Search Results (14,102)

Background/Objectives: Native to the Indo-Pacific region, edible bird’s nests (EBN; Yan Wo in Chinese) are the solidified saliva of swiftlets (Aerodramus fuciphagus and A. maximus) and have been consumed as a traditional functional food for centuries. However, the bioactive components and underlying mechanisms of EBN remain poorly understood. EBN consists of over 60% protein, much of which is heavily glycosylated, forming complex glycoconjugates that are resistant to enzymatic digestion. This study examines the properties of EBN-derived bioactive peptides and assesses their potential for skin moisturization and anti-inflammation when applied topically. Methods: EBN was double-boiled for an extended period, then digested with gastric enzymes to extract active peptides. Digestion was over 90% efficient, and peptide molecular weights were measured. The enzymatic digest was then fractionated using an activity-guided approach based on assays for skin moisturization and anti-inflammatory properties. Results: A novel bioactive heptapeptide, with the sequence LAGAAHF and designated EBN_P3, was identified and characterized. It attenuated TNF-α-induced inflammatory responses in HaCaT keratinocytes and alleviated dermatitis symptoms in a DNCB-induced C57BL/6 mouse model. Conclusions: EBN-derived peptides with skin moisturizing and anti-inflammatory activities hold significant promise for development into functional ingredients for skincare products. Full article

Emerging technologies (ETs), such as AI and reality techniques, are reshaping educational measurement. However, existing studies remain dispersed and are rarely synthesized in ways that clarify how ETs participate in the evidentiary work of educational measurement. Guided by PRISMA 2020, we systematically reviewed 933 empirical studies published between 2016 and 2025 in formal educational settings. We coded studies by (a) grain size (micro, meso, macro), (b) Constructing Measures Theory building blocks (construct map, item design, outcome space, measurement model), and (c) ET category. Results showed a strong concentration at the micro level (88.88%) and in outcome space and measurement model work (86.80% combined), indicating that ET-enabled innovation has focused primarily on transforming performances into indicators and modeling those indicators for interpretation and decision-making. Learning analytics and educational data mining, machine learning and deep learning, and automated scoring and feedback systems were the dominant ET clusters. These findings point to an uneven development of ET-enabled educational measurement. Included studies also indicating recurring concerns about transparency, fairness, and governance are linked to the field’s main areas of ET-enabled concentration. We therefore argue for closer alignment among construct claims, evidence, modeling, and intended use, and offer implications for developers, researchers, and education practitioners. Full article

Against the backdrop of expanding digital dissemination and experiential transformation in cultural heritage, visitors’ visual attention and symbolic choices increasingly shape heritage cognition and value transmission. Taking the Yellow Crane Tower as a case study, this research constructs a cultural symbol recognition dataset based on visitor-shared social media images and develops an enhanced ResNet-50 model for multi-label analysis. By integrating attention mechanisms and regularisation strategies, the model improves its capacity to capture complex cultural imagery, achieving a macro F1 score of 72.70% and a micro F1 score of 81.05% on the test set, indicating strong generalisation performance. The results reveal a significant imbalance in visual preferences: landmark symbols centred on the main architectural structure dominate at 32.95%, whereas culturally informative elements such as signage, cultural products, and interpretive facilities each account for less than 5%. Tag co-occurrence analysis further identifies three image production patterns: commemorative presentation, contextual documentation, and detail-oriented cultural photography reflecting different levels of heritage perception. Rather than directly proposing prescriptive strategies, the findings provide an empirical basis for informing future interventions aimed at shifting from landmark-focused viewing to deeper cultural perception. In this way, the study contributes to heritage display optimisation and research on visitor visual behaviour. Full article

Type 2 diabetes mellitus (T2DM) is driven by insulin resistance and β-cell dysfunction. While Ras GTPases are known for oncogenic signaling, emerging evidence implicates the Ras–Ral axis as a critical regulator of glucose homeostasis. This review synthesizes the distinct roles of Ras and Ral in metabolism. Ras hyperactivation promotes insulin resistance and inflammation via MAPK/PI3K pathways, whereas RalA supports GLUT4 translocation and insulin granule exocytosis. We propose a dual-pathway hypothesis: T2DM pathophysiology involves an imbalance characterized by excessive Ras signaling and insufficient Ral-mediated metabolic actions. Consequently, we explore the therapeutic potential of rebalancing this axis through combinatorial strategies, that selectively inhibit pathogenic Ras while enhancing protective Ral activity. We critically evaluate current Ras-targeted agents (e.g., farnesyltransferase inhibitors, allele-specific inhibitors) and discuss the emerging frontier of Ral-specific enhancers. Finally, we outline key translational challenges and future directions for validating this axis as a target for precision medicine in T2DM. Full article

Uncertainty in vessel arrival times can substantially reduce the efficiency of berth planning in port operations. To address this issue, this study proposes a unified, data-driven, predict-then-optimize framework that explicitly links vessel arrival time (VAT) prediction with downstream continuous berth allocation optimization. In the prediction stage, heterogeneous maritime data, including port call records, AIS trajectories, and vessel physical characteristics, are integrated to construct VAT prediction models. In the optimization stage, the predicted VAT is embedded into a continuous berth allocation problem (BAP) model to support berth scheduling decisions. To better reflect real operations, a two-stage evaluation framework is further developed, in which berth plans generated from estimated arrival times (ETAs) or predicted VATs are re-evaluated under realized actual arrival times while preserving the original temporal and spatial service order. Experimental results show that the proposed framework improves VAT prediction accuracy substantially, reducing the MAE and RMSE from 4.795 h and 7.255 h for the vessel-reported ETAs to 2.844 h and 4.934 h, respectively. More importantly, the predicted-VAT-based BAP consistently outperforms the ETA-based benchmark, yielding an overall 35.96% reduction in objective value across tested scenarios. These findings demonstrate that improved VAT prediction can be effectively translated into meaningful operational gains in berth allocation. Full article

Financial time series forecasting remains a persistent challenge due to the non-stationary nature, inherent noise, and multi-scale temporal dependencies present in market data. This paper presents MSTFNet, a multi-scale temporal fusion network that combines dilated causal convolutions with a frequency-enhanced sparse attention mechanism for improved financial prediction. The proposed architecture consists of three core components: a multi-scale dilated causal convolution module that extracts temporal patterns across different time horizons through parallel convolutional branches with varying dilation rates, a frequency-enhanced sparse attention mechanism that leverages Fast Fourier Transform to identify dominant periodic components and modulate attention weights accordingly, and an adaptive scale fusion gate that learns to dynamically combine representations from multiple temporal scales. Extensive experiments conducted on three public financial datasets (S&P 500, CSI 300, and NASDAQ Composite) spanning the period from January 2015 to December 2024 show two key results. First, consistent with near-efficient markets, the random-walk benchmark (${\widehat{y}}_{t+1}=y_t$) outperforms all the data-driven models on level-error metrics (MAE, RMSE, MAPE, and $R^2$), establishing the martingale as the binding lower bound on point-prediction error. Second, MSTFNet achieves the highest directional accuracy (DA) across all three indices—56.3% on the S&P 500 versus 50.0% for the martingale—representing a 6.3 percentage-point improvement that generates positive pre-cost returns in a trading strategy backtest. Among the eight data-driven baselines (LSTM, GRU, TCN, Transformer, Autoformer, FEDformer, PatchTST, and iTransformer), MSTFNet also achieves the lowest MAE, reducing it by 13.6% relative to the strongest data-driven baseline (iTransformer) on the S&P 500. These results confirm that integrating multi-scale temporal modeling with frequency-domain guidance extracts a real, if modest, directional signal from financial time series. Full article

With the increase in the demand for electronic-grade high-purity silane in the semiconductor chip industry, it is of great significance to develop a green and economical method for silane production. Therefore, a novel energy-saving fixed-bed process was proposed innovatively. In this paper, the thermodynamics and kinetics of the trichlorosilane disproportionation system were studied, and the optimal reaction conditions for the resin catalyst were determined, which were used for the subsequent simulation. Based on the conventional DR1 + DR2 process (which includes one trichlorosilane disproportionation reactor (DR1) and one dichlorosilane disproportionation reactor (DR2)), by adding an additional disproportionation reactor to the TCS recycle loop and/or DCS recycle loop, three improved process configurations were designed, including 2DR1 + DR2, DR1 + 2DR2, and 2DR1 + 2DR2 processes. Then, combined with four-column heat integration, the HI + 2DR1 + 2DR2 process was proposed to solve the bottleneck problems of high energy consumption and large circulation flow rate. The results show that the HI + 2DR1 + 2DR2 process achieved the best energy-saving effect. The TCS recycle loop flow rate reduced by 36.87%, the DCS recycle loop flow rate reduced by 12.41%, total energy consumption decreased by 62.8%, and CO₂ emissions decreased by 56.72%. The unit energy consumption is 13.8 kg steam/kg SiH₄, and the silane purity is greater than 99.9999%. This design can be easily applied to the existing production process of the silane plant, achieving energy-saving and low-cost production of silane. Full article

Background: Midkine (MDK), a secreted heparin-binding growth factor, is involved in tumor progression and metastasis. While serum MDK is widely recognized as a potential prognostic biomarker for colorectal cancer (CRC), its specific functional role and underlying mechanisms in CRC development are not fully understood. Methods: The four publicly available CRC microarray datasets—GSE41258, GSE44076, GSE81558, and GSE117606—along with TCGA-COAD and TCGA-READ datasets and their associated clinical data were obtained. MDK expression was measured at both the mRNA and protein levels using quantitative real-time PCR (qRT-PCR) and Western blotting. To investigate its oncogenic functions, a comprehensive set of assays was performed: transwell and wound healing assays for invasion and migration; CCK-8 and colony formation assays for proliferation; and tail vein/spleen injection models combined with xenograft models to study metastasis and tumor growth in vivo. To uncover underlying mechanisms, Western blotting was used to examine the involvement of epithelial–mesenchymal transition (EMT) and the PI3K/AKT signaling pathway. Results: MDK is significantly overexpressed in CRC tissues and cells compared to normal tissues and cells. Notably, patients with high MDK levels show poorer overall survival (OS). Overexpression of MDK increases CRC invasion, migration, proliferation, and metastasis both in vivo and in vitro, while its knockdown reverses these effects. Mechanistically, MDK activates the PI3K/AKT pathway, leading to increased AP2A1 expression and promotion of EMT in CRC. Conclusions: MDK promotes invasion, migration, proliferation, metastasis, and EMT in CRC cells through the PI3K/AKT pathway by inducing AP2A1 expression, which could serve as a diagnostic marker. The PI3K inhibitor LY294002 significantly reduces AP2A1 levels and inhibits MDK-induced malignant behaviors. Targeting MDK-related signaling pathways may offer new strategies for CRC treatment. Full article

Artificial intelligence (AI) systems and tools are increasingly reshaping educational practices. This study examines perspectives shared in student-focused online communities on AI’s impact on education, comparing those of computer science (CS) and business students through an analysis of Reddit posts. Using natural language processing (NLP), sentiment analysis, and Latent Dirichlet Allocation (LDA) topic modeling, we analyzed 1108 posts collected from six subreddits. Results reveal distinct thematic focuses: CS students emphasize technical aspects, including programming efficiency, coding assistance, and concerns about job displacement, while business students focus on decision-making enhancement, financial analysis applications, and operational efficiency. Sentiment analysis indicates that the Business/Finance-oriented corpus is slightly more positive than the CS-oriented corpus (51.9% vs. 50.1% positive). The CS-oriented corpus also contains a higher proportion of negative posts (36.0% vs. 33.2%). These differences reflect discipline-specific epistemological frameworks shaping AI perception. The findings provide educators with guidelines for developing tailored AI integration strategies that address discipline-specific concerns and opportunities. This study contributes to understanding how academic background influences perceptions of AI in education, offering insights for curriculum design and policy development. Full article

Extracellular vesicles (EVs) from the edible mushroom Pleurotus tuber-regium (PTR) were investigated with respect to their environmental responsiveness, molecular features, and preliminary functional properties. PTR-EVs were characterized by dynamic light scattering, nanoparticle tracking analysis, and transmission electron microscopy. Proteomic analysis revealed enrichment of ribosomal and proteasomal proteins, redox-related enzymes, and vesicle trafficking components, suggesting non-random molecular representation. Small RNA sequencing identified abundant novel miRNAs with predicted targets involved in nitrogen metabolism, cell wall remodeling, redox regulation, and ubiquitin-mediated proteolysis. Among the tested factors, temperature showed the strongest association with vesicle production, with particle concentration increasing from 1.22 × 10⁹ to 7.31 × 10⁹ particles/mL at 34 °C, approximately six-fold higher than at 30 °C. Transcriptomic profiling showed coordinated repression of cell wall-associated genes and redox enzymes, together with induction of endoplasmic reticulum proteostasis pathways, consistent with stress-associated changes in the cellular context of vesicle release. Ultrasonicated PTR-EVs exhibited enhanced DPPH and ABTS radical-scavenging activities in chemical assays, with DPPH increasing from 59.52% to 71.73% and ABTS from 38.25% to 40.51%. Encapsulation efficiencies reached 32.67% ± 1.3% for proanthocyanidins and 46.01% ± 0.5% for curcumin. PTR-EVs showed the best short-term stability at pH 7 and 4 °C, supporting their further evaluation as an edible fungal vesicle platform for food-related nanoscale delivery. Full article

Social washing refers to the strategic exaggeration or misrepresentation of an organisation’s commitment to social responsibility, ethical governance, or social impact without corresponding substantive action. It typically operates through selective disclosure, symbolic initiatives, or performative communication that aligns the organisation with socially desirable values—such as equity, human rights, community development, or inclusion—while underlying practices remain unchanged, weakly evidenced, or contradictory. The concept belongs to the wider family of “washing” phenomena associated with corporate social responsibility (CSR) and environmental, social, and governance (ESG) frameworks, especially the difficult-to-measure social (“S”) pillar. By contrast, authentic accountability refers to governance and reporting practices that connect institutional commitments to verifiable social outcomes and discernible improvements in human well-being. The institutionalisation of ESG frameworks has raised expectations of corporate responsibility while also enlarging the scope for reputational manipulation. Within this setting, social washing has become relevant not only to social policy and sustainable development debates, but also to corporate governance, ESG evaluation, and cross-sector partnership practice. This entry examines how organisations construct narratives of social responsibility that do not necessarily correspond to substantive social outcomes. It also argues that such distortions matter both for welfare systems and civil-society actors and for ESG assessment, reputational signalling, and the interpretation of social performance in market settings. Full article

Grounded in the assessment ecology framework, we examine how assessment components (school assessment policies, teacher assessment practices and training) are linked to the reading achievements of 151,969 students from 19 countries. Analyses of the 2018 PISA survey and test data yielded these results. Schools that posted assessment results for accountability, or teachers who often clarified learning goals, tracked student progress or accordingly adapted their teaching had students with higher reading scores. By contrast, schools mostly using assessment data to evaluate, teachers trained in reading comprehension assessment, or giving more feedback had students with lower reading scores. Students in richer countries or with better relationships with their teachers had higher reading scores. These findings show the complexity and interactions within assessment ecologies that shape learning outcomes. Full article

This paper documents the preliminary observations and applications of a Ka-band cloud radar newly installed at the Hong Kong International Airport. A special scanning strategy of the cloud radar was developed and is described in detail. The radar provides reasonable cloud base height data as compared with a co-located laser ceilometer, by identifying the lowest vertical layer with reflectivity > −30 dBZ and at least 150 m thick, filtering measurements influenced by rainfall, and removing noise with differential reflectivity thresholds. As demonstrated in a heavy rain case study, the radar provides good estimates of the cloud top height as well, consistent with the cloud liquid water content profiles from a microwave radiometer. The various applications of the cloud radar are then explored, including (1) observations of supercooled liquid water in clouds associated with a late-season tropical cyclone in the South China Sea, (2) monitoring of low visibility in light rain or mist at the airport region using reflectivity as well as Doppler velocity data, and (3) monitoring severe weather such as windshear and turbulence to be encountered by departing aircraft due to low-level jets and initiation of heavy rain, using the Doppler velocity and spectrum data. These observations demonstrated the robustness in the cloud radar in the observation of high clouds and the applicability of the radar’s Doppler velocity in plan position indicator scans under light rain situations. Potential research with the radar, such as visibility maps, turbulence intensity maps, and automatic cloud observations, is also discussed. Full article

As global digital transformation accelerates, artificial intelligence (AI) and blockchain technologies have evolved from theoretical concepts into practical tools within the field of accounting, particularly in financial reporting. This study conducts a systematic review of 62 sources drawn from major academic databases to develop a comprehensive framework for classifying application scenarios. The findings indicate that the application of artificial intelligence and blockchain technology can help improve the efficiency of financial report generation, enhance the reliability of data, and promote innovation in the auditing process. Nevertheless, persistent challenges remain, including concerns related to data security, technological limitations, and regulatory gaps. The study proposes a structured roadmap for the implementation of these technologies, underscoring their transformative potential in advancing the digital evolution of accounting, while also identifying key directions for future research. Full article

We propose a reaction–diffusion phase-field model to simulate the microstructure evolution of voids in systems with low vacancy concentration under irradiation. In this model, void growth and shrinkage are governed by reactions between vacancies/interstitials and the void surface, while an order parameter is introduced to describe void morphology. By avoiding the sharp increase in vacancy concentration near the void interface, the model enables the simulation of void evolution in low vacancy concentration matrices over enlarged time scales. When combined with classical nucleation theory, the approach enables quantitative, accurate three-dimensional simulations of slow void evolution processes, achieving comparability with rate theory models. Numerical results demonstrate that the model accurately captures the evolution of voids under dilution conditions. At the same time, its inherent scalability makes it broadly applicable to other material systems characterized by low solute concentrations. Full article