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In southeast Asia, males of the Siamese fighting fish, Betta splendens, have been selected across centuries for winning paired staged fights and previous work has shown that males from fighter strains are more aggressive than wild-types. This strong directional selection for winners is likely to have targeted aggression-related endocrine systems, and a comparison between fighter and wild-type strains can bring into evidence the key hormones implicated in aggression. Here, we compared the plasma levels of the androgen 11-ketotestosterone (KT) and of the corticosteroid cortisol (F) in F2 males of a fighter and a wild-type strain raised under similar laboratory conditions. We show that F was generally lower in fighter as compared with wild-type males, while no overall differences in KT levels were detected between strains. When presented with a mirror-induced aggressive challenge, post-fight levels of F increased but more significantly so in wild-type males, while KT increased in males of both strains. After the challenge, fighter males had higher levels of KT as compared with wild-type males, while the pattern for F was opposite. As compared with animals in social groups, wild-type males placed under social isolation had lower F levels, while KT decreased for fighters. Taken together, this data suggests that while wild-type males responded to aggression with an increase in circulating levels of both androgens and corticosteroids, males selected for winning fights maintained a blunt F response, increasing only KT levels. These data agree with the hypothesis that a combination of high levels of androgens and low levels of corticosteroids is associated with high aggression. Overall, these results seem to indicate that selection for winning had a stronger impact in the hypothalamus-pituitary-interrenal axis than in the hypothalamus-pituitary–gonadal axis in B. splendens.

The occurrence of endocrine disrupting chemicals (EDCs) is a major issue for marine and coastal environments in the proximity of urban areas. The occurrence of EDCs in the Pearl River Delta region is well documented but specific data related to Macao is unavailable. The levels of bisphenol-A (BPA), estrone (E1), 17α-estradiol (αE2), 17β-estradiol (E2), estriol (E3), and 17α-ethynylestradiol (EE2) were measured in sediment samples collected along the coastline of Macao. BPA was found in all 45 collected samples with lower BPA concentrations associated to the presence of mangrove trees. Biodegradation assays were performed to evaluate the capacity of the microbial communities of the surveyed ecosystems to degrade BPA and its analogue BPS. Using sediments collected at a WWTP discharge point as inoculum, at a concentration of 2 mg l−1 complete removal of BPA was observed within 6 days, whereas for the same concentration BPS removal was of 95% after 10 days, which is particularly interesting since this compound is considered recalcitrant to biodegradation and likely to accumulate in the environment. Supplementation with BPA improved the degradation of bisphenol-S (BPS). Aiming at the isolation of EDCs-degrading bacteria, enrichments were established with sediments supplied with BPA, BPS, E2 and EE2, which led to the isolation of a bacterial strain, identified as Rhodoccoccus sp. ED55, able to degrade the four compounds at different extents. The isolated strain represents a valuable candidate for bioremediation of contaminated soils and waters.

The Revenue Management (RM) problem in airlines for a fixed capacity, single resource and two classes has been solved before by using a standard formalism. In this paper we propose a model for RM by using the semi-classical approach of the Quantum Harmonic Oscillator. We then extend the model to include external factors affecting the people’s decisions, particularly those where collective decisions emerge.

Seafloor massive sulfide (SMS) deposits are important deep-sea mineral resources expected to occur predominantly on slow- and ultraslow-spreading mid-ocean ridges. Resource estimates are already available for some of the largest SMS deposits on slow-spreading ridges but not on ultraslow-spreading ridges. Based on geological mapping and sampling, this study investigates the distribution and content of sulfide-rich deposits in the Yuhuang-1 hydrothermal field (YHF), located on the ultraslow-spreading Southwest Indian Ridge. The sulfide-rich deposits in the YHF are composed of two areas ∼500 m apart: the southwest sulfide area (SWS) and the northeast sulfide area (NES). We calculated the volume of sulfide-rich mounds in the YHF and arrived at a total accumulation of ∼10.6 × 106 tons, including at least ∼7.5 × 105 tons of copper and zinc and ∼18 tons of gold. Furthermore, considering the coverage of layered hydrothermal sediment mixed with sulfide-rich breccias, which may have underlying massive sulfide deposits, the maximum total mass was estimated at ∼45.1 × 106 tons. This suggests that the YHF is one of the largest SMS deposits worldwide and confirm that ultraslow-spreading ridges have the greatest potential to form large-scale SMS deposits.

It has been claimed in \cite1, that the idea proposed in \cite2 has certain mistakes based on arguments of energy conditions and others. Additionally, some of the key arguments of the paper are criticized. Here we demonstrate that the results obtained in \cite2 are correct and that there is no violation of any energy condition. The statements claimed in \cite1 are based on three things: 1). Misinterpretation of the metric solution. 2). Language issues related to the physical quantities obtained in \cite1, where the authors make wrong interpretations about certain results over the geometry proposed in \cite2. 3). Non-rigorous evaluations of the vacuum condition defined via the result over the Ricci tensor R_\mu\nu=0.

Critical thinking (CT), as a form of higher-order thinking, is intended to help individuals form reasonable reflection and judgment to deal with increasingly severe employment situations. As the primary workforce in the labor market, undergraduates must possess a strong critical thinking disposition (CTD) to make better use of CT. Despite extensive research on components of CTD from the perspective of educational practices, there is limited emphasis on investigating the components and their relationships of CTD in the labor market and the impact of gender differences. Therefore, this study presented an analysis of 1535 Chinese undergraduates (Mage = 20.89; SD = 1.43) using the Employer-Employee-Supported Critical Thinking Disposition Inventory (2ES-CTDI), aiming to explore the CTD that undergraduates should possess before entering the labor market. The relationships among the components were examined using SmartPLS4.0 in conjunction with Partial Least Squares Structural Equation Modeling (PLS-SEM). Additionally, a multigroup analysis (PLS-MGA) with a measurement invariance (MI) test was conducted to validate the moderating effects of gender. The findings indicate that (a) self-efficacy has a significant negative effect on habitual truth-digging, and boys are more affected than girls, instant judgment plays a competitive partial mediating role in this relationship; (b) self-efficacy has a significant positive effect on instant judgment, and boys are more likely to make instant judgments than girls; (c) instant judgment significantly positively affects habitual truth-digging. These findings highlight the dynamic equilibrium among the internal components of CTD in the labor market and call for increased attention from educators to the importance of gender differences in the cultivation process.

This study explores the relationship between student teachers' beliefs and practices in early Chinese literacy instruction. Semi-structured interviews, classroom observation, and document analysis were conducted with six student teachers during their teaching practices. Findings indicated that the student teachers believed explicitly teaching literacy skills and imperceptible acquisition of literacy abilities through communication and meaning-making processes are essential in Chinese early literacy learning. However, they mainly taught Chinese literacy skills in their practices, which means the student teachers still needed to practice what they preached fully. The study suggests that possible reasons for the discrepancies include 'direct teaching' and 'rote learning' might be much easier for student teachers to design and conduct a lesson. Student teachers have limited abilities and experiences in conducting an ideal lesson, and the kindergarten curriculum and onsite supervisors highly influenced their teaching practices. The findings from this study suggested that more operational activities (such as designing lesson plans and conducting micro-teaching) should be used during pre-service training. Furthermore, the communication of educational beliefs between the university supervisor and the onsite supervisor should be strengthened.

This essay presents a mapping of the historical concepts that contributed to the emergence of post-digital aesthetics and their connections to the concept of post-media in historical terms. It also analyzes the transition from techno-positivism to discourse of resistance against the effects of the capital technological industrial complex and how these advances in technology influence artistic discourses, practices and are the leverage of art and technology which is nothing more than a representation of the aesthetics of capital. Following art and capitalism as an ideology of innovation. Is proposed an unstinting theory about technology, geology, and the importance of these conditions to the post-digital aesthetics in terms of material disponible and conceptual articulation. Producing a reconfiguration of the post-digital conceptual approach as I propose beyond the dysfunctional aesthetics and connected with the concept of radical ecology centered in the usability of electronic garbage and technical obsolescent technologies in the arts.

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.

The purpose of this paper examines the potential role of Chinese outbound tourism as a catalyst of change for the Macanese identity. There is an emerging trend to have a new identity amongst Macanese residents to valorize cultural and heritage assets. By using Bourdieu’s concept of habitus, it proposes that the social media space is built to voice concerns of language, place attachment and induced public participation. The study utilizes a mixed method approach comprising a survey for netizens and content analysis of online discussions, in order to fully understand changes due to Chinese outbound tourism.

The extraction of 21 insecticides and 5 metabolites was performed using an optimized and validated QuEChERS protocol that was further used for the quantification (GC–MS/MS) in several seafood matrices (crustaceans, bivalves, and fish-mudskippers). Seven species, acquired from Hong Kong and Macao wet markets (a region so far poorly monitored), were selected based on their commercial importance in the Indo-Pacific region, market abundance, and affordable price. Among them, mussels from Hong Kong, together with mudskippers from Macao, presented the highest insecticide concentrations (median values of 30.33 and 23.90 ng/g WW, respectively). Residual levels of fenobucarb, DDTs, HCHs, and heptachlors were above the established threshold (10 ng/g WW) for human consumption according to the European and Chinese legislations: for example, in fish-mudskippers, DDTs, fenobucarb, and heptachlors (5-, 20- and tenfold, respectively), and in bivalves, HCHs (fourfold) had higher levels than the threshold. Risk assessment revealed potential human health effects (e.g., neurotoxicity), especially through fish and bivalve consumption (non-carcinogenic risk; ΣHQLT > 1), and a potential concern of lifetime cancer risk development through the consumption of fish, bivalves, and crustaceans collected from these markets (carcinogenic risk; ΣTCR > 10–4). Since these results indicate polluted regions, where the seafood is collected/produced, a strict monitoring framework should be implemented in those areas to improve food quality and safety of seafood products.

Quality of life in general population before and during pandemic is topic need to be address by researcher in terms of mobility, self-care, usual activities, pain/discomfort and anxiety/depression. The study was carried out among Saudi population. Data were collected from general population using questionnaire during the period from 22 August 2021 to 10th January 2022. As a result, total 214 participants have included in this study. Among them prevalent age group include 40 years (n= 63, 29.4%) shadowed by the age group 25-35 (n= 61, 28.5%) while above 60 years group were least frequent (n= 1, 0.5%). On questioning the applicants whether they were satisfied with their health and how would they rate their quality of life, their answers were as follows: yes, or satisfied (n= 86, 40.2%), very Satisfied (n= 102, 47.7%) Dissatisfied (n= 11, 5.1%) and neither satisfied nor dissatisfied (n= 15, 7%). Due to pandemic, they were rate quality of life very good (n= 94, 43.9%), good (n= 63, 29.4 %) poor (n= 5, 2.3 %) and neither good and nor poor (n= 52, 24.3 %). During pandemic 96 participants feel no change in their weight but 110 participants respond that there is increase in coffee intake during the pandemic. Similarly increased in smoking habits and decrease rate in social activities (n=119,41.4%). The psychosomatic well-being of people has been interrupted by disturbing their social activities during pandemic.

The study examined the factor structure, reliability and validity of a Chinese version of the Constructivist Learning Environment Survey (C-CLES), an instrument for assessing students’ perceptions of the extent of constructivist approaches prevalent in classrooms. A convenience sample of 967 students in Secondary Three (Grade 9) in Hong Kong participated in this study by completing a self-administered questionnaire in their class time. Exploratory and confirmatory factor analyses supported the hypothesised factor structure, indicating five theoretical constructivist environment dimensions that showed goodness-of-fit to 25 items: Personal Relevance, Uncertainty, Critical Voice, Shared Control, and Student Negotiation. Criterion-related validity, involving evidence based on relations to other variables, was assessed by correlations between the constructivist environment dimensions and cognitive strategies and academic ability. Most correlations were statistically significant and in the positive direction. The C-CLES with 25 items provides a useful measure for educational practice and research among school students.

<jats:title>Abstract</jats:title><jats:p>This research unveils to predict consumer ad preferences by detecting seven basic emotions, attention and engagement triggered by advertising through the analysis of two specific physiological monitoring tools, electrodermal activity (EDA), and Facial Expression Analysis (FEA), applied to video advertising, offering a twofold contribution of significant value. First, to identify the most relevant physiological features for consumer preference prediction. We integrated a statistical module encompassing inferential and exploratory analysis tools, which identified emotions such as Joy, Disgust, and Surprise, enabling the statistical differentiation of preferences concerning various advertisements. Second, we present an artificial intelligence (AI) system founded on machine learning techniques, encompassing k‐Nearest Neighbors, Support Vector Machine, and Random Forest (RF). Our findings show that the RF technique emerged as the top performer, boasting an 81% Accuracy, 84% Precision, 79% Recall, and an F1‐score of 81% in predicting consumer preferences. In addition, our research proposes an eXplainable AI module based on feature importance, which discerned Attention, Engagement, Joy, and Disgust as the four most pivotal features influencing consumer ad preference prediction. The results indicate that computerized intelligent systems based on EDA and FEA data can be used to predict consumer ad preferences based on videos and effectively used as supporting tools for marketing specialists.</jats:p>

This research unveils to predict consumer ad preferences by detecting seven basic emotions, attention and engagement triggered by advertising through the analysis of two specific physiological monitoring tools, electrodermal activity (EDA), and Facial Expression Analysis (FEA), applied to video advertising, offering a twofold contribution of significant value. First, to identify the most relevant physiological features for consumer preference prediction. We integrated a statistical module encompassing inferential and exploratory analysis tools, which identified emotions such as Joy, Disgust, and Surprise, enabling the statistical differentiation of preferences concerning various advertisements. Second, we present an artificial intelligence (AI) system founded on machine learning techniques, encompassing k-Nearest Neighbors, Support Vector Machine, and Random Forest (RF). Our findings show that the RF technique emerged as the top performer, boasting an 81% Accuracy, 84% Precision, 79% Recall, and an F1-score of 81% in predicting consumer preferences. In addition, our research proposes an eXplainable AI module based on feature importance, which discerned Attention, Engagement, Joy, and Disgust as the four most pivotal features influencing consumer ad preference prediction. The results indicate that computerized intelligent systems based on EDA and FEA data can be used to predict consumer ad preferences based on videos and effectively used as supporting tools for marketing specialists.

With the fifth generation (5G) communication technology, the mobile multiuser networks have developed rapidly. In this paper, the performance analysis of mobile multiuser networks which utilize decode-and-forward (DF) relaying is considered. We derive novel outage probability (OP) expressions. To improve the OP performance, we study the power allocation optimization problem. To solve the optimization problem, we propose an intelligent power allocation optimization algorithm based on grey wolf optimization (GWO). We compare the proposed GWO approach with three existing algorithms. The experimental results reveal that the proposed GWO algorithm can achieve a smaller OP, thus improving system efficiency. Also, compared with other channel models, the OP values of the 2-Rayleigh model are increased by 81.2% and 66.6%, respectively.