Robust Method Research Articles

Can green trade development promote energy security in China? The role of financial development

Energy security is an essential component of overall national security. This paper investigates the impact of green trade (GRTR) on China's energy security and financial development's role in this nexus. The energy security evaluation index in this study is constructed from availability, applicability, acceptability, and affordability (the 4-As) and calculated by employing the Improved Entropy Method (IEM). Based on the two-step robust system-generalized method of moments (SYS-GMM) and data from China's 30 provinces between 2004-2020, this article investigates the impact of GRTR on energy security, the moderating effect of financial development and the impact mechanism of renewable energy development on the GRTR-energy security nexus. Our econometric analysis results indicate that increased GRTR can notably enhance energy security, and financial development can strengthen the positive impact of GRTR on energy security. The advancement of GRTR contributes positively to enhanced energy availability, acceptability, and affordability, but it could potentially decrease progress in the applicability of energy security; the promoting effect of GRTR on energy security in low-GRTR areas will be limited. The results of the mediating mechanism indicate that GRTR can accelerate energy security growth through renewable energy development. These insights provide policy implications for combining GRTR incentives to promote energy security.

Design of Experiment and green analytical chemistry principles for developing a robust and sustainable Stability-Indicating HPLC method for Bempedoic acid impurity profiling

Bempedoic acid (BEM), a drug used to lower cholesterol levels in patients with atherosclerosis. Controlling its process-related impurities is crucial due to their potential toxicity. This study details the development of a RP-HPLC method for the analysis of four process-related substances in BEM. To ensure robust chromatographic performance meeting six sigma quality standards, the method was optimized using a combined approach of response surface methodology (specifically, an I-optimal design) and tolerance analysis, integrating predefined specifications for critical method attributes such as resolution, tailing factor, and number of theoretical plates into the developmental process. Chromatographic separation was achieved using a C18 column. The mobile phase consisted of 0.1 % phosphoric acid in water and acetonitrile, utilizing a stepped gradient elution profile. The flow rate was maintained at 0.9 mL/min, with UV detection performed at 210 nm. The column temperature was set to 20 °C. The method demonstrated linearity for BEM process related impurities in the range of LOQ to 30 μg mL−1. The method ability to differentiate the drug substance from its degradation products was confirmed through forced degradation study. The environmental sustainability of the developed method was assessed using multiple Green Analytical Chemistry (GAC) tools. The National Environmental Methods Index (NEMI) showed two green-shaded quadrants, and the Green Certificate modified Eco-scale awarded a score of 70. The Complementary Green Analytical Procedure Index (Complex GAPI) confirmed the method environmentally friendly profile. Additionally, The AGREE tool generated a score of 0.77, indicating strong overall greenness, while the AGREEprep tool, focused on sample preparation, yielded a score of 0.54. Other tools such as the Sample Preparation Metric of Sustainability (SPMS) and the HPLC Environmental Assessment Tool (HPLC-EAT) scored 8.42 and 569, respectively, further demonstrating the method green credentials. This robust and environmentally conscious analytical framework offers significant potential for application in quality control of BEM.

Modelling and numerical simulation of heat transfer and hydrodynamic performance of multi-pass parallel flow condensers – A novel algebraic method to determine flow distribution

In multi-pass parallel flow condensers (MPFCs), refrigerant flow maldistribution poses a significant challenge, deteriorating heat transfer performance. A robust flow distribution prediction method is crucial for the accuracy and applicability of an MPFC model. This study originally develops a new algebraic method to determine refrigerant distribution in tube-passes for MPFCs. This algorithm can be directly integrated with the authors' previously developed 2-D distributed-parameter model for MPFCs with liquid-vapor separation (MPFCs-LS). The novel algorithm applies to conditions where: 1) the inlet tube of the first tube-pass is positioned arbitrarily along the vertical inlet header, and 2) the mediate headers have or lack liquid-vapor separation. The new condenser models are validated by comparing the predicted results with the experimental data of an MPFC and an MPFC-LS. The predicted heat transfer capacity and pressure drop show deviations within ±5% and ±35%, respectively. Results also reveal that the optimal position of the inlet tube is one tube pitch downward from the top of the inlet header under the given configuration and conditions. The best position of the inlet tube, compared with the worst case, improves the flow distribution by 67%, reduces the pressure drop by 68% and increases the heat transfer by 0.5%.

Online recruitment for an online survey study: Our experience of dealing with fraudsters

BackgroundOnline recruitment is cost efficient and can reach large sample size. It helps protect participants' privacy and confidentiality and is commonly used in research studies with sensitive topics and hard-to-reach target participants. However, widespread fraudulent responses along with missing data and multiple entries add complication to the data cleaning and analysis. This clinical-methods-brief described our experience of dealing with fraudsters in the recruitment process of an online study. MethodsBased on our experience in an online cross-sectional survey study, we reported three methods used in the recruitment process, including website recruitment (https://www.chineseinla.com/), Facebook advertisement, and Facebook group recruitment. ResultsThe detected fraudulent rates were 11.11 %, 4.55 %, and 82.69 % for website recruitment, Facebook advertisement, and Facebook group recruitment, respectively. Strategies for us to identify the fake responses mainly included internet protocol checking, conflict answers to different survey questions, participant's name, and time stamp checking. DiscussionFake data mixed in the research data threatens the validity and reliability of the study and may skew the research study results. A robust participant recruitment method and a data screening/cleaning protocol are necessary to ensure online studies' rigorous.

Markov Progressive Framework, a Universal Paradigm for Modeling Long Videos.

The computational complexity of video models increases linearly with the square number of frames. Thus, constrained bycomputational resources, training video models to learn long-term temporal semantics end-to-end is quite a challenge. Currently, the main-stream method is to split a raw video into clips, leading to incomplete fragmentary temporal information flow and failure of modeling long-term semantics. In this paper, we design the Markov Progressive framework (MaPro), a theoretical framework consisting of the progressive modeling method and a paradigm model tailored for it. Thecore idea of MaPro is to find a paradigm model consisting of proposed Markov operators which can be trained in multiple sequential steps and ensure that the multi-step progressive modeling is equivalent to the conventional end-to-endmodeling. By training the paradigm model under the progressive method, we are able to model long videos end-to-endwith limited resources and ensure the effective transmission of long-term temporal information. We provide implementations of this theoretical system on the mainstream CNN- and Transformer-based models, where they are modified to conform to the Markov paradigm. As a general and robust training method, we experimentally demonstrate that it yields significant performance improvements on different backbones and datasets. As an illustrative example, the proposed method improves the SlowOnly network by 4.1 mAP on Charades and 2.5 top-1 accuracy on Kinetics. And for TimeSformer, MaPro improves its performance on Kinetics by 2.0 top-1 accuracy. Importantly, all these improvements areachieved with a little parameter and computation overhead.

Dust event identification and characterization with one-year online observations in Beijing.

Dust storms have a profound impact on air quality, atmospheric chemistry, and human well-being by carrying vast amounts of particles over distances of thousands of kilometers. However, the overall characteristics of these dust events and their influence on secondary pollution in the northern China region are not yet well understood, due to a lack of long-term, comprehensive observations and objective identification techniques. Based on principal component analysis combined with high-time-resolution observations of particulate matter components, here we developed a robust method to identify dust storm events and identified 14 dust events in Beijing in 2019. We further classified these 14 events into two distinct types using Lagrangian particle dispersion models and backward trajectory analysis. The first type (Type I, 9 cases) is characterized by synoptic patterns in Mongolia, originating from the north and directly impacting the Beijing area. The second type (Type II, 5 cases) involves air masses from the north or northwest that temporarily pass through polluted regions south of Beijing before being carried back into the city. Consistently, during Type I dust events, we observed a sharp decrease in secondary inorganic aerosols (SIA) from 65 % to 7 %, as well as in the sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR) (from 0.52 to 0.19, and 0.27 to 0.018 respectively). In contrast, during Type II dust events, SIA concentrations increased by 91 %, along with an increase in SOR (1.7 %), NOR (69 %), and f44/f43 (3.0 %), suggesting an enhancement of secondary aerosol formation resulting from the interaction between dust aerosols and gaseous anthropogenic pollutants. Our results demonstrate that dust events and the sub-type of dust events can be identified in an objective manner using the protocol developed in this study and the dynamics should be considered when discussing impacts of dust events on atmospheric chemistry.

A high-precision automatic diagnosis method of maize developmental stage based on ensemble deep learning with IoT devices

Accurately determining the stage of crop development holds significant importance for field crop management. With the advancement of smart agriculture, an increasing number of Internet of Things (IoT) devices are being integrated into agricultural production, enabling more efficient acquisition of high-precision crop images. Currently, research on detecting crop growth stages based on IoT device images remains relatively scarce. Most existing studies rely on a single network model for detection, often encountering issues such as low accuracy and overfitting. Therefore, in this study, we collected maize images using IoT devices and constructed an integrated deep learning model by utilizing four convolutional neural networks (CNNs) to detect the growth period of maize in real time. Additionally, we implemented several improvements on these four CNNs and subsequently tested the performance of the ensemble model on the maize dataset. Regarding the ensemble strategy for the ensemble model, we proposed a dynamic weighted voting method, building upon the original voting approach, which can mitigate model training fluctuations and expedite model convergence. Ultimately, we manually simulated various lighting conditions to assess their impact on the ensemble model. Experimental results demonstrate that the ensemble deep model proposed in this paper represents a robust method for detecting maize growth stages, achieving an accuracy rate of 0.976 on the maize dataset, effectively facilitating high-precision detection of maize growth stages in complex backgrounds.

Bayesian statistical analysis reveals spatial heterogeneity in Cyclone Thane deposits from Southeast India

Modern and geological records of storm sedimentary deposits preserved on siliciclastic coastlines are important archives to evaluate the past and current magnitude and impacts of.storms. Examination of modern storm deposits also offers the opportunity to evaluate the similarities and differences between storm and other coastal overwash processes and hazards.We examined the stratigraphy and sedimentary characteristics of the 31st December 2012 Cyclone Thane and underlying coastal units from 14 pits from six sites from the coastal zone of Tamil Nadu Province, southeast India. We analysed the grain size parameters, grain shape, and heavy mineral proportions of each deposit in high resolution and examined the sedimentary structures of each unit. For the first time, we use Bayesian factors to quantitatively evaluate the similarities and differences between the storm sedimentary deposits and other co-located coastal sedimentary deposits. At several sites, the storm deposits differ in several parameters from the underlying coastal deposits, but at some locations, distinguishing between different depositional units cannot be achieved. In comparing the storm deposits from the different sites, mean grain size results in the most coherent pattern with closely located sites having similar mean grain size, and more southerly sites being finer grained. The other measured parameters show a far less coherent pattern with adjacent sites often preserving larger differences than more distal sites attesting to very local hydrodynamic variations during sediment deposition. As with the sedimentary parameters, the sedimentary structures formed during sediment deposition preserved at each site are highly variable. To date, the presence of terminal foresets at the landward edge of washover fans remains the only diagnostic feature of storm deposition, but that this feature is not ubiquitous across all storm deposits. Our findings demonstrate the spatially heterogeneous nature of storm sediment deposition and the challenges of identifying storm deposits in coastal siliciclastic sequences. The use of Bayesian statistical approaches also offers a robust method for evaluating and discriminating between coastal sediment deposits that has many advantages over traditional frequentist approaches. This method can easily be applied to other sedimentary depositional environments.

Image Forgery Detection using Deep Learning

Deepfake Detection: A Convolutional Neural Network Approach Digital authenticity is in grave danger due to the rapid advancement of deepfake technology.This research introduces a robust deep learning-based method to accurately detect deepfakeimages. A CNN (convolutional neural network) architecture is developed and trained on a diverse dataset of authentic and manipulated images. CNN model effectively learns discriminative features, enabling it to distinguish between genuine and forged content. The model's superior performance in identifying different deepfake techniques is demonstrated by experimental results, underscoring its potential to prevent the spread of false informationand protect digital integrity

Robust joint estimation of sideslip angle and tire lateral force for distributed drive electric vehicle

Accurate estimation of vehicle sideslip angle (SA) and tire lateral force (TLF) is essential for the effective functioning of vehicle active safety control systems. However, effective estimation of SA and TLF using low-cost on-board sensors is a major challenge. In this paper, a robust joint estimation method of SA and TLF for distributed drive electric vehicle (DDEV) is proposed. Adaptive sliding-mode observer (ASMO) is used to estimate the TLF and is used as the input to the subsequent filter. Maximum correntropy criterion (MCC) is introduced to improve the robustness of the unscented Kalman filter (UKF) under mixed Gaussian noise, and the maximum correntropy unscented Kalman filter (MCUKF) algorithm is composed to estimate SA. The effectiveness of the proposed ASMO and MCUKF joint estimation method is verified by Simulink/Carsim joint simulation experiments. The experimental results show that compared with the existing methods, the estimation method proposed in this paper effectively improves the estimation accuracy and robustness of SA and TLF under the mixed Gaussian environment, which is of great significance for the improvement of vehicle active safety.

The establishment of reference intervals for the ClotPro thromboelastometry device in healthy dogs

AbstractObjectiveTo establish reference intervals using a new point‐of‐care thromboelastometry device in dogs for the extrinsically activated test (EX‐test), intrinsically activated test (IN‐test), fibrin polymerization test (FIB‐test), ecarin test (ECA‐test), and tissue plasminogen activator test (TPA‐test) and to investigate the effects of storage time on the results.DesignProspective clinical study in 2022.SettingUniversity teaching hospital.AnimalsForty‐eight healthy privately or university‐owned dogs were prospectively enrolled and included on the basis of normal physical examination and normal baseline laboratory results (CBC, biochemistry profile, prothrombin time, and activated partial thromboplastin time [aPTT]).InterventionsAfter a 30‐minute storage time, the EX‐test, IN‐test, FIB‐test, ECA‐test, and TPA‐test were performed on citrated blood samples. To determine the effect of storage time, 11 samples had the EX‐test, FIB‐test, and IN‐test repeated 90 and 150 minutes after sample collection.Measurements and Main ResultsTen thromboelastometry parameters were evaluated for each test. Reference intervals were calculated using the robust method for parametric data, and the robust Box–Cox transformed or nonparametric methods were used for nonparametric data. Increasing storage time resulted in more hypocoagulable tracings. A correlation was found between the IN‐test and aPTT (r = 0.62, P < 0.0001). Other weak to moderate correlations were seen between thromboelastometry parameters and platelet count and hematocrit.Conclusions and Clinical ImportanceThe development of reference intervals for the thromboelastometry device allows for the clinical use of this technology. Analyzing samples after a prolonged storage time of more than 30 minutes may result in erroneous results. Results may also be affected by an abnormal hematocrit or platelet count.

Development of methodology to support molecular endotype discovery from synovial fluid of individuals with knee osteoarthritis: The STEpUP OA consortium

Objectives To develop a protocol for largescale analysis of synovial fluid proteins, for the identification of biological networks associated with subtypes of osteoarthritis. Methods Synovial Fluid To detect molecular Endotypes by Unbiased Proteomics in Osteoarthritis (STEpUP OA) is an international consortium utilising clinical data (capturing pain, radiographic severity and demographic features) and knee synovial fluid from 17 participating cohorts. 1746 samples from 1650 individuals comprising OA, joint injury, healthy and inflammatory arthritis controls, divided into discovery (n = 1045) and replication (n = 701) datasets, were analysed by SomaScan Discovery Plex V4.1 (>7000 SOMAmers/proteins). An optimised approach to standardisation was developed. Technical confounders and batch-effects were identified and adjusted for. Poorly performing SOMAmers and samples were excluded. Variance in the data was determined by principal component (PC) analysis. Results A synovial fluid standardised protocol was optimised that had good reliability (<20% co-efficient of variation for >80% of SOMAmers in pooled samples) and overall good correlation with immunoassay. 1720 samples and >6290 SOMAmers met inclusion criteria. 48% of data variance (PC1) was strongly correlated with individual SOMAmer signal intensities, particularly with low abundance proteins (median correlation coefficient 0.70), and was enriched for nuclear and non-secreted proteins. We concluded that this component was predominantly intracellular proteins, and could be adjusted for using an ‘intracellular protein score’ (IPS). PC2 (7% variance) was attributable to processing batch and was batch-corrected by ComBat. Lesser effects were attributed to other technical confounders. Data visualisation revealed clustering of injury and OA cases in overlapping but distinguishable areas of high-dimensional proteomic space. Conclusions We have developed a robust method for analysing synovial fluid protein, creating a molecular and clinical dataset of unprecedented scale to explore potential patient subtypes and the molecular pathogenesis of OA. Such methodology underpins the development of new approaches to tackle this disease which remains a huge societal challenge.

Variable gauge length: Processing theory and applications to distributed acoustic sensing

AbstractMost distributed acoustic sensing systems will only process acoustic data with a fixed gauge length for the entire well depth. However, it has been shown that the gauge length is a critical parameter to improve the signal‐to‐noise ratio when used as a function of certain geophysical parameters, such as the apparent velocity. It can also be responsible for significant distortions if tuned incorrectly. In this paper, we first aim to reintroduce the concept of gauge length and derive a robust method to optimize its value based on the geophysical parameters whilst ensuring no distortion to the original signal. We then present a novel method of processing the distributed acoustic sensing data using the concept of a variable gauge length. We finish by showing applications of these techniques on synthetic vertical seismic profiling data and some of the results obtained on actual field datasets.

A Novel Spectrofluorimetric Method for Vibegron in the Newly FDA Approved Pharmaceutical Formulation and in Human Plasma: Analytical QbD Strategy for Method Development and Optimization.

Vibegron is a novel selective beta-3 adrenergic receptor agonist molecule, recently approved by US Food and Drug Administration (FDA) in tablet pharmaceutical formulation for treating overactive bladder syndrome. Such formulation necessitates the development of a simple, fast and cost-effective methodology capable of assaying the drug in various real samples with high sensitivity. Herein, a facile and robust spectrofluorimetric method was introduced, for the first time, for vibegron quantification based on analytical quality-by-design approach. The method involves drug reaction with dansyl chloride at pH 9.8, as a smart approach to overcome the non-fluorescent nature of vibegron, giving a highly fluorescent yellow derivative measured at 514nm after being excited at 345nm. Plausible reaction scheme between the drug and dansyl chloride was elucidated through studying the differences in their infrared (IR) spectra. Variables affecting fluorescence intensities were carefully screened and rationally optimized via preliminary scouting studies and central composite design for accurate and robust results. Full International Council for Harmonisation (ICH) validation protocol was followed where linearity was achieved in range of 20.0-400.0 ng/mL with minimum detectability of 3.6 ng/mL. The proposed method expressed good capability in assaying the marketed dosage forms with no excipient inference. Finally, the high sensitivity of such method paved the way for extending its application to quantify vibegron in spiked human plasma at concentrations around its real human plasma concentrations for further bioavailability studies.

Enhanced industrial wastewater monitoring: method development for non-target screening of highly polar substances using ZIC-HILIC-HRMS.

Non-target screening (NTS) plays a major role in the monitoring and management of water bodies. While the NTS of moderate to non-polar substances is well-established, the screening of highly polar chemicals remains challenging. In this study, a robust separation method for highly polar substances using zwitterionic hydrophilic interaction liquid chromatography coupled with high-resolution mass spectrometry (ZIC-HILIC-HRMS) was developed. This method was specifically designed for the NTS of industrial wastewater, with the objective of capturing a wide range of polar contaminants in each acquisition run. Method validation included assessing key parameters such as repeatability, reproducibility, linearity, and limit of detection (LOD). For repeatability and reproducibility, the average %RSD of intensity and retention time across all substances in different matrices-solvent, influent, and effluent-remained below 6% and 1%, respectively (n = 10). The method demonstrated good linearity (R2 > 0.99) for 75% of the substances, while LODs varied between 0.1 and 40µg/L depending on the compound tested. The method was then applied for NTS analysis of untreated wastewater at various locations within a chemical industrial park. Additionally, the overall influent and effluent of an industrial wastewater treatment plant (WWTP) were monitored over a 10-day period. Principal component analysis (PCA) was performed to interpret the data, identifying irregularities in the wastewater content. Moreover, the method demonstrated the WWTP's ability to achieve an average removal efficiency of approximately 90% for this category of substances in this period, while also detecting their degradation products in the effluent. Finally, the method was successfully integrated into the daily monitoring routine of the WWTP, ensuring continuous surveillance and improved management of wastewater treatment processes.

Health monitoring of automotive clutch system through feature fusion and application of tree-based classifiers

Clutch systems are crucial components of automotive systems, essential for transferring engine power to the wheels through gear shifts. A failed clutch can halt gear shifts and power transmission, rendering a vehicle immobile. Effective fault diagnosis is vital for ensuring reliability, preventing breakdowns, and addressing root causes promptly. Consequently, condition monitoring of the clutch is essential for maintaining system reliability and minimizing unwanted breakdowns. This paper presents an innovative condition-monitoring technique derived from vibration analysis to detect faults in the clutch system. The study involves extracting statistical, histogram, and autoregressive moving average features from acquired vibration signals. The J48 decision tree algorithm is utilized to select the most significant features, which are then classified using tree-based classifiers across three load conditions (no load, 5 kg, and 10 kg) and six clutch conditions. The experimental results demonstrate that the feature fusion strategy significantly enhances classification accuracy. The obtained results state that the classification accuracies for no load, 5 kg load, and 10 kg load were 98.33%, 100.00%, and 99.16%, respectively, while applying feature fusion strategies. This study highlights the effectiveness of feature fusion in improving fault diagnosis accuracy for clutch systems, presenting a robust method for real-time condition monitoring in automotive applications.

A New Modified Confidence Interval Estimator of Location Parameter for Skewed Distribution

A new modified confidence interval has been proposed which incorporates a new point estimator of the location parameter mean for skewed data distribution. Traditionally, the trimmed mean confidence interval estimator (Trm-ci) is a robust method for dealing with the skewness of the underlying data distribution. However, the Trm-ci method trims a certain fraction of endpoint observations to address skewness, which may result in a loss data information. The Students’t confidence interval (t-ci) estimator, while the most efficient estimator at normal models, becomes impractical in a situation where observed data is subject to non-normality due to robustness. In between the two, the median confidence interval estimator (Med-ci) is expected to retain the robustness of Trm-ci and the efficiency of t-ci. The idea behind the proposed new modified confidence interval estimator (Mod-ci) is to consider both the sample mean and sample median simultaneously, while also using end-point information without trimming any observations. As such, the proposed Mod-ci is expected to be as good as or better than other underlying methods regarding robustness and efficiency when dealing with skewed data distributions. In this study, we examine the performance of the new method compared to most commonly used methods through examples and simulating data from skewed distribution with varying degree of skewness. The results of examples and simulations suggest that the proposed method is as good as, or better than other estimators relevant to this study, as measured by estimated coverage probability and the width of associated confidence interval. Therefore, this method is recommended for practical application while dealing with real-life data exhibiting skewness.

One-Step Palladium-Catalyzed Heterocyclic Ring Closure of Benzofurans with Aryl Iodides through a Heck-Type Pathway.

An operationally simple and robust method for the direct arylation and ring closure of benzofurans is reported. Besides the mild conditions and good reaction yields, the methodology is applicable for a wide range of derivatives using commercially available aryl iodides with complete C2 regioselectivity. The reaction is proposed to follow a Heck-type oxyarylation mechanism. The facile synthesis method will enable the development of new materials for diverse applications in biology and catalysis and as precursors for organic semiconductors.

High range resolution spectral-scanning LiDAR based on optical frequency-domain reflectometry.

Spectral scanning, which utilizes the dispersive effect of light, is a simple and robust method for solid-state beam steering in light detection and ranging (LiDAR) applications. Powered by a tunable laser source, optical frequency-domain reflectometry (OFDR) is a high-precision measurement scheme that is inherently compatible with spectral scanning. Here, we propose a spectral-scanning LiDAR based on OFDR technology and demonstrate that, by connecting the measured spectral reflectivity and group delay of the targets with the dispersion equation, their cloud point data can be obtained. Moreover, compared to the spectral-scanning LiDAR based on the frequency-modulated continuous-wave (FMCW) ranging method, our proposed LiDAR scheme offers a more than tenfold improvement in range resolution with a large number of angular pixels. This enhancement enables high-resolution 3D imaging along both the angular and range axes.

Oscillation search robust dynamic mode decomposition method and its application in rolling bearing fault diagnosis

Oscillation search robust dynamic mode decomposition method and its application in rolling bearing fault diagnosis