bluuebunny/crossref_metadata_2025_split huggingface dataset:
Professional identity of grid workers based on SPSS 22.0
Authors: Hou Zhenjiang , Kang Juan 
Published: December 2023

Abstract
The professional identity of grid workers is a significant expression of social service consciousness, and this paper utilizes statistical analysis methods to study its related factors. Under the framework of SPSS 22.0, the non-robust paradigm square of traditional PCA is replaced by a robust measure, and the AWL-RPCA model is constructed by combining the introduced data adaptive learning. Meanwhile, to address the data smoothness problem of quantile regression, the PDQR model was proposed by using the method of constructing panel data to optimize the control of individual heterogeneity. Taking the gridmen in L street of a city as the research object, the related factors of occupational identity were mined based on the AWL-RPCA model, and then the regression equation was obtained by the PDQR model. The degree of influence of the independent variables on the occupational identity of the gridmen from the highest to the lowest is the working status, the level of education, the pressure of helping and teaching, the appraisal satisfaction, the support and coordination satisfaction, and the propaganda satisfaction, and the regression coefficients are 8.36, 3.14, 5.08, 3.60, -4.35, and 3.32, respectively. Under the guidance of the statistical analysis of the SPSS, the construction of the gridmen team can be optimized in a better way.
Exploring the reform of live e-commerce talent training based on the four-way collaboration of “government, industry, enterprise and school”
Authors: Wang Xin 
Published: December 2023

Abstract
This paper takes universities and enterprises as the basic game subjects of collaborative innovation of live e-commerce talent cultivation and defines the involved interest subjects as government, school, enterprise and industry to construct a multi-party evolutionary game model. The model’s evolutionary stable equilibrium point can be calculated using the replicated dynamic equations of multiple parties. MATLAB was used to analyze the numerical simulation to explore the sensitivity of the relevant parameters for the three parties. Finally, according to the game results, a four-point practical path to optimize the live e-commerce talent training program is proposed. The results show that under the satisfaction of the equilibrium point (0, 0, 0), the critical value of the active participation willingness of the four parties is between 0.4 and 0.5, and the final equilibrium point tends to (1, 1, 1). In this paper, the cooperation between schools and enterprises is prompted to change from limited rationality to full rationality in order to realize the Pareto optimal equilibrium of the final game.
The Integration and Development of Ideological and Political Education and Student Management in Universities Based on Data Fusion Models under the Background of Three Comprehensive Education
Authors: Feng Ranjun , Zhang Lingyong 
Published: December 2023

Abstract
Mental health management of college students is a crucial aspect of college student management. To this end, this paper extracts the characteristic sequences of consumption behaviors, including the number of consumption per week, the number of times of use breakfast, the number of times of deviating from the normal time of using breakfast and the number of times fetching water from the daily consumption records of students. Then, the best model MLSTM-FCN is selected from the different existing time series classification models. Aiming at the problem that the FCN branch has a small sensory field and can only run in one direction in the network structure of the MLSTM-FCN two-branch splicing, it is proposed to use a Transformer to replace the FCN branch so as to put forward a Trans-LSTM students’ psychological abnormality early warning model based on the classification of time series. Model. According to the model comparison, the Trans-LSTM fusion model F1 has the highest mean value. The value is 0.8245. Finally, in the psychological intervention based on ideology and politics for students with psychological problems identified by the early warning model, the results show that the individual’s pressure t=8.163, df=6, p<0.001, and the social environment pressure t=9.872, df=6, p<0.001, indicating that the subject’s mental anomalies can be classified based on the time series. 0.001, indicating a significant difference between before and after the intervention for the subjects.
Exploring the construction of business management model in the context of big data
Authors: Ran Junwei 
Published: December 2023

Abstract
Big data has made it necessary for business administration to be more intelligent and informationized. This paper introduces data mining technology, explains the specific steps of data mining, and analyzes commonly used data mining algorithms. The rule mining of the C4.5 algorithm is illustrated by using information entropy, the XGBoost model is used as the base learner of Stacking integrated learning and model fusion is carried out. The regional economic prediction model was constructed using the C4.5 rule mining algorithm, while the enterprise credit rating classification model was established using the Stacking algorithm. The empirical evidence shows that the regional economy will be affected by the main body of the enterprise, the industrial structure and the development of the enterprise, in which the industrial structure and the development of the enterprise showed exponential growth in 2007-2018, and their growth rates are all around 30%. Using the Stacking algorithm for enterprise credit rating classification, the recall rate of the weighted fusion model with GRU network as a meta-learner has improved by 2.4%. By analyzing the application of big data technology in business administration data, we illustrate its role in business administration decision-making so as to provide a certain reference for the construction of the business administration informatization model.
Research on the development of AloT sensing module for new energy power information collection
Authors: Jing Zhen , Li Congcong , Zhu Hongxia , Wang Pingxin , Chen Zhiru 
Published: December 2023

Abstract
In order to improve the ability of power communication technology to support large-scale and massive services of smart electricity consumption, this paper designs a sensing model to collect information on new energy electricity consumption. Using the arrival rate calculation method of intelligent IoT, the information on new energy electricity users is collected, and the environmental measurement data is collected with the help of intelligent sensors. Monitoring the concurrency factor of all sensors in the operation state, collecting queuing theory packet loss rate through utilization maximization objective function, and setting the average queuing length of communication nodes. On this basis, the voltage amplitude of the electrical energy sampling point is measured, and the Gaussian perceptual loss vector with non-zero mean and heteroskedasticity is performed to iteratively converge the constraint matrix and error covariance. The results show that the maximum relative errors for service delay and packet loss rate are 16.8% and 7.0%, respectively. As bandwidth increases, the queuing delay and packet loss rate decrease. The broadband power line carrier can reach up to 210 Mbps, and the micropower wireless rate can reach 38.5 kbps. The annual cycle characteristics fluctuate widely, but the cumulative error of electricity load prediction results is lower, and the prediction accuracy is high. It shows that the electricity consumption information collection sensing model provides an important reference for building a new communication network with low cost, high speed and reliability for smart electricity consumption.
Distributed Power High Dynamic Group Control Technology and Grid Connection Demonstration
Authors: Wang Qing , Li Guimin , Chen Zhiru , Jing Zhen , Zhang Zhi 
Published: December 2023

Abstract
Currently, high penetration distributed power generation group control technology makes it difficult to effectively regulate complex power grids. Therefore, on the ground of the division of distributed photovoltaic power generation clusters, this study proposes a dual-layer control model for distribution networks that integrates the cuckoo particle swarm algorithm and verifies its effectiveness. The experiment showcases that the standard deviations of the research algorithm on the four benchmark functions under the 50 dimension are 2.95e-02, 6.73e+02, 6.48e-04, and 6.45e-06, respectively, most of which are lower than the comparison algorithm. On the basis of connecting to the photovoltaic system, the voltage amplitude fluctuation of the double-layer control model designed through research using control algorithms is relatively small, basically maintaining around 1.05. In addition, after implementing control in practical applications, the actual line loss of the distribution network decreased from the maximum of 15.3% to 8.9%, meeting the line loss requirements of the actual distribution network, indicating the effectiveness of the double-layer control model.
Meanwhile, after applying the double-layer control model, the voltage fluctuation of node 42 significantly decreased, and the numerical value remained at 1.05 p.u., achieving a good voltage condition effect. The voltage fluctuation of grid connection in distributed power generation has been significantly improved when applied to the actual grid connection. Overall, the proposed dual-layer control model for the distribution network is effective and practical, which can effectively promote the construction of practical grid connection demonstration projects.
The Effect of External and Internal Factors on Management Effectiveness Within Indian Construction Companies
Authors: Sanchaniya Rashmi Jaymin , Kundziņa Antra , Thomas Cijo George , Geipele Ineta , Naimavičienė Jurga 
Published: December 2023

Abstract
The aim of this study is to define and clarify the idea of improving management effectiveness within Indian construction companies. We conducted an online survey with an Indian construction company and used correlation analysis to thoroughly examine how both external and internal factors affect management effectiveness. The survey includes demographic questions, ratings on a scale of 1 to 5 for management aspects, ratings on a similar scale for internal and external factors, and ratings on a similar scale for potential improvement suggestions. With the help of descriptive statistics and frequencies, the mean efficiency values were calculated using SPSS and Excel to analyse the collected data. To establish links between the variables and the effectiveness of the company, correlation analysis was also carried out. The primary conclusions of the study highlight the critical importance of focussing on employee relations, meticulous tracking of work output, integration of technology, and adherence to established protocols as key factors in improving management effectiveness. The research also reveals significant problems with the management system, such as a preference for sanctions over incentives. It establishes that internal and external factors have equal weight in influencing management effectiveness, emphasising the need for an all-encompassing strategy that is flexible enough to support long-term advancement in construction company management practices.
Optimization Models and Strategy Approaches Dealing with Economic Crises, Natural Disasters, and Pandemics – An Overview
Authors: Guliashki Vassil , Kirilov Leoneed , Nuzi Alsa 
Published: December 2023

Abstract
The occurrence of large-scale crises is a great challenge for people. In such cases, many levels of public life are affected and recovery takes time and considerable resources. Therefore, approaches and tools for predicting and preventing crises, as well as models and methods for crisis management and crisis overcoming, are necessary. In this review, we present approaches, models, and methods that support decision-making in relation to the prevention and resolution of large-scale crises. We divide crises into three types: natural disasters, pandemics, and economic crises. For each type of crisis situation, the types of applied tasks that are solved and the corresponding models and methods that are used to support decision-makers in overcoming the crises are discussed. Conclusions are drawn on the state of the art in this area and some directions for future work are outlined.
Hybridized Cryptographic Encryption and Decryption Using Advanced Encryption Standard and Data Encryption Standard
Authors: Kanshi Avaneesh , Soundrapandiyan Rajkumar , Anita Sofia V. S. , Rajasekar V. R. 
Published: December 2023

Abstract
This research proposes an efficient hybridized approach for symmetrical encryption of image files in bitmap formats. Due to the heavy use of lightweight encryption in fields such as military and corporate workplaces, intruders try to intercept communication through illegal means and gain access to classified information. This can result in heavy losses if the leaked image data is misused. The proposed enhances the security and efficiency of one of the most used standard symmetric algorithms, Advanced Encryption Standard (AES). In the proposed method, the AES architecture has been modified using a less intensive algorithm, Data Encryption Standard (DES). DES carries a sub-process of permuting data columns rather than the AES’s mixing feature. The proposed algorithm is analyzed using a set of 16 bitmap images of varying memory sizes and resolutions. The effectiveness of the algorithm is evaluated solely in terms of perceptual invisibility as per the main objective of the research.
Robust Email Spam Filtering Using a Hybrid of Grey Wolf Optimiser and Naive Bayes Classifier
Authors: Zraqou Jamal , Al-Helali Adnan H. , Maqableh Waleed , Fakhouri Hussam , Alkhadour Wesam 
Published: December 2023

Abstract
Effective spam filtering plays a crucial role in enhancing user experience by sparing them from unwanted messages. This imperative underscores the importance of safeguarding email systems, prompting scholars across diverse fields to delve deeper into this subject. The primary objective of this research is to mitigate the disruptive effects of spam on email usage by introducing improved security measures compared to existing methods. This goal can be accomplished through the development of a novel spam filtering technique designed to prevent spam from infiltrating users’ inboxes. Consequently, a hybrid filtering approach that combines an information gain philter and a Wrapper Grey Wolf Optimizer feature selection algorithm with a Naive Bayes Classifier, is proposed, denoted as GWO-NBC. This research is rigorously tested using the WEKA software and the SPAMBASE dataset. Thorough performance evaluations demonstrated that the proposed approach surpasses existing solutions in terms of both security and accuracy.
A Novel Self-Exploration Scheme for Learning Optimal Policies against Dynamic Jamming Attacks in Cognitive Radio Networks
Authors: Sudha Y. , Sarasvathi V. 
Published: December 2023

Abstract
Cognitive Radio Networks (CRNs) present a compelling possibility to enable secondary users to take advantage of unused frequency bands in constrained spectrum resources. However, the network is vulnerable to a wide range of jamming attacks, which adversely affect its performance. Several countermeasures proposed in the literature require prior knowledge of the communication network and jamming strategy that are computationally intensive. These solutions may not be suitable for many real-world critical applications of the Internet of Things (IoT). Therefore, a novel self-exploration approach based on deep reinforcement learning is proposed to learn an optimal policy against dynamic attacks in CRN-based IoT applications. This method reduces computational complexity, without prior knowledge of the communication network or jamming strategy. A simulation of the proposed scheme eliminates interference effectively, consumes less power, and has a better Signal-to-Noise Ratio (SNR) than other algorithms. A platform-agnostic and efficient anti-jamming solution is provided to improve CRN’s performance when jamming occurs.
Exploring the Performance and Characteristics of Single Linkage and Complete Linkage Hierarchical Clustering Methods for IoT Sensor Networks
Authors: Bajaber Fuad 
Published: December 2023

AbstractThe research explores applying hierarchical clustering methods, namely single linkage and complete linkage, in IoT Sensor Networks (ISNs). ISNs are distributed systems comprising numerous sensor nodes that collect data from the environment and communicate with each other to transmit the data to a base station. Hierarchical clustering is a technique that groups nodes into clusters based on proximity and similarity. This paper implements and compares the performance of single linkage and complete linkage methods in terms of cluster size, network lifetime, and cluster quality. The study’s findings provide guidance for ISN researchers and designers in selecting the appropriate clustering method that meets their specific requirements.
Reliability Analysis of an IoT-Based Air Pollution Monitoring System Using Machine Learning Algorithm-BDBN
Authors: Saritha  , Sarasvathi V. 
Published: December 2023

Abstract
Transmission of information is an essential component in an IoT device for sending, receiving, and collecting data. The Smart devices in IoT architecture are designed as physical devices linked with computing resources that can connect and communicate with another smart device through any medium and protocol. Communication among various smart devices is a challenging task to exchange information and to guarantee the information reaches the destination entirely in real-time in the same order as sent without any data loss. Thus, this article proposes the novel Bat-based Deep Belief Neural framework (BDBN) method for the air pollution monitoring scheme. The reliability of the proposed system has been tested under the error condition in the transport layer and is validated with the conventional methods in terms of Accuracy, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Pearson correlation coefficient (r), Coefficient of determination (R2) and Error rate.
Families first: A comparative study of company responses to paid care leave programs in the COVID-19 pandemic
Authors: Freiberg Tracey 
Published: December 2023

Abstract
Despite established positive associations of paid care leave (PCL) policies on labor market outcomes such as wage replacement and job continuity, the United States is a notable outlier as the only advanced nation without a federal paid leave program. Assuming PCL programs are costly, my study examines employer perceptions and responses to PCL regulations in the US during the COVID-19 pandemic in 2020. Using a policy experiment around the 500-employee cutoff associated with the Families First Coronavirus Response Act (FFCRA), logistic regressions are used on a newly-created dataset constructed from a survey administered to 306 business managers in New York and Boston. The analysis ultimately seeks to evaluate if PCL cost concerns predict 19 different business outcomes such as changes in headcount or employee benefits. In general, while 54.6 percent of firms report cost concerns with PCL laws, the results find firms with such concerns are more likely to engage in non-employee focused operational changes such as increases in prices instead of employee-oriented outcomes such as layoffs or wage decreases. Furthermore, the policy experiment yields that large companies are more likely to increase internal paid leave, while small companies are more likely to increase the number of independent contractors at the company. My study confirms companies react to government PCL regulation in dynamic ways, dependent on the unique circumstances and culture of each company.
Passenger Transport Trends in the V4 Countries
Authors: Komorousová Veronika , Hinke Jana 
Published: December 2023

Abstract
Passenger transport, or efforts to modify it in order to minimize its negative environmental impact, is a discussed issue. This paper aims to assess the trends in passenger transport indicators in the V4 countries between1995 and 2018. Within the time series of indicators, the transport indicators of individual car transport is analyzed and compared with the public mass transport broken down into rail, bus, tram, and underground transport. The results show a clear upward trend in individual car transport performance, while rail passenger transport performance shows a convex quadratic trend in all V4 countries except Hungary. In contrast, bus transport tends to show a downward trend. CO2 emissions and traffic accidents tend to decrease in the majority of the V4 countries, despite the increasing performance of individual car transport, which is a very positive finding that corresponds with the commitments of all V4 countries, or with social responsibility.
Using Partial Solar Eclipse for the 14-Metre Radio Telescope Calibration
Authors: Kallunki J. 
Published: December 2023

Abstract
A partial solar eclipse occurred on 25 October 2022, in the central and northern parts of Europe. The partial solar eclipse was observed at Aalto University Metsähovi Radio Observatory, Finland at the radio wavelength of 8 mm (37 GHz). In Finland, the magnitude of the partial solar eclipse was 62.7 %. Solar radio maps at the time cadence of about 9 minutes were observed over the whole eclipse. The solar eclipse observations could be used for instrument calibration purposes. This paper investigates the solar brightness temperature, the limb brightening effect, the height of the chromosphere and the location of radio brightening using the aforementioned partial solar eclipse observations.
We got the confirmation that our earlier results are consistent, e.g., the solar brightness temperature matches with 8100 K ± 300 K. It was also possible to detect limb brightening effect. However, the prevailing solar activity might have distorted the final conclusions. The Moon should operate as a focusing element and the location of radio brightenings could be defined more carefully than in the normal conditions. We investigated this feature. Our results are in some parts unexpected and need further investigations.
Numerical Modelling of a Turbine Flow Meter Used as Part of the Hydrogen Compressor System
Authors: Orlova S. , Devdas T. N. , Vasudev V. P. K. , Upnere S. 
Published: December 2023

Abstract
This study analyses a turbine flow meter in the context of a hydrogen compressor system. Basic concepts of turbine flow meter, accuracy and linearity, as well as calibration are described. Physical experimental testing scheme is presented in the paper. The viscosity of the fluid is one of the major factors that affects the performance of turbine flow meters. Numerical modelling experiments for different fluids with different viscosities are performed. Performed numerical modelling experiments give a possibility to continue research into the hydrogen compression system.