Low Error Values Research Articles

Point and interval prediction for significant wave height based on LSTM-GRU and KDE

Accurate wave height prediction is significant for wave power generation, maritime traffic, and disaster prevention and reduction. This paper proposes a novel point and interval prediction framework based on deep learning methods for wave height prediction. First, multidimensional and multisource datasets are preprocessed. Subsequently, a feature selection process is executed to remove superfluous features. Third, a hybrid model that combines long short-term memory (LSTM) and a gated recurrent unit (GRU) is used to predict wave height. This model uses multivariate features that are positively correlated with wave height as input data. Finally, kernel density estimation (KDE) is leveraged to estimate the probability density distribution of the prediction errors generated the wave height prediction interval. The proposed LSTM-GRU-KDE forecasting framework is compared with benchmark models to verify its practicability. The LSTM-GRU model with multiple feature inputs has lower root mean square error and mean absolute error values and a higher coefficient of determination in predicting hourly wave height for all buoy stations compared to those of LSTM with multiple feature inputs and LSTM-GRU with a single feature input. Moreover, by the coverage width-based criterion, KDE (box) stands out as a superior choice among interval forecasting methods, offering a higher degree of reliability when constructing prediction intervals.

Smart nano generation of transgenic algae expressing white spot syndrome virus in shrimps for inner ear-oral infection treatments using the spotted hyena optimizer (SHO)-Long short-term memory algorithm

Nanotechnology offers a promising avenue to amplify the effectiveness and precision of using transgenic algae in managing WSSV in shrimp by possibly crafting nano-carriers for targeted therapeutic agent delivery or modifying algae cells at a molecular level. Leveraging the capabilities of nano-scale interventions, this study could explore innovative means to manipulate cellular processes, control biological interactions, and enhance treatment efficacy while minimizing undesirable impacts in aquatic environments. The White Spot Syndrome Virus (WSSV) is a double-stranded DNA virus with a tail and rod form that belongs to theNimaviridaefamily. There is no workable way to manage this illness at the moment. This research proposes a new model based on the Long Short-Term Memory (LSTM) and Spotted Hyena Optimizer (SHO) method to control the inner ear-oral infection, utilizing transgenic algae (Chlamydomonas reinhardtii). It is pretty tricky to modify the weight matrix in LSTM. The output will be more accurate if the weight of the neurons is exact. Histological examinations and nested polymerase chain reaction (PCR) testing were performed on the challenged shrimp every 4 h to assess the degree of white spot disease. The SHO-LSTM has shown the highest accuracy and Roc value (98.12% and 0.93, respectively) and the lowest error values (MSE = 0.182 and MAE = 0.48). The hybrid optimized model improves the overall inner ear-oral linked neurological diseases detection ratio. Additionally, with the slightest technical complexity, it effectively controls the forecast factors required to anticipate the ENT. Algal cells were found to be particularly well-suited for inner ear-oral infections, and shrimps fed a transgenic line had the best survival ratio in WSSV infection studies, with 87% of the shrimp surviving. This shows that using this line would effectively stop the spread of WSSV in shrimp populations.

Multivariable Regression 3D Failure Criteria for In-Situ Rock

Wellbore stability problems increase with the exploration and development of oil and gas reservoirs. A new 3D non-linear failure criterion is proposed as a trigonometric function considering the intermediate principal stress (2) on the triaxial compression test data. Mohr-Coulomb and Hoek-Brown are well-known failure criteria, but they do not consider the influence of (2) on rock strength. This new criterion produces a concave surface on the principal stress space (1,2, 3) with the influence of intermediate principal stress. In this study, sensitivity analysis for the variable is also done to understand the significant influence of parameters on the accuracy of the proposed criterion. Further validation of this non-linear criterion on three principal stresses (1,2, 3) was done compared with linear regression and second-degree polynomial regression results. It has been observed that the new non-linear 3D criterion with five material parameters reveals a good fit compared to linear regression and second-degree polynomial regression, which have four and six material parameters, respectively. The new non-linear criterion was further validated by comparison with existing criteria like the Priest, Drucker-Prager, and Mogi-Coulomb. It has been observed that the new 3D non-linear criterion shows a more accurate result than these existing criteria as certain rock types exhibit coefficient of determination (DC) values near one, precisely 0.95 for inada granite, 0.94 for orikabe monzonite, and 0.91 for KTB amphibolite. In contrast, other rock types have DC values ranging from 0.7 to 0.9. The new 3D non-linear criterion also yields lower root means square error (RMSE) values than the Mogi-Coulomb criterion for seven rock types. Specifically, the RMSE values by the new criterion are as follows: KTB amphibolite - 40.03 MPa, Dunham dolomite - 15.16 MPa, Shirahama sandstone - 9.08 MPa, Manazuru andesite - 22.14 MPa, Inada granite - 35.47 MPa, and Coconino sandstone - 19.047 MPa. This new 3D criterion gave precise predictions of the failure of the formation under in-situ stresses and was further helpful for the simulation of the wellbore in the petroleum industry. The variable in the new 3D criterion should be calculated from triaxial compression test data for each formation rock before applying this criterion to the wellbore stability problem and the sand production problem.

Performance Evaluation of an Immersive Virtual Reality Application for Rehabilitation after Arthroscopic Rotator Cuff Repair.

Few studies have evaluated the effectiveness of shoulder rehabilitation in virtual environments. The objective of this study was to investigate the performance of a custom virtual reality application (VR app) with a stereophotogrammetric system considered the gold standard. A custom VR app was designed considering the recommended rehabilitation exercises following arthroscopic rotator cuff repair. Following the setting of the play space, the user's arm length, and height, five healthy volunteers performed four levels of rehabilitative exercises. Results for the first and second rounds of flexion and abduction displayed low total mean absolute error values and low numbers of unmet conditions. In internal and external rotation, the number of times conditions were not met was slightly higher; this was attributed to a lack of isolated shoulder movement. Data is promising, and volunteers were able to reach goal conditions more often than not. Despite positive results, more literature comparing VR applications with gold-standard clinical parameters is necessary. Nevertheless, results contribute to a body of literature that continues to encourage the application of VR to shoulder rehabilitation programs.

Integrating topographic features and patch matching into point cloud restoration for terrain modelling

ABSTRACTPoint clouds are widely used in Earth surface research but usually exhibit gaps of missing data. Previous point cloud restoration methods used in terrain modelling have not fully considered complex terrain characteristics, which can be summarised as the controlling role of topographic features in shaping terrain surfaces and the inherent similarities observed among these surfaces. This work introduces a novel method that integrates Topographic Features and Patch Matching (TFPM) into point cloud restoration processes for terrain modelling. The method mainly contains three steps. First, identifying gap boundary points. Second, topographic feature points are extracted and subsequently interpolated into the identified gaps. Third, searching other parts of the raw point cloud for patches resembling the gaps, and the identified patches are used as templates to restore the point cloud. The proposed method is benchmarked against three state-of-the-art point cloud restoration methods. The experimental results demonstrate that the TFPM method consistently exhibits superior accuracy in terrain modelling and analysis, as evidenced by low values of the root mean square error, average elevation difference, and average slope difference. This work endeavours to incorporate topographic features into point cloud restoration processes and can benefit future research related to terrain modelling and analysis.

Deep fusible skinning of animation sequences

Animation compression is a key process in replicating and streaming animated 3D models. Linear Blend Skinning (LBS) facilitates the compression of an animated sequence while maintaining the capability of real-time streaming by deriving vertex to proxy bone assignments and per frame bone transformations. We introduce a innovative deep learning approach that learns how to assign vertices to proxy bones with persistent labeling. This is accomplished by learning how to correlate vertex trajectories to bones of fully rigged animated 3D models. Our method uses these pretrained networks on dynamic characteristics (vertex trajectories) of an unseen animation sequence (a sequence of meshes without skeleton or rigging information) to derive an LBS scheme that outperforms most previous competent approaches by offering better approximation of the original animation sequence with fewer bones, therefore offering better compression and smaller bandwidth requirements for streaming. This is substantiated by a thorough comparative performance evaluation using several error metrics, and compression/bandwidth measurements. In this paper, we have also introduced a persistent bone labeling scheme that (i) improves the efficiency of our method in terms of lower error values and better visual outcome and (ii) facilitates the fusion of two (or more) LBS schemes by an innovative algorithm that combines two arbitrary LBS schemes. To demonstrate the usefulness and potential of this fusion process, we have combined the outcome of our deep skinning method with that of Rignet—which is a state-of-the-art method that performs rigging on static meshes—with impressive results.

Simulation of the soil water-carrying capacity of Haloxylon ammodendron plantations in the Alxa Desert, China: Implications for afforestation

In recent years, China has achieved remarkable results in curbing desertification through afforestation. However, the dry soil layer resulting from the long-term water consumption of overloaded perennial plants seriously threatens the sustainability of afforestation, and quantifying the maximum vegetation carrying capacity under certain soil moisture conditions is crucial to the sustainability of afforestation in arid areas. Therefore, we aimed to simulate soil water in Haloxylon ammodendron plantations at different growth stages using the simultaneous heat and water transfer (SHAW) model to determine the vegetation carrying capacity threshold. Field-measured soil and vegetation data for 2021 were used to calibrate the SHAW model. Good consistency in soil water content (SWC) was observed between measurements and simulations in the calibration and validation phases of the model, with considerable linear correlation and low prediction error values. Therefore, the soil water dynamics of H. ammodendron plantations in the study area could be accurately predicted using the SHAW model. The simulation results show that, with increasing forest age, the SWC at 0–400 cm gradually decreases and a dry soil layer starts to form when the forest age reaches 11 years. The dry layer thickness reaches 120 cm by 22 years, thus the optimal growth age of artificial H. ammodendron plantations in the study area should not exceed 22 years, and the maximum biomass soil water carrying capacity of vegetation is 1,650 kg/hm2. A vegetation carrying capacity threshold can be determined in this study for the sustainable management of future afforestation activities.

Ensemble approaches for leveraging machine learning models in load estimation

AbstractHelicopter component load estimation can be achieved through a variety of machine learning techniques and algorithms. A range of ensemble integration techniques were investigated in order to leverage multiple machine learning models to estimate main rotor yoke loads from flight state and control system parameters. The techniques included simple averaging, weighted averaging and forward selection. Performance of the models was evaluated using four metrics: root mean squared error, correlation coefficient and the interquartile ranges of these two metrics. When compared, every ensemble outperformed the best individual model. The ensembles using forward selection achieved the best performance. The resulting output is more robust, more highly correlated and achieves lower error values as compared to the top individual models. While individual model outputs can vary significantly, confidence in their results can be greatly increased through the use of a diverse set of models and ensemble techniques.

Applicability of denoising-based artificial intelligence to forecast the environmental externalities

The current study attempts to compare the hybrid artificial intelligence models to forecast the environmental externalities in Saudi Arabia. We have used the denoising based artificial intelligence models to construct hybrid models. While comparing the denoising techniques, the CSD-based denoising has outperformed. However, we have used the CSD-based hybrid models. CSD-ANN and CSD-RNN are used for denoising-based artificial intelligence models, whereas CSD-ARIMA is used for denoising-based traditional models. All these models are used to check and compare their performance in terms of level and direction of prediction for PM10. The results show that the CSD-based ANN model has a higher predictability for PM10 levels in Saudi Arabia due to low error values and higher Dstat values. In comparing original and forecasted data, the superiority of CSD-ANN is evident in predicting the PM10 in Saudi Arabia. Hence, this hybrid model can predict the environmental externalities for non-linear and highly noised data. Moreover, the findings can be useful in achieving the sustainable development goal.

A new technique inversion Time-Domain electromagnetic data

Time-Domain Electromagnetic (TDEM) data modeling, especially for central-loop configurations, is often achieved through 1D inversion models. This study aims to enhance the accuracy and efficiency of TDEM data inversion by employing the Born Approximation method to address calculation and convergence speed issues. We also utilize the modified Symbiotic Organism Search (mSOS), a global optimization algorithm capable of handling multi-minimum problems in non-linear objective functions, to optimize the inversion process. Our research includes the assessment of the accuracy and performance of this approach through inversion modeling on both synthetic and field data. The accuracy of the synthetic data was evaluated based on the algorithm's capability to retrieve the values of the synthetic data, as indicated by the small relative error between the synthetic model parameters and the calculated model. In the case of field data modeling, the accuracy relied on the consistency achieved when modeling the data with different numbers of layers. Additionally, we considered the time required to perform the inversion as an evaluation metric for inversion performance. For the synthetic data case, the algorithm produced relatively accurate models with misfit values of approximately 0 % and low relative error values. In the field data case, the inversion models demonstrated consistency and reduced misfit values when the data was modeled with different numbers of layers, specifically 8.72 % for the 2-layer model, 3.92 % for the 3-layer model, and 2.61 % for the 4- and 5-layer models. Both datasets required less than 19 min for 10,000 iterations. These findings highlight the innovative nature of the mSOS algorithm and its potential for practical applications in TDEM inversion studies.

A novel study on forecasting the airfoil self-noise, using a hybrid model based on the combination of CatBoost and Arithmetic Optimization Algorithm

One of the important challenges in aircraft design is the light, inexpensive, and self-noise-free design of airfoil. Since the self-noise generated by rotor blades interacting with turbulent flow produced in their boundary layer can impact how well the system works. Predicting aerofoil self-noise can help shorten computational times and improve cost-effectiveness because traditional computational fluid dynamics methods flow has more time consumption. Therefore, this paper proposes a forecasting approach using the hybrid model of the CatBoost algorithm. In this way, the hyperparameters of the CatBoost are tuned by twelve optimizers to increase precision and speed up forecasting. The precision of the hybrid models is evaluated by statistical evaluation indices such as Coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MSE), and relative absolute error (MAE). The obtained results show that the CatBoost combination with the Arithmetic Optimization Algorithm better operates optimally and depicts lower error values compared to other optimizers. Also, the values of the coefficient of determination for training and testing data with 0.9999 and 0.9706, respectively, demonstrate that Arithmetic Optimizer is the most precise optimizer among studied metaheuristic algorithms.

Image Steganography Using LSB and Hybrid Encryption Algorithms

In today’s era of widespread web technology and cloud computing, ensuring data security has become a crucial concern across various industries. Instances of data breaches and vulnerabilities in cloud storage have emphasized the need for robust data protection and communication protocols, particularly in sectors like social media, military, and research. This research proposes a Multi-Level Steganography (MLS) algorithm that employs two encryption algorithms, AES and Blow-Fish, to secure the cover image and embed encryption keys as key images within the stego image. The proposed MLS algorithm incorporates a robust pixel randomization function to enhance the security of the encrypted data. Experimental results demonstrate that the proposed algorithm effectively protects data with high Peak Signal-to-Noise Ratio (PSNR) and low Mean Square Error (MSE) values, ensuring superior image quality, reliable encryption, and decryption of secret messages. The utilization of hybrid encryption with AES and BlowFish algorithms further strengthens the algorithm’s security by augmenting the complexity of the encryption process.

Utilizing Multivariate Adaptive Regression Splines (MARS) for Precise Estimation of Soil Compaction Parameters

Traditional laboratory methods for estimating soil compaction parameters, such as the Proctor test, have been recognized as time-consuming and labor-intensive. Given the increasing need for the rapid and accurate estimation of soil compaction parameters for a range of geotechnical applications, the application of machine learning models offers a promising alternative. This study focuses on employing the multivariate adaptive regression splines (MARS) model algorithm, a machine learning method that presents a significant advantage over other models through generating human-understandable piecewise linear equations. The MARS model was trained and tested on a comprehensive dataset to predict essential soil compaction parameters, including optimum water content (wopt) and maximum dry density (ρdmax). The performance of the model was evaluated using coefficient of determination (R2) and root mean square error (RMSE) values. Remarkably, the MARS models showed excellent predictive ability with high R2 and low RMSE, MAE, and relative error values, indicating its robustness and reliability in predicting soil compaction parameters. Through rigorous five-fold cross-validation, the model’s predictions for wopt returned an RMSE of 1.948%, an R2 of 0.893, and an MAE of 1.498%. For ρdmax, the results showcased an RMSE of 0.064 Mg/m3, an R2 of 0.899, and an MAE of 0.050 Mg/m3. When evaluated on unseen data, the model’s performance for wopt prediction was marked with an MAE of 1.276%, RMSE of 1.577%, and R2 of 0.948. Similarly, for ρdmax, the predictions were characterized by an MAE of 0.047 Mg/m3, RMSE of 0.062 Mg/m3, and R2 of 0.919. The results also indicated that the MARS model outperformed previously developed machine learning models, suggesting its potential to replace conventional testing methods. The successful application of the MARS model could revolutionize the geotechnical field through providing quick and reliable predictions of soil compaction parameters, improving efficiency for construction projects. Lastly, a variable importance analysis was performed on the model to assess how input variables affect its outcomes. It was found that fine content (Cf) and plastic limit (PL) have the greatest impact on compaction parameters.

Prediction of RCCI combustion fueled with CNG and algal biodiesel to sustain efficient diesel engines using machine learning techniques

This study used microalgae biodiesel as a high-reactive fuel directly injected along with various Compressed Natural Gas (CNG) energy shares (10, 20, 30, and 40%) as low-reactive fuel injected into the intake system. The experiments are performed in a single-cylinder, water-cooled, 1500 rpm, 3.5 kW power Compression Ignition (CI) engine under various loading conditions to examine the effects of CNG energy share on performance and emissions in Reactivity Controlled Compression Ignition (RCCI) combustion mode. The study found that the 30%CNG share decreased Nitrogen oxides (NOx) and smoke by 25 and 31%, as well as an increase in thermal efficiency of 4.35% in comparison to traditional biodiesel combustion. Finally, two machine learning (ML) models, namely the Gradient Boosting Regressor (GBR) and LASSO (Least Absolute Shrinkage and Selection Operator) Regression, were developed for predicting the dependent variables individually from the independent variables. Both the LASSO and GBR models achieved high accuracy with R2 values of 0.98–0.99 and relatively low Root Mean Square Error (RMSE) values.

Orthognathic surgical planning using graph CNN with dual embedding module: External validations with multi-hospital datasets

Background and objectiveDespite recent development of AI, prediction of the surgical movement in the maxilla and mandible by OGS might be more difficult than that of tooth movement by orthodontic treatment. To evaluate the prediction accuracy of the surgical movement using pairs of pre-(T0) and post-surgical (T1) lateral cephalograms (lat-ceph) of orthognathic surgery (OGS) patients and dual embedding module-graph convolution neural network (DEM-GCNN) model. Methods599 pairs from 3 institutions were used as training, internal validation, and internal test sets and 201 pairs from other 6 institutions were used as external test set. DEM-GCNN model (IEM, learning the lat-ceph images; LTEM, learning the landmarks) was developed to predict the amount and direction of surgical movement of ANS and PNS in the maxilla and B-point and Md1crown in the mandible. The distance between T1 landmark coordinates actually moved by OGS (ground truth) and predicted by DEM-GCNN model and pre-existed CNN-based Model-C (learning the lat-ceph images) was compared. ResultsIn both internal and external tests, DEM-GCNN did not exhibit significant difference from ground truth in all landmarks (ANS, PNS, B-point, Md1crown, all P > 0.05). When the accumulated successful detection rate for each landmark was compared, DEM-GCNN showed higher values than Model-C in both the internal and external tests. In violin plots exhibiting the error distribution of the prediction results, both internal and external tests showed that DEM-GCNN had significant performance improvement in PNS, ANS, B-point, Md1crown than Model-C. DEM-GCNN showed significantly lower prediction error values than Model-C (one-jaw surgery, B-point, Md1crown, all P < 0.005; two-jaw surgery, PNS, ANS, all P < 0.05; B point, Md1crown, all P < 0.005). ConclusionWe developed a robust OGS planning model with maximized generalizability despite diverse qualities of lat-cephs from 9 institutions.

Enhancing compressive strength prediction in self-compacting concrete using machine learning and deep learning techniques with incorporation of rice husk ash and marble powder

Focusing on sustainable development, the demand for alternative materials in concrete, especially for Self-Compacting Concrete (SCC), has risen due to excessive cement usage and resulting CO2 emissions. As Compressive Strength (CS) is dominant among concrete properties, this research concentrates on developing SCC by incorporating Rice Husk Ash (RHA) and Marble Powder (MP) as cement and filler replacements, respectively, while applying Machine Learning (ML) and Deep Learning (DL) techniques to forecast the CS of RHA/MP-based SCC. The research further evaluates material characteristics, with a strong emphasis on ML and DL for CS prediction. Concrete samples with various mixed ratios were cast and examined after 91 days to collect data for model application. In the experimental technique, 133 samples were gathered, and CS was predicted using seven input factors (cement, RHA, MP, superplasticizer, coarse aggregate, fine aggregate, and water) in an 80:20 ratio. Various ML algorithms, including linear regression, ridge regression, lasso regression, K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), and boosting methods such as gradient boost (GB), XG boost (XGB), and adaptive boosting (ADB) are employed, along with the DL technique of backpropagation neural network (BPNN) with different optimizer algorithms (Adam, SGD, RMSprop) to predict CS in RHA/MP-based SCC. The predicted data is further validated using evaluation parameters such as R-squared (R2), mean squared error (MSE), normalized root mean squared error (NRMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Comparatively, ML ensemble algorithms and BPNN using Adam and RMSprop optimizers demonstrate high accuracy in predicting CS outcomes, indicated by their high coefficient correlation R2 values and low error values.

Application of efficient soft computing approaches for modeling methyl ester yield from Azadirachta Indica (Neem) seed oil: A comparative study of RSM, ANN and ANFIS

This work centers on methyl ester yield modeling; by Azadirachta Indica seed oil (AISO) transesterification, using Adaptive Neuro-fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Response Surface Methodology (RSM). AISO was obtained from the seeds of Azadirachta Indica tree. The oils were extracted from the seeds using solvent extraction method. The physicochemical properties of AISO and Azadirachta Indica seed oil methyl ester (MAISOt) were determined using standard methods. Fatty acid composition was determined using, Gas Chromatography (GC). Statistical evaluations of these models show their efficacy in the order RSM < ANN < ANFIS, with ANFIS as the best; as indicated by its very high R2 value of 0.9999 and low RMS error value of 0.0011. The ANFIS predicted minimum and maximum values for % methyl ester yields were 54.66 and 90.25 %, respectively, while the other models predicted similar methyl ester yields. The physicochemical characterization results of AISO and MAISOt samples, show that their respective viscosity, dielectric strength (DS), pour and flash points values were (8.83 and 3.47 mm 2s−1), (33.42 and 48.93 KV), (9 and -6 °C), and (162 and 174 °C). These results indicated the MAISOt sample’s potential use as a transformer fluid. GC result indicated that MAISOt was unsaturated. Finally, on the basis of the gotten model results, ANFIS was adjudged as the best predictive model, followed by ANN and RSM, in that order.

Development, validation and greenness assessment of a new electro-driven separation method for simultaneous analysis of cefixime trihydrate and linezolid in their fixed dose combination

The establishment and validation of a straightforward, accurate, and eco-friendly capillary zone electrophoretic-diode array detection (CZE-DAD) procedure has been presented for concurrent measurement of two common antibiotics, namely, linezolid (LIN) and cefixime trihydrate (CEF), in their binary mixture or combined dosage form. The selected fused silica capillary has total and effective lengths equal to 58.5 cm and 50 cm, respectively, with a 50 µm internal diameter. Injections were performed utilizing 100 mM borate buffer at pH 10.2 as the background electrolyte (BGE) with a 15.0 s injection time. The finally utilized voltage was 30 kV. DAD was programmed to measure LIN at 250 nm and CEF at 285 nm. In less than 6 min, the two cited drugs were resolved at 2.51 and 5.47 min for LIN and CEF respectively. The introduced procedure had a linear response in the concentration range of 5–50 μg/mL for both analytes with correlation coefficients > 0.9999. Detection and quantification limits were 1.213 and 4.042 μg/mL, respectively, for LIN and 0.301 and 1.004 μg/mL, respectively, for CEF. Validation was conducted according to the International Council for Harmonization (ICH), concerning linearity, detection and quantitation limits, range, accuracy, precision, selectivity, and robustness. Precision was found acceptable due to the low relative standard deviation (RSD%) values that did not exceed 1.86% either for repeatability or for intermediate precision. Additionally, the adequately recovered concentrations and the low values of percentage relative error (Er%) provide evidence of the accuracy of the proposed method. On the other hand, the robustness of the introduced method was affirmed by the acceptable RSD% values that did not exceed 0.6% after deliberate changes in the following procedure parameters: buffer concentration, buffer pH, and wavelength. Finally, the ability of the presented method to quantify the two tested drugs in laboratory-prepared tablets was confirmed by the adequate recoveries (≥ 99%) utilizing the standard-addition procedure, along with the absence of any significant difference between the proposed method and the reference method as proven by the student’s t-test and the variance-ratio F-test values that did not exceed the theoretical ones. The analytical Eco-Scale and the analytical GREEness metric (AGREE) were the tools utilized for greenness assessment. This CZE procedure is the first electro-driven separation method that was utilized for the analysis of both antibiotics in their combined laboratory-prepared tablets with no interference from the co-formulated adjuvants.

A high-throughput phenotyping method for sugarcane rind penetrometer resistance and breaking force characterization by near-infrared spectroscopy

BackgroundSugarcane (Saccharum spp.) is the core crop for sugar and bioethanol production over the world. A major problem in sugarcane production is stalk lodging due to weak mechanical strength. Rind penetrometer resistance (RPR) and breaking force are two kinds of regular parameters for mechanical strength characterization. However, due to the lack of efficient methods for determining RPR and breaking force in sugarcane, genetic approaches for improving these traits are generally limited. This study was designed to use near-infrared spectroscopy (NIRS) calibration assay to accurately assess mechanical strength on a high-throughput basis for the first time.ResultsBased on well-established laboratory measurements of sugarcane stalk internodes collected in the years 2019 and 2020, considerable variations in RPR and breaking force were observed in the stalk internodes. Following a standard NIRS calibration process, two online models were obtained with a high coefficient of determination (R2) and the ratio of prediction to deviation (RPD) values during calibration, internal cross-validation, and external validation. Remarkably, the equation for RPR exhibited R2 and RPD values as high as 0.997 and 17.70, as well as showing relatively low root mean square error values at 0.44 N mm−2 during global modeling, demonstrating excellent predictive performance.ConclusionsThis study delivered a successful attempt for rapid and precise prediction of rind penetrometer resistance and breaking force in sugarcane stalk by NIRS assay. These established models can be used to improve phenotyping jobs for sugarcane germplasm on a large scale.

Accelerated screening of sensitive and selective MoO3-based gas sensing materials by combining first-principles and machine learning approach

A large amount of hybrid metal oxides can be proposed for detecting various gases from rapidly developing of 2D materials and elemental doping technologies, and the key issue is how to search the large parameter space in an efficient way. First-principles approach can calculate molecular-scale electronic properties and provide a guide of material design for experiments, however, it is too time-consuming to explore all the possible metal oxides doped with different elements. Herein, we develop a novel framework via combining first-principles and machine learning, with the aim of enabling more efficient and practical screening of MoO3-based gas sensors than by experiments or first-principles alone. Owing to the high accuracy of first-principles calculations verified by experimental results, we demonstrate the reliability of the proposed methods for evaluating gas sensing performance. By proposing a set of new descriptors including d-band center and average bond length with demanding low-cost calculations, we significantly reduce the amount of required training data. In particular, the gradient boosting regression algorithm exhibits highR-square value of 0.96 and low mean absolute error value of 0.22, indicating superior reliability of the model. This work opens an avenue for quickly screening of novel metal oxides and other nano-materials with superior sensitivity and selectivity for gas detection.