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Optical skyrmions are an emerging class of structured light with sophisticated particle-like topologies with great potential for revolutionizing modern informatics. However, the current generation of optical skyrmions involves complex or bulky systems, hindering the development of practical applications. Here, exploiting the emergent "lab-on-fiber" technology, we demonstrate the design of a metafiber-integrated photonic skyrmion generator. We not only successfully generate high-quality optical skyrmions from metafibers, but also verify their remarkable properties, such as topology switchability and topology stability with subwavelength polarization features beyond the diffraction limits. Our flexible fiber-integrated optical skyrmions platform paves the avenue for future applications of topologically-enhanced remote super-resolution microscopy and robust information transfer. Current optical skyrmion generators involve complex bulky systems, hindering further practical applications. We propose an integrated metafiber for high-quality photonic skyrmions, with subwavelength polarization features and topology tunability. [ABSTRACT FROM AUTHOR]

The outbreak of the COVID-19 pandemic caused dashboards to become widely used by the public and decision-makers. Nevertheless, dashboard interfaces have been related to business intelligence since their origins, and the search for improvements in their design is not new. This article's objective is to conduct a user evaluation of COVID-19 dashboards that contain geospatial information. This is done through a formative study to identify problematic aspects of user/dashboard interaction. This is enhanced by comparing two self-developed dashboards that, according to previous tests, have functionalities with different appearances. User evaluation is performed through mixed research that combines objective (eye-tracking) and subjective (a questionnaire and an interview) methods. The results generate recommendations for better-designed dashboard interfaces that can transfer information appropriately. The vital elements needed to achieve this are interactivity, the option to choose the metrics, and the distribution of the elements in the layout, all playing a role in a more user-friendly interaction between the user and the dashboard. [ABSTRACT FROM AUTHOR]

A striking feature of human cognition is an exceptional ability to rapidly adapt to novel situations. It is proposed this relies on abstracting and generalizing past experiences. While previous research has explored how humans detect and generalize single sequential processes, we have a limited understanding of how humans adapt to more naturalistic scenarios, for example, complex, multisubprocess environments. Here, we propose a candidate computational mechanism that posits compositional generalization of knowledge about subprocess dynamics. In two samples (N = 238 and N = 137), we combined a novel sequence learning task and computational modeling to ask whether humans extract and generalize subprocesses compositionally to solve new problems. In prior learning, participants experienced sequences of compound images formed from two graphs' product spaces (group 1: G1 and G2, group 2: G3 and G4). In transfer learning, both groups encountered compound images from the product of G1 and G3, composed entirely of new images. We show that subprocess knowledge transferred between task phases, such that in a new task environment each group had enhanced accuracy in predicting subprocess dynamics they had experienced during prior learning. Computational models utilizing predictive representations, based solely on the temporal contiguity of experienced task states, without an ability to transfer knowledge, failed to explain these data. Instead, behavior was consistent with a predictive representation model that maps task states between prior and transfer learning. These results help advance a mechanistic understanding of how humans discover and abstract subprocesses composing their experiences and compositionally reuse prior knowledge as a scaffolding for new experiences. [ABSTRACT FROM AUTHOR]

Based on the study of the professional careers of 439 parliamentary assistants of the 7th parliamentary term (2009–2014), we identify their different career patterns, especially after leaving the European parliament. We show which factors (social, biographical) influence mobility within the field of Eurocracy. We assess the importance of public/private circulation. Beyond the notorious ‘revolving doors’ and the question of the transfer of information from EU institutions to private interests, we show that parliamentary assistants form a group of specialised intermediaries of EU affairs who, after having acquired specific knowledge and know-how at the heart of European policymaking, are able to convert these assets in different sectors (public bureaucracies, private companies, or NGOs), at the EU level or in the member states. Such mobility illustrates the blurring of the public/private divide, and the hybridisation of EU governance. We thus shed new light on the structuring of the field of Eurocracy and the circulation of professionals within it. [ABSTRACT FROM AUTHOR]

Timely detection of hard exudates in fundus images can effectively avoid the severity of the disease, but the labelling of small and numerous lesion areas requires a lot of labour costs. This paper proposes a semi‐supervised segmentation network, which integrates pseudo‐labels and multi‐level features consistency correction. It achieves accurate segmentation of hard exudates by making full use of a small amount of labelled data and a large amount of unlabelled data. The network effectively extracts features from the unlabelled data through knowledge transfer of the teacher‐student model, and incorporates a Transformer network for auxiliary training to promote the quality of transfer. In addition, three unsupervised losses are introduced to improve the performance: the perturbation loss improves the robustness of the model to noise by adding different noises to the same input; the multi‐level feature consistency correction loss ensures the consistency of features of the student model at different scales; and the pseudo‐labelling cross‐supervision loss utilizes the generated pseudo‐labels for supervision between CNN and Transformer. By comparing the segmentation results with different proportion of the labelled data, it has better segmentation performance compared to other methods. The proposed methods can totally increase dice by 16.56% and mean intersection over union (MIoU) by 25.11%. [ABSTRACT FROM AUTHOR]

Social network–based data collection methods that rely on third‐party reporting have emerged as a promising approach for measuring abortion in restrictive settings. In order for these methods to accurately measure abortion incidence, they must also assess the visibility of abortions within social networks. Failure to do so may result in estimates affected by transmission bias, caused by imperfect knowledge of all abortions within one's social network. In this paper, we present exploratory research that uses respondent‐driven sampling (RDS) and the game of contacts method to measure abortion visibility in four sites in Uganda and Ethiopia. We assess the existence of potential biases in the game of contacts estimate of abortion visibility in each site by conducting several internal and external validity tests. While these tests provided some promising results, other factors such as the representativeness of the RDS samples, direct versus indirect abortion knowledge transfers, and the generalizability of the study sites may have introduced biases into the final estimates of abortion visibility in this study. We conclude by making recommendations on how applications of this methodology could be improved to better estimate abortion‐related transmission bias. [ABSTRACT FROM AUTHOR]

The creation of successful innovation ecosystems, like Silicon Valley, is challenging due to significant cultural, infrastructural, and resource differences between regions. In this context, the Innovation Reef (I-Reef) model emerges as a promising alternative, offering an approach for regions with limited resources to develop successful innovation ecosystems based on cooperation and mutual benefit among participants. This model has great potential to promote regional development, especially due to its focus on retaining and sharing the value generated. However, the role of I-Reef in sustainable regional development still needs to be further explored. Thus, the objective of this study is to deepen the theoretical understanding of the I-Reef model by analyzing its contribution to sustainable development. To achieve this, a comparison was made between I-Reef and established models such as business, innovation, knowledge, and entrepreneurial ecosystems. A systematic literature review conducted on Scopus found 704 articles published in the last three decades. The purpose was to identify the similarities and differences between the models of innovation business ecosystem models. The results show that there is alignment between I-Reef and the different ecosystems on several points. A central aspect of I-Reef is that it relies on a strong network of mutually beneficial relationships, much more oriented to sustainable development than the other models, which is a key factor in generating competitive advantage and development for the region. This characteristic is either not addressed or not placed at the core of the ecosystems discussed in the literature. For future research, empirical studies and validation of the I-Reef model with experts are suggested, as this theoretical study lays the foundation for more in-depth analyses. [ABSTRACT FROM AUTHOR]

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Surrogate-assisted evolutionary algorithms have been widely employed to solve data-driven optimization problems. However, for offline data-driven optimization, it is very challenging to perform evolutionary search efficiently as well as accurately since no new data is available during the optimization process. To mitigate this issue, a multifidelity surrogates-assisted multi-objective particle swarm optimization (MFSa-PSO) algorithm is proposed in this paper. First, two low-fidelity models with convergence and diversity characteristics separately and a high-fidelity model are constructed to assemble multifidelity surrogate models. Second, by adopting the knowledge transfer strategy, the multifidelity surrogates-assisted two-archive multi-objective particle swarm optimization is conducted to search optimal solutions more exactly and effectively. Third, the output solution set is achieved by associating the solutions of two archives with reference vectors. Finally, the proposed MFSa-PSO is compared with some popular surrogate-assisted evolutionary algorithms on benchmark problems to verify its effectiveness and outperformance. Additionally, a real-world application of the municipal solid waste incineration process is carried out to verify the engineering applicability of MFSa-PSO. [ABSTRACT FROM AUTHOR]

Background: Electroencephalogram (EEG) is widely used in emotion recognition due to its precision and reliability. However, the nonstationarity of EEG signals causes significant differences between individuals or sessions, making it challenging to construct a robust model. Recently, domain adaptation (DA) methods have shown excellent results in cross-subject EEG emotion recognition by aligning marginal distributions. Nevertheless, these methods do not consider emotion category labels, which can lead to label confusion during alignment. Our study aims to alleviate this problem by promoting conditional distribution alignment during domain adaptation to improve cross-subject and cross-session emotion recognition performance. Method: This study introduces a multi-source domain adaptation common-branch network for EEG emotion recognition and proposes a novel sample hybridization method. This method enables the introduction of target domain data information by directionally hybridizing source and target domain samples without increasing the overall sample size, thereby enhancing the effectiveness of conditional distribution alignment in domain adaptation. Cross-subject and cross-session experiments were conducted on two publicly available datasets, SEED and SEED-IV, to validate the proposed model. Result: In cross-subject emotion recognition, our method achieved an average accuracy of 90.27% on the SEED dataset, with eight out of 15 subjects attaining a recognition accuracy higher than 90%. For the SEED-IV dataset, the recognition accuracy also reached 73.21%. Additionally, in the cross-session experiment, we sequentially used two out of the three session data as source domains and the remaining session as the target domain for emotion recognition. The proposed model yielded average accuracies of 94.16 and 75.05% on the two datasets, respectively. Conclusion: Our proposed method aims to alleviate the difficulties of emotion recognition from the limited generalization ability of EEG features across subjects and sessions. Though adapting the multi-source domain adaptation and the sample hybridization method, the proposed method can effectively transfer the emotion-related knowledge of known subjects and achieve accurate emotion recognition on unlabeled subjects. [ABSTRACT FROM AUTHOR]

Data visualization is a complex matter and so is teaching it in the context of engineering and data science. Even more challenging is teaching in a midst of once in a century pandemic. In this paper, we show the results of a workshop conducted with second-year students on a university degree in data science, which took place in the context of pandemic restrictions in a hybrid learning environment. About half of the students were in the classroom and the other half at home. They learned to use STMaps, a tool developed in the scope of the SILKNOW project. We transferred research outcomes to the classroom; similarly, the results of evaluating the tool were transferred to the project. But more importantly, we found insights that the learning process of students that were online was not the same quality as of those that were physically in the classroom. While both groups performed well in terms of correctly fulfilling the tasks proposed during the workshop, the students at home were less motivated and perceived the tasks more cumbersome to achieve. From a research perspective, this activity was perceived as very useful, and similar activities are being planned for each academic year. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusion The American Speech–Language–Hearing Association (ASHA) promotes the use of evidence‐based practice (EBP) in the provision of clinical services by speech–language pathologists (SLPs). Therefore, training in the EBP process is vital in the education of future SLPs. Case‐based learning (CBL) has been found effective in facilitating transfer of knowledge and skills in various fields; however, its utilisation to teach EBP to SLP students is understudied.This phenomenological investigation explores the use of CBL, an active learning strategy that gives students the opportunity to practice application of knowledge to realistic scenarios, to teach EBP to graduate SLP students. Graduate SLP students worked in small groups to complete an article review activity centred around an assigned hypothetical clinical case. Students submitted article review papers and written reflections on their learning process. The written reflections and review papers were analysed using qualitative content analysis to understand students' approach to clinical cases and their perspectives on the CBL approach.Analysis of the papers and reflections indicated that students tended to rely heavily on scientific evidence for clinical decision making, often to the exclusion of clinical expertise or client perspectives. Students reported that the project was applicable to their future clinical practice. Further, they reported learning from multiple sources, including the instructor, class discussion and their peers.Results indicate that graduate SLP students found CBL activities beneficial for learning the principles of EBP. Students specifically indicated an appreciation for the realistic nature of the activities. Future applications are examined. [ABSTRACT FROM AUTHOR]

In 1492, a Lübeckian printer Stephan Arndes released an updated edition of a popular late medieval hagiographic collection Der Heiligen Leben in Middle Low German. From a few dozens of newly added legends, nine of them are about saints who were venerated only in Northern Germany and Scandinavia – or both. By adding these ‘Northern’ hagiographies, Arndes attempted to domesticate an otherwise rather universal collection – and not only among a Northern German but also a Scandinavian audience. By analysing these hagiographies, their possible Latin sources, and how Stephan Arndes got hold of these texts, the article shows that the edition in question is a testimony to transregional hagiographic knowledge transfer between Northern Germany and Scandinavia. Such an exchange was realized through specific editorial decisions, printing networks, and business connections, enabled by the wide usage of Middle Low German in the Nordic countries not only among foreign merchants but also locals, especially in legal and religious milieux. This case study of Stephan Arndes’ hagiographic print reveals that late medieval transregional devotional networks were bi-directional, unlike what is suggested by the centre-periphery paradigm, often applied when discussing medieval German influences on Scandinavian religious culture and institutions. [ABSTRACT FROM AUTHOR]

The tree of blobs of a species network shows only the tree-like aspects of relationships of taxa on a network, omitting information on network substructures where hybridization or other types of lateral transfer of genetic information occur. By isolating such regions of a network, inference of the tree of blobs can serve as a starting point for a more detailed investigation, or indicate the limit of what may be inferrable without additional assumptions. Building on our theoretical work on the identifiability of the tree of blobs from gene quartet distributions under the Network Multispecies Coalescent model, we develop an algorithm, TINNiK, for statistically consistent tree of blobs inference. We provide examples of its application to both simulated and empirical datasets, utilizing an implementation in the MSCquartets 2.0 R package. [ABSTRACT FROM AUTHOR]

This paper takes the experiment of “preparation of manganese carbonate” as an example, and applies the teaching mode of problem-based leaning (“PBL”) in the inorganic chemistry laboratory course. Through the design of a series of problems, students are guided to engage in independent learning, cooperative communication, problem analysis and experimental verification, thereby achieving an organic integration of knowledge transfer and value guidance. The application of “PBL” teaching mode in inorganic chemistry experimental education not only provides valuable exploration and attempt for the reform and innovation of the teaching, but also provides an excellent platform for the integration of course ideology and politics. [ABSTRACT FROM AUTHOR]

The increasing single-cell RNA sequencing (scRNA-seq) data enable researchers to explore cellular heterogeneity and gene expression profiles, offering a high-resolution view of the transcriptome at the single-cell level. However, the dropout events, which are often present in scRNA-seq data, remaining challenges for downstream analysis. Although a number of studies have been developed to recover single-cell expression profiles, their performance may be hindered due to not fully exploring the inherent relations between genes. To address the issue, we propose scDTL, a deep transfer learning based approach for scRNA-seq data imputation by harnessing the bulk RNA-sequencing information. We firstly employ a denoising autoencoder trained on bulk RNA-seq data as the initial imputation model, and then leverage a domain adaptation framework that transfers the knowledge learned by the bulk imputation model to scRNA-seq learning task. In addition, scDTL employs a parallel operation with a 1D U-Net denoising model to provide gene representations of varying granularity, capturing both coarse and fine features of the scRNA-seq data. Finally, we utilize a cross-channel attention mechanism to fuse the features learned from the transferred bulk imputation model and U-Net model. In the evaluation, we conduct extensive experiments to demonstrate that scDTL could outperform other state-of-the-art methods in the quantitative comparison and downstream analyses. [ABSTRACT FROM AUTHOR]

The integration of data from multiple modalities generated by single-cell omics technologies is crucial for accurately identifying cell states. One challenge in comprehending multi-omics data resides in mosaic integration, in which different data modalities are profiled in different subsets of cells, as it requires simultaneous batch effect removal and modality alignment. Here, we develop Multi-omics Mosaic Auto-scaling Attention Variational Inference (mmAAVI), a scalable deep generative model for single-cell mosaic integration. Leveraging auto-scaling self-attention mechanisms, mmAAVI can map arbitrary combinations of omics to the common embedding space. If existing well-annotated cell states, the model can perform semisupervised learning to utilize existing these annotations. We validated the performance of mmAAVI and five other commonly used methods on four benchmark datasets, which vary in cell numbers, omics types, and missing patterns. mmAAVI consistently demonstrated its superiority. We also validated mmAAVI's ability for cell state knowledge transfer, achieving balanced accuracies of 0.82 and 0.97 with less 1% labeled cells between batches with completely different omics. The full package is available at https://github.com/luyiyun/mmAAVI. [ABSTRACT FROM AUTHOR]

Sensory data are the basis for the intelligent health state awareness of planetary gearboxes, which are the critical components of electromechanical systems. Despite the advantages of intelligent diagnostic techniques for detecting intricate fault patterns and improving diagnostic speed, challenges still persist, which include the limited availability of fault data, the lack of labeling information and the discrepancies in features across different signals. Targeting this issue, a subdomain distribution adversarial adaptation diagnosis method (SDAA) is proposed for faults diagnosis of planetary gearboxes across different conditions. Firstly, nonstationary vibration signals are converted into a two-dimensional time–frequency representation to extract intrinsic information and avoid frequency overlapping. Secondly, an adversarial training mechanism is designed to evaluate subclass feature distribution differences between the source and target domain. A conditional distribution adaptation is employed to account for correlations among data from different subclasses. Finally, the proposed method is validated through experiments on planetary gearboxes, and the results demonstrate that SDAA can effectively diagnose faults under crossing conditions with an accuracy of 96.7% in diagnosing gear faults and 95.2% in diagnosing planet bearing faults. It outperforms other methods in both accuracy and model robustness. This confirms that this approach can refine domain-invariant information for transfer learning with less information loss from the sub-class level of fault data instead of the overall class level. [ABSTRACT FROM AUTHOR]

This study used a quasirandomized within-classroom design to investigate whether prior knowledge about physics gained in elementary school prepares students for future learning in related content areas in secondary school. A total of 433 children (intervention group) received four basic curriculum units on physics from their elementary school teachers. The units dealt with floating and sinking, air and atmospheric pressure, the stability of bridges, and sound and the spreading of sound. These children entered 60 newly composed classes in early secondary school that completed an advanced curriculum unit on hydrostatic pressure and buoyancy force with their secondary school teachers. A total of 942 students (control group) in these classes had not received the four basic physics curriculum units. On a conceptual knowledge test about hydrostatic pressure and buoyancy force, the intervention group outperformed the control group in the pretest (d = 0.28) and in the posttest (d = 0.25). Students in the intervention group showed similar learning gains as those in the control group, but when controlling for pretest performance, they achieved higher learning outcomes. Regression analyses within the intervention group revealed that this advantage resulted from the content-specific transfer of conceptual knowledge from topically related basic curriculum units. The basic physics instruction also prepared male and female students equally for future learning. Educational Impact and Implications Statement: Our study shows that students who have received early physics instruction in elementary school benefit slightly more from later, more advanced physics instruction in secondary school. This finding provides first evidence that the idea of a spiral curriculum, in which learners first build basic knowledge that is later on expanded in more demanding instruction, can work, although it is yet to be further examined how this process can be optimized in school instruction. [ABSTRACT FROM AUTHOR]

Social foraging is commonly used across taxa to increase animal foraging success in uncertain environments and is believed to be a driver of social group formation. In temperate zones, females of many bat species form seasonal colonies, whereas males are usually solitary. Males of only a few bat species form temporary colonies during sperm production, likely to benefit from social foraging, social thermoregulation, or both. We radiotracked a group of reproductive male particoloured bats, Vespertilio murinus , to test the hypothesis that they use social foraging. Foraging bats overlapped in time and space significantly more than expected by chance, suggesting that they used social information to increase detection of insect swarms. Dyads also sometimes switched foraging patches together, suggesting part-time use of the more coordinated group facilitation social foraging strategy. Our results support the hypothesis that male particoloured bats use local enhancement mixed with group facilitation during sperm production and that improved foraging success through information transfer is a likely driver of seasonal sociality in these and other male bats. • Male particoloured bats foraged socially. • Bats often changed their direction of flight at the same time. • Males did not forage together during all nocturnal activity. • Bats probably used local enhancement mixed with group facilitation. [ABSTRACT FROM AUTHOR]

Immersive virtual reality (VR) is anticipated to peak in development this decade bringing new opportunities for 3D multimodal designing across all levels of education. The need for students to gain capabilities with multimodal texts—texts that combine two or more modes, such as spoken, written, and visual—is emphasised at all levels of education from P-12 in the Australian Curriculum. Likewise, the use of technology-supported pedagogies is increasing worldwide, rendering multimodal texts ubiquitous across all knowledge domains. This original, qualitative classroom research investigated students' 3D designing of multimodal texts using an immersive VR head-mounted display. Upper primary students (ages 10–12 years, n = 48) transferred their knowledge of ancient Rome through 2D drawing, writing, speaking, and 3D multimodal designing with VR. The application of multimodal analysis to video data, screen recordings, and think-aloud protocols, and the thematic coding of student and teacher interviews yielded four key findings: (i) VR gaming supported 3D multimodal designing through haptic and embodied experience, (ii) VR improved performance through creative redesigning, (iii) VR-supported knowledge application, consolidation, and transfer, and (iv) pedagogical strengths of VR were situated and transformed practice. This research is timely and significant given the increasing accessibility and affordability of VR and the need to connect research and pedagogical practice to support students' advanced knowledge and capabilities with multimodal learning across the curriculum. [ABSTRACT FROM AUTHOR]

Approximately 70%–75% of people worldwide have no formally registered land rights. Fit-For-Purpose Land Administration was introduced to address this problem and focuses on delineating visible cadastral boundaries from earth observation imagery. Recent studies have shown the potential of deep learning models to extract these visible cadastral boundaries automatically. However, studies are limited by the small size and geographical coverage of available datasets and by the lack of information about which cadastral boundaries are visible, i.e., associated with a physical object boundary. To overcome these problems, we present CadastreVision , a benchmark dataset containing cadastral reference data and corresponding multi-resolution earth observation imagery from The Netherlands, with a spatial resolution ranging from 0.1 m to 10 m. The ratio between visible and non-visible cadastral boundaries is essential to evaluate the potential automation level in cadastral boundary extraction from earth observation images and interpret results obtained by deep learning models. We investigate this ratio using a novel analysis pipeline that overlays cadastral reference data with visible topographic object boundaries. Our results show that approximately 72% of the total length of cadastral boundaries in The Netherlands are visible. CadastreVision will enable new developments in cadastral boundary delineation and future endeavours to investigate knowledge transfer to data-scarce areas. Our data and code is available at https://github.com/jeroengrift/cadastrevision. [Display omitted] [ABSTRACT FROM AUTHOR]

Usually, high-accuracy target detection algorithms have many model parameters, which require much storage space and a long testing time and are difficult to deploy on intelligent inspection robots with limited storage capacity and computational resource capability. Although lightweight target detection algorithms have lower model parameters, they cannot meet the demand for high-precision detection. To solve this problem, we design a lightweight target detection method SSD-KDGAN using knowledge distillation and generative adversarial networks. SSD-KDGAN transfers the feature maps generated by deeper and more complex teacher networks as shared knowledge to a student model with a small number of parameters without increasing testing costs, to improve the detection accuracy of the lightweight student model. First, we align the feature maps generated by the student model and the teacher model to avoid information loss caused by forced training between feature maps of different scales. Second, we use the knowledge of the teacher network to guide the learning of the student network through distillation loss. Finally, we make the feature maps output from the student network have similar spatial distributions as the feature maps output from the teacher network through adversarial loss. Extensive experiments on the PASCAL VOC and images in real environments validate the effectiveness of our framework. The experimental results indicate that transferring the knowledge of the teacher network to the student network can significantly improve the detection accuracy of the student network without increasing the testing cost. [ABSTRACT FROM AUTHOR]

Extensive researches have been conducted on transfer learning based fault diagnosis. However, negative information transfer may arise due to significant differences in the subdomain distribution across multiple source domains (MSDs). Most existing methods focus solely on the impact of subdomains from a single source domain (SSD) on the target domain (TD). Therefore, this paper proposed a novel multi-stage alignment multi-source subdomain adaptation (MAMSA) method. The global feature extractor is designed to extract domain-invariant features. Three domain-specific feature extractors capture high-level fault features from different domains with a customized adaptation strategy, which combines adversarial learning and distribution alignment based on multiple pseudo-label-guided local maximum mean discrepancy (MP-LMMD) to learn subdomain-invariant features. MP-LMMD utilizes pseudo-labels generated from all classifiers in the TD to guide the alignment of subdomains, suppressing negative transfer from the source domains (SDs). The experimental results indicate that the MAMSA method has excellent capabilities to suppress negative transfer, and the diagnostic performance can be greatly promoted with MAMSA under cross-working conditions. • A novel multi-stage alignment multi-source subdomain adaptation (MAMSA) method is proposed. • A two-stage feature extractor based on specially designed attention mechanism is proposed. • A multiple pseudo-label-guided local maximum mean discrepancy metric is proposed. • The proposed method can suppress the negative transfer and achieve good diagnostic results. [ABSTRACT FROM AUTHOR]

A strategy centered on dynamically tunable excited-state proton transfer (ESPT) processes is proposed for the design and synthesis of luminescent compounds. An emitter based on guanidine-substituted 1,8-naphthalimide (R-1) with ESPT characteristics has been meticulously engineered. Upon incorporation into poly (methyl methacrylate) (PMMA) matrices, the tunable ESPT process, transitioning between blue and yellow-green emission within the composite film, can be precisely controlled through irradiation in different pH environments. Moreover, the luminescence of the R-1/PMMA composite film exhibits variations in response to environmental changes, and demonstrates excellent fatigue resistance. Exploiting this characteristic, information such as "2020" can be encoded, and this encoded information automatically manifests in response to fluctuations in external pH. Specifically, employing a designated method is essential for accurately deciphering the information. The pH-dependent nature of this feature imparts a higher level of security to the material and offers new insights into information encryption. [ABSTRACT FROM AUTHOR]

In hydrology, a fundamental task involves enhancing the predictive power of a model in ungagged basins by transferring information on physical attributes and hydroclimate dynamics from gauged basins. Introducing an integrated nonlinear clustering framework, this study aims to develop a comprehensive framework that augments predictive performance in basins where direct measurements are sparse or absent. In this framework, uniform manifold approximation and projection (UMAP) is used as a nonlinear method to extract the essential features embedded in hydro‐climatological attributes and physical properties. Then, the Growing Neural Gas (GNG) clustering model is used to find the basins that potentially share similar hydro‐climatological behaviors. Besides UMAP‐GNG, the integration of Principal Component Analysis (PCA) as a linear method to reduce dimensionality with common clustering methods are also assessed to serve as benchmarks. The results reveal that the combination of clustering algorithms with the PCA method may lead to loss of information while the nonlinear method (UMAP) can extract more informative features. The efficacy of the proposed framework is assessed across the Contiguous United States (CONUS) by training a single Base Model using long short‐term memory (LSTM) for the centroids of all clusters and then, fine‐tuning the model on the centroids of each cluster separately to create a regional model. The results indicate that using the information extracted by the UMAP‐GNG method to guide a Base Model can significantly improve the accuracy in most of the clusters and enhance the median prediction accuracy within different clusters from 0.04 to 0.37 of KGE in ungauged basins. Key Points: The developed framework can stand as a fundamental guide for transferring the extracted knowledge from gauged basins to ungauged basinsThe attributes of western Contiguous United States (CONUS) direct the growing neurons to create more sparsely distributed patterns compared to the eastern partsThe proposed framework could significantly improve the performance of the LSTM model for predicting streamflow in ungauged basins [ABSTRACT FROM AUTHOR]

Factual knowledge and procedural knowledge are knowing 'That' and 'How,' respectively, whereas conditional knowledge is the metacognitive knowledge of 'When' and 'Why.' As prior work has found that students with conditional knowledge spontaneously transferred such knowledge across intelligent tutoring systems, this work assesses the impact of metacognitive interventions on the knowledge transfer of factual and procedural students. Specifically, we used a between-subject, pre-/posttest design with factual and procedural students, each randomly assigned to either the example, nudge, practice, or control condition. The interventions taught how and when to use a backward-chaining (BC) strategy on a logic tutor that supports a default forward-chaining strategy. Meanwhile, conditional students received no interventions. Six weeks later, we trained all students on a probability tutor that only supports BC without interventions. Our results suggest that nudges for factual students and practice for their procedural peers are the key factors for catching up with conditional students on both tutors and for facilitating knowledge transfer from the logic to probability tutor. We discuss two potential complementary theories for our findings: a choice-based theory (from interventions to knowledge) and a metacognitive load-based theory (from knowledge to interventions). The choice-based theory maps the amount of choice in the interventions to knowledge types, while the metacognitive load-based theory associates knowledge types with the metacognitive load each intervention offers. Implications for practice are discussed. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: This study aims to develop a framework that demonstrates the role of social capital in alleviating knowledge hiding behaviour in organisations while also considering the moderating roles of perceived organisational politics and the perceived value of knowledge in this process. Design/methodology/approach: The authors conducted a systematic literature review of research papers on the topic of knowledge hiding to develop a framework for mitigating knowledge hiding. Findings: This paper conceptualises social capital into three interrelated dimensions (e.g. structural, cognitive and relational). Based on the findings of the review, all the three social capital dimensions can potentially mitigate an individual's propensity towards knowledge hiding. Additionally, the paper integrates two potential moderators: perceived organisational politics and perceived value of knowledge, which could undermine the outcomes of social capital in mitigating knowledge hiding. Research limitations/implications: Although the proposed framework may provide preliminary insights to practitioners and scholars, one of its key limitations is that it is conceptual. Future empirical research is needed to validate the proposed framework. Originality/value: Existing research has focused on studying the antecedents and consequences of knowledge hiding. However, scant scholarly work explores how such behaviour can be mitigated. This paper addresses this gap and contributes to understanding how organisations can alleviate the prevalence of knowledge hiding by developing their social capital and by focusing on contextual factors. [ABSTRACT FROM AUTHOR]

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Purpose: Using an international sample of firms affiliated with a business group, this paper aims to investigate the audit quality of common auditors, i.e. auditors shared by multiple firms affiliated with the same business group. Design/methodology/approach: This paper utilizes a large sample of group-affiliated firms from 35 regions. Following the frameworks of DeFond and Zhang (2014) and Fung et al. (2017), this paper measures audit quality using the absolute value of abnormal accruals and modified audit opinions. Ordinary least squares (OLS) regression is used to address the research question. Findings: The findings demonstrate that audit quality improves for firms that share the same auditor with other group members. Importantly, cross-country analyses reveal that this relationship is stronger when auditors operate in countries with stricter local auditor inspection programs and an overall opaque information environment. Additionally, the impact of common auditors on audit quality is more pronounced for group affiliates that have a more opaque firm-level information environment and are monitored by heightened institutional ownership. Practical implications: While affiliates are more likely to select the same audit firms as common auditors, the evidence regarding the effects of common auditors is mixed. The results of this study provide further insights into auditor choices for group-affiliated firms and offer a potential avenue for better protecting shareholders' interests. Originality/value: The results of this study add to the ongoing debates regarding the costs and benefits of common auditor choice for group affiliates. To the best of the authors' knowledge, this study is the first to demonstrate that the effects of common auditors may vary in different external environments. [ABSTRACT FROM AUTHOR]

The monitoring and diagnostic system has been suggested as a non-destructive diagnostic method. The structure and operation of the suggested system can be described by the concept of digital shadow (DS). One of the main DS subsystems is a set of sensors properly placed on the monitored object and coupled with a discrete data processing model created in Matlab/Simulink. The discrete model, as another important DS subsystem of the monitored facility, transfers information about its technical condition to the operator based on data recorded by the sensor system. The digital monitoring model processes the recorded data in the form of the object's response to actions caused by its operating conditions. This work formalized a mathematical model determining the coupling of the digital model with the sensors placed on the monitored object. The formulated method using DS, due to its sensitivity, enables the detection of the damage in the object at an early stage. The tests allowed for detecting the regularities enabling the determination of the area of damage to the shaft and its size. [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptation (UDA) focuses on transferring knowledge from the labeled source domain to the unlabeled target domain, reducing the costs of manual data labeling. The main challenge in UDA is bridging the substantial feature distribution gap between the source and target domains. To address this, we propose Polarized Attention Network Domain Adaptation (PANDA), a novel approach that leverages Polarized Self-Attention (PSA) to capture the intricate relationships between the source and target domains, effectively mitigating domain discrepancies. PANDA integrates both channel and spatial information, allowing it to capture detailed features and overall structures simultaneously. Our proposed method significantly outperforms current state-of-the-art unsupervised domain adaptation (UDA) techniques for semantic segmentation tasks. Specifically, it achieves a notable improvement in mean intersection over union (mIoU), with a 0.2% increase for the GTA→Cityscapes benchmark and a substantial 1.4% gain for the SYNTHIA→Cityscapes benchmark. As a result, our method attains mIoU scores of 76.1% and 68.7%, respectively, which reflect meaningful advancements in model accuracy and domain adaptation performance. [ABSTRACT FROM AUTHOR]

Business research has rarely explored service innovation for the traditionally conservative legal industry. Using a resource-based and practice-based view blend as its theoretical backbone we develop an understanding of the parameters underpinning law firm innovation as a facilitator of operations management enhancement and possible source of entrepreneurship. The paper presents a survey answered by 106 legal professionals from 19 countries exploring four thematic areas referring to human capital, social capital, knowledge and technology transfer that were hypothesised to define innovation. Ordered probit regression modelling was used. Evidence is presented that cybersecurity threats, inadequate and limited training on IT, excessive paperwork and lack of efficient teamwork, collaboration and communication are key challenges to innovation adoption, which is a pathway to sustainable, inclusive and resilient firm growth. Firm size and internationalisation are innovation-altering factors; SMEs differ from large global firms in their ability to operate 'outside-the-box'. Our results recommend that legal enterprises need to adopt innovation as a robust transformation-enabling toolkit that could facilitate a performance-enhancing business ethos. [ABSTRACT FROM AUTHOR]

In this interview, we explore the innovative research that earned Soomi Kim the Best Paper Award at DRUID 2024. As a partner of DRUID, Industry and Innovation presents an exclusive view into the research journey behind Kim's celebrated work. Her study 'Shortcuts to Innovation: The Use of Analogies in Knowledge Production' examines how innovators venture into uncharted territories, using analogies to transfer knowledge from related domains. Using structural biology as the empirical setting, her findings highlight both the advantages and limitations of employing analogy-based technologies, such as machine learning, to speed up discovery. In this conversation, Kim reveals the inspiration, challenges, and breakthroughs that defined her research, providing a unique perspective on the paths leading to her pioneering work. Just as her paper examines how analogical reasoning and its technological automation serve as shortcuts in knowledge production, this conversation offers a unique lens on the pathways that shape groundbreaking research. [ABSTRACT FROM AUTHOR]

Research Summary: External representations, particularly visuals, are important in strategic decision‐making. However, their pervasiveness and impact are not well understood in the strategy literature. Based on cognitive science research, we identify four cognitive functions crucial to strategic decision‐making that benefit from using external representations. We also propose a conceptual model and propositions that explain how the quality of strategic decision‐making depends on the interactions among task environment, external representations, and managers. We show that external representations influence in predictable ways the boundedly rational process of searching for new strategies. Key determinants include the manager's representational capability and the usability and malleability of the external representation. We discuss implications for users, designers, and teachers of external representations in strategy, as well as suggest avenues for future research. Managerial Summary: This research points to the pivotal role of external representations, especially visuals, in strategic decision‐making. Drawing from cognitive science, this study identifies four critical cognitive functions that benefit from these external representations—working memory, long‐term memory, pattern recognition, and knowledge transfer. Further, the study highlights that external representations significantly influence the process of strategic decision‐making in predictable ways. Finally, we show that not all external representations are alike in their ease of use and a managers' ability to operate on an external representation, referred to as representational capability, greatly affects the decision‐making quality. The implications extend to users, designers, and educators of external representations, urging attention to the design and use of external representations for improved decision outcomes. [ABSTRACT FROM AUTHOR]

Purpose: Prior research has extensively explored the dynamics of knowledge creation and transfer within entrepreneurial ecosystems (EEs). However, the research on knowledge integration within EEs, particularly by entrepreneurs, remains scant. Garnering and effectively using knowledge in such a dynamic and complex environment can provide entrepreneurs with a valuable asset for gaining a competitive advantage. To address this gap, this study aims to explore how entrepreneurs garner and capitalise on knowledge within the EE environment by using a transactive memory system lens. Design/methodology/approach: This study is based on 26 semi-structured interviews with different actors and members of the same ecosystem – the northern Finnish health tech ecosystem. The data were analysed using the Gioia methodology. Findings: This study results found that transactive memory processes (i.e. knowledge specialisation, credibility and coordination) and structures (i.e. differentiated-, shared- and meta-knowledge) influence knowledge organising in EEs. Originality/value: This study provides a conceptual interplay between the EE and the transactive memory system's processes and structures. [ABSTRACT FROM AUTHOR]

Information storage and transfer in the brain require a high computational power. Neuronal network display various local or global mechanisms to allow information storage and transfer in the brain. From synaptic to intrinsic plasticity, the rules of input-output function modulation have been well characterized in neurons. In the past years, astrocytes have been suggested to increase the computational power of the brain and we are only just starting to uncover their role in information processing. Astrocytes maintain a close bidirectional communication with neurons to modify neuronal network excitability, transmission, axonal conduction, and plasticity through various mechanisms including the release of gliotransmitters or local ion homeostasis. Astrocytes have been significantly studied in the context of long-term or short-term synaptic plasticity, but this is not the only mechanism involved in memory formation. Plasticity of intrinsic neuronal excitability also participates in memory storage through regulation of voltage-gated ion channels or axonal morphological changes. Yet, the contribution of astrocytes to these other forms of non-synaptic plasticity remains to be investigated. In this review, we summarized the recent advances on the role of astrocytes in different forms of plasticity and discuss new directions and ideas to be explored regarding astrocytes-neuronal communication and regulation of plasticity. [ABSTRACT FROM AUTHOR]

Mechanical equipment is a vital foundational support for promoting national economic development and is widely utilized in key sectors such as aerospace, shipping, construction machinery, energy, petrochemicals, and robotics. With the advancement of artificial intelligence and industrial intelligence, industrial big data and its intelligent analysis provide robust support for fault prediction and health management of equipment. Building on existing research, intelligent diagnostics for mechanical equipment based on deep learning have gained significant attention and application. However, the success of big data relies on comprehensive fault data, which is challenging to obtain in practical applications where continuous equipment operation is essential. Moreover, mechanical equipment often operates under varying conditions, leading to different data distributions for training and testing. This discrepancy can result in low diagnostic accuracy or even failure of deep learning methods. Deep Transfer Learning (DTL) is an emerging machine learning paradigm that not only leverages the advantages of deep learning (DL) in feature representation but also harnesses the strengths of transfer learning (TL) in knowledge transfer. Consequently, DTL techniques can make deep learning-based fault diagnosis methods more reliable, robust, and applicable, leading to extensive development and research in the field of intelligent fault diagnosis. This paper primarily introduces adversarial-based deep transfer learning (ADTL) models, which are fundamentally based on Generative Adversarial Network (GAN). We provide a detailed discussion of the main applications of ADTL and its recent developments in intelligent fault diagnosis, along with some future challenges and prospects. [ABSTRACT FROM AUTHOR]

Neural processes (NPs) are models for meta-learning which output uncertainty estimates. So far, most studies of NPs have focused on low-dimensional datasets of highly-correlated tasks. While these homogeneous datasets are useful for benchmarking, they may not be representative of realistic transfer learning. In particular, applications in scientific research may prove especially challenging due to the potential novelty of meta-testing tasks. Molecular property prediction is one such research area that is characterized by sparse datasets of many functions on a shared molecular space. In this paper, we study the application of graph NPs to molecular property prediction with DOCKSTRING, a diverse dataset of docking scores. Graph NPs show competitive performance in few-shot learning tasks relative to supervised learning baselines common in chemoinformatics, as well as alternative techniques for transfer learning and meta-learning. In order to increase meta-generalization to divergent test functions, we propose fine-tuning strategies that adapt the parameters of NPs. We find that adaptation can substantially increase NPs' regression performance while maintaining good calibration of uncertainty estimates. Finally, we present a Bayesian optimization experiment which showcases the potential advantages of NPs over Gaussian processes in iterative screening. Overall, our results suggest that NPs on molecular graphs hold great potential for molecular property prediction in the low-data setting. Scientific contribution: Neural processes are a family of meta-learning algorithms which deal with data scarcity by transferring information across tasks and making probabilistic predictions. We evaluate their performance on regression and optimization molecular tasks using docking scores, finding them to outperform classical single-task and transfer-learning models. We examine the issue of generalization to divergent test tasks, which is a general concern of meta-learning algorithms in science, and propose strategies to alleviate it. [ABSTRACT FROM AUTHOR]

Purpose: 360° immersive videos may be useful in procedure-based healthcare settings. We hypothesized that extended reality (XR) neonatal intubation education is a feasible alternative to live demonstration in skill and knowledge transfer. Methods: This was a prospective single center evaluation of an educational XR intubation video using Premature Anne™ (Laerdal Medical, Stavanger, Norway) comparing to a live demonstration. Participants were randomly allocated to either live demonstration or XR group. Each group received 20 min of teaching from the pediatric respiratory care education specialist, either in person or in video format. Intubation success, knowledge and skill acquisition were measured using a questionnaire and checklist. Results: We had a total of 23 participants, XR (n = 11) and live demonstration (n = 12). The groups were heterogeneous in experience level.The XR group was successful at intubating 73% of the time (n = 8), which was not significantly different to the live demonstration group 58% (n = 7) (p = 0.667). For patients who achieved intubation, there was no difference between groups in the mean time to successful intubation; 98 s (SD 73) for the XR group and 65 s (SD 22) for the live demonstration group (p = 0.685).When comparing scores between groups there were no statistically significant differences regarding knowledge scores (p = 0.514), skill scores (p = 0.826) and total scores (p = 0.926). Conclusion: These preliminary findings indicated no significant differences between the XR and live demonstration groups in terms of intubation success, knowledge acquisition, skill performance, total scores or time to successful intubation. [ABSTRACT FROM AUTHOR]

Living simultaneously in worlds of subsurface darkness and cosmic light, plants transfer knowledge across time and place thresholds. They communicate through their interactions in plain sight and beyond view, often escaping the reaches of human imagination. Medicinal plants, in particular, offer the intimacy of embodied sensory knowledge. As we consume them, ecosystem functions are reenacted inside our bodies through physiological actions. Drawing on botanically centered practices incorporating field work, herbalism, and writing, this project expands creative methods within geography. Through botanical stories, I build on established knowledge adding insights and interpretations derived from ecological observations and botanical medicine practices. These stories feature ecologically and culturally important species that serve as guides for navigating emerging and ongoing environmental and social issues. Through interconnectivity with Betony (Pedicularis spp.), Globemallow (Sphaeralcea spp.), and Vervain (Verbena spp.), I explore plant perspectives for living relationally with environmental change. [ABSTRACT FROM AUTHOR]

Collective motion, such as milling, flocking, and collective turning, is a common and captivating phenomenon in nature, which arises in a group of many self-propelled individuals using local interaction mechanisms. Recently, vision-based mechanisms, which establish the relationship between visual inputs and motion decisions, have been applied to model and better understand the emergence of collective motion. However, previous studies often characterize the visual input as a transient Boolean-like sensory stream, which makes it challenging to capture the salient movements of neighbors. This further hinders the onset of the collective response in vision-based mechanisms and increases demands on visual sensing devices in robotic swarms. An explicit and context-related visual cue serving as the sensory input for decision-making in vision-based mechanisms is still lacking. Here, we hypothesize that body orientation change (BOC) is a significant visual cue characterizing the motion salience of neighbors, facilitating the emergence of the collective response. To test our hypothesis, we reveal the significant role of BOC during collective U-turn behaviors in fish schools by reconstructing scenes from the view of individual fish. We find that an individual with the larger BOC often takes on the leading role during U-turns. To further explore this empirical finding, we build a pairwise interaction mechanism on the basis of the BOC. Then, we conduct experiments of collective spin and collective turn with a real-time physics simulator to investigate the dynamics of information transfer in BOC-based interaction and further validate its effectiveness on 50 real miniature swarm robots. The experimental results show that BOC-based interaction not only facilitates the directional information transfer within the group but also leads to scale-free correlation within the swarm. Our study highlights the practicability of interaction governed by the neighbor's body orientation change in swarm robotics and the effect of scale-free correlation in enhancing collective response. Collective motion in nature, such as flocking or turning, arises from local interactions between individuals, but vision-based mechanisms often struggle to capture critical neighbor movements. This study demonstrates that body orientation change (BOC) as a visual cue enhances coordination in fish schools and robotic swarms, improving collective responses. [ABSTRACT FROM AUTHOR]

The traditional passive heat dissipation method has low heat dissipation efficiency, which is not suitable for the heat dissipation of the concentrated heat source inside the long-focal aerial camera, resulting in temperature level changes and temperature gradients in the optical system near the heat source, which seriously affect the imaging performance of the aerial camera. To solve this problem, an active heat dissipation method of liquid cooling cycle is proposed in this paper. To improve the solving efficiency and ensure simulation accuracy, a dynamic boundary information transfer method based on grid area weighting is proposed. The thermal simulation results show that the liquid cooling method reduces the heat source temperature by 70.12%, and the boundary temperature transfer error is 0.015%. The accuracy of thermal simulation is verified by thermal test, and the simulation error is less than 6.44%. In addition, the performance of the optical system is further analyzed, and the results show that the MTF of the optical system is increased from 0.077 to 0.194 under the proposed active liquid cooling cycle heat dissipation method. [ABSTRACT FROM AUTHOR]

Using oral histories of three generations of a Tzotzil Maya Muslim family in Chiapas, Mexico, this paper examines how these members navigate and make meaning of their different identities as their traditional knowledge systems have been disrupted by land dispossession and displacement. This storied process illustrates the knowledge production and transferal processes while underscoring the critical relationship with the land that intertwines with identity formation and knowledge production of three generations of this Maya Muslim community. [ABSTRACT FROM AUTHOR]

We employ the theory of asymptotic homogenization (AH) to study the elasto‐plastic behavior of a composite medium comprising two solid phases, separated by a sharp interface and characterized by mechanical properties, such as elastic coefficients and “initial yield stresses” (i.e., a threshold stress above which remodeling is triggered), that may differ up to several orders of magnitude. We speak of “plastic” behavior because we have in mind a material behavior that, to a certain extent, resembles plasticity, although, for biological systems, it embraces a much wider class of inelastic phenomena. In particular, we are interested in studying the influence of gradient effects in the remodeling variable on the homogenized mechanical properties of the composite. The jump of the mechanical properties from one phase to the other makes the composite highly heterogeneous and calls for the determination of effective properties, that is, properties that are associated with a homogenized “version” of the original composite, and that are obtained through a suitable averaging procedure. The determination of the effective properties results convenient, in particular, when it comes to the multiscale description of inelastic processes, such as remodeling in soft or hard tissues, like bones. To accomplish this task with the aid of AH, we assume that the length scale over which the heterogeneities manifest themselves is several orders of magnitude smaller than the characteristic length scale of the composite as a whole. We identify both a fine‐scale problem and a coarse‐scale problem, each of which characterizes the elasto‐plastic dynamics of the composite at the corresponding scale, and we discuss how they are reciprocally coupled through a transfer of information from one scale to the other. In particular, we highlight how the coarse‐scale plastic distortions influence the fine‐scale problem. Moreover, in the limit of negligible hysteresis effects, we individuate two viscoplastic effective coefficients that encode the information of the two‐scale nature of the composite medium in the upscaled equations. Finally, to deal with a case study tractable semi‐analytically, we consider a multilayered composite material with an initial yield stress that is constant in each phase. Such investigation is meant to contribute to the constitution of a robust framework for devising the effective properties of hierarchical biological media. [ABSTRACT FROM AUTHOR]

A core function of the olfactory system is to determine the valence of odors. In humans, central processing of odor valence perception has been shown to take form already within the olfactory bulb (OB), but the neural mechanisms by which this important information is communicated to, and from, the olfactory cortex (piriform cortex, PC) in humans are not known. To assess communication between the 2 nodes, we simultaneously measured odor-dependent neural activity in the OB and PC from human participants while obtaining trial-by-trial valence ratings. By doing so, we could determine when subjective valence information was communicated, what kind of information was transferred, and how the information was transferred (i.e., in which frequency band). Support vector machine (SVM) learning was used on the coherence spectrum and frequency-resolved Granger causality to identify valence-dependent differences in functional and effective connectivity between the OB and PC. We found that the OB communicates subjective odor valence to the PC in the gamma band shortly after odor onset, while the PC subsequently feeds valence-related information back to the OB in the beta band. Decoding accuracy was better for negative than positive valence, suggesting a focus on negative valence. Critically, we replicated these findings in an independent data set using additional odors across a larger perceived valence range. Combined, these results demonstrate that the OB and PC communicate levels of subjective odor pleasantness across multiple frequencies, at specific time points, in a direction-dependent pattern in accordance with a two-stage model of odor processing. How is information about odor valence communicated from the olfactory bulb to the olfactory cortex in humans? This neuroimaging study reveals when the subjective valence information is communicated, as well the type of information and the frequency band in which it is transferred. [ABSTRACT FROM AUTHOR]

The study explores the clan names and praises of the Mambwe-speaking people of Northern Zambia from a social semiotics perspective. Specifically drawing on the concept of semiotic assemblages, it considers the names and praises as more than just linguistic resources but as meaning-making collections that contribute to the collective self-representation of the Mambwe people. The assembled expressions reflect their worldviews, sociocultural narratives, and identities. The study shows the interconnectedness of gender roles, bioenvironmental factors, community activities, folklore, ethnicity, and self-asserting narratives within the contexts of clan names and praises. Furthermore, it demonstrates the multi-layered meanings resulting from the emotional forces of the interactions among individuals, living and non-living objects, events and bioenvironmental factors in time-spaces. It concludes that clan names and praises are narratives of expressions that represent the psycho-social and historical paths of the Mambwe people, and they play a crucial role in the intergenerational transfer of knowledge and culture. [ABSTRACT FROM AUTHOR]

In unsupervised domain adaptation (UDA), knowledge is transferred from label-rich source domains to relevant but unlabeled target domains. Current most popular state-of-the-art works suggest that performing domain alignment from the class perspective can alleviate domain shift. However, most of them based on domain adversarial which is hard to train and converge. In this paper, we propose a novel contrastive learning to improve diversity and discriminability for domain adaptation, dubbed as IDD_ICL, which improve the discriminativeness of the model while increasing the sample diversity. To be precise, we first design a novel implicits contrastive learning loss at sample-level by implicit augment sample of the source domain. While augmenting the diversity of the source domain, we can cluster the samples of the same category in the source domain together, and disperse the samples of different categories, thereby improving the discriminative ability of the model. Furthermore, we show that our algorithm is effective by implicitly learning an infinite number of similar samples. Our results demonstrate that our method doesn't require complex technologies or specialized equipment, making it readily adoptable and applicable in practical scenarios. [ABSTRACT FROM AUTHOR]

Deep learning has significantly advanced the field of medical image segmentation. However, the complexity of network structures often leads to high computational demands, limiting their practical efficiency. To enhance the efficiency of image segmentation, this paper introduces an innovative, concise, and lightweight deep learning network. First, to reduce model complexity, we replaced the attention mechanism in the traditional vision transformer (ViT) structure with a shift operation, creating the ShiftViT architecture. This substitution significantly decreased computation and the number of parameters while preserving model performance. Second, to retain and enhance fine-grained features and facilitate more precise information transfer across different layers, we employed a full-scale progressive skip connection strategy. This approach effectively integrates multi-scale feature information, further enhancing model performance. Additionally, to further reduce network complexity, inspired by the independence of probabilities, we opted for depth-wise separable convolution over traditional convolution. This enhances the relative independence between layers. Together, these modifications achieved superior segmentation results on both the Synapse and Automated Cardiac Diagnostic Challenge (ACDC) datasets compared to mainstream models, with substantial advantages in terms of computational efficiency and parameter count. The proposed approach represents an effective solution for medical image applications with limited computational resources and holds great promise for clinical practice. [ABSTRACT FROM AUTHOR]