Mixed Class Research Articles

Finding changes within protected habitats: an assessment of landscape characteristics using geospatial techniques in the lower Shivalik landscape of the Western Himalayas

A GIS-based approach was used in this study to assess 10 years of land-use change in the lower Shivalik landscape of the Western Himalayas, India. The landscape encompasses two major protected areas which are home to a thriving population of large mammals. The main objective was to identify the changes in the land use pattern, specifically after forming a new tiger reserve in the landscape. Landsat 8 and 4 imagery were used for the time series analysis for the years 2018, 2013, and 2008, respectively. The change was calculated using nine land use classes, with mixed forest showing the highest change value, followed by agriculture, grassland, and scrub. The dense forest area increased immensely after the formation of the new tiger reserve. The results for dense and mixed forest classes also identified less fragmentation in the number of patches during the same time. Even though most of the landscape is within the boundaries of a protected area, the change in land use forms is noticeable. As development activities continue to expand, this region will face increased strain, which will impact the area’s natural biodiversity. To protect that, a long-term conservation management effort is required.

Gene Expression Analysis for Uterine Cervix and Corpus Cancer Characterization.

The analysis of gene expression quantification data is a powerful and widely used approach in cancer research. This work provides new insights into the transcriptomic changes that occur in healthy uterine tissue compared to those in cancerous tissues and explores the differences associated with uterine cancer localizations and histological subtypes. To achieve this, RNA-Seq data from the TCGA database were preprocessed and analyzed using the KnowSeq package. Firstly, a kNN model was applied to classify uterine cervix cancer, uterine corpus cancer, and healthy uterine samples. Through variable selection, a three-gene signature was identified (VWCE, CLDN15, ADCYAP1R1), achieving consistent 100% test accuracy across 20 repetitions of a 5-fold cross-validation. A supplementary similar analysis using miRNA-Seq data from the same samples identified an optimal two-gene miRNA-coding signature potentially regulating the three-gene signature previously mentioned, which attained optimal classification performance with an 82% F1-macro score. Subsequently, a kNN model was implemented for the classification of cervical cancer samples into their two main histological subtypes (adenocarcinoma and squamous cell carcinoma). A uni-gene signature (ICA1L) was identified, achieving 100% test accuracy through 20 repetitions of a 5-fold cross-validation and externally validated through the CGCI program. Finally, an examination of six cervical adenosquamous carcinoma (mixed) samples revealed a pattern where the gene expression value in the mixed class aligned closer to the histological subtype with lower expression, prompting a reconsideration of the diagnosis for these mixed samples. In summary, this study provides valuable insights into the molecular mechanisms of uterine cervix and corpus cancers. The newly identified gene signatures demonstrate robust predictive capabilities, guiding future research in cancer diagnosis and treatment methodologies.

Smart-sight: Video-based waste characterization for RDF-3 production

A material recovery facility (MRF) can transform municipal solid waste (MSW) into a valued commodity called refuse-derived fuel (RDF) as a promising solution to waste-to-energy conversion. The quality of the produced RDF significantly relies on the composition of in-feed waste and waste characterization method applied for auditing purposes, a process that is both time-consuming and fraught with potential hazards. This study focuses to enhance the workflow of the waste characterization process at an MRF. A solution named Smart Sight is proposed to detect and classify waste based on videos recorded after processing MSW through a mechanical sorting line consisting of bag breakers and trommel screens. A comprehensive dataset is created encompassing thirteen mixed waste classes from single and multi-family streams. The dataset is preprocessed with motion compensation techniques and frame differencing methods to extract and refine valuable frames. A one-stage YOLO detector model is then trained over the dataset. The experimental results show that the proposed method works efficiently at detecting and classifying waste objects in indoor MRF environments. Accuracy, precision, recall, and F1 score related to the proposed solution are found to be 0.70, 0.762, 0.69 and 0.72, respectively, with a mAP@0.5 of 0.716. The proposed approach is validated using data collected from local MRF by comparing the estimated waste composition values of the proposed solution with laboratory results obtained through current standardized industrial practices. Comparison reveals that waste characterization estimation obtained is consistent with the laboratory results, inferring that Smart-Sight is a viable tool for estimating waste composition.

Using the Lattice Method to help Basic Six (6) Pupils of Zogbeli Primary School in Ghana to understand the Concept of Multiplying Multi-digit Multiplicands

This study emerged as a result of the inability of Basic Six (6) at Zogbeli Primary School pupils in Tamale, Northern Region Ghana to understand the concept of multiplying multi-digit multiplicands. To address this problem, the action research methodology was used to provide an immediate solution. A simple random sample of 45 pupils from a total of 90 pupils of a mixed class of P.6A and P.6B was chosen to participate in the study. A Pre-test consisting of 10 test items was administered to assess the pupils’ initial understanding of the concept. The results showed that the majority of the pupils scored poorly, with a mean score of 1.51 and a standard deviation of 0.91. An intervention using the lattice method for five days was then implemented. The lattice method is a hands-on learning method that involves both physical movement and mental engagement. It was hypothesized that this method would help the pupils to develop a deeper understanding of the concept of multiplying multi-digit multiplicands. After the three-day intervention, a Post-test was administered to assess the pupils’ understanding of the concept of multiplying multi-digit multiplicands and also to assess how effective the intervention was in the study. The results showed a significant improvement, with a mean score of 8.11 and a standard deviation of 2.54. Additionally, 93.4% of the pupils scored above the average mark of 5. The findings of this study suggest that lattice technology is an effective method for teaching the concept of multiplying multi-digit multiplicands. This method is engaging and easy to understand, and it can help pupils develop a deeper understanding of the mathematical concepts involved. In conclusion, the use of lattice technology has improved the learning experiences of Basic Six pupils at Zogbeli Primary School. Pupils now have a practical method for solving multi-digit multiplication problems, and they have a better understanding of the mathematical concepts involved. Keywords: Action Research, Pre-test, Post-test, Paired Sample t-test, Lattice technology

Event recognition method based on feature synthesizing for a zero-shot intelligent distributed optical fiber sensor

Phase-sensitive optical time domain reflectometer (Φ-OTDR) is an emergent distributed optical sensing system with the advantages of high localization accuracy and high sensitivity. It has been widely used for intrusion identification, pipeline monitoring, under-ground tunnel monitoring, etc. Deep learning-based classification methods work well for Φ-OTDR event recognition tasks with sufficient samples. However, the lack of training data samples is sometimes a serious problem for these data-driven algorithms. This paper proposes a novel feature synthesizing approach to solve this problem. A mixed class approach and a reinforcement learning-based guided training method are proposed to realize high-quality feature synthesis. Experiment results in the task of eight event classifications, including one unknown class, show that the proposed method can achieve an average classification accuracy of 42% for the unknown class and obtain its event type, meanwhile achieving a 74% average overall classification accuracy. This is 29% and 7% higher, respectively, than those of the ordinary instance synthesizing method. Moreover, this is the first time that the Φ-OTDR system can recognize a specific event and tell its event type without collecting its data sample in advance.

Mapping weed infestation in maize fields using Sentinel-2 data

Weed management in maize farms is a time-specific activity and requires timely detection of weed infestations. The challenge to early detection of weeds is that many dicotyledonous crops and broad-leaved weeds often display similar reflectance profile in the early growth stage and requires hyperspectral data to detect them. However, the advent of Sentinel-2 sensor series, with enhanced spectral configuration featuring red-edge bands that are known for species-level discrimination of plants, presents an affordable opportunity to detect weeds using multispectral data. The present study explores the question of whether Sentinel-2 sensor with its advanced spectral configuration can differentiate weeds from maize (Zea mays) in the early growth stages of maize. The study recorded 165 GPS points of weeds, maize, and mixed class in six maize farms during the early stages of maize growth. These GPS points were overlaid on Sentinel-2 images acquired within two days of field data gathering to guide the collection of spectral signatures of the maize, mixed, and weed classes. Spectral signatures were divided into training (70%) and validation (30%) data in a Random Forest (RF) model with S-2 spectral bands and vegetation indices as predictor variables. Spectral signatures were firstly tested for spectral separability between classes using ANOVA. The results of spectral analysis showed that the weed class had higher interclass variability from the maize and mixed class particularly in the red-edge and NIR regions of Sentinel-2. The classification matrix consistently showed that weeds were detected with high user and producers’ accuracy of 95%. These results indicate the utility of the enhanced spectral configuration of Sentinel-2 data in the early detection of weeds in maize farms.

Automatic Detection of Rice Blast Fungus Spores by Deep Learning-Based Object Detection: Models, Benchmarks and Quantitative Analysis

The severity of rice blast and its impacts on rice yield are closely related to the inoculum quantity of Magnaporthe oryzae, and automatic detection of the pathogen spores in microscopic images can provide a rapid and effective way to quantify pathogen inoculum. Traditional spore detection methods mostly rely on manual feature extraction and shallow machine learning models, and are mostly designed for the indoor counting of a single spore class, which cannot handle the interference of impurity particles in the field. This study achieved automatic detection of rice blast fungus spores in the mixture with other fungal spores and rice pollens commonly encountered under field conditions by using deep learning based object detection techniques. First, 8959 microscopic images of a single spore class and 1450 microscopic images of mixed spore classes, including the rice blast fungus spores and four common impurity particles, were collected and labelled to form the benchmark dataset. Then, Faster R-CNN, Cascade R-CNN and YOLOv3 were used as the main detection frameworks, and multiple convolutional neural networks were used as the backbone networks in training of nine object detection algorithms. The results showed that the detection performance of YOLOv3_DarkNet53 is superior to the other eight algorithms, and achieved 98.0% mean average precision (intersection over union > 0.5) and an average speed of 36.4 frames per second. This study demonstrated the enormous application potential of deep object detection algorithms in automatic detection and quantification of rice blast fungus spores.

Sentinel-2 versus PlanetScope Images for Goldenrod Invasive Plant Species Mapping

A proliferation of invasive species is displacing native species, occupying their habitats and degrading biodiversity. One of these is the invasive goldenrod (Solidago spp.), characterized by aggressive growth that results in habitat disruption as it outcompetes native plants. This invasiveness also leads to altered soil composition through the release of allelopathic chemicals, complicating control efforts and making it challenging to maintain ecological balance in affected areas. The research goal was to develop methods that allow the analysis of changes in heterogeneous habitats with high accuracy and repeatability. For this reason, we used open source classifiers Support Vector Machine (SVM), Random Forest (RF), and satellite images of Sentinel-2 (free) and PlanetScope (commercial) to assess their potential in goldenrod classification. Due to the fact that invasions begin with invasion footholds, created by small patches of invasive, autochthonous plants and different land cover patterns (asphalt, concrete, buildings) forming heterogeneous areas, we based our studies on field-verified polygons, which allowed the selection of randomized pixels for the training and validation of iterative classifications. The results confirmed that the optimal solution is the use of multitemporal Sentinel-2 images and the RF classifier, as this combination gave F1-score accuracy of 0.92–0.95 for polygons dominated by goldenrod and 0.85–0.89 for heterogeneous areas where goldenrod was in the minority (mix class; smaller share of goldenrod in canopy than autochthonous plants). The mean decrease in the accuracy analysis (MDA), indicating an informativeness of individual spectral bands, showed that Sentinel-2 bands coastal aerosol, NIR, green, SWIR, and red were comparably important, while in the case of PlanetScope data, the NIR and red were definitely the most important, and remaining bands were less informative, and yellow (B5) did not contribute significant information even during the flowering period, when the plant was covered with intensely yellow perianth, and red-edge, coastal aerosol, or green II were much more important. The maximum RF classification values of Sentinel-2 and PlanetScope images for goldenrod are similar (F1-score > 0.9), but the medians are lower for PlanetScope data, especially with the SVM algorithm.

악기를 활용한 유아음악활동에 관한 탐구

The purpose of this study is to examine the musical experience of young children from the perspective of Deleuze's philosophy and to understand the events and meanings generated from the encounters between young children and musical instruments. To this end, 10 children aged 3-5 in a mixed class of a daycare center were observed participating in special music activities for about 4 months, and as a kind of post-qualitative research method, Deleuze's philosophical concepts were used for analyzing the data generated. As a result of the study, young children created new meanings through various sensory encounters in music activities using musical instruments, and invented their own musical styles through creative experiments and expressions. In addition, the children's musical instrument activities were interpreted as the meaning of new creation through agential practices of musical ‘becoming.’ Based on these results, it was suggested that looking at young children's musical activities with an open eye by focusing on their encounters with materials and relationships, and moving from the standardized teaching approaches to new meanings created by musical instruments through philosophical thinking.

Meningkatkan Hasil Belajar Menggunakan Alat Permainan Puzzle Pada Materi Operasi Hitung Campuran Bilangan Bulat Kelas IV SD Negeri Pogo Lede

The cause of the problem of low learning outcomes for fourth grade students at Pogo Lede State Elementary School in mathematics subjects consists of teacher and student foctors. Teachers are less creative in delivering material, do not use media that, can attract students’ attention and interest in learning, focus more on using prinred books, and do not involve students. Apart from that, he also has difficulty in calculating. This makes students feel bored, fed up and sometimes students just sit still during the learning process so that the material provided by the teacher cannot be received optimally, causing the learning outcomes of class IV students at SD Negeri Pogo Ledeto still be below average. This research uses classroom action research (PTK) techniques because the research is carried out to solve problems that occur in the claasroom. In classroom action research improvements are carried out gradually and continuously as long as the research activities are carried out. Therefore, in PTK there is an implementation cycle in the from of a planning pattern. Carrying out observations and reflections so that in the next dycle it reaches the level you want to achieve. Based on the background above, the researcher chose to apply the puzzle game learning method. This method can make students activel discuss in class or in small groups to formulate the title improving learning outcomes using puzzle game tools on operation material counting mixed integers class IV Pogo Lede State Elementary School

Machine learning and soft voting ensemble classification for earthquake induced damage to bridges

Seismic vulnerability assessment of bridges is essential for both pre-earthquake planning and post-earthquake rapid loss assessment to facilitate maintenance planning and recovery initiatives. Given the impracticality of creating numerical models for every conceivable attribute variation within bridge classes, data-driven approaches offer a versatile solution to address such variations. While data-driven techniques are widely adopted in bridge engineering, many existing models rely on numerical or experimental datasets. However, such models may not adequately capture the dynamic and complex nature of seismic response of bridges. This presents an opportunity to evaluate the effectiveness of data-driven approaches using post-earthquake damage data. This paper assesses the efficacy of machine learning models and develops a robust framework for the classification of earthquake-induced damage incurred to some bridge classes. We deployed damage data collected following the 2015 Gorkha earthquake, incorporating taxonomical, stiffness, and excitation variables as predictor variables. Considering three distinct damage classes—minor, major, and critical—in a heterogeneous bridge class environment, we evaluated multiple machine learning classifiers. To account for the influence of heterogeneous bridge classes in the dataset, we conducted separate assessments for the reinforced concrete (RCC) bridge class. The Random Forest classifier achieved a substantial accuracy improvement, reaching 83% when compared to the mixed bridge class accuracy of 79% reflected by the ANN classifier. Similarly, a minor improvement was observed when deploying soft voting classifiers for RCC only bridge data. The sum of observations suggest that tree-based classifiers consistently outperform other classifiers when considering single bridge classes. This study identifies stiffness variables as the most critical factor in bridge damage classification.

Glomerular Exostosin-Positivity is Associated With Disease Activity and Outcomes in Patients With Membranous Lupus Nephritis

IntroductionThe relationship of Exostosin 1 and Exostosin 2 (EXT1/EXT2) expression and outcomes in membranous lupus nephritis (MLN) was controversial. MethodsEXT1/EXT2 was performed by immunohistochemistry in 283 consecutive MLN patients. Clinicopathological characteristics and outcomes of EXT1/EXT2-positive patients were compared with EXT1/EXT2-negative patients.The primary endpoints were adverse renal events include death, dialysis and renal transplantation. Results29.3% MLN patients were positive for EXT1/EXT2. The prevalence of EXT1/2 positive MLN was significantly higher in pure class V MLN than those for mixed class V MLN (44.2% vs. 19.4%, P<0.001). For EXT1/EXT2-positive patients, For EXT1/EXT2-positive patients, the median time between onset of lupus and renal biopsy, and lupus nephritis and renal biopsy is shorter [6 (IQR, 2-25) months vs. 12 (IQR, 3-49) months, P=0.008 and 3 (IQR, 2-18) months vs. 6 (IQR, 2-23) months, P=0.039].) and they had significantly lower Systemic Lupus Erythematosus Disease Activity Index scores (P=0.015) and lower serum creatinine levels (P<0.001), higher hemoglobin (P=0.006) as well as lower blood pressure. The EXT1/EXT2-positive patients had significantly fewer chronicity features (glomerulosclerosis, P<0.001, interstitial fibrosis, P=0.006, and tubular atrophy, P=0.002) and fewer activity indicators (endocapillary hypercellularity, P=0.012, fibrocellular crescents, P=0.007 and cellular crescents, P<0.001) on renal biopsy. After a Median follow-up of 65 (28-126) months, EXT1/EXT2-positive patients were less likely to experience adverse renal events (2.4% vs. 16.0%, P=0.001). ConclusionCompared with EXT1/EXT2-negative patients, the EXT1/EXT2-positive patients presented with lower disease activity and were less likely to experience adverse renal events in relationship with the chronicity index.

Comparison of machine learning and parametric methods for the discrimination of urban land cover types

The aim of this study is to compare the performances of different machine learning and parametric techniques for differentiating highly mixed urban land cover classes in Ulaanbaatar, the capital city of Mongolia, using multisource data sets. For data sources, 17 features are chosen, including the original 10 spectral bands of the Sentinel-2 data; VV, VH, average of HH & HV, and simple ratio of Sentinel-1 data; and normalized difference vegetation index (NDVI), Bare soil index (BSI), and modified soil adjusted vegetation index (MSAVI). Six different feature combinations are used to identify available urban land cover classes. To discriminate existing classes, a support vector machine (SVM), artificial neural network (ANN), random forest (RF), and a statistical maximum likelihood classifier (MLC) are employed and compared. In all six feature combinations, the RF method outperforms the others with an overall accuracy ranging from 81.72% to 90.71%. The SVM has an overall accuracy ranging from 77.77%-83.50%, with the second-highest performance in four combinations and the lowest in two. The ANN has an overall accuracy ranging from 74.83%-83.31%, with poorer performance than the SVM. The MLC's performance varies across feature combinations, with an overall accuracy ranging from 70.96%-85.92%, and the second-highest performance in two feature combinations. Overall, the study shows that multisource information along with additional features/indices can significantly improve the classification of mixed urban land cover types, and for the given test site, the RF technique is the best option for producing a dependable land cover map.

A Study on the Enhancement Strategies of Physical Education Teaching in Colleges and Universities Based on the Kano Model

Abstract Physical education (PE) within higher education plays a crucial role in fostering moral integrity among students. However, traditional approaches in PE have often struggled to align with student needs, leading to suboptimal educational outcomes. To address these challenges, this study employs and refines the Kano model to better analyze and categorize student needs in PE classrooms. By adopting mixed class analysis, the ambiguity typically associated with classifying needs in the conventional Kano model is significantly reduced. This refinement allows for a more nuanced consideration of various demand elements. Utilizing the Better-Worse coefficient, the study evaluates the influence of distinct educational needs on student satisfaction within courses. This analysis facilitates the identification of specific types of needs. For instance, in a controlled study focusing on basketball training, the application of the revised Kano model yielded notable improvements in students’ basketball skills. Specifically, scores for the 5-step backward shot increased from 12.55 to 15.97, and scores for the marching shot rose from 8.64 to 11.04. Additional enhancements were observed in the marching pass and half-court folding dribble, improving by 2 and 4 points, respectively. Furthermore, the basketball theory scores of students in the experimental group surpassed those of the control group by 7.92 points, with classroom satisfaction ratings also averaging 2.2 points higher. These findings underscore the substantial impact of the enhanced Kano model on improving basketball skills, theoretical understanding, and overall classroom satisfaction, highlighting its efficacy in tailoring PE curricula to meet student needs more effectively.

혼합형 글쓰기 수업 설계

The purpose of this study is to design a Mixed Writing Class by applying Hi-Edu Model. The Hi-Edu Model is a new teaching model presented by Jeju National University, and was developed with the goal of mixing several teaching methods and teaching environments. Details of the Hi-Edu Model include two teaching methods: Problem-Based Learning(Hi-Edu 1), Flipped Learning(Hi-Edu 2), and three teaching environments: Hybrid Learning(Hi-Edu 3), Blended Learning(Hi-Edu 4), Distance Learning(Hi-Edu 5). Also depending on the mixing method, the Hi-Edu Model is classified into the first type based on Hybrid Learning(Hi-Edu 3 + (Hi-Edu 1 + Hi-Edu 2)), the second type based on Blended Learning(Hi-Edu 4 + (Hi-Edu 1 + Hi-Edu 2)), and the third type based on Distance Learning(Hi-Edu 5 + (Hi-Edu 1 + Hi-Edu 2)). When applying these classified types to the situation, Mixed Class can be designed. In particular, this study designed a Mixed Writing Class by applying the Hi-Edu Model, and looked at its features in detail.

Classifying heterogeneous urban form into local climate zones using supervised learning and greedy clustering incorporating Landsat dataset

The concept of Local climate zone (LCZ) is used to generate LCZ maps worldwide as a part of World Urban Database and Access Portal Tools (WUDAPT) project utilizing Landsat imagery. Though WUDAPT has become the standard for LCZ mapping, it has certain limitations: 1) its outcome for heterogeneous cities is less accurate. 2) Due to fine granularity and huge-sized imagery, validating WUDAPT outcome becomes difficult for an urban expert. To overcome these, we have presented the LCZ classification and greedy clustering framework for heterogeneous cities (Nagpur (India)). Three classification schemes based on random forest, deep neural network, 1D-convolutional neural network and, subset ensemble are proposed. The overall accuracies are respectively 88%, 86%, 82%, 88%. Contextual information (mean, minimum, maximum, median, 25th, and 75th quantile values) at 90 m resolution is adopted while classification to handle spatial heterogeneity. Adding contextual information increases the overall accuracy by 4–6%, urban and mixed classes' accuracy by 4–7% and, 3–4% respectively. Further, clustering groups the spatially connected but similar pixels (classified Landsat) using breadth-first search concept and deque data structure creating LCZ map. Such LCZ map is helpful for an urban expert to validate correctness, select suitable level of granularity, visualize heterogeneity and suggest improvements.

Development and Validation of a Multiclass Model Defining Molecular Archetypes of Kidney Transplant Rejection: A Large Cohort Study of the Banff Human Organ Transplant Gene Expression Panel

Gene expression profiling from formalin-fixed paraffin-embedded (FFPE) renal allograft biopsies is a promising approach for feasibly providing a molecular diagnosis of rejection. However, large-scale studies evaluating the performance of models using NanoString platform data to define molecular archetypes of rejection are lacking. We tested a diverse retrospective cohort of over 1400 FFPE biopsy specimens, rescored according to Banff 2019 criteria and representing 10 of 11 United Network of Organ Sharing regions, using the Banff Human Organ Transplant panel from NanoString and developed a multiclass model from the gene expression data to assign relative probabilities of 4 molecular archetypes: No Rejection, Antibody-Mediated Rejection, T Cell–Mediated Rejection, and Mixed Rejection. Using Least Absolute Shrinkage and Selection Operator regularized regression with 10-fold cross-validation fitted to 1050 biopsies in the discovery cohort and technically validated on an additional 345 biopsies, our model achieved overall accuracy of 85% in the discovery cohort and 80% in the validation cohort, with ≥75% positive predictive value for each class, except for the Mixed Rejection class in the validation cohort (positive predictive value, 53%). This study represents the technical validation of the first model built from a large and diverse sample of diagnostic FFPE biopsy specimens to define and classify molecular archetypes of histologically defined diagnoses as derived from Banff Human Organ Transplant panel gene expression profiling data.

Clinical significance of exostosin 1 in confirmed and suspected lupus membranous nephropathy

ObjectiveThis study aimed to investigate the clinical significance of exostosin 1 (EXT1) in confirmed and suspected lupus membranous nephropathy (LMN).MethodsEXT1 was detected in 67 renal tissues of M-type phospholipase A2...

만3-5세 혼합연령 학급의 발달지연 유아의 변화 탐구

Objectives The purpose of this study is to explore changes in pro-social and cognitive behavior of children with developmental delays in a mixed-age class aged 3 to 5 years.&#x0D; Methods The participants in the study were 5-year-old children with developmental delays in a mixed-age 3-5 year old class. Related data, including 3 years of child observation logs, parent counseling logs, and interview data with homeroom teachers, were collected to determine pro-social behavior and cognitive behavior. The contents were qualitatively categorized and analyzed.&#x0D; Results First, in a mixed class for 3-5 year olds, developmentally delayed children improved their personal emotion control ability, interpersonal relationship formation ability, and classroom adaptation ability in terms of pro-social behavior. It has been shown that children with developmental delays improve their social behavior through activities that help younger siblings who are at a lower level than themselves. Second, children with developmental delays in mixed-age classes aged 3 to 5 showed improvements in cognitive behavior, such as interest in learning, improved cognitive learning ability, and language development. Improved pro-social behavior can be seen growing together by promoting active activities with children at various levels and supporting their cognitive development.&#x0D; Conclusions As seen in the results of this study, it can be concluded that in mixed-age classes, child with developmental delays are provided with the opportunity to narrow the gap in their development level with ordinary children through social and cognitive development.

Enhancing individual tree mortality mapping: The impact of models, data modalities, and classification taxonomy

Tree mortality is rapidly increasing as a result of more frequent and extensive droughts and forest fires across the globe. The increasing pace and scale of disturbance and resulting mortality necessitates the study of tree mortality at the scale at which it occurs (individual trees) to develop effective management strategies. Achieving this objective requires high-resolution aerial or satellite remote sensing data supported by field validation and efficient modeling. However, the optimal remote sensing data modalities and models for mapping overstory tree mortality remain uncertain and there is a lack of benchmark datasets for reproducible research, comparison, and model improvement. In this research, we propose a framework to generate tree mortality maps at the scale of individual trees and a labeled benchmark dataset using open-source high-resolution remote sensing data. Our benchmark dataset consists of over 15,000 image crops that have been manually labeled via multi-annotator majority for live, dead, mixed, and other classes in National Ecological Observatory Network (NEON) RGB, NEON hyperspectral, and National Agriculture Imagery Program (NAIP) RGBNIR imagery. Specifically, we present a multi-class classification framework that categorizes lidar-derived tree segments as live, dead, mixed, or other. We compared the performance of different machine learning models, remote sensing data modalities and their associated spatial resolution, spectral depth, and orthorectification using our benchmark dataset. Among various models, we found Convolutional Neural Network (CNN) models outperformed all other models with the highest accuracies and F1 scores across all data modalities. Similarly, among remote sensing data modalities, NEON RGB data at 0.1 m resolution performed best due to its high spatial resolution and high quality orthorectification. The overall accuracy of 87.27% with a macro F1 score of 0.76 was achieved using the RGB data with the CNN model. Our framework can be applied to any forest ecosystem in which lidar and raster imagery are available. We applied the framework using the best performing model and data modality to produce an individual tree mortality map over the Teakettle Experimental Forest in the southern Sierra Nevada. The framework and high-resolution mortality map can be valuable resources for forest managers and provide an invaluable basis for studying potential mortality drivers of dominant trees, forest inventory, selective logging, fire disturbance, and succession modeling.