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[Display omitted] Thermochromic dyes (TCDs) based on a three-component color change system suffer from solid rigidity and liquid leakage issues because of the intrinsic solid–liquid phase change performance, resulting in difficulty in temperature visualization applications for smart wearable fields. Despite considerable efforts in microencapsulation of thermochromic dyes, designing and fabricating essentially flexible thermochromic phase change films still need to be explored. Herein, a one-sided adhesive gradient-crosslinked thermochromic film is reported to address these issues to make a trade-off between stability and flexibility, excellent thermochromic performance, and temperature visualization. The thermochromic wearable films have been fabricated exploiting tea polyphenol thermochromic dyes, vinyl dimethylsiloxane, and hydrosilicone oil via the salt-template-assisted method and gradient crosslinking strategy, which have porous structures with an average pore size of 12.8 μm and a porosity of 28 %. Due to the spatial limiting threshold effect of the porosity structure, interconnected 3D polysiloxane porous networks can provide ample support for tea polyphenol thermochromic dyes and effectively prevent liquid leakage. Upon heating, the thermochromic film changes from blue to white with the K/S value decreasing from 7.69 to 0.78 and the Δ E * increasing from 2.7 to 16.1 at 610 nm, and the color-changing temperature is 42 °C. Gradient crosslinked thermochromic films exhibit excellent temperature-responsive color change properties, desirable one-side adhesion, and thermal energy storage, enabling multicolor temperature displays and temperature-controlled multilevel information transfer. [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptation (UDA) aims to reduce the distribution discrepancy across domains, enabling the transfer of knowledge from the labeled source domain to the unlabeled target domain. The main focus of most current UDA methods lies on extracting domain invariant representations to reduce the gap between domains. However, this singular emphasis on domain invariance may inadvertently ignore domain relevant information, which may lead to negative transfer effects. Moreover, current adversarial DA methods give uniform weight to all samples, overlooking the distinct influence that variations within source domain data or noise may exert on the adversarial performance. To address these challenges, we propose a novel method called Mutual-weighted Feature Disentanglement for Unsupervised Domain Adaptation (MWFD). Specifically, we decouple domain invariant features from domain specific features, and then use the entropy of the classifier to rebalance the weights of the domain discriminator, and simultaneously adjust the weights of the classifier using the domain entropy of the domain discriminator to reduce domain discrepancies. Finally, to obtain more discriminative features, we adopt a self-supervised contrastive learning framework to bring positive sample pairs closer together while pushing negative sample pairs apart, enhancing the discriminability of the model on the target domain. Extensive experiments on five benchmark datasets demonstrate that our model outperforms several state-of-the-art domain adaptation methods. [ABSTRACT FROM AUTHOR]

Cloud application programming interface (API) is a software intermediary that enables applications to communicate and transfer information to one another in the cloud. As the number of cloud APIs continues to increase, developers are inundated with a plethora of cloud API choices, so researchers have proposed many cloud API recommendation methods. Existing cloud API recommendation methods can be divided into two types: content-based (CB) cloud API recommendation and collaborative filtering-based (CF) cloud API recommendation. CF methods mainly consider the historical information of cloud APIs invoked by mashups. Generally, CF methods have better recommendation performances on head cloud APIs due to more interaction records, and poor recommendation performances on tail cloud APIs. Meanwhile, CB methods can improve the recommendation performances of tail cloud APIs by leveraging the content information of cloud APIs and mashups, but their overall performances are not as good as those of CF methods. Moreover, traditional cloud API recommendation methods ignore the complementarity relationship between mashups and cloud APIs. To address the above issues, this paper first proposes the complementary function vector (CV) based on tag co-occurrence and graph convolutional networks, in order to characterize the complementarity relationship between cloud APIs and mashups. Then we utilize the attention mechanism to systematically integrate CF, CB, and CV methods, and propose a model named Content and Complementarity enhanced Attentional Collaborative Filtering (CCeACF). Finally, the experimental results show that the proposed approach outperforms the state-of-the-art cloud API recommendation methods, can effectively alleviate the long tail problem in the cloud API ecosystem, and is interpretable. [ABSTRACT FROM AUTHOR]

With the rapid growth of big data, extracting meaningful knowledge from data is crucial for machine learning. The existing Swarm Learning data collaboration models face challenges such as data security, model security, high communication overhead, and model performance optimization. To address this, we propose the Swarm Mutual Learning (SML). Firstly, we introduce an Adaptive Mutual Distillation Algorithm that dynamically controls the learning intensity based on distillation weights and strength, enhancing the efficiency of knowledge extraction and transfer during mutual distillation. Secondly, we design a Global Parameter Aggregation Algorithm based on homomorphic encryption, coupled with a Dynamic Gradient Decomposition Algorithm using singular value decomposition. This allows the model to aggregate parameters in ciphertext, significantly reducing communication overhead during uploads and downloads. Finally, we validate the proposed methods on real datasets, demonstrating their effectiveness and efficiency in model updates. On the MNIST dataset and CIFAR-10 dataset, the local model accuracies reached 95.02% and 55.26%, respectively, surpassing those of the comparative models. Furthermore, while ensuring the security of the aggregation process, we significantly reduced the communication overhead for uploading and downloading. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

Voluntary sustainability standards (VSS) aim to encourage ethical behaviors of organizations, yet studies show that many VSS adopters do not live up to these promises. Existing literature typically attributes the reason for this ineffectiveness to either policy–practice decoupling, owing to a lack of adhering to VSS requirements, or means–ends decoupling, owing to a lack of adapting to the local context. However, little is known about how the contradictory needs of adherence and adaptation evolve throughout VSS implementation. Building on the knowledge transfer literature, we develop a dynamic conceptual framework that distinguishes two phases of VSS implementation. Specifically, we theorize how tensions emerge in the transition between phases since the first phase primarily calls for adherence, whereas the second calls for adaptation. Applying this framework, we develop propositions to illustrate how these tensions relate to different VSS characteristics: stringency, enforcement, and scope. The article concludes with implications and future research directions for VSS scholarship. [ABSTRACT FROM AUTHOR]

Companies operating with an engineer-to-order (ETO) strategy are often challenged with generating the desired profit as a consequence of product volumes and high levels of product customisation. Profit margins are seen to vary greatly from project to project, which may partly be explained by a lack of references to guide design decisions. Specifically, new product offerings are often based on reuse of design knowledge, which is often not efficiently utilised, as the knowledge transfer and reuse across projects are unstructured, incomplete, or not providing a suitable reference for design specification. To address this issue, this paper presents a method for identifying reference architectures under the consideration of profitability. The method was developed by combining and extending known methods within the fields of product architecture and complexity cost estimation to cover part of the ETO domain. The method was tested in two companies, one producing industrial spray drying plants and the other providing solutions for the production of confectionary products. The findings suggest that a limited understanding of 'preferred solutions' existed in the two case companies, and applying the suggested method to identifying reference architectures could potentially support a more profitable project execution. [ABSTRACT FROM AUTHOR]

Although corporate venture capital (CVC) has been studied as part of open innovation (OI), assumptions about knowledge flows crossing organizational boundaries between "the inside" and "the outside" have limited those explorations. Drawing on an abductive approach to grounded theorizing, this article introduces an intuitive yet novel framework that traces the sources and applications of knowledge obtained from CVC to derive new conditions for how those investments allow companies to orchestrate knowledge flows to overcome barriers and increase their innovation effectiveness. Besides exploring traditional OI knowledge flows within the CVC context, this article further identifies and examines both outside-out knowledge flows (which help to shape an ecosystem for a corporate innovator) and inside-in knowledge flows (which overcome internal silos to achieve real innovation impact). [ABSTRACT FROM AUTHOR]

Background. The value of services in healthcare institutions such as hospitals is created by leveraging the knowledge, skills, and experiences of their healthcare staff. Therefore, knowledge transfer is necessary to empower the employees in the hospital. Since few studies have explored the impact of knowledge transfer on the empowerment of hospital employees, this study aimed to investigate the impact of knowledge transfer and its components on employees' empowerment in Qazvin teaching hospitals. Methods. In this descriptive-analytic and cross-sectional study, 330 teaching hospital staff members of Qazvin University of Medical Sciences were investigated between February 2020 and May 2020. The required data were collected using the Knowledge Transfer Questionnaire (De-Castro, et al., 2008) and the Empowerment Questionnaire (Spreitzer, 1992). The data were analyzed using the correlation method and structural equation analysis (SEM) as well as using SPSS-22 and AMOS software. Results. There was a direct relationship between the dimensions of knowledge transfer and personnel empowerment, and all examined dimensions were above average. Moreover, the dimensions of knowledge transfer also had a significant impact on personnel empowerment. Regarding the fit of the model, the values of GFI, CFI, and NFI indices were obtained above 0.90. In addition, the RMSEA index was 0.074, which indicated the good fit of the model. Conclusion. In summary, the staff's empowerment level was enhanced through the knowledge transfer. Therefore, it was recommended that the hospital managers should improve the level of workers' empowerment by implementing effective strategies, such as providing training courses for the employees.

Abstract Background Evidence-based practice, in conjunction with optimum care quality, improves patients’ clinical outcomes. However, its implementation in daily clinical practice continues to present difficulties. The aim of this study was to identify the strategies applied by Advanced Practice Nurses (APNs) to foster adherence to clinical practice guideline recommendations. Methods An exploratory qualitative study was conducted with six focus groups at three public hospitals belonging to the Balearic Islands Health Care Service (Spain). The study participants were 32 ward nurses and 5 advanced practice nurses working routinely with inpatients at these hospitals. The study was conducted from November 2020 to January 2021, using thematic analysis, based on the COREQ checklist. Results Four major themes related to the facilitation process were identified either by RNs and APNs: the context of the project, APN contribution to nursing team management, healthcare provision on the ward, and the acquisition and application of knowledge. Conclusions The APNs adapted their actions to the characteristics and needs of the local context, employing strategies aimed at improving teamwork, healthcare, and knowledge management. Each of these contributions enhanced the sustainability of the changes made.

Despite the growing interest in using deep reinforcement learning (DRL) for drone control, several challenges remain to be addressed, including issues with generalization across task variations and agent training (which requires significant computational power and time). When the agent’s input changes owing to the drone’s sensors or mission variations, significant retraining overhead is required to handle the changes in the input data pattern and the neural network architecture to accommodate the input data. These difficulties severely limit their applicability in dynamic real-world environments. In this paper, we propose an efficient DRL method that leverages the knowledge of the source agent to accelerate the training of the target agent under task variations. The proposed method consists of three phases: collecting training data for the target agent using the source agent, supervised pre-training of the target agent, and DRL-based fine-tuning. Experimental validation demonstrated a remarkable reduction in the training time (up to 94.29%), suggesting a potential avenue for the successful and efficient application of DRL in drone control.

Abstract Background Improving the nutritional quality of the food supply increases access to nutritious foods, which improves dietary habits and population health. Yet, knowledge mobilization initiatives between public health nutrition researchers and food industries are often not adequately considered and understood. This study explored what elements related to this specific context need to be recognized so that researchers can better mobilize nutrition science knowledge with the food industry to promote the nutritional improvement of food products. Method A case study qualitative approach was selected to answer the research question, using semi-structured interviews as the data collection technique. Québec baking industry actors were shown a mock-up of an online mobilization platform sharing the results of the Food Quality Observatory that describes the nutritional quality of breads offered in Québec, Canada. They were asked to think aloud as they explored the web platform and were interviewed. Two coders analyzed the data using an inductive approach and thematic content analysis, starting with individual open coding, and then put forward their analyses and drafted the final themes. Results The final data consisted of 10 semi-structured interviews conducted between October 2019 and August 2020. Four main themes were identified: the industry’s context, the knowledge mobilization initiative, the product-related matters stemming from the information shared and the motivation within the industry. Within each theme, sub-themes were highlighted and related to the industries’ motivation to improve their products’ nutritional quality. This study also specified key considerations for changes to the sodium and fiber content in bread. Conclusion Other steps beyond using simple language and a website format could be taken to better mobilize scientific knowledge with food industries, such as providing more consumer information, using an integrated knowledge mobilization approach that includes a consideration of ethics, working with communication professionals, collaborating with food science experts, and providing resources to act on shared information. Legislation such as the front-of-pack regulations could accelerate the pace of collaboration between researchers and industry. Overall, establishing a prior relationship with industries could help gain a better understanding of the themes highlighted in this study. Future research could build on this case study to provide more insights and solidify these findings. Classification codes Public Health, Public Private, Policy Making, Research Institutions, Use of Knowledge.

N-methyl-d-aspartate receptors (NMDARs) play a pivotal role in synaptic plasticity. While the functional role of post-synaptic NMDARs is well established, pre-synaptic NMDAR (pre-NMDAR) function is largely unexplored. Different pre-NMDAR subunit populations are documented at synapses, suggesting that subunit composition influences neuronal transmission. Here, we used electrophysiological recordings at Schaffer collateral-CA1 synapses partnered with Ca2+ imaging and glutamate uncaging at boutons of CA3 pyramidal neurones to reveal two populations of pre-NMDARs that contain either the GluN2A or GluN2B subunit. Activation of the GluN2B population decreases action potential-evoked Ca2+ influx via modulation of small-conductance Ca2+-activated K+ channels, while activation of the GluN2A population does the opposite. Critically, the level of functional expression of the subunits is subject to homeostatic regulation, bidirectionally affecting short-term facilitation, thus providing a capacity for a fine adjustment of information transfer. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'. [ABSTRACT FROM AUTHOR]

The current study employs communication visibility theory to examine the nexus between task structure, knowledge transfer, and employee agility. We further investigated the role of ESM in facilitating knowledge transfer and its effects on employee agility. Utilising cross-sectional data from Chinese employees working in different organisations, structural equation modelling (SEM) was applied to 425 samples using AMOS version 24.0. Results reveal that task interdependence significantly influences both knowledge-sharing and knowledge-hiding behaviours in organisations. Moreover, task complexity positively affects knowledge sharing but negatively impacts knowledge hiding. The results also indicate that knowledge sharing positively correlates with employee agility, while knowledge hiding has a negative association with employee agility. Additionally, knowledge sharing mediates the relationship between task interdependence, task complexity, and employee agility, as does knowledge hiding. Furthermore, ESM usage strengthens the relationship between knowledge sharing and employee agility, although it does not moderate the link between knowledge hiding and employee agility. The study concludes with substantial theoretical and practical implications and directions for future research. [ABSTRACT FROM AUTHOR]

Background Aim Methods Findings Conclusions Implications for nursing and health policy Turkey has faced a notable escalation in earthquake disasters in the last two decades. Despite initiating a health and disaster management system, nurses' pivotal roles and experiences in handling such crises have been disregarded.This qualitative study analyzed nurses' experiences before, during, and after deployment in response to the 2023 Turkey earthquakes to enhance disaster‐response efforts.This descriptive qualitative study was conducted between March and May 2023 using semistructured interviews with 15 nurses purposively  sampled among those who volunteered to work in the earthquake zone. The MIRACLE and COREQ guidelines were followed for reporting qualitative research.The analysis exposed five main themes for pre‐tasking: moral obligation, motivation, insufficient experience, balancing responsibilities, and preparation challenges. The peri‐task themes include responsibilities, skills, bravery and characteristics, workload management, teamwork, and outcomes. Post‐tasking has three themes: competence assessment, career goals and aspirations, and support. Training and coping with anxiety and stress are common themes for all phases.Disaster relief requires a comprehensive and coordinated response from healthcare organizations, government agencies, and support systems. Providing adequate training, ensuring safety protocols, offering mental health support, and fostering a fair and supportive work environment are crucial steps in mitigating the adverse effects on nurses and, by extension, the patient care process in earthquake‐affected areas.Nurse training in disaster preparedness should cover various response methods and involve multiple disciplines. Managers can help by arranging drills, simulations, online courses, and workshops and promoting partnerships for improved collaboration. Psychological support should be included to address emotional challenges. Regularly updating response policies based on past experiences is crucial for preparedness and efficiency. [ABSTRACT FROM AUTHOR]

Background Objectives Scope Methods Results Conclusion Clinician‐teachers, physicians with educational responsibilities in either classroom or clinical setting, are assumed to add value by virtue of their dual role. The clinical responsibilities are often prioritised over the educational tasks. How and under which circumstances clinician‐teachers are able to perform their educational role and create added value for different stakeholders is currently unclear.To identify for whom, how and under which circumstances educational activities executed by CTs by virtue of their dual role add value to others.CTs activities linking the system of education and clinical practice beyond direct patient interactions and purposefully executed.A realist review was conducted. Databases were searched in two stages: a narrow conventional search, followed by a comprehensive artificial intelligence‐aided search. Studies concerning clinician‐teachers' dual role were included. Realist analysis was applied to identify in which contexts resource mechanisms triggered reasoning mechanisms, which led to specific outcomes for different stakeholders.Sixty‐six studies were included. In contexts where clinician‐teachers' dual role was formally recognised and valued, clinician‐teachers benefitted from the credibility and legitimacy bestowed on them, making the transfer of domain‐specific knowledge more impactful. In contexts where sociocultural differences between both systems existed, CTs were able to mediate and adjust recommendations aligned with stakeholders' perceived relevance. Also, contexts organised to support both roles made resource mechanisms more impactful. Clinician‐teachers added value to students' clinical competency and learning environment, and to educational organisations' curricular innovation. In their clinical workspace, clinician‐teachers added value by enhancing colleague physicians' teaching expertise, implementing educational innovations and recruiting students for scarce specialisms.Clinician‐teachers add value to students, colleague physicians and the clinical and educational contexts at large. Domain‐specific knowledge of both systems was important to gain credibility and achieve added value. Openness, formal recognition and allocated time for both roles in educational and clinical contexts towards the dual role are important to strengthen the impact of the dual role. [ABSTRACT FROM AUTHOR]

Evolutionary multitask optimization (EMTO) has developed fast recently, and many algorithms have emerged that solve several different problems simultaneously through knowledge transfer. Most algorithms use a single search operator in their processing. However, different tasks have distinct characteristics, and a single operator is often inadequate to adapt to different stages of the same task. In this paper, we propose a multiple search operator selection strategy by adaptive probability allocation, named adaptive multi-operator selection (AMOS) to address EMTO that features rapid convergence of populations. It can automatically select the best multiple search operators based on the characteristics of specific tasks and different stages of evolution. The primary contributions of the proposed algorithm are as follows: (1) It combines the basic concepts of multi-operator integration and adaptive search operator selection to select the best multiple search operators for each task at different evolutionary stages; (2) It facilitates the knowledge transfer through different solving operators between tasks; (3) It can be flexibly embedded into various frameworks of general EMTO algorithms with good results. In the experiments, we validate the performance of AMOS on CEC2017 benchmark suite, CMTOPs benchmark suite, and real-world EMTO problems, and experimental results demonstrate the effectiveness and generality of the proposed strategy. [ABSTRACT FROM AUTHOR]

The multilayer multiconfiguration time-dependent Hartree (ML-MCTDH) method and the density matrix renormalisation group (DMRG) are powerful workhorses applied mostly in different scientific fields. Although both methods are based on tensor network states, very different mathematical languages are used for describing them. This severely limits knowledge transfer and sometimes leads to re-inventions of ideas well known in the other field. Here, we review ML-MCTDH and DMRG theory using both MCTDH expressions and tensor network diagrams. We derive the ML-MCTDH equations of motions using diagrams and compare them with time-dependent and time-independent DMRG algorithms. We further review two selected recent advancements. The first advancement is related to optimising unoccupied single-particle functions in MCTDH, which corresponds to subspace enrichment in the DMRG. The second advancement is related to finding optimal tree structures and on highlighting similarities and differences of tensor networks used in MCTDH and DMRG theories. We hope that this contribution will foster more fruitful cross-fertilisation of ideas between ML-MCTDH and DMRG. [ABSTRACT FROM AUTHOR]

Domain adaptation (DA) offers an effective way to align feature distributions of the source domain (SD) and the target domain (TD) without requiring any target label samples. As a method of DA, representation learning effectively realizes the alignment of feature distributions in different domains by transferring domain knowledge. However, existing representation learning methods often focus on unilateral representation transfer, which potentially results in transfer bias. Additionally, most methods ignore the connection between domain alignment and discrimination during the DA process, which easily causes negative transfer. This paper proposes a dynamic weighted dual-driven domain adaptation (DW-D $^3$ 3 A) model that effectively addresses the aforementioned issues through bilateral feature transfer between domains and a dynamic weighted scheme. Technically, we first propose a dual-driven domain adaptation (D $^3$ 3 A) model, which employs symmetrical structures to facilitate the knowledge transfer of bilateral representations between source and target domain samples, learning the subspaces of two domains and reducing distribution discrepancies between subspaces via joint distribution-driven alignment. This process mitigates transfer bias and goes beyond previous unilateral transfer methods. Then, to alleviate strong constraints on projecting SD and TD into the same subspace in existing approaches, we apply a relaxed subspace constraint to bring the projections of SD and TD closer. Furthermore, data reconstruction is incorporated to preserve discriminant information from the original data. Lastly, we expand (D $^3$ 3 A) to DW-D $^3$ 3 A using a dynamic weighted scheme, which adjusts the weights assigned to domain alignment and discrimination based on their significance to inhibit negative transfer. Extensive experimentation on three datasets indicates that DW-D $^3$ 3 A outperforms seven other DA methods, showing its superior performance. [ABSTRACT FROM AUTHOR]

Quantifying the properties of complex, self-organizing systems is increasingly important for understanding the development and state of modern systems. Case studies have recommended sustainability frameworks predominately in literature, but little emphasis has been placed on methodological evaluation. Data availability is often an obstacle that constrains conventional flow-based network analysis, but a novel information-based technique (QtAC) developed by zu Castell and Schrenk overcomes these constraints by modelling interactions between agents as information transfers. This study compares the QtAC method to conventional flow analysis by applying both to the same 90-year dataset containing socio-economic data from the island of Samothraki, Greece. Resilience indicators, based on Ulanowicz's ascendency analysis, are derived on both the information- and flow-based networks. We observe that the resulting dynamics of the information-based networks align closer with complex system dynamics as theorized by the adaptive cycle model. Additionally, we discuss how QtAC offers different interpretations of network indicators when compared to usual interpretations of flow analysis. Ultimately, QtAC is shown to provide an alternative for complex systems analysis if the data situation does not allow for conventional flow-analysis. Furthermore, we show that the combination of both approaches can yield valuable new insights. [ABSTRACT FROM AUTHOR]

The automated classification of rock images is of paramount importance in geological analysis, as it serves as the foundational criterion for the categorization of rock lithology. Despite recent advancements in leveraging deep learning technologies to enhance the efficiency and precision of image classification, a crucial aspect has been overlooked: these methods face a performance bottleneck when attempting to apply it directly to rock classification methods. To address this limitation, we propose a multiple granularity Spatial disorder Hierarchical residual Network (SHN). This approach involves learning from objects annotated at different levels, thereby facilitating the transfer of hierarchical knowledge across levels. By enabling lower-level classes to inherit pertinent attributes from higher-level superclasses, our method aims to capture the intricate hierarchical relationships among different rock types. Especially, we introduce a multi-granularity spatial disorder module to aid neural networks in discerning discriminative details across various scales. This module enables processed images to exhibit region independence, compelling the network to adeptly identify discriminative local regions at diverse granularity levels and extract pertinent features. Furthermore, in light of the absence of a comprehensive rock dataset, this study amassed 4,227 rock images of diverse compositions from various places, culminating in the creation of a robust rock dataset for classification. Rigorous experimentation on this dataset yielded highly promising results, demonstrating the effectiveness of our proposed method in addressing the challenges of rock image classification. [ABSTRACT FROM AUTHOR]

As a result of the pandemic, but also of the rapid advancement of technology in general, e-learning has emerged as a popular method of education, providing students with flexibility and accessibility. Understanding the factors that influence students' levels of learning and accomplishment in this digital learning environment is therefore critical for teachers and institutions seeking to increase the effectiveness of teaching and knowledge transfer via e-learning platforms. A number of variables that might improve or impair student use, learning, and performance affect how successful e-learning actually is. In order to maximize the benefits of e-learning and guarantee successful student results, educators and policymakers must have a thorough understanding of these elements. The purpose of this study is to investigate the impact of extrinsic and intrinsic factors on students' use, learning level, and performance in the setting of e-learning in higher education in two countries. This study evaluates the impact of extrinsic elements such as course content, e-learning system quality, institutional and teacher support, as well as intrinsic aspects such as personal innovativeness, self-efficacy, and information sharing in two countries. The study takes a quantitative approach, and the analysis was carried out using the structural equations method to examine the combined influence of numerous extrinsic and intrinsic elements on the use of e-learning, as well as learning level and performance.The research results show that the course content and e-learning system, personal innovativeness, self-efficacy, and knowledge sharing have a positive influence on the intention to use e-learning. Also, the intention of using an e-learning system will increase the actual use of e-learning technologies, which will ultimately result in better learning performance. The findings of this study will help educators, policymakers, and e-learning platform developers create effective ways for optimizing student experiences and promoting good learning outcomes in higher education settings. [ABSTRACT FROM AUTHOR]

Natural language-processing tasks have been improved greatly by large language models (LLMs). However, numerous parameters make their execution computationally expensive and difficult on resource-constrained devices. For this problem, as well as maintaining accuracy, some techniques such as distillation and quantization have been proposed. Unfortunately, current methods fail to integrate model pruning with downstream tasks and overlook sentence-level semantic modeling, resulting in reduced efficiency of distillation. To alleviate these limitations, we propose a novel distilled lightweight model for BERT named MicroBERT. This method can transfer the knowledge contained in the "teacher" BERT model to a "student" BERT model. The sentence-level feature alignment loss (FAL) distillation mechanism, guided by Mixture-of-Experts (MoE), captures comprehensive contextual semantic knowledge from the "teacher" model to enhance the "student" model's performance while reducing its parameters. To make the outputs of "teacher" and "student" models comparable, we introduce the idea of a generative adversarial network (GAN) to train a discriminator. Our experimental results based on four datasets show that all steps of our distillation mechanism are effective, and the MicroBERT (101.14%) model outperforms TinyBERT (99%) by 2.24% in terms of average distillation reductions in various tasks on the GLUE dataset. [ABSTRACT FROM AUTHOR]

Despite the recent complex intertwining of firms in fierce intellectual property disputes, the formation mechanisms of patent infringement litigation relationships between firms have been little studied from a network science perspective. We construct an inter-firm patent litigation network using longitudinal data on patent infringement litigation and the firms involved in the US, and examine its structural characteristics and the mechanisms of tie formation through network motif analysis, linking it to existing knowledge on negative ties. The results reveal the significant influence of mechanisms such as homophily, popularity, and activity on network formation, with reciprocity emerging as a pivotal factor. The absence of triadic closure is also observed. This distinct structural pattern is consistent across different technological fields and throughout the 20-year period. Furthermore, our analysis delves into the rapid countersuit strategies common within this network and provides valuable insights into patent litigation strategies between firms. [ABSTRACT FROM AUTHOR]

Background. In situ simulation‐based team training of non‐technical skills is considered an important initiative for preventing adverse events caused by poor teamwork among healthcare personnel. This study aimed to assess the non‐technical skills of anaesthesia personnel before and after in situ simulation‐based team training in a clinical setting. Methods. The study was conducted from October 2020 to June 2021 using a quasiexperimental before and after design based on video‐recorded observations and ratings of anaesthesia teams' non‐technical skills during anaesthesia induction in the operating room before and shortly after in situ simulation‐based team training. Anaesthesia personnel were divided into 20 teams and video recorded during anaesthesia induction. The Anaesthetists' Non‐technical Skills (ANTS) system was used to score the teams' non‐technical skills. A paired‐sample t‐test was used to assess the impact of the intervention on the anaesthesia teams' scores on the various ANTS categories. Interrater agreement between the two ANTS raters was assessed using weighted kappa. Results. At the category level, the overall scores had a statistically significant increase in performance after simulation‐based team training (3.48 vs. 3.71; p < 0.001). Furthermore, scores of five of the 15 elements were significantly different. Interrater agreement revealed moderate agreement between the two raters (weighted kappa = 0.51, p value <0.001). Conclusion. The anaesthesia teams' increased non‐technical skills after simulation‐based team training may indicate the transfer of knowledge from training to clinical practice. The moderate agreement between the raters could be attributed to the subjective nature of the evaluation procedure. The ANTS was originally used as an individual assessment tool; however, this study has demonstrated its potential as a team assessment tool. [ABSTRACT FROM AUTHOR]

The present research describes the implementation of a methodological evaluation strategy designed to enhance meaningful learning in fifth-year students of Basic General Education (BGE). The research focuses on the development and implementation of this strategy, with the aim of promoting deeper and more meaningful learning in students, beyond mere memorization of information. It examines how this methodological evaluation strategy can facilitate the connection of curricular content with students’ prior experiences, individual interests, and learning needs, thereby creating a more stimulating and motivating educational environment. Additionally, the effects of this strategy on critical thinking, problem-solving, and the ability to transfer knowledge to new situations are analyzed. The research is conducted through a mixedmethods approach, utilizing methods such as participant observation, teacher interviews, and analysis of curricular and evaluation documents. A pre-experimental design is employed to demonstrate the correlation between the development of the methodological evaluation strategy and its impact on meaningful learning in fifth-year BGE students through an interdisciplinary project. The results show increased motivation and engagement among students, as well as a deeper and more lasting understanding of the concepts learned. This study has significant implications for teaching practice and the design of formative evaluation strategies that promote more meaningful and relevant learning for fifth-year BGE students. [ABSTRACT FROM AUTHOR]

Numerous real-time applications rely on heterogeneous wireless sensor networks (HWSNs), including the Internet of Vehicles (IoV), Internet of Sensors (IoS), medical applications, and so on. Due to the restricted computational capacity, memory, and connectivity of wireless nodes, it is essential to establish authentication techniques that consume less energy, yet guarantee strong security. It takes a lot of time and energy to construct trust models between sensor devices that can recognize and differentiate between benign and malicious attackers, which, in turn, reduces the detection rate and increases overhead. This article suggests a hybrid forewarn approach based on improved security to attain this degree of safety in HWSN communication called the Improved Security-based hybrid forewarn model (ISBHF) and the Dispense Incline Drop Algorithm. It is recommended to offer a trust score and evaluations for messages and knowledge transfer to improve detection capabilities against attackers targeting essential data transmission. To further minimize packet loss, a reliability secure measure is created, and a weighted averaging strategy is implemented to ensure the robustness and veracity of the secure scores. Furthermore, empirical evidence from measurements and simulations will demonstrate that unsecured paths of communication are key to identifying fraudulent nodes that are causing an outsized fraction of network traffic or data breaches. The suggested protocol is superior to the current approach in terms of efficiency and security, making it ideal for use in HWSN networks by decreasing communication overhead, increasing detection performance, and other parameters such as packet delivery ratio and connectivity ratio. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the effects of Storytelling Narrated Videos (SNV) on students' knowledge retention and transferability. A total of 56 students from a university in Indonesia were randomly assigned to a quasi-experimental research design exposed to SNV and to Lecture Narrated Videos (LNV). Two videos were created to deliver content on Bloom's Taxonomy, one using a lecture-style format and the other adopting a storytelling approach. Data were collected through tests, questionnaires, and essays. The findings revealed that participants exposed to SNV had higher retention memory scores, indicating a positive impact on knowledge retention compared to those who watched LNV. Moreover, the storytelling videos facilitated cognitive skill progression, enhanced understanding through engaging visuals, and fostered a strong connection with a familiar narrator, resulting in a more dynamic and memorable learning experience. The study also examined knowledge transfer and found that participants who watched the storytelling videos performed better in applying Bloom's Taxonomy concepts to planning teaching objectives in the essay test. This suggests that the incorporation of storytelling narration and promoting transfer knowledge activities can enhance students' understanding, retention, and practical application of the learned material. Overall, the findings highlight the potential of incorporating storytelling in narrated videos to improve students' knowledge retention, transferability, and engagement in educational settings. Plain language summary: The impact of digital storytelling on students' learning and knowledge transfer This research aimed to explore how using Storytelling Narrated Videos (SNV) affects students' memory and ability to apply what they've learned. The study involved 56 students from an Indonesian university who were randomly assigned to either watch SNV or Lecture Narrated Videos (LNV). Two different videos were created to teach about Bloom's Taxonomy—one presented information in a traditional lecture style, while the other used storytelling. Data were collected through tests, questionnaires, and essays. Results showed that students who watched SNV had better memory scores, suggesting that storytelling videos helped them remember information better compared to those who watched LNV. Additionally, storytelling videos helped students improve their thinking skills, made the content more understandable with engaging visuals, and created a stronger connection with the narrator, resulting in a more interesting and memorable learning experience. The study also looked at whether students could use what they learned in practical situations, and found that those who watched storytelling videos performed better in applying Bloom's Taxonomy concepts in the essay test. This indicates that using storytelling in videos and encouraging students to apply what they learn can improve their understanding, memory, and ability to use the information in real-life scenarios. [ABSTRACT FROM AUTHOR]

Drawing on an exhaustive analysis of 112 scholarly articles dedicated to the field of Knowledge Management in Family Businesses (KMFB), this study systematically examines the prevailing research frontiers, investigates the intricate dynamics of knowledge management processes, explores the multifaceted outcomes within family business contexts, and provides an array of theoretical perspectives to underpin the empirical investigations. A pivotal focal point that emerges from this comprehensive investigation is the significance of the succession process, which serves as a pivotal link between knowledge processes and the effectiveness in harnessing innovation, entrepreneurial orientation, sustainability, and internationalization outcomes. Notably, a discernible pattern surfaces from the analysis, with a substantial proportion of the KMFB literature grounded in three prominent theoretical lenses: the knowledge-based view, resource-based view, and dynamic capabilities. These theoretical frameworks provide robust foundations for explicating the underlying mechanisms and dynamics that drive knowledge management and its impact on family business performance. Furthermore, this systematic literature review uncovers a rich landscape of opportunities for future research endeavors. It illuminates potential avenues for novel research designs, innovative investigations into knowledge processes, exploration of diverse outcomes, and the application of alternative theoretical perspectives within KMFB research. These prospects hold immense promise in advancing scholarly knowledge, refining existing theories, and providing fresh insights into the intricate interplay between knowledge management and family business performance. Plain language summary: Drawing on an exhaustive analysis of 112 scholarly articles dedicated to the field of Knowledge Management in Family Businesses (KMFB), this study systematically examines the prevailing research frontiers, investigates the intricate dynamics of knowledge management processes, explores the multifaceted outcomes within family business contexts, and provides an array of theoretical perspectives to underpin the empirical investigations. Despite its relatively modest representation and dispersion across disciplinary boundaries, KMFB research has made commendable advancements, attaining notable recognition in academia and engendering fertile prospects for future inquiry. A pivotal focal point that emerges from this comprehensive investigation is the significance of the succession process, which serves as a pivotal link between knowledge processes and the effectiveness in harnessing innovation, entrepreneurial orientation, sustainability, and internationalization outcomes. This critical junction underscores the importance of understanding how knowledge flows and is managed within family business settings, thereby shaping these enterprises' trajectory and ultimate success. Notably, a discernible pattern surfaces from the analysis, with a substantial proportion of the KMFB literature grounded in three prominent theoretical lenses: the knowledge-based view, resource-based view, and dynamic capabilities. These theoretical frameworks provide robust foundations for explicating the underlying mechanisms and dynamics that drive knowledge management and its impact on family business performance. Furthermore, this systematic literature review uncovers a rich landscape of opportunities for future research endeavours. It illuminates potential avenues for novel research designs, innovative investigations into knowledge processes, exploration of diverse outcomes, and the application of alternative theoretical perspectives within KMFB research. These prospects hold immense promise in advancing scholarly knowledge, refining existing theories, and providing fresh insights into the intricate interplay between knowledge management and family business performance. [ABSTRACT FROM AUTHOR]

Understandings of Knowledge in the University College Sector: A Discourse Analysis In 2008, a new policy for knowledge production was introduced at Denmark's university colleges. These institutions were required to actively engage in collaboration on professional and development- based knowledge production. Later, in 2013/14, the legislative foundation was adjusted, making research and development work a right and a duty. The purpose of these legislative changes was to make knowledge more application-oriented for the professions. Furthermore, knowledge was intended to move dynamically between research, professional practice, and education, thereby helping to build and maintain the knowledge base of the educations. In this article, through a discourse analysis, we describe how knowledge has been sought to be conceptualized through legislation and implementation at the university colleges. The analysis is based on a study of documents, namely legal texts and reports from The Danish Evaluation Institute. The analyzed documents describe a shift in the perception of knowledge, from a so-called trickledown model to a knowledge circulation model. The analysis shows that the many definitions and references in the texts have not given the circulation metaphor a more concrete meaning, but that the concept of knowledge about research and development in university colleges has become increasingly fluid, allowing for various discursive positionings. [ABSTRACT FROM AUTHOR]

With the increase of studies in the field of university-industry cooperation, the need to integrate studies that have investigated the research process of this interaction has become more apparent. In this regard, the present research analyzed the review and bibliometric studies of the interaction between university and industry using a scoping review. The guide provided by Daudt, Van Mossel and Scott (2013) is the basis for conducting the scoping review. We used international databases, and 2,590 articles were retrieved. After sampling based on the PRISMA diagram and using the JBI institute for the quality of the selected sources, 64 articles were selected for the final analysis. A review of studies analyzing university-industry interaction showed that research publications in this field have grown despite random fluctuations. Various factors account for the growth of studies in this field, including transforming the missions and activities of universities by moving towards entrepreneurship and commercialization of research and the formation of entrepreneurial universities, increasing technology transfer offices, the growth of inventions in universities, and expanding research collaborations. The most crucial obstacle to university-industry alliances is the difference in their goals and missions. Intermediary institutions, especially science parks, and incubators, are essential for effective communication between universities and industry. Some of the most critical topics in University-Industry Interaction are innovation, economic development, sustainable development, science parks and incubators, start-ups, spin-offs, knowledge transfer, technology transfer, entrepreneurship, and research commercialization. [ABSTRACT FROM AUTHOR]

Magnetic resonance images (MRIs) of different modalities have different reference values for pathological diagnosis. The difficulty of obtaining multimodal MRIs makes it effective to synthesize medical images of missing modalities from existing one. To train a one-for-all synthesizer with limited number of unpaired MRIs, a novel cross-dimensional knowledge-guided generative adversarial network (CKG–GAN) is proposed. In CKG–GAN, a cross-dimensional knowledge transfer network is utilized to measure the perceptual similarity of 2D images (slices of MRIs) from source and synthesized modalities, the knowledge of which is transferred from a pre-trained 3D network without accessing its private training data set. We evaluate the proposed model for three tasks on BraTs2018 and BraTs2021 data sets, using one modality of T1, T2 or Flair to synthesize the other two modalities without changing the content. The results show that compared with the current state-of-the-art methods, our method improves the performance by 2–7%. [ABSTRACT FROM AUTHOR]

Motivation As more behavioural assays are carried out in large-scale experiments on Drosophila larvae, the definitions of the archetypal actions of a larva are regularly refined. In addition, video recording and tracking technologies constantly evolve. Consequently, automatic tagging tools for Drosophila larval behaviour must be retrained to learn new representations from new data. However, existing tools cannot transfer knowledge from large amounts of previously accumulated data. We introduce LarvaTagger, a piece of software that combines a pre-trained deep neural network, providing a continuous latent representation of larva actions for stereotypical behaviour identification, with a graphical user interface to manually tag the behaviour and train new automatic taggers with the updated ground truth. Results We reproduced results from an automatic tagger with high accuracy, and we demonstrated that pre-training on large databases accelerates the training of a new tagger, achieving similar prediction accuracy using less data. Availability and implementation All the code is free and open source. Docker images are also available. See gitlab.pasteur.fr/nyx/LarvaTagger.jl. [ABSTRACT FROM AUTHOR]

Motivation Deep learning models have achieved remarkable success in a wide range of natural-world tasks, such as vision, language, and speech recognition. These accomplishments are largely attributed to the availability of open-source large-scale datasets. More importantly, pre-trained foundational model learnings exhibit a surprising degree of transferability to downstream tasks, enabling efficient learning even with limited training examples. However, the application of such natural-domain models to the domain of tiny Cryo-Electron Tomography (Cryo-ET) images has been a relatively unexplored frontier. This research is motivated by the intuition that 3D Cryo-ET voxel data can be conceptually viewed as a sequence of progressively evolving video frames. Results Leveraging the above insight, we propose a novel approach that involves the utilization of 3D models pre-trained on large-scale video datasets to enhance Cryo-ET subtomogram classification. Our experiments, conducted on both simulated and real Cryo-ET datasets, reveal compelling results. The use of video initialization not only demonstrates improvements in classification accuracy but also substantially reduces training costs. Further analyses provide additional evidence of the value of video initialization in enhancing subtomogram feature extraction. Additionally, we observe that video initialization yields similar positive effects when applied to medical 3D classification tasks, underscoring the potential of cross-domain knowledge transfer from video-based models to advance the state-of-the-art in a wide range of biological and medical data types. Availability and implementation https://github.com/xulabs/aitom. [ABSTRACT FROM AUTHOR]

Crossing the early modern Pacific Ocean presented many challenges. This chapter seeks to provide some insights into the food provisions, diseases, and medical supplies found on board galleons. I will also discuss some aspects of transcultural medical knowledge transfer and introduce some results of our ongoing ERC AdG project TRANSPACIFIC. [ABSTRACT FROM AUTHOR]

Approximately 60% of the genes and gene products in the model species Arabidopsis thaliana have been functionally characterized. In non‐model plant species, the functional annotation of the gene space is largely based on homology, with the assumption that genes with shared common ancestry have conserved functions. However, the wide variety in possible morphological, physiological, and ecological differences between plant species gives rise to many species‐ and clade‐specific genes, for which this transfer of knowledge is not possible. Other complications, such as difficulties with genetic transformation, the absence of large‐scale mutagenesis methods, and long generation times, further lead to the slow characterization of genes in non‐model species. Here, we discuss different resources that integrate plant gene function information. Different approaches that support the functional annotation of gene products, based on orthology or network biology, are described. While sequence‐based tools to characterize the functional landscape in non‐model species are maturing and becoming more readily available, easy‐to‐use network‐based methods inferring plant gene functions are not as prevalent and have limited functionality. [ABSTRACT FROM AUTHOR]