Curve Method Research Articles

Fatigue assessment of structural components through the Effective Critical Plane factor

The integrity assessment of structural components under complex loading conditions relies on the evaluation of the fatigue damage typically arising from stress concentrations, such as geometric irregularities, notches, weld beads, grooves etc.. Various methodologies, including the Notch Stress Approach (NSA), the Theory of Critical Distances (TCD), the Strain Energy Density (SED), and the Critical Plane (CP) concept, have been pivotal in assessing fatigue strength for notched and welded components. Recent works combine some of the above mentioned methodologies, while other works propose to vary the embedded parameters accounting for the loading type or the fatigue lives, trying to improve the accuracy of the fatigue assessment process. This paper introduces a novel approach, the Effective Critical Plane (ECP), which is founded on the critical plane concept. The CP factor is, however, calculated starting from an averaged, over a small volume, stress–strain field. The size of the averaging volume is assumed to be a material parameter and is determined by a best fitting procedure over different experimental data sets. The novel approach is illustrated by means of the Fatemi-Socie and the Smith-Watson-Topper CP damage factors. Its potential application to other CP formulations is straightforward, as well. Literature experimental data for low carbon steel specimens possessing different notches and loading conditions are used to validate the method’s capability in accurately determining the fatigue life and to set the radius of the averaging volume for the given material and CP parameter. A spherical volume or circular area are used in case of fully 3D or 2D numerical models, respectively. Results are compared to those of some of already existing methods, namely SED, TCD and the Modified Wöhler Curve Method.

Multiplex methylation detection assays using a blocking FRET probe with machine learning-assisted quantitative melting curve method targeting early-stage breast cancer

Breast cancer (BC) is the second most prevalent form of cancer, and poses a significant threat to public health. DNA methylation is an ideal marker for the early detection of BC. Fluorescence quantitative polymerase chain reaction (PCR)-based DNA methylation detection is simpler and faster but is constrained by its multiplexing capability and specificity. To address this, we developed a multiplex quantitative methylation PCR assay for the simultaneous analysis of methylation status at multiple sites specific to BC (cg11754974, cg13828440, cg18637238, and cg16652347). The machine learning model was trained using 1200 cases of multipeak data to enhance the melting curve resolution. Performance testing demonstrated the method’s ability to selectively amplify methylated genes at a DNA concentration of 1 × 105 copies μL−1, with high replicability (coefficient of variation <5 %) and mutation detection capabilities as low as 10 %. When applied to 80 clinical BC samples, the assay effectively distinguished patients with early-stage BC from normal controls, achieving an area under the curve of 0.8938, sensitivity of 83.33 %, and specificity of 84.62 %. Our essay exhibits superior clinical performance when compared to the quantitative methylation-specific PCR assays for noninvasive detection of early-stage BC, which is poised to become a favorable clinical diagnostic method for early-stage BC owing to its simplicity, speed, and capacity to improve diagnostic accuracy.

A novel evaluation mode of Nao An Capsules using multi-wavelength fusion fingerprint combined with quantitative analysis of multi-components by a single marker method and antioxidant activity

Nao An Capsules (NAC) are utilized in the acute phase of cerebral thrombosis and are appropriate for patients in the recovery stage exhibiting symptoms of qi deficiency and blood stasis. Nevertheless, there has been a lack of recent research on the overall quality control of NAC. The objective of this study is to establish a comprehensive quality control strategy for NAC. This article presents a methodology based on the average linear quantitative fingerprinting method (ALQFM) in conjunction with multi-wavelength high performance liquid chromatography (HPLC) fusion fingerprinting, quantized Fourier transform infrared (FT-IR) spectroscopy and ultraviolet (UV) spectral techniques to comprehensively evaluate NAC from both qualitative and quantitative perspectives. Subsequently, quantitative analysis of multi-components by a single marker (QAMS) method and the Standard Curve Method (SCM) were used to quantitatively determine 10 quality markers (Q-markers) in 22 batches of samples, with the results indicating that QAMS is a reliable method for quantifying active ingredients. Chemometrics methods such as principal component analysis (PCA) and principal component analysis (HCA) were also used to analyze the quality of samples. To achieve a thorough and methodical evaluation, a combined assessment of the three fingerprints was executed. Subsequently, an in vitro antioxidant activity experiment was performed utilizing the 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical-scavenging technique. By employing a partial least squares (PLS) model, the correlation between HPLC fingerprint chromatography and antioxidant activity was investigated. The study found that out of the 59 common peaks in the five-wavelength fusion fingerprint spectra of NAC, 33 peaks exhibit antioxidant activity. In this study, multi-wavelength fusion HPLC, quantized FT-IR and UV were jointly used to effectively identify functional groups and fingerprint information in compounds. It can fully and quickly obtain the fingerprint information of the compounds in the sample. NAC multi-components quantification and overall quality evaluation were performed using ALQFM and QAMS and chemometrics methods. We have developed a new approach for comprehensive, rapid, and cost-effective quality control of traditional Chinese medicine (TCM) compound preparations.

Renormalized Internally Contracted Multireference Coupled Cluster with Perturbative Triples.

In this work, we combine the many-body formulation of the internally contracted multireference coupled cluster (ic-MRCC) method with Evangelista's multireference formulation of the driven similarity renormalization group (DSRG). The DSRG method can be viewed as a unitary multireference coupled cluster theory, which renormalizes the amplitudes based on a flow equation approach to eliminate numerical instabilities. We extend this approach by demonstrating that the unitary flow equation approach can be adapted for nonunitary transformations, rationalizing the renormalization of ic-MRCC amplitudes. We denote the new approach, the renormalized ic-MRCC (ric-MRCC) method. To achieve high accuracy with a reasonable computational cost, we introduce a new approximation to the Baker-Campbell-Hausdorff expansion. We fully consider the linear commutator while approximating the quadratic commutator, for which we neglect specific contractions involving amplitudes with active indices. Moreover, we introduce approximate perturbative triples to obtain the ric-MRCCSD[T] method. We demonstrate the accuracy of our approaches in comparison to advanced multireference methods for the potential energy curves of H8, F2, H2O, N2, and Cr2. Additionally, we show that ric-MRCCSD and ric-MRCSSD[T] match the accuracy of CCSD(T) for evaluating spectroscopic constants and of full configuration interaction energies for a set of small molecules.

Infrared spectral library of tooth enamel from African ungulates for accurate electron spin resonance dating

Electron spin resonance coupled with uranium-series dating (ESR/U-series) of carbonate hydroxyapatite in tooth enamel is the main technique used to obtain age determinations from Pleistocene fossils beyond the range of radiocarbon dating. This chronological information allows to better understand diachronic change in the palaeontological record, especially with regard to the evolution of the genus Homo. Given the relative paucity of human teeth at palaeontological and archaeological localities, ESR/U-series is widely applied to the teeth of ungulate species. However, the accuracy of ESR/U-series ages is greatly affected by the incorporation of uranium in the enamel during burial in sediments. It has been shown that uranium content is positively correlated with an increased degree of atomic order in carbonate hydroxyapatite crystals, the latter determined using infrared spectroscopy. Here we present a reference infrared spectral library of tooth enamel from African ungulates, based on the grinding curve method, which serves as baseline to track the diagenetic history of carbonate hydroxyapatite in different species and thus select the best-preserved specimens for dating.

Quadratic Bézier curve method for continuous freeform optical surface design

A novel approach is introduced for the design of freeform axisymmetric optical surfaces using an optimization technique based on quadratic Bézier curves. Notably, the continuity (or lack thereof) of the freeform surface produced using the proposed technique is largely unaffected by the source-target mapping function. The validity of the proposed methodology is demonstrated through its application to the design of several laser beam shapers. The results show that the proposed technique requires only a small number of structural points to converge to the optimum design solution. The freeform design method presented herein is mathematically straightforward and can be easily implemented in code. Thus, it offers significant advantages for the design and analysis of a diverse range of optical systems.

A CFD-DEM-FEM coupling method for the ice-induced fatigue damage assessment of ships in brash ice channels

Polar transport ships frequently traverse in the brash ice channel opened by icebreakers. Although the substantial ice resistance caused by direct collisions with the level ice is avoided, the hull still encounters collisions with the brash ice, leading to periodic damage and exacerbating the fatigue issues of the hull structure. To address the fatigue challenges faced by ships sailing in the brash ice channels, this paper proposes an ice-induced fatigue damage assessment method based on the CFD-DEM-FEM. Referring to the brash ice model test conducted at the Hamburg Ship Model Basin (HSVA), a discrete element ice model and a numerical brash ice tank are established using the CFD-DEM coupling method. The simulated ship-ice interaction is compared with HSVA’s experimental results to validate the reliability of the numerical brash ice tank and ice load. The ice load time history resulting from the ship-brash ice collision is applied to the hull, and the hot spot stress time history under each fatigue sub-condition is calculated using the FEM. The improved rain-flow counting method is employed to determine the stress level of the hot spot stress time history, and the S-N curve method based on the linear cumulative damage criterion is used to calculate the total fatigue damage of hot spots. Finally, the results of the proposed method are compared with those of the LR method. This study can serve as a valuable reference for the ice-induced fatigue assessment of ships navigating in brash ice channels.

Machine Learning on Ultrasound Texture Analysis Data for Characterizing of Salivary Glandular Tumors: A Feasibility Study.

Objective quantitative texture characteristics may be helpful in salivary glandular tumor differential diagnosis. This study uses machine learning (ML) to explore and validate the performance of ultrasound (US) texture features in diagnosing salivary glandular tumors. 122 patients with salivary glandular tumors, including 71 benign and 51 malignant tumors, are enrolled. Representative brightness mode US pictures are selected for further Gray Level Co-occurrence Matrix (GLCM) texture analysis. We use a t-test to test the significance and use the receiver operating characteristic curve method to find the optimal cut-point for these significant features. After splitting 80% of the data into a training set and 20% data into a testing set, we use five machine learning models, k-nearest Neighbors (kNN), Naïve Bayes, Logistic regression, Artificial Neural Networks (ANNs) and supportive vector machine (SVM), to explore and validate the performance of US GLCM texture features in diagnosing salivary glandular tumors. This study includes 49 female and 73 male patients, with a mean age of 53 years old, ranging from 21 to 93. We find that six GLCM texture features (contrast, inverse difference movement, entropy, dissimilarity, inverse difference and difference entropy) are significantly different between benign and malignant tumors (p < 0.05). In ML, the overall accuracy rates are 74.3% (95%CI: 59.8-88.8%), 94.3% (86.6-100%), 72% (54-89%), 84% (69.5-97.3%) and 73.5% (58.7-88.4%) for kNN, Naïve Bayes, Logistic regression, a one-node ANN and SVM, respectively. US texture analysis with ML has potential as an objective and valuable tool to make a differential diagnosis between benign and malignant salivary gland tumors.

Bearing Capacity and Settlement of Bored Pile Foundation Based On Spt Test Data Aand CPT Test

One form of government effort for the community is the construction of flats. In the construction of Flats X, it is necessary to conduct an analysis in order to minimize the occurrence of foundation collapse beyond the permit limit requirements. In this research analysis using axial and lateral carrying capacity. For axial carrying capacity based on NSPT values using three methods, namely Wright and Reese, Mayerhoff, Luciano Decourt. As well as on shaman power based on the CPT Test using three methods, namely Aoki de Lancer, Mayerhoff, and Schmertmann and Nottingham. For lateral carrying capacity using the calculation of the p-y curve method and using LPille software. For the descent of the foundation using Settle3D. So, obtained in this study for axial carrying capacity taken from three methods in the SPT Test is using the mayerhoff method with the results obtained, namely Q (all press) = 170 tons and Q (all pull) = 85 tons. The axial carrying capacity based on the Cpt Test is obtained from the smallest value in the three methods is the Aoki de Lancer method with the results obtained, namely Q (all press) = 83 tons and Q (all pull) = 14 tons. For lateral carrying capacity using the LPille program, a lateral load of 63.2 kN was obtained. For the number of foundation needed, namely 306 foundations. As well as for the lowering of the foundation obtained using Settle3D which is 96.3 mm.

A hybrid system dynamics model for power mix trajectory simulation in liberalized electricity markets considering carbon and capacity policy

Policy design during the energy system transition requires systematic thinking to navigate its complexity and mitigate potential side effects that policies may induce. Policy design based on System Dynamics (SD) simulations offers a tool to analyze real-world complexities. Given the difficulty of strictly simulating grid dispatch through causal loops, the Screening Curve (SC) method is widely employed in SD policy simulation models within closed commercial software platforms. Nevertheless, as Variable Renewable Energy (VRE) rises, the SC method faces challenges in accurately simulating grid dispatch operation due to its oversight of the power plants' limited output adjustment capabilities. This study developed a hybrid SD model as an alternative to the typical SD-SC combined method, allowing the SD model to simulate the power mix trajectory change incorporating high VRE penetration. After examining the simulation results with Hokkaido as a case study, the SC method may overestimate the abatement outcomes of the carbon pricing, and underestimate the support from capacity mechanism for the coal power plants. The difference between the two model increases along with the VRE share rising, when the VRE share exceeds about 40 % the results of the hybrid model will be more reliable.

Further study on the crossing curves in two-delay differential equations with delay-dependent coefficients

The crossing curves method, which allows delays to vary simultaneously, is generalized to the scenario that four terms exist in the characteristic equations with delay-dependent coefficients. The crossing curves on the two-delay parameter plane are first plotted by our generalized algorithms. The criteria to determine the crossing direction are also given. Finally, an example is provided to support our method and illustrate its fortes.

Profile of Round-Trip Efficiency of Lithium-Ion Battery Energy Storage System

Lithium-ion battery energy storage systems (Li-BESSs) are expected to adjust variable renewable energies such as photovoltaic energy. In the viewpoint of the energy management cost, profiles of round-trip efficiency (RTE) are important characteristics of Li-BESSs. The profiles of RTE are related to the characteristics of both LIBs and inverters installed on Li-BESSs. The characteristics of Li-BESSs would be various because of reusing and degradation of lithium-ion batteries (LIBs). And inverters made by a lot of manufacturers would be so on. Accordingly, it could be said that the understanding of the profile of each Li-BESS’s RTE is complex and difficult. Therefore, we studied the time series analysis of operation data and tried to estimate the characteristics of both LIBs and inverters. The important ones of LIB are full-charge capacity (FCC), open-circuit voltage (OCV), and internal resistance (R). In addition, characteristics of OCV and R can be expressed as function profiles with state of charge (SOC) as elements. Similarly, the important one of inverter is the typical efficiency curve. In this study, we proposed the estimation method of the profiles of OCV and R and inverter efficiency curve. On the other hand, FCC was given as assuming that general diagnosis results would be provided. Then, a RTE profile of Li-BESS (including 9.94kWh LIB and 10kW inverter) was tried to establish from the results of it. The profiles of OCV and R were estimated by the method called MGFFD (Modified Gaussian Free-Form Deformation) that we have been established [1]. The novelty of this study could be described as: Development of the estimation method of an efficiency curve of inverter installed on BESS during operation (Fig.1). Quantitatively visualized profile of RTE of Li-BESS by combining results of MGFFD estimation applied for LIBs and inverter (Fig.2). The profile described on Fig.2 is the relationship between SOC, input-output power rate of inverter, and RTE of Li-BESS. To understand Li-BESS’s charge-discharge efficiency of from state at that time to the end of an operation plan, it would be necessary information. This proposed method could clarify various RTE of Li-BESSs, consequently, be useful for its efficient operation by information communication technology.Reference[1] M. Arima, L. Lin, and M. Fukui, "Kalman-Filter-Based Learning of Characteristic Profiles of Lithium-Ion Batteries", Sensors, vol. 22, no. 14, 5156. Figure 1

N-level complex helical structure modeling method

This paper primarily explores the modeling method of n-level complex helical structures with coal mining machine cables as the research object. The paper first elaborately introduces the modeling method of n-level helix curves based on parametric equations and coordinate transformations, and compensates for the n-level helix curves with corrected pitch, which can obtain more accurate n-level helix curves and improve the accuracy of n-level helix curves modeling. Subsequently, based on this high-precision n-level helix curves modeling method, the paper elaborates on the method of solving pitch and twisting radius of multi-layer helical structure. Calculation scripts were written based on the above methods, which can be used to batch calculate the twisting radius and pitch of each layer structure in multi-layer structures when satisfying the conditions of in-layer tangency, inter-layer tangency, and extrusion deformation, and retain the actual results through logical judgment. Then, based on the above two methods, the paper developed a modeling method for braided structures based on piecewise functions containing fifth-order polynomials, which can effectively avoid the problem of insufficiently dense arrangement of braided lines and easy interference in traditional methods. Finally, a set of modeling tools was developed using C# and Python in Grasshopper to implement the modeling algorithm. Taking the MCPT-1.9/3.3 3120 + 170 + 4 * 10 coal mining machine cable as an example. The cable was modeled using both the method proposed in this paper and the traditional method. Comparative data shows that the method proposed in this paper can reduce errors by 3.31E6 times in the second-level and above helical structures. In addition this paper compares the standard line length, measured line length, and the line length established by the proposed model, showing that the relative errors are both less than 0.1941%. This paper provides a new, systematic, high-precision, and full-process cable modeling method, in which all parameters except the process parameters are accurately solved by equations. It lays a theoretical foundation for the high-precision simulation and intelligent sensing cables, which is of great significance for improving the safety, stability, and efficient development of the coal mining industry. The research results of the paper can not only be applied to the modeling of coal mining machine cables but also can be extended to the modeling of other complex multi-layer helical structures.

An efficient pre-analysis and optimization generation method for reference curves of automated fiber placement path planning

Complex curved composite components often rely on multiple reference curve algorithms for path planning in automated fiber placement. However, the reference curves are typically manually drawn. Moreover, designing the reference curves follows an iterative planning-analysis-improvement process, which can be inefficient. A new approach for the automatic pre-analysis and optimized generation of reference curves for fiber placement is proposed in this paper to enhance the efficiency of reference curve analysis and generation. Firstly, a pre-analysis algorithm for reference curves based on triangular meshes is proposed. This algorithm analyzes the theoretical geodesic curvature and angular deviation of the path before its planning. Subsequently, a comprehensive evaluation index for reference curve generation is formulated based on the pre-analysis algorithm, and the reference curve is optimized using genetic algorithms. The results demonstrate that the pre-analysis algorithm accurately computes the steering radius distribution of the path. Areas with over-limit steering radius can be eliminated while maintaining angular deviations within 10° by utilizing optimized reference curves for path planning.

Improving service accessibility and equity for sustainable development goals without newly facilities by rural settlement reconstruction

Ensuring the provision of accessible, affordable, and high-quality public services to all individuals aligns with one of the paramount aims of the United Nations’ Sustainable Development Goals (SDGs). In the face of escalating urbanization and a dwindling rural populace in China, reconstructing rural settlements to enhance public service accessibility has become a fundamental strategy for achieving the SDGs in rural areas. However, few studies have examined the optimal methods for rural settlement reconstruction that ensure accessible and equitable public services while considering multiple existing facilities and service provisions. This paper focuses on rural settlement reconstruction in the context of the SDGs, employing an inverted MCLP-CC (maximal coverage location problem for complementary coverage) model to identify optimal rural settlements and a rank-based method for their relocation. Conducted in Changyuan, a county-level city in Henan Province, China, this study observed significant enhancements in both accessibility and equity following rural settlement reconstruction by utilizing the MH3SFCA (modified Huff 3-step floating catchment area) and the spatial Lorenz curve method. Remarkably, these improvements were achieved without the addition of new facilities, with the accessibility increasing by 44.21%, 4.97%, and 3.11%; Gini coefficients decreasing by 19.53%, 1.64%, and 3.18%; Ricci-Schutz coefficients decreasing by 21.09%, 2.09%, and 4.33% for educational, medical, and cultural and sports facilities, respectively. It indicated that rural settlement reconstruction can bolster the accessibility and equity of public services by leveraging existing facilities. This paper provides a new framework for stakeholders to better reconstruct rural settlements and promote sustainable development in rural areas in China.

Determination of five veterinary drug residues in milk by ultra-high performance liquid chromatography-quadrupole/electrostatic field orbitrap mass spectrometry based on modified chitosan membrane purification

Milk is an important consumer product with high nutritional value. The presence of veterinary drug residues in milk owing to the indiscriminate use of veterinary drugs may affect consumer health. In the mass spectrometric analysis of trace compounds, chromatographic co-eluting components easily interfere with the mass spectral signals obtained, affecting the accuracy of qualitative and quantitative analyses. Matrix purification is a promising method to reduce the matrix effect. Chitosan is a natural biopolymer with numerous active functional groups such as amino, acetyl, and hydroxyl groups; these groups can adsorb lipids through hydrophobic and electrostatic interactions. Chitosan also has the advantages of low production cost, stable chemical properties, and convenient modification. Novel chitosan-based materials are promising candidates for lipid purification. In this study, a chitosan membrane was modified with trimethoxyoctadecylsilane (C18-CSM). C18-CSM was prepared through one-step hydrolysis and used as a dispersive solid phase extraction (DSPE) adsorbent to purify the matrix during milk pretreatment. We combined C18-CSM with ultra-high performance liquid chromatography-quadrupole/electrostatic field orbitrap mass spectrometry (UHPLC-Q/Exactive Orbitrap MS) to develop an effective method for the extraction and determination of ofloxacin, enrofloxacin, ciprofloxacin, diazepam, and metronidazole in milk. C18-CSM was characterized using scanning electron microscopy, Fourier transform infrared spectroscopy, and water contact angle testing. The results indicated that the material has a rough surface and uniformly dense cross-section. The water contact angle of C18-CSM was 104°, indicating its good hydrophobicity. The pretreatment conditions (extraction solvent, dosage of NaCl, extraction frequency, and dosage of C18-CSM) that influenced the recoveries of the five veterinary drugs were investigated in detail. The optimal conditions were established as follows: 5% formic acid in acetonitrile, 1 g NaCl, extraction 1 time, 20 mg C18-CSM. Separation was performed on a Hypersil GOLD VANQUISH column (100 mm×2.1 mm, 1.9 μm). The mobile phase consisted of 0.1% formic acid aqueous solution and 0.1% formic acid in acetonitrile, and was flowed at a rate of 0.3 mL/min. The sample injection volume was 1 μL, and the column temperature was maintained at 25 ℃. Mass spectrometric analysis was performed in positive electrospray ionization mode. To verify the necessity of the purification material, the matrix effect was investigated using the matrix-matched standard curve method. The use of C18-CSM reduced the matrix effects of the five necessity drugs from the range of -22%-8.8% to the range of -13%-3.6%, indicating that C18-CSM is a highly efficient DSPE material. Under optimal conditions, the developed method showed good linearities within the range of 0.5-100 μg/L, with correlation coefficients (r2)≥0.9970. The limits of detection(LODs) and quantification (LOQs) were 0.2 μg/L and 0.5 μg/L, respectively. To assess the accuracy and precision of the method, we prepared milk samples with three spiked levels (low, medium, and high). The recoveries of the five veterinary drugs were ranged from 79.5% to 115%, and the intra-day and inter-day relative standard deviations were 7.0%-13% (n=6) and 1.3%-11% (n=3), respectively. This study provides a simple, accurate, and reliable method for the rapid and simultaneous determination of the five veterinary drug residues in milk.

Lifetime measurement in 182W using the Differential Decay Curve Method

Lifetime measurement in 182W using the Differential Decay Curve Method

CBIL-VHPLI: a model for predicting viral-host protein-lncRNA interactions based on machine learning and transfer learning

Virus‒host protein‒lncRNA interaction (VHPLI) predictions are critical for decoding the molecular mechanisms of viral pathogens and host immune processes. Although VHPLI interactions have been predicted in both plants and animals, they have not been extensively studied in viruses. For the first time, we propose a new deep learning-based approach that consists mainly of a convolutional neural network and bidirectional long and short-term memory network modules in combination with transfer learning named CBIL‒VHPLI to predict viral–host protein‒lncRNA interactions. The models were first trained on large and diverse datasets (including plants, animals, etc.). Protein sequence features were extracted using a k-mer method combined with the one-hot encoding and composition–transition–distribution (CTD) methods, and lncRNA sequence features were extracted using a k-mer method combined with the one-hot encoding and Z curve methods. The results obtained on three independent external validation datasets showed that the pre-trained CBIL‒VHPLI model performed the best with an accuracy of approximately 0.9. Pretraining was followed by conducting transfer learning on a viral protein–human lncRNA dataset, and the fine-tuning results showed that the accuracy of CBIL‒VHPLI was 0.946, which was significantly greater than that of the previous models. The final case study results showed that CBIL‒VHPLI achieved a prediction reproducibility rate of 91.6% for the RIP-Seq experimental screening results. This model was then used to predict the interactions between human lncRNA PIK3CD-AS2 and the nonstructural protein 1 (NS1) of the H5N1 virus, and RNA pull-down experiments were used to prove the prediction readiness of the model in terms of prediction. The source code of CBIL‒VHPLI and the datasets used in this work are available at https://github.com/Liu-Lab-Lnu/CBIL-VHPLI for academic usage.

Predicting forest fire probability in Similipal Biosphere Reserve (India) using Sentinel-2 MSI data and machine learning

The global escalation in forest fires, characterized by increasing frequency and severity, results from a complex interplay of natural and anthropogenic factors, exacerbated by climate change. These fires devastate habitats, threaten species, reduce biodiversity, disrupt natural cycles, and harm local ecosystems. The impacts are particularly damaging in biological reserves. The Similipal Biosphere Reserve (SBR) in Odisha State is one of India’s major forest fire hotspots, experiencing forest fires almost every year. The objective of this study is to develop a predictive model using Sentinel-2 MSI data and machine learning (ML) techniques to estimate the probability of forest fires in the SBR, India, thereby enhancing disaster management and prevention in the region. This research maps and quantifies forest fire intensity by leveraging ML algorithms, namely Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Random Forest (RF). To develop a Forest Fire Probability (FFP) map, twenty conditioning factors, along with pre- and post-fire Normalized Burn Ratio (NBR) and delta Normalized Burn Ratio (dNBR), were utilized. Furthermore, four statistical methods—Mean Absolute Error, Mean Square Error, Root Mean Square Error, and Overall Accuracy—were employed to analyze the FFP. The results were validated using the Area Under Curve (AUC) method. The analysis identifies 2021 as the year with the highest incidence of forest fires, accounting for 29.19% of the occurrences. Among the models, the GBM exhibits superior performance, highlighting its efficacy in handling large, multidimensional datasets. Predictive mapping suggests that approximately 1400–1500 km2, or 25–30% of the studied area, faces a high to very high risk of forest fires.

Measuring the water content of polymer electrolyte membranes using double points of proton magic angle spinning nuclear magnetic resonance data

Although the water content in polymer electrolyte membranes (PEMs) is a very important parameter for controlling their performance, measuring it is difficult because strong acidic groups such as sulfonic acid in PEMs make them very hygroscopic. In this study, we demonstrate that the proton magic-angle-spinning nuclear magnetic resonance spectroscopic data of a Nafion PEM accompanied by weight data measured for two different water contents can be used to determine the water content of dry as well as swollen PEM. The advantages of this method are that completely dry-PEM weight can be obtained without perfectly removing water from the PEM and no specific drying condition is required. Further, the advantages and disadvantages of the proposed method and calibration curve methods are discussed. The proposed method can be applied to various PEMs provided accurate equivalent-weight values for the PEMs are available.