Search Results (99)

Coordinating ecotourism development with economic growth is central to achieving sustainability in regions where natural assets are both a comparative advantage and a binding constraint. This study assesses the ecotourism economy coupling coordination in Hainan Free Trade Port (China) during 2017–2023. Building on sustainable development theory, systems theory, and the tourism-led growth hypothesis, we conceptualize three coordination pathways, industrial structure upgrading, clustering effects, and urban–rural linkages, and operationalize them through an 18-indicator evaluation system covering ecotourism and economic subsystems. Indicator weights are determined using the entropy weight method, and the coupling coordination degree model is applied to quantify the interaction intensity and coordination level. Gray Relational Analysis is further used as a robustness-oriented complement to identify the factors most associated with coordination changes. Results show that both subsystems improved overall with noticeable fluctuations: the ecotourism index rose from 0.239 to 0.719, while the economic development index increased from 0.370 to 0.610. The coupling coordination degree advanced from moderate dysregulation (0.230 in 2017) to near quality coordination (0.995 in 2023), while shock-sensitive years highlight the vulnerability of tourism-related performance. The findings suggest that improving industrial structure and strengthening tourism-related productive capacity and external connectivity are key levers for sustaining coordination without compromising ecological efficiency. Full article

Background: The vestibular schwannoma (VS) is the most common cerebellopontine angle tumor in adults, exhibiting a highly variable natural history, from stability to rapid growth. Accurate, the non-invasive prediction of tumor behavior is essential to guide personalized management and avoid overtreatment or delayed intervention. Objective: To systematically review and synthesize the evidence on MRI-based biomarkers for predicting VS growth and treatment responses. Methods: We conducted a PRISMA-compliant search of PubMed, EMBASE, and Cochrane databases for studies published between 1 January 2000 and 1 January 2025, addressing MRI predictors of VS growth. Cohort studies evaluating texture features, signal intensity ratios, perfusion parameters, and apparent diffusion coefficient (ADC) metrics were included. Study quality was assessed using the NOS (Newcastle–Ottawa Scale) score, GRADE (Grading of Recommendations, Assessment, Development and Evaluation), and ROBIS (Risk of Bias in Systematic reviews) tool. Data on diagnostic performance, including the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, and p value, were extracted and descriptively analyzed. Results: Ten cohort studies (five retrospective, five prospective, total n = 525 patients) met the inclusion criteria. Texture analysis metrics, such as kurtosis and gray-level co-occurrence matrix (GLCM) features, yielded AUCs of 0.65–0.99 for predicting volumetric or linear growth thresholds. Signal intensity ratios on gadolinium-enhanced T1-weighted images for tumor/temporalis muscle achieved a 100% sensitivity and 93.75% specificity. Perfusion MRI parameters (K^trans, v_e, ASL, and DSC derived blood-flow metrics) differentiated growing from stable tumors with AUCs up to 0.85. ADC changes post-gamma knife surgery predicted a favorable response, though the baseline ADC had limited value for natural growth prediction. The heterogeneity in growth definitions, MRI protocols, and retrospective designs remains a key limitation. Conclusions: MRI-based biomarkers may provide exploratory signals associated with VS growth and treatment responses. However, substantial heterogeneity in growth definitions and MRI protocols, small single-center cohorts, and the absence of external validation currently limit clinical implementation. Full article

Fractal dimension (Frac) and lacunarity (Lac) are frequently proposed as biomarkers of multiscale image complexity, but their incremental value over standardized radiomics remains uncertain. We position both measures within the Image Biomarker Standardisation Initiative (IBSI) feature space by running a fully reproducible comparison in two settings. In a baseline experiment, we analyze $N=1000$ simulated $64\times 64$ textured ROIs discretized to $N_g=64$, computing 92 IBSI descriptors together with Frac (box counting) and Lac (gliding box), for 94 features per ROI. In a wavelet-augmented experiment, we analyze $N=1000$ ROIs and add level-1 wavelet descriptors by recomputing first-order and GLCM features in each sub-band (LL, LH, HL, and HH), contributing $4\times (19+19)=152$ additional features and yielding 246 features per ROI. Feature similarity is summarized by a consensus score that averages z-scored absolute Pearson and Spearman correlations, distance correlation, maximal information coefficient, and cosine similarity, and is visualized with clustered heatmaps, dendrograms, sparse networks, PCA loadings, and UMAP and t-SNE embeddings. Across both settings a stable two-block organization emerges. Frac co-locates with contrast, difference, and short-run statistics that capture high-frequency variation; when wavelets are included, detail-band terms from LH, HL, and HH join this group. Lac co-locates with measures of large, coherent structure—GLSZM zone size, GLRLM long-run, and high-gray-level emphases—and with GLCM homogeneity and correlation; LL (approximation) wavelet features align with this block. Pairwise associations are modest in the baseline but become very strong with wavelets (for example, Frac versus GLCM difference entropy, which summarizes the randomness of gray-level differences, with $\left|r\right|\approx 0.98$; and Lac versus GLCM inverse difference normalized (IDN), a homogeneity measure that weights small intensity differences more heavily, with $\left|r\right|\approx 0.96$). The multimetric consensus and geometric embeddings consistently place Frac and Lac in overlapping yet separable neighborhoods, indicating related but non-duplicative information. Practically, Frac and Lac are most useful when multiscale heterogeneity is central and they add a measurable signal beyond strong IBSI baselines (with or without wavelets); otherwise, closely related variance can be absorbed by standard texture families. Full article

Accurate and timely prediction of diseases in water-intensive crops is critical for sustainable agriculture and food security. AI-based crop disease management tools are essential for an optimized approach, as they offer significant potential for enhancing yield and sustainability. This study centers on maize, training deep learning models on UAV imagery and satellite remote-sensing data to detect and predict disease. The performance of multiple convolutional neural networks, such as ResNet-50, DenseNet-121, etc., is evaluated by their ability to classify maize diseases such as Northern Leaf Blight, Gray Leaf Spot, Common Rust, and Blight using UAV drone data. Remotely sensed MODIS satellite data was used to generate spatial severity maps over a uniform grid by implementing time-series modeling. Furthermore, reinforcement learning techniques were used to identify hotspots and prioritize the next locations for inspection by analyzing spatial and temporal patterns, identifying critical factors that affect disease progression, and enabling better decision-making. The integrated pipeline automates data ingestion and delivers farm-level condition views without manual uploads. The combination of multiple remotely sensed data sources leads to an efficient and scalable solution for early disease detection. Full article

This study analyzes retinal fundus images to distinguish healthy retinas from those affected by diabetic retinopathy (DR) and glaucoma using a dual-framework approach: vascular texture analysis and global color distribution analysis. The texture-based approach involved segmenting the retinal vasculature and extracting eight Haralick texture features from the Gray-Level Co-occurrence Matrix. Significant differences in features such as energy, contrast, correlation, and entropy were found between healthy and pathological retinas. Pathological retinas exhibited lower textural complexity and higher uniformity, which correlates with vascular thinning and structural changes observed in DR and glaucoma. In parallel, the global color distribution of the full fundus area was analyzed without segmentation. RGB intensity histograms were calculated for each channel and averaged across groups. Statistical tests revealed significant differences, particularly in the green and blue channels. The Mahalanobis distance quantified the separability of the groups per channel. These results indicate that pathological changes in retinal tissue can also lead to detectable chromatic shifts in the fundus. The findings underscore the potential of both vascular texture and color features as non-invasive biomarkers for early retinal disease detection and classification. Full article

Understanding the spatio-temporal evolution of land consolidation is essential for optimizing regional land resource allocation and mitigating human–land conflicts during socio-economic development. This study introduced a novel framework for analyzing such patterns in China. Utilizing a two-decade (2002–2022) prefecture-level city dataset of land consolidation projects in Lianyungang, Jiangsu Province, we developed a “land consolidation intensity” metric and applied quantitative techniques—including land use transfer matrices, landscape pattern indices, Sankey diagrams, and standard deviation ellipses—to assess spatio-temporal dynamics and centroid shifts. Key findings included: (1) Land consolidation intensity exhibited distinct stages, evolving from initial development to rapid growth and eventual stabilization, closely aligning with national policy shifts. (2) The primary sources for supplemented cultivated land were ponds, rivers, and tidal flats, followed by grassland, construction land, and forest land, with cultivated land consistently dominating the consolidated landscape. (3) Land consolidation projects distribution concentrated in economic and political centers, with a spatial shift from inland western region towards the eastern coastal region. (4) Gray relational analysis identified economic development as the predominant driver, with policy and social factors providing secondary guidance. This research elucidates the spatio-temporal evolution characteristics of land consolidation at the prefecture-level city and demonstrates the utility of the proposed framework for similar analyses, offering insights relevant to national land use planning and policy formulation. Full article

As a solid mixture discharged during coal production, coal gangue possesses comprehensive utilization potential. Efficient sorting and pre-enrichment of its classification are crucial for green mining practices. This study categorizes coal gangue into four types—residual coal (RC), gray gangue (GG), red gangue (RG), and white gangue (WG)—based on their apparent color and utilization properties. The research systematically analyzed how different light sources and illumination intensities affect the visual characteristics of these gangue types. The results indicate that white light sources most accurately reproduce the real coloration and texture features of coal gangue, with optimal textural clarity achieved at moderate illumination levels. Different colored light sources selectively enhance spectral reflectance, and red light significantly improves RG recognition. Support vector machine (SVM)-based classification experiments demonstrate that white light sources achieve optimal performance under moderate illumination (23,000 Lux) with Macro-F1 = 0.90, representing a 15.38% improvement over other conditions. These findings reveal that reasonable matching of light source and illumination intensity can substantially enhance the accuracy of the visual recognition of coal gangue, providing valuable optimization guidance for future precise classification applications. Full article

Monitoring sediment concentration in water bodies is crucial for assessing water quality, ecosystems, and environmental health. However, physical sampling and sensor-based approaches are labor-intensive and unsuitable for large-scale, continuous monitoring. This study employs machine learning models to estimate suspended sediment concentration using images captured in natural light, named RGB, and near-infrared (NIR) conditions. A controlled dataset of approximately 1300 images with SSC values ranging from 1000 mg/L to 150,000 mg/L was developed, incorporating temperature, time of image capture, and solar irradiance as additional features. Random forest regression and gradient boosting regression were trained on mean RGB values, red reflectance, time of captured, and temperature for natural light images, achieving up to 72.96% accuracy within a 30% relative error. In contrast, NIR images leveraged gray-level co-occurrence matrix texture features and temperature, reaching 83.08% accuracy. Comparative analysis showed that ensemble models outperformed deep learning models like Convolutional Neural Networks and Multi-Layer Perceptrons, which struggled with high-dimensional feature extraction. These findings suggest that using machine learning models and RGB and NIR imagery offers a scalable, non-invasive, and cost-effective way of sediment monitoring in support of water quality assessment and environmental management. Full article

Xylosalsola chiwensis (Popov) Akhani & Roalson is listed in the Red Data Book of Kazakhstan as a rare species with a limited distribution, occurring in small populations in Kazakhstan, Uzbekistan, and Turkmenistan. The aim of this study is to deepen the understanding of the ecological conditions of its habitats, the floristic composition of its associated plant communities, the species’ morphological and anatomical characteristics, and its molecular phylogeny, as well as to identify the main threats to its survival. The ecological conditions of the X. chiwensis habitats include coastal sandy plains and the slopes of chinks and denudation plains with gray–brown desert soils and bozyngens on the Mangyshlak Peninsula and the Ustyurt Plateau at altitudes ranging from −3 to 270 m above sea level. The species is capable of surviving in arid conditions (less than 100 mm of annual precipitation) and under extreme temperatures (air temperatures exceeding 45 °C and soil surface temperatures above 65 °C). In X. chiwensis communities, we recorded 53 species of vascular plants. Anthropogenic factors associated with livestock grazing, industrial disturbances, and off-road vehicle traffic along an unregulated network of dirt roads have been identified as contributing to population decline and the potential extinction of the species under conditions of unsustainable land use. The morphometric traits of X. chiwensis could be used for taxonomic analysis and for identifying diagnostic morphological characteristics to distinguish between species of Xylosalsola. The most taxonomically valuable characteristics include the fruit diameter (with wings) and the cone-shaped structure length, as they differ consistently between species and exhibit relatively low variability. Anatomical adaptations to arid conditions were observed, including a well-developed hypodermis, which is indicative of a water-conserving strategy. The moderate photosynthetic activity, reflected by a thinner palisade mesophyll layer, may be associated with reduced photosynthetic intensity, which is compensated for through structural mechanisms for water conservation. The flow cytometry analysis revealed a genome size of 2.483 ± 0.191 pg (2n/4x = 18), and the phylogenetic analysis confirmed the placement of X. chiwensis within the tribe Salsoleae of the subfamily Salsoloideae, supporting its taxonomic distinctness. To support the conservation of this rare species, measures are proposed to expand the area of the Ustyurt Nature Reserve through the establishment of cluster sites. Full article

Purpose: To investigate if radiomics signatures generated from longitudinal CT scans could predict IRE treatment effectiveness and outcomes in patients with locally advanced pancreatic cancer (LAPC). Methods: A cohort of 50 (60% male, mean [SD] age 60.7 [8.7] years) LAPC patients treated with IRE were retrospectively selected. Preoperative and 12-week follow-up CT scans were reviewed by two radiologists for tumor segmentation. A total of 2078 features were extracted: shape (n = 16), texture (n = 68), filter (n = 1892), intensity (n = 18), and local texture (n = 84). Principal component analysis (PCA) was applied to develop composite radiomics features. Composite signatures and clinically relevant radiomics features were correlated with time to recurrence (TTR), time to local recurrence (TTLR), time to distant recurrence (TTDR), recurrence-free survival (RFS) and overall survival (OS). Risk stratification performance was evaluated using hazard ratios (HRs), and significance was evaluated using the log-rank test. Results: Statistically significant separation between high and low patient TTR risk groups was observed in the following: gray-level co-occurrence matrix (HR = 2.65, p < 0.01, median survival difference = 6.6 mo); composite radiomics features derived from the following feature groups: all radiomics features (HR = 2.27, p = 0.01, median survival difference = 6.4 mo), intensity features (HR = 3.13, p < 0.01, median survival difference = 14.0 mo), and filter features (HR = 2.27, p = 0.01, median survival difference = 6.4 mo). Conclusions: Pre-treatment radiomics signatures were significantly associated with LAPC patient outcomes. The observed correlations used pre-treatment CT scans, implying that the features predict the individual risk of disease recurrence. Full article

Background: Radiographic evaluation of bone regeneration following maxillary sinus floor elevation commonly emphasizes volumetric gains. However, the qualitative microarchitecture of the regenerated bone, particularly when assessed via two-dimensional imaging modalities, such as panoramic radiographs, remains insufficiently explored. This study aimed to evaluate early trabecular changes in grafted maxillary sinus regions using fractal dimension, first-order statistics, and gray-level co-occurrence matrix analysis. Methods: This retrospective study included 150 patients who underwent maxillary sinus floor augmentation with bovine-derived xenohybrid grafts. Postoperative panoramic radiographs were analyzed at 6 months to assess early healing. Four standardized regions of interest representing grafted sinus floors and adjacent tuberosity regions were analyzed. Image processing and quantitative analyses were performed to extract fractal dimension (FD), first-order statistics (FOS), and gray-level co-occurrence matrix (GLCM) features (contrast, homogeneity, energy, correlation). Results: A total of 150 grafted sites and 150 control tuberosity sites were analyzed. Fractal dimension (FD) and contrast values were significantly lower in grafted areas than in native tuberosity bone (p < 0.001 for both), suggesting reduced trabecular complexity and less distinct transitions. In contrast, higher homogeneity (p < 0.001) and mean gray-level intensity values (p < 0.001) were observed in the grafted regions, reflecting a more uniform but immature trabecular pattern during the early healing phase. Energy and correlation values also differed significantly between groups (p < 0.001). No postoperative complications were reported, and resorbable collagen membranes appeared to support graft stability. Conclusions: Although the grafted sites demonstrated radiographic volume stability, their trabecular architecture remained immature at 6 months, implying that volumetric measurements alone may be insufficient to assess biological bone maturation. These results support the utility of advanced textural and fractal analysis in routine imaging to optimize clinical decision-making regarding implant placement timing in grafted sinuses. Full article

Detecting small targets in infrared search and track (IRST) systems in complex backgrounds poses a significant challenge. This study introduces a novel detection framework that integrates directional derivative correlation filtering (DDCF) with a local relative intensity contrast measure (LRICM) to effectively handle diverse background disturbances, including cloud edges and structural corners. This approach involves converting the original infrared image into an infrared gradient vector field (IGVF) using a facet model. Exploiting the distinctive characteristics of small targets in second-order derivative computations, four directional filters are designed to emphasize target features while suppressing edge clutter. The DDCF map is then constructed by merging the results of the second-order derivative filters applied in four distinct orientations. Subsequently, the LRICM is determined by analyzing the gray-level contrast between the target and its immediate surroundings, effectively minimizing interference from background elements like corners. The final detection step involves fusing the DDCF and LRICM maps to generate a comprehensive saliency representation, which is then processed using an adaptive thresholding technique to extract small targets accurately. Experimental evaluations across multiple datasets verify that the proposed method substantially improves the signal-to-clutter ratio (SCR). Compared to existing advanced techniques, the proposed approach demonstrates superior detection reliability in challenging environments, including ground surfaces, cloudy conditions, forested areas, and urban structures. Moreover, the framework maintains low computational complexity, achieving a favorable balance between detection accuracy and efficiency, thereby demonstrating promising potential for deployment in practical IRST scenarios. Full article

Reducing enteric methane production from ruminant livestock has been positioned as a key intervention to reduce global greenhouse gas emissions. Bovaer©, a feed additive purported to reduce enteric methane emissions in dairy cows by nearly a third, has received regulatory authorization in many countries. However, there is a dearth of evidence on the consumer’s response to the use of such products. In the three weeks after 27 November 2024, there was a significant increase in media communications associated with the use of Bovaer© in Europe, and especially the United Kingdom (UK). This structured review of academic and gray literature and an iterative non-systematic survey of media discourse online explored and characterized the narratives that emerged in this three-week period of intense activity in both social media and mainstream media communications in order to critique the narratives and grammars within the public response and the implications for policymakers, industry and academia. The main narrative that emerged reflected the science-consumer tensions associated with the use of Bovaer© and the four sub-narratives shaping it (mainstream media influence and narrative framing, distrust in science and lack of relatability, conspiracy theories and fear-based narratives, consumer buycotts and market responses). Organizations adopting technological solutions to address ‘wicked’ societal problems need to understand the factors that trigger, amplify and attenuate social concern as expressed in mainstream and social media and need to adopt appropriate communication and dissemination activities to reduce the circulation of mis-dis-mal-information and promote information that is appropriate for multiple audiences and levels of understanding. Full article

Introduction: Artificial intelligence (AI) is increasingly utilized for texture analysis and the development of machine learning (ML) techniques to enhance diagnostic accuracy. ML algorithms are trained to differentiate between normal and malignant conditions based on provided data. Texture feature analysis, including first-order and second-order features, is a critical step in ML development. This study aimed to evaluate quantitative texture features of normal and prostate cancer tissues identified through ultrasound B-mode and shear-wave elastography (SWE) imaging and to develop and assess ML models for predicting and classifying normal versus malignant prostate tissues. Methodology: First-order and second-order texture features were extracted from B-mode and SWE imaging, including four reconstructed regions of interest (ROIs) from SWE images for normal and malignant tissues. A total of 94 texture features were derived, including features for intensity, Gray-Level Co-Occurrence Matrix (GLCM), Gray-Level Dependence Length Matrix (GLDLM), Gray-Level Run Length Matrix (GLRLM), and Gray-Level Size Zone Matrix (GLSZM). Five ML models were developed and evaluated using 5-fold cross-validation to predict normal and malignant tissues. Results: Data from 62 patients were analyzed. All ROIs, except those derived from B-mode imaging, exhibited statistically significant differences in features between normal and malignant tissues. Among the developed models, Support Vector Machines (SVM), Random Forest (RF), and Naive Bayes (NB) demonstrated the highest performance across all ROIs. These models consistently achieved strong predictive accuracy for classifying normal versus malignant tissues. Gray Pure SWE and Gray Reconstructed images Provided the highest sensitivity and specificity in PCa prediction by 82%, 90%, and 98%, 96%, respectively. Conclusions: Texture analysis with machine learning on SWE-US and reconstructed images effectively differentiates malignant from benign prostate lesions, with features like contrast, entropy, and correlation playing a key role. Random Forest, SVM, and Naïve Bayes showed the highest classification performance, while grayscale reconstructions (GPSWE and GRRI) enhanced detection accuracy. Full article

Liver disease is a major global health concern. Given the critical role of medical image categorization in fibrosis staging (low, moderate, severe, cirrhotic) and the challenges posed by limited medical image datasets, this paper aims to leverage ultrasound imaging to assess liver margin characteristics at the level of Glisson’s capsule—here referred to as Glisson’s line—to develop a simple, automated model for accurately distinguishing fibrosis stages. The proposed approach combines traditional image processing techniques in a pre-processing stage with machine learning algorithms for classification. The pre-processing phase introduces an attention-focusing mechanism that stretches the gray levels of Glisson’s line while shrinking the intensity levels associated with the liver parenchyma and surrounding tissues. This results in the so-called region of contrast interest (ROCI), where potential classification distractors are minimized. For classification, a convolutional neural network (CNN)-based model is used to process original, rotated, and transformed ultrasound images. To address dataset imbalance and overfitting, a 10-fold cross-validation strategy was implemented. The results demonstrate that, by effectively enhancing the information content of Glisson’s line, different liver fibrosis stages can be accurately distinguished without the need for explicit edge detection, achieving accuracy levels comparable to those reported in the literature. The novelty of this work lies in analyzing the morphology of Glisson’s capsule—obtained through this method—rather than focusing on the liver parenchyma and texture, as is traditionally carried out. Full article