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Nighttime light remote sensing imagery is limited by its single band and low spatial resolution, hindering its ability to accurately capture ground information. To address this, a dual-sampling adjustment method is proposed to enhance nighttime light remote sensing imagery by fusing daytime optical images with nighttime light remote sensing imagery, generating high-quality color nighttime light remote sensing imagery. The results are as follows: (1) Compared to traditional nighttime light remote sensing imagery, the spatial resolution of the fusion images is improved from 500 m to 15 m while better retaining the ground features of daytime optical images and the distribution of nighttime light. (2) Quality evaluations confirm that color nighttime light remote sensing imagery enhanced by dual-sampling adjustment can effectively balance optical fidelity and spatial texture features. (3) In Beijing’s central business district, color nighttime light brightness exhibits the strongest correlation with business, especially in Dongcheng District, with r = 0.7221, providing a visual tool for assessing urban economic vitality at night. This study overcomes the limitations of fusing day–night remote sensing imagery, expanding the application field of color nighttime light remote sensing imagery and providing critical decision support for refined urban management.

Traditional night light images are black and white with a low resolution, which has largely limited their applications in areas such as high-accuracy urban electricity consumption estimation. For this reason, this study proposes a fusion algorithm based on a dual-transformation (wavelet transform and IHS (Intensity Hue Saturation) color space transform), is proposed to generate color night light remote sensing images (color-NLRSIs). In the dual-transformation, the red and green bands of Landsat multi-spectral images and “NPP-VIIRS-like” night light remote sensing images are merged. The three bands of the multi-band image are converted into independent components by the IHS modulated wavelet transformed algorithm, which represents the main effective information of the original image. With the color space transformation of the original image to the IHS color space, the components I, H, and S of Landsat multi-spectral images are obtained, and the histogram is optimally matched, and then it is combined with a two-dimensional discrete wavelet transform. Finally, it is inverted into RGB (red, green, and blue) color images. The experimental results demonstrate the following: (1) Compared with the traditional single-fusion algorithm, the dual-transformation has the best comprehensive performance effect on the spatial resolution, detail contrast, and color information before and after fusion, so the fusion image quality is the best; (2) The fused color-NLRSIs can visualize the information of the features covered by lights at night, and the resolution of the image has been improved from 500 m to 40 m, which can more accurately analyze the light of small-scale area and the ground features covered; (3) The fused color-NLRSIs are improved in terms of their MEAN (mean value), STD (standard deviation), EN (entropy), and AG (average gradient) so that the images have better advantages in terms of detail texture, spectral characteristics, and clarity of the images. In summary, the dual-transformation algorithm has the best overall performance and the highest quality of fused color-NLRSIs.

Background The isolated-check visual evoked potential (ic-VEP) is an advanced electrophysiological technique that selectively evaluates the magnocellular pathway, enabling precise assessment of optic nerve damage and currently serving as a diagnostic tool for glaucoma. This meta-analysis evaluates the diagnostic performance of ic-VEP in glaucoma detection. Methods The study protocol was prospectively registered in the International Prospective Register of Systematic Reviews (PROSPERO) (Registration ID: CRD42025633065) prior to data extraction. Following PRISMA guidelines, a systematic search of PubMed, Embase, Web of Science, Scopus, Ovid Medline, and Cochrane databases was conducted from inception to January 1, 2025. Studies evaluating ic-VEP for glaucoma diagnosis were included, with confirmed cases defined by standard automated perimetry (SAP). Eligible studies underwent methodological quality was appraised using the QUADAS-2 tool and Review Manager 5.3. Meta-analysis was conducted using Meta-Disc 1.4 and Stata 13.0 based on the hierarchical summary receiver operating characteristic (HSROC) model to derive pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Publication bias was assessed using Deek’s funnel plot asymmetry test and trim-and-fill analysis. Heterogeneity was quantified via the I 2 statistic, with meta-regression identifying potential sources of variability. Results Ten studies comprising 434 patients with glaucoma and 321 healthy controls met the inclusion criteria. Pooled sensitivity and specificity for glaucoma diagnosis were 0.77 (95% CI: 0.71–0.82) and 0.93 (95% CI: 0.80–0.98), with an AUC 0.86 (95% CI: 0.83–0.89). The DOR was 44.96 (95% CI: 15.25–132.51) and the lambda value was 3.70 (95% CI: 2.73–4.66). Both Deek’s funnel plot asymmetry test ( p  = 0.30) and trim-and-fill analysis (imputed studies = 4) demonstrated no substantial publication bias. Meta-regression identified type of device (Neucodia vs. EvokeDx) and type of glaucoma (early POAG vs. others) as contributors to heterogeneity. Conclusions Despite limited included studies and sample sizes, ic-VEP exhibits high diagnostic accuracy for glaucoma, offering advantages such as objectivity, non-invasiveness, and operational simplicity. These findings support its integration into clinical workflows for glaucoma screening and diagnosis.

This study addresses the limitations of traditional nighttime light remote sensing data in ground object feature recognition and carbon emission monitoring by proposing a fusion framework based on Nonsubsampled Contourlet Transform (NSCT) and Intensity-Hue-Saturation (IHS). This framework successfully generates a high-resolution color nighttime light remote sensing imagery (color-NLRSI) dataset. Focusing on Guangzhou, an important city in the Guangdong-Hong Kong-Macao Greater Bay Area, the study systematically analyzes the spatiotemporal coupling mechanism between urban form evolution and carbon footprint by integrating multiple remote sensing data sources and socio-economic statistical information. Key findings include: (i) The color-NLRSI dataset outperforms traditional NPP-VIIRS data in built-up area extraction, providing more accurate spatial information by refining urban boundary recognition logic. (ii) Spatial correlation analysis reveals a remarkably strong positive relationship between built-up area expansion and carbon emissions, with the correlation coefficient for numerous districts exceeding 0.9. High-density built-up areas are strongly associated with a carbon lock-in effect, hindering low-carbon transformation efficiency. (iii) Geographically Weighted Regression analysis demonstrates that in population-polarized regions, the impact coefficient of built-up area expansion on carbon emissions is notably high at 0.961. This factor’s association (22.43%) surpasses economic development (10.34%) and urbanization rate (14.91%). The established “data fusion—dynamic monitoring—mechanism analysis” technical system, which generates a novel high-resolution color-NLRSI dataset and reveals a distinct ‘core-periphery’ heterogeneity pattern in Guangzhou, demonstrating that urban expansion is the dominant driver of carbon emissions. This approach offers a scientific basis for tailored urban low-carbon development strategies, spatial optimization, and enhanced precision in carbon emission monitoring.

With the increasing demand for remote sensing image applications, extracting the required images from a huge set of remote sensing images has become a hot topic. The previous retrieval methods cannot guarantee the efficiency, accuracy, and interpretability in the retrieval process. Therefore, we propose a bag-of-words association mapping method that can explain the semantic derivation process of remote sensing images. The method constructs associations between low-level features and high-level semantics through visual feature word packets. An improved FP-Growth method is proposed to achieve the construction of strong association rules to semantics. A feedback mechanism is established to improve the accuracy of subsequent retrievals by reducing the semantic probability of incorrect retrieval results. The public datasets AID and NWPU-RESISC45 were used to validate these experiments. The experimental results show that the average accuracies of the two datasets reach 87.5% and 90.8%, which are 22.5% and 20.3% higher than VGG16, and 17.6% and 15.6% higher than ResNet18, respectively. The experimental results were able to validate the effectiveness of our proposed method.

Purpose This study aimed to assess orbital magnetic resonance imaging (MRI) differences across various severity levels of thyroid-associated ophthalmopathy (TAO) and compare them to healthy controls. Additionally, it investigated the correlation and diagnostic potential of multiple MRI parameters for dysthyroid optic neuropathy (DON). Methods Patients with TAO were classified according to the EUGOGO severity scale into mild TAO (66 orbits), moderate-to-severe TAO (59 orbits), and DON (21 orbits). A control group of 25 healthy orbits was also included. Patients underwent comprehensive ophthalmic and orbital MRI examinations. Generalized estimating equation (GEE) was used to analyze the clinical and MRI data, comparing differences across groups. Univariate and multivariate binary logistic regression identified independent risk factors for DON. The predictive value of MRI parameters for DON was further evaluated using a nomogram, receiver operating characteristic curve (ROC), and decision curve analysis (DCA). Results The extraocular muscle area significantly differed between the control and mild TAO groups ( P  < 0.05), as well as between both groups and the DON group ( P  < 0.05). Significant variations in angle between the medial rectus and optic nerve (AMR-ON) and angle between the lateral rectus and optic nerve (ALR-ON) were observed across the control, mild TAO, and moderate-to-severe TAO groups compared to the DON group ( P  < 0.05). Regression analysis revealed superior rectus muscle area (SRA) and AMR-ON as independent predictors of DON ( P  < 0.05). The SRA + AMR-ON nomogram demonstrated high predictive performance for DON, with an AUC of 0.906 ( P  < 0.001), sensitivity of 90.5%, and specificity of 82.4%. DCA further confirmed that the SRA + AMR-ON model outperformed Muscle Index (MI) and Crowding Index (CI) in predictive efficacy for DON. Conclusions A reduction in AMR-ON and ALR-ON correlates with increased TAO severity, while an enlargement in extraocular muscle area, MI, and CI reflects worsening TAO. SRA and AMR-ON serve as independent predictors for DON, and the predictive model combining these parameters is highly effective in forecasting DON onset.

Meibomian gland dysfunction (MGD) secondary to thyroid-associated ophthalmopathy (TAO) represents a significant pathogenic mechanism in dry eye disease. This study provides the first systematic review and meta-analysis of MGD indicators in TAO. The study protocol was prospectively registered with the International Prospective Register of Systematic Reviews (PROSPERO) (Registration ID: CRD420251020327) before data extraction. Following PRISMA and MOOSE guidelines, a systematic search was conducted across PubMed, Embase, Web of Science, Scopus, Ovid Medline, and Cochrane from inception through March 27, 2025. Fourteen studies met the inclusion criteria. Key indicators included lipid layer thickness (LLT), meiboscore, meibum quality, first non-invasive tear film break-up time (NITBUT-f), average non-invasive tear film break-up time (NITBUT-avg), tear break-up time (TBUT), meibomian gland dropout area in the upper (MGDU) and lower eyelids (MGDL), and confocal microscopy (IVCM) markers (meibomian gland acinar density [MAD], meibomian gland acinar longest diameter [MALD], meibomian gland acinar shortest diameter [MASD]). Risk of bias was assessed using the AHRQ checklist or NOS. Meta-analysis was performed with Review Manager 5.4.1 and Stata 16.0. Publication bias was assessed using Egger's test and funnel plots. Fixed-effects models were used in the absence of significant heterogeneity ( > 0.10 or < 50%); otherwise, random-effects models were applied. Thirteen studies (813 TAO eyes, 522 controls) were included in the meta-analysis. Quality assessment revealed moderate-to-high methodological rigor across studies. Patients with TAO exhibited significantly worse meibomian gland indicators compared to controls: shorter tear film stability (NITBUT-f, TBUT), higher LLT, increased meiboscore and greater eyelid gland dropout (MGDU, MGDL). IVCM markers indicated meibomian acinar enlargement (MALD, MASD). Significant heterogeneity was observed in several outcomes, including NITBUT-f, NITBUT-avg, meiboscore, Meibum quality, TBUT and MGDU comparisons. Despite the limited number of studies and small sample sizes, TAO is linked to meibomian gland atrophy, acinar dilation, and tear film instability. Active disease is associated with more pronounced lipid layer abnormalities. Targeted evaluation and management of MGD are crucial to mitigate TAO-associated ocular surface morbidity and improve patient quality of life.

In group decision making (GDM), experts from a variety of fields collaborate to select the best alternative. Due to external influences or a lack of sufficient knowledge, experts may sometimes offer low-quality opinions on alternatives. In existing GDM problems, the opinion quality and the consensus with a quality threshold have never been explored simultaneously. To fill this gap, this paper proposes a novel GDM framework integrating opinion quality dynamic management and an improved minimum cost consensus model (MCCM) with a quality threshold in GDM. Firstly, opinion quality dynamic evaluations and management mechanisms are designed to improve the opinions of experts to some extent. Afterwards, the weights of the experts are determined by combining their social reputation and opinion quality. Furthermore, the impact of opinion quality is considered in the consensus, and an improved MCCM with a quality threshold is proposed to promote the consensus. A case study on selecting AI enterprises for an investment is provided to verify the applicability of the proposed opinion-quality-based GDM. Ultimately, the quantitative results show that the proposed model achieves a consensus cost of 411, which is 67.5% lower than the benchmark method M2. The proposed GDM framework only requires two iterations and satisfies the predefined opinion quality threshold and consensus level. The optimal alternative remains stable under various parameter settings, verifying the robustness and superiority of the proposed model.

Background Ensuring the wellness of older individuals with disabilities requires prioritising the care needs of their carers. However, current practice lacks validated tools to measure the needs of carers in home environments. Thus, this study aimed to create and test a questionnaire on the care needs of family carers of older people with disabilities in China. Methods We used a standard development process to generate the questionnaire. The pilot testing included cognitive interviews to ensure interpretation as intended. Furthermore, we used a cross-sectional study method to conveniently select 640 Chinese family carers of older people with disabilities from August 1, 2022, to June 11, 2023, for face-to-face investigation. Exploratory factor analysis (EFA) aided in project reduction and factor estimation, with 30 participants undergoing retest evaluations every two weeks. Confirmatory factor analysis (CFA) assessed the model’s structural validity, while internal consistency and retest reliability validated its accuracy. Results These tests established the model: content validity, item analysis and EFA. Six factors extracted from the initial analysis explained 62.891% of the observation variance. CFA showed good model fit, and the questionnaire had good reliability and validity. The final questionnaire included 21 items focusing on six dimensions: care assistance (three items), care environment (three items), care information (three items), formal support (four items), care ability (six items) and self-development (two items). Conclusion The care needs questionnaire effectively evaluates the needs of family carers in their caring activities.

Distrust behavior is a human behavior that has a significant impact on water pollution management, but it is neglected in existing approaches. To solve this problem, we design a large-scale group decision making in social network (LSGDM-SN) approach based on distrust behavior and apply it to water pollution management. The purpose of this paper is to develop an LSGDM-SN method to assist managers choose the optimal water pollution management plan. In the presented method, fuzzy preference relations (FPRs) are used to express experts’ assessment of alternatives. To utilize the proposed LSGDM-SN approach to solve the water pollution problem, a novel agglomerative hierarchical clustering (AHC) method is proposed by combing preference similarity and social relationships. Afterward, consensus feedback based on distrust behavior and social network analysis (SNA) is developed to encourage the subset to modify its FPR. A mechanism for the identification and management of distrust behavior is introduced. Based on the situations of distrust behaviors, two pieces of feedback advice are provided to the subset to adjust its FPR. Subsequently, a score function of the FPR is proposed to obtain the best solution for water pollution management. Finally, some comparative analyses and discussions demonstrate the effectiveness and feasibility of the proposed method.