Search Results (12,551)

The Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1) is a Chinese-developed satellite that employs both active and passive remote sensing technologies for forest carbon monitoring. Equipped with a multi-beam lidar system comprising a laser altimeter and a dedicated atmospheric lidar channel, TECIS-1 supports critical applications in forest vegetation classification and the mapping of cloud and aerosol distributions. In this study, we propose a complete workflow for cloud-aerosol detection and parameter retrieval using 532 nm and 1064 nm attenuated backscatter data from TECIS-1 Level 1B products, combined with GMAO MERRA-2 reanalysis data. The color ratio, depolarization ratio and prior statistical information are incorporated to classify the cloud and aerosol layers. Then the cloud-top height (CTH) and aerosol optical depth (AOD) are derived from the geometry of cloud layers and aerosol extinction profiles. The retrieval results from the study area, encompassing northern/central China and the western North Pacific, are compared with MODIS cloud products and MODIS AOD data, Quantitative evaluations indicate that, based on 394 samples across six tracks from latitudes 10° to 75°N in 2023, TECIS-1’s CTH measurements were consistent with those from MODIS, with a root mean square difference (RMSD) of 2.13 km for single-layer opaque clouds and 2.42 km for single-layer optically thin clouds. The AOD comparison area is located in a region of northern China prone to dust storms. When compared with MODIS products, the overall root mean square difference (RMSD) of the retrieved AOD was 0.278. These results indicate that TECIS-1 can be used for cloud and aerosol detection, and provide a methodological reference for data processing and application of Chinese spaceborne atmospheric lidar systems. Full article

In next-generation wireless networks, communication systems are expected to go beyond simple data transmission and simultaneously provide high data rates, efficiency, and security. This requirement has motivated the extensive adoption of machine learning methods to develop intelligent and real-time network management frameworks, enabling the system to continuously monitor and react to channel variations and user behavior while maintaining efficient information delivery. In this context, the integration of machine learning with beamforming enables adaptive and data-driven beam direction selection, improving both the efficiency and security of wireless links. In this work, a 3GPP-based system model is first implemented under a no-attacker scenario, and an exhaustive search is employed as a reference to identify the best beamforming configurations. The proposed framework is then evaluated in the presence of an attacker and under different network scalability conditions. We demonstrate that the reinforcement learning-based approaches, namely Q-learning and State-Action-Reward-State-Action (SARSA), consistently outperform random selection in terms of total channel capacity, attacker-detection accuracy, and performance stability. Among the evaluated reinforcement learning methods, Q-learning achieves the best overall trade-off between detection accuracy and computational efficiency. Our results indicate that the proposed framework provides a stable, scalable, and effective solution for joint beamforming and security-aware decision-making in dynamic and adversarial wireless environments. Full article

Bridge structural health monitoring (SHM) depends on sensor streams that may contain missing blocks, noise, spikes, drift, and recorded faults. We evaluated a frozen, group-aware stress testing protocol on 64 Vänersborg bridge opening events and 153 processed Z24 scenario × setup traces. Models used healthy training data only; degradations were restricted to held-out groups. Area under the receiver operating characteristic curve (ROC-AUC) and average precision (AP) were co-primary metrics. Vänersborg principal component analysis (PCA) baseline AUC and AP values were 0.500/0.454, respectively. In addition, 20% missingness yielded 0.188 and 0.327 (paired changes −0.313 and −0.126), and 0.5% spikes yielded 0.058 and 0.298 (−0.442 and −0.156), respectively. Z24 PCA baseline AUC and AP values were 0.731 and 0.929, respectively, and the studied perturbations changed both metrics only slightly. Under event power-scaled 5 dB noise, Vänersborg PCA AUC decreased by 0.221, while the other detectors increased. A new label-blind sensitivity analysis fixed one robust channel variance from the healthy training events. This equalized injected power across evaluation classes and changed the PCA ΔAUC to +0.075, showing that the original scaling asymmetry contributed materially to the detector-specific directions. Results are exact conditional summaries of two observed finite archives and do not establish universal quality, severity, or maintenance thresholds. Full article

Plant diseases pose a serious threat to agriculture, causing yield losses of 20 to 40 percent each year, resulting in more than USD 220 billion in annual economic losses and significantly affecting the global food supply. Traditional plant health monitoring practices involve visual inspection of plant tissue and can only detect the presence of disease once visual symptoms are already evident. This article presents AgriIDIA, a multispectral plant disease classification system based on 24-channel image stacks multispectral images derived from six optical filters (BlueIR, Hotmirror, K590, K665, K720, and K850) and six vegetation indices (NDVI, GNDVI, NDRE, EVI, REI, and SAVI). First, an exploratory data analysis is conducted on the diagnostic capability of the described 24-channel data representation, using 1266 image stacks labeled with six classes (diseased/healthy papaya, diseased/healthy potato, and diseased/healthy tomato). Next, using the results of the exploratory data analysis, the manuscript describes the training and cross-validation performance of AgriIDIA, with a macro-F1 score of 83.91 ± 3.42% and an accuracy of 84.00 ± 3.17% on the validation set. Finally, the performance of the trained model is evaluated on the reserved test set (N = 190), demonstrating an accuracy of 80.53%, a macro-F1 score of 0.7398, and a weighted ROC-AUC of 0.9383. The results of this study suggest that the 24-channel multispectral representation has significant diagnostic potential for distinguishing healthy and diseased plant tissue under the evaluated conditions. Full article

Against recurrent external disruptions and the accelerated transition toward sustainable business practices, this study examines whether corporate environmental, social, and governance performance uniformly strengthens agricultural supply chain resilience. Using panel data for Chinese A-share listed agricultural firms from 2016 to 2025, we construct a resilience index covering resistance and recovery capacity and investigate its nonlinear relationship with ESG performance. The results reveal a significant U-shaped association. At relatively low ESG levels, adjustment expenditure and organizational reconfiguration are associated with weaker resilience, whereas the relationship becomes positive after the turning point. Mechanism evidence further supports three complementary channels involving information flow integration, capital flow guarantee, and management flow efficacy. Further period-based analysis suggests that the estimated ESG–resilience relationship exhibits different empirical patterns across regulatory periods. The findings extend the ESG-resilience literature by identifying a stage-dependent relationship in agricultural supply chains and by clarifying how sustainability-oriented investments can shift from short-term resource pressure to longer-term organizational capability. Full article

To address the degradation of seepage-control linings and structural instability of water-conveyance channels under coupled wetting-drying-freeze-thaw conditions in cold regions, this study investigated rehabilitated channels founded on expansive mudstone in northern Xinjiang. A two-dimensional transient finite-element model with fully coupled seepage, thermal, and mechanical fields was established in COMSOL Multiphysics 6.0 (COMSOL AB, Stockholm, Sweden) and validated against field monitoring data from three canal sections to explore the interactions among seepage evolution, frost-heave deformation, and structural responses over the full operational cycle. Numerical results showed that within five months after impoundment, the groundwater table in the channel foundation rose by approximately 4 m, supplying moisture for winter frost heave. During the four-month freezing period, the maximum frost-heave displacement reached 8 cm, and cyclic tensile stress could trigger geomembrane fatigue cracking mainly within the horizontal zone of 3.5–4.5 m along the channel base. In the subsequent phase after channel shutdown, foundation soil deformation increased to 16 cm, double the magnitude of initial frost heave, and the slope safety factor dropped to 1.305, implying an elevated landslide-failure risk. As a risk-mitigation measure, an acrylic polymer-based coating was adopted for surface seepage-control with favorable water-facing impermeability. Reverse seepage occurred above a backwater pressure of 0.01 MPa for unreinforced coatings, while this threshold can be enhanced by applying reinforcing fabric and interfacial agents. This work quantitatively identifies the progressive failure chain of seepage accumulation, frost heave, geomembrane rupture and slope instability, and evaluates the applicability and limitations of the proposed anti-seepage coating. It further elucidates bypass-seepage and reverse seepage hazards arising at coated-uncoated boundaries under partial surface-sealing schemes. The findings offer theoretical support and practical references for implementing a prevention-oriented strategy integrating surface waterproofing and internal drainage for cold-region water-conveyance infrastructure. Full article

Vegetation is often retained in river channels for habitat conservation and restoration and to facilitate nature-based flood management, and its hydraulic effects are typically represented using flow resistance coefficients in two-dimensional (2D) models. This study evaluated two Manning’s roughness parameterizations in HEC-RAS 2D against large-scale experimental data for grouped and isolated willow patches under high- and low-flow conditions. A distributed approach, which represents spatially varying total resistance using a momentum-based resistance formulation, was compared with a spatially uniform bulk coefficient applied to the entire reach. Because the bulk coefficient was back-calculated from the measured experimental data, it served as an observation-based benchmark rather than an independently derived prediction. A sensitivity analysis established a terrain resolution of 0.001 m and a mesh size of 0.25 m as appropriate for the simulations. The distributed approach consistently underestimated water levels, exhibited an incomplete but directionally correct response to changes in the drag coefficient for grouped patch configurations, and was negligibly sensitive to changes in the drag coefficient for isolated patch configurations. In contrast, the bulk approach reproduced water levels more closely but overpredicted them for grouped patch configurations and could not resolve local flow structures. Patch arrangement appeared more influential than vegetation density; however, their individual effects could not be separated because the grouped layout was also the densest. These findings provide guidance for selecting resistance parameterizations in 2D models of partly vegetated channels. Full article

Hydrological regulation and local engineering can modify the planform dynamics of wandering rivers, but separating their effects is difficult where pre-regulation morphology and synchronous hydraulic observations are unavailable. This study integrates discharge and water-level records (2000–2023), SRTM-derived surface topography, and nine December satellite-image composites (2015–2023) for the urban Lhasa River reach between Gates 1 and 5. Within the available 24-year discharge record, Pettitt tests identify a mid-2000s distributional shift at the three stations; sensitivity tests place the detected change in 2005–2006. Because the pre-change segment contains only six years and no precipitation-based natural-flow control was available, the result is interpreted as temporally consistent with, but not proof of an effect of, Zhikong Hydropower Station commissioning. The 2013 Pangduo project is treated separately as an engineering timeline marker because no distinct annual mean breakpoint was detected at that date. Mapped water area increased by 45.39% between 2015 and 2023, whereas exposed bar–island and riparian-land classes decreased by 36.29% and 61.13%, respectively. These values are reported as a water-surface expansion signal consistent with gate impoundment and/or acquisition-stage differences, not as confirmed sedimentary or geomorphic conversion. The comparison also contains sensor-dependent positional uncertainty: approximately ±15 m for the 2015 and 2018 Landsat-8 inputs and ±5 m for Sentinel-2 inputs. Centerline length and sinuosity varied by less than 1%. The nearest-neighbor bankline metric declined by 9.53%, whereas the node-mean channel-regime-center metric declined by 81.99%; the latter is retained only as an exploratory indicator because it is sensitive to centerline-node sampling. Overall, the observations are consistent with reduced lateral activity in an engineered reach, but the available evidence does not isolate reservoir regulation, gate operation, bank protection, climate, land use, or sediment-supply effects. Stronger causal inference requires a pre-regulation morphological baseline, synchronous discharge and water-level observations at image acquisition, fixed-interval centerline resampling, sediment data, and surveyed hydraulic geometry. Full article

The Sanmenxia Basin, located in the southern margin of the North China Craton, is a typical representative of the numerous Meso–Cenozoic small–to–medium–sized basins in China. In recent years, an exploration breakthrough has been made in this basin through its first hydrocarbon discovery well (Well Yuxiadi–1). Based on core samples and associated analytical data from Well Yuxiadi–1, this study reveals the petrological characteristics and diagenetic evolution of the Paleogene Xiao’an Formation reservoirs and investigates the controlling factors of reservoir development and their impacts on hydrocarbon accumulation. Results demonstrate that the Xiao’an Formation reservoirs are dominated by delta plain channel sandbodies and delta front subaqueous distributary channel sandbodies, with overall medium porosity and permeability. Reservoir development is governed by provenance, deposition and diagenesis: (1) a dual–provenance system dominated by distal clastics from the Qinling orogenic belt delivers quartz–rich components, forming well–sorted fine–grained sandstone frameworks; (2) depositional processes control vertical heterogeneity, with rapidly deposited massive sandbodies in delta plain channels exhibiting optimal porosity–permeability conditions; (3) two–phase calcite cementation and dissolution critically regulate microscopic reservoir properties—syndepositional micritic calcite dissolution enhances effective storage space, while the second–phase sparry calcite cementation (26–30 Ma) significantly degrades pore connectivity. Hydrocarbon accumulation analysis reveals a single–phase charging event during the late Oligocene (24–26 Ma), characterized by fault–controlled vertical migration. The temporal relationship between cementation–dissolution processes and hydrocarbon charging directly governs hydrocarbon enrichment efficiency. This research provides a key case study and scientific basis for deepening the understanding of the petroleum geological characteristics of Meso–Cenozoic small–to–medium–sized basins in China. Full article

A companion study documented that the resource efficiency adoption gap between medium-sized and microenterprises narrowed by over 80% across the EU27 between 2017 and 2024, but its EU-aggregate design could not establish whether this was a Europe-wide transition or a few national trajectories, nor which mechanism drove it. This article decomposes that finding into 27 country-specific convergence parameters using a two-step estimation and meta-analysis strategy, and tests whether the 2021–2022 energy price shock, renewable energy endowment, or environmental policy stringency explain the cross-country heterogeneity. Heterogeneity is robust to estimator choice (I² = 53.1%, p < 0.001), ranging from intense compression in Romania, Austria, Germany, and Spain to null or reversed dynamics in Estonia, Slovenia, and Slovakia, and is driven disproportionately by the largest national SME populations. The average convergence effect is more fragile, excluding unity under a conventional interval (random-effects IRR 0.905, 95% CI 0.820–0.999) but not under the more conservative Hartung–Knapp–Sidik–Jonkman correction appropriate for k = 27 (95% CI 0.818–1.001); we treat it as indicative rather than confirmed. None of the theory-driven moderators explains the heterogeneity (all coefficients below |0.029| SD, all p > 0.57), a null substantiated by a formal power analysis and, for an internal initial-gap moderator, by a split-sample design removing a regression-to-the-mean artefact present in a naive test; this rejects national-policy-mediated coercive pressure specifically, not supranational or network-transmitted channels. Two exploratory, purely descriptive extensions accompany the analysis: a small non-EU contrast group shows convergence not visibly different from the EU27, though underpowered to adjudicate between mechanisms; and services-sector SME share shows a nominally significant, non-multiple-testing-robust, association with stronger convergence. Published as the EU’s Omnibus I reform narrows CSRD-obligated reporters and caps value-chain information requests to smaller partners, this country-level baseline provides the reference point for assessing the durability of SME sustainability convergence as new data become available, with direct implications for EU-level SME greening policy. Full article

Physical layer protection can complement conventional cryptography, but its contribution under repeated key use depends on whether an adversary can learn the channel’s input–output map. We evaluate a pump-keyed multi-core fiber (MCF) architecture in a physically parameterized simulation under full observation (FO) and a partial, attenuated, noisier wiretap-like observation (WT). A fixed-key linear baseline is rapidly learnable under FO. A key-controlled nonlinear phase construction (P2) increases error for the evaluated estimators at small known-plaintext budgets. Extending the evaluation to 50,000 known-plaintext vectors changes that conclusion: at 37 cores and the default nonlinear coefficient gamma_NL = 2, a multilayer perceptron reaches mean BER 0.033, crossing the BER < 0.05 recovery criterion on all three seeds. Increasing the core count to 61 or doubling gamma_NL prevents a crossing within the tested data and training budgets. Doubling gamma_NL raises mean Bob BER from 0.000014 to 0.000471, with a maximum of 0.001007 across the three keys. P3 also remains above the recovery criterion at 50,000 pairs at a larger, key-dependent receiver cost. The core count and nonlinear strength sweeps establish an empirical trend under the specified protocol; they do not determine a general scaling law or a lower bound on the data required by stronger attackers. The results distinguish linear identification from nonlinear decoder learning and do not establish information theoretic secrecy or formal cryptographic security. Full article

To enhance fine-grained recognition of adjacent rice ripening phases, this study employs a rice ripening-stage identification method based on multi-source UAV remote sensing data fusion and a modified MobileNetV3-Small network. RGB and multispectral images were acquired using a DJI Mavic 3 Multispectral UAV at Houbai Improved Seed Farm, Jurong City, Jiangsu Province, China, and were combined with vegetation indices to construct a fused 15-channel input. The original three-channel input layer of MobileNetV3-Small was modified to accommodate RGB, multispectral, and vegetation-index features. Based on the agronomic characteristics of late reproductive rice growth, the ripening process was divided into five phenological stages: filling, milky ripening, early waxy ripening, late waxy ripening, and full ripening. In the field-scale patch assessment using the HB31 dataset, the model correctly identified 4147 of 4185 valid patches, achieving an overall accuracy of 99.09%, with macro-averaged precision, recall, and F1-scores of 99.06%, 99.12%, and 99.08%, respectively. To reduce the influence of within-field spatial correlation, an independent field-holdout test was further conducted using the HB32 field, which was excluded from training, hyperparameter tuning, and model selection. Among 2046 valid HB32 patches, 1897 were correctly classified, yielding an overall accuracy of 92.72% and a macro-averaged F11-score of 92.60%. These results indicate that fusing RGB, multispectral, and vegetation-index features can characterize canopy color, spectral responses, and vegetation-index variations during rice maturation, providing reference information for rice ripening-stage identification, field-scale ripening mapping, and harvest scheduling. Full article

Electric vehicle (EV) charging stations and vehicle-to-grid (V2G) systems depend on outbound Domain Name System (DNS) resolution for firmware retrieval, backend discovery, and fleet synchronization, making DNS tunneling an attractive covert command-and-control and data-exfiltration channel in charging infrastructure. Machine learning detectors trained on lexical and statistical DNS features achieve excellent in-distribution accuracy, yet they are rarely stress-tested against adaptive adversaries that deliberately reshape query characteristics toward benign traffic. This paper presents a station-independent evaluation framework and an adaptive-augmented, cyber-physical detection architecture for evasive DNS tunneling in EV charging and V2G networks. Using a 200-station synthetic dataset that couples 24 DNS features with 16 EV/Open Charge Point Protocol (OCPP)/V2G telemetry features and 14 cross-modal consistency features, we evaluate every detector over ten repeated grouped station-level splits and across three attack regimes: an adaptive-strength sweep (β = 0.25–0.95) of the interpolation mechanism used in training, a separately held-out constraint-aware adaptive mechanism excluded from all training and model selection, and multiplicative perturbation of the physical-anchor telemetry at relative scales of 5–20%. Under strong interpolation-based evasion at the training strength (β = 0.90), detectors relying on DNS evidence retain almost no detection capability at their original operating point (mean F1 = 0.041 ± 0.023), although part of their threshold-free ranking ability survives, and recalibrating the decision threshold alone does not repair the collapse. We propose a safe EV-anchored fusion detector that treats physical telemetry as a protected anchor, hardens a cross-modal branch with adaptive examples drawn only from training stations, and admits DNS evidence only through a bounded, validation-selected correction. Across the ten splits, the proposed detector sustains F1 = 0.909 ± 0.013 at β = 0.90 and F1 = 0.923 ± 0.016 under the held-out mechanism, retaining approximately 94–96% of its original F1 of 0.964 ± 0.006 at a false-positive rate near 5.5% (about 55 false alarms per 1000 benign windows), and it degrades gracefully (F1 ≥ 0.911) when the anchor telemetry is perturbed at up to 20% relative scale. The results indicate that anchoring detection in physical-side telemetry, with bounded and adaptively hardened cross-modal evidence, provides consistent performance across the evaluated repeated station partitions and is computationally feasible under the evaluated conditions. Full article

Background/Objectives: Spiritual care is essential for alleviating multidimensional suffering at the end of life. However, its clinical integration in Latin America faces cultural, linguistic, and religious complexities. These complexities also challenge the traditional biomedical model. This study addresses the gap between international theoretical frameworks and empirical reality in a diverse context by examining how healthcare professionals in Ecuador perceive, understand, and apply palliative spiritual care, using three competencies from the Latin American Association of Palliative Care (ALCP). Methods: This was a qualitative, exploratory, and descriptive study. We conducted in-depth, semi-structured interviews with 20 professionals (physicians, nurses, and psychologists) with experience in palliative care. We analyzed the data using inductive–deductive thematic analysis. Results: Spirituality was conceived as an existential dimension of purpose and transcendence. Clinical empathy drew upon the professional’s personal vulnerability and was channeled through nonverbal communication and informal interdisciplinary support. However, this approach faced critical institutional (time constraints and lack of clinical records), sociocultural (language barriers and clashes with traditional Andean medicine), and ethical–familial (paternalism and familism) barriers. Conclusions: Among the professionals interviewed, palliative spiritual care relied primarily on compassionate intuition, clinical sensitivity, and professional commitment, within a context marked by limited formal training and institutional, sociocultural, and ethical–familial barriers. These findings suggest possible areas for future development, including spiritual literacy, institutional self-care, and intercultural mediation, which should be examined in broader and more diverse contexts before being translated into practice or policy recommendations. Full article

Using China Family Panel Studies (CFPS) data, this paper examines the relationship between province-level cultural orientation and inclusive growth within a complex socioeconomic system. We construct a multidimensional index covering long-term orientation, entrepreneurial spirit, openness, contractual spirit, and prosocial norms. Fixed-effects estimates show that a stronger orientation toward co-creation and sharing is associated with higher individual income and lower relative income deprivation. The results remain robust across alternative samples, measures, outcomes, and specifications. The mechanism analysis identifies non-farm employment as a plausible channel among rural residents, entrepreneurial activity among urban residents, and household transfer income in both groups. The income relationship is stronger among urban residents, men, and individuals with greater human capital, whereas the deprivation relationship is stronger among rural residents, women, and likewise those with greater human capital. Regionally, these relationships are strongest in the Central and Western regions, respectively. Overall, a multidimensional cultural orientation toward participation, opportunity creation, reliable exchange, and mutual support may provide a social foundation for inclusive growth. Full article