Selection Method Research Articles

Research on motion control strategy of athlete muscle training based on blockchain and visual image analysis

Muscle training is an important part of athletes’ physical training. Its goal is to keep athletes in high intensity and improve sports performance in physical training. With the continuous improvement of the level of competitive sports, there are still many problems in the training of competitive athletes. The research on the control strategies of some technical movements in the process of athletes’ muscle development would help to improve the efficiency and effect of athletes’ training. By analyzing and quant description of muscle movement control process based on blockchain and visual image processing technology, the muscle movement control rule is discussed and then a complete movement database is constructed. At the same time, combined with the sports experiment method and cognitive behavioral theory, this paper analyzed the exercise load, exercise time allocation, muscle training time allocation methods and the corresponding muscle training strategy selection methods under different muscle states, providing theoretical basis for athletes to conduct muscle strength training scientifically and efficiently. This paper first analyzed the importance of athletes’ muscle training action control. After that, the research was mainly carried out from the following two aspects. The first is to analyze the motion control strategy of athlete muscle training based on blockchain and visual image, and summarize its characteristics. The second is to use electronic imaging technology to analyze the data related to posture control in athletes’ muscle training, and explore the characteristics and changes of muscle posture. At last, this paper put forward relevant algorithms about visual images and conducted experimental research on athletes’ physical indicators and performance under different muscle training according to the research in this paper. It was concluded that the score ratio of athletes’ performance after muscle training action control was 7.39% higher than that of general muscle training. Therefore, it is very important to strengthen the research of movement technology in muscle training and analyze the influence of some training control strategies on the development of athletes’ muscles in training.

Research on the A* Algorithm for Automatic Guided Vehicles in Large-Scale Maps

The traditional A* algorithm faces the challenges of low search efficiency and large node extension range in the field of path planning. These directly restrict the overall performance of the algorithm. In this study, we aimed to improve the search efficiency and path planning quality of the A* algorithm in complex and large-scale environments through a series of optimisation measures, including the innovation of weight design, flexible adjustment of the search neighbourhood, improvement of the heuristic function, and optimisation of the node selection strategy. Specifically, this study innovatively introduces the local obstacle rate as the core index of weight design, and it dynamically adjusts the weights according to the change of the obstacle rate during the node movement process, which effectively reduces the search space and significantly improves the search speed. At the same time, according to the real-time change of the local obstacle rate, this study dynamically adjusts the range of the search neighbourhood, so that the algorithm can choose the optimal search strategy according to different environmental information. In terms of the improvement of the heuristic function, this study adopted the diagonal distance as the benchmark for cost estimation, and it innovatively introduces the angle coefficient to reflect the complexity of path turning, thus providing the algorithm with a more accurate guidance for the search direction. In addition, this study optimises the node selection method by drawing on the idea of simulated annealing, which eliminates the need to calculate and compare all possible surrogate values during the node selection process, thus significantly reducing the running time of the algorithm. The results of the simulation experiments fully verify the effectiveness and practicality of the improved algorithm. Compared with the traditional A* algorithm, the improved algorithm achieved significant optimisation in terms of the average running time, the number of expansion nodes, and the path length, with the average running time shortened by 84%, the number of expansion nodes reduced by 94%, and the path length also shortened by 2.3%.

Plasma Tailoring of NH2-MIL-53 with Enhanced Fluorescence Emission for Simultaneous Detection of Multiple Heavy Metals in Water.

Indium, copper, and mercury are important raw materials in the electronics industry and often coexist in factory wastewater. Therefore, the development of a highly sensitive and selective method for the simultaneous detection of these heavy metal ions is of great significance for water quality monitoring and environmental protection. Herein, we report a NH2-MIL-53 fluorescent probe for the simultaneous detection of trace In3+, Cu2+, and Hg2+ in water. After a low-temperature NH3 plasma tailoring treatment for grafting electron-donor amine groups, the obtained NH2-MIL-53-M exhibited enhanced fluorescence emission intensity (∼6 times) coupled with selective adsorption of In3+, Cu2+, and Hg2+. This quenched the NH2-MIL-53-M fluorescence and allowed to significantly increase the selectivity and sensitivity for detection of In3+, Cu2+, and Hg2+. The fluorescence quenching constant (Ksv) values were 2.23 × 105, 1.00 × 105, and 2.74 × 104 M-1, while the limit of detection (LODs) values were 0.06, 0.14, and 0.53 μM, for In3+, Cu2+, and Hg2+, respectively. The concentrations of In3+, Cu2+, and Hg2+ in real environmental samples could be determined by addition of appropriate masking agents, and the recoveries were within the range of 94-110%. This study not only supplied a strategy for constructing a highly sensitive and selective fluorescent probe but also established a platform for simultaneous detection of multiple heavy metal ions in water.

Customer Requirement Importance Determination Method Based on Uncertain Language Information Entropy

To reflect the uncertainty of the objective environment in an effective way, a customer demand selection method based on information entropy of uncertain language is proposed. First, the customer representative uses uncertain language variables to represent the competitiveness of the company’s products and those of its competitors. Secondly, the method of information entropy was used to determine the competitive priority of each customer demand item. Finally, taking the improvement of the PE crusher as an example, the practicability and reliability of the proposed method are illustrated.

Comparison of supervised machine learning and variable selection methods for body weight prediction of growth pigs using image processing data

Comparison of supervised machine learning and variable selection methods for body weight prediction of growth pigs using image processing data

Selective Methods Promote Protein Solid-State NMR.

Solid-state nuclear magnetic resonance (ssNMR) is indispensable for studying the structures, dynamics, and interactions of insoluble proteins in native or native-like environments. While ssNMR includes numerous nonselective techniques for general analysis, it also provides various selective methods that allow for the extraction of precise details about proteins. This perspective highlights three key aspects of selective methods: selective signals of protein segments, selective recoupling, and site-specific insights into proteins. These methods leverage protein topology, labeling strategies, and the tailored manipulation of spin interactions through radio frequency (RF) pulses, significantly promoting the field of protein ssNMR. With ongoing advancements in higher magnetic fields and faster magic angle spinning (MAS), there remains an ongoing need to enhance the selectivity and efficiency of selective ssNMR methods, facilitating deeper atomic-level insights into complex biological systems.

PENGEMBANGAN MICROLEARNING VIDEO 360° PADA FENOMENA EFEK FOTOLISTRIK

This research aims to develop a 360° microlearning video in a modern physics course on the phenomenon of the photoelectric effect. The research uses the R&D method with the ASSURE development model which consists of (1) Analyze Learners, (2) State Standars and Objectives, (3) Select Methods, Media and Materials, (4) Utilize Technology, Media, and Materials, (5) Require Learner Participation, (6) Evaluate and Revise. However, in this study, it was carried out only up to the Utilize Technology, Media, and Materials stage. Data collection was carried out using instruments in the form of a questionnaire of initial abilities, student characteristics, interviews, and documentation to students of physics education and physics study programs at the State University of Jakarta. The results of this development research were carried out product feasibility testing by experts, namely media experts obtained a percentage achievement of 87.5% with a very feasible interpretation and material experts obtained a percentage achievement of 93.8% with a very feasible interpretation. The results of the feasibility test by experts show that the 360° video microlearning developed is very feasible to be tested on students. The final product of this research is a 360° video as a medium in accordance with the predetermined learning design. This product is published through the YouTube platform to be easily accessed by students.

Experimental Evaluation of Under Slab Mats (USMs) Made from End-of-Life Tires for Ballastless Tram Track Applications

The growing population of urban areas results in the need to deal with the noise pollution from the transportation system. This study presents experimental test results of static and dynamic elastic characteristics of under slab mats (USMs) according to the procedure of DIN 45673-7. Prototype USMs based on recycled elastomeric materials, i.e., SBR granules and fibres produced from waste car tires, are analysed. Vibration isolation mats with different thicknesses (10, 15, 20, 25, 30, and 40 mm), densities (500 and 600 kg/m3), and different degrees of space filling (no holes, medium holes, large holes) are considered. Moreover, a practical application of the laboratory test results of USMs in the design of ballastless track structures of two different types (with a concrete slab and longitudinal beams) is presented. Deflections of the rail and the floating slab system, as well as stresses acting on the mat, are determined according to EN 16432-2. The use of shredded rubber from recycled car tires as a material component of sustainable and environmentally friendly tram track structures may be one of the most effective ways to manage rubber waste within the current trend toward a circular economy, and this study intends to introduce methods for experimental identification and analytical selection of basic static and dynamic parameters of prototype USMs.

Selective Extraction of Terbium Using Functionalized Metal–Organic Framework-Based Solvent-Impregnated Mixed-Matrix Membranes

Advancements in membrane separation techniques will expand the applications and requirements for highly specialized, inventive, efficient, and resistant separation materials. The selective separation of rare earth elements (REEs) is one of the expanding applications of membrane-based techniques, as their use is becoming more widespread. Membrane techniques are becoming increasingly desired as environmentally friendly, straightforward methods for treating wastewater and separating metals. For the separation of REEs, an innovative impregnated mixed-matrix membrane (IMMM) technique was developed in this study. It provides a selective, efficient, and reusable method that is suitable for industrial applications. Terbium was selectively adsorbed from other REEs using organophosphorus IMMM with a loading capacity of 113.2 mg/g in 3 h and was reused three times without destroying the initial membrane. Solvent impregnation is thought to offer specific chelation sites that are selective for terbium separation.

Achieving optimum balance between mechanical properties and gloss of acrylonitrile–styrene–acrylate resin through control of rubber particle size distribution

AbstractPreparation of acrylonitrile–styrene–acrylate (ASA) with high gloss and superior impact properties is still challenging. Here, we developed a new strategy for the preparation of ASA resins by a selective redox initiation method based on a semi‐continuous emulsion polymerization technique. A series of ASA core‐shell impact modifiers were firstly obtained by grafting styrene (St) and acrylonitrile (AN) onto poly(n‐butyl acrylate) (PBA) seeded latexes of different sizes. The ASA core‐shell impact modifier was then used to toughen the styrene–acrylonitrile copolymer (SAN). The synergistic effects between the single particle size of the rubber phase and different particle sizes were systematically investigated, and the mechanical properties of SAN‐toughened resins at various particle sizes of the rubber phase were analyzed. The results revealed that toughened SAN with small particle sizes possessed better gloss, and the impact strength of the doped small‐particle toughened SAN was much higher than that of the single particle size toughened SAN. The ASA graft powders synthesized from large particle PBA (particle size 316 nm) and small particle PBA (particle size 99 nm), respectively, were mixed at a 50/50 ratio and blended with SAN resin, a gloss level reaching 95, and an impact performance of 275 J/m values almost twice those of a single particle size toughened SAN.Highlights Acrylonitrile–styrene–acrylate resins with an impact strength of 275 J/m and gloss of 95 were prepared. The balance between mechanical properties and gloss of acrylonitrile–styrene–acrylate resin was obtained. Polymerization was initiated at low temperature.

A novel importance-guided particle swarm optimization based on MLP for solving large-scale feature selection problems

Feature selection is a crucial data preprocessing technique that effectively reduces the dataset size and enhances the performance of machine learning models. Evolutionary computation (EC) based feature selection has become one of the most important parts of feature selection methods. However, the performance of existing EC methods significantly decrease when dealing with datasets with thousands of dimensions. To address this issue, this paper proposes a novel method called importance-guided particle swarm optimization based on MLP (IGPSO) for feature selection. IGPSO utilizes a two stage trained neural network to learn a feature importance vector, which is then used as a guiding factor for population initialization and evolution. In the two stage of learning, the positive samples are used to learn the importance of useful features while the negative samples are used to identify the invalid features. Then the importance vector is generated combining the two category information. Finally, it is used to replace the acceleration factors and inertia weight in original binary PSO, which makes the individual acceleration factor and social acceleration factor are positively correlated with the importance values, while the inertia weight is negatively correlated with the importance value. Further more, IGPSO uses the flip probability to update the individuals. Experimental results on 24 datasets demonstrate that compared to other state-of-the-art algorithms, IGPSO can significantly reduce the number of features while maintaining satisfactory classification accuracy, thus achieving high-quality feature selection effects. In particular, compared with other state-of-the-art algorithms, there is an average reduction of 0.1 in the fitness value and an average increase of 6.7% in classification accuracy on large-scale datasets.

Comparativa de diferentes modelos radiómicos para la clasificación de lesiones adrenales indeterminadas diagnosticadas de forma incidental en TC con contraste

Purpose: A comparison of different machine learning models to discriminate adrenal incidentalomas by CT studies was performed. Methods: Sixty-two different features were obtained from a sample of 61 incidentalomas using the free license software LIFEx and 19 radiomic studies were performed with different models and feature selection methods to obtain the most efficient determination of possible malignancy. For all of them, four cross-validation methods were evaluated. Adenoma contouring was performed in duplicate by different radiologists evaluating all models in both groups. Results: ROC AUC between 0.42 (0.09-0.81) and 0.92 (0.63-1.00), and accuracy of the models between 0.63 (0.43-0.79) and 0.94 (0.82-1.00). The best-performing model was the balanced logistic regression applied to the 14 features with an intraclass coefficient greater than 0.9, with which accuracy of 0.94 (0.74-1.00), ROC AUC of 0.917 (0.63-1.00), benign recall of 0.92 (0.65-1.00) and malignant recall of 1.00 (0.71-1.00) were obtained. Conclusions: The evaluation and validation of different models has allowed us to obtain an efficient radiomic model for the discrimination of adrenal incidentalomas

AI4ACEIP: A Computing Tool to Identify Food Peptides with High Inhibitory Activity for ACE by Merged Molecular Representation and Rich Intrinsic Sequence Information Based on an Ensemble Learning Strategy.

Hypertension is a common chronic disorder and a major risk factor for cardiovascular diseases. Angiotensin-converting enzyme (ACE) converts angiotensin I to angiotensin II, causing vasoconstriction and raising blood pressure. Pharmacotherapy is the mainstay of traditional hypertension treatment, leading to various negative side effects. Some food-derived peptides can suppress ACE, named ACEIP with fewer undesirable effects. Therefore, it is crucial to seek strong dietary ACEIP to aid in hypertension treatment. In this article, we propose a new model called AI4ACEIP to identify ACEIP. AI4ACEIP uses a novel two-layer stacked ensemble architecture to predict ACEIP relying on integrated view features derived from sequence, large language models, and molecular-based information. The analysis of feature combinations reveals that four selected integrated feature pairs exhibit enhancing performance for identifying ACEIP. For finding meta models with strong abilities to learn information from integrated feature pairs, PowerShap, a feature selection method, is used to select 40 optimal feature and meta model combinations. Compared with seven state-of-the-art methods on the source and clear benchmark data sets, AI4ACEIP significantly outperformed by 8.47 to 20.65% and 5.49 to 14.42% for Matthew's correlation coefficient. In brief, AI4ACEIP is a reliable model for ACEIP prediction and is freely available at https://github.com/abcair/AI4ACEIP.

Development and Validation of an Analytical Procedure for Determining Convallatoxin in Medicinal Products Containing Cardiac Glycosides of Lily of the Valley

INTRODUCTION. The safe use of lily-of-the-valley medicinal products requires adequately controlling the content of cardiac glycosides. The State Pharmacopoeia of the Russian Federation stipulates that the content of cardiac glycosides in lily-of-the-valley medicinal products should be quantified using bioassays and spectrophotometry. However, the determination of cardiac glycosides needs more accurate and selective physicochemical methods, such as high-performance liquid chromatography (HPLC).AIM. This study aimed to develop and validate an HPLC analytical procedure for the quantitative determination of cardiac glycosides (convallatoxin) in lily-of-the-valley herbal medicinal products.MATERIALS AND METHODS. The study examined the Lily-of-the-Valley Tincture, Zelenin Drops, Valocormid, Carniland®, and Lily-of-the-Valley Extract Reference Standard samples. The content of cardiac glycosides was determined by comparison with a reference standard for convallatoxin by HPLC and spectrophotometry. The samples were prepared as outlined in Monograph 3.4.0003.18 Lily-of-the-Valley Herb Tincture of the State Pharmacopoeia of the Russian Federation. Mixed cardiac glycosides were separated on a Luna 5 µm C18(2) column by gradient elution with 0.1% orthophosphoric acid in water and acetonitrile. The analysis was performed using an autoinjector with sample cooling to 5 °C.RESULTS. The developed analytical procedure met the acceptance criteria for specificity, intermediate precision, linearity (correlation coefficient of 0.99985), and repeatability (relative standard deviation of convallatoxin measurements of 1.61%). The analytical procedure is suitable for the quantitative determination of convallatoxin in lily-of-the-valley herbal medicinal products because it produces reliable and repeatable results.CONCLUSIONS. The authors developed a highly sensitive and selective HPLC analytical procedure for the quantitative determination of convallatoxin in lily-of-the-valley herbal medicinal products. The content of convallatoxin ranged from 0.012 to 0.018 mg/mL in the liquid active substance Lily-of-the-Valley Tincture, from 0.004 to 0.013 mg/mL in the medicinal product Zelenin Drops, and from 0.005 to 0.007 mg/mL in the medicinal products Carniland® and Valocormid. For the Lily-of-the-Valley Extract Reference Standard, the content of convallatoxin amounted to 0.029 mg/mL.

Conductivity Prediction Method of Solid Electrolyte Materials Based on Pearson Coefficient Method and Ensemble Learning

In order to screen solid electrolytic materials with high ionic conductivity more quickly and accurately, a conductivity prediction method for solid electrolyte materials based on logistic regression model and random forest regression model is constructed. The method first selects 20 characteristic descriptors related to ionic conductivity from material library according to four dimensions: structural stability, metal stability, electronic conductivity and oxidative decomposition stability. The enumeration method and feature selection method are used to combine different features. Then, the dimension reduction of the feature combinations is carried out by Pearson coefficient method, so as to screen the optimal feature combinations. Finally, according to the optimal feature combination and data subset, the constructed random forest ensemble learning model is utilized to predict the ionic conductivity of other solid-state electrolyte materials in the material library, in order to find the solid-state electrolyte materials that can meet the high conductivity. The results indicate that as the number of features in the combination increases, the prediction performance of the model shows a trend of first increasing and then decreasing. When the feature descriptor in the combination is 7, the maximum AUC (Accuracy) value is reached, and the optimal feature in this case contains 7 optimal feature subsets corresponding to feature descriptors, namely the standard deviation of the average adjacency number of Li atoms, the standard deviation of Li-X ion bonds, the average electronegativity of the straight path, the average width of the straight path, the average atomic volume, the average Li-Li bond, and the filling fraction of the sublattice. On the basis of the optimal feature screening, the average prediction accuracy of the logistic regression model is 87.13%, which has a high accuracy. Using the random forest regression model for prediction, the average absolute error and root mean square error obtained are only 0.237 and 0.134, and compared with classical classification methods such as KNN, SVM and Adaboost, they are smaller, which can better predict the ionic conductivity of solid electrolyte materials. This proves that the constructed method can quickly and accurately produce high ionic conductivity materials in solid electrolyte materials, which is worthy of further research and promotion.

COST OPTIMIZATION METHOD FOR INFORMATIONAL INFRASTRUCTURE DEPLOYMENT IN STATIC MULTI-CLOUD ENVIRONMENT

Context. In recent years, the topic of deploying informational infrastructure in a multi-cloud environment has gained popularity. This is because a multi-cloud environment provides the ability to leverage the unique services of cloud providers without the need to deploy all infrastructure components inside them. Therefore, all available services across different cloud providers could be used to build up information infrastructure. Also, multi-cloud offers versatility in selecting different pricing policies for services across different cloud providers. However, as the number of available cloud service providers increases, the complexity of building a costoptimized deployment plan for informational infrastructure also increases. Objective. The purpose of this paper is to optimize the operating costs of information infrastructure while leveraging the service prices of multiple cloud service providers. Method. This article presents a novel cost optimization method for informational infrastructure deployment in a static multicloud environment whose goal is to minimize the hourly cost of infrastructure utilization. A genetic algorithm was used to solve this problem. Different penalty functions for the genetic algorithm were considered. Also, a novel parameter optimization method is proposed for selecting the parameters of the penalty function. Results. A series of experiments were conducted to compare the results of different penalty functions. The results demonstrated that the penalty function with the proposed parameter selection method, in comparison to other penalty functions, on average found the best solution that was 8.933% better and took 18.6% less time to find such a solution. These results showed that the proposed parameter selection method allows for efficient exploration of both feasible and infeasible regions. Conclusion. A novel cost optimization method for informational infrastructure deployment in a static multi-cloud environment is proposed. However, despite the effectiveness of the proposed method, it can be further improved. In particular, it is necessary to consider the possibility of involving scalable instances for informational infrastructure deployment.

Pengaruh Solvabilitas, Profitabilitas, Kepemilikan Institusional, Dewan Komisaris Independen, dan Kepemilikan Publik terhadap Pengungkapan Manajemen Risiko: Studi pada Perusahaan Sub-sektor Perbankan yang Terdaftar di Bursa Efek Indonesia Tahun 2018-2022

The object of this research is a banking sub-sector company listed on the Indonesia Stock Exchange for the period 2018-2022. This study aims to determine the effect of leevrage, profitability, institutional ownership, independent board of commissioners, and public ownership as independent variables and risk management disclosure as the dependent variable. This study used 35 companies, so there were a total of 175 samples used. The study used purposive sampling as a sample selection method used for the selection of certain criteria. This study applied descriptive statistical analysis method and panel data regression analysis. The results of this study show that all variables simultaneously influence risk management disclosure. Meanwhile, leverage, profitability, and independent board of commissioners each have no effect. Meanwhile, institutional ownership and public ownership positively affect risk management disclosure.

Segmentation-based truncated-SVD for effective feature extraction in hyperspectral image classification

ABSTRACT Remote sensing hyperspectral images (HSIs) are rich sources of information about land cover captured across hundreds of narrow, contiguous spectral wavelength bands. However, using the entire original HSI for practical applications can lead to suboptimal classification accuracy. To address this, band reduction techniques, categorized as feature extraction and feature selection methods, are employed to enhance classification results. One commonly used feature extraction approach for HSIs is Principal Component Analysis (PCA). However, PCA may fall short of capturing the local and specific characteristics present in the HSI data. In this paper, we introduce two novel feature extraction methods: Segmented Truncated Singular Value Decomposition (STSVD) and Spectrally Segmented Truncated Singular Value Decomposition (SSTSVD) to improve classification performance. Segmentation is carried out based on highly correlated bands’ segments and spectral bands’ segments within the HSI data. Our study evaluates and compares these newly proposed methods against classical feature extraction methods, including PCA, Incremental PCA, Sparse-PCA, Kernel PCA, Segmented-PCA (SPCA), and Truncated Singular Value Decomposition (TSVD). We perform this analysis on three distinct HSI datasets, namely the Indian Pines HSI, the Pavia University HSI, and the Kennedy Space Center HSI, using per-pixel Support Vector Machine (SVM) and Random Forest (RF) classification. The experimental results demonstrate the superiority of our proposed methods for all three datasets. The best-performing feature extraction methods when classification is performed using an SVM classifier are STSVD3 (89.03%), SSTSVD2 (95.55%), and STSVD3 (97.74%) for the Indian Pines, Pavia University, and Kennedy Space Center datasets, respectively. Similarly, for the RF classifier, the best-performing feature extraction methods are SSTSVD4 (88.98%), SSTSVD3 (96.04%), and SSTSVD4 (96.09%) for Indian Pines, Pavia University, and Kennedy Space Center datasets, respectively.

Evaluating the Effectiveness of Traditional Recruitment and Selection Practices in a Technologically Dominated Economy

In the contemporary business environment, there is a need for firms to plan and implement proactive traditional recruitment and selection, especially regarding employees working in the gig economy. However, the poor handling of traditional recruitment and selection processes can significantly affect the quality of employees and their performance in an organisation. This study aimed to evaluate the effectiveness of traditional recruitment and selection practices in a technologically dominated economy. The importance of attracting and selecting a highly skilled workforce in a tight and competitive market has made adopting best practices in recruitment and selection by organisations critical. Firstly, the study highlighted various traditional recruitment and selection methods in organisations. Secondly, the overall recruitment and selection process was identified. Finally, strategies to mitigate the identified challenges faced in the traditional recruitment and selection process were suggested. The study was qualitative, adopting the survey strategy. Purposive was adopted, and hematic was used for data analysis. Findings revealed that traditional recruitment and selection as a practice is important for both employees involved in the gig economy. Importantly, the strategic approach to traditional recruitment and selection practices is vital and should be the cornerstone of any projected recruitment and selection process in a gig economy. Results validate the critical role of the traditional recruitment and selection process in achieving organisational aims and objectives in a technologically dominated economy. The study also proves that collaborative methods are central to the success of any traditional recruitment and selection process. Recommendations affirmed that recruitment and selection should be planned and well implemented. Human resource planning was also measured to be vital and emphasis on significant features of traditional recruitment and selection as a process such as having a place a well and implemented traditional recruitment and selection policy was asserted.

Transcriptome Profiling of Two Camellia japonica Cultivars with Different Heat Tolerance Reveals Heat Stress Response Mechanisms

Camellia (Camellia japonica) is a semi-shaded plant that is highly vulnerable to heat stress. To investigate the mechanisms underlying heat stress in C. japonica, two C. japonica cultivars, “Xiaotaohong” and “Zhuapolian”, which exhibit significant differences in heat tolerance, were selected from four common cultivars. The selection methods included phenotypic observations and physiological index detection, including relative electric conductivity (REC), malondialdehyde (MDA) content, superoxide dismutase (SOD) enzyme activity, relative water content (RWC), and chlorophyll content. RNA-seq analysis yielded 980 million reads and identified 68,455 differentially expressed genes (DEGs) in the two C. japonica cultivars during heat stress compared to the control samples. Totals of 12,565 and 16,046 DEGs were differentially expressed at 16 h and 32 h, respectively, in “Xiaotaohong” during heat stress. In “Zhuapolian”, 40,280 and 37,539 DEGs were found at 16 h and 32 h, respectively. KEGG enrichment analysis revealed that both cultivars were enriched in the “plant hormone signal transduction” and “circadian rhythm” pathways at two stages, indicating the critical role these pathways play in the heat stress response. The differences in the tolerance between the two cultivars are likely linked to pathways such as “plant hormone signal transduction”, “photosynthesis”, and “circadian rhythm”. Some members of heat shock proteins (HSPs) are associated with the heat stress response. It is speculated that transcription factor families contributing to the tolerance differences include AP2/ERF, C3H, bHLH, bZIP, and MYB-related with a small number of heat shock factors (HSFs) also induced by the stress. In conclusion, these results reveal the changes in the physiological indices and molecular networks of two C. japonica cultivars under heat stress. This study lays the foundation for the breeding of superior heat-resistant C. japonica cultivars and for further molecular research.