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This study, focusing on the China's Yao minority community, investigates the feasibility to create a generative computational method to replicate the diversity of the existing Yao traditional wood buildings, addressing the critical issues currently facing computational design methods, in the attempt to adapt genetic-generative algorithms to the study of local ancient architecture. The project develops a computational tool to generate a network of three-dimensional prototypes, or building structures, derived from traditional wood frame village houses. It studies possible housing structures that illustrate some of the key working methods available in digital systems such as ‘generating' and ‘compositing' taking as a starting point computational strategies oriented towards geometry and where a set of local variables play a decisive role: available local technologies, use of raw materials, and the dimensioning of timber components based on data collected from Yao architecture.

Artists are increasingly using blockchain as a tool for trading digital artwork as non-fungible tokens (NFTs); however, some are also beginning to experiment with the blockchain as a medium for generative art, using it as a seed for a generative process or to continuously modify an evolving piece. This paper surveys, reviews, and classifies the state-of-the-art in blockchain-interactive NFTs and presents a liberal-arts critique of the opportunities and threats posed by this technology, whilst addressing existing criticism on the broader topic of art-related NFTs. The paper examines some of the most experimental pieces minted on the Hic et Nunc (HEN) and Teia NFT marketplaces, for which a purpose-built research tool was developed. The survey reveals some reliance on centralised infrastructure, namely blockchain indexers, placing undesired trust on third parties which undermines the potential longevity of the artwork. The paper concludes with recommendations for artists and NFT platform designers for developing more resilient and economically sustainable architectures.

YouTube has become increasingly popular for marketing purposes. As corporate and user-generated content is widely available on this platform, beauty-related professionals need to understand how to create videos that make their products more appealing and stand out from the clutter. In this study, we examine four factors (i.e., perceived usefulness of the information, perceived credibility of the information, attitude toward the purchase, and perceived video characteristics) that affect the purchase intentions of female consumers. After viewing beauty-related videos, a sample of 204 female consumers was analyzed by structural equation modeling. The findings showed that videos with more views, likes, and comments tend to have a greater effect on the respondents' intentions to purchase. Also, the factors of perceived usefulness of the information, perceived credibility of the information, and attitude toward the purchase exhibited a significant effect on the intention to buy beauty-related products. The result showed that perceived video characteristics (such as quality and visuals) did not significantly influence the purchase intention, however, there is evidence that this factor should not be ignored by content creators. Finally, our research provides insights, strategies, and future directions for industry practitioners and marketers.

Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.

It has become increasingly clear that the early use of decomposition for addition is associated with later mathematical achievement. This study examined how younger children execute a base-10 decomposition strategy to solve complex arithmetic (e.g. two-digit addition). 24 addition problems in two modalities (WA: Written Arithmetic; OA: Oral Arithmetic) with sums less than 100 were administered to 22 Japanese and 22 Singaporean 6-year-old kindergarteners. Our findings reveal that they were able to solve complex addition. For instance, Japanese kindergarteners tended to solve complex arithmetic using base-10 decomposition across the modality, whereas Singaporean kindergarteners used standard algorithms and basic counting to solve complex WA and OA problems, respectively. We speculate that Japanese kindergarteners might have a clearer understanding of the base-10 concept and were able to use this knowledge more readily than Singaporean kindergarteners. Mathematical experiences in kindergarten and number-naming systems have been put forward as two of the crucial contributors for such cross-cultural differences. This study also provides new directions for future research on the understanding of the base-10 concept and its application among young children.

The key challenge of Unsupervised Domain Adaptation (UDA) for analyzing time series data is to learn domain-invariant representations by capturing complex temporal dependencies. In addition, existing unsupervised domain adaptation methods for time series data are designed to align marginal distribution between source and target domains. However, existing UDA methods (e.g. R-DANN Purushotham et al. (2017), VRADA Purushotham et al. (2017), CoDATS Wilson et al. (2020)) neglect the conditional distribution discrepancy between two domains, leading to misclassification of the target domain. Therefore, to learn domain-invariant representations by capturing the temporal dependencies and to reduce the conditional distribution discrepancy between two domains, a novel Attentive Recurrent Adversarial Domain Adaptation with Top-k time series pseudo-labeling method called ARADA-TK is proposed in this paper. In the experiments, our proposed method was compared with the state-of-the-art UDA methods (R-DANN, VRADA and CoDATS). Experimental results on four benchmark datasets revealed that ARADA-TK achieves superior classification accuracy when it is compared to the competing methods.

The physiological mechanisms underlying variation in aggression in fish remain poorly understood. One possibly confounding variable is the lack of standardization in the type of stimuli used to elicit aggression. The presentation of controlled stimuli in videos, a.k.a. video playback, can provide better control of the fight components. However, this technique has produced conflicting results in animal behaviour studies and needs to be carefully validated. For this, a similar response to the video and an equivalent live stimulus needs to be demonstrated. Further, different physiological responses may be triggered by live and video stimuli and it is important to demonstrate that video images elicit appropriate physiological reactions. Here, the behavioural and endocrine response of male Siamese fighting fish Betta splendens to a matched for size conspecific fighting behind a one-way mirror, presented live or through video playback, was compared. The video playback and live stimulus elicited a strong and similar aggressive response by the focal fish, with a fight structure that started with stereotypical threat displays and progressed to overt attacks. Post-fight plasma levels of the androgen 11-ketotestosterone were elevated as compared to controls, regardless of the type of stimuli. Cortisol also increased in response to the video images, as previously described for live fights in this species. These results show that the interactive component of a fight, and its resolution, are not needed to trigger an endocrine response to aggression in this species. The study also demonstrates for the first time in a fish a robust endocrine response to video stimuli and supports the use of this technique for researching aggressive behaviour in B. splendens.

Hydrothermal activity on mid-ocean ridges is an important mechanism for the delivery of Zn from the mantle to the surface environment. Zinc isotopic fractionation during hydrothermal activity is mainly controlled by the precipitation of Zn-bearing sulfide minerals, in which isotopically light Zn is preferentially retained in solid phases rather than in solution during mineral precipitation. Thus, seafloor hydrothermal activity is expected to supply isotopically heavy Zn to the ocean. Here, we studied sulfide-rich samples from the Duanqiao-1 hydrothermal field, located on the Southwest Indian Ridge. We report that, at the hand-specimen scale, late-stage conduit sulfide material has lower δ66Zn values (−0.05 ± 0.15 ‰; n = 19) than early-stage material (+0.13 ± 0.15 ‰; n = 10). These lower values correlate with enrichments in Pb, As, Cd, and Ag, and elevated δ34S values. We attribute the low δ66Zn values to the remobilization of earlier sub-seafloor Zn-rich mineralization. Based on endmember mass balance calculations, and an assumption of a fractionation factor (αZnS-Sol.) of about 0.9997 between sphalerite and its parent solution, the remobilized Zn was found consist of about 1/3 to 2/3 of the total Zn in the fluid that formed the conduit samples. Our study suggests that late-stage subsurface hydrothermal remobilization may release isotopically-light Zn to the ocean, and that this process may be common along mid-ocean ridges, thus increasing the size of the previously identified isotopically light Zn sink in the ocean.

In government studies, electronic government has become a hot topic in recent decades. Many scholars believe that soon, the government might not be able to operate smoothly without the help of ICTs as the Internet has been overwhelming people's daily lives already. In analyzing people's behavioral factors towards adopting e-government services, most studies targeted the adult population, while those in the hard-to-reach groups are minimal. This study was designed especially to understand the behavioral factors of the younger generation aged between 18 and 24 and the senior citizens above 60 on their adoption of e-government services in Macao SAR. Sixteen in-depth interviews were conducted based on the semi-structured interview questions developed from the prior literature on the Theory of Planned Behavior and e-government studies. Six significant findings are yielded, which could serve as an important reference for policymakers designing e-government policy and promoting its implementation strategy. These behavioral factors also contribute empirical data to support the theoretical framework of TPB in the context of Macao SAR e-government services.

This contribution to the special issue is an historical account of Paulo Freire’s pedagogical and administrative praxis before his forced exile in 1964. It relies on interviews collected during a field trip in 1976, a conversation with Paulo Freire in Geneva one year later and on the secondary literature up to date. Being the head of the first Extension Service of a major Brazilian university in the early 1960s gave Freire and his collaborators the space and time to experiment with the today world famous literacy method bearing his name. The concept of ‘Field of Cultural Production’ (Bourdieu) is used to elucidate better Freire and his team’s avant-gardist production within the spaces opened up by Brazil’s popular movements in the early sixties. The contribution shows how the ‘Paulo Freire System’ developed in the praxis of a cultural movement and received its academic consecration in an incremental and eclectic style.

Social workers’ work engagement and burnout were tested in relation to (a) personal variable, i.e., emotional intelligence; (b) organizational variables, i.e., work satisfaction and affective commitment. Regressions revealed emotional intelligence - controlling self – negatively predicted depersonalization and reduced personal accomplishment and positively predicted three facets of work engagement. Emotional intelligence - understanding others – was a negative predictor of reduced personal accomplishment. In addition, work satisfaction negatively predicted three components of burnout and positively predicted emotional work engagement. Affective commitment was a positive predictor of three facets of work engagement and negatively predicted reduced personal accomplishment. Implications for management are discussed.

<abstract><p>About 6.5 million people are infected with Chagas disease (CD) globally, and WHO estimates that $ &gt; million people worldwide suffer from ChHD. Sudden cardiac death (SCD) represents one of the leading causes of death worldwide and affects approximately 65% of ChHD patients at a rate of 24 per 1000 patient-years, much greater than the SCD rate in the general population. Its occurrence in the specific context of ChHD needs to be better exploited. This paper provides the first evidence supporting the use of machine learning (ML) methods within non-invasive tests: patients' clinical data and cardiac restitution metrics (CRM) features extracted from ECG-Holter recordings as an adjunct in the SCD risk assessment in ChHD. The feature selection (FS) flows evaluated 5 different groups of attributes formed from patients' clinical and physiological data to identify relevant attributes among 57 features reported by 315 patients at HUCFF-UFRJ. The FS flow with FS techniques (variance, ANOVA, and recursive feature elimination) and Naive Bayes (NB) model achieved the best classification performance with 90.63% recall (sensitivity) and 80.55% AUC. The initial feature set is reduced to a subset of 13 features (4 Classification; 1 Treatment; 1 CRM; and 7 Heart Tests). The proposed method represents an intelligent diagnostic support system that predicts the high risk of SCD in ChHD patients and highlights the clinical and CRM data that most strongly impact the final outcome.</p></abstract>

In view of the complex marine environment of navigation, especially in the case of multiple static and dynamic obstacles, the traditional obstacle avoidance algorithms applied to unmanned surface vehicles (USV) are prone to fall into the trap of local optimization. Therefore, this paper proposes an improved artificial potential field (APF) algorithm, which uses 5G communication technology to communicate between the USV and the control center. The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios. Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks, the algorithm introduces the concept of dynamic artificial potential field. For the multiple obstacles encountered in the process of USV sailing, based on the International Regulations for Preventing Collisions at Sea (COLREGS), the USV determines whether the next step will fall into local optimization through the discrimination mechanism. The local potential field of the USV will dynamically adjust, and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions. The objective function and cost function are designed at the same time, so that the USV can smoothly switch between the global path and the local obstacle avoidance. The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment, and take navigation time and economic cost into account.

Nowadays, the increasing number of medical diagnostic data and clinical data provide more complementary references for doctors to make diagnosis to patients. For example, with medical data, such as electrocardiography (ECG), machine learning algorithms can be used to identify and diagnose heart disease to reduce the workload of doctors. However, ECG data is always exposed to various kinds of noise and interference in reality, and medical diagnostics only based on one-dimensional ECG data is not trustable enough. By extracting new features from other types of medical data, we can implement enhanced recognition methods, called multimodal learning. Multimodal learning helps models to process data from a range of different sources, eliminate the requirement for training each single learning modality, and improve the robustness of models with the diversity of data. Growing number of articles in recent years have been devoted to investigating how to extract data from different sources and build accurate multimodal machine learning models, or deep learning models for medical diagnostics. This paper reviews and summarizes several recent papers that dealing with multimodal machine learning in disease detection, and identify topics for future research.