Use of gamma irradiation processing for shelf stable apple-banana purée and its quality evaluation

Machine-learning prediction of retardation factor and tailing propensity in thin-layer chromatography.

Honey microbiome as a source of bioactive metabolites: the role of the microorganisms and the influence of physicochemical parameters.

Parents' perception of family-centered care in the pediatric intensive care unit: a systematic review of qualitative studies.

Mapping of CO2 emissions and soil attributes under a silvopastoral system and degraded pasture in the Brazilian Cerrado Region.

Efficient DNA extraction and sequencing protocol for keratinised materials of animals.

A QbD-informed study of permeation enhancer-octreotide nanoscale interactions influencing performance in pullulan buccal films.

Unraveling geographical heterogeneity and spatial distribution of Long pepper fruits: An integrative analytical perspective.

Genesis and quality evaluation study of high-purity quartz deposit hosted in NYF-type granite pegmatite in the Central Asian Orogenic Belt, NW China

Integrating multimodal data fusion for comprehensive characterization, antioxidant marker discovery, and geographical origin tracing of Platycodonis Radix.

Conventional asphalt testing methods inadequately and slowly represent the inherent chemical composition variations among materials. This study utilizes attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) to identify subtle changes in functional groups within asphalt binders. Additionally, it integrates machine learning technology to develop a rapid and intelligent framework for quality evaluation and brand recognition based on chemical fingerprints. This paper examines four distinct brands of No. 70 needle penetration grade (PEN 70) base asphalt binders, with spectral information acquired through ATR-FTIR analysis. This work innovatively links the characteristic differences of functional groups, as captured by FTIR, to asphalt brand affiliation and quality characteristics, enabling intelligent and high-precision classification via machine learning. Principal component analysis (PCA) indicated that various asphalt brands exhibit notable differences in spectral characteristics within the fingerprint region (1,300–400 cm−1) and in the relative content of aromatic and aliphatic functional groups, which is essential for brand differentiation. Characteristic functional group peaks at 1,376, 813, 865, and 722 cm−1 were identified as essential for brand differentiation. The differences in functional groups serve to differentiate brands and correlate with performance indicators, including ductility. Random forest (RF) is the most effective machine learning algorithm for learning patterns of functional group changes. The optimized RF model attained an overall classification accuracy of 98.4% on an independent test set, as determined by fingerprint region spectral data. This framework offers a dependable technical method for the rapid quality assurance of asphalt in location.

Underwater image quality evaluation via multidimensional perceptual characterization

Construction of a home care quality evaluation index system for disabled elderly based on "internet + nursing services".

Water quality assessment in a megacity river: Water chemical analysis integrated with AHP-Entropy weighted fuzzy comprehensive evaluation model.

Integrated bioinformatics and quantitative lipidomics reveal temporal lipid dynamics and oxidative metabolic networks in refrigerated pork.

Automated quality evaluation of Cervical Cytopathology Whole Slide Images based on content analysis

This study addresses the quality of real-world data (RWD) in oncology and proposes a comprehensive evaluation system for assessing the quality of RWD in the review of anti-tumor drugs. The proposed system provides a solid theoretical foundation and methodological tools to support drug review decisions and offers insights for further enhancing data quality evaluation in regulatory processes. Preliminary indicators for evaluating the quality of real-world data in anti-tumor drug review were identified based on established domestic and international theories and practices. Expert consultations were conducted to collect feedback from professionals across various fields, further refining the evaluation indicators. The Analytic Hierarchy Process (AHP) was then applied to develop an expert judgment matrix, assigning weights to the indicators according to their relative importance. The final weight values for each indicator were subsequently determined. This study developed a real-world data quality evaluation system for anti-tumor drug review, comprising 2 primary indicators, 8 secondary indicators, and 32 tertiary indicators. Weight analysis revealed that data accuracy, source credibility, and population representativeness were the most critical secondary indicators. Among the tertiary indicators, oncology-specific data standards, third-party ethical evaluations, and clarity in inclusion/exclusion criteria and screening processes were identified as the most significant factors. These findings suggest that regulatory agencies should emphasize these aspects more during the quality evaluation process. Based on the key indicators identified in the evaluation system, it is recommended that drug regulatory agencies focus on the following aspects when assessing the quality of real-world data in anti-tumor drug reviews: (1) clear definition of core data elements and critical variables; (2) evaluation of data quality in alignment with research objectives; and (3) ensuring data security and consistency to enhance overall data quality.

Waterbird habitat quality in small inland wetlands is jointly influenced by hydrological processes and human regulation. Although previous studies have improved our understanding of waterbird habitats, most focus on large spatial scales and therefore fail to capture fine-scale habitat variation in small wetlands. In addition, while multiple environmental factors are often considered, existing indicator systems do not fully reflect the coupled interactions among hydrology, vegetation, and waterbird communities. To address this gap, this study selected the Xianghai Wetland Nature Reserve in the western Songnen Plain as the study area. By integrating remote sensing and field survey data, we developed a comprehensive Habitat Quality Index (HQI) for waterbirds. The index incorporates multidimensional hydrological connectivity (MNDWI), vegetation cover (NDVI), water depth, and waterbird diversity (Shannon-Wiener index). Using this framework, we systematically evaluated the spatiotemporal dynamics of habitat quality from 2014 to 2023The results show that from 2014 to 2019, reduced precipitation and shrinking water bodies weakened hydrological connectivity, resulting in an overall decline in habitat quality. Since 2020, ecological water replenishment and restoration projects have significantly improved hydrological connectivity, driving rapid recovery and continuous expansion of high-quality habitats. Spatially, high-quality habitats are consistently concentrated in the wetland core and along lake margins, with habitat quality decreasing from the center toward the periphery. Habitat quality changes are primarily constrained by hydrological connectivity, while water depth and vegetation regulate habitat conditions indirectly by influencing food availability and concealment. By integrating waterbird community metrics with hydrological processes, this study provides a methodological framework for dynamic assessment and refined management of waterbird habitat quality in small-scale inland wetlands.

To resolve acoustic-mechanical conflicts and integrate research gaps in underwater coatings. Inspired by the biomechanics of jumping spiders and human bones, we design an underwater composite structure subject to hydrostatic pressure. Based on mechanisms involving weak energy entanglement driven by damping and wave-mode conversion driven by impedance mismatch. A synergistic combination of theoretical modeling, numerical simulation, and experimental validation, the structure achieves low-intensity diffuse reflection below 0.8kHz, and broadband low-frequency sound attenuation at 0.8-2.5kHz (insulation > 26dB, absorption > 0.8). Notably, this structure achieves significant sound attenuation with an absorption coefficient exceeding 0.8 below 4kHz even under 3MPa of hydrostatic pressure. The sound attenuation performance decreases by an average of only 4.5% per 1MPa increase in pressure, and the deformation nearly 100% recovers after unloading. By integrating an acoustic-electrical analogy model for component dimensionality reduction and a convolutional neural network for visual quality evaluation, we establish an integrated design-evaluation framework. This strategy provides a scalable approach for next-generation underwater acoustic skins.

The performance of breast MRI (bMRI) depends on image quality, varying due to patient-related or technical/protocol factors. The resulting artifacts can heavily reduce sensitivity and specificity. We developed a bMRI quality scoring system (bMRI-QUAL) for the bMRI standard protocol. Two independent readers with 3 years of experience evaluated 133 consecutive 1.5-T examinations (sample size calculated to ensure reproducibility). Each sequence was rated on a 4-point scale: 0 = not diagnostic; 1 = relevant artifacts with conserved diagnostic value; 2 = slight artifacts with conserved diagnostic value; 3 = excellent image quality. Each score was weighted in the following formula: (T2-weighted * 1) + (diffusion weighted imaging with b = 0 * 0.5) + (apparent diffusion coefficient maps * 0.5) + (T1-weighted-precontrast * 1) + (T1-weighted-postcontrast * 2) + (T1-weighted-subtracted * 3). This sum was divided by 2.4, obtaining a 0-to-10 global score (GS), with < 6 considered insufficient. The average GS between the two readers was 8.3 ± 1.0, with 102/133 examinations (76.7%) receiving a score ≥ 7 from both readers. Only one case (0.8%) was scored < 6 by both readers. The Bland-Altman analysis showed bias of -0.12, with limits of agreement ranging from -2.52 to +2.29. The difference in GS between the two readers (coverage probability) was 72/133 (54.1%) within ±1 and 123/133 (92.5%) within ±2. Coverage probability within ±1 point ranged from 94.0% to 99.2% across sequences. Average evaluation time/examination was 3 min. bMRI-QUAL is a reproducible quality scoring system. Breast MRI image quality at a tertiary cancer center was good to excellent in approximately 80% of cases. QuestionBreast MRI image quality varies widely and can compromise diagnostic accuracy. A standardized, clinically weighted scoring system is needed to guide repeat-scan decisions and facilitate quality assurance. FindingsIn 133 breast MRIs, the bMRI-QUAL 0-to-10 scoring system proved to be reproducible. Readers assigned a good-to-excellent mean score (8.3) with a 3-min evaluation time. Clinical relevancebMRI-QUAL provides a relatively fast and reproducible evaluation of breast MRI image quality, with each sequence weighted according to its clinical relevance for lesion detection/characterization. It can support decision-making regarding insufficient image quality and facilitate quality assurance protocols.