Search Results (11,194)

The global prevalence of lung diseases has risen markedly over the past three decades, driven by factors such as population growth, harmful environmental exposures, and increased life expectancy. Epidemiological studies have highlighted not only the growing burden of chronic respiratory disorders but also the trend toward earlier onset across age groups. Early detection and timely treatment of these conditions are essential to improving long-term clinical outcomes and reducing premature mortality. Chest radiography (X-ray) remains the most widely used and cost-effective initial diagnostic tool for respiratory diseases. However, the reliable identification of multiple thoracic abnormalities, such as cardiomegaly, emphysema, hernia, infiltration, mass, nodules, atelectasis, pneumothorax, pleural thickening, pneumonia, fibrosis, consolidation, effusion and edema, poses challenges even for experienced radiologists. Recent advances in deep learning, particularly convolutional neural networks, have improved medical image analysis and classification. In this study, we propose DualPool-DenseNet121, a modified DenseNet121 architecture combining global average and global max pooling, for multi-label classification of thoracic abnormalities on the NIH ChestX-ray14 dataset. Following removal of “No Finding” cases, our analysis used 51,759 pathological radiographs from 14,402 patients, annotated for fourteen pathological conditions, partitioned patient-wise into training, validation, and test sets with verified zero patient overlap. The pipeline incorporates standardized preprocessing, offline augmentation of under-represented classes, staged transfer learning, and per-class decision thresholds selected on the validation set. Model performance was evaluated using per-class AUC together with threshold-based clinical metrics. The proposed model achieved a macro-averaged AUC of 0.803 and a micro-averaged AUC of 0.831 on the held-out test set. Zero-shot external validation on the CheXpert validation set reached a macro-averaged AUC of 0.806 across the five overlapping pathologies with sufficient positive cases. Grad-CAM visualizations indicated that, in correctly classified cases, the model frequently attended to anatomically relevant regions. These results suggest that the proposed approach provides a reliable and reproducible retrospective benchmark for multi-label thoracic abnormality classification. External validation and expert radiological assessment are required before any clinical application can be considered. Full article

Remote sensing change captioning aims to describe disaster-related changes from bi-temporal imagery, yet existing methods typically produce image-level captions without explicit regional evidence, limiting interpretability and weakening the link between generated language and actual changed areas. We present ChangePixel, a single-backbone framework that upgrades remote sensing change captioning into pixel-level, evidence-grounded change narration without introducing new manual grounding labels. ChangePixel incorporates three lightweight modules: a Bi-Temporal Change-Aware Transfer Adapter (BCTA) that converts shared pre- and post-event visual features into change-aware grounding representations, a Change Region Grounding Planner (CRGP) that localizes a compact set of informative changed regions before narration begins, and a Weak Evidence Alignment Bridge (WAB) that converts released change captions into phrase-to-region weak supervision. Through this design, the model jointly produces a global change caption and region-level evidence in the form of pixel masks paired with corresponding local change phrases. Experiments on the Remote Sensing Change Caption (RSCC) dataset and LEVIR-CC demonstrate that ChangePixel provides caption quality (ROUGE 19.52/ST5-SCS 76.91 on RSCC; CIDEr-D 56.82 on LEVIR-CC under zero-shot transfer) that is competitive with general-purpose vision–language models (VLMs) while adding pixel-level spatial evidence to change narration; additionally, evidence localization is quantified on LEVIR-MCI through semantic change-mask metrics, reaching Change mIoU 33.8 (15.3 points higher than a non-learned pixel-difference floor of 18.5), whereas phrase-to-region alignment is assessed qualitatively pending a dedicated grounding benchmark. The proposed framework offers a practical path from coarse image-level captioning to evidence-grounded disaster understanding. Full article

Background: Language disorders and dyslexia are associated with atypical language- and reading-related processing, but findings from functional near-infrared spectroscopy (fNIRS) studies remain heterogeneous. This systematic review and meta-analysis synthesized existing fNIRS evidence, quantified hemodynamic differences relative to controls, and examined whether different outcomes showed different levels of consistency. Methods: PubMed, Embase, and Web of Science were searched through 9 January 2026. Eligible studies included individuals with language disorders or dyslexia assessed using fNIRS, with outcomes reported as oxygenated hemoglobin (HbO), deoxygenated hemoglobin (HbR), β estimates, or other hemoglobin-based measures. Random-effects meta-analyses were performed when sufficient quantitative data were available, with subgroup and sensitivity analyses conducted where feasible. Results: A total of 830 records were identified, of which 10 publications comprising 11 independent samples met the inclusion criteria. HbO was the only outcome to show a statistically significant overall between-group difference, with lower HbO in the experimental groups than in controls (SMD = −0.61, 95% CI [−1.12, −0.11], p = 0.017), although heterogeneity was substantial (I² = 58.6%). In contrast, pooled results for HbR and β were not statistically significant and showed weaker stability across analyses. Subgroup and sensitivity analyses did not provide sufficient evidence to support disorder category, region of interest (ROI), or outcome metric type as stable explanations for effect size variation. Conclusions: Among the included fNIRS measures, HbO showed comparatively more consistent cross-study evidence than HbR or β. However, because the HbO estimate pooled clinically distinct dyslexia and acquired language-disorder studies within a limited evidence base, it should be interpreted as a preliminary cross-diagnostic signal rather than as evidence of a shared disease continuum or common underlying mechanism. Full article

Background: Modern hematology analyzers integrate multiparametric technologies and adaptive flagging algorithms to support rapid detection of abnormal leukocyte populations. The Sysmex XN-9100 employs the White Blood Cell Differential (WDF) channel for first-line screening and the White Precursor Cell (WPC) channel as a reflex test for improved detection of blasts and abnormal lymphocytes. Although widely implemented, the real-world diagnostic performance and workflow implications of this integrated strategy remain incompletely characterized. Objectives: To evaluate, in a large real-world cohort, the performance of the Sysmex XN-9100 WDF–WPC workflow, focusing on its diagnostic accuracy, its capability to identify pathological cases within the WDF-negative population, and its potential implications for blood smear review workload optimization. Methods: A retrospective analysis was performed on 4531 consecutive routine blood samples reviewed between February and June 2025. All samples were analyzed using the Sysmex XN-9100. WDF-positive cases (“Blasts/Abnormal lymphocytes?”) underwent reflex WPC testing (“Blasts?” and “Abnormal lymphocytes?”). Peripheral blood smear morphology, based on the evaluation of 200 leukocytes using the DI-60 digital imaging system, constituted the reference standard. Diagnostic performance metrics, including sensitivity, specificity, predictive values, likelihood ratios, accuracy, and ROC analysis, were calculated for WDF and WPC channels using both individual and aggregated flag evaluations. Results: The WDF-B/AL (White Blood Cell Differential Channel-“Blasts/Abnormal lymphocytes?”) flag demonstrated high sensitivity (88.24%) and negative predictive value (92.73%), supporting its role as an effective first-line screening tool, although specificity was limited (48.87%; AUC = 0.75). Unlike most previous studies, our workflow allowed evaluation of pathological cases occurring within the WDF-negative population, providing a realistic estimate of the false-negative burden. Disaggregated WPC analysis revealed complementary diagnostic profiles: WPC-B (White Precursor Cell Channel-“Blasts?”) showed higher specificity (77.71%) and overall discriminative performance (AUC = 0.81), whereas WPC-AL (White Precursor Cell Channel-“Abnormal lymphocytes?”) achieved higher sensitivity (92.18%) and negative predictive value (95.99%). Aggregated evaluation of WPC flags (logical OR) maximized sensitivity (92.68%) and reduced false-negative cases, supporting the role of the WPC channel as a second-level diagnostic safety net. Furthermore, within the integrated laboratory workflow, WPC-negative results may support the optimization of manual smear-review allocation when interpreted together with predefined institutional smear-review criteria, while maintaining diagnostic safety. Conclusions: The Sysmex XN-9100 WDF–WPC workflow constitutes a multilayered high-sensitivity screening strategy that prioritizes abnormality detection while optimizing laboratory efficiency. The complementary behavior of WPC-B and WPC-AL enhances overall workflow safety, whereas evaluation of WDF-negative pathological cases provides a more realistic assessment of screening performance than conventional reflex-based studies. Beyond analytical accuracy, the workflow may contribute to balancing diagnostic reliability, patient safety, and resource utilization in high-volume laboratory practice. Full article

Background: Medical imaging datasets often suffer from class imbalance and limited availability of pathology-rich cases, which limit the performance of machine learning models for segmentation, classification, and vision–language tasks. We propose POWDR, a pathology-preserving outpainting framework for 3D MRI that uses real pathological regions as conditioning evidence while generating anatomically plausible surrounding tissue. Methods: Our approach leverages wavelet-domain conditioning to enhance high-frequency detail and mitigate blurring common in latent diffusion models. We introduce a random connected mask training strategy to reduce conditioning-induced collapse and improve diversity outside the lesion. POWDR is evaluated on brain MRI using BraTS datasets and extended to knee MRI to assess applicability beyond brain imaging. Results: Quantitative metrics (FID, MS-SSIM, LPIPS) were used to assess image realism. Random connected mask training improved diversity, reducing cosine similarity from 0.9947 to 0.9580 and increasing KL divergence from 0.00026 to 0.01494. To validate pathology preservation, we compared lesion overlap, volume, intensity, and morphology. For downstream segmentation, nnU-Net performance improved from 0.6992 to 0.7137 Dice after augmentation with 50 synthetic cases, representing a modest but statistically significant improvement (paired t-test, p = 0.016). Tissue volume analysis showed no significant differences for CSF and GM compared to real images, while WM volume was lower in synthetic images. Conclusions: POWDR provides a framework for generating diverse, pathology-preserving synthetic MRI data. The results suggest potential utility for data augmentation while maintaining clinically relevant lesion characteristics. Full article

Oncology care increasingly depends on heterogeneous sensing streams generated by computed tomography (CT), radiotherapy planning systems, wearable devices, home respiratory sensors, patient-reported outcomes, and clinical records. These data streams are often processed separately, limiting their value for longitudinal, context-aware review. This study proposes OncoSense-Agent, a reliability-aware agentic multimodal sensing architecture for CT-guided respiratory monitoring in oncology care. The architecture links CT-derived anatomical evidence with wearable physiology, respiratory symptoms, functional assessment, treatment context, and explainable human-in-the-loop review-priority generation. To move beyond a purely conceptual design, we implemented a lung-focused proof-of-concept with six bounded software agents: Imaging Reliability, Wearable Monitoring, Respiratory Review, Treatment Context, Multimodal Fusion, and Explainability. The prototype used real nnU-Net v2 3D lung segmentation metrics from 139 patients with complete bilateral lung CT data as the imaging anchor, while wearable, respiratory, symptom, and treatment-context channels were introduced as deterministic overlays for controlled validation. OncoSense-Agent changed review-priority assignment relative to CT-only assessment in 78/139 cases (56.1%), assigned 111/139 cases (79.9%) to high-priority or high-uncertainty tiers, and showed increasing Safety Gate activation as CT quality declined. Three illustrative cases demonstrate hidden respiratory deterioration, wearable data-quality uncertainty, and treatment-context risk not captured by CT-only assessment. The prototype does not establish clinical diagnostic accuracy, but demonstrates operational, auditable, reliability-aware multimodal review-priority generation for clinician-supervised oncology monitoring. Full article

Field-scale rice yield prediction in irrigated coastal systems is constrained by small plot size, persistent cloud cover, and strong interannual variability, which limit the effectiveness of single-sensor remote sensing approaches. This study proposes an integrated framework that combines multispectral time series from Sentinel-2, PlanetScope, and unmanned aerial vehicle (UAV) platforms with phenological metrics derived from the Normalized Difference Vegetation Index (NDVI), climate variables aggregated within phenology-defined windows, and machine learning algorithms to predict rice grain yield at the plot level. The framework was structured in five phases: (i) cross-sensor consistency assessment of red, near-infrared, and NDVI values across platform pairs; (ii) linear harmonization and multisource temporal fusion of NDVI time series at 5-day intervals; (iii) extraction of phenological metrics from smoothed NDVI trajectories using a relative-threshold approach; (iv) aggregation of meteorological variables within crop-stage-specific windows; and (v) yield prediction using partial least squares regression (PLSR), Random Forest, and XGBoost under nested leave-one-out cross-validation. The framework was evaluated on 72 irrigated rice plots (37 in 2022, 35 in 2023) in Lambayeque, northern Peru. Cross-sensor analysis revealed that the PlanetScope–UAV pair achieved the strongest NDVI agreement ($R^2=0.87$, RMSE $=0.07$), while Sentinel-2–PlanetScope showed higher correlation ($R^2=0.91$) but with systematic bias requiring calibration. Multi-source fusion raised temporal coverage from 53–62% (individual sensors) to 82% in 2022 and 66% in 2023. The best single-season prediction was obtained in 2022 with PlanetScope-derived phenological metrics and XGBoost ($R_{\mathrm{cv}}^2=0.72$, ${\mathrm{RMSE}}_{\mathrm{cv}}=1.23$ t ${\mathrm{ha}}^{-1}$), while the best cross-season performance was achieved with combined phenological and climate features using the PlanetScope+UAV configuration and XGBoost ($R_{\mathrm{cv}}^2=0.64$, ${\mathrm{RMSE}}_{\mathrm{cv}}=1.35$ t ${\mathrm{ha}}^{-1}$). SHAP-based interpretability analysis identified post-peak phenological descriptors and climatic conditions during the grain-filling window as the most informative predictors. These findings demonstrate that PlanetScope-centered multi-source fusion, combined with phenology-informed feature engineering, provides a robust basis for rice yield prediction in cloud-prone irrigated environments. Full article

Urban environments concentrate spatially distributed stressors, including heat, noise, pollution, and biodiversity loss, that are increasingly recognized as determinants of population mental health. However, current approaches rarely integrate environmental structure, lived exposure, and climate-related emotional responses within a unified spatial framework applicable to public health. This article introduces Eco-Affective Health Mapping (EAHM) as a conceptual, spatially explicit framework, grounded in the recently formalized Eco-Affective Health theoretical model, for understanding how environmental conditions may shape climate-related emotional responses, including eco-anxiety, solastalgia, and ecological grief, across urban socio-ecological systems. Drawing on evidence from spatial epidemiology, landscape ecology, environmental mental health, and digital phenotyping, I argue that affective responses to environmental stressors are not randomly distributed but are hypothesized to exhibit spatial clustering in relation to environmental exposures, landscape configuration, and mobility-based interactions. I propose the Eco-Affective Health Mapping (EAHM) framework, which integrates four spatial layers: environmental exposures, landscape configuration, person–place interaction, and affective indicators, together with a companion composite metric, the Eco-Affective Load Index (EALI), for which I provide a formal multi-domain specification and a purely illustrative, non-empirical worked example. EAHM is presented here as a theoretical and methodological proposal rather than as a validated instrument: no primary environmental, mobility, or affective data were collected or analyzed for this article, and the framework’s constituent relationships require prospective empirical testing, for which I outline a companion measurement strategy grounded in the Eco-Affective Health Assessment Protocol (EAHAP). By conceptualizing climate-related emotional responses as candidate measurable public health signals, EAHM is intended to support a future shift toward prevention-oriented, population-level mental health strategies aligned with planetary health and sustainable development agendas. Full article

Small harvesting compartments and steep terrain in South Korea favor harvesting systems in which a single machine performs multiple processes. Multifunctional systems based on small tracked excavators, equipped with a grapple saw for felling and a clambunk skidder for extraction, have therefore been increasingly adopted to address labor shortages and to improve operational safety. However, because these base machines lack a chassis suspension system and their operator seats provide limited vibration isolation, operators of small-excavator-based systems may be exposed to elevated levels of whole-body vibration (WBV) and shock vibration. This study quantified operator ride-vibration exposure and assessed the associated health risks during small-excavator-based timber harvesting in accordance with ISO 2631-1; the assessment was based on measured vibration exposure rather than on direct medical evaluation. Tri-axial accelerations were measured simultaneously at the operator seat and the cab floor of a 5-ton-class excavator over eight workdays, and one representative workday was analyzed at the work-element level. The harvesting process was divided into timber felling (machine travel, positioning, clearing, and cutting and bunching) and timber extraction (machine travel on forest roads, machine travel on skid trails, and loading and unloading). Exposure was evaluated using the frequency-weighted root-mean-square (RMS) acceleration, the crest factor (CF), and the total vibration dose value (TVDV). The CF exceeded 9 for all work elements, indicating shock-dominated vibration and justifying the TVDV as the primary evaluation metric. Timber extraction generated higher vibration exposure than timber felling, and the seat TVDV differed significantly among the work elements within each operation (p < 0.001). The highest seat TVDV occurred during loading and unloading (123.05 m/s^1.75), exceeding the upper ISO 2631-1 Health Guidance Caution Zone limit for an 8-h exposure by more than eight times, and all major work elements exceeded the ISO 2631-1 health guidance limits. To our knowledge, this study provides the first work-element-level quantification of WBV exposure for small-excavator-based timber harvesting systems in South Korea. The findings underscore the need for improved seat suspension systems, vibration mitigation technologies, and vibration-aware work planning strategies. Full article

Terrestrial water storage anomaly (TWSA) provides an integrated measure for characterizing long-term variations in terrestrial water storage under the combined influences of climate variability and human activities. However, conventional assessments of long-term TWSA change have relied predominantly on linear trends, which may not fully capture concurrent shifts in interannual variability or the occurrence of wet and dry extremes. Using three GTWS-MLrec reconstructions (CSR, JPL, and GSFC) and their ensemble mean, this study investigates long-term TWSA changes across China during 1961–2020. An SED-based hotspot framework, adapted from previous climate-hotspot studies, was applied to integrate changes in mean state, interannual variability, and the frequencies of wet and dry extremes into a unified statistical metric of hotspot intensity. Results reveal strong regional heterogeneity, with pronounced water-storage declines concentrated in the North China Plain, while more heterogeneous changes occur in northwestern and southwestern China. The multidimensional analysis identifies three major statistical hotspot regions: the northwestern CB, the North China Plain, and the SWB. The major hotspot locations remain broadly consistent across the three reconstructions and under alternative normalization schemes, although the magnitude and spatial contrast of SED vary with the selected normalization factor. Basin-scale comparison further reveals regional differences in the component indicator most closely associated with the spatial distribution of SED. Mean-state decline and increasing dry-extreme frequency show relatively strong spatial associations with SED in several northern basins, whereas variability-related changes show stronger spatial associations in the SWB and YZRB. These results provide a multidimensional statistical characterization of long-term TWSA change and identify regions in which changes in mean state, interannual variability, and the occurrence of wet and dry extremes are spatially concentrated. Full article

Autonomous systems must demonstrate reliable safety and robustness even under rare, safety-critical conditions. Because the vast range of possible operating situations cannot be exhaustively tested, scenario-based testing has emerged as a structured approach to expose systems to representative and challenging situations. Search-based scenario selection methods (SBSSMs) algorithmically explore the scenario space to identify critical and informative cases. Yet, existing implementations are often tightly coupled to specific domains and simulation environments, hindering reproducibility, comparability, and generalizability across application areas. This paper introduces a benchmarking framework that addresses these limitations by providing a domain-independent environment for the systematic evaluation of SBSSMs. Building on empirical observations from maritime simulation studies and the literature, the framework generates synthetic test instances that mimic the structural patterns found there while abstracting away application-specific semantics. It supplies standardized performance metrics and reference implementations of key method classes, enabling direct comparison of new approaches with established ones under controlled, repeatable conditions. Illustrative demonstrations show how the benchmark reveals characteristic strengths and weaknesses of different methods across diverse structural settings. As a reproducible and extensible baseline, the framework promotes standardized performance assessment and the transfer of methodological advances from maritime navigation to domains such as automotive safety. Full article

Background: Bacillus clausii SKB/BCL21 (MCC 0569) is a novel strain that shows promise as a probiotic for both human and animal healthcare. Objectives: The objective is to evaluate the safety profile of B. clausii SKB/BCL21 through genomic and toxicity assessments in Wistar rats, as well as to assess its efficacy as a probiotic in broiler chickens challenged with Clostridium perfringens. Methods: The identification of genus and species was performed using 16S rRNA and whole genome sequencing (WGS). A genomic analysis was conducted through bioinformatic screening of the B. clausii SKB/BCL21 genome to identify virulence factors, genes encoding toxins, mobile genetic elements, and antibiotic resistance genes. In vitro biosafety assays were conducted to evaluate mucin degradation, gelatinase, hemolytic activity, and DNase activity. The in vivo safety evaluation was performed by acute and subacute oral toxicity studies as per the OECD 423 guidelines. In efficacy testing broilers challenged with C. perfringens were administered with low dose (1 × 10⁸ cfu/kg of feed) and high dose (1 × 10⁹ cfu/kg of feed) of B. clausii SKB/BCL21. Performance metrics, such as average weight gain, feed conversion ratio (FCR), and mortality rates, were evaluated in comparison to a positive control group that received Virginiamycin 50% (15 ppm). Results: The isolate SKB/BCL21 was identified as Bacillus clausii based on 16S rRNA and whole genome sequencing (WGS). The bioinformatic analysis of the B. clausii SKB/BCL21 genome reveals that it lacks genes associated with toxins, mobile genetic elements, and virulence factors. However, it does contain intrinsic and non-transferable antibiotic resistance genes within its chromosomal DNA. In the acute toxicity study, an oral dose of 2000 mg/kg (400 billion cfu/kg) body weight was found to be nontoxic. The No Observed Adverse Effect Level (NOAEL) for B. clausii SKB/BCL21 was found to be 1000 mg/kg (200 billion cfu) body weight/day by oral route in the subacute toxicity study. The findings of in vivo toxicity studies indicate that there were no treatment-related changes in any of the endpoints assessed. The effects of low (1 × 10⁸ cfu/kg of feed) and high (1 × 10⁹ cfu/kg of feed) doses of B. clausii SKB/BCL21 on the growth performance metrics, including average weight gain, feed conversion ratio, and mortality rates in broiler chickens infected with C. perfringens, showed results similar to those of the positive control (Virginiamycin 50%, 15 ppm). Conclusions: Based on these preliminary studies, B. clausii SKB/BCL21 can serve as a potential alternative to antibiotic growth promotors in broiler production. These results suggest that the B. clausii SKB/BCL21 is safe and could be a potential probiotic for animal feed supplements. Full article

This study evaluates four assessment approaches—questionnaire-based evaluation, Quantitative Content Analysis (QCA), visual summary, and machine learning-based image analysis—for quantifying infographic style in cartographic products. These methods are compared against the IGV (Infographic eValuation) metric, a novel method proposed by the authors. All approaches were applied to a reference dataset comprising 32 images, equally divided between maps and infographics. The questionnaire survey (n = 139) employed a bipolar scale (−5 to +5) to measure perceived affiliation with cartographic or infographic categories; aggregated scores were subsequently normalised to a [0, 1] interval to ensure cross-method comparability. QCA was operationalised through 20 paired codes structured into four principal indicators (linking visual style, visualisation complexity, visual appeal and cartographic accuracy), generating composite scores. The visual summary approach was quantified by transforming predefined domain-based diagram segments into numerical values. Machine learning experiments implemented in Orange (Image Analytics) included hierarchical clustering using cosine distance and supervised classification via logistic regression trained on 50 labelled images. The article details the procedural steps of this evaluation, the results obtained, and a comprehensive statistical analysis. Furthermore, it assesses the suitability of each method for evaluating infographics and identifying specific elements within cartographic products. Full article

Background/Objectives: Non-invasive urine collection in the pediatric emergency department (ED) is frequently complicated by prolonged collection times and unacceptably high contamination rates associated with the traditional bag specimen urine (BSU) method. This prospective study aimed to compare the operational efficiency and diagnostic reliability of the bladder stimulation technique (BST)—a highly promising alternative—against the traditional BSU method. Methods: The study included 149 infants aged ≤ 6 months (BST group: n = 81; BSU group: n = 68) requiring urinalysis. We systematically evaluated procedural success rates, time-to-collection metrics, and sample contamination frequencies. Furthermore, we assessed the influence of patient age, weight, behavioral state, and sex on BST efficacy. Results: The BST demonstrated superior clinical performance, significantly reducing the total mean time-to-collection to 21 min, compared to 60 min—often extending up to 4 h—typically required for BSU. Notably, the actual stimulation maneuver required only about 78 s. While approximately half of the BSU samples were contaminated, BST markedly decreased this rate (16% vs. 58%). Factors associated with the highest procedural success included patient age ≤ 3 months, weight ≤ 6000 g, and remaining calm during the procedure. Infant sex did not significantly affect success rates. Conclusions: Functioning as both an “operational accelerator” and a “diagnostic firewall,” the BST mitigates the inherent limitations of conventional methods and has the potential to replace the traditional urine bag. By offering a rapid, predictable, and clean alternative, it facilitates the swift initiation of accurate treatment and helps prevent families from leaving the ED before sample collection. Full article

Public-sector sustainability and climate reporting increasingly address environmental exposure, governance, and financial effects, yet existing frameworks do not adequately disclose preparedness for low-probability, high-impact systemic risks whose probabilities, timing, thresholds, and transmission channels remain deeply uncertain. This article develops a Public-Sector Resilience Reporting Standard (PSRRS) as a pre-standard architecture for government preparedness disclosure. The design has three bounded objectives: diagnose cross-framework disclosure gaps, translate these gaps into a theoretically grounded capability-to-disclosure architecture, and demonstrate its analytical use through an illustrative Florida application and two hazard-neutral stress tests. The documentary corpus includes international sustainability and public-sector reporting standards, ISO and UNDRR resilience and continuity instruments, three Florida resilience documents, and peer-reviewed literature on resilience governance, decision-making under deep uncertainty, critical infrastructure interdependency, catastrophic uncertainty, climate-risk disclosure, public finance, climate-risk pricing, local-government credit risk, investor attention, and ransomware service disruption. A structured interpretive coding protocol classifies each framework as explicit, partial, or not explicit across nine disclosure dimensions; a codebook appendix identifies the assessment criteria, the a priori and inductively refined dimensions, and the validation boundaries. Florida is not treated as a basis for statistical or jurisdictional generalization. Instead, it illustrates how a comparatively developed resilience architecture may disclose statutory continuity, critical-asset data, project ranking, and output metrics while leaving systemic dependencies, adaptive triggers, long-horizon fiscal exposure, residual service risk, distributional effects, and assurance mechanisms insufficiently visible in the reviewed reporting corpus. AMOC and case-grounded cyber-fiscal stress tests show how the PSRRS shifts reporting from hazard inventories and funded projects toward auditable evidence of institutional capacity, adaptive readiness, and public-value protection. The article specifies mandatory, recommended, and optional clauses, evidence requirements, indicator examples, a disclosure index, a sample report structure, and a three-tier pilot conformity model. The contribution is conceptual and operational, but not yet a validated formal standard; cross-jurisdictional piloting, inter-rater coding, cost testing, assurance testing, and stakeholder consultation are identified as the next stage of standardization. Full article