Search Results (69,867)

Multispectral LiDAR can simultaneously obtain distance and spectral information and shows great potential for underwater detection. However, absorption and scattering caused by suspended particles in water lead to energy attenuation and multiple scattering, which affect echo intensity and ranging accuracy, while the propagation characteristics under multi-wavelength conditions remain insufficiently studied. In this study, a simplified underwater propagation simulation model for multispectral LiDAR is established based on the equivalent spherical-particle assumption, combining Mie scattering theory with a semi-analytical Monte Carlo method. The effects of particle size on echo intensity and ranging error are analyzed under fixed concentration conditions. Based on this model, a detection-threshold-constrained optimal wavelength selection criterion is formulated. Multi-distance analysis (3, 5, 8, and 15 m) confirms that the preferred wavelength is primarily governed by particle size and remains stable across depths. The results show that the optimal detection wavelength shifts with particle size, being about 560 nm for fine particles and gradually moving toward the 400–480 nm blue–green band for larger particles. Experimental validation shows that the simulation-based ranging correction reduces RMSE by 9.4–25.9% (average 18.1%) and MAE by 11.8–29.7% (average 22.0%) across five experimental distances. The results provide a preliminary reference for wavelength selection in multispectral LiDAR systems under simplified conditions. Full article

The distribution and spectral properties of colored dissolved organic matter (CDOM) in the northern Bay of Bengal were investigated in June 2016. Based on in situ data collected from 100 CDOM samples at 25 stations, the distribution characteristics of CDOM in the surface layer differed markedly from those at 30 m, 75 m, and 100 m. The CDOM spectral slope ($S_{350-500}$) exhibited a broad range, varying from 0.0026 to 0.0300 nm${}^{-1}$, and showed a significant negative correlation with the absorption coefficient $a_{\mathrm{CDOM}}\left(443\right)$. Analysis of salinity and temperature profiles revealed no obvious correlation between the distribution of $a_{\mathrm{CDOM}}\left(443\right)$ and these physical parameters. A comparative analysis with satellite-derived wind and current data indicated that elevated $a_{\mathrm{CDOM}}\left(443\right)$ values in the northeastern surface waters were primarily associated with the southwest monsoon. In contrast, $a_{\mathrm{CDOM}}\left(443\right)$ values in the lower mixed layer were mainly influenced by the combined effects of geostrophic and eddy currents. Full article

The severe continuous cropping obstacles in Morchella cultivation, driven primarily by soil microecological imbalance, critically constrain the sustainable development of the industry. To address this challenge, this study evaluated the efficacy of rotary tillage, calcium cyanamide (CaCN₂), and organic fertilizer, applied individually and in combination, in mitigating these obstacles and explored the underlying microbial mechanisms. The soil was treated on 5 August 2024, and soil samples were collected on 5 October 2024. Four treatments were established: continuous cropping control (CK), rotary tillage (XGX), rotary tillage combined with calcium cyanamide (MPD), and rotary tillage combined with calcium cyanamide and organic fertilizer (MPX). Soil chemical properties were analyzed in conjunction with metagenomic sequencing to characterize the responses of soil properties and microbial communities, including both eukaryotic and bacterial taxa. The results indicated that the MPD treatment showed a relatively pronounced effect in enhancing key soil fertility indicators, including soil organic matter (OM), total nitrogen (TN), available nitrogen (AN), available potassium (AK), and total phosphorus (TP). All amendments significantly altered microbial community structures. Specifically, the integrated MPX treatment effectively reduced the relative abundance of the pathogenic fungus Olpidium while maintaining higher overall microbial diversity. It also significantly promoted the abundance of Morchella itself and beneficial bacterial phyla such as Actinomycetota and Pseudomonadota. Redundancy analysis identified AN and AK as the primary drivers of eukaryotic community variation, whereas Availa-ble phosphorus (AP) and potential of hydrogen (pH) were the key factors shaping the bacterial community. The results indicated that MPD was the showed relatively pronounced effectiveness in rapidly improving soil fertility and suppressing pathogenic fungi. In contrast, MPX showed relatively better performance in optimizing microbial community structure, enhancing microbial diversity, and strengthening overall ecological stability. These two treatments exhibited distinct advantages in soil chemical improvement and microbial community regulation, respectively, thereby providing alternative practical strategies and a theoretical basis for the ecological management of continuous-cropping obstacles in Morchella cultivation. It should be noted that this study did not include treatments with calcium cyanamide alone, organic fertilizer alone, or their combined application without rotary tillage. This is primarily because rotary tillage is a standard land preparation practice in Morchella cultivation, and the use of soil amendments without accompanying tillage is rarely adopted under practical production conditions. Full article

The influence of rootstocks on mineral nutrient composition in the edible tissue of citrus fruits has not been explored so far. This study assessed leaf and juice mineral nutrients of sweet orange (Citrus sinensis (L.) Osbeck) cultivars (‘Pusa Sharad’ and ‘Pusa Round’) grafted onto different rootstocks (‘RLC-6’, ‘C-35’, ‘X-639’, ‘Yamma Mikan’, ‘Soh Sarkar’, ‘RLC-7’, and ‘Jatti Khatti’). Deviation from optimum percentage (DOP) index was employed as an integrative measure to assess leaf mineral nutrient balance for specific scion–rootstock combinations. The relative abundance of leaf mineral nutrients was ranked as follows: Ca > K > P > S > Mg > Na > Fe > Mn > Zn > Cu. Overall, rootstock ‘X-639’ demonstrated superior mineral nutrient uptake efficiency across grafted plants of both scion cultivars, as indicated by higher leaf mineral nutrient concentrations. Juice mineral nutrient concentrations followed the order K (930.87–1362.17 mg L⁻¹), Ca (346.40–651.33 mg L⁻¹), P (116.23–236.97 mg L⁻¹), Mg (64.60–102.50 mg L⁻¹), S (49.35–74.34 mg L⁻¹), Na (25.61–47.88 mg L⁻¹), Fe (4.76–7.92 mg L⁻¹), Zn (1.79–4.34 mg L⁻¹), Mn (0.73–1.62 mg L⁻¹), and Cu (0.41–0.71 mg L⁻¹), indicating distinct differences in the accumulation pattern of macro- and micro-mineral nutrients in the edible tissues across the studied scion–rootstock combinations. Multivariate analysis revealed that the rootstocks significantly influenced juice mineral nutrient levels, indicating rootstock-mediated agronomic biofortification. Rootstock ‘RLC-6’ enhanced juice K levels, and ‘Soh Sarkar’ improved juice Mg contents, while ‘X-639’ improved juice micronutrient (Zn, Mn, Cu) accumulation in both cultivars. This study constitutes the first comprehensive investigation that explicitly evaluates the influence of rootstocks on the enhancement of mineral nutrient content in the edible tissues of citrus fruits. It further elucidates how rootstock selection can indirectly affect dietary mineral intake, thereby highlighting its potential role for improved nutrition. Full article

The increasing integration of nonlinear loads in modern power systems has made harmonic pollution a critical challenge to the operational safety of power cables. This study develops a frequency-dependent high-voltage cable system model using the ATP-EMTP (Alternative Transients Program-Electro Magnetic Transient Program) electromagnetic transient simulation platform, systematically investigating the amplification mechanisms and propagation characteristics of grounding currents under multi-type harmonic disturbances. A frequency-dependent parameter correction model is established by integrating the conductor skin effect and the dielectric relaxation properties of the insulation layers. This model incorporates the multi-structure combination among conductors, insulation, and metallic screen. It effectively overcomes the limitations of conventional lumped-parameter models in higher frequency harmonic analysis. Key findings are as follows: (1) The combined influence of harmonic frequency and amplitude leads to a grounding current amplification of up to 445 times (at 1950 Hz with 30% distortion level). Notably, current-source excitation produces significantly greater amplification than voltage-source excitation. (2) The distributed capacitance of long-distance cables (>8 km) exacerbates resonance risks within specific frequency bands (750–1250 Hz), resulting in a maximum harmonic amplification factor of 34.73 (observed for the 17th harmonic in a 15 km cable). (3) The contribution of voltage-source harmonics diminishes to less than 5% of the total current at high frequencies (≥1250 Hz), indicating a pattern of current-dominated harmonic superposition. Full article

Background: Elevated anticardiolipin IgG (aCL IgG) has been reported in end-stage renal disease (ESRD), but its association with specific etiologies of kidney failure remains unexplored. The unique pathophysiology of diabetic–hypertensive nephropathy may be associated with a microenvironment that could potentially contribute to antiphospholipid antibody production and thrombotic complications. This study aimed to investigate whether aCL IgG elevation in hemodialysis (HD) patients is associated with combined diabetes–hypertension (DM + HTN) etiology and thrombocytopenia, thereby identifying a clinically distinct potential high-risk subgroup. In this hypothesis-generating study, we focused on within-HD patient comparisons rather than healthy controls. Methods: We enrolled 242 participants: 150 healthy controls (included only to establish local reference ranges) and 92 patients with maintenance HD. The study was conducted from 01 September to 20 November 2025 in Madinah, Saudi Arabia. Serum aCL IgG was measured by chemiluminescence immunoassay (positive ≥ 12 GPL units). Comprehensive hematological and biochemical parameters were analyzed. Multivariable logistic regression identified predictors of aCL positivity. Results: In the HD cohort, 21% demonstrated aCL positivity; this represents a substantially higher rate than the 2% observed in local healthy controls (p < 0.001). This elevation was not uniform across etiologies. Strikingly, 94.7% (18/19) of aCL-positive HD patients had DM + HTN aetiology, compared with only 17.8% of aCL-negative patients (p < 0.001). Thrombocytopenia was significantly more severe in aCL-positive patients (median platelets: 100 vs. 191 × 10⁹/L, p < 0.001). In multivariable analysis, DM + HTN etiology (HTN-alone vs. DM + HTN odds ratio [OR]: 0.0013, 95% confidence interval [CI]: 0.00002–0.0999, p = 0.003; confirmed by Firth’s penalized logistic regression sensitivity analysis, and lower platelet count (OR: 0.92 per 1 × 10⁹/L increase, 95% CI: 0.87–0.98, p = 0.006) independently predicted aCL positivity. Conclusions: These hypothesis-generating findings suggest a potential association between metabolic–vascular disease and antiphospholipid immunity in ESRD. Causality cannot be inferred from this cross-sectional design. At present, routine aCL screening is not recommended outside of research protocols; prospective studies are needed to confirm these associations. Full article

State of Health (SOH) estimation of lithium-ion batteries is a critical and challenging requirement in advanced battery management technologies. As an important parameter, battery impedance contains significant electrochemical information that can reflect the state of health of batteries. In this study, a SOH estimation method is proposed based on alternating electrical signals. First, an aging test was carried out using commercial 18650-type batteries. Considering the current uncertainty in practical applications, tests under different discharge conditions were conducted to obtain the capacity and wide frequency band impedance data of each battery throughout its life cycle. Then, important features at specific frequencies were extracted from the impedance data, and an interpretable analysis of the features was performed using the distribution of relaxation times (DRTs). Finally, the impedance features were combined with the Gaussian process regression algorithm in machine learning to estimate and validate the SOH. The results show that using fixed-frequency impedance features can achieve accurate estimation. The average value of the maximum absolute error of each battery under different working conditions can be controlled within 1.59%. With the development of embedded chips and online measurement technology, battery management systems can obtain important impedance features by applying alternating electrical signals within a certain frequency range, thus achieving online estimation of SOH. Full article

This study examines how the allocation of export expansion across sectors affects economy-wide outcomes in Bangladesh. Using a Social Accounting Matrix (SAM) framework, we combine linkage analysis with simulation to evaluate how sectoral export growth propagates through the production network. The results show that the impact of export diversification depends critically on sectoral allocation rather than export intensity alone. While aggregate differences between scenarios are modest, reallocating export growth toward sectors with stronger intersectoral linkages generates larger economy-wide gains in GDP and labor income. In particular, sectors with low initial export shares but high network connectivity—such as agriculture, hunting, forestry, and fishing; retail trade; other community, social and personal services; and inland transport—produce stronger multiplier effects than most export-intensive sectors. These findings highlight a key distinction between export intensity and network centrality, demonstrating that sectors with limited direct export participation can play a central role in transmitting economic gains. The results provide a network-based perspective on export diversification and offer policy-relevant insights for designing strategies that promote more inclusive and efficient economic growth. Full article

This study aimed to detect growth stress at the individual-tree level in chestnut (Castanea crenata Sieb. et Zucc.) plantations using UAV-based RGB orthomosaic and multispectral imagery and to determine an optimal NDVI threshold for stress classification. UAV surveys were conducted over a 21 ha chestnut orchard located in Gongju, Chungcheongnam-do, Republic of Korea. NDVI was calculated and analyzed at the individual-tree level using multispectral imagery. Based on field observations, 100 healthy trees and 23 stressed trees were selected for statistical analysis. The mean NDVI value was 0.900 ± 0.012 for healthy trees and 0.816 ± 0.013 for stressed trees, showing a highly significant difference (p < 0.001). ROC analysis showed excellent classification performance with an AUC of 1.00. The optimal NDVI threshold determined using Youden’s index was 0.855. Independent validation in another chestnut plantation approximately 1 km away achieved high classification accuracy using the same threshold. These results indicate that UAV-based multispectral imagery combined with NDVI analysis provides an effective approach for early detection of growth stress and precision monitoring at the individual-tree level in chestnut plantations. This study provides a practical and efficient approach for the early detection of growth stress at the individual-tree level, enabling early intervention against potential declines in tree vitality and proactive management in chestnut orchards. The proposed NDVI threshold-based method offers a simple yet robust tool that can be readily applied in precision forestry and smart agriculture to support large-scale monitoring and informed management decisions for maintaining orchard productivity, enabling cost-effective early intervention at the individual-tree level, which is difficult to achieve using conventional ground-based surveys in complex mountainous orchards. Full article

Background/Objectives: Ankle–foot orthoses (AFOs) are commonly used to improve gait in children with cerebral palsy (CP), but their effect on specific gait patterns is underreported. This study evaluates the utilization of the Gait Pattern Classification System for Children with Spastic CP (GaP-CP) to investigate the effects of ankle–foot orthoses on gait kinematics, spatio-temporal parameters and the energy cost of walking. Methods: In this retrospective study, 66 ambulatory children with spastic CP underwent 3D gait analysis with and without AFOs or functional electrical stimulation. Gait patterns were classified according to GaP-CP. AFOs were articulated, flexible, or rigid. Thirty-six children also performed a 5 min walk test with gas exchange measurements. Step length, walking speed, and the energy cost of walking were calculated. Gait kinematics were analyzed with statistical nonparametric mapping. Non-parametric statistics were used to investigate orthotic effects for the total group and for each gait pattern. Results: Ankle kinematics improved in swing phase and initial contact (10 degrees less plantarflexion, p < 0.05) for the total group, dropfoot and genu recurvatum. During the stance phase, reduced knee extension in genu recurvatum (by 3 degrees, p < 0.05) and increased knee extension in crouch (by 3 degrees, p < 0.05) were observed. Median changes in non-dimensional step length were clinically significant (>0.039, p ≤ 0.02, effect size ≥ 0.55) for the total group and the dropfoot, genu recurvatum, and crouch subgroups, while changes in most gait indices, walking speed and the energy cost of walking were not clinically significant. Conclusions: The combined use of GaP-CP and kinematic analysis provided new insights into the effects of ankle–foot orthoses on gait. Articulated and flexible orthoses may not have provided adequate support for genu recurvatum and crouch gait, showing a potential value in gait pattern specific orthotic design to optimize gait kinematics. Full article

SiO₂ aerogel cement composites (SACCs) are promising for building insulation, but how their residual thermal performance evolves after high-temperature exposure remains unclear, limiting fire protection assessment. In this study, SACC specimen with aerogel contents of 0%, 5%, 7%, and 10% were heat-treated at 400, 600, 700, 800, and 1000 °C. After cooling, their post-exposure thermal performance and microstructure were characterized via mass loss, density, thermal conductivity, MIP, and SEM. Results obtained at room temperature showed that with increasing treatment temperature, thermal conductivity first decreases and then increases, reaching a minimum after 700 °C treatment for the A7 specimens (from 0.092 to 0.063 W/(m·K)). Microstructural analysis of cooled specimens revealed that this non-monotonic behavior arises from three heat-induced changes: the cement matrix, aerogel aggregates, and the interfacial gap between them. After treatment at 700 °C, the gap corresponds to a Knudsen number of 0.01–0.02, entering the slip-flow regime. Combined with the low thermal conductivity of the cement matrix, this yields the best insulation. After treatment at 800 °C and above, the gap exceeded 60 μm, shifting heat transfer to the continuum regime and reducing insulation capacity. A thermal conductivity prediction model based on these post-exposure mechanisms agreed well with the experimental results. Full article

This study aimed to develop and validate a physics-informed neural network (PINN) framework for data-efficient and physically consistent process optimization in the laser powder bed fusion (LPBF) of Inconel 718 (IN718) superalloy. Laser powder bed fusion (LPBF) is widely adopted for fabricating Inconel 718 (IN718) components in aerospace and energy applications; however, navigating its high-dimensional, nonlinear process parameter space remains a central challenge. High-fidelity finite element simulations are computationally prohibitive for extensive parameter sweeps, whereas purely data-driven machine learning (ML) models are limited by data scarcity and unphysical extrapolation behavior. This study presents a physics-informed neural network (PINN) framework that embeds the transient heat conduction equation and Goldak double-ellipsoidal heat source model directly into the neural network training loss, enforcing thermophysical consistency simultaneously with data fidelity. The model was trained on a curated, multi-source dataset of LPBF IN718 parameter combinations drawn from peer-reviewed experimental studies and validated finite element simulation outputs, spanning the laser power (70–400 W), scan speed (200–2000 mm/s), hatch spacing (50–140 µm), and layer thickness (20–50 µm). The PINN predicted the melt pool width, depth, peak temperature, and relative density with mean absolute percentage errors (MAPE) of 3.8%, 4.7%, 3.1%, and 1.9%, respectively, outperforming a baseline artificial neural network (ANN) with an identical architecture. The framework correctly identified the optimal volumetric energy density (VED) window of 55–105 J/mm³, yielding relative densities ≥99.5%, consistent with the published experimental thresholds for IN718. A data efficiency analysis demonstrated that the PINN with 25% training data achieves a performance equivalent to that of the fully trained ANN with 100% data, confirming an approximately four-fold data efficiency improvement attributable to physics-informed regularization, consistent with theoretical predictions. Sensitivity analysis via automatic differentiation confirmed that laser power and scan speed were the dominant parameters (~85% combined variance), which is in agreement with previous studies. This study provides a computationally efficient, interpretable, and physically consistent ML pathway for the accelerated process qualification of IN718 components for aerospace and energy applications. Full article

Political polarization and policy uncertainty have become increasingly consequential for regional economic adjustment, yet their joint role in shaping socioeconomic resilience remains underdeveloped in the literature. This study advances the debate by conceptualizing regional resilience as the outcome of a multi-layer socioeconomic system in which external policy disturbances, institutional fragmentation, and structural adaptive capacity interact over time. Using balanced panel data for 16 Korean regions from 2004 to 2023, the analysis develops an integrated empirical framework that combines panel local projections, threshold estimation, structural moderation tests, dynamic robustness checks, and forward-looking machine-learning prediction. The results show that policy uncertainty is associated with lower regional socioeconomic resilience and that this effect persists over time. More importantly, political polarization does not simply accompany weaker resilience; it amplifies the transmission of uncertainty shocks, especially once institutional fragmentation crosses a critical threshold. Structural conditions further shape this process. Digital transformation, industrial diversification, and financial depth reduce vulnerability, whereas trade exposure intensifies it. These findings indicate that resilience is not determined by economic structure alone, nor by institutional instability in isolation. It emerges from the interaction between disturbance, amplification, and adaptive capacity within a regional system. The predictive analysis reinforces this interpretation. Variables identified as central in the econometric models also carry forward-looking information about future vulnerability states. This study therefore contributes not only by combining methods, but by linking explanation and prediction within a single systems-oriented account of regional resilience. The Korean case shows how institutional coherence and structural adaptability jointly condition resilience under uncertainty. Full article

Against the background of a warming and humidifying climate on the Qinghai–Tibet Plateau, increasing attention has been paid to the sustainability of water resources and ecosystems in the Qinghai Lake Basin. Investigating the characteristics of precipitation stable isotopes and moisture sources provides critical insights into the driving mechanisms of the regional hydrological cycle. In this study, precipitation samples collected at the Qinghai Lake Wetland Ecosystem National Observation and Research Station from June 2023 to October 2024 were analyzed for hydrogen (δ²H) and oxygen (δ¹⁸O) stable isotopes. The temporal variations of δ²H, δ¹⁸O, and deuterium excess (d-excess) were characterized, and their relationships with air temperature and precipitation amount were examined. In addition, a backward trajectory model was employed to identify the moisture sources of precipitation during the observation period. The results indicate that: (1) precipitation stable isotopes and d-excess exhibit pronounced seasonal variability, characterized by enrichment in summer and depletion in spring and autumn; (2) the Local Meteoric Water Line (LMWL) for the basin is defined as δ²H = 8.15δ¹⁸O + 38.71 (R² = 0.93), with both slope and intercept exceeding those of the Global Meteoric Water Line (GMWL); (3) precipitation isotopes show a discernible temperature effect but are jointly controlled by multiple moisture sources and meteorological factors; and (4) backward trajectory analysis combined with d-excess values reveals that precipitation moisture is primarily derived from westerly transport, while locally recycled moisture and continental air masses also exert significant influences. Overall, these findings reveal the multi-source driving mechanisms of the regional hydrological cycle and provide critical scientific support for understanding hydrological processes in alpine inland basins and their responses to future climate change, thereby contributing to the sustainable management of regional water resources. Full article

Seed germination is a critical initial stage of the plant life cycle, regulated by signaling pathways such as phytohormones and reactive oxygen species (ROS). However, the low germination rate of immature grains is a key bottleneck limiting wheat speed breeding. This study used immature grains of the winter wheat cultivar Kenong 199 (KN199) collected 18 days post anthesis to establish an efficient germination protocol. By screening individual and combined treatments of hydrogen peroxide (H₂O₂, 1%), gibberellin (GA₃, 20 μM), and varying concentrations of abscisic acid (ABA) synthesis inhibitor sodium tungstate (Na₂WO₄), alongside transcriptome analysis, we identified the optimal reagent combination and gained preliminary insight into its molecular basis. The triple reagent combination of 0.5 mM Na₂WO₄ + 20 μM GA₃ + 1% H₂O₂ exhibited the highest germination rate of 80%, approximately sevenfold higher than single reagent treatments, with germination rate peaking after 4 days. Transcriptome profiling revealed that this combination modulated the expression of key genes related to dormancy release and germination, including upregulation of GA biosynthesis gene GA3ox2 and ABA catabolism gene TaCYP707A2, and downregulation of ABA biosynthesis and signaling genes (ABI5, TaNCED1, etc.). Additionally, genes associated with energy metabolism and transport pathways were enhanced. This optimized reagent combination significantly improves immature grain germination, shortens the breeding cycle, and provides a practical tool for achieving “five generations per year” speed breeding in winter wheat. Our findings contribute to seed biology by offering a chemical strategy to overcome dormancy in immature cereal grains. Full article