The Big Five Model in Relation to Job Performance:A New Look at Organizational Psychology

The Big Five Theory is often regarded as psychology’s most influential personality theoretical approach.The goal of this study is to examine the role of the Big Five Theory in the workplace,especially which personality qualities are more likely to predict work success.Which traits should companies emphasize throughout the hiring and selection processes?How can businesses use the Big Five personality model to locate employees that are more productive,efficient,and devoted to the organization’s goals?A detailed assessment of existing recent research addresses the aforementioned issues.Following a review of many current articles on the subject,it was established that using this model had a positive influence on individual and group performance,working relationships,manager work performance,and workplace innovation.

Personality Trait Detection via Transfer Learning

Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human resources.Tra-ditional machine learning techniques have been broadly employed for personality trait identification;nevertheless,the development of new technologies based on deep learning has led to new opportunities to improve their performance.This study focuses on the capabilities of pre-trained language models such as BERT,RoBERTa,ALBERT,ELECTRA,ERNIE,or XLNet,to deal with the task of personality recognition.These models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-term dependencies.This makes them suitable to classify multi-label personality traits from reviews while mitigating computational costs.The focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from texts.Moreover,it is able to fine-tune the previous models using the MyPersonality dataset,which comprises 9,917 status updates contributed by 250 Facebook users.These status updates are categorized according to the well-known Big Five personality model,setting the stage for a comprehensive exploration of personality traits.To test the proposal,a set of experiments have been performed using different metrics such as the exact match ratio,hamming loss,zero-one-loss,precision,recall,F1-score,and weighted averages.The results reveal ERNIE is the top-performing model,achieving an exact match ratio of 72.32%,an accuracy rate of 87.17%,and 84.41%of F1-score.The findings demonstrate that the tested models substantially outperform other state-of-the-art studies,enhancing the accuracy by at least 3%and confirming them as powerful tools for personality recognition.These findings represent substantial advancements in personality recognition,making them appropriate for the development of user-centric applications.

Physical Space Was Not Expanding

Plurality of characteristic peaks observed in number density distribution of galaxy redshift reveals that extent of physical space has been finite. Significant portion of observed celestial objects is found pair-wise associated, i.e., the observed lights were emitted from one and same luminescent source but seen at different sky directions of observer, which is a unique phenomenon that can occur but only in finite space. Cosmic microwave radiation has always been interpreted as afterglow of Big Bang event. However, such radiation is shown unobservable to current observer if Hubble-Lemaître Correlation is interpreted as caused by receding motion of celestial objects. On the other hand, cosmic radiation can be understood as a common and ordinary phenomenon due to space lens, a unique property only of finite space. From Sloan Digital Sky Survey data, internal diameter of physical space is measured as 2.0 billion light years. If celestial objects were receding, hence physical space was expanding, then characteristic peaks of finite physical space should not appear evenly in number density distribution of redshift of the objects but more sparsely with respect to redshift increase. However, as revealed by the data, locations of the characteristic peaks in the distributions are rather even that do not match the locations as required by receding motion of object. Therefore, as evidenced by the data, physical space was not expanding, at least during the recent 18 billion years. In addition, considerable portion of observed quasars is found sharing a common factor of ~1/2 for their respective gravitation redshifts.

A New Model of China-US Big Power Relations： A Symbolic Sign in Foreign Affairs

To cope with the current crisis and tensions full-filled China-US relationship, Chinese President Xi Jinping put forward the concept of building a new model of China-US big power relations, which the US agrees. Yet the new model won a heated discussion. In China this new model was evaluated positively and optimistically, while in the US it was perceived as a strategic challenge or even a threat. In the present article, the author proposes that this new model of China-US big power relations is more like a symbolic sign in foreign affairs rather than a strategic challenge or a threat or an effective and workable mechanism at this moment, and meanwhile analyses this view from diachronic and semiotic perspectives. The analyses reveal that the new model functions as a symbolic sign, signifying to the world that conceptually the two big powers have a good and harmonious relationship.

Big Model Strategy for Bridge Structural Health Monitoring Based on Data-Driven, Adaptive Method and Convolutional Neural Network (CNN) Group

This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element analysis.Leveraging ensemble methods,collaborative learning,and distributed computing,the approach effectively manages the complexity and scale of large-scale bridge data.The CNN employs transfer learning,fine-tuning,and continuous monitoring to optimize models for adaptive and accurate structural health assessments,focusing on extracting meaningful features through time-frequency analysis.By integrating Finite Element Analysis,time-frequency analysis,and CNNs,the strategy provides a comprehensive understanding of bridge health.Utilizing diverse sensor data,sophisticated feature extraction,and advanced CNN architecture,the model is optimized through rigorous preprocessing and hyperparameter tuning.This approach significantly enhances the ability to make accurate predictions,monitor structural health,and support proactive maintenance practices,thereby ensuring the safety and longevity of critical infrastructure.

Simulating streamflow and water table depth with a coupled hydrological model

A coupled model integrating MODFLOW and TOPNET with the models interacting through the exchange of recharge and baseflow and river-aquifer interactions was developed and applied to the Big Darby Watershed in Ohio, USA. Calibration and validation results show that there is generally good agreement between measured streamflow and simulated results from the coupled model. At two gauging stations, average goodness of fit （ R2 ）, percent bias （ PB ）, and Nash Sutcliffe efficiency （ENS） values of 0.83, 11.15%, and 0.83, respectively, were obtained for simulation of streamflow during calibration, and values of 0.84, 8.75%, and 0.85, respectively, were obtained for validation. The simulated water table depths yielded average R2 valuesof0.77 and 0.76 for calibration and validation, respectively. The good match between measured and simulated streamflows and water table depths demonstrates that the model is capable of adequately simulating streamflows and water table depths in the watershed and also capturing the influence of spatial and temporal variation in recharge.

Layered Software Patterns for Data Analysis in Big Data Environment

The proliferation of textual data in society currently is overwhelming, in particular, unstructured textual data is being constantly generated via call centre logs, emails, documents on the web, blogs, tweets, customer comments, customer reviews, etc.While the amount of textual data is increasing rapidly, users ability to summarise, understand, and make sense of such data for making better business/living decisions remains challenging. This paper studies how to analyse textual data, based on layered software patterns, for extracting insightful user intelligence from a large collection of documents and for using such information to improve user operations and performance.

An Information Fusion Model of Innovation Alliances Based on the Bayesian Network

To solve the problem of information fusion from multiple sources in innovation alliances, an information fusion model based on the Bayesian network is presented. The multi-source information fusion process of innovation alliances was classified into three layers, namely, the information perception layer, the feature clustering layer,and the decision fusion layer. The agencies in the alliance were defined as sensors through which information is perceived and obtained, and the features were clustered. Finally, various types of information were fused by the innovation alliance based on the fusion algorithm to achieve complete and comprehensive information. The model was applied to a study on economic information prediction, where the accuracy of the fusion results was higher than that from a single source and the errors obtained were also smaller with the MPE less than 3%, which demonstrates the proposed fusion method is more effective and reasonable. This study provides a reasonable basis for decision-making of innovation alliances.