Search Results (32,532)

New indazole and pyrazole nucleoside analogues were synthesized by enzymatic transglycosylation using E. coli purine nucleoside phosphorylase (PNP). Indazoles substituted at position 5 or 6 with bromine, pyrazole or pyrimidine fragments were used as heterocyclic bases. In the case of indazole-pyrazole hybrids, the selectivity of PNP in glycosylation of the pyrazole fragment, rather than the indazole one, was established. Molecular modeling methods allowed the identification of the preferred orientations of substrates in the active site of the enzyme, leading to the formation of N1- and N2-regioisomers of indazole. Unique substrate specificity ensured the possibility of glycosylation of the bases with conversion rates of 65–100% (according to HPLC analysis of the reaction mixtures) and isolated yields of 19–94%. It was shown that the synthesized nucleoside analogues are not inhibitors of E. coli adenosine deaminase (ADA). They do not exhibit cytotoxicity towards cancer cell lines (SH-SY5Y neuroblastoma cell lines, K-562 lymphoblastic cells, and HL-60 promyeloblastic cells). The activity of indazole derivatives against Mycobacterium tuberculosis (strain H37Rv) was determined by the microdilution method. 1-(β-D-2′deoxyribofuranosyl)-5-bromo-indazole and 5-(pyrimidin-5-yl)-1H-indazole at a concentration of 50 μg/mL were able to inhibit 50% and 90% of the culture growth, correspondingly. Full article

Background and Objectives: Conduction disorders (CDs) and permanent pacemaker implantation (PPI) remain among the most common complications after transcatheter aortic valve implantation (TAVI). Given their potential impact on long-term outcomes, improved preprocedural identification of patients at high risk for new-onset CDs is increasingly important. We investigated whether selected preprocedural inflammatory and fibrotic biomarkers, along with echocardiographic indices of regional myocardial remodeling, were associated with new-onset CDs after TAVI. Materials and Methods: This single-center prospective observational study included 112 patients with severe aortic stenosis undergoing TAVI. Peripheral blood samples were obtained within 24 h before TAVI for measurement of inflammatory and fibrotic biomarkers, including interleukin-6 (IL-6), C-reactive protein (CRP), CRP-to-albumin ratio (CAR), procalcitonin, ferritin, lactate dehydrogenase, and transforming growth factor-β1. The primary outcome was the occurrence of new-onset CDs during the index hospitalization or within three months after TAVI. Results: New-onset CDs occurred in 58 patients (51.8%). IL-6 showed the strongest association with the outcome in univariable analysis (OR per 1-SD increase, 9.55; 95% CI 3.00–30.40; p < 0.001), remained associated after adjustment for selected clinical and procedural predictors in exploratory models (adjusted OR 10.70; 95% CI 2.43–47.07; p = 0.002), and demonstrated the highest, although moderate, discriminatory performance among the evaluated biomarkers (AUC 0.730; 95% CI 0.637–0.823). CRP and CAR were higher in patients with CDs and were significant in univariable analysis, but showed weaker and less consistent adjusted associations. Among echocardiographic markers, AB strain ratio ≥ 2 was the most consistent imaging correlate in exploratory biomarker–echocardiographic models. Conclusions: Elevated preprocedural IL-6 was the biomarker most consistently associated with new-onset CDs after TAVI. These findings suggest that higher preprocedural IL-6 levels may reflect patient-specific susceptibility to new-onset CDs after TAVI and could have potential value as an adjunctive biomarker, alongside echocardiographic assessment, for preprocedural risk evaluation, with further validation required in larger prospective studies. Full article

The diverse application scenarios envisioned for sixth generation (6G) are characterized by the deep integration of sensing and ubiquitous connectivity, which imposes unprecedentedly stringent security requirements. However, due to the open nature of wireless channels, mobile communications systems always face severe information security threats such as falsification, spoofing, interception, and repudiation. Cryptography-based symmetric and asymmetric encryption techniques remain mainstream solutions for information protection. Symmetric encryption is efficient and secure for legitimate users but suffers from key-distribution difficulties over open wireless channels, whereas asymmetric encryption resolves this problem but faces increasing risks from quantum computing due to its reliance on structured mathematical hardness assumptions. In response to these limitations, physical layer security (PLS) has gained a great deal of research attention as a powerful security component that leverages the features of varying wireless channels. Existing PLS schemes can be broadly classified into two categories. The first one takes advantage of the legitimate link’s opportunistic channel-quality superiority over or different spatial-domain directions from the attacking link, which still faces many practical implementation difficulties. The second category is termed physical-layer key generation (PLKG). It extracts the unique features of the legitimate link’s channel variation, which is often reciprocal, as the source of secret key generation and therefore can naturally implement secure key distribution tasks. This advantage no doubt injects new vigor to symmetric encryption as a stronger protection approach. Following this trend, we in this paper concentrate on the PLKG techniques. Specifically, we present a comprehensive overview on existing PLKG schemes, discussing diverse secret key generation and reconciliation methods. We further investigate the model-driven and deep-learning-based approaches tailored for the scenario with imperfect channel reciprocity between the sender and receiver. After comprehensively reviewing major existing schemes, we further discuss a recently proposed PLKG design based on codeword reconstruction, which makes use of the strong error-correcting capability of the forward-error-correction (FEC) codes to effectively implement secure and consistent secret key generation between the legitimate sender and receiver. Finally, we share our opinions on the unsolved challenges and potential research directions dedicated to PLKG toward meeting the security requirements of 6G. Full article

With the rapid growth of social media, online news has become increasingly multimodal, combining textual and visual information, posing new challenges for fake news detection. Existing methods often suffer from redundant features, distribution differences across modalities, and insufficient modeling of semantic interactions. To address these issues, this paper proposes an Adaptive Binary Osprey Optimization Algorithm and Cross-modal Disentangled Fusion model (ABOOA-CDF). First, an Adaptive Binary Osprey Optimization Algorithm (ABOOA) is developed for multimodal feature selection by integrating chaotic initialization, adaptive search, and binary mapping strategies to identify informative feature subsets. Then, a Cross-modal Relation Disentanglement Module (CRDM) is introduced to decompose multimodal representations into shared, discrepant, and complementary components, thereby enhancing semantic relationship modeling. Furthermore, an Adaptive Semantic Fusion Module (ASFM) dynamically learns fusion weights to generate discriminative multimodal representations. Experimental results demonstrate that ABOOA-CDF effectively improves detection performance. Compared with MFO and OOA, the proposed method achieves Accuracy improvements of 1.02 and 2.66 percentage points, respectively, verifying its effectiveness in feature optimization, cross-modal relation modeling, and semantic fusion. Full article

Damage identification in instrumented structures is often framed through modal-parameter changes, finite element updating, or supervised classifiers. These approaches are powerful, but they require an explicit structural model, identified modes, labeled damage cases, or expert choices about reference sensors. This paper introduces a new data-driven and training-free approach with limited physical priors, defining a sensor network where each sensor is a node and the edges are defined from the spatial correlation of sensor responses. The idea is to use each sensor time history as the measured structural dynamics feature while damage is localized from the edges, whose correlations change relative to a baseline. The method is demonstrated on a full-scale seven-story reinforced-concrete shear-wall building tested at UC San Diego, considering four progressive earthquake-induced damage states and one brace-modification state. The results are compared with those obtained from a previously published finite element model. The results reveal that this network-based approach localizes the damage states in agreement with previous studies with limited prior requirements and low computational cost. Beyond damage localization, this network representation provides sensor centrality, allowing informative sensors to be selected from data rather than chosen randomly or only from experimental intuitions. For the case study, using this sensor network, we find the most central sensors, those carrying the most information with reduced trial-and-error and reduced expert intervention, and use them to recover the first three natural frequencies as a secondary dynamic check. The results show that spatial correlation networks can screen for damage, localize affected regions, and guide modal parameter extraction without building an FE model. This study opens a research avenue in which network representations of multi-sensor structural dynamics complement traditional modal analysis for structural health monitoring, with dense or heterogeneous sensing systems. Full article

Fractures are weak surfaces of rock; they are common in underground engineering and are prone to causing engineering disasters. The fracture parameters of rock are the crucial foundation for stability evaluation in engineering. The accurate identification of rock fractures is important for engineering support design, as it is helpful in preventing and reducing engineering accidents caused by fractures. At present, there are few technical methods for fracture identification. Digital drilling test technology provides a new approach to rock fracture identification. In this study, a multi-functional rock mass drilling test system is employed to conduct testing in fractured rock. The response laws of drilling parameters to different fracture positions and angles are analyzed, and an identification model for rock mass fracture parameters while drilling is developed. The results from tests show that the average error in identifying rock mass fracture positions using the fracture parameter identification model is 5.34 mm, and the average error in identifying fracture angles is 2.01°. This study provides a theoretical basis for on-site testing and assessment of rock mass fractures in underground engineering. Full article

Objectives: Women with a history of breast cancer are at increased risk of developing subsequent breast cancer, including ipsilateral recurrence and contralateral new primary breast cancer. This study evaluated the discriminatory performance of a mammogram-based artificial intelligence (AI) risk model for predicting subsequent breast cancer within one year after a negative screening mammogram. Methods: This enriched retrospective case–control study included women with a prior history of breast cancer who underwent screening digital breast tomosynthesis between January 2018 and December 2023 at three affiliated academic breast imaging centers. Digital breast tomosynthesis examinations classified as BI-RADS 1 or 2 were retrospectively analyzed using the ProFound AI^® Risk model version 1.0 to estimate 1-year breast cancer risk. Patients were classified according to whether they developed subsequent breast cancer within one year of the index screening examination. Model discrimination was evaluated using receiver operating characteristic analysis. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated at an exploratory cutoff selected by maximizing the Youden index. Results: The study included 96 women (mean age, 65.3 ± 8.7 years), of whom 32 developed subsequent breast cancer within one year, and 64 did not. The mean AI risk score was significantly higher in the subsequent breast cancer group than in the control group (1.18 ± 0.59 vs. 0.49 ± 0.41; p < 0.001). The AI model demonstrated an AUC of 0.824 (95% CI: 0.728–0.921). At an exploratory cutoff of 0.39, sensitivity was 81.3%, specificity was 76.6%, PPV was 63.4%, and NPV was 89.1%. In separate exploratory analyses, the AUC was 0.790 (95% CI: 0.641–0.939) for ipsilateral recurrence and 0.860 (95% CI: 0.752–0.974) for contralateral new primary breast cancer. AI risk scores were not significantly correlated with tumor size or age at subsequent breast cancer diagnosis. Conclusions: In this enriched retrospective case–control study, higher mammogram-based AI risk scores were associated with subsequent breast cancer within one year after a negative screening examination. The model demonstrated discriminatory performance for both ipsilateral recurrence and contralateral new primary breast cancer; however, these analyses were exploratory. Because the cohort was enriched for subsequent breast cancer events, the reported predictive values are specific to the study sample and should not be extrapolated to routine surveillance populations. Larger prospective cohorts are needed to validate discrimination, calibration, and clinical utility. Full article

Implementing innovative digital teaching methods in higher education is a particularly complex endeavor that calls for adaptive and hybrid implementation strategies. This study describes the integration of digital simulations into a higher education nursing faculty. Kotter’s change model was used as an organizational framework to structure and report the process, while a design approach guided day-to-day practice. This combination was chosen to balance accountability with the flexibility required in a complex higher education context. This four-year single case study draws on annual project reports and descriptive student questionnaire data. The findings show that neither the problem definition nor the solution remained stable across project cycles: each cycle surfaced new challenges and revealed new contextual constraints. Tangible outcomes include a growing library of co-created simulation scenarios, a range of implementation tools, and the “simulatie wasstraat”, a curated arrangement of scenarios tailored to students’ placement contexts. Students reported positive experiences regarding perceived usefulness, motivation, and the perceived educational value of the simulations. Persistent challenges around organizational embedding remain. The findings nonetheless suggest that a hybrid implementation strategy was practically viable in this institutional context. Full article

Financial media risk is jointly driven by multisource content, including news reports, corporate announcements, social media posts, short videos, and livestreams, while content authenticity, propagation velocity, asset relevance, and trading infrastructure conditions can simultaneously influence short-term market fluctuations. To address the limitations of existing methods, including their reliance on either textual information or market sequences, insufficient source verification, and inadequate alignment of asynchronous multimodal signals, FMRP-Net is proposed for artificial intelligence-driven sensing. Event semantics, risk categories, and asset association information are first extracted through a pretrained financial language model and cross-modal consistency analysis. A dual-layer hardware reliability perception module is then employed to integrate sensing evidence from cameras, microphones, terminal inertial signals, server temperature, power consumption, network traffic, and transmission latency. Heterogeneous temporal propagation graphs, cross-frequency alignment, and bidirectional propagation–market coupling are further incorporated to jointly predict market direction, volatility, risk level, and propagation trends. Experimental results demonstrate that FMRP-Net achieved an Accuracy of $0.832$, a Macro-F1 of $0.824$, a ROC-AUC of $0.891$, an MCC of $0.665$, and a PR-AUC of $0.883$ for market direction prediction over future horizons of 5, 15, 30, and 60 min, indicating a balanced performance in terms of Precision and Recall. For volatility prediction, MAE, RMSE, and MAPE values of $0.0178$, $0.0271$, and $12.46\%$ were obtained, respectively, together with an $R^2$ of $0.812$. In the ablation study, the media risk Macro-F1 and source reliability AUC reached $0.842$ and $0.929$, respectively, while the propagation-scale prediction error was reduced to $0.109$ and the average early-warning lead time reached $10.6$ min. These results demonstrate that the integration of multimedia semantics, hardware sensing evidence, and propagation structures can effectively improve the accuracy, stability, and interpretability of financial market prediction and risk early warning. Full article

To better understand how journalists represent sources, it is necessary to systematically study the use of reported speech in news coverage. This paper presents a method for LLM-assisted content analysis to identify and classify reported speech in Dutch newspapers automatically. The study evaluates a three-step procedure utilising role-based instructions to prompt the model as a professional journalist. First, a codebook for identifying citation structures and source types was developed with LLM support and then manually verified. Second, inter-coder reliability between human coders and the LLM was assessed on a representative sample of Dutch news articles using a human-in-the-loop validation approach. Third, the prompt-engineered LLM was used to code a large corpus spanning seven decades (1950–2024). Manual verification of 16,689 citations shows a weighted F1-score of 0.75, which aligns with recent benchmarks for high-capacity models performing complex journalistic coding. While human oversight remains the benchmark for reliability, due to issues such as repeated citations that were given as examples in the used prompts and representational bias, LLM-based systems perform sufficiently well for large-scale analyses of journalistic source use. The paper concludes that hybrid human–AI workflows provide a practical bridge between traditional rule-based approaches and new generative models, offering scalable and cost-effective methods for studying source representation in journalism. Full article

Background/Objectives: The transition toward healthier and more sustainable diets has increased the need for reformulated bakery and confectionery products formulated with alternative ingredients. However, validated instruments for assessing consumer acceptance of these products remain limited. This study aimed to develop and psychometrically validate a multidimensional assessment framework integrating food choice motives, green (environmental) values, health consciousness, perceived nutritional value, willingness to pay a premium, and purchase intention. Methods: A cross-sectional survey was conducted among 504 consumers in Romania using an online questionnaire. The instrument was developed by adapting established scales and by developing a new Perceived Nutritional Value scale. Dimensionality was evaluated using exploratory (EFA) and confirmatory (CFA) factor analyses. Temporal stability was assessed in an independent test–retest subsample (n = 30; 14-day interval), and the proposed conceptual model was evaluated using Partial Least Squares Structural Equation Modeling (PLS-SEM) with 5000 bootstrap resamples. Robustness was examined through a sensitivity analysis excluding respondents younger than 18 years (N = 441). Results: EFA (KMO = 0.933) and CFA (CFI = 0.965, TLI = 0.959, RMSEA = 0.061, SRMR = 0.033) supported the proposed six-factor structure. All constructs demonstrated excellent internal consistency (Cronbach’s α: 0.898–0.938), convergent validity (AVE: 0.790–0.889), discriminant validity (HTMT < 0.77), and good-to-excellent test–retest reliability (ICC: 0.82–0.91). Perceived nutritional value was positively and significantly associated with both willingness to pay and purchase intention, and willingness to pay partially mediated the association between perceived nutritional value and purchase intention. The structural model explained 46.1% of the variance in purchase intention; all hypothesized relationships were supported, and the findings remained stable in the sensitivity analysis. Conclusions: The proposed framework demonstrated good psychometric performance and provides a theory-informed measurement framework for assessing consumer acceptance of reformulated bakery and confectionery products. It may support future nutrition research, consumer studies, and the evaluation of food reformulation strategies aimed at promoting healthier and more sustainable dietary choices. Full article

To address the critical challenge of extracting short-period surface deformation signals induced by extreme floods from InSAR time series, this study focuses on the catastrophic flood that struck the Haihe River Basin in July 2023 (hereinafter referred to as the “23·7” flood, with a total duration of approximately 65 days) and proposes a novel method for transient deformation signal extraction. Using Sentinel-1A satellite data and the PS-InSAR technique, we constructed a multivariate composite fitting function comprising a linear trend term, annual and semi-annual seasonal terms, a step term, and a logarithmic decay term. Through nonlinear least-squares fitting, this approach achieves effective separation of long-term tectonic deformation, seasonal fluctuations, high-frequency noise, and transient flood-related signals. The results show that the W-shaped floodplain east of Xiong’an New Area does not exhibit the expected subsidence induced by water loading but instead features pronounced surface uplift of up to 30 mm. Multi-physics forward modeling reveals the underlying mechanism: the elastic subsidence caused by surface water loading, calculated via the LoadDef spherical loading theory, amounts to only ~2 mm. In contrast, forward modeling based on the GMS three-dimensional groundwater seepage model and the principle of effective stress indicates that the pore water rebound effect can produce surface uplift of up to ~36 mm. The superposition of these two effects is highly consistent with InSAR observations in terms of magnitude, direction, and spatial distribution, confirming that the flood-induced surface deformation is dominated by the pore water rebound effect driven by rapid groundwater recharge, rather than subsidence from water loading. The proposed framework extends the application potential of geodetic techniques for monitoring short-period extreme hydrological events. Full article

Recent journalism and media research increasingly uses embeddings, transformer-based models, and large language models, yet the methodological functions remain unevenly conceptualized. This article examines how vector-based and semantically oriented methods are incorporated into journalism and media studies and proposes a theory-informed framework of four recurrent methodological functions: semantic mapping, interpretive assistance, cross-scale articulation, and reflexive auditing. We conducted a structured review of English-language journal articles and review articles indexed in Scopus and Web of Science between 2022 and April 2026. The review combined database retrieval, conservative LLM-assisted metadata screening, full-text analytical extraction, expert manual validation, descriptive analysis, exploratory association tests, and an embedding-based map built from article-level analytical profiles. The final corpus comprised 43 articles. Empirical studies of news texts predominated, and semantic mapping was the most frequent primary methodological function. The profile-based map identified four moderately differentiated and overlapping communities centered on reception and circulation, semantic mapping of news content, affective and evaluative discourse analysis, and methodological infrastructure-building. Community membership aligned more strongly with media domain than with study type or primary methodological function, although these patterns are interpreted heuristically given the small corpus and low expected cell counts. The findings show that these methods are best understood as components of hybrid research designs combining semantic formalization, interpretation, validation, and scale-sensitive analysis. Full article

Retrieval-augmented fine-tuning (RAFT) can improve small language models (sLMs) on retrieval-grounded question answering, but the synthetic training data produced by commercial large language models (LLMs) vary in quality. This paper contributes a quality-aware selection protocol—rather than a new RAFT or QLoRA method—that scores LLM-generated alternatives along four embedding-based dimensions (question relevance, answer faithfulness, QA coherence, and semantic similarity) and selects one alternative per task before parameter-efficient fine-tuning. Under pre-specified paired-bootstrap contrasts with Holm correction, the parameter-free faithfulness-based selector only-AF significantly exceeds random selection on Gemma-2-9B-IT (ΔF1 = +0.106, 95% CI [+0.043, +0.174], Holm-corrected p = 0.019), and its pre-specified weighted companion af-70 (w_AF = 0.70) shows the same confirmed pattern (Holm-corrected p = 0.002). Both effects persist under a Korean character-level F1 that removes particles and punctuation (Holm-corrected p = 0.004 and p = 0.042), indicating robustness to the choice of lexical metric. Relative to training on the full 150-row augmented pool, the quality-selected 50-row sets are statistically indistinguishable while using one third of the training data, which we interpret as data efficiency rather than superiority. Across six instruction-tuned models (2B–27B), a significant selector-by-model interaction indicates that the optimal quality axis is model-dependent, and the two smallest models show no benefit from selection. The study’s confirmatory contrasts use a small controlled Korean corpus under a transductive design; two pre-registered validation experiments probe external validity. On an independent five-fold larger corpus with a passage-level train/test split, fine-tuning transfers strongly and the selected one-third subsets show no significant difference from the full pool, while the advantage over random selection is directionally positive but small and not significant; under controlled corruption of 35% of the pool, the metrics detect the damaged rows, and for the score-sum selector the selection-versus-random benefit is significantly larger than on the clean pool (difference-in-differences p = 0.0014; directionally consistent but not significant for the faithfulness selectors). Within this scope, quality-aware selection is a promising, data-efficient safeguard for synthetic RAFT data—performing comparably to full-pool training at one third of the cost, with growing value as pool quality degrades—and larger-scale external validation remains future work. Full article

Moisture significantly reduces the load-carrying capacity of lubricating oil films, accelerates oxidative degradation, and induces equipment corrosion, making it a critical hazard factor affecting lubrication reliability. To overcome the limitations of existing methods for determining water content—such as complex operation, poor real-time performance, and high cost—this paper proposes a radio frequency identification (RFID)-based method for lubricating oil water content detection via the orthogonal fusion of phase and received signal strength indicator (RSSI) as an off-line analytical tool. The method exploits the signal variation characteristics when RF signals penetrate media with different dielectric properties; by analyzing the phase and RSSI of backscattered RFID signals, non-contact moisture sensing is achieved. First, a theoretical model integrating phase and RSSI for water content detection is established to reveal the differential response mechanisms of the two parameters to water content. Second, a detection method based on phase-RSSI orthogonal fusion is proposed, and performance evaluation metrics are constructed. Finally, comparative experiments with different detection approaches are conducted. It is found that as water content increases, the mean phase continuously rises with significantly increased fluctuation, while RSSI exhibits a linear decreasing trend, demonstrating clear complementary response characteristics. Compared with single-phase or single-RSSI methods, the proposed fusion method achieves a coefficient of determination (R²) of 0.95 over the 0–2.0% water content range, with reliable detection verified at concentrations as low as 0.1%, and exhibits superior detection sensitivity in the low-water-content range. Furthermore, it possesses a type-discrimination capability absent in single-parameter methods—that is, it can effectively distinguish whether response variations originate from moisture contamination or non-moisture interference. The method offers stable response and high detection efficiency, providing a new approach for accurate determination of water content in lubricating oil. Full article