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For ranking aggregation in crowdsourcing task, the key issue is how to select the optimal working group with a given number of workers to optimize the performance of their aggregation. Performance prediction for ranking aggregation can solve this issue effectively. However, the performance prediction effect for ranking aggregation varies greatly due to the different influencing factors selected. Although questions on why and how data fusion methods perform well have been thoroughly discussed in the past, there is a lack of insight about how to select influencing factors to predict the performance and how much can be improved of.

In this paper, performance prediction of multivariable linear regression based on the optimal influencing factors for ranking aggregation in crowdsourcing task is studied. An influencing factor optimization selection method based on stepwise regression (IFOS-SR) is proposed to screen the optimal influencing factors. A working group selection model based on the optimal influencing factors is built to select the optimal working group with a given number of workers.

The proposed approach can identify the optimal influencing factors of ranking aggregation, predict the aggregation performance more accurately than the state-of-the-art methods and select the optimal working group with a given number of workers.

To find out under which condition data fusion method may lead to performance improvement for ranking aggregation in crowdsourcing task, the optimal influencing factors are identified by the IFOS-SR method. This paper presents an analysis of the behavior of the linear combination method and the CombSUM method based on the optimal influencing factors, and optimizes the task assignment with a given number of workers by the optimal working group selection method.

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and uncovers a deeper understanding of user opinions and trends within this digital realm. Further, sentiments signify the underlying factor that triggers one’s intent to use technology like the metaverse. Positive sentiments often correlate with positive user experiences, while negative sentiments may signify issues or frustrations. Brands may consider these sentiments and implement them on their metaverse platforms for a seamless user experience.

The current study adopts machine learning sentiment analysis techniques using Support Vector Machine, Doc2Vec, RNN, and CNN to explore the sentiment of individuals toward metaverse in a user-generated context. The topics were discovered using the topic modeling method, and sentiment analysis was performed subsequently.

The results revealed that the users had a positive notion about the experience and orientation of the metaverse while having a negative attitude towards the economy, data, and cyber security. The accuracy of each model has been analyzed, and it has been concluded that CNN provides better accuracy on an average of 89% compared to the other models.

Analyzing sentiment can reveal how the general public perceives the metaverse. Positive sentiment may suggest enthusiasm and readiness for adoption, while negative sentiment might indicate skepticism or concerns. Given the positive user notions about the metaverse’s experience and orientation, developers should continue to focus on creating innovative and immersive virtual environments. At the same time, users' concerns about data, cybersecurity and the economy are critical. The negative attitude toward the metaverse’s economy suggests a need for innovation in economic models within the metaverse. Also, developers and platform operators should prioritize robust data security measures. Implementing strong encryption and two-factor authentication and educating users about cybersecurity best practices can address these concerns and enhance user trust.

In terms of societal dynamics, the metaverse could revolutionize communication and relationships by altering traditional notions of proximity and the presence of its users. Further, virtual economies might emerge, with virtual assets having real-world value, presenting both opportunities and challenges for industries and regulators.

The current study contributes to research as it is the first of its kind to explore the sentiments of individuals toward the metaverse using deep learning techniques and evaluate the accuracy of these models.

The purpose of this paper is to propose a new velocity prediction navigation algorithm to develop a conflict-free path for robots in dynamic crowded environments. The algorithm BP-prediction and reciprocal velocity obstacle (PRVO) combines the BP neural network for velocity PRVO to accomplish dynamic collision avoidance.

This presented method exhibits innovation by anticipating ahead velocities using BP neural networks to reconstruct the velocity obstacle region; determining the optimized velocity corresponding to the robot’s scalable radius range from the error generated by the non-holonomic robot tracking the desired trajectory; and considering acceleration constraints, determining the set of multi-step reachable velocities of non-holonomic robot in the space of velocity variations.

The method is validated using three commonly used metrics of collision rate, travel time and average distance in a comparison between simulation experiments including multiple differential drive robots and physical experiments using the Turtkebot3 robot. The experimental results show that our method outperforms other RVO extension methods on the three metrics.

In this paper, the authors propose navigation algorithms capable of adaptively selecting the optimal speed for a multi-robot system to avoid robot collisions during dynamic crowded interactions.

In the hybrid knowledge-sharing platform where paid and nonpaid (“free”) knowledge activities coexist, users’ free knowledge contribution may be influenced by financial factors. From the perspective of opportunity cost, this study investigates the direct effect of how the amount of monetary income from users’ contribution to paid knowledge activities influences their free knowledge contribution behavior in the future. Further, this study aims to verify the interaction effect of financial and nonfinancial factors (i.e. the experience of free knowledge contribution and social recognition) on free knowledge contribution.

Objective data was collected from a hybrid knowledge-sharing platform in China and then analyzed by using zero-inflated negative binomial regression model.

Results show that the amount of monetary income that knowledge suppliers gain from paid knowledge contribution negatively influences their free knowledge contribution. Experience of free knowledge contribution strengthens the negatively main effect, while social recognition has the weakening moderating role.

Although some studies have explored and verified the positive spillover effect of financial incentives on free knowledge contribution, the quantity dimension is ignored. This study examines the hindering influence of the quantity of monetary income from the perspective of opportunity cost. By taking the characteristic of knowledge suppliers and platforms as moderators, this study deepens the understanding of the influence of monetary income on free knowledge contribution in the hybrid knowledge-sharing platform.

In recent years, terrorist attacks have gradually become one of the important factors endangering social security. In this context, this research aims to propose methods and principles which can be utilized to make effective evacuation plans to reduce casualties in terrorist attacks.

By analyzing the statistical data of terrorist attack videos, this paper proposes an extended cellular automaton (CA) model and simulates the panic evacuation of the pedestrians in the terrorist attack.

The main findings are as follows. (1) The panic movement of pedestrians leads to the dispersal of the crowd and the increase in evacuation time. (2) Most deaths occur in the early stage of crowd evacuation while pedestrians gather without perceiving the risk. (3) There is a trade-off between escaping from the room and avoidance of attackers for pedestrians. Appropriate panic contagion enables pedestrians to respond more quickly to risks. (4) Casualties are mainly concentrated in complex terrains, e.g. walls, corners, obstacles, exits, etc. (5) The initial position of the attackers has a significant effect on the crowd evacuation. The evacuation efficiency should be reduced if the attacker starts the attack from the exit or corners.

In this research, the concept of “focus region” is proposed to depict the different reactions of pedestrians to danger and the effects of the attacker’s motion (especially the attack strategies of attackers) are classified. Additionally, the influences on pedestrians by direct and indirect panic sources are studied.

The Astroworld concert tragedy is used as an example of crisis (mis)management and the potential utility of the 4R model. Although the 4R model has been implemented in high-risk emergency management situations, it is useful in the PR field because of its actionable approach, creating a way for practitioners to prepare for and manage crisis situations.

This is an analysis of the crisis that occurred at Astroworld, spanning preparation, day-of events, casualties and enduring reputational impact. The paper applies the 4R method to the Astroworld tragedy to show how it could have lessened or even prevented the tragedy. Finally, the SCCT model is used to explain why the official post-crisis statements were ineffective.

Social media has heightened the importance of a quick and effective organizational response to risk and crisis situations because poor responses can go viral quickly. However, social media also provides intelligence and crowd sourced information that can inform PR practitioners of emerging crisis scenarios. It is also an underutilized tool for two-way communication during crises.

The 4R approach is beneficial to general practitioners as it simplifies crisis best-practices, something essential for quick action. As our world changes and becomes less predictable, practitioners must have a clear plan to protect their organizations and the public surrounding them. This approach includes reduction, readiness, response and recovery, which are all essential in crisis communication.

The 4R method has not been explored or applied in the PR field. This paper highlights how the model has been utilized in the emergency management field and illustrates the way 4R can serve the PR field.

Crowd logistics is a rising phenomenon in last-mile delivery that integrates technological applications and sources a large number of participants to do logistical activities, achieving sustainable shipping in urban environments. However, up until now, there has been limited literature in this field. This research aims to investigate the extrinsic and intrinsic factors that impact the participative behaviour of driver-partners in crowd logistics.

An integrated model is developed based on motivation theory, incorporating attitude as a contributor to both extrinsic and intrinsic motivations. A questionnaire was constructed and distributed to collect data from 303 respondents who are existing or potential driver-partners in Vietnam.

Our findings confirm (1) the influence of monetary rewards on extrinsic motivation and (2) the power of self-efficacy, trust and sense of belonging on intrinsic motivation. Further, we find that attitude positively impacts extrinsic motivation, whereas there is no effect between attitude and intrinsic motivation. Both extrinsic and intrinsic motivations are demonstrated to significantly influence driver-partners' participative intentions. Additionally, a positive association is found between extrinsic and intrinsic motivations.

Findings from this study theoretically enrich the literature on crowd logistics, especially on the supply side, and empirically contribute to implications that are valuable to crowd logistics firms on driver-partner recruitment and business strategy development.

The purpose of this study is to propose a new mathematical model that integrates strategic decision-making with tactical-operational decision-making in order to optimize production and scheduling decisions.

This study presents a multi-objective optimization framework to make production planning, scheduling and maintenance decisions. An epsilon-constraint method is used to solve small instances of the model, while new hybrid optimization algorithms, including multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm, multi-objective harmony search and improved multi-objective harmony search (IMOHS) are developed to address the high complexity of large-scale problems.

The computational results demonstrate that the metaheuristic algorithms are effective in obtaining economic solutions within a reasonable computational time. In particular, the results show that the IMOHS algorithm is able to provide optimal Pareto solutions for the proposed model compared to the other three algorithms.

This study presents a new mathematical model that simultaneously determines green production planning and scheduling decisions by minimizing the sum of the total cost, makespan, lateness and energy consumption criteria. Integrating production and scheduling of a shop floor is critical for achieving optimal operational performance in production planning. To the best of the authors' knowledge, the integration of production planning and maintenance has not been adequately addressed.

Due to the miniaturization of electronic devices, the increased current density through solder joints leads to the occurrence of electromigration failure, thereby reducing the reliability of electronic devices. The purpose of this study is to propose a finite element-artificial neural network method for the prediction of temperature and current density of solder joints, and thus provide reference information for the reliability evaluation of solder joints.

The temperature distribution and current density distribution of the interconnect structure of electronic devices were investigated through finite element simulations. During the experimental process, the actual temperature of the solder joints was measured and was used to optimize the finite element model. A large amount of simulation data was obtained to analyze the neural network by varying the height of solder joints, the diameter of solder pads and the magnitude of current loads. The constructed neural network was trained, tested and optimized using this data.

Based on the finite element simulation results, the current is more concentrated in the corners of the solder joints, generating a significant amount of Joule heating, which leads to localized temperature rise. The constructed neural network is trained, tested and optimized using the simulation results. The ANN 1, used for predicting solder joint temperature, achieves a prediction accuracy of 96.9%, while the ANN 2, used for predicting solder joint current density, achieves a prediction accuracy of 93.4%.

The proposed method can effectively improve the estimation efficiency of temperature and current density in the packaging structure. This method prevails in the field of packaging, and other factors that affect the thermal, mechanical and electrical properties of the packaging structure can be introduced into the model.