Second-order Relationship Research Articles

Statistical analysis and optimization of mechanical-chemical electro-Fenton for organic contaminant degradation in refinery wastewater

Refinery wastewater (RWW) poses significant environmental challenges due to its high concentration of organic contaminants, necessitating effective treatment solutions. This study employs the mechanical design of the Electro-Fenton (EF) technique as a feasible alternative for treating RWW. The anode and cathode electrodes, made of stainless steel and iron, respectively, were utilized to conduct organic removal (OR) from RWW. To maximize performance, two metric optimization design techniques were examined: Box Behnken Design (BBD) and response surface methodology. The analysis of variance revealed a high coefficient of determination value (R2 = 0.9685), indicating a second-order provable relationship between the response and self-governing variables. Additional statistical analysis was performed to assess the validity and reliability of the suggested approach. A predictive regression model was developed based on unresolved values, demonstrating a high level of agreement with experimental values. This model identified the optimal equation for the empirical model to predict OR based on predetermined parameters. Based on the BBD, the percentage of OR reached 93.45 % at pH 3, temperature, and electrolysis time of 60 °C, and 30 minutes, respectively. The implications of this study suggest that the optimized Electro-Fenton technique could serve as a robust solution for addressing the environmental impact of RWW, contributing to sustainable wastewater management practices.

Uncertainty impacts on China’s agricultural commodity futures: a quantile perspective

ABSTRACT This study employs rolling quantile regression and non-parametric estimation methods, specifically Quantile-on-Quantile (QQ) and Causality-in-Quantiles (QC), to investigate the impact of uncertainty indices (Geopolitical Uncertainty (GPR), Climate Policy Uncertainty (CPU), and Economic Policy Uncertainty (EPU)) on China’s agricultural commodity futures. The rolling window quantile regression coefficients vary over time, with CPU having the most significant impact on agricultural commodity futures. The empirical results from the non-parametric estimation QQ method reveal when agricultural commodities are in a prosperous market, CPU and EPU exhibit relatively strong correlation, while GPR shows weaker or insignificant correlation. According to the non-parametric estimation QC method, the evidence suggests that the second-order (variance) causality relationship is relatively stronger than the first-order (mean) causality relationship, and the first-order mean causal relationship between CPU and corn and between EPU and wheat is not significant across the entire range of quantiles. The results on Hedging Effectiveness reveal that the efficiency of uncertainty hedging is substantiated. The hedging effectiveness of EPU exhibits improvement post-COVID-19, whereas the effectiveness of GPR and CPU is more pronounced in the pre-COVID-19 period.

Third-Order Intrinsic Aberration Modification with Second-Order Accuracy Transfer of Pupil Coordinates and Extrinsic Aberration of Plane-Symmetric Optical System with a Two-Dimensional Field Light Source

A plane-symmetric optical system with a two-dimensional field light source, such as a soft X-ray and vacuum ultraviolet optical system, is widely used for synchrotron radiation source devices, microscopes and so on. Therefore, this paper studied third-order intrinsic aberration modification with second-order accuracy transfer of pupil coordinates and extrinsic aberration of these kinds of optical systems. First, we discuss the extrinsic aberration calculation method and derive the corresponding aberration expressions; in addition, we apply optical geometric relations and the polynomial fitting method to obtain a second-order accuracy transfer relationship expression of the pupil coordinates between adjacent optical surfaces and derived the modification calculation expressions of the intrinsic aberration caused by the above factor. Finally, the third-order intrinsic aberration modification with second-order accuracy transfer of pupil coordinates and extrinsic aberration expressions derived in this paper are applied to calculate ray spot diagrams of two soft X-ray and vacuum ultraviolet optical systems with meridional and sagittal field angle light sources, and these results are compared with those obtained from the ray-tracing software Shadow; this showed that the calculation accuracy of the aberration expressions is sufficient.

Quantitative Study of Sensor-Pipe Distance in Noncontact Magnetic Detection of Ferromagnetic Pipelines

Non-contact magnetic detection technology has great application potential in buried oil and gas pipelines due to the non-contact online detection of pipeline damage. However, the non-contact magnetic detecting signal is affected by the distance between the magnetometer and the ferromagnetic pipeline, leading to missed detection of pipe defects. It is essential to calibrate the detection height for the non-contact magnetic detecting signal. In this paper, the spatial analytical model of non-contact magnetic signals above ferromagnetic pipelines is established. Then, a quantitative variation law of non-contact magnetic detecting signals is obtained under different pipe diameters and internal pressures. Next, a signal calculation model based on the detection height h is established. A detection height correction method is proposed to eliminate the influence of detection heights on the magnetic detecting signal. An experimental investigation is carried out to validate the accuracy of the numerical calculation results and correction method. The results indicate that the magnetic detecting signal has a second-order exponential relationship with the detection height h within 0.1 m - 2 m. The average error of the signal correction model is 6.37%. The modified magnetic detecting signal via the signal correction method can reflect the damage status of ferromagnetic pipelines.

Progressive Context-Aware Graph Feature Learning for Target Re-Identification

This paper aims at robust and discriminative feature learning for target re-identification (Re-ID). In addition to paying attention to the individual appearance information as in most Re-ID methods, we further utilize the abundant contextual information as additional clues to guide the feature learning. Graph as a format of structured data is used to represent the target sample with its context. It describes the first-order appearance information of the samples and the second-order topological relationship information among samples, based on which we compute the feature representation by learning a graph feature embedding. We provide a detailed analysis of graph convolutional network mechanism applied in target Re-ID and propose a novel progressive context-aware graph feature learning method, in which the message passing is dominated by a pre-defined adjacency relationship followed by a learned relationship in a self-adaptive way. The proposed method fully exploits and utilizes contextual information at a low cost for Re-ID. Extensive experiments on five Re-ID benchmarks demonstrate the state-of-the-art performance of the proposed method.

Structure Destruction and Content Combination for Generalizable Anti-Spoofing

In securing the face verification systems, prior face anti-spoofing studies excavate hidden cues in original images to discriminate real persons and diverse attacks with the assistance of auxiliary supervision. However, limited by several inherent shortcomings in their training process: 1) Neglect of the multi-scale nature of spoof cues; 2) Complete integral structure in a single image; 3) Implicit subdomains in the whole dataset, these methods are weak to mine comprehensive spoof patterns and may stick on memorization of the entire training dataset, incurring overfitting. In this paper, we propose a new framework named Destruction and Combination Network (DCN) including Multi-scale Representation Extraction Module, Structure Destruction Module, and Content Combination Module to address these limitations respectively. The first mechanism exploits multi-scale representation to learn spoof cues comprehensively. The second one destroys images into patches to construct non-structural inputs, and the last scheme recombines patches from different subdomains or classes into a mixup construction. Based on the above splitting-and-splicing operation, we further introduce Local Relation Modeling Module to model the second-order relationship between patches. To show the generalizable capacity of the proposed framework, besides simple intra-dataset testing, we test our method in cross-domain, cross-content, and cross-attack scenarios. Extensive experiments on different scenarios demonstrate the reliability of our method against state-of-the-art competitors.

Assessment of Stability and Thermophysical Properties of Jojoba Nanofluid as a Metal-Cutting Fluid: Experimental and Modelling Investigation

Nanofluids based on vegetable oil have emerged as ecological alternatives to conventional cutting fluids. Jojoba-seed oil has recently been identified as adequate for use in metal cutting. Aiming to assess the stability and thermophysical properties of jojoba nanofluids, this article reports an experiment- and modelling-based investigation. The stability, viscosity and thermal conductivity of jojoba MoS2 nanofluid were studied across a broad range of temperatures and concentrations of nanoparticles. The functional relationship of the viscosity and thermal conductivity to the temperature and concentration was determined by regression analysis. In addition to confirming known phenomena, vis-à-vis the effect of the concentration and temperature on the viscosity and thermal conductivity, this study shows that the increase in the thermal conductivity in line with the concentration stagnates after an initial sharp rise due to an increase in the attractive forces between the particles. The viscosity displays a second-order interactive relationship with the temperature and concentration of the nanoparticles, whereas thermal conductivity follows a complex third-order interaction model. In addition to being economical, jojoba nanofluid matches or surpasses the nanofluid prepared using commercially available mineral-oil-based cutting fluid (LRT 30)—which is specially designed for the minimum-quantity lubrication method of metal cutting. Conclusively, this investigation paves the way for the shop-floor application of jojoba nanofluid in metal-cutting operations.

Mediation roles of pedagogical approaches and personality traits in entrepreneurial curriculum design and entrepreneurial intention nexus

Planned behaviour theory was used in a path analysis modelling to investigate the serial mediation role of teaching methods and personality traits (locus of control, need for achievement and entrepreneurial attitude) in the relationship between entrepreneurship curriculum and entrepreneurial intention among university students in Ghana. A proposed 40-item instrument was used to measure outcomes for six constructs (3 personality trait constructs, entrepreneurship curriculum, teaching methods and entrepreneurial intention) for 324 participants. Acceptable convergent, divergent and construct validity scores were observed for the instrument. Teaching methods fully mediated the first-order relationships between entrepreneurial curriculum and each personality traits. The three constructs of personality traits parallelly mediated the second-order relationship between teaching methods and entrepreneurial intention. Teaching methods and each personality trait serially mediated the relationship between entrepreneurial curriculum and entrepreneurial intention. This empirical evidence provides insight into the design of pragmatic interventions by major stakeholders including entrepreneurship educators to inspire students into start-up activities

Robust multi-view non-negative matrix factorization for clustering

Non-negative matrix factorization (NMF) has attracted much attention for multi-view clustering due to its good theoretical and practical values. Although existing multi-view NMF methods have achieved satisfactory performance to some extent, there still exist the following problems: 1) most existing methods only consider the first-order relationship to construct the similarity matrix, which cannot fully explore the neighborhood information; 2) most existing methods only consider the common information shared by different views, and the complementary information contained in different views is often ignored. To tackle the above-mentioned shortcomings, we present a novel multi-view clustering approach, called robust multi-view NMF (RMNMF). Specifically, in RMNMF, we consider the high-order relationship among features, where the first-order and second-order relationship is utilized to obtain an optimal neighborhood structure. Also, we introduce the Hilbert-Schmidt independence criterion (HSIC) to fully explore the complementary information in different views. Extensive experimental results on several benchmark datasets prove the effectiveness of our proposed approach.

A Printed LC Resonator-Based Flexible RFID for Remote Potassium Ion Detection

This article presents a flexible printed radio-frequency identification (RFID) sensor based on a printed inductive&#x2013;capacitive (LC) resonator circuit and a potassium ion-selective electrode (ISE) for remote potassium ion sensing. The potassium ion concentration of the contact solution can be monitored by measuring the change of the resonant frequency of the RFID sensor. The resonant frequency of the sensor can be directly detected by measuring the induced change in the reflection coefficient (<inline-formula> <tex-math notation="LaTeX">$S_{11}$ </tex-math></inline-formula>) of an external interrogator coil that is inductively coupled to the RFID sensor. Results obtained for the RFID sensor exhibited a second-order exponential relationship between the resonant frequency of the sensor and the K^&#x002B; concentration of the solution over 0.001&#x2013;2 mol/L dynamic range values. Effects of varying separation distance between the sensor and the interrogator coil and the effect of temperature variations on sensor&#x2019;s measurement are shown. With less than 2-s response time and the long-term stability, the wireless passive printed sensor has potential for low-cost K^&#x002B; monitoring applications such as K^&#x002B; monitoring in food packages.

Face Adaptation-Investigating Nonconfigural Saturation Alterations.

Recognizing familiar faces requires a comparison of the incoming perceptual information with mental face representations stored in memory. Mounting evidence indicates that these representations adapt quickly to recently perceived facial changes. This becomes apparent in face adaptation studies where exposure to a strongly manipulated face alters the perception of subsequent face stimuli: original, non-manipulated face images then appear to be manipulated, while images similar to the adaptor are perceived as “normal.” The face adaptation paradigm serves as a good tool for investigating the information stored in facial memory. So far, most of the face adaptation studies focused on configural (second-order relationship) face information, mainly neglecting non-configural face information (i.e., that does not affect spatial face relations), such as color, although several (non-adaptation) studies were able to demonstrate the importance of color information in face perception and identification. The present study therefore focuses on adaptation effects on saturation color information and compares the results with previous findings on brightness. The study reveals differences in the effect pattern and robustness, indicating that adaptation effects vary considerably even within the same class of non-configural face information.

Determination of the Optimal Neural Network Transfer Function for Response Surface Methodology and Robust Design

Response surface methodology (RSM) has been widely recognized as an essential estimation tool in many robust design studies investigating the second-order polynomial functional relationship between the responses of interest and their associated input variables. However, there is scope for improvement in the flexibility of estimation models and the accuracy of their results. Although many NN-based estimations and optimization approaches have been reported in the literature, a closed functional form is not readily available. To address this limitation, a maximum-likelihood estimation approach for an NN-based response function estimation (NRFE) is used to obtain the functional forms of the process mean and standard deviation. While the estimation results of most existing NN-based approaches depend primarily on their transfer functions, this approach often requires a screening procedure for various transfer functions. In this study, the proposed NRFE identifies a new screening procedure to obtain the best transfer function in an NN structure using a desirability function family while determining its associated weight parameters. A statistical simulation was performed to evaluate the efficiency of the proposed NRFE method. In this particular simulation, the proposed NRFE method provided significantly better results than conventional RSM. Finally, a numerical example is used for validating the proposed method.

Reducing Thickness Deviation of W-Shaped Structures in Manufacturing DRAM Products Using RSM and ANN_GA

Thickness deviation on a W-shaped structure of word lines in dynamic random access memories (DRAMs) is detrimental to the DRAM manufacturing's yield rate. The case company has suffered from a low yield rate in their DRAM manufacturing due to large thickness deviations on the W-shaped structures. This article proposed a new approach to find an appropriate setting of manufacturing control factors' values to decrease the thickness deviation. In the proposed method, we first used the fractional factorial design to select important control factors related to the thickness deviation. We then used the gradient descent method to find a region of control factors that contained a second-order solution to the problem and adopted the response surface method (RSM) to find the second-order solution. However, the second-order RSM was limited by its second-order relationship between the input control factors and the output thickness deviation. To further reduce the thickness deviation, we used an artificial neural network (ANN) to predict the thickness deviation given a set of control factors' input values. We then used a genetic algorithm (GA) to find a better solution to the problem with the predicted thickness deviations by the ANN. The confirmation experiment showed that the GA had found a better solution than the RSM method. With the proposed GA method, the case company has successfully reduced the thickness deviation from 45.0 to 12.9 Å, saving U.S. $205 000 per year on their DRAM manufacturing cost.

Face Adaptation Effects on Non-Configural Face Information.

Inspecting new visual information in a face can affect the perception of subsequently seen faces. In experimental settings for example, previously seen manipulated versions of a face can lead to a clear bias of the participant's perception of subsequent images: Original images are then perceived as manipulated in the opposite direction of the adaptor while images that are more similar to the adaptor are perceived as normal or natural. These so-called face adaptation effects can be a useful tool to provide information about which facial information is processed and stored in facial memory. Most experiments so far used variants of the second-order relationship configural information (e.g., spatial relations between facial features) when investigating these effects. However, non-configural face information (e.g., color) was mainly neglected when focusing on face adaptation, although this type of information plays an important role in face processing. Therefore, we investigated adaptation effects of non-configural face information by employing brightness alterations. Our results provide clear evidence for brightness adaptation effects (Experiment 1). These effects are face-specific to some extent (Experiments 2 and 3) and robust over time (Experiments 4 and 5). They support the assumption that non-configural face information is not only relevant in face perception but also in face retention. Brightness information seems to be stored in memory and thus is even involved in face recognition.

Investigating the Result of Current Density, Temperature, and Electrolyte Concentration on COD: Subtraction of Petroleum Refinery Wastewater Using Response Surface Methodology

Electrochemical oxidation (EO) investigated chemical oxygen demand (COD) subtraction from petroleum refinery wastewater (PRW) as a capable remediation process. Titanium substrates coated with iridium–tantalum oxide mixtures (Ti/IrO2–Ta2O5) were used as the dimensional stable anode (DSA). The Box-Behnken Design (BBD), a statistical experimental design and response surface methodology (RSM), was used to matrix the current density, temperature, and electrolyte (NaCl) concentration variables, with COD removal efficiency as the response factor. A second-order verifiable relationship between the response and independent variables was derived where the analysis of variance displayed a high coefficient of determination value (R2 = 0.9799). The predicted values calculated with the model equations were very close to the experimental values where the model was highly significant. Based on the BBD for current density, the optimum process conditions, temperature and electrolyte (NaCl) concentration were 7.5 mA/cm2, 42 °C and 4.5 g/L, respectively. They were resulting in a COD removal efficiency of 99.83% after a 12-hour EO period.

Effects of CO2 and H2O on oxy-fuel combustion characteristics and structural evolutions of Zhundong coal pellet at fast heating rate

To comprehensively investigate the combustion characteristics and structural evolutions of Zhundong coal pellet at fast heating rate in O2/N2, O2/CO2 and O2/H2O atmospheres, the real-time mass loss, temperature and combustion images were directly obtained on a novel concentrating photothermal reactor with heating rate of 4800 °C/min. The structural features of the coal/char with different holding times were analyzed by FT-IR and Raman. The experimental results showed that the maximum temperature and the maximum mass loss rate of coal pellet increased with the increasing of H2O concentration but decreased with the increasing of CO2 concentration. Under the experimental condition with the fast heating rate, CO2 prolonged while H2O shortened the ignition delay of coal pellet. In the initial stage of coal combustion, faster consumption rates of all functional groups were evidenced in O2/H2O while slower consumption rates were evidenced in O2/CO2 compared with those in O2/N2. Raman band area ratio I(Gr+Vl+Vr)/ID of chars also exhibited that the less condensed char was observed in O2/CO2 but more condensed char was obtained in O2/H2O. Such results indicated that the coal conversion was slowed down with the addition of CO2 but accelerated with the addition of H2O in the initial stage at fast heating rate. The specific reaction rates were reasonably characterized by the Raman band area ratio of char in O2/N2, O2/CO2 and O2/H2O atmospheres, and they were accorded with the second-order function relationship expressions.

Mechanical Model of the Initial Leg Pressure Increment of the Shield and Its Application to Monitor Roof Load in Longwall Panels

The shield pressure cannot always be used to represent the upper load of longwall panels, since its value is steady or even decreases by the yielding action. However, the leg pressure increment of the shield (LPIS) at the initial stage is not influenced by yielding and could therefore be an important factor to judge the state of overlying loads. In this study, a mechanical model is established to analyze the relationship between the overlying loads of the main roof and LPIS after cutting. There is a linear positive correlation between leg pressure increment and overlying loads and a second-order relationship between leg pressure increment and length of main roof cantilever in the proposed model. Therefore, it can be used to determine the magnitude of roof weighting strength in different periods as well as the length of the main roof cantilever in a period. Finally, the mine pressure difference between the period of fully mechanized mining and the period of fully mechanized caving mining in the MinDong-1 coal mine serves to verify the rationality of the proposed model.

오버헤드 교반기에서 자동 점도 측정을 위한 교반 공정 조건의 영향 분석

To monitor the stirring state by on-line estimation of liquid viscosity instead of stopping the stirring process and measuring the viscosity using viscometer, a basic study clarifying the effect of stirring conditions was carried out. For this purpose, the relationship between liquid viscosity and the stirring conditions, such as stirring torque, stirring speed, the duty ratio of PWM, impeller and beaker type, and the blade position in the beaker was analyzed. The second-order relationship between the stirring speed and torque, the linear relationship between viscosity and torque, and the linear relationship between duty ratio of PWM and stirring speed were observed, thereby indicating that the liquid viscosity could be estimated by an experimental formula based on the duty ratio and the stirring speed. In addition, the type of impeller and beaker was identified using the magnetic field inside impeller and RFID technology. For reducing the error in calculating the liquid viscosity, the employment of an impeller blade with a large rotational resistance and stirring with the blade at the center zone of the beaker were found to be necessary.

Validation of the Czech Version of the Relational Needs Satisfaction Scale.

AimIf we want to understand people’s satisfaction in their relationships, it is essential to have a valid and reliable measure of relational needs satisfaction. The aim of this study was to test the factor structure of the Czech version of the Relational Needs Satisfaction Scale (RNSS) as well as the scale’s measurement invariance and convergent validity.MethodIn total, 419 adults answered a battery of measures, including the RNSS, in an online survey. Confirmatory factor analysis was conducted to test the factor structure and the measurement invariance of the RNSS across gender and age. A correlational analysis was conducted to assess the convergent validity.ResultsThe five-factor structure of the RNSS was confirmed. Furthermore, support for a second-order global relationship satisfaction factor was found. The hierarchical model was strictly invariant with respect to gender and age. Furthermore, the RNSS demonstrated an expected pattern of correlations with the reference instruments.ConclusionThe Czech version of the RNSS can be considered a valid and reliable method.

Experimental and Numerical Study of Electroporation Induced by Long Monopolar and Short Bipolar Pulses on Realistic 3D Irregularly Shaped Cells.

In this article, the reversible electroporation induced by rectangular long unipolar and short bipolar voltage pulses on 3D cells is studied. The cell geometry was reconstructed from 3D images of real cells obtained using the confocal microscopy technique. A numerical model based on the Maxwell and the asymptotic Smoluchowski equations has been developed to calculate the induced transmembrane voltage and pore density on the plasma membrane of real cells exposed to the pulsed electric field. Moreover, in the case of the high-frequency pulses, the dielectric dispersion of plasma membranes has been taken into account using the second-order Debye-based relationship. Several numerical simulations were performed and we obtained suitable agreement between the numerical and experimental results.