Search Results (6,933)

Early-season crop yield prediction is critical for food security assessment and timely agricultural decision-making, yet large-scale remote-sensing applications are often constrained by a mismatch between pixel-level observations and county-level yield labels. This mismatch limits the use of fine-grained spatial heterogeneity, especially under partial-season observations. Single-scale approaches are also limited in capturing complementary pixel-level and county-level information, restricting representation of yield formation processes. To address this, we propose TandemNet, a multi-scale multiple-instance learning framework for early-season prediction of japonica rice yield in Northeast China. TandemNet treats each county–year as a bag of rice pixels and adopts a dual-branch architecture to jointly learn pixel-level growth trajectories and county-level statistical responses. A phenology-conditioned cross-attention module fuses the two scales under varying growing-season windows. Using Sentinel-1, Sentinel-2, and MODIS data from 2018 to 2023, leave-one-year-out validation shows that TandemNet outperforms Random Forest, XGBoost, LSTM, and Transformer baselines across most phenological stages. It achieves reliable prediction at the tillering stage, approximately 2–3 months before harvest, with an $R^2$ of 0.69 and an RMSE of 655.98 kg/ha. Ablation and attention analyses further indicate a stage-dependent shift from local heterogeneity to county-level consistency. These results demonstrate that modeling cross-scale interactions improves the timeliness, accuracy, and interpretability of rice yield prediction. Full article

Coxiella burnetii is the causative agent of Q fever, a zoonotic disease of public health concern. Despite the recent expansion of the domestic goat industry in Korea, epidemiological data regarding C. burnetii exposure in Korean goat populations remain limited. This pilot study aimed to assess the serological distribution of C. burnetii exposure in the southwestern region of Jeonnam Province, the major goat-breeding region of Korea. Goat serum samples were originally collected for routine Foot-and-Mouth Disease monitoring in 2025. Using a two-stage random sampling approach, 92 samples were selected from 16 farms across five southwestern counties of Jeonnam Province. Specific antibodies against C. burnetii were detected using a commercial ELISA. The overall unadjusted apparent seropositivity was 44.6% (41/92). By county, seropositivity was 61.1% in both Haenam and Wando, 38.9% in Gangjin, 36.8% in Yeongam, and 26.3% in Jangheung. Furthermore, the overall herd-level apparent seropositivity was 68.8% (11/16). At the herd level within the surveyed farms, intra-herd seropositivity ranged from 0.0% to 100.0%, showing wide variation among the tested farms. The results indicate high serological exposure to C. burnetii within the surveyed southwestern counties. While constrained by a small sample size, these findings show that targeted longitudinal surveillance is required in this region. Expanding systematic monitoring efforts nationwide is recommended to establish effective veterinary and public health control measures. Full article

Background: Two-stage coarse-to-fine architectures are commonly used in medical image synthesis and refinement. However, when the distributional gap between coarse inputs and high-resolution targets is large, the second-stage generator may produce unstable or weakly conditioned refinements rather than operating as a true residual corrector. Methods: We propose intentional degradation, a target-side distribution-alignment strategy that deliberately degrades high-resolution targets toward the coarse prediction space before residual learning. The degradation profile is guided by measured differences in contrast, saturation, and edge energy. We further evaluate refinement quality using two residual-space metrics: Var(Δ), the variance of the predicted residual (target minus input), and Δ-SSIM, the structural similarity between the predicted and true residual maps; both are designed to reveal refinement instability that conventional image-level metrics (e.g., SSIM computed on the full image) may not capture. We evaluated the approach in three medical imaging domains: wound-healing photography, retinal fundus imaging, and dermoscopy. Results: Across all three domains, intentional degradation consistently improved Δ-SSIM and normalized residual variance recovery relative to the baseline, and predicted residuals showed substantially stronger directional correspondence with the true residual (assessed via sign-agreement and cosine-similarity metrics) than the baseline, which converged toward directionally random predictions. These findings suggest that residual-space and direction-sensitive metrics can reveal refinement instability that may not be captured by conventional image-level metrics alone. Conclusions: Intentional degradation provides a simple, architecture-agnostic-by-design strategy for stabilizing residual learning in coarse-to-fine medical image refinement, validated within a single ConvLSTM-based refinement framework; extension to other architectures remains for future validation. Rather than introducing a new network architecture, the method modifies the target-side training distribution to reduce the mismatch between coarse inputs and high-resolution targets. Full article

The subtropical hilly regions of southern China are characterized by dense vegetation and highly concealed landslides, making it difficult for traditional, single-source remote sensing methods to meet disaster prevention needs. The core scientific contribution of this study is the development of a hierarchical, progressive hazard identification framework that bridges the gap between InSAR deformation detection and landslide risk identification. This study focuses on Mayang County, Hunan Province, China, and combines time-series InSAR data from C-band Sentinel-1 and L-band ALOS-2 with a random forest (RF) algorithm to construct an early-stage identification model for landslide risks. All SAR data were processed under controlled baseline conditions (perpendicular baseline <150 m; polarization: VV for Sentinel-1, HH for ALOS-2). By screening highly reliable deformation points through dual-source cross-validation and integrating nine evaluation factors including slope, we established a two-layer coupled identification model combining InSAR deformation and susceptibility indices at the slope unit scale. The results showed that the dual-source InSAR approach achieved an identification accuracy of 71% (precision 68%, recall 65%, F1-score 0.66, Cohen’s κ 0.62), significantly outperforming single-source methods (62% for Sentinel-1 alone and 58% for ALOS-2 alone); the AUC was 0.815 under spatial block cross-validation, with an out-of-bag error of 16.8%. The dual-source InSAR approach identified a total of 59 potential hazard sites, 83.1% of which were located in medium- to high-risk zones. Following field surveys and LiDAR verification, 35 of these were confirmed as active landslide sites, demonstrating identification accuracy significantly superior to that of a single data source. The multi-source coupling framework proposed in this study effectively overcomes the decoherence issues associated with single-source SAR data in subtropical vegetated areas, providing reliable technical support for the early identification of landslides in humid hilly regions of southern China. Full article

Artificial intelligence is becoming deeply embedded in higher education, yet how the characteristics of AI-based e-learning systems relate to students’ perceived learning effectiveness and satisfaction remains insufficiently understood. This study examines the relationships of AI Functionality Compatibility, AI Instructional Process Coverage, and AI-Assisted Learning Cognitive Usability with Perceived Online Learning Effectiveness and Online Learning Satisfaction. Data from 384 students at Chinese universities were analyzed using a two-stage approach combining partial least squares structural equation modeling and artificial neural networks (PLS-SEM-ANN). The results showed that all three system characteristics were positively associated with perceived learning effectiveness, with instructional process coverage showing the strongest relationship. Cognitive usability also had a significant direct association with learning satisfaction, whereas functionality compatibility and instructional process coverage showed significant indirect effects through perceived learning effectiveness. The findings reveal an outcome-oriented pattern in which perceived learning effectiveness occupies a central position between system characteristics and satisfaction. This study extends understanding of AI-supported learning systems by emphasizing the alignment of technical functions with pedagogical processes and learners’ cognitive needs. It also provides practical guidance for universities and developers seeking to better align the design and evaluation of AI-based e-learning systems with learners’ instructional and cognitive needs. Full article

Tobamoviruses (viruses in the genus Tobamovirus, family Virgaviridae) lead to major yield losses in economically important crops around the world. In this review, we go beyond the canonical gene expression framework by integrating recent discoveries of reverse open reading frames (rORFs) on the negative-strand RNA. These rORFs have only been experimentally validated in cucumber green mottle mosaic virus (CGMMV), with predicted sequence-conserved homologs across a subset of the genus, including TMV, ToBRFV, and PMMoV. However, they are not universally present in all tobamoviruses. We systematically dissect the infection cycle—from disassembly and replication to cell-to-cell and systemic movement—with an emphasis on the host factors hijacked at each stage. We synthesize current understanding of plant antiviral immunity, focusing on RNA silencing and NLR receptor-mediated resistance as two pillars of defense, along with the transcription factors and microRNAs that orchestrate these responses. We critically evaluate the experimental evidence for both plant defenses and viral counter-strategies, noting that many mechanistic models derive from limited model systems. We further characterize host genetic resistance and susceptibility factors applicable to crop breeding. These resources include dominant NLR and non-NLR resistance, as well as recessive resistance derived from modified host susceptibility genes. We address how viral mutations, recombination and fitness trade-offs undermine resistance durability. We then evaluate their practical deployment through conventional breeding, the exploitation of quantitative resistance, and genome editing, and outline associated agronomic drawbacks and regulatory constraints. Using ToBRFV as a case study, we analyze its epidemiological traits and assess the current arsenal of surveillance tools, from field diagnostics to remote sensing. Finally, we survey management strategies across a spectrum of maturity. Some approaches, including sanitation protocols and conventionally bred resistant cultivars, have proven effective under field conditions. The first dsRNA-based biopesticide has recently been registered in China, while other biological control agents and low-risk chemical approaches remain largely at the experimental stage. We also discuss the bottlenecks that impede lab-to-field transition and highlight promising solutions such as precision breeding and evolution-oriented cultivar deployment. By bridging molecular virology, epidemiology, and integrated disease management, this review provides a critical, bench-to-field framework for the sustainable control of tobamoviruses. Full article

Free chlorine is commonly applied at treatment works as a disinfectant residual but decays throughout the water supply system. Chlorine decay consists of a bulk and a wall decay component. The bulk component depends on water composition (i.e., it does not vary across the network) and is often characterized at the entrance to the system by fitting a first-order decay model based on bottle test data. The bulk decay coefficient ($k_b$) is known to vary widely depending on the water source characteristics and temperature. Recent studies have shown that the uncertainty of $k_b$ can be significant (>15%), and its quantification is essential to avoid misinterpreting imprecise $k_b$ values. This work aims to explore how bottle test sampling conditions affect the uncertainty of $k_b$ through State Estimation (SE) and Uncertainty Assessment (UA) techniques. First, a sensitivity analysis is presented to explore the effect of the number of measurement replications and sampling times. Then, an experimental comparison is carried out to highlight the relevance of $k_b$ uncertainty and assess the effect of different types of chlorination (laboratory vs. onsite chlorination). This two-stage approach is essential for deriving a set of practical recommendations for the appropriate design of bottle tests, controlling (i.e., limiting) the associated $k_b$ uncertainty for each application. This is key to better understanding and modeling free chlorine residual bulk decay and, thus, chlorine dynamics, through water supply systems. Full article

Objective: Ovarian, fallopian tube, and primary peritoneal cancers remain among the deadliest gynecological malignancies, largely because most cases are diagnosed at an advanced stage and no effective screening strategy is currently available. Increasing evidence suggests that many high-grade serous ovarian carcinomas originate from the fallopian tube. We aimed to explore the diagnostic performance of ex vivo fallopian tube cytology and the CytoSaLPs score for detecting tubal and adnexal malignancies in women undergoing salpingectomy or salpingo-oophorectomy. Methods: We conducted a prospective single-center observational study including 304 women undergoing salpingectomy or salpingo-oophorectomy for benign, premalignant or malignant gynecological indications between 2020 and 2023. Ex vivo cytological brushing of the distal fallopian tube was performed before fixation, followed by histopathological examination using the SEE-FIM protocol where appropriate. The primary analysis was performed at the specimen level. Of 544 paired specimens initially available for cytology–histology correlation, 53 non-diagnostic cytological specimens were excluded from the primary diagnostic performance analysis, leaving 491 evaluable paired specimens. Fallopian tube cytological findings were compared with histopathology as the reference standard. The discriminatory ability of the CytoSaLPs score was explored using receiver operating characteristic analysis. Results: Fallopian tube cytology demonstrated high sensitivity for histologically confirmed tubal malignancy, although specificity was moderate. Based on the primary specimen-level analysis, sensitivity was 94.4% and specificity was 71.0%. When fallopian tube cytology was compared with ovarian histology, sensitivity was 72.9% and specificity was 72.4%. For the adnexa considered as a single anatomical entity, sensitivity was 76.5% and specificity was 70.7%. The CytoSaLPs score showed good discriminatory ability for fallopian tube malignancy (AUC 0.8534), moderate discrimination for ovarian malignancy (AUC 0.6790), and fair discrimination for adnexal malignancy (AUC 0.730). The optimal score thresholds were derived from the same dataset and should therefore be considered provisional. Three serous tubal intraepithelial carcinoma lesions were identified histologically; two showed cytological abnormalities and elevated CytoSaLPs scores, whereas one specimen was non-diagnostic. Conclusions: This exploratory proof-of-concept study suggests that ex vivo fallopian tube and the CytoSaLPs score may provide a structured approach for detecting cytological abnormalities associated with tubal and adnexal malignancy. However, the findings were obtained in a tertiary gynecologic oncology population under ex vivo conditions, non-diagnostic specimens occurred in approximately 10% of samples, and the scoring system was developed and evaluated within the same cohort. Independent external validation and evaluation using clinically applicable in vivo sampling methods are required before any clinical implementation can be considered. Full article

Efron’s double bootstrap and hierarchical Bayesian nonparametric methods have largely developed along separate paths. This paper connects them and uses that connection to motivate a new resampling procedure. We show that a suitable two-level Dirichlet construction, with base measures matched to the data, can approach the classical double bootstrap when its concentration parameters become large, while the hierarchical Dirichlet process of Teh et al. does not share this limit. This distinction identifies the double bootstrap as the endpoint of a construction of hierarchical Dirichlet type—though not of the hierarchical Dirichlet process itself—and as the boundary of a broader family of two-level resampling methods. Moving away from that boundary leads to the Informative Bayesian Double Bootstrap (IBDB). The method introduces prior information at the first stage while keeping the second stage focused on calibration, as in the classical double bootstrap. We also establish finite-concentration bounds describing how the proposed construction differs from its classical counterpart and when it can move beyond the support of the observed data. In simulations against four competing methods, the IBDB performs best for tail-sensitive quantities and heavy-tailed settings, while it tends to over-cover simple location parameters. Similar patterns appear in the Danish fire-insurance and Siemens equity-loss examples. Its main advantage is improved calibration through interval repositioning rather than simply wider intervals. The gains are most relevant when sample information is limited. Full article

Soil salinization is a major abiotic constraint on tomato production worldwide. To evaluate adult-stage tomato performance under contrasting saline and non-saline conditions, 23 tomato accessions (13 large-fruited and 10 cherry types) were evaluated at two solar-greenhouse locations in Hebei Province, China: a non-saline site in Shijiazhuang and a saline–alkali site in Huanghua, Cangzhou. Eleven growth, biomass, yield, and quality traits were assessed, and salt tolerance coefficients (STCs) were calculated to characterize relative trait performance between the two sites. Accessions were also genotyped using a PARMS marker linked to SlHAK20. Biomass accumulation was generally lower at the saline site, with above-ground and root dry weights showing the largest relative differences between the two sites, whereas plant height differed comparatively little. Total soluble solids (TSS) were higher at the saline site in most accessions, with the largest relative difference (91.7%) observed in large-fruited accession 305. Correlation analysis revealed strong positive associations between fresh and dry weights of both shoot and root tissues (p < 0.0001). Large-fruited accession 305 and cherry accession 436 showed comparatively favorable biomass retention and relatively small morphological differences between the two sites and therefore represent candidate accessions for further evaluation. The observed SlHAK20-linked marker genotypes were not consistently aligned with phenotypic performance among accessions, indicating that the predictive value of this marker requires further validation. These findings should be interpreted cautiously because salinity was confounded with location, the experiment was conducted during a single growing season, and the marker analysis was not validated in an independent population. This study provides a preliminary comparative evaluation approach and candidate accessions for further investigation and breeding of tomatoes adapted to saline environments. Full article

Consumers face increasing pressure to choose more environmentally sustainable foods and evidence-based behavioural interventions can support consumers in this task. This study seeks to provide insight into intervention design in the Australian context using two behavioural frameworks: the Capability, Opportunity, and Motivation Model of Behaviour Change (COM-B) and the Transtheoretical Model of Behaviour Change (TTM). Consumer data were collected through an online survey conducted by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) from November 2021 to February 2022 (n = 1307). Behavioural barriers affecting environmentally sustainable food choice (based on COM-B) were assessed, and chi-square tests of independence were used to identify barriers and other participant variables associated with different ‘stages of change’ (based on TTM). The top three barriers reported were: “know how to identify these food products” (n = 837, 64%); “have more information about these food products” (n = 640, 49%); and “have more access to these food products” (n = 614, 47%). Regarding intent to make environmentally sustainable food choices, the most common ‘stages of change’ were ‘Action’ (n = 573), ‘Maintenance’ (n = 411), and ‘Pre-Action’ (n = 266). These groups were found to be significantly associated with participant characteristics, including age, diet quality, and climate concern, as well as specific COM-B barriers. For example, Action-stage individuals were found to have a statistically significant and strong association with statements relating to product identification (χ2(2) = 43.54, p = 0.017, V = 0.19) and requiring more triggers to prompt action (χ2(2) = 28.67, p = 0.017, V = 0.15). Based on the presented findings, which were collected using a dual COM-B and TTM framework, a range of behaviour-stage tailored behavioural intervention approaches are discussed that may ease the consumer transition to more consistent environmentally sustainable food choices. Full article

UAV-based visual inspection has become an effective approach for acquiring surface information from three-dimensional building structures. However, existing coverage path planning methods usually treat viewpoint planning and path sequencing as two separate stages, which may introduce redundant viewpoints, long connection paths, and high computational cost. To address this problem, this paper proposes a skeleton-guided spiral coverage path planning method for UAV inspection of 3D structures. The target building model is first converted into a watertight triangular mesh, from which a one-dimensional skeleton line is extracted to guide both viewpoint generation and path construction. Surface sampling points are generated using rotating radial rays along the skeleton line, and UAV viewpoints are obtained by offsetting these points according to a predefined viewing distance. The ordered viewpoints are then connected to construct spiral coverage paths, while visibility checking, safety-distance constraints, and collision detection are incorporated to ensure path feasibility. Parameter sensitivity analysis shows that the sampling interval has a dominant influence on coverage performance and path cost, while the angular increment mainly affects path compactness and construction efficiency. Comparative experiments on the Christ, Wind Turbine, and Big Ben models demonstrate that the proposed method achieves high coverage rates of 96.62%, 97.72%, and 99.67%, respectively, while generating shorter paths and requiring substantially less computation time than ACO−OPD and Zhao’s method. These simulation results indicate that the proposed method can generate compact coverage paths with substantially lower computation time for UAV coverage inspection of 3D structures. Full article

Geological CO₂ storage represents one of the main strategies for mitigating greenhouse gas emissions within the framework of global energy transition. This study presents an integrated workflow for assessing the geological CO₂ storage potential of the Mugrosa Formation in the Middle Magdalena Valley Basin (Colombia). The methodology integrates multicriteria analysis, seismic interpretation, sedimentological and petrophysical characterization, three-dimensional geocellular modeling, geomechanical analysis, and fault seal evaluation using Shale Gouge Ratio (SGR) approach to assess reservoir-seal integrity and storage feasibility. Based on regional evaluation, two targets were selected: North and Central Structures. Seismic interpretation identified faulted anticline traps associated with reverse faults within a compressional regime, revealing different degrees of reservoir compartmentalization. Petrophysical characterization showed a predominance of clay-rich facies, with shale volume > 90% in some intervals, limiting reservoir quality and restricting storage potential to discrete sandy bodies. Reservoir intervals exhibit effective porosity from 13–15% and permeability between 500–800 mD. Geomechanical analysis allowed the evaluation of system stability under injection conditions, main faults are close to critical reactivation conditions, although they remain stable under moderate injection pressures. High SGR values (>0.75) suggest favorable fault-sealing capacity. Storage feasibility is controlled by the balance between reservoir quality, structural integrity, and geomechanical behavior. The integrated workflow provides a robust framework for subsurface integrity assessment and early-stage screening of geological CO₂ storage prospects in structurally complex sedimentary basins. Full article

Machine learning has become a widely adopted approach for URL-based phishing detection, with many studies reporting F1 scores exceeding 0.95 on benchmark datasets. However, recent adversarial machine learning research has questioned the robustness of these models, suggesting that small input perturbations can severely degrade detection performance. In this study, we argue that a substantial part of this reported vulnerability stems from the way adversarial attacks are evaluated. Specifically, many existing studies assess attacks in the feature space, where feature values are modified directly without ensuring that the resulting samples correspond to valid, functional URLs. To investigate this issue, we conduct a two-stage empirical study using both a benchmark feature dataset and a dataset of real phishing URLs. Crucially, to avoid confounding the attack space with dataset differences, we additionally evaluate both feature-space and problem-space attacks on the same real-URL dataset, using an identical model and manipulable-feature budget. Our experiments reveal a striking contrast between these evaluation settings. While feature-space attacks reduce the detection rate of a Random Forest classifier on the benchmark dataset from 0.96 to 0.36, analogous manipulations performed on real URLs have almost no effect on detection performance, as the most informative signals originate from host-related attributes that are difficult for attackers to manipulate. Building on this observation, we propose a set of robust features that capture stable domain characteristics, including lexical word validity, homoglyph disguises, brand impersonation, subdomain depth, character entropy, and transport-related signals. Incorporating these features substantially improves robustness under adversarial conditions, maintaining phishing detection rates between 0.24 and 0.76 where the lexical-only baseline deteriorates to zero under a non-adaptive attacker, while also increasing the clean-data F1 score from 0.985 to 0.994. We further evaluate an adaptive attacker that explicitly targets the proposed features; although the proposed representation raises the attacker’s cost and helps under moderate attacks, host-derived features remain the only strictly attack-invariant component, so we position the proposed features as a complement to host-based signals rather than a standalone defense. Additional analyses, including model comparison, hyperparameter sensitivity analysis, feature ablation, SHAP-based interpretation, multi-seed confidence intervals, a domain-disjoint evaluation, and host-only evaluation, consistently support the proposed approach. The findings demonstrate that problem-space evaluation provides a more realistic assessment of adversarial robustness than conventional feature-space testing and show that robust feature engineering offers a practical strategy for developing phishing detection systems that remain effective under realistic adversarial conditions. Full article

Global seaports play a pivotal role in international supply chains; however, prevailing port performance evaluation frameworks remain predominantly intraport-oriented, limiting their capacity to support sustainability transitions and the integration of Environmental, Social, and Governance (ESG) criteria. Although ports are increasingly conceptualised as multiscale systems embedded within maritime and inland networks, existing approaches remain fragmented across intraport, foreland (seaside connectivity) and hinterland dimensions. Using a systematic literature review following PRISMA 2020, combined with bibliometric mapping through VOSviewer and covering 2019–2024, this study adopts a two-stage analytical design. First, a broad corpus of 238 peer-reviewed articles is used to develop a six-category port performance framework. Second, this corpus is refined to 95 articles focused on container ports, examined by integrating six methodological approaches with three spatial scales. This layered design ensures that the broad corpus defines the categories, while the refined corpus supports the multiscale application of the matrix. The findings reveal a persistent dominance of intraport-focused and efficiency-oriented approaches, alongside limited integration across spatial scales. Sustainability and governance perspectives are increasingly present but remain weakly connected to logistics network performance and rarely operationalise ESG criteria. The study develops a Multiscale Diagnostic Matrix that synthesises the literature and diagnoses fragmentation in port performance evaluation. Full article