Results Of Evaluation Research Articles

Intelligent Evaluation of Chinese Hard-Pen Calligraphy Using a Siamese Transformer Network

The essence of Chinese calligraphy inheritance resides in calligraphy education. However, it encounters challenges such as a scarcity of calligraphy instructors, time-consuming and inefficient manual assessment methods, and inconsistent evaluation criteria. In response to these challenges, this paper introduces a deep learning-based automatic calligraphy evaluation model. Initially, hard-pen handwriting samples from 100 volunteers were collected and preprocessed to create a dataset consisting of 4800 samples, along with the corresponding label files for hard-pen calligraphy evaluation. Subsequently, YOLOv5 was utilized for region detection and character recognition on the evaluation samples to obtain the corresponding standard samples. Lastly, a Siamese metric model, with VGG16 as the primary feature extraction submodule, was developed for hard-pen calligraphy evaluation. To improve feature extraction and propagation, a transformer structure was introduced to extract global information from both the evaluated and standard samples, thereby optimizing the evaluation results. Experimental results demonstrate that the proposed model achieves a precision of 0.75, recall of 0.833, and mAP of 0.990 on the hard-pen calligraphy evaluation dataset, effectively realizing automatic calligraphy evaluation. This model presents a novel approach for intelligently assessing hard-pen calligraphy.

Does Urban Digital Construction Promote Economic Growth? Evidence from China

In order to explore the causal relationship between the level of urban digital construction and urban economic growth, this paper takes 280 cities in China as the research object and constructs a comprehensive indicator evaluation system covering digital infrastructure, overall economic level, innovation development level, digital industry development status, and ecological environment conditions. Using the entropy method to weigh various indicators, this paper has obtained the evaluation results of the digital construction level of each city from 2011 to 2021. Furthermore, a panel data regression model is used to empirically analyze the impact of urban digital construction level on urban economic growth. The results show that for every 1% increase in the level of urban digital construction, the GDP will increase by 0.974. Through the above research, we hope to further enrich the theoretical and empirical research in the field of the digital economy, provide a scientific and reasonable method for quantitatively evaluating the level of urban digital construction, and provide decision-making references for improving the level of urban digital construction and promoting sustainable urban development.

Deep neural networks based method to islanding detection for multi-sources microgrid

One of the significant issues in the field of microgrids is their islanding, where in many cases, the lack of awareness of microgrid islanding can lead to interference in the protective and control functions of the microgrid. Therefore, the accurate detection of microgrid islanding is of utmost importance. In this article, a method based on deep neural networks is presented. The proposed approach utilizes terminal parameters of microgrid resources, such as sequence current components, voltage, and other parameters, to detect islanding. Various operational states of the microgrid are simulated offline as standard test cases, and the parameters of each of them are recorded for later use. These data are then used to extract statistical features using discrete wavelet transform. Subsequently, the extracted features are fed into deep neural networks for training, and the training and evaluation results demonstrate an accuracy of over 99% for the proposed method in terms of precision and reliability. Furthermore, the accuracy of the proposed method is compared with some similar approaches for islanding detection.

EVALUATION OF THE APPLICATION OF THE ASSURE MODEL BASED ON THE COUNTENANCE STAKE MODEL ON RUKHSAH MATERIAL IN BUILDING EFFECTIVE LEARNING

The learning process determines student success in learning, so teachers need to build effective learning that supports these achievement goals. This study aims to analyze the evaluation of the application of the ASSURE model based on the Countenance Stake model on rukhsah material in building effective learning at SMP Hikmah Teladan. This research uses a qualitative approach using a descriptive analysis method. The results and conclusions of this study show that the application of the ASSURE model to rukhsah material in SMP Hikmah is exemplary based on the results of evaluation using the countenance stake model has been applied very well and supports building effective learning; the average good student learning outcomes show this.

The Importance of Eating Healthy and Nutritionally Balanced Food for Elementary School Children


 Health and well-being are important aspects of human life. One of the key factors that affect health is diet. The food we consume directly affects our physical and mental state. This activity aims to increase elementary school children's knowledge of the importance of eating healthy and nutritionally balanced foods. Method As an alternative solution to the problems that have been identified, counselling activities are carried out on the importance of eating healthy and nutritionally balanced foods in elementary school children. The method to increase this knowledge is counselling on the importance of eating healthy and nutritionally balanced foods. The techniques used in this activity include advising on the importance of eating healthy, nutritionally balanced foods and distributing leaflets. Based on the evaluation results, there has been a significant increase in knowledge about eating healthy and nutritionally balanced foods. Education and awareness about a healthy and nutritionally balanced diet are very important. The public needs to be adequately informed about the nutritional value of food and ways to adopt a healthy diet. Thus, it is expected to improve the quality of life, reduce disease risk, and create a healthier and more productive society overall.

Development and application of a comprehensive assessment method of regional flood disaster risk based on a refined random forest model using beluga whale optimization

To scientifically characterize regional flood disaster risks, a comprehensive regional flood disaster risk assessment index system was constructed based on the R cluster-factor analysis method. To improve the regional flood disaster risk identification ability, a random forest model refined by the beluga whale optimization (BWO-RF) was proposed. It was applied to the flood disaster risk assessment of the Jiansanjiang Branch of the Beidahuang Agricultural Reclamation Group in Heilongjiang Province, China. The research results show that the constructed BWO-RF model has strong generalization ability and optimization efficiency. The evaluation results have high rationality and stability, which fully demonstrates the applicability of the BWO-RF model in flood disaster risk assessment. On a time scale, the flood risk of the Jiansanjiang Branch shows a clear downward trend. However, due to weak economic development and insufficient water conservancy and flood control planning and construction, the risk of flood disasters fluctuates greatly in the later period, and the prevention and control situation is still not optimistic. On a spatial scale, the risk of flood disasters on farms in the northeast of Jiansanjiang Branch is low, while the risk on farms in the southwest is higher. Future flood control and drainage should focus on this area. The research results provide a technical method system for reference in flood disaster risk assessment and provide valuable information for reducing regional flood disaster risks.

Exploring data quality and seasonal variations of N2O in wastewater treatment: a modeling perspective

Abstract In this work, operational data collected from four Danish wastewater treatment plants (WWTP) are assessed for quality issues and analyzed to investigate the feasibility of data-driven modeling for control purposes. All plants have permanent N2O sensors installed in the biological reactors, and N2O data are collected on the same terms as other operational data. We present and deploy a six-dimensional data quality assessment to the operational data evaluating (1) relevance, (2) accuracy, (3) completeness, (4) consistency, (5) comparability, and (6) accessibility. To increase the accuracy and completeness of the stored data, it is suggested that future initiatives are taken toward the collection and storing of metadata in WWTPs. Furthermore, seasonal variations and time-varying relationships between N2O, nitrogenous variables, and oxygen are investigated and compared across various case plants and process designs. Results show that the quality of the operational data varies substantially between plants. The investigation of time-varying interrelation between N2O and nitrogenous variables showed no clear pattern within or across different case plants. Furthermore, it is recommended that future research should consider adapting models so that more influence is linked to reliable measurements, contrary to assuming that all variables are of equal quality.

Investigating twelve mainstream global precipitation datasets: Which one performs better on the Tibetan Plateau?

Acquiring precipitation information is a critical yet challenging task for the Tibetan Plateau (TP) with sparse gauges and high altitudes. Presently, there is a multitude of operational precipitation products merged from different data sources. A major concern within the TP science community is which one of these available precipitation datasets performs best on the TP. To address this concern, we selected and compared twelve widely used global precipitation datasets, including six satellite-based precipitation estimates (GSMaP-Gauge, IMERG-Final, TMPA-v7, CMORPH-BLD, PERSIANN-CDR, CHIRPS), five reanalysis data (ERA5, JRA-55, MERRA2, GLDAS, NCEP2), and one multi-source fusion product (MSWEP). Benchmarked against both automatic weather stations and independent field observations, we comprehensively investigate the spatiotemporal accuracy and regional applicability of these global precipitation products on the TP. The evaluation results indicate that almost all the datasets have a consistent tendency to overestimate lower precipitation rates and underestimate higher ones. However, four out of the twelve studied datasets (i.e., GSMaP-Gauge, IMERG-Final, ERA5, and MSWEP) significantly outperform others. Specifically, the two GPM-based satellite precipitation products, GSMaP-Gauge and IMERG-Final, can better reflect the difference between different rain intensities and perform better at moderate rain rates. In contrast, the reanalysis-based ERA5 exhibits a more clustered data series, with relatively higher accuracy at low precipitation rates. Moreover, ERA5 demonstrates a stable superiority in the mountainous areas characterized by complex local terrains. Furthermore, we find that the applicability of different precipitation datasets has an obvious region-related feature. Thus, a regional distribution map was made to clearly outline the optimal choice of current mainstream global precipitation datasets over different climate zones within the TP. This study can offer valuable insights for TP researchers, especially aiding them to select the appropriate precipitation data for their specific studies.

Evaluation of electric car styling based on analytic hierarchy process and Kansei engineering: A study on mainstream Chinese electric car brands

To evaluate the fuzzy and uncertain factors brought by human's emotional sensibilities during the design of new energy vehicle models, a scientific design evaluation method was adopted. This study combines the Analytic Hierarchy Process (AHP) and Kansei Engineering to assess the design of new energy vehicle models. It uses both objective criteria and subjective user perceptions. First, it quantified the design imagery vocabulary through Kansei Engineering and identified the target imagery vocabulary. Secondly, the AHP method was used to establish a relationship between design features and imagery vocabulary. Thrid, the study created an evaluation index system for design imagery, and adopted the fuzzy comprehensive evaluation matrix to rank the representative vehicles and brands in the Chinese new energy vehicle market. Based on the weight analysis results, the vehicle functionality (whether it is safe and reliable), the elegance of design, and the intricacy of vehicle structure were identified as the top three aspects that affect the impression of new energy vehicles. Therefore, we need to focus on these aspects during the design of new energy vehicles. Additionally, by integrating objective evaluation and emotional analysis results, we assessed the design for each new energy vehicle brand. The results of this study are important for developing the new energy vehicle market.

Design and fabrication of improved motorized plantain slicing machine for chips production

The majority of small-scale food companies and shops in Nigeria are unable to meet the enormous demand for plantain chips because of a few crucial considerations. Being a semi-perishable crop, a significant number of plantains are lost to deterioration at the highest point of harvest. Adopting an efficient method for turning food into chips for year-round availability and preservation is vital to guarantee food security and minimize waste. Plantain slicing with a knife has always been a hard, time-consuming, and unclean process with poor results. Although the slicer can be used for domestic purposes, it was intended for usage in medium and small-scale industries. The plantain slicing machine's design and construction aim to address the aforementioned issues. The design parameters that have been determined are the belt and pulley size, the power requirement (P), and the shaft diameter (D). The complete apparatus consists of a slider-crank mechanism that is powered by an electric motor through a v-belt. The driven pulley is supported by two bearings on a shaft. The hopper, which holds the plantains, the guide, and the stanchion make up the slider. Two cranks, one on each side, make up the mechanism. Through the use of a connecting rod, which transfers motion to the hopper (slider), they are connected to the slider. The intended chip formation is accomplished by moving the slider back and forth along the guide's bed, which holds the cutting blade. The device, which operates on the shear cutting concept, can cut raw plantains into chips with consistent sizes in a shorter amount of time. In just two to three seconds, it can cut a raw plantain finger up to 70 mm in diameter. According to the performance evaluation results, it takes an average of 267 seconds to slice 30 plantain pieces with an average length of 260 mm, weight of 0.3 kg, and diameter of 65 mm. All that's needed for machine maintenance is appropriate cleaning after usage and lubrication of the spinning parts. Even while this plantain slicer uses less labor and is more efficient than a chip cutter, it nevertheless yields chips with a consistent thickness. This updated device is easy to use, clean, lightweight, removable, and requires less maintenance. Consequently, the aforementioned constraints will be removed with this new design's enhancement, making the processing of plantain chips quite simple.

From coarse to fine: Enhancing multi-document summarization with multi-granularity relationship-based extractor

Multi-Document Summarization (MDS) is a challenging task due to the fact that multiple documents not only have extremely long inputs but may also be overlapping, complementary, or contradictory to each other. In this paper, we propose to capture complex cross-document interactions to handle lengthy inputs for better multi-document summarization. Specifically, we present MDS-MGRE, a coarse-to-fine MDS framework that introduces Multi-Granularity Relationships into an Extract-then-summarize pipeline. In the coarse-grained stage, multi-granularity embedding, heterogeneous graph construction, and MGRExtractor work together to convert redundant multi-documents into compact meta-documents. We first utilize pre-trained language model BERT to obtain semantically rich embeddings for documents at different granularities, including documents, paragraphs, sentence-sets, and sentences. Then, we construct a heterogeneous graph with 4 types of nodes (document nodes, paragraph nodes, sentence-set nodes, and sentence nodes) and corresponding connecting edges to model rich document relationships. Furthermore, we propose a novel Multi-Granularity Relationship-based Extractor (MGRExtractor) to produce meta-documents by efficiently pruning heterogeneous graphs. More precisely, it consists of 4 main modules: noise removal, redundancy removal, multi-granularity scoring, and sentence-set selection. In the fine-grained stage, we employ the large configuration of BART as our abstractive summarizer to generate system summaries from the extracted meta-documents. Experimental results on two benchmark datasets show that our framework significantly outperforms strong baselines with comparable parameters, and slightly underperforms methods with a maximum encoding length of 16,384 tokens. For Multi-News and WCEP, automatic evaluation results show that MDS-MGRE achieves an average performance improvement of 1.75% and 8.77% compared to the state-of-the-art systems with comparable parameters, respectively. Such positive results demonstrate the benefits of generating high-quality meta-documents to enhance MDS by modeling rich document relationships.

A network security situation assessment method based on fusion model

This research introduces an innovative network security situation assessment (NSSA) model, designed to overcome the shortcomings in feature extraction quality and efficiency observed in existing methods. At the core of this model is a fusion model (FM), which uniquely combines an attention mechanism with a bi-directional gated recurrent unit (BiGRU). This FM framework is adept at extracting pivotal information pertinent to various cyber threats. It employs the attention mechanism to assign appropriate weights to these crucial features, thereby elevating the model’s precision. The BiGRU, in synergy with newly proposed quantitative indicators, is responsible for generating the final evaluation results, offering a more refined measure of the cybersecurity stance. Comparative threat detection experiments reveal that the FM model exhibits superior performance across multiple evaluation metrics, marking a significant advancement in the field of network security assessment.

Prediction of sulfate concentrations in groundwater in areas with complex hydrogeological conditions based on machine learning

The persistent and increasing levels of sulfate due to a variety of human activities over the last decades present a widely concerning environmental issue. Understanding the controlling factors of groundwater sulfate and predicting sulfate concentration is critical for governments or managers to provide information on groundwater protection. In this study, the integration of self-organizing map (SOM) approach and machine learning (ML) modeling offers the potential to determine the factors and predict sulfate concentrations in the Huaibei Plain, where groundwater is enriched with sulfate and the areas have complex hydrogeological conditions. The SOM calculation was used to illustrate groundwater hydrochemistry and analyze the correlations among the hydrochemical parameters. Three ML algorithms including random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN) were adopted to predict sulfate levels in groundwater by using 501 groundwater samples and 8 predictor variables. The prediction performance was evaluated through statistical metrics (R2, MSE and MAE). Mine drainage mainly facilitated increase in groundwater SO42− while gypsum dissolution and pyrite oxidation were found another two potential sources. The major water chemistry type was Ca-HCO3. The dominant cation was Na+ while the dominant anion was HCO3−. There was an intuitive correlation between groundwater sulfate and total dissolved solids (TDS), Cl−, and Na+. By using input variables identified by the SOM method, the evaluation results of ML algorithms showed that the R2, MSE and MAE of RF, SVM, BPNN were 0.43–0.70, 0.16–0.49 and 0.25–0.44. Overall, BPNN showed the best prediction performance and had higher R2 values and lower error indices. TDS and Na+ had a high contribution to the prediction accuracy. These findings are crucial for developing groundwater protection and remediation policies, enabling more sustainable management.

A novel hybrid forecasting model with feature selection and deep learning for wind speed research

AbstractAccurate wind speed prediction is of great importance for the operation of wind farms, and extensive efforts have been made to develop effective forecasting methods in this regard. However, the feature selection of data input as well as optimization of deep learning models have received comparatively less attention, leading to unreliable forecasting results. This research proposes a novel hybrid model that integrates data preprocessing, feature selection, and optimized forecasting for improved wind speed prediction. Specifically, a powerful preprocessing technique is utilized to reduce data noise disturbances, while an innovative two‐stage feature selection is designed to achieve the optimal input data format for forecasting purposes. Moreover, a hybrid forecasting module based on long‐short term memory, which is optimized by the Bayesian optimization algorithm, has been developed to enhance the efficiency and reliability of the model. The empirical study used 10‐min interval wind speed data of four seasons for presentation and evaluation results demonstrated its superior performance in effectively learning the volatility and irregularity features of wind speed series, which established a solid foundation for practical applications in wind power systems.

Ridge Preservation and Augmentation Using a Carbonated Apatite Bone Graft Substitute: A Case Series.

The newly developed mineral carbonated apatite has recently been proposed as a bone graft material for bone regenerative treatment in implant therapy. This case series details the clinical and radiographic outcomes of ridge preservation and ridge augmentation using only carbonated apatite as bone graft material for implant treatment. Twenty patients (36 sites) who required bone regeneration and implant placement were retrospectively assessed. Simultaneous carbonated apatite implant placement was performed using the simultaneous ridge preservation or augmentation approach on 24 sites in 13 patients with sufficient bone quantity for primary stabilization based on preoperative evaluation results. A staged ridge preservation or augmentation approach was used for the remaining 12 sites in seven patients with insufficient bone quantity. The mean regenerated bone height for each treatment method was as follows: simultaneous preservation, 7.4 ± 3.3 mm; simultaneous augmentation, 3.6 ± 2.3 mm; staged preservation, 7.2 ± 4.5 mm; and staged augmentation, 6.1 ± 2.7 mm. The mean regenerated bone width for each treatment method was as follows: simultaneous preservation, 6.5 ± 2.9 mm; simultaneous augmentation, 3.3 ± 2.5 mm; staged preservation, 5.5 ± 1.7 mm; and staged augmentation, 3.5 ± 1.9 mm. Ultimately, the use of carbonated apatite alone as a bone graft material in implant therapy resulted in stable and favorable bone regeneration.

The Effect of Effort and Result-Oriented Contracts on Human Resource Productivity: An Important Component of Sustainable Development Study of Dairy Farm in Iran

Human resource productivity is one of the essential components of the sustainable development of human resources. Human resource efficiency means the optimal use of people's capabilities, skills, knowledge, and experiences in society or organization. The sustainable development of human resources can be aided by raising productivity levels. The management of human resources systems and performance evaluation techniques may need help in evaluating the performance of employees under the influence of its complexities, which can cause inaccurate evaluation results. As a result, the organization will need help achieving the predetermined goals with proper productivity. Therefore, The present study examines the effect of effort and result-oriented contracts on the productivity of human resources in a 20,000-head state cattle farm in Iran. A comparative approach was used to investigate the effect of effort-based and result-based payments on human resource productivity. It analyzed ten projects in a set, where five were used for effort-based contracts and five similar projects used for results-based contracts. The findings of the study show that result-based contracts have a substantial impact on the productivity of labor in terms of effectiveness and efficiency. The study likewise discussed the implications of its results.

Construction and Application of a Seasonal River Health Evaluation System in Arid and Semi-Arid Areas

Addressing the inadequacy of theoretical frameworks and evaluation indicators for assessing the health of seasonal rivers in arid and semi-arid regions, this study aims to enrich the theoretical foundation for the management and ecological restoration of seasonal river systems. By selecting seven indicators from three aspects: hydrology, habitat, and social services, a seasonal river health assessment indicator system was constructed for the Tabu River basin. The weights of the indicators were determined using the analytic hierarchy process and entropy weight method, and a classification standard for seasonal river health was established. The health status of the Tabu River in 2021 was evaluated accordingly. The results revealed that the upstream, midstream, and downstream reaches of the Tabu River were categorized as healthy, sub-healthy, and diseased, respectively, with poor hydrological conditions being the primary concern. The satisfaction level of ecological flow within the basin was low, accompanied by deep groundwater levels and water scarcity issues, aligning with the actual situation. This indicator system effectively reflects the true state of seasonal river ecosystems. Based on the evaluation results, measures such as establishing ecological water storage projects and adjusting agricultural planting structures were proposed to alleviate the impacts caused by water scarcity. Additionally, implementing river and lake management systems, water resource protection measures, and water-saving technologies can directly regulate the pressures imposed on rivers by human socio-economic activities, thus alleviating regional water resource shortages and promoting the health of rivers and regional water resources. These findings provide scientific decision-making support for the management of seasonal river basins and the maintenance of river health.

Adapting the Donabedian model in undergraduate nursing education: a modified Delphi study

BackgroundDonabedian conceptual and multidimensional framework has been used in several studies in an educational context. In this study, we aimed to adapt the Donabedian three-component model (structure, process, and outcome) in undergraduate nursing education. This conceptual framework provides a comprehensive image of nursing education institutions and can help to evaluate institutions by measuring different aspects of their performance. A comprehensive understanding of the various elements of an educational institution helps to develop a complete, appropriate relevant set of performance indicators.MethodsThis was a modified Delphi study. It had three rounds. The expert panel consisted of nursing faculty members and nursing Ph.D. students. In the first round, a questionnaire was designed based on interviews, focus groups, and a literature review. Experts rated their agreement with each element on a 5-point Likert scale in rounds two and three. The consensus level was set as 75%. The stability between rounds was also determined by calculating kappa coefficients. One Sample T-Test was also calculated for new items in round three.ResultsAll 55 items of the questionnaire were confirmed in the second round based on the consensus percentage of 75. Five new items were added to the third round based on comments in round two. Eventually, all elements except one were confirmed according to the consensus level, kappa values, means, and One-Sample T-Test in round three. The structure's key elements include staff (academic and non-academic); equipment; guidelines; resources and facilities; and students’ demographics and characteristics. Process key elements include communication; education; evaluation; cooperation; and consultation. Outcome key elements include knowledge development; nursing image; alumni’s outcome; students’ outcome; related medical centers’ performance; accreditation and evaluation results; and satisfaction.ConclusionsDifferent elements of a nursing education institution at the bachelor's level were determined. The results of this study can help related bodies to develop and implement a comprehensive and systematic evaluation. These results can also be a basis for making this model useful in other nursing courses or education in other fields.

Efficiently improving the Wi-Fi-based human activity recognition, using auditory features, autoencoders, and fine-tuning

Human activity recognition (HAR) based on Wi-Fi signals has attracted significant attention due to its convenience and the availability of infrastructures and sensors. Channel State Information (CSI) measures how Wi-Fi signals propagate through the environment. However, many scenarios and applications have insufficient training data due to constraints such as cost, time, or resources. This poses a challenge for achieving high accuracy levels with machine learning techniques. In this study, multiple deep learning models for HAR were employed to achieve acceptable accuracy levels with much less training data than other methods. A pretrained encoder trained from a Multi-Input Multi-Output Autoencoder (MIMO AE) on Mel Frequency Cepstral Coefficients (MFCC) from a small subset of data samples was used for feature extraction. Then, fine-tuning was applied by adding the encoder as a fixed layer in the classifier, which was trained on a small fraction of the remaining data. The evaluation results (K-fold cross-validation and K = 5) showed that using only 30% of the training and validation data (equivalent to 24% of the total data), the accuracy was improved by 17.7% compared to the case where the encoder was not used (with an accuracy of 79.3% for the designed classifier, and an accuracy of 90.3% for the classifier with the fixed encoder). While by considering more calculational cost, achieving higher accuracy using the pretrained encoder as a trainable layer is possible (up to 2.4% improvement), this small gap demonstrated the effectiveness and efficiency of the proposed method for HAR using Wi-Fi signals.

Production of quality planting material in commercial banana cvs. Rasthali (Silk, AAB) and Neypoovan (Neypoovan, AB) through farmer-friendly macropropagation technique and their field evaluation

Macropropagation is a low cost farmer friendly alternative to tissue culture propagation to cater to the farmers’ need for quality planting material at an affordable cost. Attempts were made to study the effect of age and hot water treatment on macropropagation of cvs. Rasthali (AAB, Silk) and Neypoovan (AB, Neypoovan) in pot and bed methods. Age of the sucker, hot water treatment and propagation method played a vital role on shoot bud production in both the test cultivars. In cv. Rasthali, bed method produced maximum number of plants (50.5) in five months old suckers subjected to hot water treatment in 42.2 days but the same treatment produced only 24.7 plants under pot method. In cv. Neypoovan, five months old suckers subjected to hot water treatment resulted in 23.3 and 16.7 plants in bed and pot methods respectively. Therefore, five months old suckers with hot water treatment under bed method are more appropriate for macropropagation purpose in banana. Results of field evaluation indicated that macropropagated bananas were at par with control in respect of all plant parameters like plant height, bunch weight and fruit parameters like total soluble sugar (TSS) and acidity.