Traditional Indices Research Articles

Prediction of soil organic carbon in black soil based on a synergistic scheme from hyperspectral data: Combining fractional-order derivatives and three-dimensional spectral indices

Monitoring soil organic carbon (SOC) content is crucial for climate change mitigation and sustaining ecological balance. Despite the unparalleled advantages of hyperspectral data in capturing nuanced variations in soil properties through its high spectral resolution, effectively extracting useful features from numerous bands via spectral processing techniques remains a formidable challenge. This study proposes an integrated approach combining fractional-order derivative (FOD) technique and optimal band combination algorithm using ZY1-02D satellite hyperspectral data to estimate SOC in Northeast China’s Black soil region. Three modeling strategies were compared: (1) FOD-transformed reflectance (FOD spectra), (2) FOD spectra with traditional 2D spectral indices (FOD + 2D SI), and (3) FOD spectra with new 3D spectral indices (FOD + 3D SI). These strategies were implemented using the random forest model with the aim of the optimal SOC prediction. Results showed that the application of FOD technique for spectral transformation effectively addressed the challenges posed by overlapping peaks and baseline drift inherent in the original spectral reflectance. Additionally, FOD transformation enhanced subtle soil spectral features and yielded more pronounced spectral variations with increasing fractional order, as compared to the original spectral data and conventional integer-order derivatives (i.e., first and second-order derivatives). However, as the FOD order continued to increase beyond 1.4, the spectral curve exhibited amplified noise and distortion, thereby adversely impacting subsequent model development. The 3D spectral indices correlate more robustly with SOC than 2D indices. The model that combines 0.6-order FOD and 3D spectral indices achieved the best accuracy (R2 = 0.66, RMSE = 2.99 g/kg and MAE = 2.42 g/kg), significantly outperforming the models built by 0.6-order FOD spectra (R2 = 0.48, RMSE = 3.65 g kg−1, and MAE = 2.93 g kg−1) and 0.8-order FOD + 2D SI modeling strategy (R2 = 0.55, RMSE = 3.54 g kg−1, and MAE = 2.85 g kg−1). These findings indicated that FOD and 3D spectral indices exhibit superior synergistic performance in SOC prediction, demonstrating their feasibility and providing valuable insights for large-scale soil property prediction and mapping using satellite hyperspectral data.

Air pollution concentration fuzzy evaluation based on evidence theory and the K-nearest neighbor algorithm

Background: Air pollution, characterized by complex spatiotemporal dynamics and inherent uncertainty, poses significant challenges in accurate air quality prediction, and current methodologies often fail to adequately address these complexities.Objective: This study presents a novel fuzzy modeling approach for estimating air pollution concentrations.Methods: This fuzzy evaluation method integrates an improved evidence theory with comprehensive weighting and the K-nearest neighbor (KNN) interval distance within the framework of the matter-element extension model. This involves generating the basic probability assignment (BPA) based on interval similarity, performing sequential fusion using the Dempster–Shafer evidence theory, enhancing the fusion results via comprehensive weighting, and conducting fuzzy evaluation of air pollution concentrations using the matter-element extension KNN interval distance.Results: Our method achieved significant improvements in monitoring air pollution concentrations, incorporating spatiotemporal factors and pollutant concentrations more effectively than existing methods. Implementing sequential fusion and subjective–objective weighting reduced the error rate by 38% relative to alternative methods.Discussion: Fusion of multi-source air pollution data via this method effectively mitigates inherent uncertainty and enhances the accuracy of the KNN method. It produces more comprehensive air pollution concentration fusion results, improving accuracy by considering spatiotemporal correlation, toxicity, and pollution levels. Compared to traditional air-quality indices, our approach achieves greater accuracy and better interpretability, making it possible to develop more effective air quality management strategies. Future research should focus on expanding the dataset to include more diverse geographical and meteorological conditions, further refining the model to integrate external factors like meteorological data and regional industrial activity, and improving computational efficiency for real-time applications.

Using vegetation and chlorophyll indices to model above ground biomass over time in an urban arboretum in subtropical queensland

As urban forests grow, they can provide a range of ecosystem services, including by sequestering carbon in the form of above ground biomass (AGB). Data from satellites is starting to be used to estimate ecological functions of urban forests including AGB. In this study, we compare individually and in combination five vegetation index and three chlorophyll specific indices based on Sentinel-2 satellite images in spring (September to November) of a newly planted multi-species arboretum over eight years in subtropical Queensland, Australia. There was variation in the eight indices within the arboretum, but some clear patterns among indices over time reflecting the growth of the forest. Using a published simple regression model of AGB as a function of Leaf Area Index (LAI), we found Modified Soil Adjusted Vegetation Index (MSAVI) and Modified Chlorophyll Absorption Ratio Index (MCARI) best predicted AGB (r-square = 0.52 and r-square = 0.84, respectively), and in combination gave the best estimate (r-square = 0.85). The performance of these chlorophyll-specific indices (MSAVI and MCARI) reinforces their benefits over traditional vegetation indices in characterizing vegetation patterns and could complement field-based sampling when modelling carbon sequestration potentials including in mixed species urban forests.

On Ve-Degree and Ev-Degree Based Topological Invariants of Chemical Structures

In the realm of graph theory, recent developments have introduced novel concepts, notably the ν ε -degree and ε ν -degree, offering expedited computations compared to traditional degree-based topological indices (TIs). These TIs serve as indispensable molecular descriptors for assessing chemical compound characteristics. This manuscript aims to meticulously compute a spectrum of TIs for silicon carbide S i C 4 - I [ r , s ] , with a specific focus on the ε ν -degree Zagreb index, the ν ε -degree Geometric-Arithmetic index, the ε ν -degree Randić index, the ν ε -degree Atom-bond connectivity index, the ν ε -degree Harmonic index, and the ν ε -degree Sum connectivity index. This study contributes to the ongoing advancement of graph theory applications in chemical compound analysis, elucidating the nuanced structural properties inherent in silicon carbide molecules.

The Retrieval of Ground NDVI (Normalized Difference Vegetation Index) Data Consistent with Remote-Sensing Observations

The Normalized Difference Vegetation Index (NDVI) is widely used for monitoring vegetation status, as accurate and reliable NDVI time series are crucial for understanding the relationship between environmental conditions, vegetation health, and productivity. Ground digital cameras have been recognized as important potential data sources for validating remote-sensing NDVI products. However, differences in the spectral characteristics and imaging methods between sensors onboard satellites and ground digital cameras hinder direct consistency analyses, thereby limiting the quantitative application of camera-based observations. To address this limitation and meet the needs of vegetation monitoring research and remote-sensing NDVI validation, this study implements a novel NDVI camera. The proposed camera incorporates narrowband dual-pass filters designed to precisely separate red and near-infrared (NIR) spectral bands, which are aligned with the configuration of sensors onboard satellites. Through software-controlled imaging parameters, the camera captures the real radiance of vegetation reflection, ensuring the acquisition of accurate NDVI values while preserving the evolving trends of the vegetation status. The performance of this NDVI camera was evaluated using a hyperspectral spectrometer in the Hulunbuir Grassland over a period of 93 days. The results demonstrate distinct seasonal characteristics in the camera-derived NDVI time series using the Green Chromatic Coordinate (GCC) index. Moreover, in comparison to the GCC index, the camera’s NDVI values exhibit greater consistency with those obtained from the hyperspectral spectrometer, with a mean deviation of 0.04, and a relative root mean square error of 9.68%. This indicates that the narrowband NDVI, compared to traditional color indices like the GCC index, has a stronger ability to accurately capture vegetation changes. Cross-validation using the NDVI results from the camera and the PlanetScope satellite further confirms the potential of the camera-derived NDVI data for consistency analyses with remote sensing-based NDVI products, thus highlighting the potential of camera observations for quantitative applications The research findings emphasize that the novel NDVI camera, based on a narrowband spectral design, not only enables the acquisition of real vegetation index (VI) values but also facilitates the direct validation of vegetation remote-sensing NDVI products.

Toward Development of a Novel Proprioceptive Trail-Making Test: Comparing Healthy Dancers and Non-Dancers.

There is a pressing need for ecologically valid versions of traditional neuropsychological tests indexing executive function (EF), such as the Trail-Making Task (TMT), that incorporate movement and bodily awareness in healthy participants with varying abilities. We designed a proprioceptive version of the TMT (pTMT) that involved coordinated gross motor movement and proprioceptive awareness to investigate whether this measure of visual attention, task switching, and working memory positively correlated with a computerized version of the TMT (the dTMT). We aimed to establish the initial validity of our proprioceptive TMT (pTMT) by comparing performances on the dTMT and pTMT among a cohort of 36 healthy participants (18 dancers, 18 non-dancers; M age = 22, SD = 5.27; 64% female) anticipating that dancers would express higher intrinsic bodily awareness than non-dancers. Results revealed a mild to moderate but statistically significant positive correlation between dTMT and pTMT completion times [part A: r (36) = .33, p = .04; part B: r (36) = .37, p = .03] and numbers of errors [part B: r (36) = .41, p = .01] across both participant groups. These data suggest partial measurement convergence between these two TMT versions. Relative to non-dancers, dancers exhibited a higher level of performance (likely due to their better proprioceptive ability) through their faster completion times on dTMT-B [t (34) = 3.81, p = .006, d = 1.27] and pTMT-B [t (34) = 2.97, p = .005, d = .99], and their fewer errors on dTMT-B [t (34) = 2.93, p = .006, d = 1.0]. By identifying cognitive differences between these different groups of healthy individuals, our data contribute to both a theoretical understanding and the initial development of gross motor movement-based cognitive assessments, providing a path toward the further refinement of an ecologically valid full-body TMT.

Effects of irrigation dams on riverine biota in mountain streams

Mountain streams harbor unique biodiversity and provide essential ecosystem services to human societies. Yet, these ecosystems face numerous threats, such as the construction of dams and land use changes, leading to rapid habitat degradation, water pollution, and biodiversity loss. In this study, we assess the effect of irrigation dams on mountain riverine biota using traditional biotic indices and trait-based approaches. We selected diatom and macroinvertebrate communities surveyed between 2015 and 2017 in mountain streams located in different regions in northern Spain (Cantabrian Cordillera, Iberian System, and Pyrenees) under natural and altered flow conditions (i.e., downstream of irrigation dams). Hydrological and biological changes related to the presence of dams, the mountain range, and the interaction between these two factors were identified. Summer flows, frequency of high flow events, and minimum annual flows timing were significantly affected by irrigation dams, independently of the region. Winter flows, the magnitude of high flow extremes, and the number of flow rises and falls varied significantly with the dam-mountain range interaction. The frequency and duration of flow pulses depended on the mountain range only. In the Cantabrian Cordillera, a region with larger reservoirs (>150 hm3), impacted sites showed a marked inversion of the seasonal flow patterns (i.e., increased summer flows but reduced winter flows). In the other mountain ranges, reservoirs had smaller storage volumes and multiple purposes, causing significant flow change frequency variations. Diatom traits, taxonomic richness, diversity, and IPS score varied with dam presence and mountain ranges, while macroinvertebrate traits and biotic indices responded weakly. These findings suggest that diatom communities might be more sensitive to hydrological alteration, while macroinvertebrates might be more influenced by space-related factors, such as biogeography and dispersal, overriding dam-related impacts. Furthermore, dam-related changes in ecosystems may depend not only on the presence of dams and their characteristics (e.g., reservoir size and operation), but also on local conditions and biogeography. Our findings emphasize that, when using pre-existing biomonitoring datasets, although some dam-related patterns emerge (e.g., with diatoms), other patterns may be constrained by the datasets’ low spatio-temporal coverage and taxonomic resolution, highlighting the need of well-structured study designs.

Hemodynamic Determinants of Cardiac Surgery-Associated Acute Kidney Injury.

Examine the: 1) relative role of hemodynamic determinants of acute kidney injury (AKI) obtained in the immediate postcardiac surgery setting compared with established risk factors, 2) their predictive value, and 3) extent mediation via central venous pressure (CVP) and mean arterial pressure (MAP). Retrospective observational study. The main outcome of the study was moderate to severe AKI, per kidney disease: improving global outcomes, within 14 days of surgery. U.S. academic medical center. Adult patients undergoing cardiac surgery between January 2000 and December 2019 (n = 40,426) in a single U.S.-based medical center. Pulmonary artery catheter measurements were performed at a median of 102 minutes (11, 132) following cardiopulmonary bypass discontinuation. None. The median age of the cohort was 67 years (58, 75), and 33% were female; 70% had chronic hypertension, 29% had congestive heart failure, and 3% had chronic kidney disease. In a multivariable model, which included comorbidities and traditional intraoperative risk factors, CVP (p < 0.0001), heart rate (p < 0.0001), cardiac index (p < 0.0001), and MAP (p < 0.0001), were strong predictors of AKI, and superseded factors such as surgery type and cardiopulmonary bypass duration. The cardiac index had a significant interaction with heart rate (p = 0.026); a faster heart rate had a differentiating effect on the relationship of cardiac index with AKI, where a higher heart rate heightened the risk of AKI primarily in patients with low cardiac output. There was also significant interaction observed between CVP and MAP (p = 0.009); where the combination of elevated CVP and low MAP had a synergistic effect on AKI incidence. Hemodynamic factors measured within a few hours of surgery showed a strong association with AKI. Furthermore, determinants of kidney perfusion, namely CVP and arterial pressure are interdependent; as are constituents of stroke volume, that is, cardiac output and heart rate.

Formation mechanisms of fluvial residues from eastern watersheds of China: Insights from corrected weathering process and sediment provenance

Investigating the weathering process and provenance of fluvial residues plays a crucial role in revealing superficial circulation on earth. Although the transfer and inheritance processes have been well elaborated for intra-watersheds, the limitations and applications of geochemical proxies need to be further explored for cross-watersheds. Geochemical analysis was performed on 541 surficial sediment samples from 12 intertidal zones in China, combined with traditional weathering indices, mass transfer coefficients, and rare earth element parameters, to gain a better understanding of their geochemical behavior. Based on the interactions of lithology, hydrodynamic sorting, and sedimentary recycling on various weathering indices, the metamorphic and succession processes of the residues were fully characterized during the source-to-sink process. The weathering index of Parker (3.9–66.2) was more susceptible to the hydrodynamic sorting effect, whereas the chemical index of alteration (45.8–83.9) was heavily influenced by the sedimentary recycling process. In comparison, the mass transfer coefficient of Na provides better performance in the early weathering stage and is optimally protected from the effects of hydrodynamic sorting and sedimentary recycling. As confirmed by the corrected mass transfer coefficients and weathering indices, regional temperature and precipitation co-dominated the weathering heterogeneity. Parameters excluding heavy rare earth elements were hardly affected by the weathering process and grain-size effect, and were the most sensitive geochemical proxies for tracing the provenance of fluvial residues. This could provide a scientific basis for cross-watershed research, source apportionment, and regional geological background.

Research on Tail Risk Hedging in the Digital Asset Market

In recent years, the digital asset market has experienced rapid growth, but at the same time, its implicit tail risks have gradually raised concerns, especially amidst the global economic downturn caused by the pandemic. This paper aims to study tail risks and propose hedging strategies. Based on the GARCH and VaR models, this paper uses weekly return data from July 2017 to December 2021 for Bitcoin digital cryptocurrency and traditional non-digital asset market indices such as stocks, bonds, gold, and commodities. Utilizing EViews and Excel, the study explores tail risk measurement and portfolio diversification effects in the digital asset market. The main findings are as follows: (1) During the sample period, cryptocurrency returns exhibited significant volatility, with weak correlations with returns from traditional non-digital assets. Further analysis revealed strong hedging effects of digital assets against the bond market but weaker effects against the gold and stock markets, as well as commodity markets; (2) Cryptocurrency in traditional investment portfolios can enhance the risk-return ratio by adding it to the asset mix.

Early Detection of Rice Leaf Blast Disease Using Unmanned Aerial Vehicle Remote Sensing: A Novel Approach Integrating a New Spectral Vegetation Index and Machine Learning

Leaf blast is recognized as one of the most devastating diseases affecting rice production in the world, seriously threatening rice yield. Therefore, early detection of leaf blast is extremely important to limit the spread and propagation of the disease. In this study, a leaf blast-specific spectral vegetation index RBVI = 9.78R816−R724 − 2.08(ρ736/R724) was designed to qualitatively detect the level of leaf blast disease in the canopy of a field and to improve the accuracy of early detection of leaf blast by remote sensing by unmanned aerial vehicle. Stacking integrated learning, AdaBoost, and SVM were used to compare and analyze the performance of the RBVI and traditional vegetation index for early detection of leaf blast. The results showed that the stacking model constructed based on the RBVI spectral index had the highest detection accuracy (OA: 95.9%, Kappa: 93.8%). Compared to stacking, the detection accuracy of the SVM and AdaBoost models constructed based on the RBVI is slightly degraded. Compared with conventional SVIs, the RBVI had higher accuracy in its ability to qualitatively detect leaf blast in the field. The leaf blast-specific spectral index RBVI proposed in this study can more effectively improve the accuracy of UAV remote sensing for early detection of rice leaf blast in the field and make up for the shortcomings of UAV hyperspectral detection, which is susceptible to interference by environmental factors. The results of this study can provide a simple and effective method for field management and timely control of the disease.

Estimating high-density aboveground biomass within a complex tropical grassland using Worldview-3 imagery

A large percentage of native grassland ecosystems have been severely degraded as a result of urbanization and intensive commercial agriculture. Extensive nitrogen-based fertilization regimes are widely used to rehabilitate and boost productivity in these grasslands. As a result, modern management frameworks rely heavily on detailed and accurate information on vegetation condition to monitor the success of these interventions. However, in high-density environments, biomass signal saturation has hampered detailed monitoring of rangeland condition. This issue stems from traditional broad-band vegetation indices (such as NDVI) responding to high levels of photosynthetically active radiation (PAR) absorption by leaf chlorophyll, which affects leaf area index (LAI) sensitivity within densely vegetative regions. Whilst alternate hyperspectral solutions may alleviate the problem to a certain degree, they are often too costly and not readily available within developing regions. To this end, this study evaluated the use of high-resolution Worldview-3 imagery in combination with modified NDVI indices and image manipulation techniques in reducing the effects of biomass signal saturation within a complex tropical grassland. Using the random forest algorithm, several modified NDVI-type indices were developed from all potential dual-band combinations of the Worldview-3 image. Thereafter, linear contrast stretching and histogram equalization were implemented in conjunction with Singular Value Decomposition (SVD) to improve high-density biomass estimation. Results demonstrated that both contrast enhancement techniques, when combined with SVD, improved high-density biomass estimation. However, linear contrast stretching, SVD, and modified NDVI indices developed from the red (630–690 nm), green (510–580 nm), and near-infrared 1 (770–895 nm) bands were found to produce the best biomass predictive model (R2 = 0.71, RMSE = 0.40 kg/m2). The results generated from this research offer a means to alleviate the biomass saturation problem. This framework provides a platform to assist rangeland managers in regionally assessing changes in vegetation condition within high-density grasslands.

Unveiling Outperformance: A Portfolio Analysis of Top AI-Related Stocks against IT Indices and Robotics ETFs

In this study, we delve into the financial market to compare the performance of prominent AI and robotics-related stocks against traditional IT indices, such as the Nasdaq, and specialized AI and robotics ETFs. We evaluate the role of these stocks in diversifying portfolios, analyzing their return potential and risk profiles. Our analysis includes various investment scenarios, focusing on common AI-related stocks in the United States. We explore the influence of risk management strategies, ranging from “buy and hold” to daily rebalancing, on AI stock portfolios. This involves investigating long-term strategies like buy and hold, as well as short-term approaches, such as daily rebalancing. Our findings, covering the period from 30 April 2021, to 15 September 2023, show that AI-related stocks have not only outperformed in recent years but also highlight the growing “AI bubble” and the increasing significance of AI in investment decisions. The study reveals that these stocks have delivered superior performance, as indicated by metrics like Sharpe and Treynor ratios, providing insights into market trends and financial returns in the technology and robotics sectors. The results are particularly relevant for investors and traders in the AI sector, offering a balanced view of potential returns against the risks in this rapidly evolving market. This paper adds to the financial market literature by demonstrating that investing in emerging trends, such as AI, can be more advantageous in the short term compared to traditional markets like the Nasdaq.

Classifying Multimorbidity Using Drug Concepts via the Rx-Risk Comorbidity Index: Methods and Comparative Cross-Sectional Study.

The study objective was to update a method to identify comorbid conditions using only medication information in circumstances in which diagnosis codes may be undercaptured, such as in single-specialty electronic health records (EHRs), and to compare the distribution of comorbidities across Rx-Risk versus other traditional comorbidity indices. Using First Databank, RxNorm, and its web-based clients, RxNav and RxClass, we mapped Drug Concept Unique Identifiers (RxCUIs), National Drug Codes (NDCs), and Anatomical Therapeutic Chemical (ATC) codes to Rx-Risk, a medication-focused comorbidity index. In established rheumatoid arthritis (RA) and osteoarthritis (OA) cohorts within the Rheumatology Informatics System for Effectiveness registry, we then compared Rx-Risk with other comorbidity indices, including the Charlson Comorbidity Index, Rheumatic Disease Comorbidity Index (RDCI), and Elixhauser. We identified 965 unique ingredient RxCUIs representing the 46 Rx-Risk comorbidity categories. After excluding dosage form and ingredient related RxCUIs, 80,911 unique associated RxCUIs were mapped to the index. Additionally, 187,024 unique NDCs and 354 ATC codes were obtained and mapped to the index categories. When compared to traditional comorbidity indices in the RA cohort, the median score for Rx-Risk (median 6.00 [25th percentile 2, 75th percentile 9]) was much greater than for Charlson (median 0 [25th percentile 0, 75th percentile 0]), RDCI (median 0 [25th percentile 0, 75th percentile 0]), and Elixhauser (median 1 [25th percentile 1, 75th percentile 1]). Analyses of the OA cohort yielded similar results. For patients with a Charlson score of 0 (85% of total), both the RDCI and Elixhauser were close to 1, but the Rx-Risk score ranged from 0 to 16 or more. The misclassification and under-ascertainment of comorbidities in single-specialty EHRs can largely be overcome by using a medication-focused comorbidity index.

Evaluation of sustainable feeds for “caviar” production in the Mediterranean sea urchin Paracentrotus lividus (Lamarck, 1816)

To improve the sustainability of aquaculture practices, a step towards the use of alternative nutrient sources (such as food processing discards) may secure the future of aquaculture sector, namely for emergent species, such as sea urchins. In this context, adult females of the commercial sea urchin Paracentrotus lividus were reared using four feeds based on lettuce discards (72%) and enriched (8%) with an animal-source ingredient (fish Sardina pilchardus, Feed-S; krill Euphausia superba, Feed-K; mussel Mytilus galloprovincialis, Feed-M; anchovy Engraulis encrasicolus discards, Feed-AD). A fifth feed, used as control treatment, was composed of macroalgae (Laminaria sp. and Ulva sp., Feed-UL). Feed performance was evaluated employing a new productive protocol, the Raking method, which propose testing feed effects on sea urchin caviar (oocytes rather than gonads) production. Thus, ingestion rates and absorption efficiency were measured to evaluate feed palatability. Somatic growth and caviar production, expressed introducing the ovosomatic index (OI) instead of the traditional gonadosomantic index, were measured to assess feed productive performances. Caviar quality was assessed by nutritional content and color. Ingestion rate results showed that all feeds were palatable, while findings on absorption efficiency showed differences between the five proposed feeds, with Feed-M and Feed-AD presenting the worst results. Somatic growth was promoted regardless the provided feeds, while OI resulted higher with Feed-K and Feed-M than the other feeds. All produced caviar resulted suitable for human consumption with high protein and fatty acid content, but caviar produced by Feed-UL showed the poorest nutritional profile. Similarly, Feed-UL led to the production of caviar with the lowest quality color, while Feed-S showed the best orange color. Lettuce-based feeds were therefore effective for feeding P. lividus as they stimulated production of high quality caviar. Findings support the exploitation of food discards for the production of eco-friendly feeds for sea urchin aquaculture.

Direct Discrepancy Dynamic Fit Index Cutoffs for Arbitrary Covariance Structure Models

Despite the popularity of traditional fit index cutoffs like RMSEA ≤ .06 and CFI ≥ .95, several studies have noted issues with overgeneralizing traditional cutoffs. Computational methods have been proposed to avoid overgeneralization by deriving cutoffs specifically tailored to the characteristics of the model being evaluated. Simulations show favorable performance of these methods; however, these methods support a narrow set of scenarios (e.g., certain models or response scales) and the interpretation of cutoffs is not always standardized, which affects empirical researchers’ ability to confidently and broadly adopt these methods to evaluate model fit. In this paper, we propose an extension to one recently developed computational method—dynamic fit index cutoffs—that (a) permits application to any covariance structure model (e.g., CFA, mediation, bifactor), (b) standardizes interpretation of cutoffs across any covariance structure model, and (c) supports normal, nonnormal, categorical, and missing data. Software is provided to facilitate implementation of the method.

Evaluation of surface quality and error compensation for optical aspherical surface grinding

Optical aspherical lenses are widely used in the aerospace, electronics, and optical communication industries. Grinding is the primary processing method used to ensure their precision. It is difficult for traditional evaluation indexes to fully reflect the optical properties of optical surfaces after grinding. This paper presents a novel method for characterizing surface quality of optical aspherical surface oriented to the application of optical properties. The proposed evaluation indicators include the profile error, surface roughness, slope error, and projection-area scale-deviation value, which can be used to comprehensively evaluate the surface quality. Subsequently, a novel grinding compensation method for the optical aspherical surface is proposed, and its compensation precision is evaluated using the abovementioned evaluation indicators. The results show that the maximum surface-shape error decreases to less than 150 nm; additionally, the central cusp point is restrained. The precision and efficiency of error compensation for the optical aspherical surface are significantly better than those of the conventional method. The proposed evaluation indicators can be applied to reflect the features of optical aspheric surface directly. The high efficiency and precision compensation of optical aspherical surface can be achieved with the proposed evaluation method and modified error compensation algorithm.

Identifying critical weak points of power-gas integrated energy system based on complex network theory

Prompt and precise identification of critical weak points is essential in integrated energy systems (IES) to mitigate system reliability issues and effectively prevent cascading failures. This study proposes methods to identify critical weak points in the power-gas integrated system (PGIS) based on complex network theory for this task. Firstly, we improve the traditional betweenness indices in complex network theory by integrating power output of energy supply equipment as weighted factors to capture diverse types of energy flows, and incorporating the efficiency coefficient to account for energy losses via the PGIS coupling equipment. By integrating these refinements, our approach could further identify some points that are not weak from the viewpoint of topology but are crucial for energy transmission. Secondly, we propose an approximate algorithm for shortest-paths calculations (AA-SPC), which preserves information of total node and some specific topological, and simplifies the calculation of target shortest path, aiming at increasing the efficiency of obtaining path information. Additionally, vulnerability assessment indicators, including system fragmentation and network betweenness loss, measure PGIS vulnerability under different attack strategies. The aforementioned methods are applied to a self-built virtual European PGIS based on the open-source database SciGRID, successfully identifying critical weak points overlooked by traditional betweenness indices.

Bare soil detecting algorithms in western iran woodlands using remote sensing

This study presents the Modified Bare Soil Index (MBI), an innovative remote sensing instrument that employs Short Wave Infrared (SWIR) and Near Infrared (NIR) wavelengths sourced from Landsat 8 satellite observations. Its purpose is to refine the identification and distinction of bare soil within woodland regions. Addressing the limitations of existing methods, MBI represents a significant advancement in land cover change detection. Through a focused study in the Sardasht forests of western Iran, the MBI demonstrates superior accuracy, achieving an overall accuracy of approximately 96 % with a Kappa coefficient exceeding 0.94, surpassing traditional bare soil indices. This innovation improves the classification of land surfaces and also aids in the efficient monitoring and safeguarding of changes in land cover. The MBI's robust performance underscores its potential in smart forestry, enabling more precise identification and surveillance of bare soil areas. This advancement is pivotal for ecological studies and resource management, offering a practical solution to the challenges of soil erosion and land degradation. By integrating advanced spectral analysis with satellite imagery, the MBI sets a new standard in remote sensing techniques, offering vital insights for environmental conservation and agricultural practices.

The Association Between Lean-to-Fat Mass Ratio and Cardiometabolic Abnormalities: An Analytical Cross-Sectional Study.

Cardiovascular diseases (CVDs) are a global health threat, significantly impacting Latin America. Cardiometabolic abnormalities (CAs), encompassing lipid profile, fasting plasma glucose, and blood pressure, contribute to CVD prevalence. Despite high CA incidence, research in Latin America has primarily focused on traditional adiposity indices, overlooking the intricate relationship between fat and lean body components. The study aimed to analyze the association between the lean-to-fat mass ratio (LFMR) and CAs in the adult Peruvian population. This was an analytical cross-sectional study using secondary data from the PERU MIGRANT study (2007, 989 participants). The main outcome variable was CA defined as having ≥ 2 out of six metabolic components (high triglycerides, impaired fasting glucose, high blood pressure, low high-density lipoprotein (HDL)-cholesterol, insulin resistance, and high C-reactive protein). The main exposure variable LFMR was divided into tertiles. A generalized linear model was used with log link and robust variance Poisson family to calculate crude (cPR) and adjusted prevalence ratios (aPRs) and 95% confidence intervals (95% CIs). A total of 959 adults aged 30 years or older were included in the analysis (53% females). The prevalence of CA was 50.9%. Females aged 30 - 44 years old showed statistically significant inverse associations for the middle (aPR: 0.57, 95% CI: 0.42 - 0.78) and highest (aPR: 0.22, 95% CI: 0.14 - 0.35) LFMR categories. Similar trends were seen for females aged 45 - 59 years and ≥ 60 years, and males aged 30 - 44 years, while for males aged 45 - 59 years, only the middle LFMR category was associated. No statistically significant association between LFMR and CA was found among old males. LFMR was negatively associated with CA, among the Peruvian adult population. These findings underscore the relevance of LFMR in understanding cardiometabolic health disparities.