Search Results (1,426)

Time-series Interferometric Synthetic Aperture Radar (InSAR) techniques encounter substantial reliability challenges, primarily due to the presence of gross errors arising from phase unwrapping failures. These errors propagate through the processing chain and adversely affect displacement estimation accuracy, particularly in the case of a small number of SAR datasets. This study presents a unified data fusion framework designed to enhance the detection of gross errors in multi-source InSAR observations, incorporating a robust Least Squares Adjustment (LSA) methodology. The proposed framework develops a comprehensive mathematical model that integrates the fusion of multi-source InSAR data with robust LSA analysis, thereby establishing a theoretical foundation for the integration of heterogeneous datasets. Then, a systematic, reliability-driven data fusion workflow with robust LSA is developed, which synergistically combines Multi-Temporal InSAR (MT-InSAR) processing, homonymous Persistent Scatterer (PS) set generation, and iterative Baarda’s data snooping based on statistical hypothesis testing. This workflow facilitates the concurrent localization of gross errors and optimization of displacement parameters within the fusion process. Finally, the framework is rigorously evaluated using datasets from Radarsat-2 and two Sentinel-1 acquisition campaigns over the Tianjin Binhai New Area, China. Experimental results indicate that gross errors were successfully identified and removed from 11.1% of the homonymous PS sets. Following the robust LSA application, vertical displacement estimates exhibited a Root Mean Square Error (RMSE) of 5.7 mm/yr when compared to high-precision leveling data. Furthermore, a localized analysis incorporating both leveling validation and time series comparison was conducted in the Airport Economic Zone, revealing a substantial 42.5% improvement in accuracy compared to traditional Ordinary Least Squares (OLS) methodologies. Reliability assessments further demonstrate that the integration of multiple InSAR datasets significantly enhances both internal and external reliability metrics compared to single-source analyses. This study underscores the efficacy of the proposed framework in mitigating errors induced by phase unwrapping inaccuracies, thereby enhancing the robustness and credibility of InSAR-derived displacement measurements. Full article

Despite the advances on data analysis methodologies in the last decades, most of the traditional regression methods cannot be directly applied to large-scale data. Although aggregation methods are especially designed to deal with large-scale data, their performance may be strongly reduced in ill-conditioned problems (due to collinearity issues). This work compares the performance of a recent approach based on normalized entropy, a concept from information theory and info-metrics, with bagging and magging, two well-established aggregation methods in the literature, providing valuable insights for applications in regression analysis with large-scale data. While the results reveal a similar performance between methods in terms of prediction accuracy, the approach based on normalized entropy largely outperforms the other methods in terms of precision accuracy, even considering a smaller number of groups and observations per group, which represents an important advantage in inference problems with large-scale data. This work also alerts for the risk of using the OLS estimator, particularly under collinearity scenarios, knowing that data scientists frequently use linear models as a simplified view of the reality in big data analysis, and the OLS estimator is routinely used in practice. Beyond the promising findings of the simulation study, our estimation and aggregation strategies show strong potential for real-world applications in fields such as econometrics, genomics, environmental sciences, and machine learning, where data challenges such as noise and ill-conditioning are persistent. Full article

Neopestalotiopsis zimbabwana is an emerging phytopathogen with multiple hosts. Considering the environmental, toxicological, and resistance issues linked to synthetic fungicides, Origanum vulgare L. essential oil (OEO) was evaluated through in vitro, in vivo, and in silico approaches. The pathogen, isolated from Watsonia borbonica L., was molecularly identified. Gas chromatography–mass spectrometry (GC–MS) analysis showed hexadecanoic acid (15.98%), dodecanoic acid (15.74%), terpinen-4-ol (11.61%), and thymol (7.65%) as the main components. In vitro assays determined a minimum inhibitory concentration (MIC) of 30% OEO and a minimal fungicidal concentration (MFC) of 60% OEO. Growth chamber trials demonstrated that preventive sprays maintained 0% foliar damage—similar to Captan^®—while controls reached ≈98%; suspending applications after week 4 resulted in ≈45% damage by week 8. These results confirm that OEO lacks systemic residual activity, acting only as a protectant within preventive integrated pest management (IPM) schemes. Docking to cytochrome b (protein data bank, PDB: 5TL8) indicated strong binding of α-farnesene (−7.638 kcal·mol⁻¹), isoterpinolene (−6.944), and α-terpineol (−6.918), suggesting disruption of mitochondrial respiration via Complex III. OEO represents a promising eco-friendly alternative for managing N. zimbabwana under controlled conditions and reducing reliance on synthetic fungicides. Full article

This study examines the complex relations between corruption, income inequality, and sustainable economic development within the European Union (EU) for the 2003–2023 period. Employing panel data for all 27 EU member states, as well as for the subgroups of Old (OMS) and New Member States (NMS), the analysis applies pooled OLS, random- and fixed-effects models, and panel-corrected standard errors (PCSE) estimations. The results indicate that higher perceived corruption is robustly associated with greater income inequality, while higher tertiary education attainment, greater social protection expenditures, and increased urbanization apparently reduce inequality. Subsample evidence reveals that institutional context conditions the strength of these relationships, with NMS exhibiting a more significant corruption–inequality nexus. These findings highlight that achieving sustainable and inclusive economic growth in the EU depends on institutional integrity and good governance. Strengthening anti-corruption frameworks, investing in human capital, and enhancing social protection are essential policy instruments for supporting the EU’s sustainable development objectives. Full article

This paper proposes a new family of robust estimators of means, depending on the Orthogonalized Gnanadesikan–Kettenring (OGK) covariance matrix. These estimators are computationally feasible and robust replacements of the Minimum Covariance Determinant (MCD) estimator in survey sampling contexts involving auxiliary information. With the growing popularity of outliers in environmental data, as in the case of measuring solar radiation, conventional estimators like the sample mean or the Ordinary Least Squares (OLS) regression-based estimators are both biased and unreliable. The suggested OGK-based exponential-type estimators combine robust measures of location and dispersion and have a considerable advantage in the estimation of the population mean when auxiliary variables such as temperature are highly correlated with the variable of interest. The MSE property of OGK-based estimators is also obtained through a detailed theoretical derivation with the expressions of optimal weights. Performance was further proved using real-world and simulated data on solar radiation, as well as by demonstrating lower MSEs and higher PREs in comparison to MCD-based estimators. These results show that OGK-based estimators are highly efficient and robust in actual and artificially contaminated situations and hence are a good option in robust survey sampling and environmental data analysis. Full article

The aim of this study was to present a comprehensive analysis of honey varieties from different botanical origins, focusing on their phenolic compounds’ composition, volatile profiles, and antioxidant activity. We simultaneously identified and quantified 37 bioactive compounds, including anthocyanins, flavonols, flavones, flavan-3-ols, proanthocyanidins, and phenolic acids, across various honey samples by HPLC-MS/MS. Total phenolic content (TPC), total flavonoid content (TFC), and antioxidant activity (AOA) were determined using UV-Vis spectrophotometric analysis. The content of phenolic compounds quantified by HPLC-MS/MS ranged from 19.56 to 243.94 mg·kg⁻¹, highlighting a high presence of these antioxidant compounds (mainly phenolic acids), confirmed also by the positive correlation between TPC and DPPH values. Among volatiles compounds, analyzed by HS-SPME-GC-MS, benzene acetaldehyde and furfural resulted specific for two types of honey samples (H-7 and H-9), highlighting the possibility of searching for chemical markers to characterize honeys of different specie/origin. This study enhances our understanding of the bioactive potential of honey from different botanical origins and provides a foundation for future research on its health benefits. Full article

This study investigates the relationship between artificial intelligence (AI) adoption and environmental, social, and governance (ESG) performance among 100 listed Saudi Arabian firms over the period 2015–2024. Drawing on panel data regression techniques, including fixed effects models with Driscoll–Kraay standard errors, pooled OLS with industry and year controls, and dynamic panel estimations using system GMM, the analysis reveals a significant and positive association between AI implementation and overall ESG scores. Disaggregated analysis shows that AI adoption is particularly associated with improvements in the environmental and social dimensions, with a more moderate relationship to governance practices. To address potential issues of cross-sectional dependence and heterogeneity, the study applies the Common Correlated Effects Mean Group (CCEMG) and Mean Group (MG) estimators as robustness checks, which confirm the consistency of the main findings. In addition, the Dumitrescu–Hurlin panel Granger causality test indicates that AI adoption Granger-causes ESG performance—especially in the environmental and social dimensions—while no reverse causality is observed. The results suggest that AI technologies are positively linked to firms’ sustainability strategies and performance, supporting the integration of digital transformation into national and corporate ESG agendas, particularly in emerging markets like Saudi Arabia. Full article

This study aims to explore whether the dual-scale management model, formed by integrating service-scale management with land-scale management, can further break through the benefit limits of single land-scale management and unlock additional profit potential in agricultural scale operations. This study used data from a 2024 questionnaire survey of 2166 farming households in Anhui Province and employed a coupling coordination degree model to measure the level of dual-scale management. Subsequently, we utilized OLS regression and mediation effect models to empirically examine the impact of dual-scale management on agricultural operational benefit and their underlying mechanisms. We find that dual-scale management significantly improves agricultural operational benefit. Our measurements show that dual-scale management not only breaks through the upper limit of the optimal operating area inherent in single land-scale management but also yields a greater improvement in agricultural operational benefit than single land-scale management. Heterogeneity analysis reveals that dual-scale management significantly enhances the agricultural operational benefit of farmers in plain areas and farmers with fully developed high-standard farmland. Mechanism analysis indicates that dual-scale management enhances agricultural operational benefit through an endogenous efficiency improvement mechanism and an exogenous risk-burden-sharing mechanism. These findings suggest that fostering a synergistic development system for land-scale management and service-scale management is conducive to improving the economic returns for land scale operators and unlocking new dividend spaces for agricultural scale operation in China’s post-land transfer era. Full article

The aim of this work was to systematically evaluate the effects of Lion’s Mane Mushroom powder (LMM, 0–40%) on the physicochemical properties, structural characteristics, and flavor profile of soy protein isolate-based high-moisture meat analogues (HMMAs). Optimal incorporation of 20% LMM significantly enhanced product quality by acting as a secondary phase that inhibited lateral protein aggregation while promoting longitudinal alignment, achieving a peak fibrous degree of 1.54 with dense, ordered fibers confirmed by scanning electron microscopy. Rheological analysis showed that LMM improved viscoelasticity (G′ > G″) through β-glucan; however, excessive addition (≥30%) compromised structural integrity due to insoluble dietary fiber disrupting protein network continuity, concurrently reducing thermal stability as denaturation enthalpy (_ΔH) decreased from 1176.6 to 776.3 J/g. Flavor analysis identified 285 volatile compounds in HMMAs with 20% LMM, including 98 novel compounds, and 101 flavor metabolites were upregulated. The mushroom-characteristic compound 1-octen-3-ol exhibited a marked increase in its Relative Odor Activity Value of 18.04, intensifying mushroom notes. Furthermore, LMM polysaccharides promoted the Maillard reaction, increasing the browning index from 48.77 to 82.07, while β-glucan induced a transition in protein secondary structure from random coil to β-sheet configurations via intramolecular hydrogen bonding. In conclusion, 20% LMM incorporation synergistically improved texture, fibrous structure, and flavor complexity—particularly enhancing mushroom aroma. This research offers valuable insights and a foundation for future research for developing high-quality fungal protein-based meat analogues Full article

Origanum petraeum Danin, an endemic medicinal shrub from Jordan, belongs to the Lamiaceae family and possesses significant pharmaceutical potential, yet its secondary metabolite profile remains largely unexplored. This study evaluated the effects of two types of nanoparticles, silver (Ag) and copper (Cu), on in vitro propagation and secondary metabolite composition in O. petraeum microshoots. Sterilized buds were used to initiate in vitro cultures on Murashige and Skoog (MS) medium supplemented with gibberellic acid (GA₃) at 0.5 mg/L. Microshoots were treated with nanoparticles at concentrations of 0, 25, 50, 100, and 150 mg/L. AgNPs at 100 mg/L promoted growth, increasing the number of microshoots to 11.6 and shoot height to 9.22 cm. Transmission electron microscopy confirmed nanoparticle uptake and translocation, with AgNPs observed in root cells as small particles (≤24.63 nm), while CuNPs formed aggregates in leaves (47.71 nm). GC-MS analysis revealed that nanoparticles altered the volatile composition; 50 mg/L CuNPs enhanced monoterpenes, including α-terpinyl acetate (29.23%) and geranyl acetate (12.76%), whereas 50 mg/L AgNPs increased sesquiterpenes, such as caryophyllene oxide (28.45%). Control in vitro cultures without nanoparticles showed simpler profiles dominated by caryophyllene oxide, while wild plants contained both monoterpenes and sesquiterpenes, with eudesm-7(11)-en-4-ol (25.10%) as the major compound. Nutrient analysis indicated that nanoparticles influenced nutrient composition in microshoots. This study is the first to report nanoparticle-assisted growth and essential oil composition in O. petraeum, demonstrating their potential to enhance growth and secondary metabolite production for pharmacological and biotechnological applications. Full article

Background and Objectives: Anthocyanin-rich barley varieties have recently gained attention due to their high (poly)phenolic content and potential health benefits, yet human data on their bioavailability remain scarce. This study aimed to characterize the absorption, metabolism, and excretion of (poly)phenolic compounds from a novel hull-less purple whole-grain barley (WGB) genotype. Methods: Eleven healthy volunteers consumed 140 g of purple WGB biscuits, and plasma and urine samples were collected over 6 h and 48 h, respectively. Results: UPLC-MS/MS analysis revealed a broad range of metabolites, with 11 (poly)phenolic compounds identified in plasma and 80 in urine. The biscuits were particularly rich in flavones (217 mg/140 g, mainly chrysoeriol derivatives), followed by hydroxycinnamic acids (~54 mg, mainly 4′-hydroxy-3′-methoxycinnamic acid), anthocyanins (44.8 mg), and flavan-3-ols (16.8 mg). In plasma, glycosylated anthocyanins and flavone conjugates (e.g., peonidin-3-O-glucuronide, chrysoeriol-O-glucuronide) were detectable within 1–2 h, consistent with early absorption. In contrast, microbial-derived catabolites—including valerolactones, phenylacetic and benzoic acids—were mainly excreted in urine between 8 and 24 h, reaching concentrations above 1000 nM. Conclusions: These findings provide novel insights into the bioavailability and metabolic fate of barley (poly)phenols, supporting their potential contribution to host and gut health. As a proof-of-concept study, it complements the limited data available from pigmented cereals and underscores the need for validation in larger cohorts. Full article

Flotation separation of fluorite and calcite, by adding AWG (acidified water glass) as inhibitor and NaOL (sodium oleate) as collector, has been investigated by means of micro-flotation tests, flotation solution chemistry, zeta potential measurements, Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS) in this study. The micro-flotation results demonstrate that NaOL exhibits strong collecting power toward both fluorite and calcite, making effective separation of the two minerals unachievable in the absence of depressants. When AWG is applied as a depressant, it shows selective depression toward calcite, while exhibiting little effect on fluorite. Solution chemistry analysis, contact angle measurements, zeta potential measurements, FTIR, and XPS analyses collectively confirm that AWG can adsorb onto the calcite surface, but not onto fluorite. This adsorption prevents NaOL from interacting with the calcite surface. In contrast, the absence of AWG adsorption on fluorite allows NaOL to freely adsorb and thereby collect fluorite particles. Full article

Ensiling forage under low-temperature conditions often leads to poor fermentation and nutrient losses. This study evaluated the effects of a cold-tolerant Pediococcus pentosaceus OL77 strain on oat silage. Silages were prepared with or without Pediococcus pentosaceus inoculation (1 × 10⁵ cfu/g FM). After 90 days, OL77-treated silage showed markedly higher lactic acid (45.83 vs. 30.51 g/kg DM), lower pH (3.88 vs. 4.443), and better preservation of WSC (64.68 vs. 47.60 g/kg DM) and crude protein (89.26 vs. 65.52 g/kg DM) than the control. Microbial analysis revealed accelerated colonization by Pediococcus, reduced bacterial diversity, and faster stabilization of the fermentation process. Functional predictions indicated enhanced carbohydrate and energy metabolism. These findings demonstrate that OL77 can effectively improve fermentation quality and nutrient preservation of oat silage under low-temperature conditions, offering a practical inoculant option for cold regions. Full article

This study investigates the impact of working capital management (WCM) on profitability in the Indian cement industry, an energy-intensive sector central to the country’s infrastructure growth. Using a balanced panel of listed firms over 2010–2024, we employ pooled OLS, two-way fixed effects, quantile regressions, and dynamic system GMM to address heterogeneity and endogeneity concerns. The results demonstrate that reductions in the cash conversion cycle (CCC), accelerated receivables collection, leaner inventories, and prudent use of payables significantly improve profitability. Quantile regressions reveal that highly profitable firms capture larger absolute gains from CCC reductions, while size-split analysis indicates that smaller and liquidity-constrained firms achieve proportionally greater marginal relief. These findings represent complementary perspectives rather than unified statistical relationship, a limitation we acknowledge. Dynamic estimates confirm the robustness of results after accounting for persistence and reverse causality. Beyond firm-level outcomes, the study contributes conceptually by linking WCM efficiency to sustainability financing: liquidity released from shorter operating cycles can be redeployed into green and energy-efficient investments, offering a potential channel for ESG alignment in carbon-intensive industries. Policy implications highlight the role of digital reforms such as TReDS and e-invoicing in strengthening liquidity efficiency, particularly for mid-sized firms. The findings extend the international WCM profitability literature, provide sector-specific evidence for India, and suggest new avenues for integrating financial and sustainability strategies. Full article

Tyrosyl-DNA phosphodiesterase 1 (TDP1) is a key enzyme for the repair of stalled topoisomerase 1 (TOP1)-DNA complexes. Previously, we obtained HEK293A cells with homozygous knockout of the TDP1 gene by the CRISPR/Cas9 method and used them as a cell model to study the mechanisms of anticancer therapy and to investigate the effect of TDP1 gene knockout on gene expression changes in the human HEK293A cell line by transcriptome analysis. In this study, we investigated the effect of a TDP1 inhibitor ((R,E)-2-acetyl-6-(2-(2-(4-bromobenzyliden) hydrazinyl) thiazol-4-yl)-3,7,9-trihydroxy-8,9b-dimethyldibenzo[b,d] furan-1(9bH)-one, OL9-119, an usnic acid derivative), capable of potentiating the antitumor effect of topotecan, as well as its combination with topotecan, on the transcriptome of wild-type and TDP1 knockout HEK293A cells. OL9-119 was found to be able to reduce cell motility by decreasing the expression of a number of genes, which may explain the antimetastatic effect of this compound. Differentially expressed genes (DEGs) related to electron transport, mitochondrial function, and protein folding were also identified under TDP1 inhibitor treatment. Full article