Search Results (650)

Background/Objectives: There is significant interest in using texture features to extract hidden image-based information. In medical imaging applications using radiomics, AI, or personalized medicine, the quest is to extract patient or disease specific information while being insensitive to other system or processing variables. While we use digital breast tomosynthesis (DBT) to show these effects, our results would be generally applicable to a wider range of other imaging modalities and applications. Methods: We examine factors in texture estimation methods, such as quantization, pixel distance offset, and region of interest (ROI) size, that influence the magnitudes of these readily computable and widely used image texture features (specifically Haralick’s gray level co-occurrence matrix (GLCM) textural features). Results: Our results indicate that quantization is the most influential of these parameters, as it controls the size of the GLCM and range of values. We propose a new multi-resolution normalization (by either fixing ROI size or pixel offset) that can significantly reduce quantization magnitude disparities. We show reduction in mean differences in feature values by orders of magnitude; for example, reducing it to 7.34% between quantizations of 8–128, while preserving trends. Conclusions: When combining images from multiple vendors in a common analysis, large variations in texture magnitudes can arise due to differences in post-processing methods like filters. We show that significant changes in GLCM magnitude variations may arise simply due to the filter type or strength. These trends can also vary based on estimation variables (like offset distance or ROI) that can further complicate analysis and robustness. We show pathways to reduce sensitivity to such variations due to estimation methods while increasing the desired sensitivity to patient-specific information such as breast density. Finally, we show that our results obtained from simulated DBT images are consistent with what we see when applied to clinical DBT images. Full article

Human memory is inherently susceptible to errors, including the formation of false memories—instances where individuals mistakenly recall information they were never exposed to. While prior research has largely focused on neural activity associated with false memory, the structural brain correlates of this phenomenon remain relatively unexplored. This study bridges that gap by investigating gray matter structure as it relates to individual differences in false memory performance. Using publicly available magnetic resonance imaging datasets, we analyzed cortical thickness (CT) in neural regions implicated in memory processes. To assess false memory, we applied signal detection theory, which provides a robust framework for differentiating between true and false memory. Our findings reveal that increased CT in the parietal lobe and middle occipital gyrus correlates with greater susceptibility to false memories, highlighting its role in integrating and manipulating memory information. Conversely, CT in the middle frontal gyrus and occipital pole was associated with enhanced accuracy in memory recall, emphasizing its importance in perceptual processing and encoding true memories. These results provide novel insights into the structural basis of memory errors and offer a foundation for future investigations into the neural underpinnings of memory reliability. Full article

Background: This review explores the influence of eco-anxiety on sustainable consumption, with a specific focus on the urban context. While the literature on green consumerism continues to expand, the role of emotional and psychological factors, especially eco-anxiety, in shaping sustainable consumption decisions remains underexplored. Most existing studies emphasize cognitive, social, or contextual drivers, often overlooking affective dimensions that may significantly influence consumer behavior. Addressing this gap, the review examines how emotional responses to climate change, such as eco-anxiety, inform and potentially motivate eco-friendly consumption patterns. Understanding these affective pathways offers valuable insights on how individuals and urban communities can effectively adapt to climate change and establish a sustainable consumption culture. Methods: A systematic literature search was conducted in Scopus and Web of Sciences databases, following a predefined keyword strategy, resulting in 56 initial records. We further implemented a supplementary search of gray literature on Google Scholar to search for additional reports. The full-text screening process identified 12 eligible studies based on the following inclusion criteria: quantitative or mixed-methods studies focusing on adult and young adult individuals, including both measures of eco-anxiety and green consumption and assessing their direct or indirect relationship. Results: Findings suggest that eco-anxiety functions as a cognitive–affective motivator for sustainable consumer choices; however, the strength and direction of this influence appear contingent on moderating emotional and psychological variables and cross-cultural and demographic moderators. Discussion: This review highlights the need for urban-focused intervention tailored communication, marketing, and business strategies that address the emotional dimensions of climate change. Policymakers and businesses are encouraged to consider affective drivers as eco-anxiety to promote sustainable consumption stewardship within urban communities. By addressing these psychological responses, urban societies can become more resilient and proactive in confronting climate change challenges. Full article

The spatial genes of rural settlements show a lot of different traditional settlement traits, which makes them a great starting point for studying rural spatial morphology. However, qualitative and macro-regional statistical indicators are usually used to find and extract rural settlement spatial genes. Taking Yubei Village in the Nanxi River Basin as an example, this study combined remote sensing images, real-time drone mapping, GIS (geographic information system), and space syntax, extracted 12 key indicators from five dimensions (landform and water features (environment), boundary morphology, spatial structure, street scale, and building scale), and quantitatively “decoded” the spatial genes of the settlement. The results showed that (1) the settlement is a “three mountains and one water” pattern, with cultivated land accounting for 37.4% and forest land accounting for 34.3% of the area within the 500 m buffer zone, while the landscape spatial diversity index (LSDI) is 0.708. (2) The boundary morphology is compact and agglomerated, and locally complex but overall orderly, with an aspect ratio of 1.04, a comprehensive morphological index of 1.53, and a comprehensive fractal dimension of 1.31. (3) The settlement is a “clan core–radial lane” network: the global integration degree of the axis to the holy hall is the highest (0.707), and the local integration degree R3 peak of the six-room ancestral hall reaches 2.255. Most lane widths are concentrated between 1.2 and 2.8 m, and the eaves are mostly higher than 4 m, forming a typical “narrow lanes and high houses” water town streetscape. (4) The architectural style is a combination of black bricks and gray tiles, gable roofs and horsehead walls, and “I”-shaped planes (63.95%). This study ultimately constructed a settlement space gene map and digital library, providing a replicable quantitative process for the diagnosis of Jiangnan water town settlements and heritage protection planning. Full article

Background/Objectives: Successful discourse relies not only on linguistic but also on prosodic information. Difficulty recognizing emotion conveyed through prosody (receptive affective aprosodia) following right hemisphere stroke (RHS) significantly disrupts communication participation and personal relationships. Growing evidence suggests that damage to white matter in addition to gray matter structures impairs affective prosody recognition. The current study investigates lesion–symptom associations in receptive affective aprosodia during RHS recovery by assessing whether disruptions in distinct white matter structures impact different underlying affective prosody recognition skills. Methods: Twenty-eight adults with RHS underwent neuroimaging and behavioral testing at acute, subacute, and chronic timepoints. Fifty-seven healthy matched controls completed the same behavioral testing, which comprised tasks targeting affective prosody recognition and underlying perceptual, cognitive, and linguistic skills. Linear mixed-effects models and multivariable linear regression were used to assess behavioral performance recovery and lesion–symptom associations. Results: Controls outperformed RHS participants on behavioral tasks earlier in recovery, and RHS participants’ affective prosody recognition significantly improved from acute to chronic testing. Affective prosody and emotional facial expression recognition were affected by external capsule and inferior fronto-occipital fasciculus lesions while sagittal stratum lesions impacted prosodic feature recognition. Accessing semantic representations of emotions implicated the superior longitudinal fasciculus. Conclusions: These findings replicate previously observed associations between right white matter tracts and affective prosody recognition and further identify lesion–symptom associations of underlying prosodic recognition skills throughout recovery. Investigation into prosody’s behavioral components and how they are affected by injury can help further intervention development and planning. Full article

Aims: To systematically review and synthesize qualitative research exploring the Breast Cancer Survivors (BCSs)’ perception of health promotion interventions and informing strategies to mitigate recurrence risk within five years post-treatment and improve clinical outcomes. Specifically, this study addresses the question: “How do women diagnosed with breast cancer perceive health promotion interventions for recurrence prevention?” Design: A systematic review and qualitative meta-synthesis were performed. Data Sources: A systematic search of scientific databases (CINAHL, MEDLINE, and Scopus) was undertaken in November 2024. The reference list was cross-referenced and hand-searched to identify additional articles. Review Methods: Studies were included if they met the following criteria: they were primary qualitative studies focusing on BCSs within five years post-treatment, involving participants who had completed surgery, radiotherapy, or chemotherapy in the same time frame, as this period is critical for monitoring recurrence and implementing health promotion interventions. Only studies published in peer-reviewed journals and written in Italian, English, French, or Spanish were considered, provided that an abstract and the full text were available. Moreover, eligible studies had to be conducted in high-income or middle-income countries. Studies were excluded if they focused exclusively on advanced or metastatic breast cancer, if they involved mixed cancer populations without reporting separate data for BCSs, or if they were non-qualitative studies or gray literature. The review study protocol was registered in the PROSPERO database (CRD42024626033). Results: The literature search identified 490 records, 13 articles from databases, and 3 articles identified via other methods (web and citation searching) that met inclusion criteria. A narrative synthesis approach allowed the emerging five themes: (I) Challenges, (II) Self-motivation and empowerment, (III) The relationships as a facilitator, (IV) Barriers to change, and (V) Proactive support strategies. Conclusions: Addressing internal and external factors that influence health behaviors is essential to improve adherence, reduce recurrence risk, and enhance quality of life. Tailored interventions, social support, and healthcare engagement are crucial in this effort. Impact: Our meta-synthesis highlighted significant challenges as well as valuable resources for health promotion among BCSs, suggesting practical and tailored approaches to improving the adoption of healthy behaviors, supported by relationships and targeted support strategies. Full article

Background/Objectives: Value frameworks are useful tools to explicitly define the dimensions and criteria important for decision-making, but no existing frameworks capture the broad value domains of population genomic programs. Using a mixed methods approach, we aimed to develop a novel value framework for evaluating population genomic programs (PGPs). Methods: We first conducted a targeted literature review of published evidence on the value of PGPs and existing frameworks to evaluate and quantify their impact. Value domains and elements were extracted and summarized to develop a preliminary framework. Semi-structured stakeholder interviews on the preliminary framework were conducted from March 2024 to October 2024 with 11 experts representing 9 countries. A thematic analysis of interview transcripts was conducted to map value elements to domains of the final framework. Results: We identified 348 potentially relevant articles from MEDLINE-indexed and the gray literature sources. After title and abstract screening, 23 articles met the inclusion criteria and underwent full-text review, and 8 reported value elements were extracted and mapped to a preliminary framework for testing in interviews. Stakeholder themes were summarized into the value domains and elements of the final framework, which included health as a primary domain, education and research, enterprise and finance, and labor as the core domains, and agriculture and security as extended domains. Domains and elements may be excluded based on stakeholder objectives and program characteristics. Conclusions: This novel framework for assessing the comprehensive value of PGPs provides a foundational step to assess the value of these programs and may promote more efficient and informed allocation of resources. Full article

The expansion of human activities can degrade natural habitats, thereby increasing threats to wildlife conservation. The wild populations of many species have declined due to the modification of natural habitats by humans. The Arabian wolf (Canis lupus arabs) is a subspecies of the gray wolf that is of conservation concern across its distribution range. The Arabian wolf is understudied in certain habitats (e.g., mountainous areas), which limits understanding of its overall ecology. Given its vulnerable conservation status, this study aimed to collect relevant data and information on incidents and potential threats facing this predator in the rugged mountainous habitats of western Tabuk province, Saudi Arabia, and how the effects of these threats can be minimized. In these mountain habitats Arabian wolves encounter various severe threats that challenge relevant conservation efforts. Observations of such threats—some of which result in wolf mortality—represent serious challenges to the survival of wild Arabian wolves. Conflicts with humans and livestock represent considerable threats that must be appropriately managed. Additionally, the potential association between Arabian wolves and free-ranging dogs requires further investigation. Various conservation scenarios and mitigation approaches can be applied to help reduce negative impacts on Arabian wolf populations and maximize their likelihood of survival. Overall, ensuring the persistence of such a unique desert-adapted apex predator in this ecosystem must become a conservation priority. Full article

For the first time, the numerous scattered data on birds (wild and domestic) have been collected based on their medieval bone remains discovered on the modern territory of the Republic of Bulgaria. The collected information is about a total of 37 medieval settlements from the time of the First and Second Bulgarian Empires. Among the settlements studied are both the two medieval Bulgarian capitals (Pliska and Veliki Preslav), as well as other cities, smaller settlements, military fortresses, monasteries, and inhabited caves. The data refer to a total of 48 species of wild birds and 6 forms of domestic birds of 11 avian orders: Accipitriformes, Anseriformes, Ciconiiformes, Columbiformes, Falconiformes, Galliformes, Gruiformes, Otidiformes, Passeriformes, Pelecaniformes, and Strigiformes. The established composition of wild birds amounts to over one tenth (to 11.5%) of the modern avifauna in the country. Five of the established species (10.4%) have disappeared from the modern nesting avifauna of the country—the bearded vulture, the great bustard, the little bustard, the gray crane, and the saker falcon (the latter two species have reappeared as nesters in the past few years). First Bulgarian Empire (681–1018): Investigated settlements—22. Period covered—five centuries (7th to 11th c.). Found in total: at least 44 species/forms of birds, of which 39 species of wild birds and 5 forms of poultry. Second Bulgarian Empire (1185–1396): Investigated settlements—15. Period covered—3 centuries (12th to 14th c.). Found in total: at least 39 species/forms of birds, of which 33 species of wild birds and 6 forms of poultry. The groups of raptors, water, woodland, openland, synanthropic and domestic birds were analyzed separately. The conclusion was made that during the two periods of the Middle Ages, birds had an important role in the material and spiritual life of the population of the Bulgarian lands. Birds were mainly used for food (domestic birds), although some were objects of hunting. No traces of processing were found on the bones. Birds were subjects of works of applied and monumental art. Their images decorated jewelry, tableware, walls of buildings and other structures. Full article

To address the dynamic decision-making and control problem in unmanned aerial vehicle (UAV) swarms, this paper proposes a cumulative prospect theory-driven pigeon-inspired optimization (CPT-PIO) algorithm. Gray relational analysis and information entropy theory are integrated into cumulative prospect theory (CPT), constructing a prospect value model for Pareto solutions by setting reference points, defining value functions, and determining attribute weights. This prospect value is used to evaluate the quality of each Pareto solution and serves as the fitness function in the pigeon-inspired optimization (PIO) algorithm to guide its evolutionary process. Furthermore, incorporating individual and swarm situation assessment methods, the situation assessment model is constructed and the information entropy theory is employed to ascertain the weight of each assessment index. Finally, the reverse search mechanism and competitive learning mechanism are introduced into the standard PIO to prevent premature convergence and enhance the population’s exploration capability. Simulation results demonstrate that the proposed CPT-PIO algorithm significantly outperforms two novel multi-objective optimization algorithms in terms of search performance and solution quality, yielding higher-quality Pareto solutions for dynamic UAV swarm decision-making. Full article

This paper proposes an advanced forecasting model for predicting carbon dioxide (CO₂) emissions in Thailand’s electricity generation sector. The model integrates a multivariate gray model with the fminsearch optimization algorithm in MATLAB (R2025a) to address the critical challenge of accurate emission forecasting, a key driver of climate change. Historical data on CO₂ emissions, gross domestic product (GDP), peak electricity demand, and electricity user numbers are utilized to enhance predictive accuracy. Comparative analysis demonstrates that the optimized model significantly outperforms the conventional multivariate gray model, achieving mean absolute percentage error (MAPE) values of 7.74% for the training set and 1.75% for the testing set. The results highlight the effectiveness of the proposed approach as a robust tool for policymakers and stakeholders in Thailand’s energy sector, offering actionable insights to support informed decision-making in managing and reducing CO₂ emissions. Full article

Soil organic carbon (SOC) is a critical indicator of soil health and carbon sequestration potential. Accurate, efficient, and scalable SOC estimation is essential for sustainable orchard management and climate-resilient agriculture. However, traditional visible–near-infrared (Vis–NIR) spectroscopy often suffers from limited chemical specificity and weak adaptability in heterogeneous soil environments. To overcome these limitations, this study develops a five-stage modeling framework that systematically integrates Fourier Transform Infrared (FTIR) spectroscopy with hybrid machine learning techniques for non-destructive SOC prediction in citrus orchard soils. The proposed framework includes (1) FTIR spectral acquisition; (2) a comparative evaluation of nine spectral preprocessing techniques; (3) dimensionality reduction via three representative feature selection algorithms, namely the Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Principal Component Analysis (PCA); (4) regression modeling using six machine learning algorithms, namely the Random Forest (RF), Support Vector Regression (SVR), Gray Wolf Optimized SVR (SVR-GWO), Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and the Back-propagation Neural Network (BPNN); and (5) comprehensive performance assessments and the identification of the optimal modeling pathway. The results showed that second-derivative (SD) preprocessing significantly enhanced the spectral signal-to-noise ratio. Among feature selection methods, the SPA reduced over 300 spectral bands to 10 informative wavelengths, enabling efficient modeling with minimal information loss. The SD + SPA + RF pipeline achieved the highest prediction performance (R² = 0.84, RMSE = 4.67 g/kg, and RPD = 2.51), outperforming the PLSR and BPNN models. This study presents a reproducible and scalable FTIR-based modeling strategy for SOC estimation in orchard soils. Its adaptive preprocessing, effective variable selection, and ensemble learning integration offer a robust solution for real-time, cost-effective, and transferable carbon monitoring, advancing precision soil sensing in orchard ecosystems. Full article

Public media serves as a significant source of scientific information for non-scientists. However, the simplifications and omissions inherent in media reporting often alter the nature of scientific information, potentially influencing understanding and perceptions of science and the nature of science. This study investigates how mechanistic explanations about biological processes are represented in public media, focusing on two forms of incomplete mechanistic information: “gray boxes” and “black boxes”. Using COVID-19 as a case study, we analyzed 122 media reports of biological mechanisms to understand how incomplete parts are masked by more complete explanations and their implications. Our findings highlighted three main points. First, incomplete information often appears alongside complete information within other parts of the explanation. Second, some parts of similar mechanisms are presented differently, which can create a sense of conflicting information if incompleteness is not recognized. Third, multiple filler terms are used to mask black boxes within biological explanations (e.g., “cause”, “fight”, or “mutate”). While filler terms enhance narrative flow, they can obscure gaps in scientific knowledge and lead to anthropocentric or teleological explanations. We categorized these filler terms into three groups and discussed their relevance to teaching and learning. Implications for addressing partial information in the science classroom are discussed. Full article

Drinking Water Safety Plans (DWSP) in buildings serve to identify health hazards associated with the drinking water system. Sanitation Safety Plans (SSP) fulfill the same purpose for the sewage system. Water Safety Plans (WSP) include DWSPs, SSPs, and water systems like gray water and firefighting water. WSPs are based on a high-quality description of the water systems. This paper presents a new methodology for describing water systems. In contrast to previous approaches, the system description begins at the point where the water is consumed. These points of use are described using ecomaps, which are then supplemented with information about the pipe network. This approach makes it possible to fulfill four relevant premises: (1) the system description includes all essential parts of the drinking water installation, (2) the system description is possible with usual equipment, (3) the system description can be carried out with the least possible additional personnel costs, and (4) the system description is controllable, versionable, changeable, and forgery-proof. The ecomaps created in this way are suitable for the next step within the WSP framework, namely hazard and risk assessment. In addition, the ecomaps can be integrated into a quality, occupational safety, or environmental management system. Aspects of water security can be added to enable the ecomaps to be used as the basis for a total integrated water management system. Full article

Location-based services have important economic and social values. The positioning accuracy and cost have a crucial impact on the quality, promotion, and market competitiveness of location services. Dead reckoning can provide accurate location information in a short time. However, it suffers from motion pattern diversity and cumulative error. To address these issues, we propose a PCA-GWO-KELM optimization gait recognition indoor fusion localization method. In this method, 30-dimensional motion features for different motion patterns are extracted from inertial measurement units. Then, constructing PCA-GWO-KELM optimization gait recognition algorithms to obtain important features, the model parameters of the kernel-limit learning machine are optimized by the gray wolf optimization algorithm. Meanwhile, adaptive upper thresholds and adaptive dynamic time thresholds are constructed to void pseudo peaks and valleys. Finally, fusion localization is achieved by combining with acoustic localization. Comprehensive experiments have been conducted using different devices in two different scenarios. Experimental results demonstrated that the proposed method can effectively recognize motion patterns and mitigate cumulative error. It achieves higher localization performance and universality than state-of-the-art methods. Full article