic losses, and can be extended to other agricultural applications requiring early disease detection.

Quality control of elbow joint radiography using a YOLOv8-based artificial intelligence technology

BackgroundTo explore an artificial intelligence (AI) technology employing YOLOv8 for quality control (QC) on elbow joint radiographs.MethodsFrom January 2022 to August 2023, 2643 consecutive elbow radiographs were collected and randomly assigned to the training, validation, and test sets in a 6:2:2 ratio. We proposed the anteroposterior (AP) and lateral (LAT) models to identify target detection boxes and key points on elbow radiographs using YOLOv8. These identifications were transformed into five quality standards: (1) AP elbow positioning coordinates (XA and YA); (2) olecranon fossa positioning distance parameters (S17 and S27); (3) key points of joint space (Y3, Y4, Y5 and Y6); (4) LAT elbow positioning coordinates (X2 and Y2); and (5) flexion angle. Models were trained and validated using 2,120 radiographs. A test set of 523 radiographs was used for assessing the agreement between AI and physician and to evaluate clinical efficiency of models.ResultsThe AP and LAT models demonstrated high precision, recall, and mean average precision for identifying boxes and points. AI and physicians showed high intraclass correlation coefficient (ICC) in evaluating: AP coordinates XA (0.987) and YA (0.991); olecranon fossa parameters S17 (0.964) and S27 (0.951); key points Y3 (0.998), Y4 (0.997), Y5 (0.998) and Y6 (0.959); LAT coordinates X2 (0.994) and Y2 (0.986); and flexion angle (0.865). Compared to manual methods, using AI, QC time was reduced by 43% for AP images and 45% for LAT images (p < 0.001).ConclusionYOLOv8-based AI technology is feasible for QC of elbow radiography with high performance.Relevance statementThis study proposed and validated a YOLOv8-based AI model for automated quality control in elbow radiography, obtaining high efficiency in clinical settings.Key PointsQC of elbow joint radiography is important for detecting diseases.Models based on YOLOv8 are proposed and perform well in image QC.Models offer objective and efficient solutions for QC in elbow joint radiographs.Graphical

A Condition Assessment Tool for Steel Bridge Deck Pavement Systems Based on Data Balancing Methods and Machine Learning Algorithms

The primary challenge in the operation of steel deck pavement systems lies in the inspection and assessment of their condition. Traditionally, manual inspection methods are employed. However, these approaches are not only time-consuming and labor-intensive but also prone to human error. As a result, integrating data-driven machine learning technologies into the evaluation of pavement systems presents a significant advantage in addressing these issues. This study proposes a decision-making tool for estimating the condition levels of steel bridge deck pavement systems by employing classification techniques. To address the issue of class imbalance in the dataset, the SMOTE algorithm is utilized. Additionally, seven different machine learning methods—Light Gradient Boosting Machine, Extreme Gradient Boosting, Random Forest, Adaptive Boosting, K-Nearest Neighbor, Multilayer Perceptron, and Logistic Regression—are applied for training. Comparative analysis reveals that the Light Gradient Boosting performs optimally, achieving classification accuracies of 0.841 and 0.929 on the original and synthetic datasets, respectively.

Comparative Study on Cost Analysis of Mechanical and Manual Transplanting of Rice

In Telangana, rice cultivation predominantly relies on traditional manual transplanting, which requires a significant labor force. However, the migration of agricultural laborers to urban areas in search of better wages has led to a shortage of workers, particularly during the critical transplanting season. To address this labor shortage and reduce cultivation costs, an efficient and cost-effective method of rice transplanting is essential, without compromising grain yield. To explore such a solution, Krishi Vigyan Kendra in Jammikunta, Karimnagar district, conducted 30 frontline demonstrations between 2018-19 and 2020-21, showcasing rice cultivation using a self-propelled, walk-behind six-row mechanical transplanter. The performance of this mechanical method was evaluated by collecting data on various growth parameters, including plant height, number of productive tillers per hill, panicle length, grains per panicle, and grain yield, and comparing it with traditional manual transplanting. Economic analysis revealed that the gross returns from mechanical transplanting were Rs. 135,956, compared to Rs. 128,629 for manual transplanting. Similarly, the net returns for mechanical and manual methods were Rs. 95,106 and Rs. 82,204, respectively. The benefit-cost ratio was found to be 3.32 for mechanical transplanting and 2.77 for manual transplanting. These findings indicate that mechanical rice transplanting offers a viable, cost-effective alternative, reducing labor requirements and drudgery while delivering higher yields.

Validation of the Sysmex XN-V hematology analyzer for feline specimens.

The Sysmex XN-V is derived from the new Sysmex XN series of human hematology analyzers. The main changes from the previously validated XT-2000iV analyzer include an optic-fluorescent analysis for platelets and a nucleated red blood cell (NRBC) count. We aimed to validate the Sysmex XN-V for feline blood following the American College for Veterinary Clinical Pathology and International Council for Standardization in Hematology recommendations. Feline EDTA blood specimens were analyzed on the Sysmex XN-V to evaluate repeatability, linearity, comparison with the XT-2000iV analyzer and manual methods, stability, and to verify the previously established Sysmex XT-2000iV RIs. Repeatability was excellent for most variables. Visually determined linearity was excellent or good for most variables except eosinophils and platelet variables. The correlation between the XN-V and XT-2000iV analyzers was good (≥0.82) for all variables except reticulocyte indices. Correlations between the Sysmex XN-V and manual differential counts were good to excellent for most variables, acceptable for neutrophils, and fair for monocytes and NRBC. The previously established Sysmex XT-2000iV RIs can be used to interpret results from the Sysmex XN-V analyzer for most variables except red cell distribution width and reticulocyte variables. The RI for platelet variables could not be evaluated because of platelet clumps. Changes in the Sysmex XN-V measurements after storage at 4 and 24°C were similar to those described for the Sysmex XT-2000iV analyzer. The performance of the Sysmex XN-V analyzer was good and compared favorably with the Sysmex XT-2000iV analyzer.

Research on Applying Deep Learning to Visual–Motor Integration Assessment Systems in Pediatric Rehabilitation Medicine

In pediatric rehabilitation medicine, manual assessment methods for visual–motor integration result in inconsistent scoring standards. To address these issues, incorporating artificial intelligence (AI) technology is a feasible approach that can reduce time and improve accuracy. Existing research on visual–motor integration scoring has proposed a framework based on convolutional neural networks (CNNs) for the Beery–Buktenica developmental test of visual–motor integration. However, as the number of training questions increases, the accuracy of this framework significantly decreases. This paper proposes a new architecture to reduce the number of features, channels, and overall model complexity. The architectureoptimizes input features by concatenating question numbers with answer features and selecting appropriate channel ratios and optimizes the output vector by designing the task as a multi-class classification. This paper also proposes a model named improved DenseNet. After experimentation, DenseNet201 was identified as the most suitable pre-trained model for this task and was used as the backbone architecture for improved DenseNet. Additionally, new fully connected layers were added for feature extraction and classification, allowing for specialized feature learning. The architecture can provide reasons for unscored results based on prediction results and decoding rules, offering directions for children’s training. The final experimental results show that the proposed new architecture improves the accuracy of scoring 6 question graphics by 12.8% and 12 question graphics by 20.14% compared to the most relevant literature. The accuracy of the proposed new architecture surpasses the model frameworks of the most relevant literature, demonstrating the effectiveness of this approach in improving scoring accuracy and stability.