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PurposeMethodology/approachFindingsOriginality/valueImplicationsThe effectiveness of channel integration performed by manufacturers in facilitating their online market entry is still an under-researched topic. This study fills the research gap by investigating and comparing the effects of channel integration strategies and their components implemented by manufacturers.A questionnaire was designed and distributed to purchasing managers in downstream dealers. The survey yielded 2051 valid responses, and data analysis employed hierarchical and polynomial regressions, along with structural equation modeling and response surface analyses.First, dealers’ dependence plays a crucial role in triggering manufacturers’ online market entry. Second, manufacturers’ channel consistency and transparency are more relevant to dealers’ active dependence, while information and interaction integration lead to dealers’ passive dependence. Third, channel ambidexterity is superior to integration ambidexterity in ensuring dealers’ dependence.Significant contributions to omnichannel integration research are made by introducing four distinct types of channel integration strategies. Enhancements to the interfirm dependence literature are achieved through a comparative analysis of the effectiveness of channel integration strategies in shaping dealers’ dependency behavior.Manufacturers should carefully evaluate the effectiveness of these strategies, avoiding a blind pursuit of all forms of channel integration ambidexterity. Strategic leverage of channel feature ambidexterity is recommended as a cornerstone for fostering dealers’ dependence and achieving success in online market entry. [ABSTRACT FROM AUTHOR]

Recent years have witnessed a rise in the popularity of information integration without sharing of raw data. By leveraging and incorporating summary information from external sources, internal studies can achieve enhanced estimation efficiency and prediction accuracy. However, a noteworthy challenge in utilizing summary-level information is accommodating the inherent heterogeneity across diverse data sources. In this study, we delve into the issue of prior probability shift between two cohorts, wherein the difference of two data distributions depends on the outcome. We introduce a novel semi-parametric constrained optimization-based approach to integrate information within this framework, which has not been extensively explored in existing literature. Our proposed method tackles the prior probability shift by introducing the outcome-dependent selection function and effectively addresses the estimation uncertainty associated with summary information from the external source. Our approach facilitates valid inference even in the absence of a known variance-covariance estimate from the external source. Through extensive simulation studies, we observe the superiority of our method over existing ones, showcasing minimal estimation bias and reduced variance for both binary and continuous outcomes. We further demonstrate the utility of our method through its application in investigating risk factors related to essential hypertension, where the reduced estimation variability is observed after integrating summary information from an external data. [ABSTRACT FROM AUTHOR]

Abstract To avoid exploitation by defectors, people can use past experiences with others when deciding to cooperate or not (‘private information’). Alternatively, people can derive others’ reputation from ‘public’ information provided by individuals within the social network. However, public information may be aligned or misaligned with one’s own private experiences and different individuals, such as ‘friends’ and ‘enemies’, may have different opinions about the reputation of others. Using evolutionary agent-based simulations, we examine how cooperation and social organization is shaped when agents (1) prioritize private or public information about others’ reputation, and (2) integrate others’ opinions using a friend-focused or a friend-and-enemy focused heuristic (relying on reputation information from only friends or also enemies, respectively). When agents prioritize public information and rely on friend-and-enemy heuristics, we observe polarization cycles marked by high cooperation, invasion by defectors, and subsequent population fragmentation. Prioritizing private information diminishes polarization and defector invasions, but also results in limited cooperation. Only when using friend-focused heuristics and following past experiences or the recommendation of friends create prosperous and stable populations based on cooperation. These results show how combining one’s own experiences and the opinions of friends can lead to stable and large-scale cooperation and highlight the important role of following the advice of friends in the evolution of group cooperation.

Abstract Humans learn both directly, from own experience, and via social communication, from the experience of others. They also often integrate these two sources of knowledge to make predictions and choices. We hypothesized that when faced with the need to integrate communicated information into personal experience, people would represent the average of experienced exemplars with greater accuracy. In two experiments, Mturk users estimated the mean of consecutively and rapidly presented number sequences that represented bonuses ostensibly paid by different providers on a crowdsource platform. Participants who expected integrating these values with verbal information about possible change in bonuses were more accurate in extracting the means of the values compared to participants who did not have such expectation. While our study focused on socially communicated information, the observed effect may potentially extend to other forms of information integration. We suggest that expected integration of experience with additional information facilitates an abstract representation of personal experiences.

Humans learn both directly, from own experience, and via social communication, from the experience of others. They also often integrate these two sources of knowledge to make predictions and choices. We hypothesized that when faced with the need to integrate communicated information into personal experience, people would represent the average of experienced exemplars with greater accuracy. In two experiments, Mturk users estimated the mean of consecutively and rapidly presented number sequences that represented bonuses ostensibly paid by different providers on a crowdsource platform. Participants who expected integrating these values with verbal information about possible change in bonuses were more accurate in extracting the means of the values compared to participants who did not have such expectation. While our study focused on socially communicated information, the observed effect may potentially extend to other forms of information integration. We suggest that expected integration of experience with additional information facilitates an abstract representation of personal experiences. [ABSTRACT FROM AUTHOR]

To avoid exploitation by defectors, people can use past experiences with others when deciding to cooperate or not ('private information'). Alternatively, people can derive others' reputation from 'public' information provided by individuals within the social network. However, public information may be aligned or misaligned with one's own private experiences and different individuals, such as 'friends' and 'enemies', may have different opinions about the reputation of others. Using evolutionary agent-based simulations, we examine how cooperation and social organization is shaped when agents (1) prioritize private or public information about others' reputation, and (2) integrate others' opinions using a friend-focused or a friend-and-enemy focused heuristic (relying on reputation information from only friends or also enemies, respectively). When agents prioritize public information and rely on friend-and-enemy heuristics, we observe polarization cycles marked by high cooperation, invasion by defectors, and subsequent population fragmentation. Prioritizing private information diminishes polarization and defector invasions, but also results in limited cooperation. Only when using friend-focused heuristics and following past experiences or the recommendation of friends create prosperous and stable populations based on cooperation. These results show how combining one's own experiences and the opinions of friends can lead to stable and large-scale cooperation and highlight the important role of following the advice of friends in the evolution of group cooperation. [ABSTRACT FROM AUTHOR]

Configurators have recently emerged as essential tools in the construction industry to enable builders to offer a wide range of customizable designs. Due to significant challenges in information integration between construction suppliers and clients, existing configurator systems often lack crucial usability and supply chain information, presenting barriers to wider adoption among residential communities, especially in single-family residence development that requires a high degree of customization. To address this challenge in the design and construction supply chain, this study presents a lightweight cloud-based modular home configuration methodology as a robust unified platform solution to integrate parametric design options with a certified kit-of-parts library to meet local design codes. The configurator prototype developed under this framework seamlessly integrates essential design and supply chain information by leveraging (1) a generative layout design with pre-approved blueprints, (2) a knowledge-based recommender system to link the design process with certified material catalogs, and (3) a user-friendly web interface to present possible designs. The implementation of a single-family housing design adhering to the building codes in the British Columbia Province of Canada illustrates the benefits of the proposed configurator functionalities and efficient supplier data integration. Lightweight and automated, the proposed configurator has substantial potential to be scaled and adopted across different communities. [ABSTRACT FROM AUTHOR]

AbstractA full parametric and linear specification may be insufficient to capture complicated patterns in studies exploring complex features, such as those investigating age-related changes in brain functional abilities. Alternatively, a partially linear model (PLM) consisting of both parametric and nonparametric elements may have a better fit. This model has been widely applied in economics, environmental science, and biomedical studies. In this article, we introduce a novel statistical inference framework that equips PLM with high estimation efficiency by effectively synthesizing summary information from external data into the main analysis. Such an integrative scheme is versatile in assimilating various types of reduced models from the external study. The proposed method is shown to be theoretically valid and numerically convenient, and it ensures a high-efficiency gain compared to classic methods in PLM. Our method is further validated using two data applications by evaluating the risk factors of brain imaging measures and blood pressure. [ABSTRACT FROM AUTHOR]

The expansion planning of distribution network is based on the implementation information fusion management of distributed generation in distribution network. In order to improve the management and scheduling capability of distributed generation, an implementation information management platform for distributed generation in distribution network based on information integration is proposed, and a comprehensive optimization model for distributed generation and energy storage of distribution network is constructed. The load change from the planning base year to the level year is simulated by Monte Carlo Method; According to the installed capacity of distributed generation, Monte Carlo Method is used to simulate the output of distributed generation, and the planning model of township distribution network expansion is established to meet all power supply needs. The line loss calculation is based on the simulation results of load and distributed generation output. The corresponding steady state model for all kinds of distributed generations is established, and the type of interface connected to the power grid should be confirmed. The information integration method is used to optimize the design of the information management platform for distributed generation in the distribution network. The test shows that the designed implementation information management platform for distributed generation in distribution network can accurately realize the information fusion and management of distributed generation, solve the simultaneity problem caused by the fluctuation and interval of load data and distributed generation output on power flow calculation, and integrate distribution network planning with distributed generation planning. [ABSTRACT FROM AUTHOR]

Risk analysis is essential for promoting hiking-based tourism. Our objective in the present study was to map 395 mountain hikers' positions on risk judgment and risk taking, according to how they integrated three antecedent factors of confidence (environment, team, and self). For integrating information, people can develop an additive rule whereby they apply the same weight to all information or use interaction rules (i.e., conjunctive or disjunctive), to give different weights to information. In the questionnaire our participants completed, there were eight scenarios that combined the three confidence antecedent factors as information cues. We applied cluster analysis, repeated-measures analyses of variance, chi-square tests, and bivariate correlation analyses to the questionnaire results to identify three participant risk positions. In the first risk position (cluster 1), participants used a disjunctive integration rule for both risk judgment and risk taking. In the second risk position (Clusters 2 and 4), they used an additive integration rule for risk judgment while they used a disjunctive integration rule for risk taking. In the third risk position (cluster 3), they used an additive integration rule for both risk judgment and risk taking. In each risk position, confidence in the three antecedent factors (environment, team, and self) negatively affected risk judgment and positively affected risk taking. We found the compositions of the clusters to be related to the participants' sex, and we discuss various advantages of applying information integration for mountain hiking practitioners and promoters. [ABSTRACT FROM AUTHOR]

In order to solve the problems of methods that use a single form of sensing, the ease of causing deformation damage to the targets with a low hardness during grasping, and the slow sliding inhibition of a prosthetic hand when the grasping target slides, which are problems that exist in most current intelligent prosthetic hands, this study introduces an adaptive control strategy for prosthetic hands based on multi-sensor sensing. Using a force-sensing resistor (FSR) to collect changes in signals generated after contact with a target, a prosthetic hand can classify the target's hardness level and adaptively provide the desired grasping force so as to reduce the deformation of and damage to the target in the process of grasping. A fiber-optic sensor collects the light reflected by the object to identify its surface roughness, so that the prosthetic hand adaptively adjusts the sliding inhibition method according to the surface roughness information to improve the grasping efficiency. By integrating information on the hardness and surface roughness of the target, an adaptive control strategy for a prosthetic hand is proposed. The experimental results showed that the adaptive control strategy was able to reduce the damage to the target by enabling the prosthetic hand to achieve stable grasping; after grasping the target with an initial force and generating sliding, the efficiency of slippage inhibition was improved, the target could be stably grasped in a shorter time, and the hardness, roughness and weight ranges of targets that could be grasped by the prosthetic hand were enlarged, thus improving the success rate of stable grasping under extreme conditions. [ABSTRACT FROM AUTHOR]

One central question in the scientific and philosophical study of consciousness is regarding the scope of human consciousness. There is a lively debate as to whether high-level information integration is necessarily dependent on consciousness. This study presents a new form of unconscious integration based on the facingness between two individuals. Using a breaking continuous flash suppression paradigm, Experiments 1-3 found that two facing human heads got a privilege in breaking into awareness compared to nonfacing pairs. Experiments 4 and 5 demonstrated that the breakthrough difference between facing and nonfacing pairs could not be attributed to low-level or mid-level factors. Experiments 6, 7a, and 7b showed that the unconscious priority of facing pairs was significantly diminished when the holistic processing of the two agents was disrupted. Experiments 8-11 demonstrated that the advantage of facing pairs was only observable for human agents and not for daily objects, directional arrows, or nonhuman animals. These findings have critical implications for better understanding the scope of human consciousness and the origins of social vision. [ABSTRACT FROM AUTHOR]

The fourth industrial revolution promotes the numerous applications of the industrial internet of things to transmit integrated information. Scheduling multi-data of a distributed network involves many complications in maintaining network performance. This paper introduces a linear mixed model approach to multi-data integration and transmission in an industrial enterprise network. This paper includes the computational scheme and its performance evaluation of the linear mixed model. The proposed computation scheme incorporates the following contributions: i) a simulation of an industrial enterprise network configured with distributed network domains of multi-data. ii) Development of a linear mixed model approach to integrated multi-data for transmission through the shared network links. iii) The performance evaluation of the proposed scheme using the R squared value of Bayesian linear regression. The proposed scheme realizes the type of data and their level of integration at shared network links of an industrial enterprise network. The expected outcome is to exhibit healthy throughput in scheduling both level 1 and level 2 integration of multiple data types promising to shape the path toward Industry 4.0 applications. [ABSTRACT FROM AUTHOR]

An assessment of the causal treatment effect in the development and progression of certain diseases is important in clinical trials and biomedical studies. However, it is not possible to infer a causal relationship when the treatment assignment is imbalanced and confounded by other mechanisms. Specifically, when the treatment assignment is not randomized and the primary outcome is binary, a conventional logistic regression may not be valid to elucidate any causal inference. Moreover, exclusively capturing all confounders is extremely difficult and even impossible in large-scale observational studies. We propose a multiple-model-based robust (MultiMR) estimator for estimating the causal effect with a binary outcome, where multiple propensity score models and conditional mean imputation models are used to ensure estimation robustness. Furthermore, we propose an enhanced MultiMR (eMultiMR) estimator that reduces the estimation variability of MultiMR estimates by incorporating secondary outcomes that are highly correlated with the primary binary outcome. The resulting estimates are less sensitive to model mis-specification compared to those based on state-of-the-art doubly-robust methods. These estimates are verified through both theoretical and numerical assessments. The utility of (e)MultiMR estimation is illustrated using the Uniform Data Set (UDS) from the National Alzheimer's Coordinating Center with the objective of detecting the causal effect of the short-term use of antihypertensive medications on the development of dementia or mild cognitive impairment. The proposed method has been implemented in an R package and is available at. [ABSTRACT FROM AUTHOR]

The relationship between integration and awareness is central to contemporary theories and research on consciousness. Here, we investigated whether and how information integration over time, by incorporating the underlying regularities, contributes to our awareness of the dynamic world. Using binocular rivalry, we demonstrated that structured visual streams, constituted by shape, motion, or idiom sequences containing perceptual‐ or semantic‐level regularities, predominated over their nonstructured but otherwise matched counterparts in the competition for visual awareness. Despite the apparent resemblance, a substantial dissociation of the observed rivalry advantages emerged between perceptual‐ and semantic‐level regularities. These effects stem from nonconscious and conscious temporal integration processes, respectively, with the former but not the latter being vulnerable to perturbations in the spatiotemporal integration window. These findings corroborate the essential role of structure‐guided information integration in visual awareness and highlight a multi‐level mechanism where temporal integration by perceptually and semantically defined regularities fosters the emergence of continuous conscious experience. [ABSTRACT FROM AUTHOR]

Ontology merging is an important task in ontology engineering to date. However, despite the efforts devoted to ontology merging, the incorporation of relevant features of ontologies such as axioms, individuals and annotations in the output ontologies remains challenging. Consequently, existing ontology-merging solutions produce new ontologies that do not include all the relevant semantic features from the candidate ontologies. To address these limitations, this paper proposes a novel algorithm for multi-criteria ontology merging that automatically builds a new ontology from candidate ontologies by iteratively updating an RDF graph in the memory. The proposed algorithm leverages state-of-the-art Natural Language Processing tools as well as a Machine Learning-based framework to assess the similarities and merge various criteria into the resulting output ontology. The key contribution of the proposed algorithm lies in its ability to merge relevant features from the candidate ontologies to build a more accurate, integrated and cohesive output ontology. The proposed algorithm is tested with five ontologies of different computing domains and evaluated in terms of its asymptotic behavior, quality and computational performance. The experimental results indicate that the proposed algorithm produces output ontologies that meet the integrity, accuracy and cohesion quality criteria better than related studies. This performance demonstrates the effectiveness and superior capabilities of the proposed algorithm. Furthermore, the proposed algorithm enables iterative in-memory update and building of the RDF graph of the resulting output ontology, which enhances the processing speed and improves the computational efficiency, making it an ideal solution for big data applications. [ABSTRACT FROM AUTHOR]

The intricacy of the construction supply chain and the interactions among various actors in construction projects highlight the important role of Information Technology capability and efficient information flow integration to guarantee seamless coordination and communication among stakeholders. This study investigates the association between Client/Owner, Contractors, supplier's Information Technology capabilities, and information flow integration. Regression analysis is used to examine the data and ascertain the relationships between these variables. The findings indicate that information flow integration, each entity's Information Technology capability, and construction project performance are positively correlated. Information Technology capability allows key players in a construction project to integrate information and ultimately improves the performance of construction projects. According to the research, better project performance may result from strengthening Information Technology capability and integrating information flow among the Construction Project's partners. The significance of investing in Information Technology Capability and encouraging efficient communication and cooperation between clients, contractors, and suppliers are important takeaways from these findings for patrons in the construction industry. [ABSTRACT FROM AUTHOR]

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In prognostics and health management, the system's degradation condition assessment and corresponding remaining useful life prediction are the most important tasks. Both of these processes are heavily dependent on information gathered by multiple sensors, which eventually causes data fusion-related complex problems. Typically, sensor information contains the speed, pressure, temperature, and similar other types of various system data. These systems' data obtained through sensors can be utilized as a part of the evidence in the evidence-based estimation method. In this work, an artificial intelligence-based novel framework for estimating the remaining useful life using data fusion has been presented. The Dempster–Shafer extended theory is adopted for sensor information modeling and data fusion. Besides, two different scenarios are introduced to determine the similarity between the studied system and the available evidence. As a case study, the turbofan dataset is demonstrated to assess the proposed method. Based on the results, our integrated proposed method performs very competitively compared with the existing methods based on standard scores and performance criteria. [ABSTRACT FROM AUTHOR]

Objective: Examine the extent to which increasing information integration across displays in a simulated submarine command and control room can reduce operator workload, improve operator situation awareness, and improve team performance. Background: In control rooms, the volume and number of sources of information are increasing, with the potential to overwhelm operator cognitive capacity. It is proposed that by distributing information to maximize relevance to each operator role (increasing information integration), it is possible to not only reduce operator workload but also improve situation awareness and team performance. Method: Sixteen teams of six novice participants were trained to work together to combine data from multiple sensor displays to build a tactical picture of surrounding contacts at sea. The extent that data from one display were available to operators at other displays was manipulated (information integration) between teams. Team performance was assessed as the accuracy of the generated tactical picture. Results: Teams built a more accurate tactical picture, and individual team members had better situation awareness and lower workload, when provided with high compared with low information integration. Conclusion: A human-centered design approach to integrating information in command and control settings can result in lower workload, and enhanced situation awareness and team performance. Application: The design of modern command and control rooms, in which operators must fuse increasing volumes of complex data from displays, may benefit from higher information integration based on a human-centered design philosophy, and a fundamental understanding of the cognitive work that is carried out by operators. [ABSTRACT FROM AUTHOR]

The objective of the present study was to confirm the convergent validity of information integration theory in the judgment of fatigue in sport, using information integration, subjective, and physiological data. Twenty healthy athletes were confronted with six cycling scenarios in two experimental conditions. In the laboratory condition, the athletes imagined the scenarios and had to cognitively combine the exercise intensity (30%, 50%, and 70% of the maximal intensity) and the exercise duration (15 and 30 min) when judging their expected level of fatigue. In the real sports condition, the athletes enacted each scenario and then rated their subjective fatigue. The heart rate was recorded continuously, so that the physiological training impulse could be calculated. We applied analyses of variance to the data and analyzed correlations between variables. The information integration data from the laboratory condition, the subjective data from the real sports condition, and the objective (physiological) data from the real sports condition were strongly correlated. The information integration patterns concerning fatigue as a function of the exercise duration and intensity obtained respectively from the three data sets were extremely similar.

Configurators have recently emerged as essential tools in the construction industry to enable builders to offer a wide range of customizable designs. Due to significant challenges in information integration between construction suppliers and clients, existing configurator systems often lack crucial usability and supply chain information, presenting barriers to wider adoption among residential communities, especially in single-family residence development that requires a high degree of customization. To address this challenge in the design and construction supply chain, this study presents a lightweight cloud-based modular home configuration methodology as a robust unified platform solution to integrate parametric design options with a certified kit-of-parts library to meet local design codes. The configurator prototype developed under this framework seamlessly integrates essential design and supply chain information by leveraging (1) a generative layout design with pre-approved blueprints, (2) a knowledge-based recommender system to link the design process with certified material catalogs, and (3) a user-friendly web interface to present possible designs. The implementation of a single-family housing design adhering to the building codes in the British Columbia Province of Canada illustrates the benefits of the proposed configurator functionalities and efficient supplier data integration. Lightweight and automated, the proposed configurator has substantial potential to be scaled and adopted across different communities.

In order to solve the problems of methods that use a single form of sensing, the ease of causing deformation damage to the targets with a low hardness during grasping, and the slow sliding inhibition of a prosthetic hand when the grasping target slides, which are problems that exist in most current intelligent prosthetic hands, this study introduces an adaptive control strategy for prosthetic hands based on multi-sensor sensing. Using a force-sensing resistor (FSR) to collect changes in signals generated after contact with a target, a prosthetic hand can classify the target’s hardness level and adaptively provide the desired grasping force so as to reduce the deformation of and damage to the target in the process of grasping. A fiber-optic sensor collects the light reflected by the object to identify its surface roughness, so that the prosthetic hand adaptively adjusts the sliding inhibition method according to the surface roughness information to improve the grasping efficiency. By integrating information on the hardness and surface roughness of the target, an adaptive control strategy for a prosthetic hand is proposed. The experimental results showed that the adaptive control strategy was able to reduce the damage to the target by enabling the prosthetic hand to achieve stable grasping; after grasping the target with an initial force and generating sliding, the efficiency of slippage inhibition was improved, the target could be stably grasped in a shorter time, and the hardness, roughness and weight ranges of targets that could be grasped by the prosthetic hand were enlarged, thus improving the success rate of stable grasping under extreme conditions.

In a seminal article (Plessner & Haar, 2006) a social cognition approach was proposed as framework to study how people judge their own or others’ performance in competitive sport contexts. An important goal of this work was to better understand the processes that underlie potential biases in judgments of sports performance and, thus, receive some hints on how to prevent them. Numerous studies have since been published in which judgments of sports performance have been subjected to scientific analysis. The present paper provides an updated overview on related research from the past 15 years. Again, it follows central building blocks of social information processing (i.e., information input, rules of information integration, situational influences, and characteristics/states of the judging person). Additionally, we organize this overview according to whether the research addresses judgments typically performed by athletes, officials, managers/coaches, or observers. On the one hand, it becomes clear that not all possible combinations of judging persons and information processes are addressed to the same extent in research, which opens up a number of future research tasks. On the other hand, it is suggested that future research in this area should not only be developed broadly, but also that the goal of further theoretical development should not be lost sight of.

Prefabricated construction addresses the extensive construction timelines and environmental pollution associated with traditional building methods. However, challenges such as inconsistent design standards and insufficient information exchange persist across various stages. This paper harnesses Building Information Modeling (BIM) technology to mitigate these practical challenges in prefabricated buildings. It adapts the Industry Foundation Classes (IFC) standard framework to BIM technology and proposes an integrated management model for prefabricated building information using BIM. The engineering construction process is segmented into phases, and a combination of structured and unstructured data is employed to construct a hierarchical BIM model that facilitates the scheduling of large-scale data. For optimizing information scheduling, the Dynamo software programmatically divides components of the prefabricated building, and Python is utilized to streamline the development of corresponding nodes. A case study involving a multi-story residential building in Shijiazhuang City, Hebei Province, demonstrates the benefits of this parametric design approach. Optimizations in the production and construction processes led to a reduction in total project duration by one day and an increase in direct economic benefits by 1,590,500 yuan, thereby underscoring significant financial advantages. This research contributes to the rapid implementation of functional requirements in prefabricated buildings, enhances work efficiency, and offers a novel approach to parametric modeling for this sector.

A fundamental objective in Auditory Sciences is to understand how people learn to generalize auditory category knowledge in new situations. How we generalize to novel scenarios speaks to the nature of acquired category representations and generalization mechanisms in handling perceptual variabilities and novelty. The dual learning system (DLS) framework proposes that auditory category learning involves an explicit, hypothesis-testing learning system, which is optimal for learning rule-based (RB) categories, and an implicit, proceduralbased learning system, which is optimal for learning categories requiring predecisional information integration (II) across acoustic dimensions. Although DLS describes distinct mechanisms of two types of category learning, it is yet clear the nature of acquired representations and how we transfer them to new contexts. Here, we conducted three experiments to examine differences between II and RB category representations by examining what acoustic and perceptual novelties and variabilities affect learners' generalization success. Learners can successfully categorize different sets of untrained sounds after only eight blocks of training for both II and RB categories. The category structures and novel contexts differentially modulated the generalization success. The II learners significantly decreased generalization performances when categorizing new items derived from an untrained perceptual area and in a context with more distributed samples. In contrast, RB learners' generalizations are resistant to changes in perceptual regions but are sensitive to changes in sound dispersity. Representational similarity modeling revealed that the generalization in the more dispersed sampling context was accomplished differently by II and RB learners. II learners increased representations of perceptual similarity and decision distance to compensate for the decreased transfer of category representations, whereas the RB learners used a more computational cost strategy by default, computing the decision-bound distance to guide generalization decisions. These results suggest that distinct representations emerged after learning the two types of category structures and using different computations and flexible mechanisms in resolving generalization challenges when facing novel perceptual variability in new contexts. These findings provide new evidence for dissociated representations of auditory categories and reveal novel generalization mechanisms in resolving variabilities to maintain perceptual constancy. [ABSTRACT FROM AUTHOR]

My country is currently in a new era of industrial revolution and technological development, among which the most representative is the rapid development of information technology. And the action plan has been formulated, and it is pointed out that it is necessary to deeply integrate the real economy and artificial intelligence, the Internet and digital China, as well as the smart society and big data. As the most critical part of community governance, the complexity and difficulty of community sports center governance is relatively high. Therefore, one of the key goals that all sectors of society are currently concerned about is how to build and develop community sports through more advanced technical means center. The innovative governance community sports center must effectively combine and integrate the current advanced technologies such as the Internet of Things and big data, so that the level of community governance can be effectively improved. This paper draws on the academic background of "governance" in the field of public management. Different from social management, "governance" mainly reflects the administrative aspect, the cooperation between the government and all sectors of society, while the traditional social management mainly highlights the state's administrative management. It reflects the concept that the government's management power is inviolable and unshareable, which is also very different from the development direction of the gradual liberalization of the administrative power advocated by the Chinese government. Therefore, the effective way to solve various social problems at this stage is to change the concept of social management to community governance, and this approach has also been supported and valued by the government in recent years. The research shows that the management efficiency of community sports facilities can be effectively improved by optimizing the community sports governance system, making full use of the convenience brought by information resources, and using its own advanced technology. [ABSTRACT FROM AUTHOR]

Objective: Quantification of disease-disease association (DDA) enables the understanding of disease relationships for discovering disease progression and finding comorbidity. For effective DDA strength calculation, there is a need to address the main challenge of integration of various biomedical aspects of DDA is to obtain an information rich disease representation. Materials and Methods: An enhanced and integrated DDA framework is developed that integrates enriched literature-based with concept-based DDA representation. The literature component of the proposed framework uses PubMed abstracts and consists of improved neural network model that classifies DDAs for an enhanced literature-based DDA representation. Similarly, an ontology-based joint multi-source association embedding model is proposed in the ontology component using Disease Ontology (DO), UMLS, claims insurance, clinical notes etc. Results and Discussion: The obtained information rich disease representation is evaluated on different aspects of DDA datasets such as Gene, Variant, Gene Ontology (GO) and a human rated benchmark dataset. The DDA scores calculated using the proposed method achieved a high correlation mainly in gene-based dataset. The quantified scores also shown better correlation of 0.821, when evaluated on human rated 213 disease pairs. In addition, the generated disease representation is proved to have substantial effect on correlation of DDA scores for different categories of disease pairs. Conclusion: The enhanced context and semantic DDA framework provides an enriched disease representation, resulting in high correlated results with different DDA datasets. We have also presented the biological interpretation of disease pairs. The developed framework can also be used for deriving the strength of other biomedical associations.

Ontology merging is an important task in ontology engineering to date. However, despite the efforts devoted to ontology merging, the incorporation of relevant features of ontologies such as axioms, individuals and annotations in the output ontologies remains challenging. Consequently, existing ontology-merging solutions produce new ontologies that do not include all the relevant semantic features from the candidate ontologies. To address these limitations, this paper proposes a novel algorithm for multi-criteria ontology merging that automatically builds a new ontology from candidate ontologies by iteratively updating an RDF graph in the memory. The proposed algorithm leverages state-of-the-art Natural Language Processing tools as well as a Machine Learning-based framework to assess the similarities and merge various criteria into the resulting output ontology. The key contribution of the proposed algorithm lies in its ability to merge relevant features from the candidate ontologies to build a more accurate, integrated and cohesive output ontology. The proposed algorithm is tested with five ontologies of different computing domains and evaluated in terms of its asymptotic behavior, quality and computational performance. The experimental results indicate that the proposed algorithm produces output ontologies that meet the integrity, accuracy and cohesion quality criteria better than related studies. This performance demonstrates the effectiveness and superior capabilities of the proposed algorithm. Furthermore, the proposed algorithm enables iterative in-memory update and building of the RDF graph of the resulting output ontology, which enhances the processing speed and improves the computational efficiency, making it an ideal solution for big data applications.

Purpose: To solve the problems of low integration accuracy and long integration time of traditional prefabricated construction information integration methods. Method: A method of assembling building information integration based on BIM and RFID technology was proposed. By analyzing the information integration principle of BIM RFID (Building Information Modeling Radio Frequency Identification) technology, starting with rfid technology, we use rfid technology to collect the information of prefabricated building components and obtain the coding information of component data. Experiment: Combining Markov model and fuzzy algorithm, the obtained coding information is preprocessed. According to the processing results, statistical feature clustering algorithm is introduced to integrate the construction information of prefabricated buildings. Result: The precision polyline of the prefabricated building construction information integration method based on BIM and RFID technology showed a steady increase, and it was close to 100% in the later stage. At the same time, the time consumed by this method was within 0.41 s, with high accuracy, high efficiency and high practicability. [ABSTRACT FROM AUTHOR]

Bed and breakfast (B&B) tourism stimulates the economic development of underdeveloped areas. However, although Taitung is a renowned B&B hotspot, it is the poorest area in Taiwan. This phenomenon has prompted studies on the contribution of B&B tourism to counties. Previous literature related to B&B tourism has the lack of non-economic measures typically results in contradictory phenomena. In addition, most studies have been empirical and have incorporated expert opinions, which are susceptible to human semantical uncertainty and incomplete expression. To overcome these limitations, a framework based on human-land interactions focused on cultural landscape, locality, and regional revitalization was constructed in present study. An advanced decision method, Z-DEMATEL, in which Z-numbers and decision-making trial and evaluation laboratory (DEMATEL) are combined, was applied. The expression confidence and consistency were evaluated through information integration using exclusive sematic, confidence, and perception scales. Subsequently, the DEMATEL was used to analyze the internetwork relations between Dimensions/Criteria. In the overall dimensions, the locality was a nexus of interrelations, while the cultural landscape was a cause. In each dimension, such nexus and cause-starter were identified using specific criteria. The findings were synthesized as improvement plans for easy indexing. Therefore, the results can improve poverty alleviation through B&B tourism. [ABSTRACT FROM AUTHOR]

In the landscape of natural language processing and artificial intelligence, sentiment analysis and emotion recognition hold crucial roles in deciphering human emotions across diverse communication channels. This study addresses a significant research gap by venturing into the promising domain of emotion identification through sentiment analysis, capitalising on the potential of multimodal deep learning. Through an exhaustive literature review, the study seeks to bridge the gap between conventional sentiment analysis methods and the intricate subtleties of human emotions, achieved by fusing data from various modalities. This integration, coupled with fine-grained emotion recognition, strives to heighten the precision of emotion comprehension. Anchored by a robust conceptual framework encompassing variables like information integration, information variability and model type, this research probes the interplay of these factors in shaping emotion consistency. The research methodology involves a two-tailed t-test, a potent statistical tool for hypothesis testing using SmartPLS. The outcomes shed light on the intricate interplay among these variables. While information integration may not exert a significant impact on information consistency, information variability and model type surface as critical factors, each distinctively contributing to the enhancement of information consistency. These insights offer a deeper comprehension of the complexities within this domain, charting a path towards refined insights into the examined relationships. [ABSTRACT FROM AUTHOR]

The detection and recognition of traffic signs is an essential component of intelligent vehicle perception systems, which use on-board cameras to sense traffic sign information. Unfortunately, issues such as long-tailed distribution, occlusion, and deformation greatly decrease the detector's performance. In this research, YOLOv5 is used as a single classification detector for traffic sign localization. Afterwards, we propose a hierarchical classification model (HCM) for the specific classification, which significantly reduces the degree of imbalance between classes without changing the sample size. To cope with the shortcomings of a single image, a training-free multi-frame information integration module (MIM) was constructed, which can extract the detection sequence of traffic signs based on the embedding generated by the HCM. The extracted temporal detection information is used for the redefinition of categories and confidence. At last, this research performed detection and recognition of the full class on two publicly available datasets, TT100K and ONCE. Experimental results show that the HCM-improved YOLOv5 has a mAP of 79.0 in full classes, which exceeds that of state-of-the-art methods, and achieves an inference speed of 22.7 FPS. In addition, MIM further improves model performance by integrating multi-frame information while only slightly increasing computational resource consumption. [ABSTRACT FROM AUTHOR]

Digital technologies are dramatically reshaping the maritime industry and transforming how ports operate in a global transport system. To remain competitive and improve its productivity, a port needs to build its digital capability and become a 'smart' port. Despite its increasing importance, port digitalisation is largely under-researched. Our research explores how information integration afforded by the recent development of Port Centric ICT systems (PCIS) may impact port performance. A survey was conducted in Qingdao Port, the seventh-largest comprehensive port in the world. We adopted the partial least squares structural equation modelling (PLS-SEM) technique for analysis. Our model focuses on testing the mechanism of information integration on port performance among the port community members; that is, both internal information integration and external information integration and their direct and indirect impact on port performance. Our main contribution lies in that our study identifies two clear pathways for developing a port's digitalisation processes to achieve competitive advantages: a) the mediated path, where port performance will improve if information integration is coupled with port community operational capability (PCOC); b) the direct path, where the innovative use of PCIS leads to radical ideas, new business models and transformative changes. [ABSTRACT FROM AUTHOR]

In a seminal article (Plessner & Haar, 2006) a social cognition approach was proposed as framework to study how people judge their own or others' performance in competitive sport contexts. An important goal of this work was to better understand the processes that underlie potential biases in judgments of sports performance and, thus, receive some hints on how to prevent them. Numerous studies have since been published in which judgments of sports performance have been subjected to scientific analysis. The present paper provides an updated overview on related research from the past 15 years. Again, it follows central building blocks of social information processing (i.e., information input, rules of information integration, situational influences, and characteristics/states of the judging person). Additionally, we organize this overview according to whether the research addresses judgments typically performed by athletes, officials, managers/coaches, or observers. On the one hand, it becomes clear that not all possible combinations of judging persons and information processes are addressed to the same extent in research, which opens up a number of future research tasks. On the other hand, it is suggested that future research in this area should not only be developed broadly, but also that the goal of further theoretical development should not be lost sight of. [ABSTRACT FROM AUTHOR]