Search Results (115,647)

In the present work, we investigated the role of polydopamine coatings in anchoring TiO₂ P25 nanoparticles on gold and ITO surfaces. For this purpose, we studied the adhesion of polydopamine-coated aggregates of TiO₂ nanoparticles on bare substrates and of aggregates of bare TiO₂ nanoparticles on polydopamine-coated substrates. Coating with polydopamine was accomplished by oxidation in air of alkaline aqueous dopamine solutions in which the nanoparticles or the substrates were immersed. Dynamic light scattering, Fourier transform infrared spectroscopy, and transmission electron microscopy were used for the chemical and morphological characterization of aggregates of the nanoparticles. The adhesion of aggregates of the nanoparticles on the substrates was evaluated by means of AFM and XPS. The results of this study suggest that the adhesion of TiO₂ P25 nanoparticles on polydopamine films, as well as of polydopamine-coated TiO₂ P25 nanoparticles, is a balance of several contributions: chemical interactions, electrostatic interactions, and coating roughness. Electrostatic attraction and repulsion between nanoparticles and the substrate appear to play an important role, as indicated by the ζ-potential values of nanoparticles and substrates. Full article

Constructing wind and solar energy bases is an effective way to promote the green transformation, and the optimal dispatch of renewable energy bases is essential to their high-quality development. However, wind and solar generation are characterized by intermittency, fluctuations, and unpredictability, which pose new challenges to the economic operation of power systems. This paper proposes an enhanced data-driven robust optimization method to establish an economic dispatch model for a new energy base that accounts for uncertainties in wind turbine and photovoltaic power output. It also develops uncertainty intervals for wind turbine and photovoltaic output using a random forest regression method. Compared with traditional robust optimization methods, the proposed method fully utilizes historical data to establish a more precise and flexible uncertain variable interval model, avoiding the overly conservative issues inherent in traditional robust optimization approaches. Lastly, the proposed method is validated in a case study, demonstrating that the data-driven uncertainty set is more in line with the actual situation. Full article

Digital transformation has emerged as one of the most influential drivers of contemporary socio-economic change, shaping development trajectories, social resilience, and the capacity of societies to adapt to external shocks. Despite the growing body of research on digital transformation, relatively little attention has been devoted to societal digitalization as a distinct analytical category. Existing approaches remain largely focused on technological infrastructure, economic performance, or organizational transformation, while the social dimension of digital development is frequently treated as secondary. Addressing this gap, the present study conceptualizes societal digitalization as an autonomous dimension of digital transformation and advances a human-centred perspective that emphasizes digital capabilities and meaningful technology use rather than mere access to technological resources. The study examines the digital development trajectories of Indonesia, Malaysia, and Thailand (ASEAN-3) between 2016 and 2023, with particular attention to the transformative effects of the COVID-19 pandemic. To this end, an original Digital Development of Society (DDS) Index was developed and applied. The index is grounded in a hierarchical framework encompassing three interrelated dimensions: Access, Skills, and Use. The findings reveal substantial cross-country differences in both the level and structure of societal digitalization. More importantly, they provide empirical evidence of a second-level digital divide, demonstrating that improvements in digital access do not automatically translate into higher levels of digital competence or more advanced forms of technology utilization. The results further indicate that a structural shift in digital development—moving the focus from connectivity towards digital skills and meaningful use—accelerated during the 2020–2023 period. Consequently, human capital emerges as a more decisive determinant of digital maturity than infrastructure alone. The study contributes to the literature by offering a new conceptual framework for understanding societal digitalization and by introducing a multidimensional measurement tool capable of identifying structural bottlenecks in socio-digital development. Furthermore, the findings extend the policy debate on digital transformation by providing a diagnostic framework that enables policymakers to identify structural bottlenecks in national digital ecosystems and to align infrastructure investments with human capital development and meaningful digital participation. Full article

Environmental accumulation issues arise from discarded waste, synthetic, and natural polymers in landfills, whose slow degradation exacerbates the problem. These materials can be upcycled as renewable feedstocks and properly reused. Therefore, this research examines the fabrication of an extruded wood–plastic suitable for large-scale production, made from recycled polypropylene (PP) and reinforced with plant-based fillers extracted from the lignocellulosic shell of Bertholletia excelsa, well-known as Castanha-do-Pará (CDP), offering a cost-effective and sustainable solution that minimizes waste and fosters a circular economy, enhancing its mechanical properties and environmental benefits. The fractions of 10%, 20%, and 30% by mass of CDP were tested. X-ray diffraction (XRD) analyses indicated that CDP acts as a nucleating agent for the polymer’s beta phase. Fourier-transform infrared spectroscopy (FTIR) indicated interactions between the components as the filler content increased. Scanning Electron Microscopy (SEM) images revealed increased void regions at different CDP contents, corroborating the impact results. These outcomes indicate that incorporating 20%–30% Castanha-do-Pará filler can enhance the wood–plastic flexural properties. This reduces reliance on purely synthetic materials and positions Bertholletia excelsa wood–plastic as a valuable, potentially large-scale product. Full article

Transport Layer Security (TLS) 1.3 strengthens Internet privacy by encrypting protocol metadata increasingly used by network monitoring and intrusion-detection systems, while Encrypted ClientHello (ECH) further reduces visibility into connection establishment. This paper presents a systematic and technically grounded review of encrypted-traffic detection approaches under TLS 1.3 and ECH, with an emphasis on their observable features, analytical formulations, and practical limitations. Unlike previous reviews that primarily classify detection techniques, this study explicitly examines the methodological assumptions and mathematical foundations underlying representative approaches, including Random Forest aggregation, Kullback–Leibler divergence, Discrete Fourier Transform (DFT), and Shannon entropy. The literature is systematically organised into machine learning, statistical/rule-based, and behavioural/flow-level approaches and assessed against feature dependency, interpretability, reproducibility, scalability, deployment feasibility, and resilience to reduced visibility. The review identifies continued dependence on TLS-specific or handshake-derived features and highlights persistent challenges in dataset representativeness, cross-environment generalisation, explainability, and adversarial robustness. In contrast, residual observables—including packet timing, size distributions, directional asymmetry, flow dynamics, frequency-domain characteristics, and burst behaviour—remain potentially useful without inspecting encrypted payloads or concealed protocol fields. The synthesis identifies behavioural–statistical fusion as a promising research direction; however, its effectiveness remains empirically unvalidated as an integrated framework. Future research should therefore prioritise reproducible datasets, cross-environment and adversarial evaluation, and lightweight, interpretable detection mechanisms capable of operating under progressively restricted network visibility. Full article

Heart rate variability (HRV) forms the basis of non-invasive autonomic nervous system assessment. However, its analysis is constrained by the non-stationary nature of physiological signals. Standard analytical methods, which assume stationarity within fixed time windows, fail to capture dynamical effects of interest, such as the response to a physiological stressor. This limitation obstructs the development of mechanistic hypotheses about autonomic control. Here, we address this challenge by introducing a probabilistic framework for modeling non-stationary HRV dynamics during transient, single-event perturbation-recovery paradigms. We propose a hypothesis-driven, generative model that transforms the physiological response into a continuous-time stochastic process controlled by a double-logistic function. This approach deconstructs the R-R interval (RRi) series into a set of interpretable parameters representing the latency, rate, and magnitude of distinct response and recovery phases. Through simulation, we show that the model achieves high-fidelity parameter recovery and describes these dynamics more accurately than conventional fixed-time window methods under conditions matching its own generative assumptions. We then apply the framework to an empirical exercise-recovery recording, generating a precise, falsifiable hypothesis of “dissonant autonomic recovery”, where the baseline RR interval and its variability recover to distinct extents. The biphasic autonomic non-stationary decomposition (BAND) framework provides a formal methodology for translating RRi time series into quantitative, testable estimates of their generative processes. Full article

Sudden collapse accidents in mine roadways occur frequently, and post-disaster emergency rescue faces major challenges in terms of safety and efficiency. Therefore, efficient demolition equipment and intelligent prediction methods are urgently needed. Abrasive waterjet technology has considerable potential for complex disaster environments owing to its high efficiency, environmental friendliness, and cold-cutting characteristics. However, its cutting performance is affected by multiple coupled factors, including jet parameters, material properties, and environmental conditions. This makes accurate prediction difficult, especially under extreme or unseen operating conditions where available samples are limited. To address this problem, this study proposes a zero-shot learning-based multi-physics coupling prediction framework for the “jet–material–environment–effect” relationship. The framework is designed to predict abrasive waterjet cutting performance under unseen working conditions. First, a multi-factor cutting-performance dataset is constructed through a hierarchical experimental design. A generative adversarial network (GAN) is then introduced to expand the sample space and compensate for the discrete nature and limited distributional coverage of the experimental data. Second, a lightweight self-attention mechanism is employed to model high-dimensional input features globally, thereby improving the model’s ability to capture complex feature interactions. Finally, a joint loss function is designed to collaboratively optimize the generation and prediction processes. The experimental results show that the proposed model achieves a prediction accuracy of 98.3% on the test set, with a coefficient of determination R² of 0.967, outperforming WOA-SVM, BP neural network, EML, and Transformer models. The inference response time is approximately 3.2 s, indicating good engineering applicability. The results demonstrate that GAN effectively expands the sample space and improves model generalization, while the LightTransformer structure provides advantages in modeling high-dimensional coupled inputs. The proposed method can provide theoretical support and technical reference for intelligent demolition rescue and cutting-depth prediction under mine disaster conditions. Full article

Background/Objectives: Assessment of Internet Gaming Disorder (IGD) relies on retrospective self-reports and clinical interviews, which may be affected by recall and social desirability biases and may be insensitive to behavioral change. This study evaluated an artificial intelligence (AI)-enabled, privacy-preserving digital phenotyping framework for personalized IGD risk stratification under controlled simulation assumptions. Methods: A synthetic dataset of 1000 virtual user profiles was generated with a 20% elevated-risk prevalence and 5% balanced stochastic label noise. Four aggregated telemetry features were modeled: average session duration, sessions per week, Late-Night Index, and application-switching rate. Random Forest, Logistic Regression, and Gradient Boosting classifiers were evaluated using a stratified 80:20 hold-out split, five-fold cross-validation, playtime-only baselines, label-noise sensitivity analysis, and 200 synthetic realizations. Results: The primary Random Forest model achieved a balanced accuracy of 0.850, a sensitivity of 0.800, a specificity of 0.900, an area under the receiver operating characteristic curve (ROC-AUC) of 0.909, an average precision (AP) of 0.779, and a Brier score of 0.089. As an internal consistency check under the pre-specified synthetic signal structure, all-feature models showed higher performance than playtime-only baselines, and feature importance analyses recovered the encoded signal hierarchy. Performance declined with increasing label noise. Across 200 realizations, mean ROC-AUC values for the three all-feature models ranged from 0.888 to 0.904, with overlapping empirical 95% intervals. Conclusions: The framework demonstrates the methodological feasibility of transforming aggregated telemetry into interpretable risk signals while avoiding content-level monitoring. These findings are hypothesis-generating and do not establish clinical validity or diagnostic performance. Longitudinal validation in clinically characterized cohorts is required before deployment. Full article

The growing demand for multifunctional textile materials has stimulated extensive research into the development of antimicrobial finishing agents capable of providing long-term protection against pathogenic microorganisms while preserving the performance characteristics of fabrics. In this study, cotton fabrics were functionalized using a nitrogen- and sulfur-containing antiseptic composition based on sulfosalicylic acid, copper acetate treated fabrics were characterized by Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and thermogravimetric analysis (TGA/DTG) to investigate their structural, morphological, and thermal properties. The antimicrobial activity of the modified fabrics was evaluated against representative microorganisms. In addition, the influence of the antiseptic treatment on the functional properties of the cotton fabrics, including tensile strength, elongation at break, wrinkle resistance, abrasion resistance, hygroscopicity, air permeability, color fastness, and water permeability, was assessed. The results demonstrated successful incorporation of the antiseptic composition onto the fiber surface, improved thermal stability, and pronounced antimicrobial activity. Furthermore, the treated fabrics retained satisfactory mechanical and hygienic properties, indicating the suitability of the developed composition for the production of protective and hygienic textile materials. The proposed approach offers a promising route for the fabrication of multifunctional cellulose-based textiles with enhanced performance and biological protection. Full article

The Transformer project at TU Wien exemplifies how universities can act as initiators for sustainability education and STEM science communication. Addressing the urgent need for climate change adaptation and the strengthening of key competencies for sustainability, this paper examines the university’s role in creating a temporary, participatory learning space. The Transformer bridges academic research and societal practice, demonstrating how higher education institutions can actively shape sustainable development. As both a knowledge producer and a facilitator of public dialogue, TU Wien designs the Transformer as a hands-on laboratory for children and adolescents, providing low-threshold access to STEM and sustainability content. By integrating university students and researchers from architecture, civil and environmental engineering, mechanical engineering, electrical engineering, informatics, and technical chemistry, the project transforms abstract scientific concepts into tangible, co-created solutions. This approach is designed to foster systemic thinking, practical skills, and participant agency—outcomes for which this paper presents documented but preliminary evidence—while enriching academic teaching and research through real-world applications. This paper analyses how TU Wien’s commitment to interdisciplinary cooperation and participatory science communication positions the Transformer as a model for other institutions, with a focus on circular economy as the anchor theme. Through reflective practice on four analytical dimensions—place-based learning, staged sustainability education, co-creative STEM communication, and the engaged university—this study offers insights into the challenges and opportunities of university-led co-creation spaces and considerations for institutions seeking to develop comparable initiatives. Full article

Background: Oral squamous cell carcinoma (OSCC) is a multifactorial malignancy traditionally associated with tobacco exposure, alcohol consumption, and human papillomavirus (HPV) infection. It may also arise through the malignant transformation of oral potentially malignant disorders (OPMDs). However, a substantial proportion of tumors arise without clinically detectable precursor lesions, suggesting heterogeneous carcinogenic pathways. This study aimed to compare the clinicopathological characteristics of OPMDs-associated and apparently de novo OSCC. Methods: A retrospective analysis was conducted on patients with histologically confirmed OSCC referred to the department between 2015 and 2025. Baseline patient characteristics, clinical, and histopathological diagnostic management data were collected from medical records, and patients were classified as the OSCC with OPMD group or the de novo OSCC group based on the presence or absence of a history of OPMDs. Results: Seventy-seven patients were included (mean age: 70.4 years; 53.2% males, 46.75% females). OPMDs were documented in 37.66% of cases, while 62.34% of tumors developed without documented precursor lesions. No significant difference was observed in histological grade distribution between the two groups (p = 0.206), although G1 tumors were more frequent in the OSCC with OPMD group (25.93%) than in the de novo OSCC group (9.30%). The de novo OSCC group showed a significantly higher prevalence of denture-related lesions. No significant differences were observed between the two considered groups in age, sex, tobacco or alcohol exposure, HPV status, systemic diseases, or oncological history. Patients with OPMDs underwent significantly more biopsies in total and biopsies per year. Conclusions: OSCC may develop through distinct pathogenic pathways, emphasizing the need for broader clinical vigilance beyond OPMD surveillance to improve early diagnosis. Full article

Contemporary mural paintings contribute a significant part of urban cultural heritage, yet their conservation remains challenging due to the complex materials used and the aggressive conditions of the urban environment. Among the main deterioration factors, vandalic graffiti is particularly problematic, as its removal must be carried out without damaging the original paint layer, which often has a similar chemical composition. In this context, laser cleaning is a promising alternative to conventional mechanical and chemical methods. This study evaluates the effectiveness and selectivity of a nanosecond Nd:YAG laser (1064 nm) for the removal of a blue alkyd graffiti spray paint applied over mock-ups prepared with alkyd and polyvinyl acetate (PVA) paints on concrete substrates. The cleaning results were evaluated by stereomicroscopy, colour spectrophotometry, measurement of static contact angle, profilometry, near-infrared (NIR) hyperspectral imaging, Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS) to assess physical and chemical changes after laser treatment. The results show that the effectiveness and selectivity of the process depend strongly on the chemical composition of both the vandalism layer and the original paint system, highlighting the importance of preliminary material characterisation prior to laser cleaning interventions. Although laser cleaning enabled the partial or substantial removal of the blue alkyd graffiti in all cases, alkyd-based paints exhibited greater resistance to laser irradiation and allowed more effective graffiti removal with fewer surface alterations than PVA-based paints. Among them, the green alkyd paint achieved the highest cleaning efficiency. These results indicate that the interaction between laser radiation and the materials was governed not only by the binder type, but also by the pigment composition and the optical properties of the paint layers. Full article

The high-temperature crystalline transformation of spodumene is critical for efficient lithium extraction and utilization. In this study, we employ Raman spectroscopy to investigate the phase transition behavior of spodumene under thermal treatment. The results reveal that the optimal transformation conditions for Nigerian spodumene are achieved at 1050 °C with a 30 min dwell time, as validated by X-ray diffraction (XRD). Comparative analysis demonstrates that Raman spectroscopy offers distinct advantages over XRD, including minimal sample preparation and the ability to conduct ex situ measurements of spectral changes after heat-induced phase evolution. These findings highlight the potential of Raman spectroscopy to guide process optimization in lithium extraction by precisely identifying crystallographic transformation thresholds. Full article

Mpox has emerged as a global public health concern, highlighting the limitations of traditional vaccinia-based vaccination strategies and the urgent need for precision vaccinology approaches. Advances in structural virology and immunoinformatics have enabled the identification of conserved immunodominant antigens from both mature virion and extracellular virion forms, supporting the development of multivalent antigen combinations capable of inducing broad neutralizing antibody (nAb) responses. Emerging delivery technologies including mRNA-lipid nanoparticles, viral vectors, and self-assembling protein nanoparticles offer rapid scalability, enhanced immunogenicity, and improved safety compared with conventional live-attenuated vaccines. Addressing antigenic evolution, vaccine supply limitations, and population-specific immune variability will be crucial for optimizing vaccine effectiveness. This review synthesizes current evidence on antigen design, vaccine delivery platforms, and immunological correlates of protection to outline a framework for next-generation mpox vaccines. Precision vaccinology therefore represents a transformative strategy for developing durable, clade-specific mpox vaccines and strengthening preparedness against future orthopoxvirus outbreaks worldwide. Full article

The Portuguese agricultural sector has suffered a profound transformation over recent decades, evolving from traditional to increasingly technology-driven systems. Throughout this transition, climate and meteorological conditions have remained key drivers of agricultural productivity. Today, Portuguese agriculture faces growing challenges associated with climate change, including more frequent and intense heatwaves, droughts, and floods. Consequently, reliable climate information and decision-support tools are essential for strengthening resilience and promoting sustainable management. To address these needs, the Portuguese Institute for the Sea and Atmosphere (IPMA) developed two complementary climate service platforms for mainland Portugal: AgroClima and DataClima. The first provides observations from IPMA’s meteorological network, ECMWF forecasts, and agroclimatic indicators such as temperature, precipitation, soil water, and so-called agroclimatic warnings. The second offers historical climate information including WRFv4.2 simulations dynamically downscaled from ERA5 (1981–present), in situ observations (1941–present), and climate normals. Evaluation of the WRFv4.2 regionalization against IPMA observations shows a systematic underestimation of precipitation and air temperature, while mean wind speed is generally overestimated. Despite these biases, the downscaled WRFv4.2 dataset demonstrates sufficient accuracy to support operational climate services, providing valuable help for environmental monitoring, climate adaptation, and decision-making in agriculture and water resource management across Portugal. Full article