Search Results (195)

Multi-source remote sensing is transforming landslide susceptibility assessment from static terrain-based zonation toward observation-driven spatiotemporal inference and dynamic map updating. Satellite precipitation products, interferometric synthetic aperture radar (InSAR) deformation time series, optical image sequences, land-cover products, and multi-temporal terrain observations provide complementary evidence of hydrometeorological forcing, slope kinematics, land-system regulation, and geomorphic reorganization. However, these observation streams differ substantially in spatial support, temporal resolution, physical meaning, and uncertainty structure and therefore cannot be reliably integrated as generic predictors without process-aware interpretation. This review synthesizes recent progress in remote sensing-enabled dynamic landslide susceptibility assessment by linking four key components: dynamic factor construction from Earth observation data, spatiotemporal representation and learning, susceptibility map updating, and validation under temporal and spatial independence. The reviewed literature is organized around four process roles: rainfall- and soil moisture-related forcing, kinematic state and response captured by InSAR, land-system and ecological regulation derived from optical time series, and geomorphic memory represented by multi-temporal digital elevation models (DEMs). We further examine how these signals are encoded and integrated through temporal models, graph-based representations, attention mechanisms, and hybrid frameworks, with particular emphasis on consistency among process role, data structure, mapping unit, inference target, and validation design. Current progress remains constrained by temporally coarse landslide inventories, cross-scale incompatibility among remote sensing products, uneven and insufficiently process-aware multimodal fusion, and limited physical interpretability. Future advances require event-resolved inventories, uncertainty-aware multimodal fusion, process-consistent spatiotemporal learning, and validation designs that explicitly test whether susceptibility maps can be updated in a scientifically defensible manner as new Earth observation data become available. Full article

Puerariae Lobatae Radix (PLR) is a commonly used herbal medicine in traditional Chinese medicine, whereas Puerariae Lobatae Caulis (PLC), its aerial part, is frequently utilized as an adulterant due to similar morphological features. Conventional identification methods and pharmacopoeial criteria fail to effectively distinguish these two homologous herbs, creating a demand for a precise identification method. Therefore, a Digital ID method was established for their identification. UPLC-QE-Orbitrap-MS coupled with Progenesis QI software was applied to analyze multi-batch samples of PLR and PLC. Common ion information was extracted, and a differential ion matrix was constructed after eliminating blank ions and cross-shared components. High-intensity characteristic ions were defined as exclusive Digital ID for authenticity identification. The Matching Confidence (MC) values of both herbs matching their own Digital IDs were >85%, while cross-matching MC values were <4%. Unlike subjective macroscopic authentication, compendial TLC and single-marker quantification that fail to discriminate the two materials, and spectroscopic methods incompatible with powdered samples, this digital ID framework enables automated objective authentication via quantitative Matching Confidence without manual spectral comparison. This technique achieves accurate and efficient digital identification of the two herbs, facilitating the quality control of PLR and offering a new strategy for the quality assessment of traditional Chinese medicine (TCM). Full article

This study employed a quantitatively driven mixed-methods approach to investigate crop-specific musculoskeletal disorder (MSD) prevalence and ergonomic risks among Taiwanese watermelon farmers, comparing them with pear (canopy-based) and pineapple (static-stooping) cohorts. A total of 218 participants were recruited (60 watermelon, 60 pear, 63 pineapple, and 35 non-farmers). Structured questionnaires quantified MSD prevalence and ergonomic exposures, while qualitative interviews provided a supportive operational context. Watermelon farmers reported a prominent lower-limb dominant discomfort profile, with a hip/thigh disorder prevalence (36.7%) significantly higher than pear (13.1%) and pineapple (11.1%) farmers. Multivariate logistic regression showed that daily working hours (aOR = 1.38) and uncomfortable posture duration (aOR = 1.33) were independent predictors of hip/thigh disorders. This elevated prevalence may be associated with the combined effects of prolonged deep squatting, dynamic heavy lifting, and unstable sandy terrain. Furthermore, low personal protective equipment adoption was primarily related to environmental incompatibility (sand accumulation and thermal stress). Although the cross-sectional design limits causal inferences, these findings highlight the need for targeted, crop-specific ergonomic interventions, such as breathable, sand-resistant joint supports. Full article

Hidden hunger and grassland degradation represent interconnected governance challenges in northern China’s pastoral areas. Nutrition-sensitive agriculture (NSA) has been conceptualised largely around crop-based systems, with limited attention to grassland grazing systems, where nutritional value is shaped by ecology, feeding practices, seasonality, local knowledge, and market institutions. Drawing on five rounds of fieldwork (2019–2025) across meadow, typical, and desert steppes in Inner Mongolia, this study employs a multi-case comparative design involving 92 semi-structured interviews, 58 policy documents, and long-term observations. Using reflexive thematic analysis, we develop an ecology–nutrition synergy framework to explain local practices and institutional constraints in nutrition-sensitive livestock farming. Three pathways are identified: grass–livestock nutritional balancing, scientific valorisation of native forage, and market experimentation linking ecological origin to nutritional quality. These pathways operate through three mechanisms: ecological mediation of nutritional quality, endogenous quality fluctuation as an inherent feature, and scientific codification of traditional pastoral knowledge. Four structural dilemmas constrain scaling: incompatibility between natural quality fluctuation and industrial standardisation; absence of institutional trust in nutritional premiums; short-term trade-offs between stocking control and nutritional enhancement; and fragmented cross-sectoral governance. The study extends NSA to grassland systems and offers a framework for integrating ecological protection, livestock quality, and nutrition-oriented governance in arid and semi-arid rangelands. Three theoretical contributions are advanced: (i) extending NSA’s conceptual boundary from cropping systems to natural grassland pastoral systems; (ii) embedding a nutrition-output dimension within Ostrom’s SES framework, thereby creating a triple-nested ecology–nutrition synergy framework; and (iii) specifying three grazing-system-specific mechanisms that distinguish grassland livestock systems from both crop-based and confined animal production systems. Full article

Tomato is one of Thailand’s most significant economic crops, generating substantial export value and serving as a primary source of income for local farmers. However, the traditional manual grading process often fails to comply with the Thai Agricultural Standard TACFS 1503–2007, as grading decisions rely heavily on individual experience and subjective perception, resulting in inconsistent quality. Existing automated systems face the challenges of low accuracy, high costs, and complex hardware, while many are incompatible with Thailand’s grading standards. This study presents a multi-view tomato grading system (MVT-Grader), utilizing a dataset acquired from Doi Kham Food Products Co., Ltd. (Third Royal Factory, Tao Ngoi) under controlled lighting conditions. Subsequently, MVT-Grader is built on a custom-designed lightweight CNN architecture with an adjusted spatially aware loss function to enhance the model’s sensitivity in detecting subtle surface defects and color variations. The proposed model was trained using tomato images captured from two and three different viewpoints via a low-cost webcam setup and processed by a GPU-embedded system. Experiments conducted using stratified 5-fold cross-validation on a real-world industrial dataset demonstrate average grading accuracies of 99.43% (two-view) and 99.64% (three-view). Furthermore, the proposed Real-Time Lightweight CNN with Spatially Aware Loss Optimization achieves processing speeds of 87 ms and 114 ms per tomato for two- and three-view cases, respectively. Compared with MVCNN-Siamese, SDF-ConvNets, and Multi-View Spatial Network, the proposed system outperforms the others in both accuracy and speed, improving accuracy by 1.6–6.11% and reducing processing time by 39–49 ms. Full article

Conventional machine learning models for perovskite solar cells predict photovoltaic parameters independently, disregarding the physical constraint $\mathrm{PCE}=V_{oc}\times J_{sc}\times \mathrm{FF}/100$. This approach can yield mutually incompatible predictions for the four parameters, a failure mode that has not been hitherto quantified in the perovskite solar cell literature. This paper proposes a multi-head neural network with a shared backbone, physics-guided feature construction, and task-specific prediction heads, and validates it on 7176 SCAPS-1D simulations across 12 perovskite compositions. When benchmarked against architecturally matched single-task baselines, the multi-task model, optimized via 5-fold cross-validation, achieves $R^2$ values of at least 0.994 for all four targets, with cross-fold standard deviations of 0.001. In particular, fill factor prediction improves from $R^2=0.617\pm 0.254$ (single-task) to $0.994\pm 0.001$ (multi-task), a 233-fold reduction in cross-fold standard deviation. Application of a physical consistency metric developed in this work reveals that 36.5% of single-task predictions exceed a 2 PCE-unit implausibility threshold, compared to only 0.01% for the multi-task model. The multi-task model outperforms the single-task baseline in all 20-fold target comparisons, with large effect sizes (Cohen’s $d=1.33$–$8.93$). These results confirm multi-task learning as an effective approach for achieving robust, stable, and internally consistent predictions in simulation-based photovoltaic virtual screening. Full article

High-order implicit-explicit (IMEX) schemes are effective for stiff parabolic partial differential equations when temporal regularity is compatible with the active multistep stencil. In moving-interface problems, a fixed Eulerian node may undergo a rapid transition as a diffuse interface crosses the grid, allowing stored multistep history to mix incompatible local regimes. This paper develops a conditional hybrid Deep Neural Galerkin-IMEX (DNG-IMEX) framework for this order-degradation mechanism. A classical IMEX-BDF3 backbone is retained on smooth intervals, whereas flagged event windows are treated by a localized neural/subcycle bridge followed by restart-consistent history reconstruction. The formulation separates the weak parabolic setting from the additional smoothness used for pointwise interface kinematics and proves a Sobolev-level transport estimate, a weak energy estimate, and a conditional propagation result under explicit flagged/restart defect bounds. Numerical tests on a manufactured Allen–Cahn benchmark show that event-aligned restarting suppresses the dominant history-contamination defect. A benchmark diagnostic realization localizes corrections with available event information and improves the baseline when event windows are resolved and the detector remains selective. Interface-thickness and cost tests indicate that sharper interfaces require stronger event resolution and that the present correction pipeline has non-negligible overhead. These findings support selective interface-aware enhancement of classical IMEX time integration. Full article

Resource-constrained edge processors deployed on unmanned aerial vehicles and wearable platforms require compact, drift-robust gas classification models for a range of environmental and security monitoring applications, including CBRN-motivated scenarios. Existing approaches rely on server-grade architectures incompatible with edge-board-scale deployment, or on classifiers that chemically degrade severely under long-term sensor drift. Each UCI gas class was mapped to a CBRN behavioral category based on physicochemical analogy (molecular functional group, vapor pressure, and metal-oxide semiconductor (MOS) cross-sensitivity pattern), following established precedent. Analyzed were Ammonia (NH₃), Acetaldehyde (CH₃CHO), Acetone ((CH₃)₂CO), Ethylene (C₂H₄), Ethanol (C₂H₅OH), Toluene (C₆H₅CH₃). We propose herein an end-to-end pipeline integrating a novel 1-D convolutional neural network with depth-wise separable convolutions (LiteSensor-Net), INT8 post-training quantization, structured magnitude pruning, and a knowledge-distillation domain-adaptation module (KD–DM) for sensor drift compensation. Using the UCI Gas Sensor Array Drift Dataset (13,910 measurements; 16 metal-oxide sensors; six analyte gases; a 36-month work span). LiteSensor-Net achieved accuracy = 92.63 ± 2.02%, macro-F1 = 0.898, model size = 5.99 kB INT8 pruned, inference latency = 6.3 ms, RAM footprint = 31.7 kB, and energy per inference = 0.04 mJ (all metrics on Raspberry Pi 4B, ARM Cortex-A72). Under chronological forward-chaining evaluation, KD–DM–20 achieved 47.91 ± 18.79% mean accuracy over Batches 2–10, representing a +9.25 pp improvement over uncompensated NC (38.66%). A six-metric benchmark framework—accuracy, macro-F1, model size, inference latency, RAM footprint, and energy per inference—is introduced to standardize edge-AI gas classifier evaluation. The proposed pipeline provides an open-source, deployable foundation for edge-class gas classification systems, with CBRN detection as a motivating application. Full operational validation on certified chemical simulants remains as future work. Full article

Fruit production in many crops depends on pollen transfer by animals, and many self-sterile crops rely on long-distance pollen transfer by animals between different genotypes, i.e., between different cultivars. Foragers such as honeybees may need to visit and carry the pollen of more than one cultivar to be effective pollinators, but we currently have little understanding of how many foragers are carrying more than one cultivar of pollen. We determined the number of cultivars carried by honeybees returning to their hive with pollen at two locations in an orchard of a predominantly self-sterile tree crop, macadamia. The locations were either (i) close to only one macadamia cultivar or (ii) between two macadamia cultivars. We sampled honeybees early in the flowering period, when the floral resource availability was lower, and at peak flowering when the resource availability was higher. We identified the cultivars carried by individual honeybees using a SABER-MassARRAY method that distinguishes cultivar-specific SNPs in pollen DNA. We found that most honeybees carried pollen from only one identified macadamia cultivar, regardless of the hive location and time within the flowering period. Only 15% of the honeybees were carrying pollen from more than one identified macadamia cultivar. This suggests that most honeybee foraging visits to flowers in the orchard were unlikely to have resulted in cross-pollination. Pollenizer trees could be interplanted among the main cultivars in macadamia orchards to increase the number of honeybees carrying cross-pollen and increase their pollination efficiency. Full article

The escalating sophistication of ransomware requires defensive strategies that are both proactive against zero-day attacks and operationally efficient. Existing solutions often force a trade-off—sacrificing low false-positive rates for broad detection, or vice versa. This work introduces an integrated framework designed to transcend this limitation. Our dual-stage approach synergizes pre-encryption behavioral analysis with definitive post-encryption confirmation. The first stage employs a specialized artificial immune system (AIS) that monitors a curated set of 47 features, including API-call n-grams and file entropy dynamics, to identify malicious activity before file encryption begins. This pre-emptive analysis is complemented by an enhanced, cross-platform R-Locker mechanism, which uses Windows named pipes and symbolic links to deploy honeyfiles that trap ransomware during I/O operations, providing a high-fidelity trigger for automated containment. We subjected this framework to a rigorous evaluation against 3500 real-world ransomware samples and 12,000 benign applications. The results demonstrate a 98.2% detection rate with a 0.8% false-positive rate, achieving a mean response time of 1.3 s. A key finding is the framework’s efficiency on both Windows and Linux (the only platforms tested), with the AIS and R-Locker modules consuming a combined 101 MB of memory. While the system excels in real-time detection, we note that its current memory forensics capability for key recovery is incompatible with certain ransomware families due to architectural obfuscations. Our findings suggest that the integrated approach performs well under laboratory conditions; further real-world validation is required to confirm robustness in diverse environments. Full article

Self-incompatibility (SI) is an important plant mechanism that prevents inbreeding depression by recognizing and rejecting self-pollen, thereby promoting outcrossing. However, SI can also act as a barrier in breeding programs, presenting significant challenges to breeders. Passion fruit (Passiflora edulis), a tropical fruit species of substantial economic importance, also serves as a valuable system for investigating SI mechanisms within the Passifloraceae. Nevertheless, the molecular basis of SI in passion fruit has not yet been elucidated. In this study, we investigated the SI system in yellow passion fruit (P. edulis f. flavicarpa) and employed transcriptomic analysis to examine the time-course transcriptional responses following different pollination treatments. Transcriptomic analysis revealed distinct gene expression dynamics under different pollination treatments: self-pollinated samples exhibited stronger and earlier transcriptional changes, whereas the number of differentially expressed genes (DEGs) in cross-pollinated samples was relatively lower. Numerous pathways previously associated with sporophytic self-incompatibility (SSI) were enriched in the stigma samples after self-pollination. Reactive oxygen species (ROS) are crucial signaling molecules involved in pollen germination and pollen tube growth during SI responses. Our results showed that ROS-related pathways were enriched in stigma tissues after self-pollination. In addition, oxidative stress-related responses were detected in the style shortly after self-pollination, suggesting that plastid-associated or general oxidative stress processes may also be involved, although the precise source of ROS requires further validation. FERONIA, ROP9, and ARC1 are key genes related to the SI system in Brassica. In the passion fruit SI response, the expression levels of these genes increased in the style, indicating a spatial expression pattern different from that reported in classical Brassicaceae SSI systems. Together with cytological observations showing that self-pollen rejection occurs at the stigma surface, our results suggest that yellow passion fruit may employ an SSI-like regulatory framework while exhibiting a lineage-specific spatial deployment of SI-related regulators. Overall, this study provides new transcriptomic insights into the SI mechanism of yellow passion fruit, establishes a molecular framework for understanding SI in P. edulis f. flavicarpa, and offers novel insights into the diversity of plant SI systems. Full article

Fine-grained vehicle orientation estimation is widely reported with strong in-domain accuracy, yet performance degrades substantially when models are applied across datasets; the relative contributions of visual domain shift and annotation label incompatibility to this degradation remain poorly understood. A controlled cross-dataset benchmark was conducted using two publicly available datasets—Car Full View (CFV) and Freiburg Static Cars 52 v1.1 (UnsupCar)—under a fixed ConvNeXt-Small predictor with a varied training source, test target, and image preprocessing strategy. All conditions were evaluated with five-fold cross-validation at the vehicle-instance level. Annotation label incompatibility was identified as the dominant source of transfer error: correcting the angular convention mismatch in UnsupCar orientation labels reduced cross-dataset circular mean absolute error (CMAE) by approximately $3.5$–$4.5^{\circ }$. Crop protocol was a similarly large factor—train/test crop mismatch raised CMAE into the 9–${12}^{\circ }$ range. Square cropping with mirrored boundary padding provided the most robust preprocessing across both in-domain and cross-dataset conditions. After label harmonization, a residual transfer gap of approximately $2^{\circ }$ remained, with a consistent directional asymmetry favoring the UnsupCar-to-CFV transfer direction. Joint training on both harmonized datasets achieved the best-balanced performance ($3.{77}^{\circ }$ on CFV; $5.{38}^{\circ }$ on UnsupCar). These results demonstrate that instance-level splitting, explicit label harmonization, and consistent crop definition are necessary preconditions for credible cross-dataset vehicle orientation evaluation. Full article

Background: Artificial intelligence (AI) is reshaping oncology at every stage of the cancer care pathway, from population-level screening through molecular diagnosis, treatment planning, and post-treatment surveillance. Despite an exponential growth in AI oncology publications exceeding 5000 peer-reviewed studies annually, a critical and persistent gap separates demonstrated algorithmic performance from genuine patient benefit. Most published evidence derives from retrospective, single-institution studies conducted in curated dataset environments that systematically differ from real-world clinical deployment conditions. This comprehensive review examines the translational maturity of AI applications across 18 major malignancies, providing an evidence-stratified, cross-cancer assessment of where AI has fulfilled, approaches, or remains far from fulfilling its transformative potential in oncological care. Methods: A structured narrative review was conducted across PubMed/MEDLINE, Embase, IEEE Xplore, and the Cochrane Library, supplemented by regulatory grey literature including FDA 510(k) decision summaries, CE Technical Files, and ClinicalTrials.gov. Search terms combined cancer site-specific terminology with AI methodology terms and translational outcome descriptors. Studies were only included if they applied an AI or machine learning methodology to a defined clinical oncological task, reported a clearly specified performance evaluation, and involved human subjects or human-derived clinical data. Evidence quality was assessed using QUADAS-2, PROBAST, and Cochrane RoB 2. A five-tier translational readiness framework, grounded in the NIH T0–T4 translational spectrum and CONSORT-AI/SPIRIT-AI guidelines, was applied a priori to enable cross-cancer comparison. A rigorous distinction was maintained between diagnostic accuracy and clinical utility, defined as demonstrated impact on clinical decision-making or patient-centered outcomes. Results: Across all 18 malignancies, AI development varied profoundly by cancer type. Breast cancer and prostate cancer (Tier 1) represent the most mature AI ecosystems, with multiple FDA-cleared tools for mammographic screening and digital pathology achieving prospective multi-institutional validation; however, randomized evidence demonstrating reduced cancer-specific mortality remains absent. Lung, hepatocellular, and melanoma AI (Tier 2) have achieved regulatory milestones but face documented performance disparities across demographic subgroups, including DermaSensor’s 20.7% specificity in primary care settings and HCC model failures in non-viral disease etiologies. Colorectal, glioma, pancreatic, and ovarian cancers (Tier 3) exhibit technical maturity without clinical clarity: colorectal CADe systems increase adenoma detection but meta-analyses of 18,232 patients across 21 RCTs fail to demonstrate improvement in advanced neoplasia detection or cancer incidence reduction. A full study-level presentation of pooled estimates, confidence intervals, and heterogeneity statistics for each cited randomized evidence base across all cancer types would extend beyond the intended scope and format of this cross-cancer narrative review. Gastric, esophageal, cervical, bladder, head and neck, and endometrial cancers (Tier 4) demonstrate promising single-institutional or geographically restricted results without multi-institutional external validation, particularly notable for cervical cancer AI’s transformative potential in low- and middle-income countries constrained by absent regulatory frameworks. Hematologic malignancies, sarcoma, and pediatric solid tumors (Tier 5) face structural barriers, workflow incompatibility in hematopathology, extreme rarity in sarcoma (>70 subtypes, <15,000 US cases annually), and irreducible ethical constraints in pediatric data governance, that cannot be resolved through algorithmic refinement alone. Conclusions: Oncological AI has not yet fulfilled its clinical promise. Across all five translational tiers, a single finding is consistent: diagnostic accuracy is not a surrogate for patient benefit. AI tools with high sensitivity and specificity have repeatedly failed to demonstrate equivalent reductions in cancer-specific mortality, overdiagnosis, or procedural harm under real-world outcome scrutiny. Simultaneously, documented performance disparities across races, ethnicity, disease etiology, and geographic setting reveal that current AI systems risk amplifying the very health inequities they are positioned to resolve. Bridging this translational gap requires three coordinated systemic shifts: regulatory frameworks mandating post-market outcome surveillance as a condition of clinical clearance; prospective trial designs measuring patient-centered endpoints rather than diagnostic concordance alone; and sustained infrastructure investment in federated data governance, demographically inclusive training datasets, and LMIC-accessible regulatory pathways. AI holds genuine potential to reduce cancer mortality on a global scale—but only if held to the evidentiary and equity standards that the stakes of oncological care demand. Full article

Reproductive barriers severely limit interspecific hybridization success among Hydrangea macrophylla, H. paniculata, and H. arborescens, thereby restricting the combination of ornamental traits and cold hardiness. We evaluated cross-compatibility, pollen tube growth, and embryo development in both direct and reciprocal crosses involving H. macrophylla with H. paniculata and H. arborescens. Both species pairs exhibited pronounced unilateral incompatibilities. When H. macrophylla served as the maternal parent, the percentages of seedling emergence were higher, whereas reciprocal crosses produced >84% ovary swelling but resulted in almost no seedlings. Fluorescence microscopy revealed mild prezygotic barriers in direct crosses but strong inhibition of pollen germination and pollen tube growth in reciprocal crosses. Paraffin section observations showed that postzygotic barriers were the primary cause of hybrid failure, with endosperm-type abortion predominating in direct crosses and embryo-type or complete abortion in reciprocal crosses. Consistent with these abortion patterns, direct crosses maintained higher proportions of normal embryos, whereas reciprocal crosses dropped below 10% at the globular stage and approached 0% at later stages. These findings support the use of timely embryo rescue for direct crosses and targeted mitigation of prezygotic barriers in reciprocal crosses to improve Hydrangea interspecific hybridization efficiency. Full article

To achieve autonomous ultrasound scanning skill transfer across different physical equipment instances and address the limitations of traditional imitation learning methods—which struggle with cross-instance generalization due to their reliance on specific manipulator parameters—this study proposes a physical-parameter-decoupled imitation learning method based on waypoint representation. This approach utilizes a greedy algorithm to automatically extract key nodes within the task space from expert demonstration trajectories, constructing a trajectory representation decoupled from low-level kinematic parameters and base calibration errors. Simultaneously, a velocity-aware adaptive error precision adjustment mechanism is introduced to dynamically modulate waypoint extraction thresholds, simulating the speed-accuracy strategies employed by sonographers across different scanning phases. Cross-validation across two mainstream generative architectures—Action Chunking Transformer (ACT) and Diffusion Policy—on an offline dataset confirms the plug-and-play capability of waypoint representation in suppressing long-horizon error accumulation, with both architectures achieving significant reductions in prediction errors. For physical deployment, a complete ACT-waypoint system featuring low-level triple safety redundancy was validated. In kidney long-axis standard plane scanning tasks, the system achieved a 92% success rate on the source domain manipulator and maintained an 84% success rate on the target deployment manipulator, despite incompatible low-level kinematic parameters and base coordinates. Force control accuracy remained stable around the target value of 12 N. The results demonstrate that the proposed method effectively overcomes base coordinate and D-H parameter discrepancies to achieve cross-instance zero-shot skill transfer, significantly enhancing the adaptability across physical instances and the scanning success rate of imitation learning models. Full article