Search Results (1,171)

Photosynthetically active radiation (PAR) is a fundamental driver of terrestrial photosynthesis and a key input for light use efficiency-based estimates of gross primary productivity (GPP). However, existing PAR products are typically designed for regional to global applications and often remain spatially mismatched with the finer-resolution land surface variables now commonly derived from optical satellite observations. In this study, we present a semi-empirical framework for deriving daily clear-sky and all-sky PAR from Sentinel-2 Level-2A imagery. The approach combines solar geometry, daily extraterrestrial radiation, and simplified atmospheric transmittance parameterizations using Sentinel-2 aerosol, water vapor, and scene classification information to estimate clear-sky PAR, and further extends this formulation to all-sky conditions through a cloud-transmission factor derived from cloud probability to generate a spatially explicit PAR product aligned with Sentinel-2 observations. The resulting estimates are evaluated against flux tower observations from 172 AmeriFlux sites across North and South America for the period 2017–2024 and compared with MODIS MCD18, VIIRS VNP18, and CERES SYN1deg PAR products. The clear-sky Sentinel-2 formulation showed a moderate positive bias of 6.38 W m⁻², while the all-sky cloud adjustment reduced the mean bias to −1.44 W m⁻² with an RMSE of 23.53 W m⁻² and correlation of r = 0.87. The largest improvements occurred in spring and summer seasons, when atmospheric attenuation has the strongest influence on the clear-sky estimates. MODIS and CERES all-sky PAR products achieved lower overall errors with RMSE of 17.60 W m⁻² and 15.56 W m⁻², respectively, but at substantially coarser spatial resolution. The proposed framework therefore provides a practical high-resolution approximation of daily PAR that is spatially consistent with Sentinel-2 observations. Rather than replacing dedicated radiative transfer-based products, the method is intended to support analyses in which PAR needs to be evaluated together with Sentinel-2 bands, vegetation indices, and other Sentinel-2-derived variables within a common observational framework. Full article

The maximum carboxylation rate (V_cmax) is a key parameter determining photosynthetic capacity in terrestrial biosphere models, yet its variability is often oversimplified as a plant functional type-dependent constant, increasing uncertainties in gross primary productivity (GPP) simulations. In this study, we inverted the daily V_cmax normalized to 25 °C (V_m25) by coupling the Breathing Earth System Simulator (BESS) model with the light response curves (LRC) method using eddy covariance observations at five maize sites in the United States and China. Then, four machine learning (ML) models—KNN, SVM, Random Forest (RF) and XGBoost—were employed to simulate V_m25, with RF showing the best performance (R² = 0.83, RMSE = 7.57 μmol m⁻²s⁻¹ for testing). Cross-validation further demonstrated the RF model’s ability to capture seasonal trends across sites. Incorporating the RF-simulated V_m25 into the BESS model significantly improved GPP estimates compared to the original BESS, with R² increasing from 0.53–0.81 to 0.77–0.84. Through the SHAP method and ablation experiments, leaf age was identified as the most influential factor, with the largest SHAP value of 8.37 μmol m⁻²s⁻¹, higher than that for temperature (4.47 μmol m⁻²s⁻¹), solar radiation (4.21 μmol m⁻²s⁻¹) and leaf area index (2.32 μmol m⁻²s⁻¹). And vapor pressure deficit had the minimal SHAP value of 0.87 μmol m⁻²s⁻¹. Notably, the effect of leaf age on V_m25 exhibited a unimodal pattern, with a strong coupling effect with leaf area index. This study demonstrates that interpretable machine learning not only provides a robust approach for simulating seasonally dynamic V_cmax, but also enhances our understanding of its driving biological and environmental factors, offering a valuable pathway for improving carbon cycle modeling in agroecosystems. Full article

The evolution of Advanced Metering Infrastructure (AMI) requires reliable, energy-efficient, and scalable communication technologies for connecting large numbers of smart meters and gateways with utility backend systems. Among 3GPP Cellular Internet of Things (CIoT) technologies, Narrowband IoT (NB-IoT) and LTE-M are promising candidates due to their extended coverage, low cost, and power efficiency. However, selecting between them remains challenging because performance depends on deployment environments, spatial distribution, and radio signal conditions. This study addresses the CIoT network selection problem in AMI networks by applying machine learning to predict the appropriate communication technology from smart meter location and Reference Signal Received Power (RSRP). Three supervised learning algorithms, namely Decision Tree, Support Vector Machine, and XGBoost, were evaluated using field measurement datasets from two AMI deployment areas. A spatial holdout strategy was applied to assess performance in unseen geographical regions. Decision Tree achieved the best performance in Area 1, with an accuracy of 0.7143 and an F1-score of 0.6154. In Area 2, XGBoost achieved the highest performance, with an accuracy of 0.9732 and an F1-score of 0.9388. The results demonstrate the feasibility of ML-based CIoT selection under spatially heterogeneous and imbalanced deployment conditions. Full article

General-purpose polystyrene (GPPS) is widely used in packaging and insulation, although its recyclability poses environmental challenges that require circular economy strategies. The objective of this study was to evaluate the mechanical and tribological behavior of virgin GPPS (100%N), a 50% virgin–50% recycled blend (50N–50R), and 100% recycled GPPS (100%R), processed in Mexico under controlled injection molding conditions. Micro-tensile, flexural, surface roughness, and sliding wear (pin-on-disk) tests were conducted in accordance with ASTM/ANSI standards. The results show that virgin GPPS exhibited the highest strength (micro-tensile: 31.1 MPa; flexural: 87.8 MPa), while the 50N–50R blend maintained comparable tensile strength (27.5 MPa) with greater ductility, making it viable for secondary applications. Recycled GPPS exhibited a significant reduction in strength (micro-tensile: 18.6 MPa; flexural: 41.1 MPa), although with greater deformability. In tribological tests, the coefficients of friction were 0.480 (100%N), 0.128 (50N–50R), and 0.143 (100%R), all with relative errors of less than 4%, confirming statistical validity. Surface roughness analysis showed that the virgin material had the most uniform surface (Ra = 1.0 µm), while the blends exhibited greater variation (Ra ≈ 1.2 µm). In conclusion, although recycled GPPS has limitations compared to virgin material, it retains acceptable mechanical and tribological properties for non-structural applications. These findings support its potential in circular economy strategies in Mexico and provide a framework for countries with similar recycling infrastructure conditions. Full article

This paper presents FedGAT, a federated graph attention network for joint user association and inter-remote radio head (RRH) interference mitigation in cloud radio access networks (C-RAN). Centralized optimization requires global channel state information (CSI) at the baseband unit (BBU) pool, which adds fronthaul overhead and exposes user data. Existing federated learning schemes use flat local models that do not represent the interference graph, which lowers performance in heterogeneous multi-cell settings. In FedGAT, each RRH builds a local interference graph from its own CSI and trains a GATv2 model by local Adam gradient steps. Only the parameter increments are sent to the BBU pool, which combines them by weighted FedAvg without exchanging raw CSI. We formulate the joint user association and power allocation problem as a mixed-integer non-convex program and derive a graph neural network (GNN)-based continuous relaxation suitable for distributed training. A non-asymptotic convergence bound is obtained for non-identically distributed local interference graphs, showing that the optimality gap grows with the local step count and a measure of graph heterogeneity. Under the 3GPP Urban Macrocell channel model, FedGAT increases the steady-state weighted sum-rate by 18.7% over a federated multilayer perceptron baseline and by 49.2% over a centralized Graph Attention Network version 2 (GATv2) model at equal training budgets, while keeping raw CSI local. These margins are averaged over ten Monte Carlo channel realizations and reported with their standard deviations, and the fronthaul benefit of FedGAT refers to privacy and CSI-free inference after training rather than training-phase traffic. Full article

Background: Siraitia grosvenorii (monk fruit) is a dioecious medicinal crop native to southern China, yet its terpene synthase (TPS) gene family and the molecular basis of floral sexual dimorphism remain unexplored. Methods: Genome-wide identification of the TPS gene family was performed using HMMER and BLAST-based approaches. Phylogenetic classification, gene structure and conserved motif characterization, and comparative synteny analyses were conducted. Transcriptome data from leaves and fruits at different developmental stages were analyzed for tissue-specific expression profiling. Promoter cis-element analysis, protein–protein interaction network prediction, and molecular docking were performed to characterize the fruit-specific candidate SgTPS49. Results: A total of 58 SgTPS genes were identified and classified into six subfamilies, with TPS-a and TPS-b comprising 72.4% of the family. Approximately 88% of SgTPS genes arose from lineage-specific tandem duplication. Female flowers exhibited monoterpene-dominant scents and smaller corollas, whereas male flowers displayed a mid-morning sesquiterpene burst and greater morphological variation. SgTPS49 was specifically upregulated at 20 days post-pollination and possessed a unique promoter architecture devoid of classical hormone-responsive elements. Molecular docking supported its annotation as a putative monoterpene synthase with favorable GPP binding. Conclusions: This study provides the first comprehensive genomic resource for the SgTPS family in S. grosvenorii, reveals significant sexual dimorphism in floral traits, and identifies SgTPS49 as a key candidate for future functional validation. Full article

Generalized pustular psoriasis (GPP) is a rare, potentially life-threatening, chronic cutaneous inflammatory disease characterized by unpredictable, recurrent acute flares of painful sterile pustules on a widespread erythematous background. In addition to cutaneous manifestations, patients may experience fever, pruritus, pain, chills, and general malaise, which may be further complicated by secondary infection, sepsis, and organ failure, thus requiring urgent medical treatment and, in some cases, hospitalization. Prompt therapeutic management of the acute phase is crucial for severe cases, and proactive treatment to prevent flares should always be considered. However, early recognition of acute flares can be challenging due to the low frequency of the disease, the rapid onset of flares, the lack of hematological biomarkers and the absence of standardized diagnostic criteria. Moreover, despite the approval of new targeted therapies, there are still several unmet needs, as these treatments are highly expensive, not always readily available, and may have limited efficacy in patients with advanced or complicated disease. For these reasons, multidisciplinary round-table discussions and shared diagnostic and therapeutic algorithms involving dermatologists, who are responsible for diagnosing and treating GPP, and other medical specialists are desirable to facilitate prompt referral to dermatologists for accurate diagnosis and appropriate treatment. We report the updated literature discussed during a multidisciplinary meeting with the aim of providing practice recommendations for clinicians involved in GPP management. Full article

Economic development is multidimensional, yet provincial comparisons in Thailand often rely on individual indicators or broad sustainability indices. This study constructs separate TOPSIS rankings of economic prosperity and distributional outcomes for all 77 Thai provinces. Six prosperity indicators capture economic output, human capital, spatial agglomeration, labor market outcomes, and structural transformation, while four distributional indicators capture income inequality, economic vulnerability, informal employment, and household dependency. The analysis uses 2024 provincial accounts and Labor Force Survey microdata, with comparisons for 2022–2024. Prosperity is concentrated in Bangkok, the surrounding metropolitan region, and the Eastern Seaboard, while Phuket represents a distinct tourism-led pathway. Favorable distributional outcomes are concentrated within the metropolitan–industrial corridor, whereas disadvantage is more prevalent in the North and Northeast. The rankings are associated but conceptually distinct, and the prosperity ranking differs meaningfully from one based solely on GPP per capita. The prosperity ranking is highly stable over time, while the distributional ranking exhibits substantial stability after harmonizing income coverage. Both rankings are robust to alternative normalization procedures, weighting schemes, and aggregation methods. Spatial statistics confirm clustering. The findings demonstrate the value of separating economic prosperity from distributional disadvantage when benchmarking provincial development and assessing urban–regional disparities. Full article

The expansion of Internet-of-Things (IoT) deployments in 5G New Radio (NR) networks has made periodic measurement reporting a growing burden for low-mobility devices, which benefit little from frequent updates yet must report as often as highly mobile ones. At present, User Equipment (UE) transmits MeasurementReport messages at a fixed ReportInterval—typically 240 ms—regardless of mobility. This continuous transmission needlessly depletes UE battery energy and consumes critical uplink signaling capacity. This paper proposes a five-level adaptive ReportInterval selection scheme driven by the statistical properties of Reference Signal Received Power (RSRP) and Signal-to-Interference-plus-Noise Ratio (SINR). A low-mobility criterion combines four statistical conditions—the variance and gradient of both RSRP and SINR—through a logical AND, while a two-stage hysteresis mechanism (a 3 dB margin and a 2 s holding timer) suppresses unnecessary level transitions. The scheme is slice-agnostic: By relying on observed signal statistics rather than network-slice labels, it serves low-mobility mMTC and stationary eMBB devices while leaving URLLC and high-mobility UEs at their standard configuration. In Monte Carlo simulations over the 3GPP TR 38.901 Urban Micro (UMi) channel model (200 UEs, 300 s, 100 iterations), the algorithm attains a classification accuracy of 91.32% and a sensitivity of 98.77%. Based on the DRX energy model, it yields an estimated 10.87% reduction in average UE power (from 28.15 to 25.09 mW) together with a 51.09% reduction in the network-wide MeasurementReport count. The hysteresis mechanism cuts level transitions by a factor of 31.33 (from 6852.7 to 218.7 per iteration), substantially lowering RRC reconfiguration signaling. Operating at O(n) complexity on the gNodeB and using only conventional MeasConfig signaling, the scheme requires no protocol additions or UE-side modifications, making it directly deployable on existing 3GPP Release 17 infrastructure as a gNB-side software update. Full article

Understanding how the spatiotemporal variability of precipitation affects ecosystem respiration (RE) is central to carbon–climate feedback in climate-smart agriculture, yet remains unresolved for the alpine agricultural region of the southern Qinghai–Tibet Plateau, where flux observations are sparse. Using 25 years (2000–2024) of monthly gridded climate and remote sensing data for the Yarlung Zangbo River Basin and Its Two Tributaries Basin, we developed a flux tower-constrained reference–respiration (R_ref) environment-matching model in which R_ref varies with the enhanced vegetation index (EVI) and land surface temperature (LST) to correct the Lloyd–Taylor parameterization. The correction reduced the RE root mean square error by 54.8% (1.04 → 0.47 gC·m⁻²·month⁻¹) and eliminated systematic bias (+0.80 → −0.001) relative to an independent gridded RECO product. We then constructed a multidimensional index of precipitation variability (intra-annual concentration, interannual variability, long-term trend, spatial clustering) and combined random forest, spatial regression, lag analysis, and structural equation modeling (SEM) to disentangle direct and indirect pathways from precipitation variability to RE. The central finding is an indirect-conduction mechanism: precipitation concentration (PCI) affects RE almost entirely through vegetation productivity (PCI → GPP → RE, indirect effect −0.676) rather than directly (direct effect +0.076, opposite in sign), because low temperature and high soil water holding capacity buffer the immediate soil moisture response. The basin functions as a net carbon source (mean NEP = −0.550 gC·m⁻²·month⁻¹) with a significant warming-driven interannual RE increase (Sen’s slope = 0.0025 yr⁻¹, p = 0.022) that is independent of the stable precipitation total. The framework offers a transferable paradigm for carbon flux attribution in alpine regions under sparse observation. Full article

Ascorbic acid (vitamin C) is a critical antioxidant influencing the nutritional quality of fruits. This study integrates metabolomic and transcriptomic analyses to investigate high-ascorbic-acid (HAA), medium-ascorbic-acid (MAA), and low-ascorbic-acid (LAA) jujube cultivars. Metabolomic profiling revealed significant enrichment of ascorbic acid and its precursors in HAA cultivars, whereas MAA and LAA cultivars showed lower levels. Transcriptomic analysis identified 107 differentially expressed genes (DEGs) across the three groups. Compared with LAA, 139 differentially accumulated metabolites (DAMs) were identified in HAA, with more down-accumulated (77) than up-accumulated (62); compared with MAA, 235 DAMs were found in HAA, with significantly more up-accumulated (180) than down-accumulated (55); 250 DAMs with different accumulation patterns between LAA and MAA included 199 up-accumulated and 51 down-accumulated; and 16 DAMs were common in all three comparison combinations. Potential key regulatory genes such as GDP-mannose-3′,5′-epimerase (GME) and L-galactose-1-phosphate phosphatase (GPP) were highlighted. Weighted Gene Co-expression Network Analysis (WGCNA) detected possible core genes participating in ascorbic acid biosynthesis pathways. This study provides novel insights into the genetic and metabolic basis of ascorbic acid accumulation, potentially providing valuable targets for molecular breeding. Full article

Advanced metering infrastructure (AMI) is the sensing backbone of the smart grid, and its reliability underpins urban energy services such as state estimation, demand response, and distributed-energy integration. When AMI uses cellular spectrum leased through a cognitive mobile virtual network operator (C-MVNO), allocating channels to data aggregation points (DAPs) each frame is difficult because three uncertainties interact: imperfect spectrum sensing, time-varying and cross-channel-correlated primary-user activity, and stochastic urban propagation. Classical Hungarian assignment is optimal per frame but blind to primary-user dynamics, while cognitive-radio heuristics ignore queue state and cross-channel structure. We propose a two-timescale hierarchy that couples these established tools in a new way: a Proximal Policy Optimization (PPO) agent decides, once per epoch, which opportunistic channels to expose, and an exact Hungarian solver performs the per-frame DAP-to-channel assignment. To our knowledge this is the first coupling of a learned cognitive layer with exact Hungarian assignment for cognitive-radio resource allocation. On a 3GPP TR 38.901-compliant simulator, PPO significantly outperforms a Bayesian-belief baseline and the Hungarian-only configuration in delivery ratio, latency, and a strict per-meter satisfaction metric, and is robust across independent seeds and sensitivity sweeps. An architectural ablation shows the DAP tier is a precondition for viability, not merely an optimization. Full article

Accurate estimation of evapotranspiration (ET) and transpiration (T) is crucial in enhancing irrigation schedules in agricultural ecosystems. A simplified canopy resistance ($r_s^c$) algorithm grounded in the Ball-Berry model was integrated into Penman–Monteith (PM)-based models and assessed in a humid-region kiwifruit orchard. The Shuttleworth–Wallace (SW) and Clumping (CL) models agreed well with eddy covariance ET (ET_EC) throughout the growth season (R² = 0.79 and 0.89, RRMSE = 0.73–0.75 and 0.22, at the sub-daily and daily scales), outperforming the Two-Patch (TP) and topography- and vegetation-based surface energy partitioning (TVET) models. SW and CL also best reproduced sap-flow-based T (T_SF) (R² = 0.72 and 0.71–0.72, RRMSE = 0.85–0.87 and 0.38–0.39), primarily due to their higher accuracy during the mid stage. In this ecosystem, canopy interception evaporation had only a limited influence on the simulation performance of the SW and CL models. ET and T were most sensitive to changes in $r_s^c$ and related environmental factors, including gross primary productivity (GPP), the empirical parameter (a₁), and soil water content (θ). The sensitivity of T to θ was higher during the early stage but lower during the mid and late stages due to seasonal drought. ET was more sensitive to θ than T, due to its direct effect on soil surface resistance ($r_s^s$). T simulated by TP, TVET, and CL models showed greater sensitivity to leaf area index (LAI) than SW, while contrasting T and soil evaporation (E) responses to LAI caused ET to remain relatively insensitive. Both ET and T were highly sensitive to net radiation (R_n), air temperature (T_a), and vapor pressure deficit (VPD), all of which directly affect the energy balance. Overall, integrating the simplified canopy resistance algorithm with PM-based models improves ET and T estimation in humid-region orchards, supporting more efficient water management. Full article

Signalling traffic in 5G Interconnect and Roaming Networks (IRNs) over the IP eXchange (IPX) faces man-in-the-middle (MITM) interception on N32 and GTP-U, billing fraud, and “Harvest Now, Decrypt Later” (HNDL) adversaries. This paper develops and validates qSiP, a hybrid quantum–classical framework integrating Quantum Key Distribution (QKD, BB84 decoy-state protocol) and Quantum Secure Direct Communication (QSDC, DL04) at OSI Layers 1–2 with a classical-plus-quantum ML classifier for signalling anomaly and billing-fraud detection. Evaluation spans NS-3, Mininet, and MicroK8s on a dual-PLMN Open5GS testbed with PacketRusher NB-IoT traffic and STRIDE-L threat modelling. Cryptographically, qSiP holds BB84 QBER within decoy-state thresholds, sustains key rates matched to N32 timing, and under HNDL conditions bounds adversary exposure to one key-rotation interval; N32 captures confirm 3GPP message format and handshake semantics end-to-end. For detection, the Hybrid configuration reaches 98.5–99.5% accuracy across environments (vs. 89.4–96.0% classical), reduces undetected billing fraud over four roaming paths, and adds under 5 ms latency per signalling exchange. Statistical tests confirm hybrid > classical at p < 0.05 across eMBB, uRLLC, mMTC, and BCE. The work contributes an integrated framework with simulation-based empirical validation in a standards-aligned 5G SA testbed and a methodological commitment for future quantum-roaming research: quantum ML detects but does not encrypt, while QKD/QSDC encrypt does not classify; these are two complementary, non-interchangeable roles. Full article

The water use efficiency (WUE) in North China is undergoing rapid changes due to climate warming and vegetation “greening”, significantly impacting the ecosystem’s carbon and water cycles. Existing research lacks quantitative analysis of WUE or an understanding of future trends. This study selected the rapidly greening Agro-Pastoral Ecotone of Northern China (APENC) as a case study, utilizing linear regression, Hurst index analysis, and residual analysis to analyze the past and future changes and driving mechanisms of WUE. The results indicated that: (1) The multi-year (2001–2023) annual mean WUE in the APENC spatially ranged from 0.32 to 2.50 g C kg⁻¹ H₂O. (2) Gross primary productivity (GPP), evapotranspiration (ET), and WUE showed significant increasing trends of 10.22 g C m⁻² yr⁻², 5.62 kg H₂O m⁻² yr⁻², and 0.01 g C kg⁻¹ H₂O yr⁻¹, respectively. (3) Precipitation had highly positive impacts on GPP and ET, while non-climatic factors (land use, human activities, etc.) explained 62% of WUE variations in the APENC, and energy conditions (air temperature and solar radiation) were not the decisive factor of WUE. (4) The Hurst exponent of WUE indicates that WUE in the APENC region generally exhibits anti-persistent behavior. In terms of future trends, WUE is projected to shift from rising to declining in 58.9% of the region, while 28.5% is expected to continue increasing. Full article