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Emotions are an important basis for decision making, and their influence is increasingly attracting attention from the academic community. However, existing research mainly focuses on the impact of a single emotion on people and lacks information on the impact of ambivalent emotions. Based on the Cognitive-Affective processing system theory, this study explores tourists' willingness to travel, while experiencing the emotions of hope and anxiety through two situational experiments. This study used the Sojump platform to recover data, and a total of 200 valid samples were collected in Experiment 1 to explore the chain-mediating role of proactive coping and well-being. A total of 240 valid samples were collected in Experiment 2, which verified the boundary conditions of the interaction between hope and anxiety on tourists' willingness to travel. The results broaden the theoretical research on tourism ambivalence and provide a reference source for tourism companies to use ambivalence in precision marketing. [ABSTRACT FROM AUTHOR]

The paper examines the various uncertainties encountered in high-frequency trading (HFT) environments and delves into the multiple challenges faced by HFT firms in navigating the Dodd–Frank Wall Street Reform and Consumer Protection Act (referred to as the "Dodd–Frank Act"), particularly during the initial stages of its enactment. These challenges include the ambiguity surrounding the definition of HFT, the lack of clarity regarding regulatory requirements and boundaries, inconsistencies in enforcement resulting from deviations in understanding the content, and the absence of detailed descriptions of the Act's provisions. These hurdles significantly impact not only the daily operations of HFT firms but also pose higher demands on their long-term strategic planning and risk management. Drawing upon the Data, Information, Knowledge, Wisdom, and Purpose (DIKWP) model, this study employs an innovative analytical framework. Through the comprehensive application of Cognitive Space, Concept Space, and Semantic Space, it provides a systematic methodology for identifying and analyzing the aforementioned issues. This approach not only aids firms in better comprehending and adhering to complex regulatory requirements but also enables them to explore new business opportunities and competitive advantages while ensuring compliance. [ABSTRACT FROM AUTHOR]

The doctor–patient relationship has received widespread attention as a significant global issue affecting people's livelihoods. In clinical practice within the medical field, applying existing artificial intelligence (AI) technology presents issues such as uncontrollability, inconsistency, and lack of self-explanation capabilities, even raising concerns about ethics and morality. To address the problem of doctor–patient interaction differences arising from the doctor–patient diagnosis and treatment, we collected the textual content of doctor–patient dialogues in outpatient clinics of local first-class hospitals. We utilized case scenario analysis, starting from two specific cases: multi-patient visits with the same doctor and multi-doctor interaction differences with the same patient. By capturing the external interactions and the internal thought processes, we unify the external expressions and internal subjective cognition in doctor–patient interactions into interactions between data, information, knowledge, wisdom, and purpose (DIKWP) models. We propose a DIKWP semantic model for the doctor–patient interactions on both sides, including a DIKWP content model and a DIKWP cognitive model, to achieve transparency throughout the entire doctor–patient interaction process. We semantically–bidirectionally map the diagnostic discrepancy space to DIKWP uncertainty and utilize a purpose-driven DIKWP semantic fusion transformation technique to disambiguate the uncertainty problem. Finally, we select four traditional methods for qualitative and quantitative comparison with our proposed method. The results show that our method performs better in content and uncertainty handling. Overall, our proposed DIKWP semantic model for doctor–patient interaction processing breaks through the uncertainty limitations of natural language semantics in terms of interpretability, enhancing the transparency and interpretability of the medical process. It will help bridge the cognitive gap between doctors and patients, easing medical disputes. [ABSTRACT FROM AUTHOR]

Based on the cognitive fit theory, this study constructs a model to examine the impact mechanism of AI tour guides' visual and linguistic cues on consumers' intentions to continue using them. Two experiments are conducted to validate this model. The results showed that when an AI tour guide is dressed formally and informally, a competent and warm conversational style, respectively, enhances consumers' intention to continue using them. Identity identification and para-social interaction play partial mediating roles and jointly play a chain-mediating role in the above relationships. Additionally, this study also examines the role of conversational form (text vs. voice). [ABSTRACT FROM AUTHOR]

High-utility sequential pattern mining (HUSPM) helps researchers find all subsequences that have high utility in a quantitative sequential database. The HUSPM approach appears to be well suited for resource transformation in DIKWP graphs. However, all the extensions of a high-utility sequential pattern (HUSP) also have a high utility that increases with its length. Therefore, it is difficult to obtain diverse patterns of resources. The patterns that consist of many low-utility items can also be a HUSP. In practice, such a long pattern is difficult to analyze. In addition, the low-utility items do not always reflect the interestingness of association rules. High average-utility pattern mining is considered a solution to extract more significant patterns by considering the lengths of patterns. In this paper, we formulate the problem of top-k high average-utility sequential pattern mining (HAUSPM) and propose a novel algorithm for resource transformation. We adopt a projection mechanism to improve efficiency. We also adopt the sequence average-utility-raising strategy to increase thresholds. We design the prefix extension average utility and the reduced sequence average utility by incorporating the average utility into the utility upper bounds. The results of our comparative experiments demonstrate that the proposed algorithm can achieve sufficiently good performance. [ABSTRACT FROM AUTHOR]

In large-scale public security equipment projects, long-term equipment operation often leads to equipment failures and other problems, so it is particularly important to choose the appropriate operation and maintenance (O&M) scheme based on the content of the equipment work orders. However, there are a variety of equipment models in the work orders; not only is the data complex, but also due to the long project cycle, there are often problems such as loss of content, which bring great challenges to the O&M work. This paper defines these problems as "3-No problems": inconsistency, inaccuracy, and incompleteness. In this paper, an improved DIKWP model is proposed and combined with a random forest classifier to construct data graphs, information graphs, knowledge graphs, and wisdom graphs. Through the above model, the 3-No problem in equipment work orders can be solved, and the importance of each equipment model can be obtained. Eventually, combined with the purpose graph, the selection of models, the bid score calculation, and the selection of O&M schemes are carried out based on the obtained conclusion in a purpose-driven manner to achieve the evaluation of O&M efficiency and safety. Finally, an example is assumed to illustrate the application of the method in actual projects, which provides a certain reference value for the selection of an O&M scheme for large-scale equipment projects. [ABSTRACT FROM AUTHOR]

In modern maritime activities, the quality of ship communication directly impacts the safety, efficiency, and economic viability of ship operations. Therefore, predicting and analyzing ship communication status has become a crucial task to ensure the smooth operation of ships. Currently, ship communication status analysis heavily relies on large-scale, multi-source heterogeneous data with spatio-temporal and multi-modal features, which presents challenges for ship communication quality prediction tasks. To address this issue, this paper constructs a multi-modal spatio-temporal ontology and a multi-modal spatio-temporal knowledge graph for ship communication, guided by existing ontologies and domain knowledge. This approach effectively integrates multi-modal spatio-temporal data, providing support for subsequent efficient data analysis and applications. Taking the scenario of fishing vessel communication activities as an example, the query tasks for ship communication knowledge are successfully performed using a graph database, and we combine the spatio-temporal knowledge graph with graph convolutional neural network technology to achieve real-time communication quality prediction for fishing vessels, further validating the practical value of the multi-modal spatio-temporal knowledge graph. [ABSTRACT FROM AUTHOR]

For positive numbers t, p,q,c and an analytic function f(z) in an annulus R1 < |z| < R2, let Mt,ϕ,q,c(f,r) be the area integral means of f with respect to the weighted area measure ϕ' (|z| q)|z| q−2 dA(z), where R1 ≤ c < R2 . We show that Mt,ϕ,q,c(f,r) 1/p is a convex function of r if f and ϕ satisfy certain conditions. The convexities of logMt,ϕ,q,c(f,r) in r and log r can be obtained as special cases. [ABSTRACT FROM AUTHOR]

Wastewater treatment plants play a critical role in reducing point source pollution in watersheds; however, in taking on the task of reducing pollutants such as COD and NH3-N, they also consume energy intensively, which can result in additional greenhouse gas (GHG) emissions and operating (OAM) costs. Therefore, appropriate effluent targets should be implemented to achieve a balance between pollution load reduction, greenhouse gas emissions and operating costs, depending on the field conditions of the wastewater plant. In this study, four different wastewater treatment technologies, namely, A2/O, CASS, MBR and A/O-MBR, which are mainly operated in the Liaohe River Basin of Jilin Province, were selected using the coupled AHP and fuzzy TOPSIS models, and the appropriate effluent standards were preferred according to the characteristics of these technologies. Firstly, the AHP model is used to determine the criteria layer (environmental, economic and social benefits) of the four treatment technologies and the weights of each indicator, and then the fuzzy TOPSIS model is used to determine the indicator values of the different alternatives (effluent standards) of the four treatment technologies, and finally the optimal value of the effluent standard is determined, in terms of COD, 30 mg/L for A2/O and CASS process and 50 mg/L for MBR and A/O-MBR. [ABSTRACT FROM AUTHOR]

This paper reveals the hidden dangers of reverse data modifications on distributed software with network synchronization, during the era of 5G, which may occur in more important domains, such as telemedicine and automatic driving. We used pseudo-codes to formally elaborate the distributed software architectures and design patterns. It is necessary to deal with three challenges for the modification of binary code and data in the distributed software architectures: (1) the base virtual addresses of software objects are changed frequently for safety; (2) prior knowledge of the reverse is not considered; (3) system memory values of some target objects are changed with extreme speed. For this purpose, a novel reverse modification method for binary code and data is proposed. According to the knowledge-based rules, our method can manipulate physical data, sight data, animation data, etc., while the game synchronization mechanism cannot detect the changes. The implementation details of our method are presented using high-level programming languages (C++) and low-level programming languages (assembly), based on multiple snippets, so that readers can understand both the overall distributed software developments and the corresponding reverse processes. In particular, two network games are used for the demonstrations in this paper. The demonstration results show that our proposed methodology is efficient (as proved by formulas and practices) to manipulate the codes and data of distributed software using a synchronization mechanism. [ABSTRACT FROM AUTHOR]

With the development of cloud computing, service computing, IoT(Internet of Things) and mobile Internet, the diversity and sociality of services are increasingly apparent. With the increasing complexity of collaborative relationships between services, service ecosystems are beginning to emerge with the characteristics of natural ecosystems, economic systems and complex networks. Under this context, how to realize systematic evaluation of service ecosystem is of great significance to promote its sound development. Based on this, this article proposes a value entropy model that links the operating state of the system with the efficiency of value creation, which helps to clarify the performance of the service ecosystem from the perspective of multi-dimensional integration. In addition, a computational experiment system is established to verify the effectiveness of value entropy model, which stimulates the competitive evolution process of two service ecosystems with different strategies. The result shows that our model can provide new ideas for the analysis of service ecosystem evolution, and can also provide decision support for the optimization of operation strategy. [ABSTRACT FROM AUTHOR]

The quality stability and batch consistency of laser powder bed fusion products are key issues that must be solved in additive manufacturing. The melt pool radiation intensity data of laser powder bed fusion contain a significant amount of forming process information, and studies have shown that the analysis of melt pool radiation intensity using data-driven methods can achieve online quality judgment; however, there are still speed and accuracy problems. In this study, we propose a data-driven model for hardness predictions of laser powder bed fusion products based on process parameters fused with power spectrum features of melt pool intensity data, which quickly and accurately predicts the microhardness of laser powder bed fusion specimens and can make constructive guidance for closed-loop feedback quality regulation in practical production. The effects of three integrated learning models, Random Forest, XGBoost and LightGBM, are also compared. The results indicate that random forest has the highest prediction accuracy in this dataset; however, it has the limitation of slow training and prediction speeds. The LightGBM algorithm has the fastest training and prediction speeds, about 1.4% and 4.4% of the random forest, respectively; however, the prediction accuracy is lower than that of random forest and XGBoost. XGBoost has the best overall comparative performance with adequate training and prediction speeds, about 23.7% and 37.9% of the random forest, respectively, while ensuring a specified prediction accuracy, which is suitable for application in engineering practices. [ABSTRACT FROM AUTHOR]

In this paper, we have considered Over Design (OD) and Under Design (UD) as two basic forms of negative variability construction that lead to both functional incompleteness and unexpected software quality degradation during design time and run time. To make our approach economically practical to the quality assurance at the whole life cycle of software production process, we aim to ensure that OD and UD are minimized in terms of values under economical goals. These requirements are concretized with the functionality implementation and the quality management of a quality lifecycle.

The unprecedented outbreak of the Corona Virus Disease 2019 (COVID-19) pandemic has seriously affected numerous countries in the world from various aspects such as education, economy, social security, public health, etc. Most governments have made great efforts to control the spread of COVID-19, e.g., locking down hard-hit cities and advocating masks for the population. However, some countries and regions have relatively poor medical conditions in terms of insufficient medical equipment, hospital capacity overload, personnel shortage, and other problems, resulting in the large-scale spread of the epidemic. With the unique advantages of Artificial Intelligence (AI), it plays an extremely important role in medical imaging, clinical data, drug development, epidemic prediction, and telemedicine. Therefore, AI is a powerful tool that can help humans solve complex problems, especially in the fight against COVID-19. This study aims to analyze past research results and interpret the role of Artificial Intelligence in the prevention and treatment of COVID-19 from five aspects. In this paper, we also discuss the future development directions in different fields and prove the validity of the models through experiments, which will help researchers develop more efficient models to control the spread of COVID-19. [ABSTRACT FROM AUTHOR]

Summary: With the ever‐increasing popularity of web application programming interfaces (APIs) sharing communities, it is becoming a promising way for software developers to design and create their interesting Apps through composing a set of selected web APIs that can collectively fulfill the App functions expected by the App developer. However, the App developer's web APIs selection decision‐makings are often nontrivial due to the massive candidate APIs as well as their diverse functions. Furthermore, it is difficult to guarantee that the selected web APIs are compatible enough. Moreover, traditional web APIs recommendation approaches only return a recommended APIs list, which are often not sufficient to accommodate the App developer's undetermined and fuzzy personalized preferences. Considering the above challenges, a novel keywords‐driven web APIs recommendation approach called keywords‐driven and compatibility‐aware multiple API group recommendation is proposed in this article for green and compatible software, which cannot only satisfy the App developer's functional requirements, but also return a group of web APIs recommended lists. Each returned list includes a set of compatible web APIs. Finally, we design a series of experiments based on a real‐world web APIs dataset, that is, PW dataset crawled from www.programmableWeb.com. Experimental reports compared with other competitive approaches in existing literatures indicate the effectiveness and efficiency of our proposal in this work. [ABSTRACT FROM AUTHOR]

Service-oriented architecture (SOA) provides a flexible IT infrastructure in order to deal with global competition. However, real-world services change their architectures and functions frequently. This work uses data integration, information synthesis, and knowledge fusion to analyze SOA evolution and complexity. A quantificational approach is provided to estimate the change effects on a service-oriented system. We established a cloud service community to provide rich cloud services and a service mashup platform that connects API service providers and service users. The platform user can invoke all API services on the platform without jumping to the third-party website. The platform users can filter and accept the services recommended by the platform, and also freely compose the services which are real-world services. Data integration, information synthesis, and knowledge fusion are applied to mashups that users constructed, helping users avoid complexity and unreliability that is our objective. We also showed that our method achieved significant quantitative results. [ABSTRACT FROM AUTHOR]

γ′ precipitates, the main strengthening phase of Ni-based single-crystal superalloys, are difficult to determine in the forming zone of selective electron beam melting (SEBM), which is best for realizing the direct manufacturing of single crystal superalloys. Herein, the relationship between parameters and γ′ precipitates is urgently needed to be fabricated, and solution heat treatment is an efficient way to obtain a desired uniform γ′ microstructure. In the initial work, an array of SEBM experiments with various processing parameters were carried out, the processing window and parameter influence on γ′ precipitates were fabricated successfully, and a solution heat treatment protocol was applied for obtaining a homogeneous γ′ microstructure in the as-built SEBM microstructure. In processing, the bulks can be manufactured with a beam current of 10–12.4 mA and a scan speed of 1.7–2.4 m/s. The dimensions of the γ′ precipitates present regular changes with parameters such as current and scan speed, and a great change had also been observed in building height. After the heat treatment includes recovery-annealed (1100 °C, 6 h), solution-treated (1300 °C, 0.5 h), and aged (1100 °C, 6 h + 870 °C, 5 h) precipitates, the size of γ′ precipitates gradually increases and homogenizes, and an optimized solution and aging heat treatment protocol was established. [ABSTRACT FROM AUTHOR]

Privacy protection has recently been in the spotlight of attention to both academia and industry. Society protects individual data privacy through complex legal frameworks. The increasing number of applications of data science and artificial intelligence has resulted in a higher demand for the ubiquitous application of the data. The privacy protection of the broad Data-Information-Knowledge-Wisdom (DIKW) landscape, the next generation of information organization, has taken a secondary role. In this paper, we will explore DIKW architecture through the applications of the popular swarm intelligence and differential privacy. As differential privacy proved to be an effective data privacy approach, we will look at it from a DIKW domain perspective. Swarm intelligence can effectively optimize and reduce the number of items in DIKW used in differential privacy, thus accelerating both the effectiveness and the efficiency of differential privacy for crossing multiple modals of conceptual DIKW. The proposed approach is demonstrated through the application of personalized data that is based on the open-source IRIS dataset. This experiment demonstrates the efficiency of swarm intelligence in reducing computing complexity. [ABSTRACT FROM AUTHOR]

Artificial General Intelligence (AGI) has leaped forward in capabilities, offering applications that reach far beyond conventional machine learning systems. These graphs not only facilitate the representation of data but also encapsulate the purpose, thereby fulfilling the vision of a holistic and purpose-oriented AGI system [[8]]. Purpose-driven data graphs offer a way to understand this complex relationship, ensuring that AGI models remain aligned with the specific objectives they serve. [Extracted from the article]

With the rapid development of RGB‐D cameras and pose estimation techniques, action recognition based on three‐dimensional skeleton data has gained significant attention in the artificial intelligence community. In this paper, we incorporate temporal pattern descriptors of joint positions with the currently popular long short‐term memory (LSTM)–based learning scheme to obtain accurate and robust action recognition. Considering that actions are essentially formed by small subactions, we first utilize a two‐dimensional wavelet transform to extract temporal pattern descriptors in the frequency domain for each subaction. Afterward, we design a novel LSTM structure to extract deep features, which model a long‐term spatiotemporal correlation between body parts. Since temporal pattern descriptors and LSTM deep features can be regarded as multimodal representations for actions, we fuse them with an autoencoder network to achieve a more effective feature descriptor for action recognition. Experimental results on three challenging data sets with several comparative methods demonstrate the effectiveness of the proposed method for three‐dimensional action recognition. [ABSTRACT FROM AUTHOR]

Wide application of the Internet of Things (IoT) system has been increasingly demanding more hardware facilities for processing various resources including data, information, and knowledge. With the rapid growth of generated resource quantity, it is difficult to adapt to this situation by using traditional cloud computing models. Fog computing enables storage and computing services to perform at the edge of the network to extend cloud computing. However, there are some problems such as restricted computation, limited storage, and expensive network bandwidth in Fog computing applications. It is a challenge to balance the distribution of network resources. We propose a processing optimization mechanism of typed resources with synchronized storage and computation adaptation in Fog computing. In this mechanism, we process typed resources in a wireless-network-based three-tier architecture consisting of Data Graph, Information Graph, and Knowledge Graph. The proposed mechanism aims to minimize processing cost over network, computation, and storage while maximizing the performance of processing in a business value driven manner. Simulation results show that the proposed approach improves the ratio of performance over user investment. Meanwhile, conversions between resource types deliver support for dynamically allocating network resources. [ABSTRACT FROM AUTHOR]

Business process modeling is a way to the organizational change management, which provides abstract workflows to describe business logics for customer demands analysis and IT infrastructures improvement. In order to reuse business process for covering different application scopes, it requires the capacity of configuring each task of business process by selecting appropriate services from the candidate services set, and then assembling them together to properly work with each other as domain-specific software. Considering choosing an executable business process with high quality service (QoS), the service selection is regarded as the pivotal step of determining process instances since service displays probabilistic behaviors under the uncertainty of Internet making business process shown different reliability. In this paper, it proposes an approach to the service selection for business process modeling. In the first phase, the function similarity method is used to pick out services from service repository in order to build a set of candidate services, which checks the functions description to find matching services, especially service may publish one or more functions through multiple interfaces. In the second phase, the probabilistic model checking-based method is employed to the quantitative verification of process instances, which involves services composition and stochastic behaviors computing according to workflow structures. Then, corresponding algorithms are discussed for service selection purposes. Furthermore, it introduces the probabilistic model checking-based framework for prototype design and implementation. Finally, experiments are conducted to demonstrate the effectiveness and efficiency of the proposed method comparing with traditional methods. [ABSTRACT FROM AUTHOR]