Search Results (26,571)

Conventional macroeconomic indicators are released with a time lag and may therefore provide limited information about rapidly changing market expectations, creating a need for complementary high-frequency indicators capable of capturing the informational content of financial narratives. This study examines whether sentiment extracted from unstructured financial text contains incremental predictive information for short-horizon market dynamics in the context of Eastern Europe’s green and digital transition. Using financial news and discourse collected between May 2025 and May 2026, a FinBERT-based Daily Sentiment Index (DSI) is constructed for selected technology, energy, and sustainability-related narratives and linked to market indicators and representative assets, including the DAX, UiPath, and OMV Petrom. The empirical strategy combines Granger predictability tests, vector autoregression (VAR), GARCH(1,1) models with sentiment effects, lead–lag analysis, Bai–Perron structural-break tests, and Markov-switching specifications to assess predictive temporal relationships, volatility dynamics, and time variation in sentiment–market interactions. Forecasting utility is further evaluated through an expanding-window out-of-sample exercise in which a sentiment-augmented VAR is compared with a benchmark autoregressive specification using one-day-ahead DAX returns, with relative predictive accuracy evaluated via the Diebold–Mariano test. The results indicate that lagged sentiment contains statistically significant predictive information for subsequent market returns and that incorporating the DSI significantly improves out-of-sample forecast accuracy relative to the benchmark specification. The evidence also reveals heterogeneous and time-varying responses across the selected cases: technology-related assets exhibit comparatively differentiated sensitivity to macro-sentiment conditions, whereas the energy case displays stronger exposure to transition-related narratives. Structural-break and regime-dependent estimates further suggest instability in the relationship between ESG sentiment and market performance over the sample period, but do not establish a permanent structural transformation in investor preferences. The study contributes to the narrative-economics and computational-finance literature by providing applied evidence that domain-specific textual sentiment contains incremental short-horizon information beyond conventional market dynamics and can complement lagged macroeconomic indicators in financial forecasting and risk-monitoring applications. Full article

Mitigating structural failure and improving the fire safety of concrete infrastructure during severe thermal events depends critically on the high-temperature resilience of Portland cement paste. Given the increasing production of Portland blended cements, understanding their high-temperature behavior is crucial for ensuring the safety of building structures. This study investigates the effects of exposure to high temperatures (up to 1200 °C) on Portland cement pastes containing ground granulated blast furnace slag and quartz powder, focusing on their thermal stability and the chemical reactions occurring under these conditions. In situ X-ray diffraction (XRD) with a heating module was employed to observe real-time phase transformations as the temperature increased, supported by ex situ scanning electron microscopic analysis. The results showed changes in the mineralogical composition, with particular attention to the decomposition of calcium hydroxide and the formation of melilite above 900 °C. These transformations suggest thermal reactions between cement hydrate products (calcium silicates and aluminates) in the presence of slag and quartz powder. Mixtures containing quartz powder exhibited increased porosity and phase transformation shifts at lower temperatures, reflecting the combined effects of quartz addition, reduced reactive binder content, and an increased effective water-to-binder ratio. Notably, lower strength-grade cements containing fly ash (additional alumina source) show higher degrees of formation of calcium–aluminate silicate phases such as melilite upon heating. Compared to conventional studies, the novelty of this study lies in the use of an in situ experimental setup, which uniquely identifies the temperature thresholds of chemical changes and the formation of new phases such as melilite in cement–selected slag mixes, while also capturing their recrystallization upon cooling. Full article

Microorganisms are the core drivers of ripened Pu-erh tea (RpPT) quality formation. However, the mechanisms of the microbiological and flavor chemical changes during the fermentation process remain unclear, which limits the quality control of ripened Pu-erh tea fermentation. A physicochemical analysis revealed that fermentation significantly reduced the contents of water extracts, tea polyphenols, free amino acids, soluble sugars, and most catechins (p < 0.05), while flavonoids and tea pigments were significantly accumulated. Amplicon sequencing revealed that Pantoea and Bacillus were the dominant bacterial genera during the early and late fermentation stages, with maximum relative abundances of 97.89% and 63.66%, respectively. Aspergillus and Thermomyces were the dominant fungal genera, reaching maximum relative abundances of 98.34% and 62.59%, respectively. During fermentation, bacterial diversity initially increased and then declined, whereas fungal diversity dropped sharply and subsequently stabilized. Co-occurrence network analysis indicated that microbial interactions were primarily cooperative, with network complexity gradually diminishing as fermentation progressed. Functional prediction showed enhanced microbial metabolism of amino acids and carbohydrates during the mid-to-late stages. Crucially, the stage-specific microbial communities were significantly correlated with distinct flavor compounds, shaping the final aroma profile of ripened Pu-erh tea. These dynamics explain the variations in tea quality and provide critical insights for optimizing the fermentation process. Full article

Informal caregiving for individuals with chronic obstructive pulmonary disease (COPD) places substantial emotional and physical demands on caregivers. However, many educational resources remain difficult to use due to high reading levels, dense medical language, and limited accessibility. Given that a large proportion of U.S. adults experience challenges with health literacy, existing caregiver toolkits may not provide the clear, actionable guidance needed to support caregiver self-efficacy or improve patient outcomes. This study introduces a reproducible, three-phase workflow that uses generative artificial intelligence (AI) to simplify and refine caregiver education materials, with application to the COPD Caregiver’s Toolkit. Four AI tools were used to simplify text, followed by human-guided refinement using the Sydney Health Literacy Lab (SHeLL) health literacy editor to ensure conceptual accuracy and usability. Readability indices and blinded assessments were used to evaluate changes in understandability and actionability across versions. AI-assisted simplification improved readability and clarity while maintaining high levels of actionability and preserving key instructional content. This scalable, adaptable approach may help public health researchers and practitioners improve the accessibility of educational resources across settings. Multi-phase, human-in-the-loop AI workflows may reduce linguistic complexity and improve the dissemination of essential health information to caregiving populations. Full article

Soil salinity restricts sugar beet growth and productivity, creating a need for effective agronomic approaches to improve plant performance under salt-affected conditions. However, the comparative responses of sugar beet to different foliar-applied exogenous substances under controlled NaCl stress and field saline–alkali conditions remain insufficiently understood. In this study, sugar beet (Beta vulgaris L.) cultivar ‘HI0479’ was used to evaluate the effects of different concentrations of PAA@Mn₃O₄ nanoparticles (PMO), methyl jasmonate (MeJA), and γ-aminobutyric acid (GABA). A pot experiment was first conducted under controlled NaCl stress to identify concentrations for subsequent field evaluation, followed by a one-season field experiment under non-saline–alkali and saline–alkali soil conditions. Based on the overall responses of growth and physiological traits, 100 mg L⁻¹ PMO, 100 mg L⁻¹ MeJA, and 1000 mg L⁻¹ GABA were selected for field evaluation. Under the imposed NaCl treatment, these foliar treatments improved growth and physiological performance and were associated with changes in photosynthetic characteristics, antioxidant enzyme activities, MDA content, osmolyte accumulation, and selected growth-related hormone levels. Under saline–alkali field conditions, PMO, MeJA, and GABA increased storage-root yield by 13.59%, 12.69%, and 12.37%, respectively, compared with the control. Although estimated storage-root sugar concentration decreased, the corresponding estimated sugar yields increased by 10.77%, 9.79%, and 7.41%, respectively. These results show that the three foliar treatments produced favorable growth- and yield-related responses under the experimental conditions evaluated while also revealing a trade-off between storage-root yield and estimated storage-root sugar concentration. The findings provide a basis for further agronomic evaluation of foliar PMO, MeJA, and GABA in sugar beet grown under salt-affected conditions. Full article

Background/Objectives: Protein supplementation may elevate advanced glycation end products (AGEs) through higher dietary AGE (dAGE) content and increase amino acid-driven endogenous formation. Protein supplementation from different sources may differentially affect AGE levels, possibly through differences in amino acid composition, digestion and metabolism, but evidence from randomized controlled trials (RCTs) remains limited. This study aimed to examine the effect of protein supplementation from either casein (animal-based) or soy (plant-based) protein isolates on circulating, skin, and dAGE levels in older adults following a healthy dietary pattern (HDP). Methods: In this 16-week RCT, 55 older adults (mean age: 66 years old) in Singapore were assigned either to control (no supplementation), casein protein isolate supplementation, or soy protein isolate supplementation. This study was registered at clinicaltrials.gov as NCT05400005. Results and Discussion: Overall, there were no alterations in dietary, skin and total circulating AGE levels after protein supplementation when adhering to an HDP such as My Healthy Plate (MHP). However, different sources of protein supplementation resulted in differential changes in specific plasma AGE markers. Two-way ANOVA analysis showed that soy protein supplementation led to a significant increase in plasma N(6)-(1-carboxymethyl)-L-lysine (CML) compared to the other groups (week 0: 0.72 ± 0.10, week 16: 0.80 ± 0.10, p_int = 0.040), while a paired t-test showed that casein protein supplementation led to a significant within-group increase in plasma pentosidine (week 0: 0.77 ± 0.06, week 16: 0.80 ± 0.05, p-value = 0.045). Other AGEs showed no significant changes. Conclusions: Protein supplementation while adhering to MHP did not significantly influence overall circulating and skin AGEs in older adults in Singapore. A differential response was observed only for plasma CML levels with soy protein supplementation, whereas the within-group increase in plasma pentosidine following casein supplementation should be interpreted with caution and requires further investigation. Full article

This study aims to explore women school administrators’ experiences of sustainable leadership by examining how they define sustainable leadership, the leadership practices they adopt, the challenges they encounter, the resilience strategies they employ, and their perspectives on the future of educational management. The study was conducted using a qualitative case study design. The participants consisted of 23 women school administrators working in the provinces of Manisa and Hakkâri, Türkiye. Data were collected through semi-structured interviews and analyzed using content analysis. The findings revealed that women school administrators primarily perceived sustainable leadership as a future-oriented leadership approach that emphasizes building a strong organizational culture, continuously improving the quality of education, and adopting a human-centered management philosophy. Sustainable leadership practices were found to revolve around collaboration, stakeholder participation, supporting teachers’ professional development, the effective use of school resources, empathy, and inclusive leadership. However, participants also reported significant challenges in implementing sustainable leadership, including traditional gender roles, work–life balance difficulties, limited access to leadership positions, and the perceived preference for male leadership. To overcome these challenges, participants emphasized continuous professional development, collaboration, strong communication networks, fair decision-making, emotional regulation, and maintaining work–life balance as key resilience strategies. Furthermore, they envisioned the future of educational management as being shaped by leadership approaches that are inclusive, collaborative, empathetic, innovative, and adaptable to change. Full article

This work presents a compact square complementary split-ring resonator (CSRR) microwave sensor, combined with a machine-learning-based calibration strategy, to achieve superior texture-aware soil moisture quantification. Implemented on a Rogers RO3010 substrate with a 20 × 30 mm² footprint and operating near 1.3 GHz, the sensor exploits shifts in resonance/notch frequency and insertion loss (S21) to probe both the real and imaginary components of the soil’s complex permittivity. Full-wave 3D electromagnetic simulations guided optimisation of the CSRR topology and T-shaped microstrip feedline, yielding strong field confinement, high quality factor, and high Frequency Detection Resolution (FDR). Experiments on sand and loam across 0–30% and 0–40% moisture content ranges, respectively, demonstrate FDR values of 6.09 MHz (sand) and 6.86 MHz (loam), enabling discrimination of subtle permittivity changes. Several calibration strategies are developed and compared for complex permittivity extraction from measured S-parameters: linear and polynomial regression, a multivariable least-squares sensitivity-matrix model, and a delta-referenced multilayer perceptron (MLP) with z-score standardization. While polynomial and least-squares models significantly outperform linear regression (R² > 0.997), the MLP combined with the optimized CSRR architecture delivers the best performance, achieving near-ideal accuracy (R² ≈ 1, MAE < 0.001, RMSE < 0.001) for both soil types. These results demonstrate that the synergy between the novel CSRR sensor design and data-driven MLP calibration enables high-resolution, robust, and field-deployable soil moisture sensing, offering a compelling solution for next-generation agricultural and geotechnical monitoring systems. Full article

Background/Objectives: Evidence on longitudinal changes in nutrition, inflammation, and body composition during adjuvant chemotherapy for Stage II/III colorectal cancer (CRC) remains limited. We therefore evaluated these changes and explored preoperative factors associated with treatment discontinuation and prognosis. Methods: This single-center retrospective study included patients with Stage II/III CRC who underwent curative surgery and adjuvant chemotherapy. Body mass index (BMI), computed tomography (CT)-derived psoas muscle index (PMI), psoas muscle density (PMD), modified intramuscular adipose tissue content (mIMAC), Geriatric Nutritional Risk Index (GNRI), Prognostic Nutritional Index (PNI), hemoglobin–albumin–lymphocyte–platelet (HALP) score, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) were assessed preoperatively and 6 months after chemotherapy initiation. Analyses were stratified into oxaliplatin-based doublet therapy (CAPOX/FOLFOX) and fluoropyrimidine monotherapy. Results: Among 142 patients, GNRI, PNI, and HALP score increased, whereas BMI, NLR, and PLR decreased from preoperative assessment to follow-up. Adjuvant therapy was discontinued in 45 patients (31.7%). In the doublet therapy group, discontinuation was associated with lower preoperative GNRI, PNI, and HALP scores and higher NLR and PLR. In the monotherapy group, only older age was associated with discontinuation. Lower preoperative PMD and mIMAC, but not PMI, were associated with poorer overall survival. No other nutritional or inflammatory markers were associated with overall survival. Conclusions: Several nutritional and inflammatory markers improved from preoperative assessment to follow-up. Preoperative nutritional and inflammatory markers were associated with treatment discontinuation, particularly in the doublet therapy group. CT-derived muscle quality markers were associated with overall survival, suggesting prognostic relevance of muscle quality in this setting. Full article

In this study, La_1-xCa_xMnO₃ (LCMO) samples (x = 0.3, 0.4 and 0.5) were synthesized using a sol–gel method and their structural, electronic, magnetic and magneto–transport properties were investigated. Structural analysis by XRD confirmed orthorhombic perovskite structure. FTIR studies indicated the changes in bond length and bond angle of Mn–O/Mn–O–Mn bonds; FESEM/EDAX confirmed a polycrystalline nature. Magneto–transport studies revealed a decrease in ferromagnetic metal–paramagnetic semiconductor transition temperature with the increase in Ca²⁺ content. Magneto–transport studies also revealed that the resistivity in the ferromagnetic metallic region was predominantly governed by the extrinsic spin–polarized tunneling (SPT) mechanism along with double-exchange interaction, whereas charge transport in the paramagnetic semiconducting region at higher temperatures was due to variable-range hopping conduction. Magnetoresistance was at its maximum near the transition temperature, but decreased at lower temperature (77 K). XPS and ⁵⁵Mn IFNMR confirmed coexistence of Mn³⁺/Mn⁴⁺, while IFNMR results at 77 K confirmed high-frequency electron exchange among Mn³⁺ and Mn⁴⁺ ions due to double exchange interaction. VSM studies at 300 K showed that the paramagnetic nature of the samples and susceptibility decreased with the increase in Ca²⁺ content. The close agreement between the magnetoresistance behavior and the ⁵⁵Mn IFNMR results confirms the role of the intrinsic DE interaction in governing the magnetotransport properties of LCMO. Full article

Background/Objectives: Women undergoing endocrine therapy after breast cancer often experience sexual-health concerns, but opportunities for individualized nursing support are limited. This case series implemented and preliminarily evaluated a novel sexual health-care model for women undergoing endocrine therapy after breast-cancer treatment. Methods: A care model was developed from Comfort Theory, a literature review, and clinical experience and underwent content-validity review by experts, including one nurse with lived experience of breast cancer. It was implemented individually for 6 months in five premenopausal women receiving endocrine therapy after breast-conserving surgery or total mastectomy. Data included baseline characteristics, care records, final interviews, and Self-affirmation Scale scores. Nursing goal attainment and final interviews were evaluated qualitatively. Results: All nursing goals appeared to have been achieved based on provider assessment and participants’ narratives. Participants gradually expressed distress, questions, and sexual health needs related to bodily and sexual changes, developed greater understanding of treatment-related changes, identified individualized coping strategies, and showed greater self-worth and self-care. Self-affirmation scores were descriptively higher after care. Phenomenological analysis identified four constituents: having a place to discuss a body marked by breast and fertility loss; sharing with a midwife who accepted them as they were; reconnecting with their changed bodies; and discovering new meaning in life with altered fertility and bodily integrity. Conclusions: The care model may provide a safe, individualized space for women undergoing endocrine therapy after breast cancer to express concerns, understand bodily changes, and reconstruct meaning. The findings are preliminary and represent provider-assessed observations rather than evidence of effectiveness. Full article

Federal Reserve press conferences convey policy path information and uncertainty beyond formal decisions. This study tests whether transcript-derived risk language predicts the magnitude of financial market responses after policy surprises and event characteristics enter the model. The dataset contains 93 official press conference transcripts from April 2011 to June 2026, with 90 scheduled events in the primary sample. A Q&A lexical risk index combines standardised frequencies of negative, uncertainty, and weak modal terms from the Loughran–McDonald dictionary. The outcome is an equal weight composite of absolute S&P 500 returns, two- and ten-year Treasury yield changes, and US dollar returns during a 70 min press conference window. OLS models use HC3 standard errors, asset-specific regressions, influence analysis, permutation testing, and leave-one-out cross-validation. Q&A lexical risk does not predict larger responses. The full-model coefficient equals −0.063 (p = 0.444), incremental R² equals 0.0034, and prediction error rises by 0.60% after adding the index. Policy surprise magnitude remains the strongest predictor. The 90-event sample limits precision for small effects, with an approximate 80% minimum detectable effect of 0.230 response index units. The estimates describe conditional association and incremental predictive content. They do not test acoustic delivery, realised intraday volatility, or a causal communication effect. Full article

The continued digitization of open museum collections provides new opportunities for the intelligent organization and visual discovery of cultural heritage. However, morphological similarity between ceramic forms, intra-class variation, and changing photographic conditions remain challenges for automated classification, model interpretation, and similar-object retrieval. This study uses 3305 Chinese ceramic objects from the open collection of The Metropolitan Museum of Art (The Met) to develop an explainable and traceable workflow for ceramic-form classification and visual retrieval. Museum metadata were standardized into 13 form categories, from which 2755 objects were used to establish a seven-class primary classification task. Explicit morphological features, handcrafted visual features, ResNet50 representations, DINOv2 representations, and morphology–deep feature fusion were evaluated under a unified data split and evaluation protocol. Explainable artificial intelligence (XAI) methods were further used to examine spatial model responses and feature attributions of explicit morphological variables, while different representations were evaluated for content-based visual retrieval. The results show that deep visual representations effectively support ceramic-form classification, with DINOv2 demonstrating comparatively stable performance across multiple random seeds. Morphology–deep feature fusion did not provide a consistent classification advantage over DINOv2-only, but the fused representation showed clearer complementary value in visual retrieval, achieving the highest Precision@5 (0.819) and mean average precision at 10 (mAP@10; 0.773). XAI analyses further indicated that structurally meaningful spatial responses and explicit geometric descriptors contributed to form discrimination. By linking classification, interpretation, and retrieval outputs to Object IDs and original collection records, the proposed workflow provides a practical computational approach for ceramic-form organization, similar-object discovery, and traceable visual retrieval in digital museum collections. Full article

The emplacement of mafic dikes into carbonate-bearing sedimentary successions commonly produces localized mineralogical and geochemical variations in adjacent country rocks. However, the spatial distribution of these variations remains poorly documented in the Permian sedimentary successions of Thailand. The study investigates spatial mineralogical and geochemical variations across intrusive rocks and adjacent sedimentary rocks in the Permian Nam Duk Formation, Phetchabun Province, Thailand. Petrographic observations and whole-rock geochemical analyses (XRF and ICP-MS), supported by qualitative XRD phase identification, were integrated to characterize mineral assemblages and whole-rock geochemistry. The intrusive rocks display porphyritic textures dominated by plagioclase and hornblende, whereas the adjacent country rocks are characterized by quartz, calcite, feldspar, clay minerals, and secondary alteration phases. Spatial variations in mineral assemblages are accompanied by changes in contents of some major oxides (SiO₂, Al₂O₃, Fe₂O₃, MgO, and CaO) and selected trace and rare earth elements. The sedimentary country rocks generally contain higher total REE concentrations, particularly La and Ce, whereas the contact-proximal sample shows values closer to the mafic dike. Site A shows the clearest spatial variations across the exposed mafic dike–country rock contact, whereas site B exhibits compositional variability associated with strongly altered porphyritic andesite and heterogeneous sedimentary rocks. The integrated results document localized mineralogical and geochemical variations across the investigated intrusive–sedimentary rock systems, although primary lithological heterogeneity and secondary alteration may also contribute to these patterns. Full article

Moisture in building materials, particularly in masonry structures, can negatively affect the durability, serviceability, and safety of buildings. Therefore, it is important to develop non-destructive diagnostic methods that enable reliable assessment of moisture content. Microwave techniques provide a method for determining a material’s moisture content indirectly from changes in its electromagnetic properties, particularly the apparent permittivity. The aim of this article is to present the application of machine learning methods for estimating the moisture content of masonry materials based on laboratory microwave measurements. The study uses a measurement setup with a single antenna operating simultaneously as both transmitting and receiving antenna, which increases the practical applicability of the method in in-situ diagnostics but also makes the interpretation of the measured signal more challenging. Predictive models were developed using the scikit-learn library and dedicated software designed to automatically select the best-performing model and its parameters. The obtained results make it possible to assess the potential of machine learning as a tool supporting the interpretation of microwave data and improving the accuracy of non-destructive moisture diagnostics in masonry structures. Full article