Limited Accuracy Research Articles

Improving carbon flux estimation in tea plantation ecosystems: A machine learning ensemble approach

Tea plant (Camellia sinensis) is a major global crop consumed as a drink after water. Quantifying carbon flux, specifically the net ecosystem exchange (NEE), in tea plantations is essential for determining carbon sequestration and ecosystem carbon balance. The Eddy covariance (EC) system is widely used for continuous monitoring of carbon flux but high costs associated with installation and maintenance limit its widespread adoption. In addition, EC flux data is often discarded due to malfunction of instruments caused by adverse weather conditions. Therefore, additional approaches for estimating NEE are necessary to overcome these challenges and ensure accurate NEE measurement. For this purpose, three standalone tree-based machine learning (ML) models were used for NEE estimation using EC flux data collected from tea ecosystem located in subtropical region (Danyang county of Zhenjiang city) of China. To address the accuracy limitations inherent in standalone ML models, the ensemble mechanism based on voting regressor method was proposed. In addition, k-fold cross-validation based on early stopping process was also used to enhance the performance of standalone ML models. Based on visual plots (scatter diagram, heatMap, Taylor diagram) and performing indices (root-mean-square error (RMSE), determination coefficient (R2), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash–Sutcliffe efficiency (NSE), correlation coefficient (r), Kling Gupta Efficiency (KGE) and index of agreement (d)), the findings indicated that non-linear ensemble-generalized regression neural network (NLE-GRNN) significantly improved standalone ML model's results. In current study, the highest NSE, r and d in case of standalone ML model (DT) achieved 0.49, 0.73 and 0.75 respectively while our proposed NLE-GRNN model improved 48 % in NSE value (NSE = 0.97), 25 % in r value (r = 0.98) and 24 % in d value (d = 0.99). Likewise, NLE-GRNN significantly reduce errors (MAE, MAPE and RMSE) and provides NEE estimate closet to the observed value. The impact of climatic variables on NEE using shapley additive explanations (SHAP) analysis revealed that Rg (solar radiation) and Tair (air temperature) were the prime factors controlling NEE variation in the tea ecosystem. Considering the high accuracy and stability of the studied ML models, it is recommended to apply developed ensemble ML model (NLE-GRNN) for significant improvement of NEE estimate in the tea biomes or other ecosystems.

(Invited) On the Definition of Velocity in Statistically Accurate, Discrete-Time Simulations of Thermodynamics

One of the most basic struggles in simulations of statistical and dynamical systems is how to appropriately balance the desire for simulation accuracy, obtained in the small time step limit, with simulation efficiency, obtained for large time steps. Obviously, we would like to have the benefits of both limits of accuracy and efficiency. Thus, understanding the influence of discrete time on the behavior of equations of motion is crucial for the understanding and optimization of numerical simulations in physical science and engineering. There are two different types of crucial discretization errors. One is that the simulated, discrete-time trajectory increasingly deviates from the true, continuous-time trajectory as the time step is increased. Another is that the simulated discrete-time velocity increasingly deviates from the expected velocity of the corresponding simulated trajectory as the time step is increased. While the former error is a signature that the simulated trajectory is not exactly the intended one, the simulation may still be representative of the true system. In contrast, the latter error is a signature of a fundamental internal inconsistency in the simulation, which may greatly complicate physical interpretation of the simulation results, especially when both configurational and kinetic measures are required. It is therefore crucially important to understand the relationship between a discrete-time coordinate and its corresponding velocity.It has previously been shown that, unlike in continuous-time, discrete-time configurational dynamics does not need a discrete-time velocity variable. This realization allowed for the derivation of the complete configurational GJ set of stochastic Verlet-type algorithms for simulating the Langevin equation. This set has the key feature of simultaneously giving exact, time-step independent statistical sampling of damped, noisy harmonic oscillators, and giving exact spatial transport values, drift and Einstein diffusion, on flat surfaces. The apparent fact that a discrete-time velocity is not required to simulate a coordinate trajectory implies that discrete-time velocity is an ambiguous concept, which, in turn, implies that multiple velocity definitions can be entertained in order to avoid internal simulation inconsistencies between configurational and kinetic measures. From the derivation of the stochastic GJ methods it was shown that a statistically correct velocity obtained at the same time as the coordinate (an on-site velocity) cannot be obtained. In contrast, a velocity at the half-step between consecutive configurational time steps (a half-step velocity) was identified to produce exact statistical kinetic response for harmonic oscillators regardless of the applied time step. However, only for a single GJ method does this velocity also give the correct drift value.We here derive new, practical velocity definitions that are optimized for statistical kinetic measures, such that, e.g., measures of kinetic energy and correlations with the configurational coordinate are exact for the noisy harmonic oscillator, while also producing the correct kinetic measure of transport on flat surfaces. We identify and analyze three half-step velocity definitions that fulfill these criteria, and we outline simple and practical Verlet-type algorithms that incorporate these velocities within the statistically desirable GJ format that provides the optimized configurational statistics. The application of the methods are demonstrated through characteristic Molecular Dynamics simulations of both simple and complex systems, illustrating how the resulting methods are able to efficiently and simultaneously simulate configurational and kinetic statistical ensembles in different situations using very large time steps.

Machine learning approaches to predict whether MEPs can be elicited via TMS

BackgroundTranscranial magnetic stimulation (TMS) is a valuable technique for assessing the function of the motor cortex and cortico-muscular pathways. TMS activates the motoneurons in the cortex, which after transmission along cortico-muscular pathways can be measured as motor-evoked potentials (MEPs). The position and orientation of the TMS coil and the intensity used to deliver a TMS pulse are considered central TMS setup parameters influencing the presence/absence of MEPs. New methodWe sought to predict the presence of MEPs from TMS setup parameters using machine learning. We trained different machine learners using either within-subject or between-subject designs. ResultsWe obtained prediction accuracies of on average 77 % and 65 % with maxima up to up to 90 % and 72 % within and between subjects, respectively. Across the board, a bagging ensemble appeared to be the most suitable approach to predict the presence of MEPs. ConclusionsAlthough within a subject the prediction of MEPs via TMS setup parameter-based machine learning might be feasible, the limited accuracy between subjects suggests that the transfer of this approach to experimental or clinical research comes with significant challenges.

Enhanced Trajectory Forecasting for Hypersonic Glide Vehicle via Physics-Embedded Neural ODE

Forecasting hypersonic glide vehicle (HGV) trajectories accurately is crucial for defense, but traditional methods face challenges due to the scarce real-world data and the intricate dynamics of these vehicles. Data-driven approaches based on deep learning, while having emerged in recent years, often exhibit limitations in predictive accuracy and long-term forecasting. Whereas, physics-informed neural networks (PINNs) offer a solution by incorporating physical laws, but they treat these laws as constraints rather than fully integrating them into the learning process. This paper presents PhysNODE, a novel physics-embedded neural ODE model for the precise forecasting of HGV trajectories, which directly integrates the equations of HGV motion into a neural ODE. PhysNODE leverages a neural network to estimate the hidden aerodynamic parameters within these equations. These parameters are then combined with observable physical quantities to form a derivative function, which is fed into an ODE solver to predict the future trajectory. Comprehensive experiments using simulated datasets of HGV trajectories demonstrate that PhysNODE outperforms the state-of-the-art data-driven and physics-informed methods, particularly when training data is limited. The results highlight the benefit of embedding the physics of the HGV motion into the neural ODE for improved accuracy and stability in trajectory predicting.

Dissipation behaviour and dietary risk evaluation of pesticides on cucumber.

Cucumber (Cucumis sativus L.) is the world's most widely consumed salad vegetable, and it is frequently treated with pesticides to prevent pest and disease outbreaks. Pesticide residues in food commodities impede trade and pose a major health risk. Prior to residue estimation, the QuEChERS approach was validated utilising criteria such as limit of detection, limit of quantitation, linearity, accuracy, and precision. The residues of carbendazim, cypermethrin, ethion, profenofos, quinalphos, and triazophos were examined using a Gas Chromatograph equipped with an Electron Capture Detector or a Flame Photometric Detector and a high-performance liquid chromatography coupled to a photo diode array. The initial deposits of carbendazim, cypermethrin, ethion, profenofos, quinalphos, and triazophos at the prescribed dose were 1.235, 0.407, 0.817, 0.960, 0.628, and 0.985mg/kg, respectively, with a pre-harvest interval of 5.58-11.30days. According to the consumer risk evaluation data, the Hazard Quotient is less than one, and the Theoretical Maximum Dietary Intake is less than the Maximum Permissible Intake and Maximum Residue Limit, both of which are considered safe for human consumption at the authorised dose.

Salting-out assisted liquid-liquid extraction for the simple and rapid determination of veterinary antibiotic residues in aquatic products by HPLC-MS/MS

Based on salting-out assisted liquid-liquid extraction (SALLE) and high performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS), a simple, rapid pretreatment without complex clean-up for the determination of 22 veterinary drug residues in aquatic products was developed and validated. In order to improve the efficiency of the method, the key procedural parameters of SALLE were fabricated. Na2EDTA-Mcllvaine buffer/ACN was used as the extraction solvent, anhydrous MgSO4 and NaCl as the extraction salts. The relationship between extraction efficiency and logD was initially evaluated during the optimization process. This study was well validated in various aquatic samples such as bass, large yellow croaker, carp, and shrimp, the limits of detection (LOD) and accuracy for all compounds ranged from 0.5 to 1.0 μg/kg, 71.4% to 120%. This method has the advantages of rapidity, simplicity, low cost, and high efficiency, and has broad potential for risk monitoring and evaluation of veterinary antibiotics in aquatic products.

Incidence and Predictors of Cerebrovascular Accidents in Patients Who Underwent Transcatheter Mitral Valve Repair With MitraClip

Transcatheter mitral edge-to-edge repair (TEER) with transcatheter devices has become a mainstay in the minimally invasive treatment of patients with severe mitral regurgitation at increased surgical risk. Despite its apparently favorable risk profile, there is uncertainty on the risk and features of cerebrovascular accidents (CVAs) early and long after transcatheter mitral valve repair. We aimed to appraise the incidence and predictors of CVA in patients who underwent TEER. We explicitly queried the data set of an ongoing multicenter prospective observational study dedicated to TEER with MitraClip (Abbott Vascular, Santa Clara, California). The incidence of CVAs after TEER was formally appraised, and we explored potential predictors of such events. Descriptive, bivariate, and diagnostic accuracy analyses were performed. Of 2,238 patients who underwent TEER, CVAs occurred in 33 patients (1.47% [95% confidence interval 1.02% to 2.06%]), including 6 (0.27% [0.10% to 0.58%]) in-hospital strokes and 27 events after discharge (0.99% [0.66% to 1.44%]), over a median follow-up of 14 months. Most CVAs were major ischemic strokes during and after the in-hospital phase. Overall, CVAs were more common in patients with atrial fibrillation (p = 0.018), renal dysfunction (p = 0.032), higher EuroSCORE II (p = 0.033), and, as expected, higher CHA2DS2-VASc score (p = 0.033), despite the limited prognostic accuracy of the score. Notably, the occurrence of CVA did not confer a significantly increased risk of long-term (p = 0.136) or cardiac death (p = 0.397). The incidence of CVA in patients who underwent TEER is low, with most events occurring after discharge and being associated with preexisting risk features. These findings, although reassuring on the safety of TEER, call for proactive antithrombotic therapy whenever CVA risk is increased before and after TEER.

Accurate and efficient Bloch-oscillation-enhanced atom interferometry

Bloch oscillations of atoms in optical lattices are a powerful technique that can dramatically boost the sensitivity of atom interferometers to a wide range of signals by large momentum transfer. To leverage this method to its full potential, an accurate theoretical description of losses and phases is required, going beyond existing treatments. Here, we present a comprehensive theoretical framework for Bloch-oscillation-enhanced atom interferometry and verify its accuracy through comparison with a numerical solution of the Schrödinger equation. Our approach establishes design criteria to reach the fundamental efficiency and accuracy limits of large momentum transfer using Bloch oscillations and allows us, in a broader context, to define the fundamental efficiency limit of the transport of neutral atoms using optical lattices. We compare these limits to the capabilities of current state-of-the-art experiments and make projections for the next generation of quantum sensors. Published by the American Physical Society 2024

One-pot derivatization/magnetic solid-phase extraction combined with high-performance liquid chromatography-fluorescence detection for rapid analysis of biogenic amines in alcoholic beverages

The determination of biogenic amines (BAs) in alcoholic beverages is crucial for assessing their health impact, ensuring beverage quality, and guaranteeing safety. Herein, a rapid one-pot derivatization/magnetic solid-phase extraction (OPD/MSPE) method was proposed using 6-aminoquinolinyl-N-hydroxysuccinimide carbamate as the derivatization reagent and magnetic hydroxyl-functionalized multi-walled carbon nanotubes as the extraction material. Integration of derivatization and extraction steps simplifies the sample preparation process, taking only three minutes and eliminating the need for centrifugation by utilizing magnetic sorbent. The resulting desorption solution was directly analyzed by high-performance liquid chromatography-fluorescence detection (HPLC-FLD) without any evaporation or reconstitution steps. The integrated OPD/MSPE-HPLC-FLD method demonstrates excellent linearity (R2 > 0.992), accuracy (relative recoveries: 85.1–109.2%), precision (RSDs≤9.7%) and detection limits (limits of detection: 0.3–2 ng/mL). It has been successfully applied to determine free BAs in various alcoholic beverages, including red wine, Baijiu, Huangjiu, and beer. This method enables rapid, sensitive and precise analysis of BAs in alcoholic beverages.

Bridge the gap between simulated and real-world data in optical fiber mode decomposition for accuracy improvement: A deep learning-based co-learning framework with visual similarity-based matching

In the area of optical fiber mode decomposition (MD), data-driven deep-learning (DL) algorithms have achieved remarkable strides with unlimited theoretically synthetic data, but the differences between theoretical and real-world data in the DL context are rarely investigated. The two differences, namely the information bottleneck and the domain discrepancy, between the simulation and realistic domain remain significant obstacles that limit the practical deployment of DL-based MD approaches in real-world optical systems. The paper aims to mitigate their influence on the accuracy limitation and the accuracy degradation of DL-based MD. First, we present a DL-based co-learning framework for MD for the first time. With the co-learning process, the knowledge of the teacher model learned from rich input information sources (near-field, far-field, and phase-distribution optical images) is transferred to the student model with the only near-field input source, which breaks the information bottleneck by allowing the student model to learn more knowledge in a simulation context. Furthermore, to settle the domain discrepancy between ideal data and practical observation, we propose a visual similarity-based matching to assign real values of modal coefficients to the practical optical images whose ground truth values cannot be easily acquired, which enables the transfer learning from the synthetic data to the real-world data for accuracy enhancement. Simulated and experimental results show that the proposed co-learning framework with visual similarity-based matching has realized high precision and robustness in MD simulated and practical tasks. By bridging the gap between theoretical and practical data, our study offers novel perspectives on DL-based MD practice.

Direct Determination of Glyphosate and Its Metabolites in Foods of Animal Origin by Liquid Chromatography-Tandem Mass Spectrometry.

Glyphosate is the most used herbicide in agriculture. Its major metabolite is AMPA (aminomethylphosphonic acid), but N-acetyl-AMPA and N-acetylglyphosate are also metabolites of interest. For risk assessment, a general residue definition was proposed as the sum of glyphosate, AMPA, N-acetyl-glyphosate and N-acetyl-AMPA, expressed as glyphosate. A confirmatory method for glyphosate in fat, liver and kidneys, as well as a confirmatory method for AMPA and N-acetyl-glyphosate in all matrices, are still missing. In this paper, we present a method for the quantitative determination of glyphosate residues and its metabolites AMPA, N-acetyl-AMPA and N-acetyl-glyphosate by liquid chromatography-mass spectrometry (LC-MS/MS) in adipose tissue, liver, eggs, milk and honey without derivatization. Different chromatographic columns were tested, with the Hypercarb column providing the best results. The analytes were eluted with mobile phases of acidified water with 1.2% formic acid and 0.5% formic acid in acetonitrile. Sample purification procedures were also optimized by varying the solvent extraction mixtures (water, methanol and mixture ψ (methanol, water) = 1:1, each with the addition of 1% formic acid (v/v)), using different sorbents in solid phase extraction (SPE) (polymeric cationic (PCX) and anionic (PAX)) and using dispersive solid phase extraction (dSPE) (C18 and PSA) by modifying the extraction procedures. Finally, the analytes were extracted from the samples with 1% formic acid in water (v/v). Milk and adipose tissue were purified by the addition of dichloromethane, while liver and egg samples were purified by SPE with a mixed cation exchange sorbent and ultrafiltration with cut-off filters. The proposed analytical procedures were validated according to SANTE/11312/2021 guidelines: linearity, limits of quantification, precision and accuracy were determined for all matrices. The limits of quantification (LOQs) ranged from 0.025 to 0.2 mg kg-1. Precision, expressed as relative standard deviation, was <20%, while accuracy, expressed as analytical recovery, ranged from 70% to 120%. During method validation, the measurement uncertainty was estimated to be <50% for all analytes. Good validation parameters according to the SANTE document were achieved for all analytes. Therefore, the method can be considered reliable and sensitive enough for routine monitoring of polar pesticides. The application of the accredited method in routine analysis will provide data that are useful for the re-evaluation of risk assessment studies in foods of animal origin.

Zeolitic imidazolate framework-8 as a high-affinity adsorbent for dispersive solid-phase extraction in the analysis of plant growth regulators in fruits.

Facile and sensitive determination of plant growth regulators (PGRs) in food samples is crucial but still poses a significant challenge. In this study, to enhance the sensitivity of the HPLC-DAD method for PGR detection, a dispersive solid phase extraction (d-SPE) method using zeolitic imidazolate framework-8 (ZIF-8) as the highly effective adsorbent is developed. ZIF-8 nanoparticles are formed through the coordination of Zn2+ with 2-methylimidazole. Due to its high porosity, large surface area, abundant π electronics and nitrogen electronics, ZIF-8 exhibits a strong affinity to PGRs due to the synergistic effects of π-π interaction, van der Waals force, H-bond, and surface effect. Under the optimal d-SPE conditions, the sensitivity of the method is significantly enhanced with outstanding performances, including a wide range of linearity (2.0-200 ng g-1) with high correlation coefficients (R ≥ 0.9989), low limits of detection (LODs, 0.9-8.0 ng g-1 for all PGRs), satisfactory precision (intra-day RSDs ≤ 3.3%, inter-day RSDs ≤ 4.2%), and high accuracy (recovery: 86.6-101.5%). The developed method was successfully applied to quantitatively detect 9 PGRs in fruit samples, yielding satisfactory results. This d-SPE-HPLC-DAD method, characterized by high sensitivity, simplicity, efficiency, ease of practice and cost-effectiveness for PGR detection, shows potential for detecting PGRs in other complex samples and provides a strategy for designing target-affinity adsorbents.

Therapeutic drug monitoring of free perampanel concentrations in practice: A practical analytical technique based on centrifugal ultrafiltration sample separation

ObjectivesThe centrifugal ultrafiltration–high-performance liquid chromatography–tandem mass spectrometry (HPLC–MS/MS) method was established to determine the free perampanel (PER) concentration in children with epilepsy. MethodsFree PER concentration was obtained using centrifugal ultrafiltration devices. The internal standard was PER-D5. The method was investigated for selectivity, carryover, lower limit of quantification, calibration curve, accuracy, precision, matrix effects, recovery, and stability. The Spearman's correlation coefficient was used to evaluate the correlation between the free and total PER concentrations. A nonparametric test was used to estimate the effects of PER along with other antiepileptic drugs on the total and free PER concentrations. ResultsThe free PER concentration was positively correlated with the total PER concentration in the 57 plasma samples (r = 0.793 > 0, P < 0.001). Additionally, the free PER concentrations were significantly (P < 0.05) increased in valproic acid (VPA) co-therapy (9.87 ± 5.83) compared with non-VPA co-therapy (5.03 ± 4.57). ConclusionsThe proposed method is efficient, sensitive, and suitable for detecting free PER concentrations in children with epilepsy. Simultaneously, the free PER concentration response to clinical outcomes in children with epilepsy was more clinically significant, particularly when combined with VPA.

Dissecting the natural phytochemical diversity of carrot roots with its colour using high performance liquid chromatography and UV–Visible spectrophotometry

The research provides insights into the phytoconstituents of black, orange and red carrots (Daucus carota subsp. Sativus (Hoffm.) Schübl. & G. Martens), a highly nutritious food crop widely appreciated across age groups. Recognising carrots as a repository of health-promoting compounds, our study employs UV–Visible spectrophotometric and HPLC methods to discern significant variations in bioactive components among carrot varieties. Black carrots emerge as potent contenders, displaying the highest levels of total phenolics (2660 ± 2.29 mg GAE/100 g F W.), total flavonoids (831 ± 1.74 mg QE/100 g F W.), proanthocyanins (10910 ± 1.11 mg CE/100 g F W.), and tannins (713 ± 0.84 mg/100 g F W.). Red carrots, conversely, showcase higher anthocyanin content (6870 ± 1.85 mg CyGE/100 g F W.) by UV–Vis spectrophotometry. Additionally, orange carrots exhibit heightened β-carotene levels, confirmed at 0.03 μg/mg through HPLC. HPLC analysis unveils substantial chlorogenic acid variability (1.29 μg/mg) in black carrots, accompanied by the discovery of unique compounds such as cryptochlorogenic acid (0.05 μg/mg), caffeic acid (0.01 μg/mg), ferulic acid (0.11 μg/mg), methyl caffeate (0.01 μg/mg), and quercetin (0.02 μg/mg), marking the first detection of methyl caffeate in black carrots. The analytical methodology was meticulously validated encompassing optimal parameters such as linearity, precision, limit of detection, limit of quantification, accuracy, and robustness, within the range. In conclusion, our study underscores the health benefits of black carrots due to their rich polyphenolic content and endorses orange carrots for elevated β-carotene levels. These findings contribute to a deeper understanding of the diverse phytoconstituents in carrots, aid in informed dietary choices for improved health.

Serum albumin-to-globulin ratio and CRP-to-albumin ratio did not outperform serum CRP in diagnosing periprosthetic joint infections.

Serum inflammatory parameters are widely used to aid in diagnosing a periprosthetic joint infection (PJI). Due to their limited performances in the literature, novel and more accurate biomarkers are needed. Serum albumin-to-globulin ratio (AGR) and serum CRP-to-albumin ratio (CAR) have previously been proposed as potential new parameters, but results were mixed. The aim of this study was to assess the diagnostic accuracy of AGR and CAR in diagnosing PJI and to compare them to the established and widely used marker CRP. From 2015 to 2022, a consecutive series of 275 cases of revision total hip (n = 129) and knee arthroplasty (n = 146) were included in this retrospective cohort study. Based on the 2021 European Bone and Joint Infection Society (EBJIS) definition, 144 arthroplasties were classified as septic. Using receiver operating characteristic curve (ROC) analysis, the ideal thresholds and diagnostic performances were calculated. The areas under the curve (AUCs) were compared using the z-test. AGR, CAR, and CRP were associated with PJI (p < 0.001). Sensitivities were 62.5% (95% CI 54.3 to 70.0), 73.6% (95% CI 65.8 to 80.1), and 71.5% (95% CI 63.6 to 78.3), respectively. Specificities were calculated with 84.7% (95% CI 77.5 to 89.9), 86.3% (95% CI 79.2 to 91.2), and 87.8% (95% CI 80.9 to 92.4), respectively. The AUC of CRP (0.797 (95% CI 0.750 to 0.843)) was significantly higher than the AUC of AGR (0.736 (95% CI 0.686 to 0.786), p < 0.001), and similar to AUC of CAR (0.799 (95% CI 0.753 to 0.846), p = 0.832). Decreased sensitivities were observed in PJIs caused by low-virulence organisms (AGR: 60%, CAR: 78%) compared to high-virulence pathogens (AGR: 80%, p = 0.042; CAR: 88%, p = 0.158). Higher sensitivities were seen in acute haematogenous (AGR: 83%, CAR: 96%) compared to chronic PJIs (AGR: 54%, p = 0.001; CAR: 65%, p < 0.001). Serum AGR and CAR showed limited diagnostic accuracy (especially in low-grade and chronic infections) and did not outperform the established marker CRP in our study. Hence, neither parameter can be recommended as an additional tool for diagnosing PJI.

Active learning-based metamodeling for hybrid uncertainty quantification of hydro-mechatronic-control systems: A case study of EHA systems

The Electro-Hydrostatic Actuator (EHA) is a typical hydro-mechatronic control system. Due to the limited accuracy of measurement, inadequate knowledge, and vague judgments, hybrid uncertainties, including aleatory or epistemic uncertainty, inevitably exist in the performance assessment of EHA systems. Existing methods ignored the hybrid uncertainties which can hardly obtain a satisfactory result while wasting a lot of time on the experimental design. To overcome this drawback, a metamodeling method for hybrid uncertainty propagation of EHA systems is developed via an active learning Gaussian Process (GP) model. The proposed method is bifurcated into three pillars: (A) Initializing the GP model and generating the optimum candidate sampling set by an Optimized Max-Minimize Distance (OMMD) algorithm, which aims to maximize the minimum distance between the added samples and original samples, (B) maximizing the learning function and generating new samples by a developed farthest or nearest judgment strategy, while updating the original GP model, and (C) judging the convergence by three uncertainty metrics, i.e., area metric, maximum variance metric, and mean value metric. A numerical example is exemplified to evaluate the effectiveness and efficiency of the proposed method. Meanwhile, the EHA system of aircrafts is examined to show the application of the proposed method for high-dimensional problems. The effects of the uncertainties in the Proportional-Integral-Differential (PID) of the EHA system are also examined.

High-performance and wavelength-transplantable on-chip Fourier transform spectrometer using MEMS in-plane reconfiguration

On-chip spectrometers with high compactness and portability enable new applications in scientific research and industrial development. Fourier transform (FT) spectrometers have the potential to realize a high signal-to-noise ratio. Here we propose and demonstrate a generalized design for high-performance on-chip FT spectrometers. The spectrometer is based on the dynamic in-plane reconfiguration of a waveguide coupler enabled by an integrated comb-drive actuator array. The electrostatic actuation intrinsically features ultra-low power consumption. The coupling gap is crucial to the spectral resolution. The in-plane reconfiguration surmounts the lithography accuracy limitation of the coupling gap, boosting the resolution to 0.2 nm for dual spectral spikes over a large bandwidth of 100 nm (1.5–1.6 μm) within a compact footprint of 75 μm×1000 μm. Meanwhile, the in-plane tuning range can be large enough for arbitrary wavelengths to ensure the effectiveness of spectrum reconstruction. As a result, the proposed spectrometer can be easily transplanted to other operation bands by simply scaling the structural parameters. As a proof-of-concept, a mid-infrared spectrometer is further demonstrated with a dual-spike reconstruction resolution of 1.5 nm and a bandwidth of 300 nm (4–4.3 μm).

Hybrid six sigma based on recursive kalman filter and weibull distribution to estimate the lifespan of Bulb LEDs

The durability of LED bulbs ensures that the lights can operate for a long time without needing to be replaced or maintained too often. The brightness of high-power LED bulbs provides a strong and efficient light source, saves energy, and has a long lifespan. The traditional method of testing LED bulb life span has limitations such as long testing time, limited accuracy, high cost, inability to predict accurately, and failure to respond quickly. Therefore, the development of new and improved testing methods is necessary to effectively and accurately evaluate and ensure the durability of LED bulbs. This research paper proposes a hybrid Six Sigma method based on the recursive Kalman filter and Weibull distribution to estimate the lifespan of Bulb LEDs. The goal of the new method for evaluating LED bulb life is to provide accurate, reliable, and multi-dimensional information about the life of LED lamps, meeting the requirements of accuracy, diversity, time-saving, resources, credibility, and the ability to evaluate from a variety of perspectives. As a result of the research, the recursive Kalman filter method has strengths such as high accuracy, flexibility, saving computational resources, the ability to handle inaccurate data, and scalability. When applied to LED bulb life assessment, it provides an accurate and reliable estimate of LED lamp life. Research results on longevity testing were reduced from 1650 h to 710 h. The end-of-life test time is 710 h, and the aging time per life test cycle is 135 h. The cost of life testing is reduced from 2100.17 USD to 410.12 USD. This LED bulb lifespan testing method can be applied to similar types of LED products to improve the company's productivity and business efficiency.

The Development of Digital Colorimetric-Based Thiourea Receptor for Highly Sensitive Detection of Dihydrogen Phosphate

T his study aimed to synthesize a new chemosensor, namely 1,1’-(1,4-phenylene)bis(3-(4-nitrophenyl)thiourea), specifically designed for detection of H2PO4−. The compound was characterized using FTIR, LC-MS, 1H NMR, and 13C NMR, while the colorimetric sensing abilities were validated through various analyses. These include linearity, limit of detection (LOD), limit of quantification (LOQ), precision, and accuracy using UV-Vis spectrophotometry, RGB, and HSV color components. Digital images of color changes were captured using a smartphone camera and Color Grab app was used to quantify color components. Subsequently, anions test (NO3−, Cl–, HSO4−, and H2PO4−) were conducted in acetonitrile, where the addition of H2PO4− induced a distinct color change from colorless to yellow. The results showed that detection of H2PO4− depended on pH of the solution, with no color change observed at pH 1-3 due to anions converting to acidic species. At pH 5, H2PO4− was detected as a yellow color change, and alkaline conditions led to different color responses. Furthermore, UV-Vis detection showed recovery values of 99.80% to 100.6%, indicating high accuracy. RGB and HSV color component-based sensors showed recovery values of 101.6% to 114.4% and 92.50% to 110.2%, respectively. The LOD of the UV-Vis, RGB, and HSV methods were 0.16 μM, 0.46 μM, and 0.18 μM, while LOQ values were 0.54 μM,1.52 μM, and 0.61 μM, respectively. In conclusion, the synthesized chemosensor showed potential for accurate and sensitive detection of H2PO4− using UV-Vis spectrophotometry and digital colorimetric-based image.

Ultra-precision diamond finishing of reaction-sintered silicon carbide enhanced by vibration-assisted photocatalytic oxidation

RS-SiC faces limitations in surface accuracy with conventional finishing processes due to its high mechanical hardness and multiphase nature. In this study, vibration-assisted photocatalytic oxidation is introduced to enhance the efficiency and precision of RS-SiC diamond polishing. By utilizing the photocatalytic reaction, the hard heterogeneous SiC phase and Si phase on the RS-SiC surface are transformed into a "softer" homogeneous oxide layer that can be removed easily. The introduction of vibration increases the relative velocity between the workpiece and the abrasive and prevents the aggregation of the photocatalyst, thus improving polishing efficiency. By reducing the abrasive size to the nanometer scale, the boundary between the Si phase and SiC phase can be smoothed out to further improve the workpiece surface finish. Using TiO2 as the photocatalyst, H2O2 as the oxidant, 25 nm diamond as the abrasive, and introducing vibration, an ultra-smooth surface with a surface roughness of 0.195 nm in Ra is obtained by the finishing method proposed in this paper. The proposed photocatalysis/vibration-assisted finishing approach offers a novel method for ultra-smooth polishing reaction-sintered silicon carbide.