As the importance of Electric Vehicles (EVs) in environmental protection and energy sectors increases, and with various countries implementing supportive policies, major automakers are launching a range of EVs to keep up with this trend. This research aims to analyze the core issues consumers focus on regarding EVs. Previous related studies often conducted experiments using qualitative analyses such as surveys or market research. This research employs a topic model LDA (Latent Dirichlet Allocation) combining TF-IDF to explore trends related to EVs, using quantitative analysis to replace the traditional qualitative approach, and supports the research findings with relevant data or events. The data for this research were sourced from professional reviews on Greencarreports and Electrek, both major American news websites reviewing EVs. Reviews were collected from January 2020 to November 2023 using a web crawler. Data preprocessing steps included cleaning, word segmentation, and stemming. Subsequently, TF-IDF was used for keyword filtering, allowing weighted allocation of vocabulary before inputting into the topic model. The experimental results analyzed three topics: Topic 1 concerns EV policies, Topic 2 pertains to the construction of charging stations, and Topic 3 relates to factory construction, capacity demands, and performance indicators. These findings align with the content of the International Energy Agency's 2024 white paper “Trends in Electric Cars,” and subsequent news reports also corroborate the research method effectiveness. The analysis reveals that consumer concerns about EVs differ from those about traditional cars, where attention typically focuses on mileage, fuel consumption, and interior design. In contrast, for EVs, the focus is on policies, charging station construction, and capacity, which are more forward-looking topics. This research aids in understanding EV market trends and provides reference points for relevant companies and policy-making.

One main problem of Fuzzy c-Means (FCM) is deciding on an appropriate number of clusters. Although methods have been proposed to address this, they all require clustering algorithms to be executed several times before the right number is chosen. The aim of this study was to develop a method for determining cluster numbers without repeated execution. We propose a new method that combines FCM and singular value decomposition. Based on the percentage of variance, this method can calculate the appropriate number of clusters. The proposed method was applied to several well-known datasets to demonstrate its effectiveness.

A recommendation system is based on the user and the items, providing appropriate items to the user and effectively helping the user to find items that may be of interest. The most commonly used recommendation method is collaborative filtering. However, in this case, the recommendation system will be injected with false data to create false ratings to push or nuke specific items. This will affect the user’s trust in the recommendation system. After all, it is important that the recommendation system provides a trusted recommendation item. Therefore, there are many algorithms for detecting attacks. In this article, it proposes a method to detect attacks based on the beta distribution. Different researchers in the past assumed that the attacker only attacked one target item in the user data. This research simulated an attacker attacking multiple target items in the experiment. The result showed a detection rate of more than 80%, and the false rate was within 16%.

With the diffusion of cloud computing, public and private enterprise resource planning (ERP) have received increasing attention. However, several prior studies have investigated the factors that affect cloud system adoption for either public or private cloud systems. This article attempts to compare public and private cloud ERP. A research model is developed based on Two-Factor Theory and empirically test our hypotheses by collecting 253 public cloud samples and 227 private cloud samples. The results show that system quality and perceived ease of use positively affect switching benefits, which in turn positively affect switching intention. Security risk, satisfaction, and breadth of use positively affect switching costs. The moderating effects of public and private clouds affect the relationships between perceived ease of use and switching benefits, between breadth of use and switching costs, and between switching costs and switching intention. The findings offer suggestions for promoting organizations to switch to cloud public and private ERP.

A major challenge for multinational companies is to motivate employees with different individual cultural characteristics and national cultures to share knowledge. Although comparative studies across different countries have been conducted, little is known about the effects of individual cultural differences in this context. The purpose of this study is to investigate the effects of individual and national cultures in knowledge sharing. The individual cultural characteristics of power distance, individualism/collectivism, and uncertainty avoidance are incorporated into the model as antecedents of knowledge-sharing motivations (organizational rewards, image, and reciprocal benefits). National cultural differences are examined by subjects conducted in the U.S. and China. Results show that power distance is significantly related to reciprocal benefits for the U.S. but not for China. Individualism/collectivism is related to organizational rewards and image for the U.S. but not for China, while individualism/collectivism is significantly related to reciprocal benefits for China but not for the U.S. Uncertainty avoidance is significantly related to reciprocal benefits for the U.S. but not for China. This study provides knowledge-sharing practices and managements for multinational companies attempting to motivate U.S. and Chinese employees to share knowledge.

In recent years, the amount of information has been growing quickly within organisations. Thus, several organisations have implemented analytics and business intelligence (BI) for Big Data solutions. To effectively foster the acceptance and usage of BI, this study examines how the motivational factors of both expectancy theory and social exchange theory (SET) affect managers' willingness to utilise BI. A survey of 271 managers supports the proposed model. The results show that intangible rewards of expectancy theory significantly influence the intention to read information (IRI) through the desire to make good decisions. Benefit factors (organisational rewards, reputation, and reciprocity) of SET are significantly associated with the desire to exchange reports (DER). The IRI significantly directly and indirectly influences the intention to create reports through the DER. Given the empirical findings, this study provides theoretical and managerial insights for organisations.

In today’s knowledge-based economy, most of the knowledge-sharing studies have investigated the effects of cultural values at the national level. However, individual beliefs and behaviors might also be influenced and modified by individual cultural values. To understand the effects of cultural values, cultural effects at both the individual and country levels should be considered. This study is to investigate the integrative effects of the individual’s and country’s cultural values on knowledge sharing. We examine motivational factors affecting knowledge-sharing intentions based on social exchange theory and incorporate individualism/collectivism and uncertainty avoidance as moderators. On the basis of a survey of 394 employees conducted in the United States and China, the results show that rewards are significantly related to knowledge-sharing intentions for Chinese employees but not for American employees. Reciprocity and knowledge self-efficacy significantly impact knowledge-sharing intentions in the two countries. Our results also demonstrate that the individual’s and country’s cultural values play important roles in knowledge sharing. In the United States, individualism/collectivism is found to moderate the relationship between rewards and knowledge-sharing intentions. In China, individualism/collectivism is found to moderate the relationship between reputation and knowledge-sharing intentions, while uncertainty avoidance is found to moderate the relationship between knowledge self-efficacy and knowledge-sharing intentions.

Petri nets have long been used in modeling and simulating project execution because of their great capability to describe concurrent activities and simulate the evolvement of processes. Although a number of extended Petri net models have been proposed to model and simulate resource sharing and activity dependence in projects, none of them has ever included a resource assignment mechanism into their models. Because resource assignments influence how limited resources are allocated among conflicting activities, they may heavily affect the availability of resources and the execution of projects. Therefore, a model without considering resource-sharing and resource assignment strategies may lead to a misunderstanding about project scheduling, resource consumption behaviors, and estimated project time. Accordingly, this paper proposes a new extended Petri net model that can describe how resources are shared and assigned among concurrent activities of multiple projects. The proposed model is named as resource assignment Petri net (RAPN), which extends an object composition Petri net with new places, transitions, attributes, and firing rules to model resource-sharing and resource assignment strategies. Finally, we prove that RAPN can correctly model the resource consumption behaviors of projects and can correctly compute the total elapsed time of projects.

As enterprises worldwide race to embrace real-time management to improve productivity, customer services and flexibility, large amount of resources have been invested in enterprise systems (ESs). As comprehensive feature of these modern systems, they utilize a n-tier client-server architecture that includes several application servers to serve users and host applications. The load and user distributions become a critical issue in performance tuning of these enterprise systems, as any other multi-server environments. This paper proposes an algorithm to distribute users by evoking similar transactions to same servers, which have limited buffer sizes. The number of transactions can be hosted in each server is constrained by the buffer size multiplied by a factor specified by system administrators. Based on user profiles, the algorithm return suggestions of user distributions, the number of servers needed, and similar user requests in each server. In addition, it discusses how to apply the knowledge of existing user patterns to distribute new users, who do not have enough entries in the profile and have no distribution suggestion during run-time.

A multimedia presentation model provides designers a tool to formally specify the temporal and spatial relationships of objects. The formality helps designers to communicate with others, to check the integrity of designs, and provides a chance to simulate the designs. Although much research has been devoted to this subject, to the best of our knowledge, no multimedia models are able to describe the spatial-temporal relations of moving objects that may refer to each other for computing displaying addresses. The addresses may be recomputed several times during the objects' lifetimes to reflect their movements. Without a formal model, designers are forced to specify the relationships in an ad hoc manner that causes misunderstanding and hampers integrity check. The check includes if an object gets its addresses in time from another object, if an object is displayed in the right places, etc. The difficulty of designing such a formal model lies in integrating temporal constraints of objects with a real-time address transferring mechanism. In this paper, we present an extended Petri-net model, which models concurrent relationships of objects with new places, transitions, and firing rules to transfer and transform addresses in real time. Its descriptive power and correctness is demonstrated by five patterns of multimedia presentations and a sample play scripts.

A generalized quantifier is a particular kind of operator on sets. Coming under increasing attention recently by linguists and logicians, they correspond to many useful natural language phrases, including phrases like: three, Chamberlin's three, more than three, fewer than three, at most three, all but three, no more than three, not more than half the, at least two and not more than three, no student's, most male and all female, etc. Reasoning about quantifiers is a source of recurring problems for most SQL users, and leads to both confusion and incorrect expression of queries. By adopting a more modern and natural model of quantification these problems can be alleviated. We show how generalized quantifiers can be used to improve the SQL interface.>