Optimal Index Research Articles

The Impact of Triglyceride-Glucose (TyG) Index on Future Cardio and Cerebrovascular Events in Patients with Acute Coronary Syndrome, During 3 Years of Follow-Up

Background: The triglyceride-glucose (TyG) index is a new alternative insulin resistance (IR) biomarker. The purpose of this study was to assess whether the TyG index can have a prognostic value in patients with acute coronary syndrome (ACS). Moreover, we wanted to compare the TyG index with HOMA index. Methods: We retrospectively enrolled 115 consecutive subjects, 81 males and 34 females, referred for ACS to our Unit of Cardiovascular Care of Policlinico Paolo Giaccone, Palermo. The subjects were divided into tertiles according to TyG index values and we performed a 3-year follow-up study. We considered as an end point new cardiovascular and cerebral events (MACCEs) during follow-up. Results: We found a significant statistical correlation between the HOMA index and the TyG index (p = 0.001). Patients with elevated TyG index have a higher incidence of MACCE at a 3-year follow-up. In our study the TyG index was an independent predictor of MACCEs (95% CI 1.8158 to 16.8068; P 0.0026) and the optimal TyG index cut-off for predicting MACCEs was 4.92 (sensitivity 76.56% and specificity 72.55%). Conclusions: The TyG index seems to significantly have an important prognostic role in patients with ACS and high values of TyG index are superior to HOMA-IR in predicting MACCEs.

Enhancing Soil Salinity Evaluation Accuracy in Arid Regions: An Integrated Spatiotemporal Data Fusion and AI Model Approach for Arable Lands

Soil salinization is one of the primary factors contributing to land degradation in arid areas, severely restricting the sustainable development of agriculture and the economy. Satellite remote sensing is essential for real-time, large-scale soil salinity content (SSC) evaluation. However, some satellite images have low temporal resolution and are affected by weather conditions, leading to the absence of satellite images synchronized with ground observations. Additionally, some high-temporal-resolution satellite images have overly coarse spatial resolution compared to ground features. Therefore, the limitations of these spatiotemporal features may affect the accuracy of SSC evaluation. This study focuses on the arable land in the Manas River Basin, located in the arid areas of northwest China, to explore the potential of integrated spatiotemporal data fusion and deep learning algorithms for evaluating SSC. We used the flexible spatiotemporal data fusion (FSDAF) model to merge Landsat and MODIS images, obtaining satellite fused images synchronized with ground sampling times. Using support vector regression (SVR), random forest (RF), and convolutional neural network (CNN) models, we evaluated the differences in SSC evaluation results between synchronized and unsynchronized satellite images with ground sampling times. The results showed that the FSDAF model’s fused image was highly similar to the original image in spectral reflectance, with a coefficient of determination (R2) exceeding 0.8 and a root mean square error (RMSE) below 0.029. This model effectively compensates for the missing fine-resolution satellite images synchronized with ground sampling times. The optimal salinity indices for evaluating the SSC of arable land in arid areas are S3, S5, SI, SI1, SI3, SI4, and Int1. These indices show a high correlation with SSC based on both synchronized and unsynchronized satellite images with ground sampling times. SSC evaluation models based on synchronized satellite images with ground sampling times were more accurate than those based on unsynchronized images. This indicates that synchronizing satellite images with ground sampling times significantly impacts SSC evaluation accuracy. Among the three models, the CNN model demonstrates the highest predictive accuracy in SSC evaluation based on synchronized and unsynchronized satellite images with ground sampling times, indicating its significant potential in image prediction. The optimal evaluation scheme is the CNN model based on satellite image synchronized with ground sampling times, with an R2 of 0.767 and an RMSE of 1.677 g·kg−1. Therefore, we proposed a framework for integrated spatiotemporal data fusion and CNN algorithms for evaluating soil salinity, which improves the accuracy of soil salinity evaluation. The results provide a valuable reference for the real-time, rapid, and accurate evaluation of soil salinity of arable land in arid areas.

Diagnosis of Enlarged Vestibular Aqueduct Using Wideband Tympanometry

Background: Wideband tympanometry (WBT) has the potential to distinguish various mechanical middle ear and inner ear pathologies noninvasively. This study investigated the diagnostic value of WBT in the diagnosis of enlarged vestibular aqueduct (EVA). Methods: The absorbance and resonance frequency (RF) of patients with EVA (40 ears, 25 patients) and matched population controls (39 ears, 28 subjects) were compared, alongside receiver operating characteristic (ROC) analysis. Correlations between VA width and RF were also examined. Results: Patients with EVA had higher absorbance at low frequencies (226–917 Hz) and lower absorbance at high frequencies (2520–4896 Hz) compared to controls. The RF of the EVA group was significantly lower versus controls (751 [391–1165] vs. 933 [628–1346] Hz). The ROC analysis revealed area under the curve values of 0.771 and 0.801, respectively, for absorbance and RF. RF had a sensitivity, specificity, positive predictive value, and negative predictive value of 74.4%, 82.5%, 76.7%, and 80.6%, respectively, for diagnosing EVA. In the EVA group, the VA midpoint width (r = −0.334) and VA petrous width (r = −0.402) both significantly correlated with RF. Conclusions: Our findings support the utility of WBT for diagnosing EVA, with RF as the optimal index used.

Combining Remote Sensing Data and Geochemical Properties of Ultramafics to Explore Chromite Ore Deposits in East Oltu Erzurum, Turkey

The present research’s main objective was to apply thorough exploration approaches that combine remote sensing data with geochemical sampling and analysis to predict and identify potential chromitite locations in a complex geological site, particularly in rugged mountainous terrain, and differentiate the ultramafic massif containing chromitite orebodies from other lithologies. The ultramafic massif forming the mantle section of the Kırdağ ophiolite, located within the Erzurum–Kars Ophiolite Zone and emerging in the east of Oltu district (Erzurum, NE Turkey), was selected as the study area. Optimum index factor (OIF), false-color composite (FCC), decorrelation stretch (DS), band rationing (BR), minimum noise fraction (MNF), and principal and independent component analyses (PCA-ICA) were performed to differentiate the lithological features and identify the chromitite host formations. The petrography, mineral chemistry, and whole-rock geochemical properties of the harzburgites, which are the host rocks of chromitites in the research area, were evaluated to verify and confirm the remote sensing results. In addition, detailed petrographic properties of the pyroxenite and chromitite samples are presented. The results support the existence of potential chromitite formations in the mantle section of the Kırdağ ophiolite. Our remote sensing results also demonstrate the successful detection of the spectral anomalies of this ultramafic massif. The mineral and whole-rock geochemical features provide clear evidence of petrological processes, such as partial melting and melt–peridotite interactions during the harzburgite formation. The chromian spinels’ Cr#, Mg#, Fe3+, Al2O3, and TiO2 concentrations indicate that the harzburgite formed in a fore-arc environment. The Al2O3 content and Mg# of the pyroxenes and the whole-rock Al2O3/MgO ratio and V contents of the harzburgite are also compatible with these processes. Consequently, the combined approaches demonstrated clear advantages over conventional chromitite exploration techniques, decreasing the overall costs and supporting the occurrence of chromite production at the site.

Multi-objective optimization of laser perforated fuel filter parameters based on artificial neural network and genetic algorithm

In precise fuel circuit systems, the filtration of particulate impurities seriously affects the efficiency and service life of various components. For filtration process intensification of high-pressure fuel laser perforated filters, the two-phase flow characteristics in filters is studied. The size, position and number of filtration holes are taken as optimization variables, and take the filtration efficiency and flow pressure drop as optimization objectives. Computational fluid dynamics (CFD) is used to simulate the two-phase motion of continuous phase and discrete particles in a periodic unit. Artificial neural networks (ANN) are utilized for objectives prediction, and the NSGA-II genetic algorithm is employed for multi-objective optimization, resulting in the Pareto front solution set. Furtherly, the reasonable solution is selected by introducing TOPSIS to ensure that two optimization indexes are relatively smaller and balanced. The optimized filter element scheme allows the filter to have a pressure drop of less than 3.2 MPa under high pressure and a filtration efficiency of over 80% for spherical particle impurities with a diameter of 5 microns or more.

Analyzing tube arrangements of a finned-tube heat exchanger to optimize overall efficiency using artificial neural network and response surface methodology

Temperature is a critical factor in numerous equipment, industries, and daily life applications. Heat exchangers are essential devices that help regulate and optimize this factor. The finned-tube heat exchanger (FTHE) is widely favored due to its high efficiency in facilitating heat transfer between liquids and gases. Improving the performance of FTHE can significantly provide thermal requirements in industrial and engineering processes and reduce energy consumption. The present research includes a numerical study of an FTHE and the optimization of the performances based on the input variables by response surface methodology (RSM) and artificial neural network (ANN) methods. The tubes' transverse and longitudinal pitches and inlet Reynolds number were selected as input variables. Also, the examined responses were the Colburn and friction factors. Changing tubes' pitches makes it possible to significantly affect the thermo-hydraulic performance without incurring additional costs and processes. The acquired results showed that the responses predicted by the models are very close to the numerical results, which indicates the high accuracy and validity of these models. According to the results, the optimum heat exchanger's efficiency index was obtained at Pt=26.128mm, Pl=28mm, and Re=800. It was also observed that the overall performance of the optimal design is 185% higher than the weakest FTHE.

MQGA: A Quantitative Analysis of Brain Network Hubs Using Multi-Graph Theoretical Indices

Recent advancements in large-scale network studies have shown that connector hubs and provincial hubs are vital for coordinating complex cognitive tasks by facilitating information transfer between and within specialized modules. However, current methods for identifying these hubs often lack standardized measurement criteria, hindering quantitative analysis. This study proposes a novel computational method utilizing multi-graph theoretical index calculations to quantitatively analyze hub attributes in brain networks. Using benchmark network, random simulation network (N = 100), resting fMRI data from the ADHD-200 NYU dataset (HC = 110, ADHD = 146), and the Peking dataset (HC = 120, ADHD = 83), we introduce the Multi-criteria Quantitative Graph Analysis (MQGA) method, which employs betweenness centrality, degree centrality, and participation coefficient to determine the connector (con) hub index and provincial (pro) hub index. The method's accuracy, reliability, and stability were validated through correlation analysis of hub indices and labels, vulnerability tests, and consistency analysis across subjects. Results indicate that as network sparsity increases, the con hub index increases while the pro hub index decreases, with the optimal hub node index at 4% sparsity. Vulnerability tests revealed that removing con nodes had a greater impact on network integrity than removing pro nodes. Both con and pro exhibited stability in consistency analyses, but con was more stable. The stability of hub scores in disease groups was significantly lower than in the healthy control group. High con values were found in the precuneus, postcentral gyrus, and precentral gyrus, whereas high pro values were identified in the precentral gyrus, postcentral gyrus, superior parietal lobule, precuneus, and superior temporal gyrus. This approach enhances the accuracy and sensitivity of hub node identification, facilitating precise comparisons and producing consistent, replicable results, advancing our understanding of brain network hub nodes, their roles in cognitive processes, and their implications for brain disease research.

Site selection for biomass-solar hybrid renewable energy facilities: Spatial modelling based on fuzzy logic-geographic information systems

The aim of this study is to propose a fuzzy logic-geographic information systems (GIS) based spatial model within the context of hybrid renewable energy, rural areas, and spatial planning approach in the site selection process of renewable energy facilities. In this context, potential suitability index and site selection suitability index for biomass-solar hybrid renewable energy facilities are determined within the spatial model, through a case study conducted in Türkiye. The study involves a two-stage process: the first stage focuses on establishing the national-level potential, and the second stage involves site selection in areas with high potential. Suitable locations are determined based on the optimal suitability index values, which are derived from fuzzy logic operators including AND, OR, SUM, PRODUCT, and GAMMA, applied in the GIS. The study results indicate that by assigning different γ values to the fuzzy GAMMA operator, results for the fuzzy AND (γ = 0.45–0.60), fuzzy OR (γ = 1.00), fuzzy SUM (γ = 1.00), and fuzzy PRODUCT (γ = 0.00–0.15) operators can be obtained. Furthermore, as the γ value increases, the suitability index also rises, and the optimal site selection occurs at γ = 0.90. The performance of the fuzzy GAMMA operator in big data sets and two-stage processes demonstrates its applicability in site selection.

The influence of epicardial resistance on Microvascular Resistance Reserve (MRR)

Abstract Background The optimal index of microvascular function should be specific for the microvascular compartment. Yet, coronary flow reserve (CFR), despite being widely used to diagnose coronary microvascular dysfunction (CMD), is influenced by both epicardial and microvascular resistance. Conversely, microvascular resistance reserve (MRR) adjusts for fractional flow reserve (FFR), and thus is theoretically independent of epicardial resistance. Purpose We tested the hypothesis that MRR, unlike CFR, is not influenced by increasing epicardial resistance, and thus is a more specific index of microvascular function. Methods In a cohort of 16 patients that had undergone proximal left anterior descending artery stenting, we created four grades of artificial stenosis (no stenosis, mild, moderate, and severe) using a coronary angioplasty balloon inflated to different degrees within the stent. For each stenosis grade, we calculated CFR and MRR using continuous thermodilution (64 measurements of each) in order to assess their response to changing epicardial resistance. Results Graded balloon inflation resulted in a significant sequential decrease in mean FFR (no stenosis: 0.82 ±0.05; mild: 0.72 ±0.04; moderate: 0.61 ±0.05; severe: 0.48 ±0.09, p<0.001). This translated into a linear decrease in mean hyperaemic coronary flow (no stenosis: 170.5 ±66.8 ml/min; mild: 149.8 ±58.8 ml/min; moderate: 124.4 ±53.0 ml/min; severe: 94.0 ±45.2 ml/min, p<0.001) (Figure 1). CFR exhibited a marked linear decrease with increasing stenosis (no stenosis: 2.5 ±0.9; mild: 2.2 ±0.8; moderate: 1.8 ±0.7; severe: 1.4 ±0.6), corresponding to a decrease of 0.3 for a decrease in FFR of 0.1 (p<0.001). Conversely, MRR exhibited a minimal decrease across all stenosis grades (no stenosis: 3.0 ±1.0; mild: 3.0 ±1.0; moderate: 2.9 ±1.0; severe: 2.8 ±1.0), corresponding to a decrease of just 0.05 for a decrease in FFR of 0.1 (p<0.001) (Figure 1). Conclusion MRR, unlike CFR, is minimally influenced by epicardial resistance, and thus should be considered the most specific index of microvascular function.Figure 1.Absolute blood flow (Q) vs FFRFigure 2.MRR/CFR vs FFR

Influence of Spatial Scale Effect on UAV Remote Sensing Accuracy in Identifying Chinese Cabbage (Brassica rapa subsp. Pekinensis) Plants

The exploration of the impact of different spatial scales on the low-altitude remote sensing identification of Chinese cabbage (Brassica rapa subsp. Pekinensis) plants offers important theoretical reference value in balancing the accuracy of plant identification with work efficiency. This study focuses on Chinese cabbage plants during the rosette stage; RGB images were obtained by drones at different flight heights (20 m, 30 m, 40 m, 50 m, 60 m, and 70 m). Spectral sampling analysis was conducted on different ground backgrounds to assess their separability. Based on the four commonly used vegetation indices for crop recognition, the Excess Green Index (ExG), Red Green Ratio Index (RGRI), Green Leaf Index (GLI), and Excess Green Minus Excess Red Index (ExG-ExR), the optimal index was selected for extraction. Image processing methods such as frequency domain filtering, threshold segmentation, and morphological filtering were used to reduce the impact of weed and mulch noise on recognition accuracy. The recognition results were vectorized and combined with field data for the statistical verification of accuracy. The research results show that (1) the ExG can effectively distinguish between soil, mulch, and Chinese cabbage plants; (2) images of different spatial resolutions differ in the optimal type of frequency domain filtering and convolution kernel size, and the threshold segmentation effect also varies; (3) as the spatial resolution of the imagery decreases, the optimal window size for morphological filtering also decreases, accordingly; and (4) at a flight height of 30 m to 50 m, the recognition effect is the best, achieving a balance between recognition accuracy and coverage efficiency. The method proposed in this paper is beneficial for agricultural growers and managers in carrying out precision planting management and planting structure optimization analysis and can aid in the timely adjustment of planting density or layout to improve land use efficiency and optimize resource utilization.

Analyzing the Spatiotemporal Dynamics of Drought in Shaanxi Province

Drought, as a natural disaster with wide-ranging impacts and long duration, has an adverse effect on the global economy and ecosystems. In this paper, four remote sensing drought indices, namely the Crop Water Stress Index (CWSI), Vegetation Supply Water Index (VSWI), Temperature Vegetation Dryness Index (TVDI), and Normalized Difference Water Index (NDWI), are selected for drought analysis. The correlation analysis is carried out with the self-calibrated Palmer Drought Severity Index (sc-PDSI), and based on the optimal index (CWSI), the spatiotemporal characteristics of drought in Shaanxi Province from 2001 to 2021 were studied by SEN trend analysis, Mann–Kendall test, and a center of gravity migration model. The results show that (1) the CWSI performs best in drought monitoring in Shaanxi Province and is suitable for drought studies in this region. (2) Drought in Shaanxi Province shows a decreasing trend from 2001 to 2021; the main manifestation of this phenomenon is the decrease in the occurrence of severe drought, with severe drought covering less than 10% of the area in 2010 and subsequent years. The most severely affected regions in the province are the northern Loess Plateau region and Guanzhong Plain region. In terms of the overall trend, only 0.21% of the area shows an increase in drought, primarily concentrated in the Guanzhong Plain region and the outskirts of the Qinling–Bashan mountainous region. (3) Drought conditions are generally improving, with the droughts’ center of gravity moving northeastward at a rate of 3.31 km per year. The results of this paper can provide a theoretical basis and a practical reference for drought control and decision-making in Shaanxi Province.

Model study on transmission loss of the split-stream rushing exhaust muffler for diesel engine

Numerical simulation was carried out on the transmission loss of the split-stream rushing exhaust muffler, and the accuracy of the simulation method was verified through experiments. Taking the transmission loss as the response value, the experiment was designed by using Box-Behnken module of Design Expert software. A mathematical regression model of transmission loss with experiment factors was established by using the Box-Behnken experimental design scheme, the experiment factors include the diameter of the interior pipe, the shape of the rushing hole, the center distance of the rushing hole, the cone angle of the interior pipe as well as the number of rushing holes, and the mathematical regression model's significance was tested. The response curvatures of second order interaction to the transmission loss by different variables were obtained and the interaction relationships among variables were analyzed. The results showed that the diameter of the interior pipe and the center distance of the rushing hole are the main factors that affect the transmission loss. The transmission loss increases with the increase of the diameter of the interior pipe. When the diameter of the interior pipe is between 70 mm and 80 mm, the transmission loss firstly increases and then decreases with the center distance of the rushing hole changes from Smin to Smax. When the diameter of the interior pipe is between 80 mm and 90 mm, the transmission loss decreases with the center distance of the rushing hole changes from Smin to Smax. The effect of the rushing hole shape on transmission loss is not significant. The transmission loss increases with the increase of the number of rushing holes, but the increase of transmission loss is not significant with the number of rushing holes changes from 4 to 6 groups. Taking the transmission loss as the optimization index, the better experimental condition was obtained. Compared to the not optimized muffler of the sample engine, the average transmission loss of the optimized muffler is increased by 48.70 % when the frequency is 0-1000 Hz, the average insertion loss of the optimized muffler is increased by 7.4 %. At inlet air velocity of 40 m/s, the pressure loss is reduced by 56.8 %.

Character association and path analysis for yield and its attributing traits among urdbean landraces

In the present study, character association and path analysis were studied among 23 urdbean genotypes for 11 quantitative traits. Correlation analysis divulged that seed yield/plant proclaimed a significant positive relationship between branches/plant, biological yield/plant, pods/plant, and pod length at both genotypic and phenotypic levels and also showed a direct effect on yield. Biological yield/plant, branches/plant and pods/plant had the most beneficial direct and indirect influences on the overall relationship between yield and other component attributes, indicating the high inter-dependent interaction among these traits. Therefore, these characteristics could be considered as optimum selection indices on a priority basis, which would be remarkable for improving genotype efficiency during genetic improvement.

Directed interactive topology optimization design for multi-agent affine formation maneuver control

This paper investigates the directed interactive topology optimization design problem for multi-agent affine formation maneuver control. Firstly, considering the optimization indexes such as information interaction cost and information spreading energy consumption, a directed topology optimization model satisfying affine formation maneuver is established, including two sub-models of topology structure construction and weight allocation. Secondly, aiming at the topological structure construction for affine formation maneuver, a directed k-rooted graph detection method is proposed, which can realize the solution of d +1 -rooted constraint for directed information interaction topology, and then an improved NSGA-II topological structure construction optimization algorithm is designed. Finally, a formation of seven agents in twodimensional space is taken as an example for simulation verification. The results show that the improved topology NSGA -II topology construction optimization The algorithm has better optimization effects, can effectively provide a variety of feasible directed interactive topologies for affine formation maneuver control, and the generated interactive topology can meet the requirements of directed d +1 -rooted graph.

Synthesis and antibacterial potential of novel thymol derivatives against methicillin-resistant Staphylococcus aureus and P. aeruginosa pathogenic bacteria.

The increasing threat of antibiotic resistance has created an urgent need for new antibacterial agents, particularly plant-based natural compounds and their derivatives. Thymol, a natural monoterpenoid phenolic compound derived from Monarda citriodora, is known for its aromatic and therapeutic properties, including antibacterial activity. This study focuses on synthesizing dihydropyrimidinone and dihydropyridine derivatives of thymol and exploring their antibacterial properties. The synthesized compounds were tested for their in vitro antibacterial potential against pathogenic microorganisms, specifically Pseudomonas aeruginosa (Gram-negative) and methicillin-resistant Staphylococcus aureus (MRSA) (Gram-positive). Among the synthesized derivatives, compound 3i (ethyl 4-(4-hydroxy-5-isopropyl-2-methylphenyl)-2-imino-6-methyl-1,2,3,4-tetrahydropyrimidine-5-carboxylate) exhibited the most promising antibacterial activity, with minimum inhibitory concentration (MIC) values of 12.5µM against P. aeruginosa and 50.0µM against MRSA. Additionally, compound 3i demonstrated a synergistic effect when combined with vancomycin, enhancing its antibacterial efficacy. The optimum fractional inhibitory concentration index (FICI) observed was 0.10 and 0.5 for MRSA and P. aeruginosa, respectively, in combination with vancomycin. In silico analysis of the physiochemical properties of 3i indicated compliance with all drug-likeness rules. Furthermore, molecular docking studies revealed that compound 3i has a stronger binding affinity to the target protein than thymol, providing valuable insights into its potential mechanism of action.

Optimal attack strategy in system identification with binary‐valued observations: An information entropy‐based approach

AbstractWith the advancements in network technology and artificial intelligence, the interaction between network nodes is getting more frequent. The malicious data tampering can be extremely damaging to system stability, therefore the network security is of great importance. In this paper, we address the problem of system identification with binary‐valued observations under data tampering attacks and an information entropy‐based approach to optimal attack strategy from the viewpoint of attacker. Under the constrained attack energy, a method is proposed to obtain the optimal attack strategy using the information entropy as the optimization index. It is interestingly demonstrated that such optimal attack strategy is equivalent to the one by using the estimation error of the identification algorithm as the optimization index. The rationality of the proposed method is verified through numerical simulations.

Free convective flow of Ostwald-de Waele fluid in a Π-shaped cavity with heated strip and lateral wall cooling

This paper studies the free convection of Ostwald-de Waele fluid in a Π-shaped chamber with a Π-shaped heated strip at the lower mid-section. The upper wall and the remaining sections of the lower wall are thermally insulated, while the lateral walls are maintained at a low temperature. The primary objective is to identify the optimal power-law index (n) and the geometric configurations of the strip that yield enhanced heat transfer. Furthermore, the impact of Rayleigh number (Ra) on the thermal performance of the cavity in that optimum condition is to be investigated. The Galerkin finite element approach has been employed to resolve the governing equations and auxiliary conditions. Within the study, the power-law index of the Ostwald-de Waele fluid has been systematically varied (0.6 ≤ n ≤ 1.4). At the same time, the variations in the non-dimensional height (0.1 ≤ ζ ≤ 0.5) and width (0.1 ≤ ε ≤ 0.3) of the heated strip have been explored. The results show improved thermal performance with a growth in Rayleigh number, particularly when n < 1. The average Nussetl number for n = 0.6 at Ra = 106 is approximately 14 % higher than for n = 1.4. The findings further indicate an increment of 35 % in the heat transfer rate when the height of the strip decreases from 0.5 to 0.1. This investigation provides valuable insights into the natural convection phenomena of Ostwald-de Waele fluid in complex geometries, paving the way for advanced thermal management techniques in various engineering applications.

Inversion of Crop Water Content Using Multispectral Data and Machine Learning Algorithms in the North China Plain

(1) Background: Accurate inversion of crop water content is key to making an intelligent irrigation decision. However, little effort has been devoted to accurately estimating the crop water content of winter wheat in the North China Plain. (2) Method: The crop water content of winter wheat was measured at jointing, flowering and grain-filling stages, respectively. UAV-based multispectral remote sensing images were used to calculate thirteen vegetation indices, including SAVI, EVI, R-M, NDRE, OSAVI, GOSAVI, REOSAVI, GBNDVI, NDVI, RVI, DVI, GNDVI, and TVI. Five machine learning (ML) algorithms (i.e., MLR, RF, PLSR, ElasticNet, and ridge regression) were adopted to estimate the crop water content of winter wheat at the three growth stages. The benchmark datasets, which include CWC as well as vegetation indices calculated based on spectral indices, were adopted to validate the performance of the ML models. (3) Results: The correlation coefficients ranged from 0.64 to 0.82 at different growth stages. The optimal vegetation indices were GNDVI for the jointing stage, NDRE for the flowering and the grain-filling stage, respectively. Among the five machine learning methods, random forest (RF) showed the best performance across the three growth stages, with its coefficient of determination (R2) of 0.80, or an increase by 20.1% than those of other models. In addition, the RMSE and RPD of the RF model at the flowering stage were 3.00% and 2.01, which significantly outperformed other models and growth stages. (4) Conclusion: This study may provide theoretical support and technical guidance for monitoring current water status in wheat crops, which is useful to develop a precise irrigation prescription map for local farmers. (5) Limitation: The main limitation of this study is that the sample size is relatively small and may not fully reflect the characteristics of the target groups. At the same time, subjectivity and bias may exist in the data collection, which may have a certain impact on the accuracy of the results. Future studies could consider expanding sample sizes and improving data collection methods to overcome these limitations.

Impact of the hopper-air and overfire-air distribution on the low-NOx combustion performance within a 600 MWe staged arch-firing furnace

Within a 600 MWe staged arch-firing furnace (SAF) having the staged hopper air (HA) and overfire air (OFA) as its last two air-staging layers, the present attention was focused on evaluating the impact of the HA:OFA distribution and determining an appropriate HA:OFA ratio used to improve the hopper environment and low-NO x performance. Accordingly, by varying the HA:OFA distribution at a total air ratio of 31%, four HA:OFA ratio settings of 11:20, 13.5:17.5, 16:15, and 18.5:12.5 were formed to compare the in-furnace flow, coal combustion, and NO x emissions. With increasing the HA flux to raise the HA:OFA ratio, all of combustion symmetry, the downward flame penetration, and the HA's combustion-aided effect on char firstly improved and then weakened, the OFA penetration became shallower, and the high-temperature flame in the upper furnace extended. Final performance indexes showed that both NO x emissions and burnout loss first declined and then ascended, meaning a rise-to-drop trend in the low-NO x performance as deepening the HA:OFA ratio. Among the four settings, the moderate HA:OFA = 13.5:17.5 achieved the optimal indexes with NO x emission of 543 mg/m3 (6% O2) and carbon in fly ash of 4.89%. Compared with its previous reference furnace (holding levels of 905 mg/m3 and 5.1% respectively in the two index aspects), the SAF furnace using such a HA:OFA distribution mode reduced significantly NO x by 40%, improved a little burnout, and dropped apparently the hopper temperatures by 500 K to eliminate its high overheating risk.

Analysis of Optimal HVDC Back-to-Back Placement Based on Composite System Reliability

HVDC is a promising interconnection solution for connecting asynchronous systems and ensuring power control. In Indonesia, a remote industrial system in Sumatra is experiencing load growth and has the option to draw power from the Sumatra system. However, due to frequency differences, the use of HVDC is crucial. The Generation Expansion Planning has proposed six converters but not their interconnection points. This study will determine the most reliable interconnection locations. The chosen converters are modular multilevel converters (MMCs) with high modularity. The converter reliability modeling considers voltage levels, the number of modules, and redundancy strategies. This modeling is then implemented at the power system level to obtain the best placement at the available high-voltage (HV) substation options. Determining the best placement is based on the optimal reliability index. The optimal placement also includes the option to convert from HV to medium-voltage (MV) interconnection. MV interconnection offers higher flexibility but tends to be more expensive. The availability for HV converters is 99.69%, while for MV converters, it is slightly higher, at 99.81%. Additionally, converting from HV to MV reduces the SAIFI (system average interruption frequency index) from 0.2668 to 0.2284 occurrences per year, lowering the interruption cost from 7.804 million USD to 5.737 million USD per year. The sensitivity of interruption, investment, and maintenance costs shows that converting at least one HV converter to MV remains economical. In this case study, the optimal converter placement includes Area VI–2, recommended for conversion from HV to a more distributed MV configuration, improving reliability and economic efficiency.