bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of Soil and Water Assessment Tool for Runoff Simulation in a Data Scarce Himalayan Watershed
Authors: Tabasum Rasool  , Rohitashw Kumar
Published: April 2024

The present study was conducted to simulate runoff in the watershed of Dal (188.72 km2 ) located in the temperate region of Kashmir using Soil and Water Assessment Tool (SWAT). The study watershed is characterized by significant climatic contrast, abrupt topography, and soil fragility, thereby resulting in flash floods and water erosion. Such situation requires interventions to preserve soil and water resources, and a decision tool for integrated watershed management. The SWAT was calibrated for the year 2010, and validated for the year 2011, based on a comparison of simulated and observed monthly runoff at the watershed outlet. The model performance was evaluated using coefficient of determination (R2 ), Nash-Sutcliffe efficiency (ENS), relative root means square error (RRMSE) and percent bias (PBIAS). During the calibration period, R2 , ENS, RRMSE and PBIAS were 0.98, 0.87, 1.3 and (-) 26, respectively. During the validation period, the values of R2 , ENS, RRMSE and PBIAS were 0.97, 0.84. 1.1 and (-) 26.0, respectively. The modelled values showed reasonably good agreement with the observed values of runoff, both during calibration and validation periods. The runoff in the watershed was quantified under the prevailing land use conditions. The study demonstrated a satisfactory application of the SWAT model for quantification of hydrological processes in a watershed under data scarce condition.
Combined Microwave and Hot Air Drying of Sapota (Acharas zapota L.) Slices: Analysis of Quality and Mass Transfer Parameters
Authors: Harshala Patil  , N. G. Shah  , Girish Kumar
Published: April 2024

This effects of combined microwave and hot air on drying of sapota (Acharas zapota L.) slices were studied. The slices were dried from the initial moisture level of 75-78 % (w.b.) to a final moisture content of ~10 % (w.b.). Process parameters such as drying time, moisture diffusivity, specific energy consumption and quality attributes of dried sapota slices were evaluated. Drying time at the power level of 2 W.g-1 reduced up to 14 to 21 times compared to only hot air drying. Dincer and Dost model was used to estimate Biot number and mass transfer coefficient which ranged from 0.29 to 1.23, and 0.23×10-7 m.s-1 to 8.81×10-7 m.s-1, respectively. Specific energy consumption was reduced by 12 to 17 times during combination drying (2 W.g-1, 50-70 °C) compared to only hot air drying (50-70 °C). Process modelling results indicated that Midilli model was best fitted describing the drying kinetics of sapota slices during combination drying. Optimization for better quality dried sapota slices suggested slice thickness of 5 mm using combination of microwave (1.5 W.g-1) and hot air drying (60 °C).
Land Suitability Assessment for Potato Crop using Analytic Hierarchy Process Technique and Geographic Information System
Authors: Chiranjit Singha  , Kishore Chandra Swain  , Binoy Kumar Saren
Published: April 2024

Agricultural land-suitability analysis is a prerequisite to achieve optimum utilization of the available land resources for sustainable agricultural production. The Analytical Hierarchy Process (AHP) technique coupled with Geographic Information System (GIS) can be a unique tool for land-suitability studies. AHP-GIS technique based on soil nutrient criteria of soil texture, pH, organic carbon, electric conductivity, available nitrogen, phosphorous, potassium, and zinc was used for land-suitability assessment in Tarkeswar Block, Hooghly district, West Bengal for growing potato. The study area was classified into suitable land categories based on soil nutrient levels analysed from 50 randomly-selected plot-based soil samples. Pair-wise comparison matrix-based ranking was computed considering the importance of each criterion for potato crop in the area. Suitability maps were developed and analysed in ArcGIS software environment. The total area was classified into ‘highly suitable’ class occupying 61 ha (21%), ‘moderately suitable’ class in 195 ha (67%), ‘marginally suitable’ class in 32 ha (11%) and ‘not suitable’ class in 3 ha (1%) land areas. Yield distribution map of potato crop showed nearly 106 ha (36.5%) area producing higher tuber yield of 20 t.ha-1. The proposed suitability map was validated against crop yield map where nearly 253 ha (87%) area classified under ‘highly suitable’ to ‘moderately suitable’ classes was found to give better potato yield of 15 t.ha-1or more. The AHP-GIS technique can be used for crop rotation analysis for multiple seasons for better crop selection in long run.
On-farm Evaluation of Furrow Irrigated Raised Bed System with Plastic Mulch for Brinjal Crop
Authors: R. C. Maurya  , A. K. Singh  , Ratna Sahay  , Archana Singh
Published: April 2024

An on-farm experiment was conducted on farmers’ field in two villages of Unnao district, Uttar Pradesh to evaluate the effectiveness of mulched and non-mulched furrow irrigated raised bed(FIRB) system over farmers’ practice of check basin irrigation for brinjal crop. The system performance was assessed in terms of crop physiological parameters, water saving, yield, water productivity and cost of cultivation. Mulch significantly affected the physiological parameters of crop like plant height, number of branches, girth, root depth, leaf area and fruit weight. Results revealed that the water savings under furrow irrigated raised bed system without mulch (T2) and with mulch (T3) was 167.7 mm (31.2 %) and 237.0 mm (44.1 %), respectively, as compared to farmers’ practice (T1). Significantly higher brinjal yield (21.29 t.ha-1) was observed in T3, which was 26.7 % higher over T1. Higher yield and comparatively lesser application of irrigation water resulted in significantly high water productivity (88.49 kg.ha-mm-1) in T3 as compared to T1 (31.34 kg.ha-mm-1). The average benefit-cost ratio (BCR) for the treatments T1, T2, and T3 were 2.21, 2.58 and 2.62, respectively. Furrow irrigated raised bed cultivation of brinjal with black plastic mulch is recommended to the farmers of central Uttar Pradesh to achieve twin objectives of improved crop yields and increased water productivity.
Радиационно-стимулированные эффекты в органических сцинтилляторах UPS-923A
Authors: Юлдашев Б.С. , Нуритдинов И. , Эсанов З.У. , Ирисов Ш.
Published: April 2024

Исследованы оптическое поглощение и фотолюминесценция органических сцинтилляторов UPS-923A с полистирольной основой и добавками птерфенила (рTP) и 1,4-бис(5фенилоксазолилбензола (POPOP), край поглощения которых находится на уровне ∼390 нм. В спектре фотолюминесценции образцов в спектральной области 300−380 нм и 380−500 нм наблюдаются группы свечений, относящихся к флуоресценции люминесцирующей добавки рTP и спектросмещающей добавки POPOP соответственно. После γ-облучения, начиная с дозы 106 рад, происходит увеличение поглощения и появление широкой полосы поглощения (ПП), простирающейся до 550 нм, а также уменьшение интенсивности люминесценции в указанных полосах ФЛ, которые обусловлены деградацией структуры ароматического бензольного кольца в матрице полимерной основы и деструкцией добавок.
Organization of veterinary services in the territories of the Leningrad region
Authors: Orekhov D. A.
Published: April 2024

The problem of organizing veterinary activities in the territories of horticultural, horticultural and other non-profit associations of citizens needs attention. Many land owners keep agricultural, pet companions. They often do not comply with the norms of legislation in the field of veterinary medicine, there is a concealment of facts and violation of the rules of maintenance, animal diseases, we see ignorance of the owners about the signs of manifestation, lack of awareness of the threats of a particular disease. At the same time, there is no centralized accounting of animals kept on the territory of the non-commercial gardening, as there is no work to perform veterinary knowledge. It is necessary to plan veterinary and educational work: organize meetings with representatives of the non-commercial gardening administration, animal owners, and present information material in the form of posters and leaflets on the ground. It is necessary to place sections on the electronic resources of the animal disease control station, non-commercial gardening containing in an accessible form the information on the maintenance, feeding of agricultural and domestic unproductive animals, carrying out antiepizootic measures, it is advisable to provide regulatory and legal information, inform in an accessible form about the need for veternary and sanitary examination of products obtained on the territory of the non-commercial gardening. The issue of registration and vaccination of animals can also be solved by organizing field events of representatives of the district animal disease control station in the non-commercial gardening. It seems necessary to change the approach to the list of acceptable ways of using land plots belonging to the category of agricultural land with an established type of permitted use for horticulture, so that among them there is the maintenance of farm animals.
Dynamics of the content of immunoglobulin classes in blood serum and vaginal secretions of cows with genital mycoplasmosis with various methods of therapy
Authors: Vasiliev R. M.
Published: April 2024

The economic efficiency of industrial animal husbandry in modern conditions provides not only for an increase in productivity, but also for maintaining the optimal level of basic physiological functions, including reproductive ones. The successful implementation of the task of preserving reproductive function is hindered by diseases of females of various etiologies that negatively affect their reproductive ability. One of these diseases is genital mycoplasmosis of cattle, which has a significant prevalence on dairy farms, both abroad and in the Russian Federation. The effectiveness of therapy for this disease largely depends on the initial state of the immune system. The objective of our research was to study the therapeutic efficacy and effect on the immunoglobulin composition of blood serum and vaginal secretions of patients with genital mycoplasmosis of cows of the use of the antibiotic tulathromycin and its combination with the immunomodulator thymalin. The results obtained demonstrated that the use of tulatromycin for treatment leads to the recovery of 75% of experimental animals and ensures the restoration of the immunoglobulin profile of vaginal secretions, whereas the immunodeficiency state observed in blood serum is only partially restored. The combination of an antibiotic and an immunomodulator ensured the recovery of all experimental animals and the restoration of the immunological characteristics of blood serum and vaginal secretions to the level of healthy cows.
Antibacterial therapy for kidney pathology of small pets
Authors: Chirkova A. S. , Sidorova C. A. , Skosyrskikh L. N. , Shchipakin M. V.
Published: April 2024

The article considers etiological factors, clinical manifestations, laboratory and instrumental methods for diagnosing kidney pathology, which requires the use of antibacterial drugs – pyelonephritis. However, this pathology can be difficult to diagnose and not every antibacterial drug is suitable for its treatment. The main ways of kidney damage by bacterial infection in small domestic animals are ascending infection and hematogenous introduction, when pathogenic microorganisms enter the kidneys along with the bloodstream. The article provides predisposing factors leading to the development of pyelonephritis. Pyelonephritis has several types of course. The acute form of the disease is most easily diagnosed due to the vivid clinical signs and pronounced changes in blood, urine, and kidney sonography tests. Whereas the chronic form and latent course may be overlooked due to the nonspecific clinical picture. In the treatment of pyelonephritis, the occurrence of antibiotic resistance in bacteria can be fatal for the patient, because if it is impossible to eliminate infection in the kidneys, the development of renal failure is inevitable, leading to all manifestations of uremia, anemia and other changes that occur with impaired renal function. To prevent the development of antibiotic resistance, it is extremely important to make a final diagnosis through bacteriological urine culture, but pyelonephritis is a pathology that requires a comprehensive diagnostic approach and, if suspected, it is advisable to start etiological treatment immediately, correctly choosing an antibiotic. Not every antibacterial drug is suitable for the treatment of pyelonephritis. For example, one of the first-line drugs of choice in the treatment of urinary tract infection (UTI), amoxicillin with clavulanic acid does not penetrate the renal parenchyma. The group of choice for the treatment of bacterial inflammation of the kidneys are fluoroquinolones, but not every drug from this group is suitable for treatment in animals and there are certain restrictions in their use in the case of puppies, kittens, as well as in cats as a species, therefore, the selection of an antibiotic should be carried out individually in each clinical case, taking into account the pharmacokinetics and pharmacodynamics of the drug, the result of urine sampling and other studies, as well as the physiological characteristics of the patient.
Description of the linear features of the exterior of the first –calfs of different origin
Authors: Paderina R. V. , Vinogradova N. D.
Published: April 2024

In our country, a linear assessment of the exterior of animals is becoming increasingly widespread, which allows us to objectively assess the influence of the producer bull on the change in the most important exterior features that ensure the se-verity of the dairy type while maintaining high productivity and intensity of use in the herd. This ensures the ranking of animals by body type and helps to accelerate the genetic progress of productivity. In this regard, the introduction of a linear assessment of the daughters of breeding bulls in the herd of a breeding plant is relevant. We conducted research aimed at studying and analyzing the signs of a linear description of the exterior of the first-calf cows of the daughters of Holstein bulls and identifying the most common exterior flaws in one of the breeding plants of the Kirov region. The analysis of the data showed that the evaluated first-calf cows of different origins are characterized by exterior features: average height, body of medium depth, strong physique, pronounced dairy type. The most common disadvantages of the exterior of the examined heifer cows are: interception of the shoulder blades (25.6%), asymmetry of the udder lobes (23.0%), weak headstock (15.4%).
АГРЕГИРОВАННЫЙ КРИТЕРИЙ ОЦЕНКИ СОСТОЯНИЯ ЖИЛОГО ФОНДА РЕГИОНОВ РОССИЙСКОЙ ФЕДЕРАЦИИ
Authors: Noskov Sergey Ivanovich , Ovsyannikov Ivan Vladimirovich
Published: April 2024

Данное исследование нацелено на разработку и усовершенствование методов агрегирования частных критериев, используемых для оценки состояния жилого фонда регионов Российской Федерации. В контексте задач векторной оптимизации, основанных, в частности, на построении линейной свертки этих критериев, рассматриваются способы формирования агрегированных критериев, позволяющих более объективно оценить эффективность формирования жилого фонда. Основными элементами подхода к формированию агрегированного критерия являются анализ характеристик ввода в эксплуатацию жилищных объектов и экспертные оценки. Это позволяет свести задачу агрегирования к задаче построения линейной свертки частных характеристик, реализация которой приводит к использованию методов линейного программирования. В работе также представлено описание специализированного программного обеспечения, разработанного для автоматизации процесса агрегирования критериев, которое способно выявлять возможную несовместность экспертных оценок и предлагать при этом способы поиска решения задачи линейного программирования. Исследование ориентировано на анализ состояния жилищного фонда с целью поддержки управленческих решений и оптимизации используемых ресурсов.
Exploring the Learning Outcomes of College Education Expected by Highly Educated Adult Learners
Authors:   The Korean Society for Lifelong Education, Jung Hyeryung , Noh Ilkyung , Kim Myungjin , Woo Younghee
Published: April 2024

This study aimed to examine the growing trend of adult learners with bachelor's degrees or higher returning to university education, prompted by the widespread accessibility to higher education. Additionally, it sought to explore the learning outcomes they expect from their re-engagement with university education. To achieve this goal, the perceptions of the importance of 16 learning outcomes and the current performance of universities were investigated among adult undergraduate students enrolled at a 4-year distance education university. The responses of 841 participants were analyzed using IPA(Importance-Performance Analysis), Borich Needs Assessment, and the Locus for Focus model. The analysis revealed that while there were high expectations for higher-order thinking skills, information technology proficiency, writing skills, and fostering civic awareness, there was a need for intensive improvement efforts by universities as they were not rated highly in current performance. Conversely, degree or certification attainment, achieving high grades, and acquiring general knowledge were identified as areas where universities are overly focused compared to the expectations of highly educated learners, indicating a need for improvement in university education strategies. Based on these findings, discussions and implications are presented.
Cattle Body Intelligent Measurement based on Improved CenterNet
Authors: Xu Huixiang
Published: April 2024

Traditional cattle body measurement methods have limitations such as manual contact measurement and low efficiency. To improve the efficiency of measuring cattle bodies and reduce labor costs, an improved intelligent measurement network based on CenterNet was proposed. In this study, we used the DenseNet-100 to replace the ResNet-101 to alleviate the vanishing gradient problem. We also improved the cattle body size measurement efficiency by reducing the network parameters. In addition, we conducted a comparative experiment on the cattle posture dataset collected by ourselves to verify the feasibility of the network. The experiment confirmed that the proposed intelligent measurement based on improved CenterNet outperforms the traditional networks and other advanced object measurement networks in accuracy and measurement efficiency, and can effectively solve most of the error problems caused by manual measurement. Besides, our network has good applicability and strong stability, which can meet the requirements of the evaluation of cattle body size measurement indicators.
Semantic Similarity Caculating based on BERT
Authors: Dengyun Zhu, Hailong Gai, Fucheng Wan Denghui Yang,
Published: April 2024

The exploration of semantic similarity is a fundamental aspect of natural language processing, as it aids in comprehending the significance and usage of vocabulary present in a language. The advent of pre-training language models has significantly simplified the process of research in this field. This article delves into the methodology of utilizing the pre-trained language model, BERT, to calculate the semantic similarity among Chinese words. In order to conduct this study, we first trained our own model using the bert-base-chinese pre-trained model. This allowed us to acquire the word embeddings for every single word, which served as the basis for calculating semantic similarity. Essentially, word embeddings are vector-based depictions of words that encapsulate word’s significance and surroundings, allowing for the measurement of the semantic similarity between words. Next, we executed a sequence of experiments to assess the efficiency of the BERT model in managing semantic similarity tasks within the Chinese language. The results were encouraging, as the BERT model demonstrated remarkable performance in these tasks. Furthermore, it was observed that the BERT model outperformed traditional methods in terms of performance and generalization capabilities. This study, therefore, underscores the potential of the BERT model in natural language processing, particularly in the Chinese language. This emphasizes the model’s capacity to accurately calculate semantic similarity, paving the way for its widespread adoption in related fields.
The Carbon Emission Accounting and Prediction of the Power Generation Side based on LSTM in Jilin Province
Authors: Yu Lu, Huixu Xiao, Minglei Jiang, Weiguo Hu Yuanmei Zhang,
Published: April 2024

In the context of global warming and the dramatic increase in greenhouse gas emissions, the power industry is the largest source of carbon emissions. Adjusting and optimizing the carbon dioxide emissions from the power industry will help China achieve its “dual carbon” goals and is of great significance for mitigating global carbon dioxide emissions. This paper takes six power plants in Jilin Province as the research objects, and firstly accounts for the carbon emission production data between January 2020 and December 2023 according to the "Accounting Methods and Reporting Guidelines for Greenhouse Gas Emissions from Enterprises - Power Generation Facilities". Then, LSTM was used to establish carbon emission prediction models for six different power plants in Jilin Province, and the analysis of each model showed that that the single-step prediction RMSEs are all less than one, with higher prediction accuracy, but only can used in short-term prediction, the multi-step prediction RMSEs are bigger than one, with lower prediction accuracy, but can used in long-term carbon emission trend prediction can be achieved. The carbon emission trend prediction of six power plants in Jilin province between January 2024 and August 2031 confirms that the carbon emissions of power plants will be affected by seasons and shown cyclical changes. Finally, reasonable policy recommendations are provided for the successful realisation of the "double carbon" target for electricity in Jilin Province.
Deep Learning Enabled Efficient Net with KLM for Pragmatic Plant Disease Diagnosis
Authors: G. Kalpana A. Pavithra,
Published: April 2024

Research is still being done on the automated identification and classification of plant diseases. A quick and precise methodology for identifying plant diseases can improve both small-scale commercial crop protection and large-scale food security. Deep learning (DL) methods also enable the monitoring of plant health and the early diagnosis of illnesses. This study's DLEN-KLM model, which is built on EfficientNet and Kernel extreme Learning Machine (KLM) and is designed to diagnose plant diseases intelligently, addresses this issue. In contrast to limited adaptive histogram equalisation as a preprocessing method, the suggested DLEN-KLM model uses median filtering in its construction. A feature extractor built on EfficientNet B0 is also part of the DLEN-KLM model, which is used to create the best feature vectors before categorising them with the KLM model. Utilising a benchmark dataset, the effectiveness of the DLEN-KLM technique is validated. The experimental results showed that the technique outperformed more contemporary methods in terms of disease diagnosis.
Employee Turnover Prediction Based on Ensemble Learning DGNK Model
Authors: Kechao Wang, Yan Wang, Lin Liu, Ning Sha, Lin Ma Lihe Ma,
Published: April 2024

Employee turnover is a problem that can have significant negative impacts on an enterprise. It not only results in the loss of valuable talent and knowledge but also incurs substantial costs in terms of hiring, onboarding, and training new employees. Therefore, predicting employee intent to quit can be crucial for organizations to take proactive measures to prevent it from happening. Early detection of employee turnover intention will help enterprise develop and enhance core competitiveness. This study aims to predict the employee intension to quit. In the present study, more than 1,400 samples containing 31 features of a company’s employees were collected from Kaggle website as data sets. A two-layer DGNK model was designed with decision tree, gradient boosting, naive bayes and k-nearest neighbor model as the primary classifier and gradient boosting as the secondary classifier to build the predictive model of employee turnover intention. The experimental outcomes show that DGNK model based on two-layer ensemble learning has the best outcome, while naive bayes model has the worst outcome. In conclusion, this study highlights the importance of predicting employee turnover intention as an effective strategy to enhance organizational performance and competitive advantage. Furthermore, the success achieved in the study suggests that machine learning models like DGNK can play a crucial role in achieving this goal.
Application of Statistical Features in Vibro-acoustic Signals to Detect Early Browning Disorder in Pears Compared with Food Chemistry Method
Authors: Kai Wang, Jiahui Gu, Zhijun Feng, Feng Zhang Hui Zhang,
Published: April 2024

- Browning in pears is one of the most serious diseases in pear fruit, which is caused by Alternaria alternata. The browning process is accompanied by changes in the chemical properties of the fruit, which affect its taste and nutritional composition. Additionally, as a typical postharvest disease, internal browning in pears can cause fruit tissue decay during storage that can reduce the shelf stability of fruit, and bring serious losses to sellers. Because it is difficult to identify the browning pears by appearance, a non-destructive detection technology is highly desirable to correctly discriminate a pear at the early stage of browning for increasing the market value. Firstly, 11 and 7 statistical features were calculated from the time-domain and frequency-domain, respectively. Then, sensitive features in time-domain set, frequency-domain set and combined feature set were selected by the distance evaluation technology, respectively. These selected features were used to train classifier based on K-nearest neighbor algorithm under different K-values. With the selected combined features adopted, the constructed KNN classifier performed the best classification performance. It allowed a high overall accuracy of 91.8 % to classify the healthy and browning pears. Also, the F1 value of 92.6 % indicated that the classifier can be successfully generalized. Therefore, the classification model established in this study is effective for identifying the early browning disorder in pear fruit.
Auto encoders with Cellular Automata for Anomaly Detection
Authors: Prasun Chakrabarti, Martin Margala, SSSN Usha Devi N. Pokkuluri Kiran Sree,
Published: April 2024

This work combines auto encoders with cellular automata (CA) to present a novel hybrid strategy for anomaly identification. For feature learning, auto encoders are used to identify spatial patterns in the input data. Simultaneously, temporal and geographical dependencies are captured by CA, which improves the model's capacity to identify complicated anomalies. Training on spatially altered data, the auto encoder-CA hybrid model makes use of CA's temporal evolution to reveal dynamic patterns. Reconstruction errors between the input data and its decoded representation are computed to identify anomalies. A comprehensive framework for anomaly identification is provided by the synergy between spatial-temporal analysis of CA and auto encoder-based feature learning. Performance is optimized by fine-tuning the model's parameters, which include the auto encoder architecture and CA setup. The hybrid model's dynamic adaptation ensures robustness over time by accommodating changing data distributions. Evaluation measures show how well the suggested method captures abnormalities that appear in both temporal and geographical dimensions. A promising method for identifying abnormalities in complicated datasets with detailed spatial and temporal patterns is presented by this novel combination of auto encoders and cellular automata. The proposed method is evaluated with various parameters like reconstruction error, precision , recall, F1 score and Area Under the Receiver Operating Characteristic (ROC-AUC). The average accuracy is reported as 97.36% which is promising when compared with baseline methods.
“Web Services Performance Prediction with Confusion Matrix and K-Fold Cross Validation to Provide Prior Service Quality Characteristics”
Authors: C. Kalaiarasan K. Prakash,
Published: April 2024

The Information Technology sector has experienced substantial growth, especially in the realm of application development, over the last few decades. This ongoing evolution highlights the complexity of contemporary IT applications, which no longer rely on a singular component. Developers now integrate a multitude of components from diverse sources, including users and vendors. Thoroughly examining the quality of external software components before integration into an application is imperative for ensuring optimal service performance. Numerous methodologies and approaches are available for appraising the quality of software components, including the Software as a Service Model (SaaS), Quality of Experience (QoE), and alternative models for identifying quality. These models employ traditional techniques to assess factors like availability, integrity, accessibility, security, performance, and reliability, collectively contributing to the measurement of Quality of Service (QoS). The suggested method enables thorough scrutiny of software components through a 360-degree evaluation, utilizing the Confusion Matrix for predicting performance. This evaluation method ranks web services based on throughput and response time, providing tangible values for decision-making by service users. The classification mechanism aids in categorizing standards within a benchmark web service dataset. By utilizing this performance measuring method, one can determine service quality through the confusion matrix, aiding in the identification of the best web services and contributing to the optimization of application performance.
Reference Image Aided Color Matching Design Based on Interactive Genetic Algorithm
Authors: Yubin Wu
Published: April 2024

This paper proposes an interactive genetic algorithm to assist designers in color selection and match different color combinations extracted. Then, based on the CorelDraw development environment, this paper establishes a graphic-based man-machine dialogue model. Through simulation experiments, genetic algorithms such as random selection, hybridization, variation, color region adjustment, etc. Finally, the best color combination is obtained and combined with the image. Within the scope of color extraction value, designers can use interactive genetic algorithm rules to view color planning from a holistic perspective. Under the influence of human-computer interaction, each scheme is optimized step by step, so that the color ideas matching the designer can be found quickly.
Application of computer technology to numerical simulation of hydrothermal variation of subgrade enhanced by seepage drainage geoglage under freeze-thaw cycle
Authors: Yuzhuo Wang, Zhichao Xu, Yunshan Chen, Kai Li
Published: April 2024

In seasonal frozen soil areas, engineering problems are common due to the high water content of subgrade soil. In view of this phenomenon, a new type of percolating drainage geogrid (SDG) is proposed for cooling and drainage. The changes of soil temperature and moisture at different time points were analyzed through computer simulation laboratory model test, and the changes and distribution of soil temperature and moisture under freeze-thaw conditions were obtained. The temperature and moisture changes and their distribution obtained by computer modeling agree with the experimental results. By optimizing the soil materials of the model samples, the changes of temperature and moisture with time under freeze-thaw conditions were more significant. Therefore, it can be inferred that, without changing the configuration of the percolation drain grid, the cooling drainage performance of the percolation drain grid can be improved under the same conditions by changing the heat transfer coefficient or the macroporous filling material. It provides theoretical support and data guarantee for the reinforcement of subgrade percolation and drainage grid in cold area.
Geo-positioning Method of Optical Satellite Unconventional Stereo Imagery Based on Optimal Intersection
Authors: Yang Wu, Lei Wu, Fen Tang, Bo Wang, Yaofeng Su
Published: April 2024

Generally, the classic three-dimensional (3D) geometric positioning of optical satellite imagery uses the least squares principle to calculate the coordinates of a ground point by minimizing the sum of the squares of the distances between two imaging rays, which requires the standard stereo data with good imaging geometric conditions. As for unconventional stereo images, the undesirable and ubiquitous weak intersection phenomena exist in data will lead to bad results or even calculation failures for the conventional method. By selecting the highest precision intersection point in block adjustment, a new method that can solve the 3D coordinates with higher accuracy and stability was proposed. Tests of two data sets covering different landscapes validated the effectiveness of the method. The results showed that the geo-positioning performance and robustness of the proposed method was better than that of the conventional method, and this advantage is even greater in areas with more undulating terrain and more images with weak convergence.
Research on engine multiple fault diagnosis method based on cascade model
Authors: Genghong Jiang, Chao Wang, Zhonghua Yao, Haixuan Qiu
Published: April 2024

The engine is the core component of the power system, and the health status of the components of the engine is very important for the normal operation of the power system. Most of the exist-ing fault diagnosis methods diagnose the engine fault type without further analysis of the sever-ity of the fault. Different fault severity requires different maintenance measures. Therefore, this paper proposes a cascading fault diagnosis model based on Gated Recurrent Unit (GRU) to di-agnose the fault type and the severity of the corresponding fault type.Firstly, the effective fea-tures are extracted from the vibration signals, and then the features are input into the GRU for fault type diagnosis to obtain the sub-fault diagnosis model. After training, one fault type diag-nosis model and four fault severity diagnosis models are obtained. Then the obtained model is cascaded to obtain the total fault diagnosis network. Fault type diagnosis is located at the first level, and four fault severity diagnosis is located at the second level. The effectiveness of the proposed method is verified by experimental data
The Correction Strategy of Artificial Intelligence Embedding into the Negative Social Mentality of the Marginal Group of College Students in the New Era
Authors: Wenqu Xu
Published: April 2024

Artificial intelligence is a rapidly developing technology that is gradually changing the pace of modern people's lives and ways of working. As AI enters the field of higher education, it is generating increasingly significant functions and effects. Artificial intelligence is having a certain impact on the growth of college students, and as a marginalized group of college students in the new era, it also presents unique social attitudes and patterns. Strengthening the correction of negative social attitudes towards marginalized groups of college students is of great practical significance for promoting social progress and development. This article takes the marginalized group of college students in a certain university in China as the research basis and concludes that: 1.there are significant differences in the scores of social emotions among marginalized groups of college students of different genders; 2.There are significant differences in the scores of social needs, social cognition, and social emotions among marginalized groups of college students from different disciplines; 3.There are significant differences in the scores of social needs, social cognition, social emotions, social values, and social behavioral tendencies among marginalized groups of college students of different grades. Research has found that artificial intelligence can have a positive impact on academic achievement, career planning, social responsibility, and creativity of marginalized groups of college students. It is suggested that universities should take artificial intelligence as their starting point and think from four dimensions: family factors, school factors, social factors, and personal factors, striving to guide marginalized groups of college students to establish a healthy and positive social mentality
Data-driven study of container handling charges in coastline ports of China
Authors: Tianchao Guan, Zhehui Zhang, Di Xu, Peng Cai
Published: April 2024

With the rapid development of networking and digitization, the data generated by various industries has shown exponential growth. The seaport industry is the fundamental industry to support the operation of the national economy, and undertakes the vast majority of China's foreign trade, materials, and coal transport along the sea. In response to some of China's major seaports of container handling charges for different extent of the upward event. In this paper, by researching the main seaports of China's coastal ports, we have obtained the level of container handling charge, and operating cost of container section. Meanwhile, this paper combines the reform history of China's port container charging policy and the comparison of domestic and foreign charging levels to discover the cost structure of the container handling charges. It establishes the benchmark for the optimization of the container handling charges. Utilizing data analysis related to production and operations, this paper proposes an optimization method of container handling charges based on the cost analysis of container port companies with macro and micro coupling and puts forward effective suggestions on this basis. This paper suggests the establishment of a mechanism for regulating container handling charges. It also proposes a mechanism for monitoring and regular announcement of container costs.
Cause Analysis of Elevator Accidents Based on Complex Network
Authors: Wenhui Du, Yiwei Chen, Qian Zhou, Minghui Zhang, Ru Yi
Published: April 2024

In order to excavate the key causes of elevator accidents, 70 elevator accident investigation reports from 2018 to 2023 were collected, and data mining was carried out to extract "4M" unsafe factors. On this basis, based on the complex network, the model of elevator accident causation network is constructed, and the statistical characteristics of this model are analyzed. Finally, the entropy weight-TOPSIS is used to construct a comprehensive evaluation model of network node importance, which comprehensively evaluates the causes of elevator accidents from four indicators, including degree centrality, intermediary centrality, proximity centrality and aggregation coefficient of nodes, and identifies the key factors of elevator accidents. The results show that unsafe factors such as defects of equipment and facilities, illegal operation, improper operation, weak safety awareness, inadequate implementation of safety responsibilities, and inadequate on-site supervision are the key causes of elevator accidents, which need to be controlled and controlled.
Design and Development of an Intelligent Connected Access Integrated Training and Assessment Platform
Authors: Shuo Kang Guanghong Xin, Haozheng Xie,
Published: April 2024

This study addresses the issues of poor interdisciplinary integration and misalignment between experimental content and industry demands in the construction of applied undergraduate laboratory facilities, based on the background of industry-academia collaboration. This paper presents the design and development of an intelligent connected access integrated training and assessment platform, utilizing a full-stack development approach that combines knowledge graph, sensor principles, communication protocol integration, microcontroller programming, networking, connected access, and cloud platform development. The platform adopts a topological hierarchical structure, facilitating layered instruction for teachers and helping students to understand the logical development of Internet of Things products. Furthermore, the platform utilizes equipment commonly used in enterprises to enhance students' technical application, practical, and innovative capabilities. Through teaching practice, students' engineering awareness and practical skills have significantly improved, effectively promoting the development of relevant courses and practical training.
Autonomous Vehicle Traffic Accident Prevention using Mobile-Integrated Deep Reinforcement Learning Technique
Authors: J. Praveenchandar
Published: April 2024

When it concerns autonomous traffic management, the most effective decision-making reinforcement learning methods are often utilized for vehicle control. Surprisingly demanding circumstances, however, aggravate the collisions and, as a consequence, the chain collisions. In order to potentially offer guidance on eliminating and decreasing the danger of chain collision malfunctions, we first evaluate the main types of chain collisions and the chain events typically proceed. In an emergency, this study proposes mobile-integrated deep reinforcement learning (DRL) for autonomous vehicles to control collisions. Three essential influencing substances are completely taken into consideration and ultimately achieved by the offered strategy: accuracy, efficiency, and passenger comfort. Following this, we investigate the safety performance currently employed in security-driving solutions by interpreting the chain collision avoidance problem as a Markov Decision Process problem and offering a decision-making strategy based on mobile-integrated reinforcement learning. All of the analysis's findings have the objective of aid academics and policymakers to appreciate the positive aspects of a more reliable autonomous traffic infrastructure and to smooth out the way for the actual adoption of a driverless traffic scenario.
Monitoring and Detecting Plant Diseases Using Cloud-Based Internet of Things
Authors: Sasidhar Attuluri
Published: April 2024

The farming field is currently going through an upheaval as a consequence of the Internet of Things (IoT), which is offering farmers a respectable spectrum of tackles that include precision and sustainable agriculture for confronting problems within the industry. IoT technology is used to obtain data on ambient conditions which include soil moisture, air temperature, and atmospheric moisture that are advantageous to the enhancement of the various microorganisms and the propagation of farm-related problems. Farmers' ambition to connect through their farms from any location around the globe at any time is fostered by IoT. Wirelessly connected devices record the situation of the farm, and microcontrollers are implemented to automate and regulate farm operations so that conditions may be observed remotely. Farmers can leverage IoT on their mobile devices to be warned about the ongoing state of their cultivated land at any time and at nearly any point across the world. IoT technology can help traditional farming become more profitable while lowering the hurdles it faces. Farmers possess real-time access to the details, which allows them to determine a health condition that originates from favorable ambient conditions. Farmers subsequently become professionals at sustaining the ecosystems of their crops. Furthermore, farming industries necessitate greater numbers of workers and more staff people. Unfortunately, a rising number of human beings are departing the agriculture industry, which has rendered the lack of employees severe. As an outcome, intelligent agricultural technologies are essential in agriculture to shrink the amount of workforce demanded while providing the rising requirements imposed by rising populations.
Application of Neural Network in Seismic Risk Survey
Authors: Hao Wang, Ping Fu Changxian Zhou, Shaopeng Zheng, Youquan Ye,
Published: April 2024

The accuracy of inversion data is determined by the reliability of linear inversion method. Therefore, the study of nonlinear seismic inversion method is a meaningful work. This paper mainly aims at the application of nonlinear neural network inversion method in Fujian Risk Survey, the following work has been accomplished: (1) The theory and algorithm of the conventional multilayer feedforward neural network and some problems in the design of the model are analyzed and summarized. Aiming at the gradient descent function search algorithm in the BP algorithm, a method for global optimization is proposed by combining simulated annealing (global search algorithm) with Conjugate gradient method (local search algorithm) , the principle and implementation of simulated annealing and conjugate gradient algorithm are analyzed in detail. (2) Based on the analysis of conventional multilayer feedforward neural networks, the model structure and basic principle of probabilistic neural networks are studied. The object function search algorithm and its realization are analyzed. The method is applied to the risk elimination prediction for the first time, and the results are satisfactory. (3) The method and principle of inversion of reservoir parameters by using various seismic attribute information and neural network technology are discussed. In order to make full use of the effective information contained in seismic signal, the method of seismic attribute extraction and optimization is studied, the principle and implementation method of attribute optimal ranking by stepwise recursive method and selecting the most important attribute combination by interactive verification method are presented.
Digital Technology: Research on the Construction and Application of Blended Teaching Curriculum of Practical Physical Education Classroom Teaching Skills in the Big Data Background
Authors: Pengfei Chen, Zhibing Zou, Nianmao Li Wenxing Tan,
Published: April 2024

Big data is bringing discipline to education. Under the guidance of digital technology, education has ushered in the upsurge of digital reform, digital course teaching has gradually entered the public's vision, and the upsurge of digital course open education is coming. The paper adopts the research methods of literature, field investigation and logical analysis, and takes the construction of digital curriculum mixed teaching course based on the platform of Superstar Learning as the research object. This paper constructs the curriculum construction ideas in three stages of "pre-class, mid-class and after-class", and draws the following conclusions through the research of blended teaching curriculum practice in the big date background: Build digital media teaching resource library, record multi-platform online teaching videos, practice dual-track mixed teaching, pre-set interactive classroom teaching activities, after-school homework to consolidate and improve classroom learning, online examination to achieve teaching effect evaluation and feedback, etc. And summed up four practical experiences of multi-style one-click check-in function to help class attendance, interactive teaching activity function to achieve subject participation, paperless online evaluation function to detect learning effect, digital courses to break the limitation of time and space. Digital technology improves the convenience of course teaching and forms a visual teaching effect. Digital technology provides new ideas for digital curriculum teaching. The existence of digital curriculum provides more possibilities for personalized learning, while hybrid teaching promotes the interaction and cooperation between students and teachers. Build an online learning platform based on big data to support computer technology, and guide students to conduct simulation and practice in the teaching process.