A review on robust M-estimators for regression analysis

A robust method to improve the regression accuracy of LIBS data: determination of heavy metal Cu in Tegillarca granosa

Tegillarca granosa (T. granosa) is susceptible to contamination by heavy metals, which poses potential health risks for consumers. Laser-induced breakdown spectroscopy (LIBS) combined with the classical partial least squares (PLS) model has shown promise in determining heavy metal concentrations in T. granosa. However, the presence of outliers during calibration can compromise the model's integrity and diminish its predictive capabilities. To address this issue, we propose using a robust method for partial least squares, RSIMPLS, to improve the accuracy of Cu prediction in T. granosa. The RSIMPLS algorithm was employed to analyze and process the high-dimensional LIBS data and utilized diagnostic plots to identify various types of outliers. By selectively eliminating certain outliers, a robust calibration method was achieved. The results showed that LIBS spectroscopy has the potential to predict Cu in T. granosa, with a coefficient of determination (Rp2) of 0.79 and a root mean square error of prediction (RMSEP) of 11.28. RSIMPLS significantly improved the prediction accuracy of Cu concentrations with a 43% decrease in RMSEP compared to the PLS. These findings validated the effectiveness of combining LIBS data with the RSIMPLS algorithm for the prediction of Cu concentrations in T. granosa.

Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference-grade performance

Particulate Matter (PM) low-cost sensors (LCS) present a cost-effective opportunity to improve the spatiotemporal resolution of airborne PM data. Previous studies focused on PM-LCS-reported hourly data and identified, without fully addressing, their limitations. However, PM-LCS provide measurements at finer temporal resolutions. Furthermore, government bodies have developed certifications to accompany new uses of these sensors, but these certifications have shortcomings. To address these knowledge gaps, PM-LCS of two models, 8 Sensirion SPS30 and 8 Plantower PMS5003, were collocated for one year with a Fidas 200S, MCERTS-certified PM monitor and were characterised at 2 min resolution, enabling replication of certification processes, and highlighting their limitations and improvements. Robust linear models using sensor-reported particle number concentrations and relative humidity, coupled with 2-week biannual calibration campaigns, achieved reference-grade performance, at median PM_2.5 background concentration of 5.5 μg/m³, demonstrating that, with careful calibration, PM-LCS may cost-effectively supplement reference equipment in multi-nodes networks with fine spatiotemporality.

Strain stiffening of Ndc80 complexes attached to microtubule plus ends

In the mitotic spindle, microtubules attach to chromosomes via kinetochores. The microtubule-binding Ndc80 complex is an integral part of kinetochores, and is essential for kinetochores to attach to microtubules and to transmit forces from dynamic microtubule ends to the chromosomes. The Ndc80 complex has a rod-like appearance with globular domains at its ends that are separated by a long coiled coil. Its mechanical properties are considered important for the dynamic interaction between kinetochores and microtubules. Here, we present a novel method that allows us to time trace the effective stiffness of Ndc80 complexes following shortening microtubule ends against applied force in optical trap experiments. Applying this method to wild-type Ndc80 and three variants (calponin homology (CH) domains mutated or Hec1 tail unphosphorylated, phosphorylated, or truncated), we reveal that each variant exhibits strain stiffening; i.e., the effective stiffness increases under tension that is built up by a depolymerizing microtubule. The strain stiffening relation is roughly linear and independent of the state of the microtubule. We introduce structure-based models that show that the strain stiffening can be traced back to the specific architecture of the Ndc80 complex with a characteristic flexible kink, to thermal fluctuations of the microtubule, and to the bending elasticity of flaring protofilaments, which exert force to move the Ndc80 complexes. Our model accounts for changes in the amount of load-bearing attachments at various force levels and reproduces the roughly linear strain stiffening behavior, highlighting the importance of force-dependent binding affinity.

Wideband signal detection in multipath environment affected by impulsive noise

Detecting the presence or absence of a known signal is an important aspect of underwater acoustic (UWA) communications as it is a vital first step to process the received data. The influence of impulsive noise and multipath propagation on detection are both considered in this paper. Many robust detectors have been designed to cope with impulsive noise, but they tend to ignore the impact of multipath propagation, which may decrease the detection probability. To emphasize the role of multipath channel, we propose two-stage detection methods. In the first stage, the channel is estimated based on robust orthogonal matching pursuit, while in the second stage, detectors that use the channel estimation from the first stage, namely augmented log-likelihood ratio detector and augmented pseudo-correlation detector, are developed. The former detector is parametric and the latter one is non-parametric. The improved performance is demonstrated in simulated multipath channel and actual UWA channel with both simulated noise and recorded snapping shrimp noise, indicating that the proposed detectors are resistant to impulsive noise. It is also shown that the use of multipath information helps to improve the detection performance.

Design of PM2.5 monitoring and forecasting system for opencast coal mine road based on internet of things and ARIMA Mode

The dust produced by transportation roads is the primary source of PM2.5 pollution in opencast coal mines. However, China’s opencast coal mines lack an efficient and straightforward construction scheme of monitoring and management systems and a short-term prediction model to support dust control. In this study, by establishing a PM2.5 and other real-time environmental information to monitor, manage, visualize and predict the Internet of things monitoring and prediction system to solve these problems. This study solves these problems by establishing an Internet of things monitoring and prediction system, which can monitor PM2.5 and other real-time environmental information for monitoring, management, visualization, and prediction. We use Lua language to write interface protocol code in the APRUS adapter, which can simplify the construction of environmental monitoring system. The Internet of things platform has a custom visualization scheme, which is convenient for managers without programming experience to manage sensors and real-time data. We analyze real-time data using a time series model in Python, and RMSE and MAPE evaluate cross-validation results. The evaluation results show that the average RMSE of the ARIMA (4,1,0) and Double Exponential Smoothing models are 12.68 and 8.34, respectively. Both models have good generalization ability. The average MAPE of the fitting results are 10.5% and 1.7%, respectively, and the relative error is small. Because the ARIMA model has a more flexible prediction range and strong expansibility, and ARIMA model shows good adaptability in cross-validation, the ARIMA model is more suitable as the short-term prediction model of the prediction system. The prediction system can continuously predict PM2.5 dust through the ARIMA model. The monitoring and prediction system is very suitable for managers of opencast coal mines to prevent and control road dust.

A Lévy Distribution Based Searching Scheme for the Discrete Targets in Vast Region

This paper investigates the Discrete Targets Search Problem, (DTSP), which aims to quickly search for discrete objects scattered in a vast symmetry region. Different from continuous function extremal value search, the discrete points search cannot make use of the properties of regular functions, such as function analytic, single/multiple extreme, and monotonicity. Thus, in this paper a new search scheme based on Lévy random distribution is investigated. In comparison with the TraditionalCarpet search or Random search based on other distributions, DTSP can provide much faster search speed which is demonstrated by simulation with different scales problems for the selected scenarios. The simulations experiment proves that DTSP is faster for searching for a discrete single target or multiple targets in a wide area. It provides a new method for solving the discrete target search problem.

Do E-Government Services Affect Jordanian Customer Loyalty?

E-governance could be understood as the performance of the governance via the electronic medium to facilitate a transparent, efficient, and speedy process for making information readily available to the public, government organs, and other social agencies to perform administration activities of the government. Likewise, e-governance has become the most adopted instrument to transform government functionaries in service delivery. Therefore, the current study was carried out to investigate the effect of e-government services dimensions (Protection Perception, Data Protection Act, Available Information, and Government Commitment) on Jordanian customer loyalty. This study adopted a quantitative research method to gather data from customers related to the e-governance service facilities in Jordan. The findings showed that Information and Communication Technology (ICT) advancement through the adoption of e-governance is not significant in Jordan. Along with adoption, the performance indices related to e-governance showed poor performance of the Jordanian government's official website. The commitment such as REACH25 of Jordan aims to digitize government services on the public platform. Absent of a dedicated data protection act and regulation makes it harder for government agencies to deal with the data breach issue. To protect customers' loyalty to the e-governance of Jordan, its government needs to address the grievances of the customers that suffer from online fraud. Accordingly, it has become imperative for the Jordanian government to adopt the best practices of e-governance from developed countries to enhance their customer's experiences. Jordanian government could adopt the data protection act in line with the European Union's general data protection regulation to protect data flow across boundaries. By adopting robust data regulation, the Jordanian government could ensure that the generated data is processed and stored in the country and other regulations regarding sharing data between countries

Shedding Light on Data Reconciliation Techniques Applied to Analytical Chemistry

Historically, owing to the increase in processing capacity over the years, validation and adjustment of measurements have become imperative. In particular, concerning discussions related to data and results in analytical chemistry, there is always a need to improve their reliability. The data reconciliation technique has the objective of using measurement redundancies to obtain the best estimate of the true value and, consequently to minimize its uncertainty. Unfortunately, this powerful tool is less known and used by analytical chemists compared to other areas. This approach can be satisfactorily performed in decision-making procedures that focus on chemical analysis, chemometrics, biochemistry analysis, forensics, and environmental sciences, such as in a characterization study, regarding conformance or nonconformance with the specification, doubts related to the malfunctioning of meters and about the compatibility of test methods. This work discusses and sheds light on the importance of data reconciliation, including data reconciliation statistics and application of the technique, traditional data reconciliation in analytical chemistry, principal component analysis based on data reconciliation in analytical chemistry, and fuzzy data reconciliation in analytical chemistry.

Estimation of Feeding Composition of Industrial Process Based on Data Reconciliation

For an industrial process, the estimation of feeding composition is important for analyzing production status and making control decisions. However, random errors or even gross ones inevitably contaminate the actual measurements. Feeding composition is conventionally obtained via discrete and low-rate artificial testing. To address these problems, a feeding composition estimation approach based on data reconciliation procedure is developed. To improve the variable accuracy, a novel robust M-estimator is first proposed. Then, an iterative robust hierarchical data reconciliation and estimation strategy is applied to estimate the feeding composition. The feasibility and effectiveness of the estimation approach are verified on a fluidized bed roaster. The proposed M-estimator showed better overall performance.