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Access points to Heliophysics information are often poorly inter-linked to one another, making it challenging for researchers to integrate information, mature their understanding, and incorporate more complex ideas and relationships into their analyses. The authors reason this behavior to be a direct conseaquence of the lack of infrastructure for knowledge and data discovery in Heliophysics. Building an online library resource for Heliophysics addresses this gap in resources, and can also address other troublesome gaps related to community-building, such as access to help from other scientists, example analysis tutorials, and collaboration opportunities. This paper envisions a resource that better connects existing Heliophysics information and knowledge sources, the LIbrary for KnowledgE and Discovery (LIKED), which will be an online resource that interlinks information across databases (e.g., observational and publication), researcher interactions (e.g., conferences and discussion boards), and educational materials. The result is a rich and searchable environment to provide users a more productive experience for research and collaboration. In this paper, we provide the conceptual model for LIKED. We expand to detail how LIKED will serve to connect the various knowledge and research components available internationally together in an uniformly searchable manner. Finally, this work outlines various contributions the community can make towards this goal. [ABSTRACT FROM AUTHOR]

Historically, economic models of memory have neglected a pivotal aspect: decay, the established notion that memories deteriorate over time, resulting in a loss of fidelity. In this paper we show that a framework of rational memory with decay can produce the recency effect and other intuitively appealing memory phenomena. We apply the framework to both finite time and dynamic programming settings, deriving a wide range of results with a focus on comparative statics and asymptotic behaviors. Many of our results can also be applied to multi-dimensional rational inattention settings where individuals have to split their attention across several information sources. [ABSTRACT FROM AUTHOR]

• A framework is presented to explore the influence of friendships in a social network on drivers' route choice. • This paper simulates the evolution of drivers' route choice in a hypothetical transportation network under different assumptions. • This research offers insight into the route choice process when drivers can access information about other drivers' travel experience in a social network. • The simulation results show that the information types mainly affect the change rate, and the memory length mainly affects the time for convergence. Route choice is a decision that drivers face daily under varying traffic conditions. Although many studies have focused on route choice, the actual behavior considering real-world networks and the mechanisms that govern it have eluded complete characterization. Part of the difficulty has been in accounting for the growing availability of information sources that may influence drivers' route choices, precluding collection of adequate observational data. In addition, with the continuing emergence of new forms and sources of information, such as social media with varying degrees of interactivity, engagement, and immediacy, existing frameworks and models for representing these choices and the influence of new information sources have been lacking. This study uses an agent-based modeling approach to investigate the effects of social influence on drivers' route choices. In the model, all drivers communicate with each other in the same social network, and this information is combined with their previous experiences. The first aim of this study is to investigate the impact of social network connectivity and information exchange on route choice. The second is to characterize situations in which all drivers in the social network are satisfied with their route choices. The simulation results show that an equilibrium is achieved when all drivers in the given social network are satisfied with their current route choices. [ABSTRACT FROM AUTHOR]

Background: Given the potential for social media to spread health misinformation, it is important to understand how trusts impact adolescents' engagement with health content on social media. Objective: To explore the concept of trust when adolescents (13-18 years) engage with health information on social media. Five relevant databases (MEDLINE, EMBASE, PsycINFO, ERIC, and CINAHL) were systematically searched alongside Google Scholar and reference lists of included papers. Studies were included if they examined adolescents' trust when engaging with health information on social media. Study Appraisal and Synthesis Methods: Thematic analysis was used to synthesize the findings from this review. Results: Thirty-four papers were included. Three key domains were explored: trust in the social media platform/ service (general distrust of social media for health information; safety and privacy); trust in other users (mistrust of unknown users; fear of bullying or judgment; trust in friends or peers; celebrities and popularity; trust in others' experience and the importance of social support); trust in content (tone and appearance of health information; expertise and verification; advertising, pushed, and suggested content). Limitations: Narrow geographic representation of papers and limited quantitative studies. Conclusions and Implications of Key Findings: Adolescents' trust in health information on social media involves a complex interplay between trust in: social media platforms, other users, and health content. Central to many of the findings is the social and identity work done by adolescents on and through social media. [ABSTRACT FROM AUTHOR]

The conceptualization of the value-based healthcare (VBHC) model as a way to improve the quality of patient care and control the costs of that care has been widely debated. However, few studies describe how to practically implement VBHC from the perspective of health information systems as technical platforms for data collection, analysis, and sharing. This study presents a scoping review of implementation experiences of this model, specifically focusing on the requirements and barriers of the information system. Out of the initially identified 1866 references, 24 papers were selected, from which the facilitating factors and barriers associated with the information system were extracted. The quality of data, its analysis, and resources (infrastructure and specialized human resources in information and technology communication) emerge as indispensable categories for implementation. [ABSTRACT FROM AUTHOR]

The rapid expansion of information resources on the internet has made it increasingly challenging for users to get valuable and relevant information. Recommendation systems (RSs) play a vital role in addressing this issue by providing personalized suggestions based on user preferences. However, the open nature of RSs allows for the injection of fake profiles by malicious users, leading to biased ratings and manipulation of the overall system results. These fake profiles have been intended to promote or degrade specific items, which creates bias in the system and compromises the user experience. The motivation behind these fake profiles is to promote or degrade a particular item, which affects the system and makes it more biased toward certain items. This research paper aims to detect and eliminate such fake profiles in RSs, ensuring users receive genuine and reliable results. The proposed approach utilizes a voting ensemble (VE) method, combining the strengths of multiple classification algorithms including Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor (KNN), and Naive Bayes classifier (NBC). By utilizing these classifiers, the system enhances its robustness against malicious activities. The proposed VE method effectively identifies the fake profiles with a minimum number of parameters, and it shows high detection accuracy on Movielens datasets. It has been demonstrated that the proposed method gives 99.4%, 99.5%, 99.4% and 99.6% accuracy using NBC, QDA, KNN, and VE, respectively and it outperforms all other competing methods in terms of accuracy, precision, recall and MCC. [ABSTRACT FROM AUTHOR]

The atom-bond sum-connectivity index (A B S index) of a graph G = (V , E) is defined as A B S (G) = ∑ ξ ζ ∈ E (G) d ξ + d ζ − 2 d ξ + d ζ , where d ξ represents the degree of vertex ξ ∈ V. By using A B S index, the heat of formation of octane and heptane can be accurately predicted. The approximate value of the range for topological indices via certain parameters of molecular graphs is a major source of information in studying the molecular structure. In this paper, we determine the maximum values of A B S index in the collection of graphs which was provided with some predetermined parameters containing clique number, chromatic number etc., furthermore, the relevant extremal graphs were depicted. [ABSTRACT FROM AUTHOR]

• A new equivalent radiation source method, which equates the radiation effect produced by the unknown real radiation sources, is proposed in this paper. • A limited number of detection points can be planned to build the equivalent source model, which helps deal with the problems of limited coverage of radiation detection points and improves the detection efficiency. • The effectiveness and feasibility of the proposed method is verified by simulations of different source distributions and experiments using a nuclear detection robot. Reconstructing nuclear radiation field is essential for a safe and efficient treatment strategy, especially when a nuclear facility fails and there is a need to go inside. Since prior radiation source information may not be accurate in the abnormal situation, this paper proposes a new equivalent radiation source method, which equates the radiation effect produced by the unknown real radiation sources. A limited number of detection points can be planned to build the equivalent source model, which helps deal with the problems of limited coverage of radiation detection points and improves the detection efficiency. The effectiveness of the proposed method is verified by simulations of different obstacles and different source distributions. The feasibility is experimentally tested by detecting and reconstructing the radiation field around a nuclear irradiation facility using a nuclear detection robot. In addition, the influence of the parameters is also analyzed to guide the practical usage of the proposed method. [ABSTRACT FROM AUTHOR]

Chandrayaan-1 and Chandrayaan-2, the Indian Mars Orbiter Mission (MOM), and a multi-wavelength astronomical observatory (AstroSat) are the missions launched by the Indian Space Research Organisation (ISRO) under the broader category of space science and exploration. The results generated from the periodically released data from these missions, are published in the form of journal articles, conference papers, The Astronomer's Telegrams, GRB Coordinates Network circulars, etc. The goal of this study is a bibliometric analysis of 592 journal articles on ISRO's four space science and exploration missions which were published between 2002 and 2021 (20 years). The Web of Science Core collection database and Google Scholar are used to retrieve the data needed for the study. Bibliometric indicators like mission-wise distribution of articles; year-wise distribution of articles, relative growth rate and doubling time of publications; prolific authors; leading institutions; international collaboration; highly ranked journals; highly cited articles and keyword occurrences were considered for analysis. The doubling time of publications of those missions which yielded the highest number of articles were separately calculated using an exponential equation and built in algorithms in Python. Advanced data visualization tool such as VosViewer was used for generating the network maps. The bibliometric analysis revealed the usage of mission output data not only by ISRO scientists but also by academia, research labs, etc. Overall, the study can be considered as a comprehensive reference source for information on research trends related to the four Indian space science and exploration missions. [ABSTRACT FROM AUTHOR]

Paediatric health research is fraught with both ethical and practical challenges that can hinder the successful completion of research studies. Listening to, and acting on, the voices of children and young people in the design and delivery of paediatric health research (otherwise known as Patient and Public Involvement) is one way to overcome such challenges. This paper describes our experiences of working directly with children and young people in various health research initiatives. We recount our journey of involving children and young people as partners and give examples to demonstrate the unique knowledge and insights gained in the production of high-quality research. This short review showcases the many benefits gained if meaningful involvement occurs, including greater understanding of children and young people's perspectives and the impact this has on research studies. We provide various practical resources for researchers on how to meaningfully involve children and young people as partners, how to develop age-appropriate information and guidance to support the ethical review process. [ABSTRACT FROM AUTHOR]

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This paper examines the accessibility options for SRDs in university libraries in Nigeria. Access to the library's information resources is a benchmark for effectively utilizing information resources and services and fulfilling information demands for the educational growth of people within a community. Students that struggle with reading and it affect their capacity to learn are students with reading disabilities. University libraries should support and encourage scholarly work and research in all areas of human endeavor, regardless of ability or disability. As a result, this paper explores the idea of students with reading disabilities (SRDs), their traits, and the responsibility of university libraries in Nigeria in supporting SRDs. The article looks at the discrepancies between accessible and necessary information sources and the difficulties Nigerian university libraries experience in bridging such gaps and presents pertinent suggestions for improving access to SRDs. • The paper examined the provision of access for students with reading disabilities (SRDs) in Nigerian university libraries. • The study adopted a conceptual approach. • The takeaways are its emphasis on the critical role university libraries should play in improving information access and availability for SRDs. • The article examined the discrepancies between accessible and necessary information sources and the difficulties Nigerian university libraries experience in bridging such gaps. • The study recommended the repackaging and recreating university library information resources, facilities, and services to improve access to SRDs. [ABSTRACT FROM AUTHOR]

Nature- and society-inspired metaheuristic algorithms have recently become the most promising technological model. To solve more complex optimization problems and complicated engineering applications, this paper proposes a new people duality psychological tendency and feedback mechanism-based Inherited Optimization Algorithm(IOA), which is inspired by people showing positive-negative duality cognitive tendency and adaptive feedback behavior when selecting information resources with different identity attributes. The IOA algorithm contains the symmetric two exploration phases. The exploitation phase adaptively regulates the dualistic psychological balance of people in inheriting the information resources with better existence value through a feedback regulation mechanism controlled by the profitability awareness to increase population diversity. This paper qualitatively and quantitatively evaluates the optimization performance of IOA on 84 benchmarks, including swarm convergence behavior, effectiveness, convergence, robustness, and significance. The scalability of the IOA is investigated using the CEC2017 suites. The algorithm performance in solving constrained optimization is verified on 8 engineering problems. All statistical results of the IOA are compared with the most promising 12 metaheuristics, which shows that the absolute computational efficiency of IOA on four types of functions is 95%, 96.67%, 80.95%, and 76.92%, respectively, the average rank (rank sum ratio) of IOA is 1.08 (1.19%) among the 13 algorithms, ranking first. The Wilcoxon signed rank test results on the CEC2017 suites show that IOA contains 1437 significance indicators out of 1440 comparisons, with the proportion of significant differences 99.79%, which suggests the proposed IOA maintains efficient search efficiency. [ABSTRACT FROM AUTHOR]

Merchant vessels often require icebreaker (IB) assistance to create safe pathways and improve efficiency when navigating in the Baltic Sea. Since IB resources are limited, an accurate estimation on the need for IB assistance is important. Whether IB assistance is needed depends on multiple factors. While practical experience from captains is naturally a source of valuable information for the decision on the need for IB assistance, systematic analysis of the reasoning is limited. The primary aim of this paper is to holistically investigate the influencing factors and their effect on estimating the need for IB assistance through data-driven techniques. Based on a comprehensive list of potential factors, different of data such as traffic history, environmental conditions, and ship specifications are gathered to present complex navigational scenarios. Each scenario is labeled by different navigation modes (independent navigation or IB assistance), laying the foundation for influencing factor identification and effect quantification. Logistic regression is applied to evaluate the effect of the factors on the need for IB assistance. The results show that the impact of the factors is diverse, and ridged ice concentration has the most significant impact. The effectiveness of identified factors is measured by comparing it to that of the factors that have been implemented by the existing studies (e.g., the combination of ice concentration, thickness, and ship ice class, or only ship speed). By considering the factors in this study, the classification performance can be improved by at least 5.6%. The findings in this paper can provide insights for predicting IB workloads and optimizing IB resources and have the potential to support the development of an intelligent decision-support system for winter navigation. • A data-driven model was proposed to analyze navigation mode determination in ice-covered waters. • Influencing factors for the need for icebreaker assistance in ice were quantitively identified and assessed. • The impact of various influencing factors on ships with different ice classes was examined. • The innovation of influencing factor analysis was highlighted through model performance comparison. [ABSTRACT FROM AUTHOR]

The information fusion field has recently been attracting a lot of interest within the scientific community, as it provides, through the combination of different sources of heterogeneous information, a fuller and/or more precise understanding of the real world than can be gained considering the above sources separately. One of the fundamental aims of computer systems, and especially decision support systems, is to assure that the quality of the information they process is high. There are many different approaches for this purpose, including information fusion. Information fusion is currently one of the most promising methods. It is particularly useful under circumstances where quality might be compromised, for example, either intrinsically due to imperfect information (vagueness, uncertainty, ...) or because of limited resources (energy, time, ...). In response to this goal, a wide range of research has been undertaken over recent years. To date, the literature reviews in this field have focused on problem-specific issues and have been circumscribed to certain system types. Therefore, there is no holistic and systematic knowledge of the state of the art to help establish the steps to be taken in the future. In particular, aspects like what impact different information fusion methods have on information quality, how information quality is characterised, measured and evaluated in different application domains depending on the problem data type or whether fusion is designed as a flexible process capable of adapting to changing system circumstances and their intrinsically limited resources have not been addressed. This paper aims precisely to review the literature on research into the use of information fusion techniques specifically to improve information quality, analysing the above issues in order to identify a series of challenges and research directions, which are presented in this paper. [Display omitted] • High information quality levels are crucial in information systems. • Information fusion is a promising and active field for improving information quality. • There is no synthesised and organised knowledge of the state of the art in this field. • Key aspects such as information fusion techniques, information quality measurement and applicability require research. • We surveyed the literature, analysed major issues and listed a series of challenges and opportunities. [ABSTRACT FROM AUTHOR]

The major hurdle in building a visible and infrared fusion model is the necessity of large-scale pixel-aligned and time-synchronized image pairs. In this paper, we propose an easy-to-learn visible and infrared image fusion framework that does not require pairs for training. In addition to easy training using unpaired sets, our framework provides fusion images with more textures and meaningful scene information compared to previous works. In particular, to mix features from each spectrum, we newly present a feature line-up module to identify important information in each source. Additionally, in order to provide a new option for benchmark evaluation, we construct a new sequence-based visible and infrared paired dataset that is aligned and synchronized. Finally, we perform extensive experiments to verify the performance of the proposed method by both public and proposed datasets. [Display omitted] • The proposed image fusion framework can be learned with an unpaired set. • Aligning images and preparing ground truth is not needed at all. • A feature line-up method is designed to mix features from each spectrum. • Our loss function targets both rich textures and well-represented scene information. • A new sequence-based dataset for validation of need-to-fuse situations is introduced. [ABSTRACT FROM AUTHOR]

• A scoping study provides a better understanding of how local authorities in China support care-experienced young people. • Twenty-two social policies in China are mapped and examined. • Across localities, housing and employment services are prioritised to support care-experienced young people. • Current policy responses are insufficient, and people's opportunities to access the stipulated support are quite limited. An increasing amount of attention is being paid to young people with care experience on an international scale. They are often associated with negative outcomes after leaving care, such as low educational attainment, unstable housing, job instability and social exclusion. While many countries have prescribed social policies for supporting them for a more fruitful transition out of care, there is little information available in the context of China at local levels. Given this, this paper maps and examines existing local policies, exploring how local authorities support care-experienced young people in China. To achieve, it adopts a scoping review. The findings highlight (i) China's local polices as a whole prescribe a variety of services, resources, and/or measures to support care-experienced young people, of which housing and employment are at the top of the list; (ii) current policy responses are insufficient, and opportunities of this group of people to access the stipulated support are quite limited. The findings of this study indicate that policymakers need to improve local policies in order to make them more operable, sophisticated, and comprehensive, so that care-experienced young people can reap the greatest benefits from them. Also, a national leaving-care policy that provides guidance to local authorities should be considered, adding consistency and clarity to relevant policies across the country. The limitations of this paper relate mainly to its review methods, which point to the need to expand data sources and conduct cross-reviews to enhance the findings' credibility and robustness. [ABSTRACT FROM AUTHOR]

There are a wide variety of Land-use Regulation (LuR) restrictions, ranging from those that need simple 2D spatial analyses to be verified (e.g., area calculation), to those that need complex 3D analyses (e.g., overshadowing and overlooking). Assessing LuR restrictions to automatically detect LuRs' potential conflicts with physical objects, such as building elements, could be a challenging task in 2D land administration systems relying on 2D drawings and 2D representations, especially when 3D/vertical analyses are required. Land administration systems can include 3D representations of LuR restrictions to identify their impacts on land parcels and vice versa since the LuRs contain legally binding restrictions for all parties including governments and citizens. As part of the transition from the 2D representation of LuR restrictions and manual conflict detection between LuR restrictions and physical objects such as building elements towards a 3D digital representation and automatic conflict detection, this paper proposes a three-stage conceptual framework for automatic 3D modelling and conflict detection of 3D LuR restrictions. The three-stage conceptual framework, as a generic approach, proposes required principles and procedures to automatically (1) model 3D LuR restrictions (called 3D CityLuR), (2) combine them with other sources of information (e.g., zoning maps, 3D city models, and BIM), and (3) detect LuRs' potential conflicts between the modelled LuRs and physical objects (i.e., proposed/existing building elements). For modelling and verifying 3D LuRs automatically, the framework organises all the required information for IFC (Industry Foundation Classes) as the proposed building, CityGML as the existing buildings, and most importantly, the planning and zoning requirements. It proposes two categories of required information as "level of information need" for (1) modelling and combing 3D CityLuR with other sources of information automatically and (2) automatically detecting potential spatio-semantic conflicts between LuR restrictions and physical objects such as proposed/existing building elements. Based on a case study in Victoria, Australia, 3D LuR restrictions subject to planning approvals and building subdivisions are investigated. The paper concludes with a discussion arguing the feasibility of automating the verification of LuR restrictions to support decision-making in planning permit applications and building subdivisions. • A Three-Stage Conceptual Framework for Automatic 3D Modelling and Conflict Detection of Land-use Regulation Restrictions. • Transitioning from Manual 2D Verification to Automatic 3D Modelling and Verification of Land-use Regulation Restrictions. • Modelling 3D City Land-use Regulations Automatically (Called 3D CityLuR) and Combining it with 3D City Models and BIM. • Required Information as "Level of Information Need" to Combine 3D CityLuR with Other Sources of Information Automatically. • Required Information as "Level of Information Need" to Detect 3D CityLuR's Potential Spatio-Semantic Conflicts Automatically. [ABSTRACT FROM AUTHOR]

Statistical matching aims to integrate two statistical sources. These sources can be two samples or a sample and the entire population. If two samples have been selected from the same population and information has been collected on different variables of interest, then it is interesting to match the two surveys to analyze, for example, contingency tables or covariances. In this paper, we propose an efficient method for matching two samples that may each contain a weighting scheme. The method matches the records from the two sources. Several variants are proposed in order to create a directly usable file integrating data from both information sources. • Optimal transport using calibration can produce an efficient statistical match. • Method uses calibration to create a weighting system respecting matching variables. • Method can be used for micro and macro approaches. [ABSTRACT FROM AUTHOR]

Multi-exposure image fusion (MEF) targets to integrate multiple shots with different exposures and generates a single higher dynamic image than each. Existing deep learning-based MEF approaches only adopt reference high dynamic images (HDR) as positive samples to guide the training of fusion networks. However, simply relying on these positive samples are difficult to find the optimal parameters for the network as a whole. Thus, the structure or texture information is blurred or missed in the generated HDR results. Moreover, few approaches attempted to prevent illumination degeneration during the fusion process, resulting in poor color saturation on their fused results. To address such limitations, in this paper, we introduce a novel holistic and local constraint built upon contrastive learning, namely HoLoCo, to discover the intrinsic information of both source LDR images and the reference HDR one. In this manner, the generated fused images are pulled to the HDR image and pushed away from the LDR source images in both image-based and path-based latent feature spaces. Besides, inspired by Retinex theory, we propose a color correction module (CCM) to refine illumination features. CCM involves dual streams that can collaborate to ensure the nature of color information and details consistency. Extensive experiments on two datasets show that our HoLoCo can continuously generate visual-appealing HDR results with precise detail and vivid color rendition, performing favorably against the state-of-the-art MEF approaches. Source code is available in github https://github.com/JinyuanLiu-CV/HoLoCo. • We introduce contrastive learning to multi-exposure image fusion. • A holistic and local contrastive constraint is built into the loss function. • We propose a color correction module to alleviate color bias. • Our HoLoCo can generate visual-appealing on various datasets. [ABSTRACT FROM AUTHOR]

The paper examines to what extent expressing information sources with a fully grammaticalized evidential system or with lexical means may have a different pragmatic impact in conversation. A contrastive corpus of Tibetan (TSC) and English (CSC/LAC) allows me to investigate several dimensions: frequency, semantic schematicity, optionality, economy, and information hierarchy. The data from the contrastive corpus indicate that Tibetan evidentials are on average 6.7 times more frequent than English evidentials. Tibetan evidentials are also functionally more schematic, since English evidentials usually contain more semantic features. This compels English speakers to be more specific when referring to their information sources. Grammatical evidential systems may be less optional than lexical systems, but the degree of optionality differs depending on the linguistic level. The morpho-syntactic obligatoriness of many Tibetan evidentials may indeed be compensated by the availability of evidentially neutral forms in the same paradigm. However, evidentiality seems to be pragmatically less optional in Tibetan than in English. Tibetan evidentials are also more economical on a morpho-phonetic, syntactic, and cognitive level. Finally, all these parameters influence the informational status of evidentials. Because they are more frequent, schematic, obligatory, and economical, Tibetan evidentials appear more backgrounded. • Tibetan evidentials are on average 6.7 times more frequent than English evidentials. • Tibetan evidentials are semantically more schematic than English evidentials. • Tibetan evidentials are pragmatically more optional than English evidentials. • Tibetan evidentials are more economical than English evidentials. • Tibetan evidentials tend to be more backgrounded than English evidentials. [ABSTRACT FROM AUTHOR]

Domain adaptation aims to exploit useful information from the source domain where annotated training data are easier to obtain to address a learning problem in the target domain where only limited or even no annotated data are available. In classification problems, domain adaptation has been studied under the assumption all classes are available in the target domain regardless of the annotations. However, a common situation where only a subset of classes in the target domain are available has not attracted much attention. In this paper, we formulate this particular domain adaptation problem within a generalized zero-shot learning framework by treating the labelled source-domain samples as semantic representations for zero-shot learning. For this novel problem, neither conventional domain adaptation approaches nor zero-shot learning algorithms directly apply. To solve this problem, we present a novel Coupled Conditional Variational Autoencoder (CCVAE) which can generate synthetic target-domain image features for unseen classes from real images in the source domain. Extensive experiments have been conducted on three domain adaptation datasets including a bespoke X-ray security checkpoint dataset to simulate a real-world application in aviation security. The results demonstrate the effectiveness of our proposed approach both against established benchmarks and in terms of real-world applicability. [ABSTRACT FROM AUTHOR]

Off-Policy Actor-Critic methods can effectively exploit past experiences and thus they have achieved great success in various reinforcement learning tasks. In many image-based and multi-agent tasks, attention mechanism has been employed in Actor-Critic methods to improve their sampling efficiency. In this paper, we propose a meta attention method for state-based reinforcement learning tasks, which combines attention mechanism and meta-learning based on the Off-Policy Actor-Critic framework. Unlike previous attention-based work, our meta attention method introduces attention in the Actor and the Critic of the typical Actor-Critic framework, rather than in multiple pixels of an image or multiple information sources in specific image-based control tasks or multi-agent systems. In contrast to existing meta-learning methods, the proposed meta-attention approach is able to function in both the gradient-based training phase and the agent's decision-making process. The experimental results demonstrate the superiority of our meta-attention method in various continuous control tasks, which are based on the Off-Policy Actor-Critic methods including DDPG and TD3. [ABSTRACT FROM AUTHOR]

Since it is widely conveyed on the Web, tabular data is mostly organized in a table structure. Indeed, this data format is widely used in multiple scenarios on the web and even within data storage entities. In addition, tabular data is a source of information that deserves to be interpreted and exploited. In this framework, efforts to extract meaningful information from tabular data based on semantic approaches, such as an ontology or a knowledge graph, are commonly referred to as Semantic Table Interpretation (STI). In this paper, we present an interactive tool that deploys a mapping approach to overcome possible semantic gaps in tabular data against a Knowledge Graph. Indeed, the ultimate goal of this tool is to provide an application and adaptable framework that combines search and filtering services associated with text preprocessing techniques. The experimental evaluation was conducted under the SemTab challenge and yielded encouraging and promising results regarding its performance and rankings. [ABSTRACT FROM AUTHOR]

• Propose to explore two kinds of topic signals and tailor them to rumor detection. • Propose a multi-task learning strategy and jointly train the model on both topic credibility prediction and user credibility prediction. • Experimental results demonstrate our model outperforms all state-of-the-art methods. Detecting rumors on social media and preventing its spread play a critical role for politics, economy, etc. Conventional studies mainly focus on exploiting the content or context of the source post, while they always ignore the rich topic information within the source post. To tackle this issue, in this paper, we propose a Topic and Structure Aware Neural Network (TSNN) for rumor detection. To be specific, we explore two kinds of topic signals, including a coarse-grained topic signal (i.e., topic credibility) and a fine-grained topic signal (i.e., latent topic representation), and tailor them to the task of rumor detection. Moreover, we introduce a new auxiliary task, i.e., topic credibility prediction, in order to effectively leverage the rich topic information within source posts. Finally, we develop a multi-task learning strategy that helps improve rumor detection performance by jointly learning the task of topic credibility prediction and user credibility prediction. Extensive experiments on three real-world datasets demonstrate that the proposed approach TSNN is superior to the state-of-the-art baseline methods. [ABSTRACT FROM AUTHOR]

Web Accessibility refers to apps, websites, and digital tools designed to be accessible by all users, including those with disabilities. The web is a source of public information, education, employment, government, commerce, health, entertainment, and many others, and every person, regardless of ability, has the right to access it. Imagine missing out on thousands of new customers because your competitor has an easier-to-use, WCAG-compliant website. This systematic literature review aims to analyze the empirical methods of evaluating accessibility to educational websites, disabilities, and errors. Some of the papers we will review here use automated tools, real users, or experts to evaluate web accessibility. After analyzing the results, we will determine if the websites comply or not with the Web Accessibility Content Guidelines (WCAG). Also, what are the differences between automated tools, real users, and experts in evaluating websites. [ABSTRACT FROM AUTHOR]

Topic modelling is a probabilistic based statistical model used to find the latent topics that best depicts the content of the documents. Community Question Answering websites such as Quora, Stack Overflow and Yahoo! Answers have been prevalently in use, performs topic modeling as lot of queries pour in on daily basis which make it challenging to understand, summarize and synthesize the main topic of discussions. On these websites there are basically two sources of information that are available to analyze the key latent topics: questions text and tags. Questions are in textual format and tags are the keywords or tokens that are related to the question being asked which describes the content of the question. In past studies, most of the researchers have used question text for the purpose of topic modeling. It is still unclear why tag is not being considered for topic modeling. To combat this issue, this paper performs topic modeling using both question tags and text. The topic modeling based on tags has been compared with text based on two metrics namely coherence and perplexity. Experiment has been conducted on three real time datasets namely Artificial intelligence, Software Engineering and quantum computing from Stack exchange website. At high level tag-based topic modelling looked promising but closer observation revealed the opposite. It has been found that topic modeling using question text is preferable as topic modelling using tags collapses after a certain number of topics. [ABSTRACT FROM AUTHOR]

In recent decades, there has been an increase in people learning about and practicing beekeeping in rural, peri-urban and urban across the United States. Yet, much recent honey bee research focuses on the biotic (e.g., mites and viruses) and abiotic (e.g., pesticides and management practices) factors contributing to colony loss. However, beekeepers themselves play an essential role in stewarding healthy and productive colonies. This study focuses on backyard and sideliner beekeepers who responded to an annual online survey distributed in the U.S. (n = 2155) in 2018. This paper investigates beekeepers' concerns, and their use and perception of honey bee management information sources. We found survey respondents to be a relatively homogeneous group. Our findings suggest beekeepers' primary concerns relate to biotic and abiotic drivers of colony loss and the socio-political aspects of honey bee management. Also, beekeepers use, perceive as useful, and trust different types of information in consistent ways. For example, networks developed in beekeeping club interactions with beekeeping scientists are often used, viewed as useful, and highly trusted. Thus, information distribution through such channels may increase the likelihood of impactful and resonant communication exchanges among researchers, educators, and beekeepers, perhaps leading to the more widespread use of Best Management Practices that address risk factors to mitigate future colony loss. • Beekeepers are concerned about Varroa, general disease, and pesticide exposure. • Personal experience, mentors, and clubs are perceived as useful information sources. • Scientists, experience, and mentors are perceived as trustworthy information sources. • Clubs and mentor programs can be resonant sites for information sharing and practice. [ABSTRACT FROM AUTHOR]

This paper investigates pervasiveness effect of information technology on the technological progress of logistics in China. Using panel data of 30-regions in China during 2008 to 2020, we propose both static and dynamic panel data models, and find both resource level and spreading rate of information technology have negative influences on technological progress of logistics. The resource level of information technology has a short-term effect on technological progress of logistics, whereas the spreading rate of information technology has a long-term effect. Moreover, the result reveals that the discordant problem of information construction of logistics between resource level and spreading rate cause the negative influence of information technology. [ABSTRACT FROM AUTHOR]

Incumbents are central regime actors who simultaneously adapt to transitions and actively shape them. This study has two objectives: advance the conceptualization around these actors' behaviors in contemporary transition processes and demonstrate a novel research approach to study this behavior in practice. The focus is on incumbents' responses to transition-related external pressures. These responses are studied by reviewing related work from organization and management literature, as well as conducting an empirical case study in the aviation sector. The theoretical and empirical insights are combined through flexible pattern matching into a conceptual framework, which simultaneously accommodates a holistic view of different types of responses incumbents use to navigate the complex operating environment, while also supporting particularity that goes beyond strategy talk. For the empirical investigation, this research utilizes incumbent airlines' transition risk work as a source of information on what external pressures these actors themselves perceive and the responses they operationalize. The empirical case study illustrates how by distinguishing between standard, inconsistent, and unconventional response areas that incumbents take part in, even a snapshot analysis of a contemporary transition phenomenon can reveal what is happening in socio-technical regimes. The analysis also pinpoints how responses have two important characteristics that signify the importance of acknowledging the variety of responses that incumbents have at their disposal: multi-functionality and parallelity. Together, the insights of this paper can support the study and understanding of the complex, heterogeneous, and nuanced behavior of incumbent organizations, and encourage more research efforts on transitions in the making. [ABSTRACT FROM AUTHOR]

Deep learning-based medical image segmentation models often suffer from performance degradation across domains due to domain discrepancies arising from data collected by various healthcare centers. Recent advancements, particularly the Segment Anything Model (SAM), have shown promising generalization abilities in this field, inspiring the development of several SAM-based approaches to address domain discrepancy issue. Nevertheless, these methods seldom exploit the full potential of the rich source domain knowledge to improve segmentation accuracy in unseen target domains. In this paper, we propose a source domain prior-assisted module for generalizable medical image segmentation based on SAM. Specifically, we store diverse features of the source domain data in a memory bank. When applying the model to the target domain, the features of the target domain first match with the features in the memory bank to obtain invaluable prior information. This strategy enables the model to utilize the prior information from the source domain to adapt to the target domain. We validate the proposed method on two widely used medical image segmentation tasks across multiple domains and achieve state-of-the-art performance. • Improved Segment Anything Model (SAM) generalization for single domain generalization. • Addressing medical image domain generalization challenges. • Source domain prior-assisted medical image segmentation. • Memory bank mechanism used for medical image segmentation. [ABSTRACT FROM AUTHOR]

In this paper, a W-shaped network combined with dual transformers and edge protection is proposed for multi-focus image fusion. Different from the traditional Convolutional Neural Network (CNN) fusion method, a heterogeneous encoder network framework is designed for feature extraction, and a decoder is used for feature reconstruction. The purpose of this design is to preserve the local details and edge information of the source image to the maximum extent possible. Specifically, the first encoder uses adaptive average pooling to downsample the source image and extract important features from it. The source image pair for edge detection using the Gaussian Modified Laplace Operator (GMLO) is used as input for the second encoder, and adaptive maximum pooling is employed for downsampling. In addition, the encoder part of the network combines CNN and Transformer to extract both local and global features. By reconstructing the extracted feature information, the final fusion image is obtained. To evaluate the performance of this method, we compared 16 recent multi-focus image fusion methods and conducted qualitative and quantitative analyses. Experimental results on public datasets such as Lytro, MFFW, MFI-WHU, and the real scene dataset HBU-CVMDSP demonstrate that our method can accurately identify the focused and defocused regions of source images. It also preserves the edge details of the source images while extracting the focused regions. • By using GMLO for edge extraction, this algorithm can accurately depict the boundary between the focused area and the defocused area. • We utilize CNN and Transformers for local and global feature extraction, enabling the capture of both local and global information from the source image. • Our network is trained using a mixed loss function, and its training results demonstrate excellent image fusion performance on three common datasets: Lytro, MFFW, MFI-WHU, and the real scene dataset HBU-CVMDSP. [ABSTRACT FROM AUTHOR]

Response forecasting is used for offshore decision-making by continuously evaluating wave conditions using the forecasted numerical wave spectrum. While forecasted wave spectra come directly from mathematical models, forecasted wave parameters undergo meteorologist inspection, are corrected based on measured wave data, and may be available at higher spatial resolution. This paper compares decision-making using forecasted numerical wave spectra against conventional lookup of forecasted wave parameters by simulating responses in an offshore construction vessel and applying two different response criteria at two locations in the North Sea and the Norwegian Sea. Five years of forecast and hindcast data, including operational forecasted wave parameters and spectra, are used. Results show that response forecasting reduces uncertainty and aids decision-making under some circumstances but may not necessarily result in both reduced risk and increased operability. However, response forecasting significantly increases operability for a particularly sensitive case at the end of the installation season without additional risk. Still, none of the considered methods guarantee against severe misclassifications that could result in operational failure. Decision-makers should rely on multiple sources of information to avoid such situations, and improved methods for accounting for forecast uncertainty should be investigated by the industry. • Response forecasting could improve offshore decision-making. • Both operability and the risk of exceeding threshold responses are included in the comparison. • Response forecasting provides valuable information, but the effect on risk and operability depends on the response criteria. • More suitable methods for accounting for forecast uncertainty are needed in the industry. [ABSTRACT FROM AUTHOR]

Accurate segmentation of polyps in colonoscopy images is important for the prevention and treatment of colorectal cancer. However, samples collected from different centers often possess diverse distributions, leading to poor generalization when a segmentation model trained in one center is directly employed in another. This paper proposes a multi-source boundary-aware prototype alignment domain adaptation network (MBDA-Net) to improve the performance of cross-center colonoscopy image segmentation. Specifically, we first design an image translation (IT) module based on the discrete cosine transform (DCT) to reduce the distribution gap between source and target domains by translating source domain styles into target domain styles. Then we propose a mutual perception prototype alignment (MPPA) module containing prototype inference, prototype interaction and adaptive mutual feature fusion. By learning relationships between prototypes and features among multiple domains, one can obtain mutual perception features that fuse prototype information from multiple domains. In order to fully exploit the supervised information of the source domains and optimize the prediction boundaries, we develop a boundary-aware learning (BAL) module to align the boundaries of the source domain predictions and ground truths. Moreover, to mitigate the foreground–background imbalance present in small-sized polyp images and reduce the biased predictions of the model, this study proposes a double normalization strategy (DNS) during the inference stage to improve the detection rate of small polyps. Experimental results on three challenging public datasets show that the proposed MBDA-Net outperforms existing methods on cross-center colonoscopy image segmentation, achieving state-of-the-art performance. • We propose a novel multi-source prototype alignment polyp segmentation network. • We design an IT module to harmonize the style between the source and target domains. • We develop an innovative MPPA module to achieve the indirect alignment of prototypes. • We propose AMFF to learn inter-domain relationships between prototypes and features. • We design a BAL module to enhance segmentation boundary accuracy. • We implement a double normalization strategy to mitigate model prediction bias. [ABSTRACT FROM AUTHOR]

The goal of infrared and visible image fusion is to synthesize an image that contains complementary information as much as possible in paired infrared and visible images. However, due to using the convolution operation with local receptive field to extract features, most of the existing deep learning-based fusion methods fail to effectively exploit global contextual information in source images and so obtain a limited fusion performance. To address this issue, this paper proposes an end-to-end skip-connecting group convolutional attention network, termed as SCGAFusion, to fuse infrared and visible images. In SCGAFusion, a group convolutional attention block (GAB) is purposefully developed to promote the extraction and utilization capability of the fusion network for informative features. GAB introduces group convolutional layer to obtain hierarchical features, and employs a residual non-local attention module to capture long-range dependencies between pixels. In this way, it can not only focus more on the important regions or details, but also leverage both local neighborhood and global contextual information. Additionally, a hierarchical feature compensation mechanism based on skip connections is devised to integratedly exploit both local and global features. Experimental results on several public datasets qualitatively and quantitatively demonstrate the advantage of SCGAFusion over other state-of-the-art fusion methods. [Display omitted] • Propose an end-to-end skip-connecting group convolutional attention network for infrared and visible image fusion. • Introduce group convolution for hierarchical feature extraction. • Devise residual non-local attention module to capture long-range dependencies between pixels. • Comprehensive experiments are conducted to demonstrate the superiority of our method over other fusion methods. [ABSTRACT FROM AUTHOR]

Syntax-guided paraphrase generation (SGPG) refers to generating a paraphrase sentence that satisfies the given syntactic structure without changing the source sentences' semantics. The commonly utilized syntactic structures are part-of-speech (POS) sequence, constituency parse tree, and masked template, with constituency parse tree achieving State-of-The-Art (SOTA) performance because of its rich syntactic information. As a result, further mining of syntactic information in parse trees has grown popular, yet fewer works pay attention to investigating semantic information in source sentences. A sentence is made up of two parts: syntax and semantics. Multiple studies have shown that improving the model's ability to learn semantic information is critical for paraphrase construction as well as syntax learning. In this paper, we propose Fusing Semantic Information for Syntax-guided Paraphrase Generation (FS-SPG). Specifically, we propose a transformer-based semantic encoder to obtain detailed semantics from source sentences. This encoder contains a Semantics-Aware Attention mechanism for mining semantic information. In addition, we apply contrastive learning to improve the accuracy of parse tree nodes' guidance to semantic sentences. Experiments on ParaNMT and QQP-Pos show that our model beats the SOTA model SI-SCP by 4.92% in syntactic metrics and 1.35% in semantic metrics. [ABSTRACT FROM AUTHOR]

Graph domain adaptation, which falls under the umbrella of graph transfer learning, involves transferring knowledge from a labeled source graph to improve prediction accuracy on an unlabeled target graph, where both graphs have identical label spaces but exhibit distribution discrepancies due to temporal data shifts or distinct data collection methods. This adaptation is complicated by the challenges of graph-specific domain discrepancies and cross-graph label scarcity. This paper proposes a semi-supervised G raph domain adaptation method via I nformation F iltering and I nterpolating (GIFI). Specifically, GIFI utilizes a parameterized graph reduction module and a variational information bottleneck to adequately filter out irrelevant information from the source and target graphs to eliminate distribution discrepancy. GIFI also introduces an interpolation-enhanced pseudo-labeling strategy for cross-graph semi-supervised learning, which can mitigate model over-fitting on domain-specific features and limited labeled nodes, thus improving the model's adaptation and discriminative capability. Experimental results on various graph domain adaptation benchmarks demonstrate GIFI's superior performance over state-of-the-art methods. Our code is available at https://github.com/joe817/GIFI. • Delve into graph domain adaptation for knowledge transfer between graphs with distribution differences. • Filter out irrelevant information explicitly to reduce distribution discrepancies. • Interpolate unlabeled and labeled samples for better model adaptation. • Experiments show GIFI's superior performance over other methods. [ABSTRACT FROM AUTHOR]

Under the background of the COVID-19 pandemic, our paper attempts to advance a dual-process model from online social support to individual resilience via diverse social capital (bonding and bridging) and political beliefs (political efficacy, political trust and city identification). Drawing on a survey of 446 residents in Shanghai, China, the findings indicate that neither informational nor emotional social support directly affects individual resilience, but indirectly influences it through different pathways. Specifically, bridging social capital mediates informational social support and individual resilience, but bonding social capital might not follow a similar path. Notably, political efficacy and political trust are involved in the above mediation between informational social support and individual resilience. Besides, the serial mediation effect of emotional social support on individual resilience via bonding social capital and then city identification or political trust is also significant. Our findings are expected to reveal the mechanism of resilience construction and prepare for the future public health crisis. • Neither informational nor emotional social support directly predicts individual resilience in China during the COVID-19. • Bridging social capital mediates the relationship between informational support and resilience. • Political trust explains the mechanism from social support to resilience via both bonding and bridging social capital. • Political efficacy acts in the sequential mediation of informational support, bridging social capital, and resilience. • City identification acts as the pathway connecting emotional support, bonding social capital, and resilience. [ABSTRACT FROM AUTHOR]

The fine texture, noise and structure information in a source medical image are treated equally, which may cause effective information to be masked or lost, making the fusion result unsatisfactory. To solve this problem, this paper proposes a novel image fusion method guided by internal structural information. First, an adaptive direction smoothing filter is proposed to decompose the input image into a fine-texture layer, a coarse-structure layer and an energy layer to weaken the texture interference in structure extraction. Second, guided by the pre-fused structure image obtained by the neighbor spatial frequency method, a structure similarity balance optimization (SSBO) method for fusing detail layers is proposed. SSBO achieves complete preservation of the fine texture of the image under the influence of interference details. Third, a multi-feature saliency map is calculated based on the direction and intensity information of the energy layer, and adaptive fusion of the energy layer is achieved by selectively emphasizing effective information. Last, the fusion results are obtained through image reconstruction. The experimental results show that compared with several state-of-the-art medical image fusion methods, the proposed fusion method shows superior performance in both subjective perception and quantitative evaluation, and can better retain the details and structure information of the source medical images. • An adaptive direction smoothing filter (ADSF) is designed to extract image features. • A three-scale decomposition framework based on ADSF is proposed. • The SSBO is designed to obtain a fused detail layer guided by structure information. • The MFSM rules is proposed to achieve energy layer fusion. [ABSTRACT FROM AUTHOR]

In the era of big data, People's lives are filled with all kinds of information. Scientific and technological information is utilized for scholars to understand the current technology trends, and to think about the source of information for future development prospects. More and more scholars are no longer satisfied with single-modal retrieval methods. However, to get more intelligent cross-media retrieval results we should give higher requirements to the search engine. And how to span the semantic gap between different modalities is a key issue that needs to be solved. In response to the above problems, this paper proposes a Multi-feature Fusion based Cross-Media Retrieval (MFCMR) method. Our method is capable of integrating multiple features to promote semantic understanding, and adopting adversarial learning to further improve the accuracy of public subspace representation. Then we use similarity in the same space to sort the retrieval results. We conduct a lot of experiments on real datasets, and the results show that our method obtains better cross-media retrieval performance than other methods. [ABSTRACT FROM AUTHOR]

Through an in-depth study of the literature, as well as some other sources of information, geographical distribution maps of the various taxa of the European freshwater planarian genus Crenobia (Platyhelminthes, Tricladida) are provided, which document their records and distributional ranges in a detail that was previously unavailable. The genus Crenobia ranges across Northern, Western, Southern, and Eastern Europe, including several islands in the Mediterranean region, but is absent from a major part of the Iberian Peninsula; it is distributed also in Turkey. The chromosome portrait of Crenobia is complex, as it exhibits chromosome numbers that are multiples of 7, such as 28, 35, 42, 56, and 63. Sexually reproducing populations are tetraploid or hexaploid, while asexually reproducing populations exhibit high chromosome numbers. All taxa of Crenobia are stenothermic, cold-loving species. Although historical biogeographic scenarios interpreting the taxonomic diversification of the genus as being the result of the vicissitudes of the Last Ice Age are still captivating, it may be the case that the genus already diversified 6-20 million years ago. The paper sketches in some detail the anatomy of the copulatory complex of the various taxa. Previously, it went unnoticed that in two of the taxa (Crenobia alpina and C. anophthalma) the two sperm ducts have different, asymmetrical trajectories. Further, it was largely undocumented that in several taxa the sperm ducts exhibit a pronounced recurvature, as well as a dorsal loop before entering the penis papilla. Neither did previous studies document that in C. montenigrina the mouth opening is not located at the most posterior end of the pharyngeal pocket, but has shifted anteriad for some distance. Polypharyngy is a stable, species-specific character, with C. montenigrina usually exhibiting 11-17 pharynges, while C. anophthalma has never more than three pharynges. Different views have been expressed on the taxonomic status of several geographic and anatomical forms of Crenobia. It is shown here that Crenobia comprises five, well-defined full species, viz., C. alpina , C. corsica , C. montenigrina , C. anophthalma , and C. bathycola. [ABSTRACT FROM AUTHOR]

Several operators have emerged in the framework of Choquet integral with the purpose of simultaneously generalizing weighted means and ordered weighted averaging (OWA) operators. However, on many occasions, not enough attention has been paid to whether the constructed operators behaved similarly to the weighted means and OWA operators that have been generalized. In this sense, it seems necessary that these new operators preserve the weights assigned to the information sources (which are established through the weighting vector associated with the weighted mean) and that they are able to rule out extreme values (which is an important characteristic of OWA operators). In this paper we analyze a family of operators recently introduced in the literature through the Crescent Method. First, we introduce a broad class of weighting vectors that allow us to guarantee that the games generated with the Crescent Method are capacities. Next we analyze the conjunctive/disjunctive character of the Choquet integrals associated with these capacities and we also give closed-form expressions of some tolerance and importance indices such as k -conjunctiveness/disjunctiveness indices, the veto and favor indices, and the Shapley values. Finally, we give two examples to show the usefulness of the results obtained. [ABSTRACT FROM AUTHOR]

In general, when users search in academic databases, the search result page directly shows the download count and citation count next to each paper. The download count and citation count are likely to produce information cascade effects. Based on the cognitive load theory, this study employs the semi-structured interview and grounded theory methods to explore herd behavior in academic information use in the university digital academic platform context, and develops a theoretical model of influencing mechanism of herd behavior in academic information use through three levels of coding. The results show that intrinsic cognitive load, extraneous cognitive load, and germane cognitive load all have direct impacts on herd behavior in academic information use. Germane cognitive load has moderating effects on the links from intrinsic cognitive load and extraneous cognitive load to herd behavior in academic information use. Meanwhile, information quality, source reliability, reputation, and convenience have direct impacts on information usefulness. Scientific research literacy and prior experience are elicited as germane cognitive load. It is recommended that researchers' scientific research literacy should be cultivated as far as possible, and researchers should be greatly encouraged to become familiar with scientific research tasks and become familiar with academic databases. [ABSTRACT FROM AUTHOR]

Remanufacturing is a valuable process with significant environmental benefits. However, remanufacturing companies face challenges in assembly due to small-scale production and reliance on manual labor. To address these problems and promote reuse and assembly efficiency, this paper presents a novel approach to fine-grained process retrieval and reuse, based on the process constituent elements model. The study highlights the importance of analyzing process information in four dimensions: input, auxiliary activities, value-added control activities, and output of process constituent elements. Additionally, a method for representing process multi-source information based on genetic structure is proposed. To measure similarity, the paper examines the base attributes of process constituent elements across different procedures and implements the grey relational analysis to integrate diverse information. The effectiveness is demonstrated through a set of remanufacturing assembly procedure cases. [ABSTRACT FROM AUTHOR]

Achieving sufficient COVID-19 vaccination coverage has been hindered in many areas by vaccine hesitancy. Many studies based on large survey samples have characterized vaccine refusal, but there are fewer in-depth qualitative studies that explore hesitant adoption: the middle-ground between vaccine acceptance and refusal, and how individuals may move across this continuum depending on their lived experience. For this paper, we use the narratives of 25 adults living in off-road, predominately Alaska Native communities to describe the complex decision-making processes undertaken by ' hesitant adopters', defined in our study as those who completed their initial COVID-19 series despite reporting hesitancy. Interviewees' stories help illustrate how hesitant adopters' decision-making processes involved making sense of information through interactions with trusted individuals, lived experiences, observations, emotions, and personal motivations. For the majority of these hesitant adopters' (n = 20, 80%) interpersonal interactions were key in helping to make the decision to get vaccinated. Over half of the interviewees (n = 14, 56%) described how conversations with individuals they trusted, including healthcare providers, family, friends, and interactions through their professional network made them feel safe. One third of the hesitant adopters (n = 7, 28%) attributed their decision to get vaccinated based on the influence of Alaska Native Elders including their knowledge, personal experiences, as well as being motivated by the desire to protect them. Independent research was also important to about a quarter of hesitant adopters (n = 6, 24%), and for these interviewees it was the process of gathering information on their own and learning from others, especially healthcare providers who could answer their questions and alleviate their concerns. This paper illustrates the temporality of vaccine decision-making: vaccine acceptance for those who are hesitant may be an ongoing process that is influenced by personal experience, relationships, and context. • Understanding hesitant adoption provides insights into increasing vaccine acceptance • In remote Alaska, Elders may influence the decisions community members make • Images and conversations reinforced narratives of safety and efficacy • Agency in information-seeking increased vaccine confidence [ABSTRACT FROM AUTHOR]

This paper studies the event-triggered distributed fusion estimation problem for a class of networked asynchronous multi-rate systems with nonlinear disturbances and bounded noises. Firstly, the asynchronous multi-rate systems are transformed into synchronous ones at the measurement sampling points (MSPs) by iterating the state equation. This paper proposes an event-triggered strategy to alleviate the communication burden to meet the finite communication resources during information transmission. Since the reduction of information will inevitably reduce the estimation performance, a compensation mechanism is adopted to restructure the untransmitted measurement. Then, a local estimator at the MSP is designed by the compensation information, and the local estimation at the unsampled measurement point is obtained by the multi-step prediction. Furthermore, by constructing the upper bound of the fusion estimation error, a robust recursive optimization approach, which can deal with nonlinear disturbances and bounded noises, is proposed to determine the distributed fusion criterion. Meanwhile, the stability conditions are derived such that the square errors of the designed estimators are asymptotically bounded. Finally, a target tracking example is used to demonstrate the effectiveness of the proposed method. • The proposed compensation strategy can maintain a satisfactory estimation performance. • The complex nonlinearities arising from the iterative state equation do not need to be eliminated. • The statistical information on the complex iterative noises does not need to be calculated. • The stability conditions are derived by the proposed robust recursive optimization approach. [ABSTRACT FROM AUTHOR]

Within agri-food scholarship, longstanding debates have focused on the empirical applicability and conceptual utility of differing agricultural paradigms. These have often dovetailed with considerations around the future of agriculture in countries such as the UK given a shift in emphasis away from the central tenets of a high-input, yield-oriented productivism. Alternative diagnoses, particularly in notions of a post-productivist paradigm, have proven influential in capturing broad changes in the restructuring of agricultural activity. Whilst debates around the characterisation of these paradigms continue, they highlight distinct contestations in broader (and shifting) questions around what agriculture should be 'for', e.g. whether environmental 'goods' should be valued more highly than material production. Situating our paper within these ongoing debates, we draw on farmer perspectives in examining the adoption of regenerative agricultural practices in two farmer clusters in the East and South-West of England respectively. As a diverse array of practices which appear favourably positioned in a post-Brexit policy environment, we explore the ways in which regenerative agriculture can be understood as seeking to reconcile productivist and post-productivist tendencies. Drawing attention to the tensions, contradictions and uncertainties at play in these farmer perspectives, we highlight: the environmental and emotional appeal of regenerative agriculture compared to its adoption for more pragmatic (economic) reasons; the parallels and differences between regenerative agriculture and organic production; and the multiple sources of trusted information drawn upon by farmers in an uncertain policy environment. We conclude the paper by reflecting on the need to better contextualise and conceptualise contemporary interest in regenerative agriculture within longer theoretical lineages, in which the voice of farmers themselves must be central. • The paper presents the results of in-depth interviews with members of two farmer clusters regarding the adoption of regenerative farming practices in the UK • The results are situated within theoretical debates about the transition from productivism to post-productivism • Farmers discuss the emotional appeal of regenerative farming and other (environmental and economic) motivations for adopting regenerative practices • Comparisons with organic farming are discussed including the potential benefits of certification and the risks of corporate capture • Uncertainties in the policy environment, the volatile costs of agricultural inputs and the geopolitical situation in Ukraine were all mentioned as factors affecting the uptake of regenerative farming in the UK, prompting a need for reliable information from trusted sources. [ABSTRACT FROM AUTHOR]

Infrared and visible image fusion aims to synthesize a single fused image containing salient targets and abundant texture details even under extreme illumination conditions. However, existing image fusion algorithms fail to take the illumination factor into account in the modeling process. In this paper, we propose a progressive image fusion network based on illumination-aware, termed as PIAFusion, which adaptively maintains the intensity distribution of salient targets and preserves texture information in the background. Specifically, we design an illumination-aware sub-network to estimate the illumination distribution and calculate the illumination probability. Moreover, we utilize the illumination probability to construct an illumination-aware loss to guide the training of the fusion network. The cross-modality differential aware fusion module and halfway fusion strategy completely integrate common and complementary information under the constraint of illumination-aware loss. In addition, a new benchmark dataset for infrared and visible image fusion, i.e. , Multi-Spectral Road Scenarios (available at https://github.com/Linfeng-Tang/MSRS), is released to support network training and comprehensive evaluation. Extensive experiments demonstrate the superiority of our method over state-of-the-art alternatives in terms of target maintenance and texture preservation. Particularly, our progressive fusion framework could round-the-clock integrate meaningful information from source images according to illumination conditions. Furthermore, the application to semantic segmentation demonstrates the potential of our PIAFusion for high-level vision tasks. Our codes will be available at https://github.com/Linfeng-Tang/PIAFusion. • We propose a progressive IR/VIS fusion method based on illumination aware. • It can round-the-clock fuse meaningful information according to illumination. • It can integrate complementary and common information at various stages. • We release a new benchmark dataset for IR/VIS fusion, termed MSRS. • Our method has the potential for facilitating high-level vision tasks. [ABSTRACT FROM AUTHOR]

In view of the current demand of heterogeneous, cross domain, many service platforms, wide distribution and low-cost resource sharing platform construction of agricultural public welfare information service resources, this paper puts forward a set of standardized and orderly data acquisition and interpretation sharing methods. Methods combined with the characteristics of multi domain resource sharing, metadata semantic modeling based on cognition is proposed to establish the mapping between metadata cognition and metadata semantics to resource description model, build a basic framework of resource sharing, extract the minimum core metadata required for resource sharing, define the basic rules for the existence of resource sharing data, and give the resource sharing description model. At the same time, in order to clearly explain the semantic annotation information in the model, the construction method of shared resource semantics is introduced. In order to reduce the difficulty of resource description, sharing and publishing on heterogeneous platforms, a shared resource description tool based on "resource sharing semantic description model" is introduced. This method combines the advantages of existing methods. At the same time, through the production of description tools and the introduction of semantics, the development difficulty and development volume of each service platform are reduced, the development cost is saved, the data sharing efficiency is improved, and the requirements of low-cost, cross domain, light dependence, resource readability and machine solvability for the construction of agricultural public welfare information service resource sharing platform are better solved. [ABSTRACT FROM AUTHOR]

Image fusion has become a hot issue in the field of information processing. In this paper, a novel image fusion method based on guided filter random walk and improved spiking cortical model (ISCM) in non-subsampled shearlet transform (NSST) is presented. The core process is composed of three steps. Firstly, the source images to be fused are decomposed into the low-frequency parts and high-frequency parts via NSST. Then, two models including guided filter and random walk are combined to complete the fusion of low-frequency sub-images. As for the fusion of the high-frequency parts, the traditional spiking cortical model is improved to be ISCM to capture and fuse the details information of the source images. Finally, the fused image can be obtained by inverse NSST. In order to verify the effectiveness of the proposed method, lots of datasets with different categories are selected as the source images to conduct the simulation experiments. Experimental results demonstrate that the proposed method own obvious superiorities over the representative ones in terms of both subjective visual performance and objective evaluation data. [ABSTRACT FROM AUTHOR]