Quality Classification Research Articles

Quality assessment of garden strawberries of foreign selection

At present, «Strategy for Improving the Quality of Food Products in the Russian Federation until 2030» is the main document in the field of healthy nutrition. According to it, the main direction in this area is to provide the Russian population with nutritious food products, which will ensure the prevention of nutritional diseases and lead to to increase the life expectancy of the population, as well as improve their quality of life and to stimulate the production of quality products. At the same time, the need to use domestically produced raw materials, as well as functional ingredients from these raw materials in the production of a wide range of products for healthy and preventive nutrition, is clear lystated. The importance of using local plant, fruit and other raw materials, which are a source of many deficient vitamins, minerals, insoluble and soluble dietary fiber, as well as other valuable nutrients should be noted. In addition, local fruit raw materials are climatically adapted to the living conditions of humans in a particular region. In this regard, research aimed at analyzing the nutritional value of common regional fruit raw materials of the Central Chernozem Region (CCHR) and other regions of Russia is necessary. Of particular importance in the study of the nutritional value of berries and fruits as multivitamin crops that have a complex of medicinal and preventive properties. Among them are, undoubtedly, garden strawberries. The goal of the research is to study the consumer qualities and nutritional value of new varieties of garden strawberries. The article presents the results of a comprehensive assessment of the consumer qualities of the berries of seven new, currently zoned varieties of garden strawberries of foreign selection grown in the Tambov region: Aprika, Vivara, Zephyr, Kwiki, Letitia, Lord and Sibilla. The quality of berry raw materials is determined by a combination of organoleptic and physicochemical indicators, nutritional value, as well as safety indicators. The results of organoleptic studies showed that the berries of all studied varieties had excellent taste. At the same time, the berries of the Aprica, Kwiki, Zephyr and Vivara varieties had excellent quality. These fruits are classified as having an excellent quality. The berries of Laetitia, Sibylla and Lord varieties have been classified in the first quality category due to their good quality. Studies of the nutritional value of berries have shown that the berries of the Laetitia variety are leading in terms of ascorbic acid content, and the berries of the Zephyr variety in terms of anthocyanins. In terms of safety indicators, the berries of all studied varieties of garden strawberries meet the requirements of TR CU 021/2011 «On the safety of food products».

Identification of agricultural surface source pollution in plain river network areas based on 3D-EEMs and convolutional neural networks.

Agricultural non-point sources, as major sources of organic pollution, continue to flow into the river network area of the Jiangnan Plain, posing a serious threat to the quality of water bodies, the ecological environment, and human health. Therefore, there is an urgent need for a method that can accurately identify various types of agricultural organic pollution to prevent the water ecosystems in the region from significant organic pollution. In this study, a network model called RA-GoogLeNet is proposed for accurately identifying agricultural organic pollution in the river network area of the Jiangnan Plain. RA-GoogLeNet uses fluorescence spectral data of agricultural non-point source water quality in Changzhou Changdang Lake Basin, based on GoogLeNet architecture, and adds an efficient channel attention (ECA) mechanism to its A-Inception module, which enables the model to automatically learn the importance of independent channel features. ResNet are used to connect each A-Reception module. The experimental results show that RA-GoogLeNet performs well in fluorescence spectral classification of water quality, with an accuracy of 96.3%, which is 1.2% higher than the baseline model, and has good recall and F1 score. This study provides powerful technical support for the traceability of agricultural organic pollution.

Performance of vision transformer and swin transformer models for lemon quality classification in fruit juice factories

Assessing the quality of agricultural products holds vital significance in enhancing production efficiency and market viability. The adoption of artificial intelligence (AI) has notably surged for this purpose, employing deep learning and machine learning techniques to process and classify agricultural product images, adhering to defined standards. This study focuses on the lemon dataset, encompassing ‘good’ and ‘bad’ quality classes, initiate by augmenting data through rescaling, random zoom, flip, and rotation methods. Subsequently, employing eight diverse deep learning approaches and two transformer methods for classification, the study culminated in the ViT method achieving an unprecedented 99.84% accuracy, 99.95% recall, and 99.66% precision, marking the highest accuracy documented. These findings strongly advocate for the efficacy of the ViT method in successfully classifying lemon quality, spotlighting its potential impact on agricultural quality assessment.

Optimization and classification control of permanent basic farmland based on quality classification

Permanent basic farmland plays an important role in stabilizing agricultural production and ensuring national food security. Therefore, it is necessary to reasonably delineate and control permanent basic farmland. This article is based on the idea of classifying the quality of cultivated land resources, combined with the rules for the delineation of permanent basic farmland, and from the perspective of the synergy of “suitability-connectivity-stability” of cultivated land, a indicator system is constructed. Application of suitability, connectivity, and stability evaluation methods. Taking Zhangshu City, Jiangxi Province as an example, the status of cultivated land resources is comprehensively evaluated, and permanent basic farmland is optimized and graded for protection. The results show that: (1) the arable land in Zhangshu City is mainly of medium suitability, medium continuity and high stability. (2) Zhangshu City is divided into 43218.80 hm2 of permanent basic farmland, accounting for 79.35% of the total area of cultivated land. (3) Control and partition permanent basic farmland into three categories: core protected areas for permanent basic farmland, quality improvement areas, and key transformation areas. The above results indicate that this evaluation has a supportive role in supporting the spatial optimization and hierarchical management of permanent basic farmland, and is of great significance for the unified management of natural resources.

Relationship between Diet Quality and Nutritional Status among Adolescents

Adolescence is a transition period from childhood to adulthood. According to the World Health Organization (WHO), the limit for adolescents is between 10 - 19 years of age. Nutritional status in adolescents can influence growth and development and continue into adulthood. Low diet quality is one of the direct causes of nutritional status problems in adolescents. Data from the Jambi City Health Service in 2021 shows 280 cases of malnutrition at the junior high school and equivalent level in Jambi City. This research aims to determine the relationship between diet quality and nutritional status in adolescents at junior high schools of SMPN 9 Jambi City. The design used in this research was quantitative research with a cross-sectional approach. This research was carried out at SMPN 9 Jambi City in May 2023. The population and sample in this research were students of SMPN 9 Jambi City, totaling 88 people. The sampling technique was purposive random sampling. The data analysis used was univariate and bivariate. The highest frequency of diet quality was in the poor diet quality category, 52 respondents (59.1%). It can be concluded that a relationship exists between diet quality and nutritional status (p-value ≤ 0.05).

Flexural vibration test method for determining the dynamic elastic modulus of full-size strawboards for use in transportation framed cases

This paper proposes an improvement in the test method for determining the flexural dynamic modulus of elasticity of strawboard with two triangular prisms as supports, for quality control and classification. Free-plate modal and free-plate transient excitation methods were used to test the elastic modulus of 1/4-plate and whole-plate strawboards. The dynamic test results were verified with the four-point bending method and tensile method. The results show that the elastic moduli of strawboards is approximately 2 GPa. The dynamic test method proposed is efficient, simple, repeatable, and accurate. This method is more suitable for factory applications than existing dynamic testing methods. The framed cases produced by the strawboard all meet the performance requirements in GB/T 7284 (2016).

Development of a STREAM-Based Self-Study Module to Stimulate HOTS in Petroleum Study Materials

This study aimed to develop a STREAM-based self-study module to stimulate HOTS in petroleum study materials and to test the effectiveness of the module. This developmental research used the 4D method. This research began with a literature study and observations of class XI MIPA students to see the material used and the learning process, as well as to find out the obstacles to learning. The initial conditions provided ideas for making STREAM-based self-study modules that are expected to stimulate students’ HOTS. The module was tested on a limited basis on online classes of XI MIPA 3 to see its practicality. In completing the module, an expert feasibility test was carried out. The expert test showed that the STREAM-based learning module to stimulate HOTS was in the very appropriate category with an average score of 94.55%. For the very decent quality category with a value of 93.13%, for the learning quality category, it was in the very feasible category with a score of 94, 28%., and 96.25% in the very decent category for technical quality (media). The very feasible category was at 96.67% for student readability. The use of STREAM-based self-study modules to facilitate HOTS was proven to be effective and can be used in the learning process. In modules with the STREAM approach to learning chemistry, students can develop independent learning skills and higher thinking skills.
 Keywords: STREAM-based self-study, HOTS, petroleum

Effectiveness of Sleep Hygiene Intervention on the Sleep Quality of the Elderly in Nursing Homes in Salatiga City

The aging process, physiologically, brings about changes and increases the risk of disease. Twenty-eight older adults in three different nursing homes in Salatiga City had difficulty sleeping at night. Poor sleep quality can cause disorders, such as being at higher risk of diseases, forgetfulness, confusion, disorientation, and decreased concentration ability. This research contributed to identifying the effectiveness of sleep hygiene on elderly sleep disorders in Salatiga City nursing homes. The research method is a quasi-experiment with a one-group pretest-posttest design research design among 28 older people who lived in the nursing home from April to July 2023. Sleep hygiene intervention in the form of aerobic exercise and education was carried out five times in five weeks. Sleep quality was measured through the Pittsburgh Sleep Quality Questionnaire (PSQI), and data was analyzed using descriptive statistics (percentage and mean). The results of the pre-experiment study revealed a 33.21% prevalence of sleep disturbances among older individuals, with the majority falling into the category of moderate sleep quality (64%). After receiving a sleep hygiene intervention, there was a reduction of 2.89 in the number of elderly individuals experiencing sleep disturbances. Additionally, 36% of the elderly were found to be sleeping without any disorders, while 64% experienced only mild sleep disturbances. This means that there will be a significant change after the intervention. Conclusion Sleep hygiene effectively improves sleep quality in older people in nursing homes. Future research is expected to conduct physical and laboratory examinations to support the respondent's sleep quality results

Assessment Of Remote Sensing Approach For Urban Ecological Quality Evaluation In Pekanbaru City, Riau Province Indonesia

There are obstacles in estimating environmental dynamics behind its convenience, beginning with the development of effective policies for sustainable urban development. The objectives of this research were to comprehend the ability and performance of ecological indices integration and to identify the spatial distribution of changes from 2018 to 2021 in Pekanbaru City, Riau province, Indonesia. This study employed remote sensing data to create ecological parameters including the build-up index, vegetation index, soil index, and moisture index, as well as principal component analysis to generate ecological index integration. The findings indicate a correlation of over 90% among these parameters from 2018 to 2021. Overall, there has been a significant decrease in the ecological quality index’s high-quality categories, such as good and excellent, covering a total of 19.6% over 127 km². Conversely, the poor ecological quality category increased to 2.2%, encompassing an area of 15 km², up from the initial 21.2% covering 122 km². Additionally, the fair and moderate categories also experienced increases of 4% and 13.4%, respectively, reaching 28 km² and 84 km². The study area’s ecological quality is largely affected by increased anthropogenic activities, leading to a drastic decrease in the presence of ecological quality in the good and excellent categories. The importance of spatial planning is emphasized to incorporate aspects of ecological assessment rather than solely focusing on increasing economic activity. This outcome can be used to respond to the concept of sustainable development by caring for the ecological environment, particularly in urban areas, and mitigating ecological damage.

Формирование морфологии деревьев культивируемых сосновых древостоев

The aim of this study is to assess the qualitative characteristics of the crowns, stems and growth of Scots pine (Pinus sylvestris L.) trees in cultivated stands of a dry forest on gently sloping hills thinned in a regular-uniform manner, as well as to develop proposals for production based on the data obtained. The stands under study grow in the southwestern part of the ribbon forests of the Altai Territory in the territory of the Klyuchevskoye forestry. The thinning was carried out in 2016 as part of the planned forest maintenance activities. The field stage of the research was completed in 2022. The sample plots laid had a rectangular shape and a square of at least 0.25 ha. The assessment of the qualitative characteristics of the trees has been carried out in accordance with the classification proposed by G.A. Chibisov et al. The significance of differences (or their absence) in the tree distribution by quality categories in the plots has been established using the χ2 test for comparing two distributions. The article presents the taxation characteristics of the main forest element of the stands under study, the quality indices for the assessed tree indicators, and the graphs of tree distribution by diameter and quality classes. It has been found that most of the trees have poor crowns (27–45 %) and growth (14–41 %). Such indicators are a consequence of an excessively high density of the stands and the lack of timely thinning. Regular-uniform thinning of moderate and moderately high intensity has contributed to an increase in the quality class of the trees. The impact of high and very high intensity thinning on the stands has been negative. Based on the conducted research, it is recommended to carry out thinning at an earlier age of the stand, abandon the regular-uniform thinning in favor of selective thinning using the grassroots method (if possible) and carry out thinning along the largest exploitable diameter of 10 cm in the studied forest stands and similar ones. The study can be continued by establishing the optimal exploitable diameter in younger plantations and in other types of forests, as well as determining the intervals of optimal density of pine stands of different ages and different types of forest. The results obtained can be useful in designing economic activities in the study area.

Klasifikasi kualitas mutu susu pasteurisasi menggunakan metode klasifikasi k-Nearest Neighbor

Milk is a product that is often consumed because it has many good benefits for the body. One of the benefits of milk is that it contains calcium, which is useful for the growth of bones and teeth. However, many consumers still choose dairy products based only on their appearance, even though milk quality is not based on its appearance but is found during milk processing. Pasteurization is the process of heating whole milk at a specific temperature and for a certain period to increase the milk's shelf life and maintain the milk's quality. Two types of pasteurization processes are Long Temperature Long Time and High Temperature Short Time. k-Nearest Neighbor or k-NN is a data mining method used to classify objects from the data, using distance calculations or Euclidean Distance to look for similarities between neighbors. So, based on this problem, milk quality classification is carried out. The research was carried out by classifying milk quality using the K-NN method. The public data used to classify is 1,059 pasteurized milk data, with seven regular or ordinary attributes and one special or class attribute. The K values ​​that will be used in this research are k=3, k=5, and k=7. From the analysis of calculation results using the k-NN method, the accuracy values ​​obtained are k=3 is 97%, k=5 is 89%, and k=7 is 87%.

Development of a portable electronic nose for the classification of tea quality based on tea dregs aroma

Abstract The current assessment of tea quality is considered subjective. This study aims to develop a portable electronic nose to assess the aroma of tea dregs objectively by relying on the aromatic capture process through sensors and using multilayer perceptron (MLP). A MLP with some hyperparameter variations is used and compared with five machine-learning classifiers. The classification using MLP model with ReLU activation function and 3 hidden layers with 100 hidden nodes resulted in the highest accuracy of 0.8750 ± 0.0241. The MLP model using ReLU activation function is better than Sigmoid while increasing the number of hidden layers and hidden nodes does not necessarily enhance its performance. In the future, this research can be improved by adding sensors to the portable electronic nose, increasing the number of datasets used, and using ensemble learning or deep learning models.

Improving accuracy of vascular access quality classification in hemodialysis patients using deep learning with K highest score feature selection.

To develop and evaluate a novel feature selection technique, using photoplethysmography (PPG) sensors, for enhancing the performance of deep learning models in classifying vascular access quality in hemodialysis patients. This cross-sectional study involved creating a novel feature selection method based on SelectKBest principles, specifically designed to optimize deep learning models for PPG sensor data, in hemodialysis patients. The method effectiveness was assessed by comparing the performance of multiple deep learning models using the feature selection approach versus complete feature set. The model with the highest accuracy was then trained and tested using a 70:30 approach, respectively, with the full dataset and the SelectKBest dataset. Performance results were compared using Student's paired t-test. Data from 398 hemodialysis patients were included. The 1-dimensional convolutional neural network (CNN1D) displayed the highest accuracy among different models. Implementation of the SelectKBest-based feature selection technique resulted in a statistically significant improvement in the CNN1D model's performance, achieving an accuracy of 92.05% (with feature selection) versus 90.79% (with full feature set). These findings suggest that the newly developed feature selection approach might aid in accurately predicting vascular access quality in hemodialysis patients. This advancement may contribute to the development of reliable diagnostic tools for identifying vascular complications, such as stenosis, potentially improving patient outcomes and their quality of life.

The development of color histogram method to identify air quality index based on sky images

Air quality index measurements in Indonesia are carried out by ministry of environment and forestry (KLHK). The Ministry divides air quality levels into 5 categories, namely good, moderate, unhealthy, very unhealthy and dangerous. In this study, 3 air quality categories were used as primary research data, namely good, moderate and unhealthy because the others, never occurred in Indonesia from the time this research was conducted until its completion. This research develops the color histogram method in order to recognize the shape of an object in an image. First stage in this research is inputting the sky image into the system. Then carry out pre-processing in the form of cropping the image to obtained is only an image of sky. Next, convert the red, green and blue (RGB) colored sky image to Grayscale, then image enhancement, then noise reduction. After that is processed using development of the color histogram method. Refinement of color histogram method has yielded an impressive accuracy level of 90%, validated through the analysis of 30 sky images. The method successfully detected 27 images accurately, while three images posed detection challenges. The findings of this research is color histogram method can be used to identify objects especially air pollution from sky images.

Enhancing Hospital Pharmacy Operations Through Lean and Six Sigma Strategies: A Systematic Review.

Hospital pharmacies are integral to the healthcare system, and evaluating the factors influencing their efficiency and service standards is imperative. This analysis offers global insights to assist in developing strategies for future enhancements. The objective is to identify the optimal Lean Six Sigmamethodologies to improve workflow and quality of hospital pharmacy services. A strategic search, aligned with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, encompassed an extensive range of academic databases, including Scopus, PubMed/Medline, Web of Science, and other sources for relevant studies published from 2009 to 2023. The focus was on management tactics and those examining outcomes, prioritizing publications reflecting pharmacy operations management's state. The quality of the selected articles was assessed, and the results were combined and analyzed. The search yielded 1,447 studies, of which 73 met the inclusion criteria. The systematic review found a low to moderate overall risk of bias. The number of publications roseduring the coronavirus disease (COVID-19) outbreak. Among studies, research output in the United States of Americarepresented26% of the total. Other countries such asIndonesia, Spain, Canada, China, Saudi Arabia, the United Arab Emirates, and the United Kingdom also made significant contributions. Each country accounted for 12%, 8%, 7%, 5%, 5%, 5%, and 5%, respectively. The pharmacy journals led with26 publications, and healthcare/medical with 14. The quality category came next with 12 articles, whileseven journals represented engineering.Studies used empirical and observational methods, focusing on practice quality enhancement. The process control plan had 26 instances, and the define, measure, analyze, improve, and control (DMAIC) was identified 13 times. The sort, set in order, shine, standardize, and sustain (5S)ranked third, totaling seven occurrences. Failure mode and effects analysis (FMEA) and root cause analysiswere moderately utilized, with six and four instances, respectively. Poka-Yoke (mistake-proofing measures) and value stream mappingwere each counted three times.Quality improvement and workflow optimization dominated managerial strategies in 22 (30.14%) studies each, followed by technology integration in 15 (20.55%). Cost, patient care, and staffing each featured in three (4.11%) studies, while two (2.74%) focused on inventory management. One (1.37%) study each highlighted continuing education, collaboration, and policy changes. Analysis of the 73 studies on Lean and Six Sigma in hospital pharmacy operations showed significant impacts, with 26% of studies reporting decreased medication turnaround time, 15% showing process efficiency improvements, and 11% each for enhanced inventory management and bottleneck/failure mode reduction. Additionally, 9% of studies observed decreased medication errors, 8% noted increased satisfaction and cost savings, 6% identified enhancements in clinical activities, 3% improved prescription accuracy, 2% reduced workflow interruptions, and 1% reported increased knowledge.Also, this study has identified key strategies for service delivery improvement and the importance of quality practices and lean leadership. To the best of the author's knowledge, this research is believed to be the first in-depth analysis of Lean and Six Sigma in the hospital pharmacy domain, spanning 15 years from 2009 to 2023.

Trajectory classification to support effective and efficient field-road classification.

Field-road classification, which automatically identifies in-field activities and out-of-field activities in global navigation satellite system (GNSS) recordings, is an important step for the performance evaluation of agricultural machinery. Although several field-road classification methods based only on GNSS recordings have been proposed, there is a trade-off between time consumption and accuracy performance for such methods. To obtain an optimal balance, it is important to choose a suitable field-road classification method for each trajectory based on its GNSS trajectory quality. In this article, a trajectory classification task was proposed, which classifies the quality of GNSS trajectories into three categories (high-quality, medium-quality, or low-quality). Then, a trajectory classification (TC) model was developed to automatically assign a quality category to each input trajectory, utilizing global and local features specific to agricultural machinery. Finally, a novel field-road classification method is proposed, wherein the selection of field-road classification methods depends on the trajectory quality category predicted by the TC model. The comprehensive experiments show that the proposed trajectory classification method achieved 86.84% accuracy, which consistently outperformed current trajectory classification methods by about 2.6%, and the proposed field-road classification method has obtained a balance between efficiency and effectiveness, i.e., sufficient efficiency with a tolerable accuracy loss. This is the first attempt to examine the balance problem between efficiency and effectiveness in existing field-road classification methods and to propose a trajectory classification specific to these methods.

Assessment of the Physico-Chemical Quality of Untreated and Treated Water Using Water Quality Index (WQI) in the Ain Zada Dam, East Algeria

The Ain Zada dam is experiencing a deficit in recent years due to scanty precipitation and reduced water flow, resulting in the dam’s water reserve dropping to 8.3%. Our study’s primary objective is to assess the quality of both untreated (raw) and treated water from the Ain Zada dam by conducting physico-chemical analyses on both samples. Based on the findings, the physico-chemical quality of the treated water is deemed satisfactory, as all samples adhere to Algerian standards. The treated water exhibits an organic matter concentration of 4.94 mg/l, which is the chief contributor to the unfavourable odour and taste of the dam water. To address this, enhancements will be made to the Ain Zada dam treatment station this year through the adoption of a novel ozone disinfection method (ozonation). The Water Quality Index (WQI) for treated water from the Ain Zada dam indicates a favourable suitability for drinking water production from February to May 2022. Two quality categories are discernible for each scenario based on the obtained WQI values: excellent and good for treated water, and excellent and poor for raw water. Water quality diminishes notably in April, where the treated water quality index rises (25 < WQI . 50), while the raw water’s quality classifies as poor (WQI > 50). The decline in water quality during this month can be attributed to stream variations, agricultural activities, and urban wastewater discharges. Consequently, it is imperative to prioritise the reduction of these pollution sources to safeguard water resources and enhance water quality within the watershed.

DSNetax: a deep learning species annotation method based on a deep-shallow parallel framework.

Microbial community analysis is an important field to study the composition and function of microbial communities. Microbial species annotation is crucial to revealing microorganisms' complex ecological functions in environmental, ecological and host interactions. Currently, widely used methods can suffer from issues such as inaccurate species-level annotations and time and memory constraints, and as sequencing technology advances and sequencing costs decline, microbial species annotation methods with higher quality classification effectiveness become critical. Therefore, we processed 16S rRNA gene sequences into k-mers sets and then used a trained DNABERT model to generate word vectors. We also design a parallel network structure consisting of deep and shallow modules to extract the semantic and detailed features of 16S rRNA gene sequences. Our method can accurately and rapidly classify bacterial sequences at the SILVA database's genus and species level. The database is characterized by long sequence length (1500 base pairs), multiple sequences (428,748 reads) and high similarity. The results show that our method has better performance. The technique is nearly 20% more accurate at the species level than the currently popular naive Bayes-dominated QIIME 2 annotation method, and the top-5 results at the species level differ from BLAST methods by <2%. In summary, our approach combines a multi-module deep learning approach that overcomes the limitations of existing methods, providing an efficient and accurate solution for microbial species labeling and more reliable data support for microbiology research and application.

Soil Quality under Polyhouse and Open Field Conditions as Affected by Continuous Fertilizer and Manure Applications

ABSTRACT Intensive cultivation coupled with scrupulous use of fertilizers could be detrimental to soil health and quality of the produce in the long run. Therefore, this study aimed to assess the predominant protected/polyhouse and open field vegetable production on soil quality. The changes in soil quality in comparison to control (fallow) indicate, whether the management practices being adopted under both the systems are sustainable or not. Thus, a study was conducted to identify the most appropriate soil quality indicators and finally to know about the status of soil quality both under polyhouse and open field system of cultivation in mid hills of North western Himalayas. Principal component analysis (PCA), a data reduction tool was employed to get the minimum data set for the assessment of soil quality. Geo-referenced soil samples were collected from 25 progressive farmers’ cultivating vegetables under polyhouse, as well as open field conditions and analyzed for various physical, chemical and biological indicators of soil quality. The PCA was performed which screened out the three principal components (PCs) with eigen value > 1. Average soil quality index (SQI) for different sites was observed to be on higher under polyhouse conditions (0.815) as compared to open field cultivation for vegetables (0.760) and control (0.703). Where, soil organic carbon contributed maximum to soil quality (75.6%) followed by pH (13.9%), water holding capacity (6.8%) and EC (3.6%). Conclusively, polyhouse cultivation system revealed high category of soil quality. Whereas, open field cultivation system revealed medium category of soil quality.

A system for automatic classification of endodontic treatment quality in CBCT.

An evaluation of the effectiveness of a new computational system proposed for automatic classification, developed based on a Siamese network combined with Convolutional Neural Networks (CNNs), is presented. It aims to identify endodontic technical errors using Cone Beam Computed Tomography (CBCT). The study also aims to compare the performance of the automatic classification system with that of dentists. One thousand endodontically treated maxillary molars sagittal and coronal reconstructions were evaluated for the quality of the endodontic treatment and the presence of periapical hypodensities by three board-certified dentists and by an oral and maxillofacial radiologist. The proposed classification system was based on a Siamese network combined with EfficientNet B1 or EfficientNet B7 networks. Accuracy, sensivity, precision, specificity, and F1-score values were calculated for automated artificial systems and dentists. Chi-square tests were performed. The performances were obtained for EfficienteNet B1, EfficientNet B7 and dentists. Regarding accuracy, sensivity and specificity, the best results were obtained with EfficientNet B1. Concerning precision and F1-score, the best results were obtained with EfficientNet B7. The presence of periapical hypodensity lesions was associated with endodontic technical errors. In contrast, the absence of endodontic technical errors was associated with the absence of hypodensity. Quality evaluation of the endodontic treatment performed by dentists and by Siamese Network combined with EfficientNet B7 or EfficientNet B1 networks was comparable with a slight superiority for the Siamese Network. CNNs have the potential to be used as a support and standardization tool in assessing endodontic treatment quality in clinical practice.