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This article reports a case study of older adults learning English in China. It indicates how, founded on consequentialist ethics, risk analysis, and safeguarding, it was decided to use covert research, drawing on the confluence of risk analysis, risk evaluation, risk management, safeguarding, research ethics, and important contextual and cultural features. Ethical principles of nonmaleficence, beneficence, safeguarding, and protection were addressed, and account was taken of the strength, likelihood, and consequences of risks, safeguards, and benefits, informed by Chinese cultural contexts, values, behaviors, and features of teaching and learning based on andragogy and geragogy. Implications are drawn for teaching and learning with older adults, advocating significant account to be taken of contextual factors.

All-electric aircraft (AEA) have garnered significant attention due to their potential to achieve zero carbon emissions and reduce noise pollution, contributing to global environmental sustainability. This study examines consumer behavioural intentions toward AEA in Guangdong, China, using the Theory of Planned Behavior (TPB) as the theoretical framework. Data were collected through structured questionnaires distributed to 100 potential adopters, assessing their awareness, influencing factors, and willingness to adopt the technology. Structural Equation Modeling (SEM) was employed to analyse the responses. The findings indicate that consumers’ attitudes, subjective norms, and environmental concerns positively influence their adoption of AEAs. Interestingly, perceived behavioural control and perceived risk were found to have no significant effect. These insights offer practical implications for accelerating AEA adoption in Guangdong's aviation sector. For airlines, the results highlight the importance of emphasising environmental benefits and social endorsements in marketing campaigns. Manufacturers can strengthen safety perceptions to align with consumer expectations, while policymakers may consider infrastructure investments to mitigate adoption barriers. Collectively, these measures could foster broader acceptance of AEAs, supporting regional decarbonisation goals in the regional air transport sector.

While the initial adoption of technology is widely studied, the factors driving its long-term retention remain a critical gap. This research shifts the focus from adoption to sustained use by applying the Model for Sustained Technology Use (MSTU) to investigate generative AI engagement among Vietnamese university students. A cross-sectional survey of 100 students measured the key constructs of Habit, Satisfaction, and Perceived Usefulness, as well as their impact on Sustained Technology Use. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that Habit is the strongest direct predictor of sustained use, surpassing the influence of Perceived Usefulness (PU). While PU drives initial adoption, its effect diminishes over time, whereas Satisfaction (ST) plays a crucial mediating role in long-term engagement. These results challenge the prevailing assumption that perceived usefulness alone is sufficient to ensure long-term success. The study offers key implications for researchers and practitioners, emphasizing the importance of designing AI educational tools that seamlessly integrate into and adapt to user workflows to promote habitual use. For educators and developers, this means prioritizing features that create engaging, positive, automatic user experiences to ensure generative AI remains a retained educational resource, not a momentary novelty.

<jats:p>Personalized recommendation plays an important role in many online service fields. In the field of tourism recommendation, tourist attractions contain rich context and content information. These implicit features include not only text, but also images and videos. In order to make better use of these features, researchers usually introduce richer feature information or more efficient feature representation methods, but the unrestricted introduction of a large amount of feature information will undoubtedly reduce the performance of the recommendation system. We propose a novel heterogeneous multimodal representation learning method for tourism recommendation. The proposed model is based on two-tower architecture, in which the item tower handles multimodal latent features: Bidirectional Long Short-Term Memory (Bi-LSTM) is used to extract the text features of items, and an External Attention Transformer (EANet) is used to extract image features of items, and connect these feature vectors with item IDs to enrich the feature representation of items. In order to increase the expressiveness of the model, we introduce a deep fully connected stack layer to fuse multimodal feature vectors and capture the hidden relationship between them. The model is tested on the three different datasets, our model is better than the baseline models in NDCG and precision.</jats:p>

The marine medaka is emerging as a potential behavioral model organism for ocean studies, namely on marine ecotoxicology. However, not much is known on the behavior of the species and behavioral assays lack standardization. This study assesses the marine medaka as a potential model for chemical communication. We investigated how short exposure to visual and chemical cues mediated the stress response to social isolation with the light/dark preference test (LDPT) and the open field test (OFT). After a 5-day isolation period, and 1 h before testing, isolated fish were randomly assigned to one of four groups: (1) placed in visual contact with conspecifics; (2) exposed to a flow of holding water from a group of conspecifics; (3) exposed to both visual and chemical cues from conspecifics; or (4) not exposed to any stimuli (controls). During the LDPT, the distance traveled and transitions between zones were more pronounced in animals exposed to the conspecific's chemical stimuli. The time spent in each area did not differ between the groups, but a clear preference for the bright area in all animals indicates robust phototaxis. During the OFT, animals exposed only to chemical cues initially traveled more than those exposed to visual or both stimuli, and displayed lower thigmotaxis. Taken together, results show that chemical cues play a significant role in exploratory behavior in this species and confirm the LDPT and OFT as suitable tests for investigating chemical communication in this species.

The investigation of pharmaceuticals as emerging contaminants in marine biota has been insufficient. In this study, we examined the presence of 51 pharmaceuticals in edible oysters along the coasts of the East and South China Seas. Only nine pharmaceuticals were detected. The mean concentrations of all measured pharmaceuticals in oysters per site ranged from 0.804 to 15.1 ng g–1 of dry weight, with antihistamines being the most common. Brompheniramine and promethazine were identified in biota samples for the first time. Although no significant health risks to humans were identified through consumption of oysters, 100–1000 times higher health risks were observed for wildlife like water birds, seasnails, and starfishes. Specifically, sea snails that primarily feed on oysters were found to be at risk of exposure to ciprofloxacin, brompheniramine, and promethazine. These high risks could be attributed to the monotonous diet habits and relatively limited food sources of these organisms. Furthermore, taking chirality into consideration, chlorpheniramine in the oysters was enriched by the S-enantiomer, with a relative potency 1.1–1.3 times higher when chlorpheniramine was considered as a racemate. Overall, this study highlights the prevalence of antihistamines in seafood and underscores the importance of studying enantioselectivities of pharmaceuticals in health risk assessments.

<jats:p>Rapid urbanization and changing climatic procedures can activate the present surface urban heat island (SUHI) effect. An SUHI was considered by temperature alterations among urban and rural surroundings. The urban zones were frequently warmer than the rural regions because of population pressure, urbanization, vegetation insufficiency, industrialization, and transportation systems. This investigation analyses the Surface-UHI (SUHI) influence in Kolkata Municipal Corporation (KMC), India. Growing land surface temperature (LST) may cause an SUHI and impact ecological conditions in urban regions. The urban thermal field variation index (UTFVI) served as a qualitative and quantitative barrier to the SUHI susceptibility. The maximum likelihood approach was used in conjunction with supervised classification techniques to identify variations in land use and land cover (LULC) over a chosen year. The outcomes designated a reduction of around 1354.86 Ha, 653.31 Ha, 2286.9 Ha, and 434.16 Ha for vegetation, bare land, grassland, and water bodies, correspondingly. Temporarily, from the years 1991–2021, the built-up area increased by 4729.23 Ha. The highest LST increased by around 7.72 °C, while the lowest LST increased by around 5.81 °C from 1991 to 2021. The vegetation index and LST showed a negative link, according to the correlation analyses; however, the built-up index showed an experimentally measured positive correlation. This inquiry will compel the administration, urban planners, and stakeholders to observe humanistic activities and thus confirm sustainable urban expansion.</jats:p>

Bridging theory and practice, the up-to-date evidence from these proceedings marks an important contribution to the advancement of children and youth health and well-being professions in the issues of technology, health, stress, inclusion, and resilience. The empirical research reported here examines the perceptions of parents, social workers, counselors, and other helping professionals concerning their awareness of child protection and parent-child relationships. These proceedings serve as a catalyst for action, enabling researchers and practitioners to reference and view the newest research through the lenses of diverse themes that focus on children and youth health and well-being, and to impact the younger population at micro and macro levels. This key text has several important features: 1. It emphasizes the impact of digital technology on well-being among children and young people in this digital age, and how to involve different stakeholders who can help to respond to emergent and existing challenges. 2. It introduces learning disabilities and issues in the field of mental stress and the biopsychology of developmental needs in school settings in addressing the UN Sustainable Development Goals. 3. It advances health knowledge and care practice through practice-oriented research, establishing new benchmarks in health care work, identifying its possibilities and constraints. 4. It enriches knowledge in the field of safeguarding for adults, including parental involvement in identifying and responding to children and youth well-being.

A greater amount of existing literature suggests that personal electronic devices (PEDs), such as smartphones, are detrimental to individuals in different aspects; a smaller amount of existing literature looks at the positive impacts of PEDs. Also, most of the literature used a quantitative approach, whereas very few of them used qualitative and mixed methods approaches. Based on my observation and experience, and talking to some young adults, it seems that what the existing literature suggests may not be truly revealing what is actually happening nowadays. With this, the present study was conducted to answer the questions: 1) What is young adults' PED use? 2) How does PED use affect (associate with) young adults' development? The present study used explanatory sequential mixed methods research design, with quantitative survey conducted first, and then followed by qualitative interviews in which questions were developed based on the findings in the quantitative phase. A sample of 736 undergraduates from five universities in Macau (M = 21.9, SD = 4.1) participated in quantitative phase, and a subsample of 13 participants from quantitative phase participated in the qualitative phase interviews. Respondents’ scores on a self-report measure of personal electronic device (PED) use were compared sociodemographic factors (i.e., age, gender, maternal language, and type of family). Qualitative data were analysed using thematic analysis. Quantitative results showed that PED use is a continuous and integral part of young adults' daily lives in Macau. Increased internet use and specific activities correlate with developmental outcomes, but only extreme use is associated with negative outcomes. Interaction and communication with others are key to happiness, regardless of call duration. Using diverse devices relates to less smartphone addiction and more happiness and social satisfaction, but mobile phones and laptops are not linked to positive or negative outcomes. PED use itself is not harmful; it is only problematic when used excessively. Qualitative results showed that PED use is an integral part of young adults' daily lives in Macao due to the powerful characteristics of PEDs that enable various tasks (Theme 1), and the necessity of PED use across different contexts and with different people (Theme 2). PEDs are used for fundamental purposes like communication, productivity, and psychosocial needs (Theme 3), leading to both positive and negative impacts on individuals' lives (Theme 4). PED use is a spectrum, not a dichotomy, distinguished by factors like maladaptation, compulsivity, overuse, and attachment (Theme 5). This qualitative study deepens the understanding of PED use beyond the quantitative findings. By linking and integrating quantitative and qualitative data and applied the theoretical framework of the present study, an extension of the bioecological theory, cloudsystem is proposed. It is believed that the cloudsystem contributes to a better understanding of the person in this specific moment of human’s existence. Practical implications, strengths and limitations of the study, suggestions for future studies were also discussed.

The convergence of air pollution control and climate change mitigation is critical in the pursuit of sustainable development. Therefore, technological innovations are pivotal in addressing the dual challenges of air pollution and global warming. This work presents an overview of technological solutions aimed at reducing air pollution and mitigating GHG emissions. While evaluating their technological strengths and limitations in real applications, this work offers a framework to promote a transition toward blue skies and net-zero emissions. This work also identifies the main sources and negative impacts of air pollution on public health and the environment. A literature overview of published articles from 1976 to 2024 showed that integrating emission reduction technologies are vital in real-word applications. More than 98% of the SO2 in the flue gas can be removed using cutting-edge desulfurization technology. SO2 is eliminated from the environment either unaltered or as sulfuric acid and sulfates. Meanwhile, thermal incinerators boast an impressive efficiency, capable of eliminating 99% of gaseous pollutants. Although existing pollution control technologies are promising to mitigate climate change, they still require further research, development, demonstration, and deployment to overcome barriers and achieve their potential. By examining the effectiveness of control technologies and proposing adaptable strategies, this work highlights the potential of integrating air quality improvement efforts with climate actions. Not only this addresses the global need for cleaner air, but also contributes to the overarching goal of climate stabilization. © The Author(s), under exclusive licence to the Institute of Chemistry, Slovak Academy of Sciences 2024.

Anticancer peptides (ACPs) are promising future therapeutics, but their experimental discovery remains time-consuming and costly. To accelerate the discovery process, we propose a computational screening workflow to identify, filter, and prioritize peptide sequences based on predicted class probability, antitumor activity, and toxicity. The workflow was applied to identify novel ACPs with potent activity against colorectal cancer from the genome sequences of Candida albicans. As a result, four candidates were identified and validated in the HCT116 colon cancer cell line. Among them, PCa1 and PCa2 emerged as the most potent, displaying IC50 values of 3.75 and 56.06 μM, respectively, and demonstrating a 4-fold selectivity for cancer cells over normal cells. In the colon xenograft nude mice model, the administration of both peptides resulted in substantial inhibition of tumor growth without causing significant adverse effects. In conclusion, this work not only contributes a proven computational workflow for ACP discovery but also introduces two peptides, PCa1 and PCa2, as promising candidates poised for further development as targeted therapies for colon cancer. The method as a web service is available at https://app.cbbio.online/acpep/home and the source code at https://github.com/cartercheong/AcPEP_classification.git.

This study sought to determine the strategy that allowed the Las Vegas Sands Corporation (LVS) to attain its leading status in the casino industry and to gain insight whether this status would continue given (i) the passing of the LVS founder, Sheldon Adelson, in January 2021, (ii)the sell-off of the company's Las Vegas properties early in 2022, and (iii) the firm's greater sensitivity to events in China caused by the company's increased reliance for most of its customers on the mainland China market. The study first identified the nature of the LVS competitive advantages when Adelson was directing the firm and then assessed whether these had been adversely impacted due to changes in the firm's markets, management or strategy. The study relied initially on the work of David Baron, Professor of Political Economy and Strategy at Stanford University who as early as 1981 advanced the view that corporate strategy needed to be divided in a Marketing Strategy (MS) and a Non-Market Strategy (NMS). The NMS component for LVS was critically important since government determined who could acquire a Macau casino concession and what level of visas would be provided to Mainland China gamblers to fill the Macau casinos. The key question became the nature of Adelson's Political Effectiveness as determined by the NMS he directed towards the China market. To resolve this issue, we adopted the Wellner & Lakotta proposal to extend Porter's Five Forces analytical framework by two additional dimensions, Government Interventors and Complementor Organizations. We concluded that it was highly likely that Goldman Sachs, the long-term financial backer of Sheldon Adelson, played a significant if not the major role in the success Adelson was able to achieve in the Greater China market.

近年來，博物館的休閒、教育、娛樂功能等日益重要，其對文化教育的貢 獻也越來越受到博物館參觀者的認可。相關的文獻大多數探討於博物館的服務 質量與參觀者參觀博物館體驗的滿意度之間的關係。目前，以參觀者的博物館 體驗如何影響他們重遊博物館的專門論述或深入研究並不多。因此，本研究的 研究目的是以博物館的體驗為中心，探討哪些因素會影響參觀者參觀博物館的 體驗，以及這些因素如何影響參觀者對博物館的重遊意願。本研究以篩選的方 式隨機抽查了 10 位參觀過博物館體驗的參觀者進行半結構式訪談(Semi- structured interview)，深入地了解受訪者的博物館體驗以及影響他們重遊博物 館的因素等問題，並以文獻及理論為基礎探討參觀者在參觀博物館過程會影響 他們的因素。採訪會在博物館附近進行，並採用深度定性訪談法(In-depth qualitative interview)，研究對象為澳門的本地參觀者。

<jats:p>To comply with the United Nations Sustainable Development Goals (UN SDGs), in particular with SDG 3, SDG 11, and SDG 13, a reliable air pollution prediction model must be developed to construct a sustainable, safe, and resilient city and mitigate climate change for a double win. Machine learning (ML) and deep learning (DL) models have been applied to datasets in Macau to predict the daily levels of roadside air pollution in the Macau peninsula, situated near the historical sites of Macau. Macau welcomed over 28 million tourists in 2023 as a popular tourism destination. Still, an accurate air quality forecast has not been in place for many years due to the lack of a reliable emission inventory. This work will develop a dependable air pollution prediction model for Macau, which is also the novelty of this study. The methods, including random forest (RF), support vector regression (SVR), artificial neural network (ANN), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), were applied and successful in the prediction of daily air pollution levels in Macau. The prediction model was trained using the air quality and meteorological data from 2013 to 2019 and validated using the data from 2020 to 2021. The model performance was evaluated based on the root mean square error (RMSE), mean absolute error (MAE), Pearson’s correlation coefficient (PCC), and Kendall’s tau coefficient (KTC). The RF model best predicted PM10, PM2.5, NO2, and CO concentrations with the highest PCC and KTC in a daily air pollution prediction. In addition, the SVR model had the best stability and repeatability compared to other models, with the lowest SD in RMSE, MAE, PCC, and KTC after five model runs. Therefore, the results of this study show that the RF model is more efficient and performs better than other models in the prediction of air pollution for the dataset of Macau.</jats:p>