Search Results (158)

This article presents a novel use case of the deployment of agent-based modeling (ABM) to explore developers’ decision-making along a spectrum of reuse in the built environment. This research project used ABM to model multiple pathways to realizing circularity at the community scale through the reuse of existing buildings and adoption of deconstruction as alternatives to demolition in Ithaca, New York. This exploratory analysis demonstrates (1) the use of ABM in scenario planning, (2) the use of ABM to assess how preservation and building material reuse options affect city-wide greenhouse gas emissions in the form of embodied carbon, (3) and the potential to use the model results in policy analysis. The results of the analysis emphasize trade-offs in local government policies. A Business As Usual scenario produces the most CO₂ emissions and the highest total dollars invested and total square footage developed. This is in contrast to the other three scenarios. A Maximize Preservation scenario achieves the lowest total embodied carbon emissions through retention of existing buildings while maintaining the second highest total investment in urban development. The Deconstruction Incentive and Mandatory Material Reuse scenarios, which involve incentivizing building material reuse, lead to strong ROI signals for developers, reducing the search for suitable properties for new construction as deconstruction becomes a viable avenue for generating returns. ABM-generated scenarios exhibit differences in the performance of developers according to their type and scale. This research produced a readily available ABM and graphical interface that could be used by researchers and local government agencies exploring pathways to achieving circular cities through policies that can affect urban development processes and embodied carbon emissions. Full article

To quantitatively describe changes in skeletal radiopharmaceutical uptake during a training program including swimming in horses with neck and back pain, eighteen showjumping horses with documented vertebral lesions were prospectively included in a training program composed of 4 weeks of ridden work and then 8 weeks combining swimming and ridden exercise. Previously published data on this cohort did not demonstrate any clear change in thoracolumbar mobility. Bone scintigraphy was performed at the 4th and 12th weeks. Radiopharmaceutical uptake was quantified in 205 regions of interest (ROIs) and normalized using a Z-score approach. For each ROI, a linear mixed-effects model was fitted to evaluate the effect of the time point (W4, W12) on the normalized radiopharmaceutical uptake with the horse as a random effect. Overall variation in normalized signal intensity (Z-score difference W12–W04) was small (mean < 0.001; range −0.28 to 0.24). Significant changes between time points were identified in 21 ROIs. Thirteen ROIs located in the axial skeleton showed a decreased radiopharmaceutical uptake, whereas eight ROIs located in the limbs, including the humeral tubercles on both lateral views, showed an increased uptake. During a 2-month training program including swimming, axial skeletal radiopharmaceutical uptake did not increase in this cohort of horses with neck and back pain. However, substantial interindividual variability in scintigraphic changes was observed. Further controlled studies are needed to confirm these preliminary results and to better explore the effects of swimming on horses with axial musculoskeletal disorders. Full article

The 70-gene signature (70-GS; MammaPrint) assay is useful for prognosis assessment in HR+/HER2− early breast cancer, but limited accessibility motivates development of noninvasive alternatives. We retrospectively enrolled 219 women with preoperative grayscale ultrasound and 70-GS results, including a development cohort (n = 125), an internal validation cohort (n = 53), and a temporally independent validation cohort (n = 41). Radiomic features were extracted from manually delineated ROIs using PyRadiomics, and a radiomics score was derived after LASSO selection. Candidate radiomics-only, clinicopathologic-only, and full clinicoradiomic models were explored. To reduce overfitting, we selected a parsimonious model combining the radiomics score and Ki67 as the primary model. The simplified model achieved AUCs of 0.878, 0.816, and 0.831 in the development, internal validation, and temporally independent validation cohorts, respectively. In 1000 bootstrap resamples, the optimism-corrected AUC was 0.872 and the corrected calibration slope was 0.953. Adding the radiomics score to a Ki67-only model significantly improved model fit (likelihood-ratio chi-square = 17.14, df = 1, p < 0.001). An ultrasound radiomics and Ki67 model may provide a noninvasive reference for estimating MammaPrint risk categorization, but it should be considered only as a supportive adjunct and not as a replacement for genomic testing. Full article

Aflatoxin B₁ (AFB₁) contamination in maize poses serious risks to food and feed safety; however, conventional laboratory-based assays are often constrained by time and throughput for large-scale screening. This study proposes a hyperspectral imaging (HSI) workflow for rapid, non-destructive prediction of AFB₁. A total of 236 hyperspectral images were acquired in the 400–1000 nm range (256 bands) from maize samples covering a broad gradient of AFB₁ contamination, and spectral features were extracted from regions of interest (ROI) for model development. The results demonstrate that appropriate spectral preprocessing and wavelength selection play a critical role in improving model robustness, with the SNV–CARS–KNN model achieving the best prediction performance (test R² = 0.9341, RMSE = 4.8938). Based on the predicted AFB₁ values, contamination grading was further explored to enable rapid screening and risk management in agricultural applications. Aflatoxin B₁ (AFB₁) contamination in maize poses substantial risks to food and feed safety, creating a need for rapid screening tools that can support high-throughput prescreening. In this study, hyperspectral imaging (HSI) was explored as a non-destructive approach for the preliminary assessment of AFB₁ contamination in maize kernels. A total of 236 hyperspectral images were collected in the 400–1000 nm range, and region-of-interest spectra were extracted for model development. Spectral preprocessing and wavelength selection strategies were compared in combination with several conventional regression models to examine their influence on predictive performance using the current dataset. Among the tested combinations, the SNV–CARS–KNN model showed the most favorable performance on the held-out test set (R² = 0.9341; RMSE = 4.8938). In addition, a grade-based classification derived from predicted values was explored as an application-oriented extension for rapid risk sorting. However, the study was conducted on artificially contaminated samples and relied on rapid-test-derived reference values, so the findings should be interpreted as a proof of concept rather than a fully validated quantitative method. Overall, the results support the potential of HSI for rapid prescreening of AFB₁ in maize and provide a basis for further validation using more rigorous reference analysis and independent sample sets. Full article

Lithological mapping is essential for geological studies, mineral exploration, and environmental assessment. Satellite remote sensing combined with machine learning provides a scalable, cost-effective approach for regional lithological discrimination. This study evaluates hyperspectral and multispectral satellite imagery for lithological mapping in a geologically complex region of northwestern Queensland, Australia. The study area, within the Mount Isa Inlier, comprises diverse sedimentary, volcanic, intrusive, and metamorphic lithologies. PRISMA hyperspectral and ASTER multispectral imagery were analyzed using supervised classification algorithms, including Support Vector Machine (SVM), Mahalanobis Distance (MaDC), Minimum Distance (MDC), and Maximum Likelihood (MLC). Image-derived endmembers from representative lithologies were used as training data. Classification accuracy was assessed using confusion matrices, Overall Accuracy (OA), and the Kappa coefficient. PRISMA imagery outperformed ASTER data. SVM achieved the highest performance for PRISMA (OA = 82.03%, Kappa = 0.81), whereas MLC achieved the highest performance for ASTER (OA = 33.29%, Kappa = 0.30). Classification accuracy was evaluated using an independent set of validation ROIs that were spatially separated from the training samples, providing a more reliable estimate of model performance. These results highlight the benefits of hyperspectral remote sensing with machine learning for lithological discrimination in complex terrain and emphasise the importance of spatially independent validation. The approach demonstrates strong potential for regional-scale applications and may support more efficient mineral exploration and geological mapping workflows. Full article

The Chihuahuan Desert Biome (CDB), the largest semi-arid region in North America, has undergone repeated climatic fluctuations during the Interglacial–Glacial Oscillation (IGO) of the last eight million years. We investigated biogeographic and evolutionary patterns of endemic cacti within the present-day Interglacial and the Last Glacial by examining 119 strict endemics, including 75 suitable for Species Distribution Modeling (SDM) and 44 microareal strict endemics, together representing 36.17% of the 329 species in the biome. Cacti probably originated in South America after substantial separation from Africa, with pollen fossils documenting their presence in Mexico by 51.6 Ma. Climatic reconstructions for each phase were developed using regional numerical and co-kriging methods following Sánchez-Santillán and García, complemented by paleoclimatic evidence from Scotese, Roy-Priyadarsi, Van Devender, and Betancourt. MAXENT SDMs and PANBIOTRACKS’ track-node analyses were applied to 3719 specimens representing 2015 localities to explore the colonization patterns and broad evolutionary trends. Combined suitability layers and panbiogeographic analyses revealed a predominant southeastern-to-northwestern colonization pattern, largely following the western flank of the Sierra Madre Oriental and intermontane valleys. The northern sectors were less diverse, more arid, and apparently colonized more recently, whereas the southern sectors concentrated much of the endemic richness and connectivity. The concordance among climatic suitability patterns, tracks, nodes, and the available phylogenetic evidence supports a major role of climatic oscillations in shaping the spatial and evolutionary history of endemic cacti throughout the CDB. Full article

Small cell lung cancer (SCLC) is an aggressive neuroendocrine malignancy with limited publicly available spatial transcriptomic resources, particularly for extensive-stage disease (ES-SCLC), which remains absent from major initiatives such as The Cancer Genome Atlas (TCGA). To improve accessibility, interoperability, and downstream analytical utility of existing spatial transcriptomic data, SCAPeSCLC was developed as a harmonized dataset derived from two publicly available Gene Expression Omnibus (GEO) series, GSE261345 and GSE261348, generated using the NanoString GeoMx Digital Spatial Profiler platform. The resource integrates normalized expression measurements from 296 tumor regions of interest (ROI) across 58 ES-SCLC patients treated with first-line chemoimmunotherapy. Normalized expression matrices were reformatted into survival-ready column-based datasets at both ROI and patient levels following log₂-transformation and standardization. Clinical metadata were curated and harmonized, and progression-free survival (PFS), disease-specific survival (DSS), overall survival (OS), time-on-treatment (ToT), follow-up intervals, and censoring indicators were reconstructed from the original clinical records. Biological pathway (BP) activity scores were generated using Cancer Transcriptome Atlas (CTA) annotations encompassing 106 BPs. To account for variable ROI sampling across patients, Bayesian hierarchical modeling was applied to estimate patient-level pathway activity, yielding posterior estimates and corresponding credible intervals. The resulting resource includes harmonized expression matrices, pathway enrichment profiles, Bayesian posterior estimates, survival-ready clinical annotations, and standardized Cox proportional hazards modeling outputs, along with a dedicated GitHub repository. SCAPeSCLC is intended to facilitate confirmatory analyses, integrative statistical modeling, methodological benchmarking, and reproducible exploration of spatial transcriptomic determinants of survival in ES-SCLC. Full article

This article explores the structural paradox of the byeong-gut (Korean shamanic healing ritual): why it adheres to the rigid and canonical format of the jaesu-gut (shamanic blessing ritual) instead of adopting a specialized clinical procedure. Critiquing the instrumental trap of previous scholarship that reduces shamanic healing to psychological comfort or social liberation, this study proposes a relational displacement model by integrating Roy Rappaport’s theory of ritual invariance with the relational ontologies of Bruno Latour and Tim Ingold. The article demonstrates that shamanic healing operates through a dual mechanism. First, at the non-discursive (material) level, the ritual functions as an ontological technology that objectifies and displaces individual suffering onto external surrogates. Second, at the discursive (linguistic) level, a meticulous analysis of the manse-baji (invocation chant) illustrates how the patient’s fragmented life is re-assembled into a meshwork of human and non-human agencies. Ultimately, this article argues that the byeong-gut transcends mere functional curing; it serves as a sophisticated knowledge system that re-maps the isolated ego onto a relational cosmology, transforming the Geertzian bafflement of suffering into an intelligible event within a shared and sacred cosmic order. Full article

This article examines dialogic presence as articulated by Martin Buber and explores its continued relevance within contemporary technoetic and AI-mediated new media art. Drawing on Buber’s early writings on art, theatre, and dance—particularly Daniel (1913)—the article first analyses the dialogic relations between artist, art form, and viewer, with attention to the aesthetic principles of distance, unity, and presence that structure the I–Thou encounter. The second part explores the correlation between Buber’s dialogic philosophy and the principles of technoetic art as theorised by Roy Ascott, focusing on the telematic installation Aspects of Gaia: Digital Pathways across the Whole Earth (1989) as a paradigmatic example of dialogic encounter within technologically mediated environments. The third part examines seven artworks from the Infinite Self Pavilion, curated for The Wrong Biennale (2025–2026), as illustrative examples. These works engage AI-mediated aesthetics to interrogate the relation between Self and Other through modes of dialogic encounter and presence induced by orbital apparatus, installation, and screen practices, positioning the viewer at the centre of the encounter while challenging the limits of human consciousness. The article concludes by foregrounding Buber’s ethical stance toward advanced technologies, emphasising relational responsibility and humility in dialogue with Ascott’s technoetic ethics. Full article

Background/Objectives: This study aimed to explore the potential value of narrow-band-imaging (NBI)-derived mean optical intensity (MOI) in monitoring the progression of oral squamous cell carcinoma (OSCC), from the normal oral mucosa through epithelial dysplasia to invasive carcinoma. We compared differences in the NBI MOI among distinct pathological stages, so as to provide preliminary evidence for its clinical application in auxiliary diagnosis and progression assessment for OSCC. Methods: A total of 40 human oral mucosal specimens (15 normal, 15 oral leukoplakia, 10 OSCC) were enrolled for NBI image acquisition and MOI measurements. A 4-nitroquinoline-1-oxide (4NQO)-induced mouse OSCC model (n = 34) was used to dynamically record MOI changes across different pathological stages. A syngeneic tongue tumor mouse model (n = 16) was further established to evaluate whether MOI could reflect tumor formation and growth. All MOI values were quantified using ImageJ software with standardized region-of-interest (ROI) selection and background correction. Results: In clinical samples, MOI values decreased progressively from the normal mucosa (129.6 ± 5.991 arbitrary units (a.u.)) to oral leukoplakia (OLK) subgroups, including mild dysplasia (104.6 ± 3.757 a.u.) and moderate-to-severe dysplasia (91.77 ± 4.345 a.u.), and further to OSCC (54.41 ± 14.40 a.u.). In the 4NQO model, the MOI of the lingual mucosa was highest in the healthy control group (167.3 ± 10.05 a.u.) and gradually declined with increasing dysplasia severity, reaching the lowest level at the OSCC stage (48.67 ± 10.07 a.u.). In the syngeneic tumor model, the MOI was significantly lower in tumor-bearing mice than in healthy controls (47.85 ± 10.44 a.u. vs. 119.7 ± 14.20 a.u., p < 0.001). Receiver operating characteristic (ROC) analysis demonstrated good diagnostic performance of the MOI in distinguishing healthy tissue from cancerous lesions. Conclusions: NBI-derived MOI may quantitatively reflect the dynamic alterations of the oral mucosa during oral carcinogenesis and could represent a potential biomarker enabling the non-invasive, repeatable early evaluation and dynamic monitoring of OSCC. Full article

Objective: To explore the feasibility and effectiveness of an enhanced CT deep learning model based on regional attention for the preoperative multi-classification of rectal cancer T stages. Methods: Five hundred eligible patients with rectal cancer (48 in T1 stage, 127 in T2, 259 in T3, and 66 in T4) were randomly divided into a training group (n = 400) and a validation group (n = 100). Regions of interest (ROIs) in rectal cancer lesions were pixel-wise annotated by experienced radiologists. A deep learning algorithm based on regional attention was used to train a binary classification model (early stage—T1 and T2, advanced stage—T3 and T4) and a multi-classification model (T1, T2, T3 and T4 stages), which were compared against radiomics approaches. Features were extracted from manually segmented ROIs using pyradiomics, radiomics-based binary and multi-classification models using ten different algorithms. In addition, baseline clinical data-based binary and multi-classification models were also constructed. The performance of both binary and multi-classification models were evaluated by plotting receiver operating characteristic (ROC) curves. The area under the curve (AUC) and accuracy were calculated for the binary model, and the micro-average AUC, macro-average AUC, and accuracy were calculated for the multi-classification model. Results: The ROI-based binary classification model for T stage (ROITransStage; AUC = 0.878, accuracy = 0.850) outperformed the best among ten radiomics-based binary models (AdaBoost; AUC = 0.802, accuracy = 0.76), as well as the best-performing baseline clinical data binary model (AdaBoost; AUC = 0.836, accuracy = 0.76). In addition, ROITransStage (micro-average AUC = 0.873, macro-average AUC = 0.862, accuracy = 0.81) also demonstrated superior diagnostic performance for the T1, T2, T3 and T4 stages compared to the best-performing radiomics-based (SVM; micro-average AUC = 0.845, macro-average AUC = 0.777, accuracy = 0.6) and baseline clinical data-based (SVM; micro-average AUC = 0.841, macro-average AUC = 0.76, accuracy = 0.61) multi-classification models. Conclusions: The CT deep learning binary and multi-classification models based on regional attention exhibited superior predictive performance for rectal cancer staging compared to both radiomics and clinical data-based models. Full article

UAV aerial cinematography has become increasingly important in film production, surveying, and smart-city applications due to its efficiency and creative potential. However, existing UAV filming workflows still rely heavily on manual operation and professional piloting skills, resulting in complex mission design, limited planning autonomy, and inconsistent visual quality. To address these challenges, this paper proposes a unified aesthetics-aware trajectory planning framework for multi-region-of-interest (multi-ROI) UAV aerial cinematography that automatically generates safe, efficient, and visually coherent flight paths from user-specified ROIs. The proposed framework consists of three main components. First, for each ROI, candidate viewpoints are sampled using a spiral trajectory, and a learning-based aesthetic evaluation network is applied to select visually optimal viewpoints for local trajectory generation. Second, transition trajectories between ROIs are generated using a Goal-biased Bidirectional Rapidly exploring Random Tree Star (Goal-biased BiRRT*) planner and evaluated through a multi-objective cost function to determine the most suitable transition paths. Third, the global connection of multiple ROIs is formulated as a Set Traveling Salesman Problem (STSP) to obtain an efficient visiting sequence. By integrating learning-based aesthetic evaluation with hierarchical trajectory planning and coordinated multi-ROI route organization, the proposed framework jointly considers flight feasibility, planning efficiency, visual composition quality, and trajectory continuity within a unified planning pipeline. Experimental results demonstrate that the proposed method generates more visually appealing and coherent aerial trajectories than traditional manual or rule-based approaches, while significantly reducing operational complexity. The proposed system provides an effective solution for autonomous UAV aerial cinematography with improved global consistency, aesthetic performance, and practical planning capability in complex environments. Full article

Solar radio bursts at very low frequencies are key phenomena in the Sun–Earth space environment, providing crucial diagnostics of the acceleration and propagation of solar wind, coronal mass ejection (CME), and non-thermal energetic particles and serving as important indicators for space weather forecasting. To meet the demand for rapid screening of burst events in large-scale observational datasets, we present an end-to-end automatic detection and evaluation framework tailored for Type III bursts, built upon long-term radio dynamic spectra from STEREO-A/SWAVES. We formulate radio burst detection as a one-dimensional interval localization task along the time axis and, in view of the scarcity of annotated samples, cast it as a few-shot object detection task. Building upon the Faster R-CNN architecture with a ResNet50-FPN backbone, we propose the Meta-FSOD framework, which adopts an episodic training paradigm to construct support–query episode pairs. The framework incorporates a metric-guided prototype learning branch to semantically align and calibrate region-of-interest (RoI) features via class prototypes, and integrates a dynamic Beta-Gating mechanism coupled with Soft-NMS to effectively suppress false positives while preserving high-recall performance. Experimental results demonstrate that, despite being trained on a significantly smaller dataset than comparable studies, Meta-FSOD achieves competitive performance, closely matching that of conventional supervised model. The proposed framework exhibits strong cross-temporal generalization capabilities and holds considerable potential for engineering applications in deep space exploration missions. Full article

Objective: To investigate the integrated relationship between Cerebral Small Vessel Disease (CSVD) markers and quantitative facial soft-tissue measurements in Alzheimer’s disease (AD) continuum, utilizing peripheral muscle health as a potential biomarker for systemic frailty and neurodegeneration. Methods: Retrospective analysis of 3T brain MRI data from 67 patients (AD, N = 45; Mild Cognitive Impairment [MCI], N = 22). CSVD markers were assessed using STRIVE and standardized scales (Fazekas, Potter). Facial soft-tissue metrics, including masseter and tongue volume, temporal muscle thickness (TMT), and fat infiltration (Mercuri Scale), were quantified via semi-automatic segmentation on T1-weighted sequences. Group comparisons (AD vs. MCI) used regression models adjusted for age and sex. The overall central–peripheral relationship was explored via Canonical Correlation Analysis (CCA). Results: The AD group showed a highly significant cognitive decline (MMSE: 23.2 ± 4.1 vs. 28.2 ± 1.4, p < 0.0001). Centrally, the presence of PVSs in the mesencephalic region was the most robust predictor for AD (p = 0.003). Peripherally, average masseter muscle volume was significantly lower in the AD group (p = 0.0273), and masseter fat infiltration was significantly higher (p = 0.025), supporting localized sarcopenia. The CCA demonstrated a statistically significant positive multivariate relationship (r = 0.51, Roy’s Largest Root p = 0.015) between a higher combined CSVD burden and a worse soft tissue profile across the cohort. Conclusions: Quantitative indices of facial soft tissues, particularly masseter muscle volume and quality, reflect systemic frailty and cognitive deterioration along the AD continuum. The strong central–peripheral correlation suggests that sarcopenia and CSVD are interconnected manifestations of a shared pathobiological process. These easily measurable facial markers could serve as valuable, non-invasive peripheral biomarkers, complementing traditional neuroimaging risk stratification in AD. Full article

Precision agriculture has become a major direction of agricultural technological development in recent decades, addressing efficiency, environmental, and economic challenges simultaneously. Input optimization based on site-specific data collection—particularly variable-rate nutrient application, precision irrigation systems, and targeted crop protection—has been shown to generate measurable cost and resource savings. The aim of the study is to explore and systematically evaluate the economic impacts influencing precision technology in crop production. Although the technical and environmental benefits of precision technologies are widely documented, their economic performance and farm-level profitability remain inconsistently interpreted. The study is based on a systematic literature review of peer-reviewed English-language journal articles retrieved from the Web of Science, Scopus, ScienceDirect, and JSTOR databases. Study selection and evaluation were conducted in accordance with the PRISMA 2020 methodological framework. The literature indicates that precision technologies achieve average input savings of 8–20% and yield increases of 2–6%, while reported return on investment (ROI) values typically range between 5% and 15%. Economic viability is strongly dependent on farm size, with most studies identifying profitability above 100–200 ha. Additional benefits include improved management of soil heterogeneity, enhanced nutrient-use efficiency, and reduced excess input application, although adoption remains constrained by high investment costs and technological complexity. Full article