During the nano-cutting process, monocrystalline gallium arsenide is faced with various surface/subsurface deformations and damages that significantly influence the product’s performance. In this paper, molecular dynamics simulations of nano-cutting on gallium arsenide are conducted to investigate the surface and subsurface deformation mechanism. Dislocations are found in the machined subsurface. Phase transformation and amorphization are studied by means of coordination numbers. Results reveal the existence of an intermediate phase with a coordination number of five during the cutting process. Models with different cutting speeds are established to investigate the effects on the dislocation. The effect of crystal anisotropy on the dislocation type and density is studied via models with different cutting orientations. In addition, the subsurface stress is also analyzed. Gallium arsenide (GaAs) is a typical kind of III–V compound, and it is also one of the most important semiconductor materials. Favored by its great properties such as direct bandgap, high electron mobility and high resistivity, monocrystalline gallium arsenide finds wide applications in various areas such as infrared optical devices and microwave devices. With the continuous development of its production, the strict requirement of surface roughness, surface form accuracy, and subsurface damage are increasingly demanded. Nano-cutting technology is beneficial in improving the performance of gallium arsenide optical elements and in expanding the field of its applications. The nano-cutting process of gallium arsenide crystal is faced with many difficulties such as brittle crack, anisotropy, and other subsurface damages. Therefore, the study on the nano-cutting mechanism of gallium arsenide is of great significance in solving the machining problems, improving the surface quality of gallium arsenide, and developing related nanometric processing technology.Many studies performed nanoscale experiments on gallium arsenide. Fang et al.  comparedMolecular dynamics (MD) simulation is one of the most efficient methods to explore the mechanism of nanometric machining. Shimada et al. , 7 proveAs for the studied materials, most MD simulation studies in nanometric machining focus on the element semiconductors such as silicon and germanium or metals such as copper and aluminum. Fewer studies have been reported about the numerical analysis on nano-cutting of gallium arsenide. Fan et al.  investigMD simulation models are established to study the deformation behaviors of brittle crystal gallium arsenide in nano-cutting process. The three-dimensional MD simulation model is shown in Fig. The potential is the basis for calculating the force and energy between atoms, and it is also one of the most important settings in MD simulation. In the previous study, a potential system consisting of three kinds of potential was determined for the simulation of gallium arsenide and diamond tool model . In thisLAMMPS is used to carry out the MD simulation, while the visualization and analysis are via OVITO, including the dislocation analysis (DXA), stress calculation and coordination analysis. The relaxation of the workpiece is set as an NPT ensemble to minimize the system’s energy and stabilize the free surface, while the nano-cutting process is set as an NVE ensemble. The timestep for the integration in the simulation is 1.0 fs.y-direction. The atoms in front of the tool are observed to flow up, becoming chips by extrusion. A mount of atoms flow downwards and form the machined surface. On both sides of the machined groove, the atoms pile up with a height of 2 nm due to extrusion and plowing of the tool. The result revels that the deformation and removal of the brittle gallium arsenide crystal represents a ductile behavior when the machining is in nanometric scale. Few arsenide clusters precipitating to the machined surface are evident in the simulation process. This study reveals the presence of arsenide precipitation after annealing [As is shown in Fig. nnealing . The Ternnealing . In the z-direction. When the forces are stabilized, the normal force and tangential force fluctuate around 1700 nN and 700 nN, respectively. It is found that the force in the y-direction is dominant in the cutting process because of the large effective negative rake angle of the diamond tool as presented in the model. The negative rake angle brings a large compressive stress, which causes a high normal force in the cutting process.Compare the cutting force curve during the machining process in Fig. The subsurface damage formation is an important part of the study of the nano-cutting mechanism. It is necessary to figure out the damage formation mechanism of gallium arsenide during machining to further optimize the processing parameters. Crystals have anisotropy on atomic structure, and it is one of the most serious problems in the machining process, which would affect the process performance with different orientations.The distribution of dislocations in the machined subsurface is shown in Fig. The von Mises stress, which is calculated including the shear stress components, is usually used to determine the formation of dislocation. Figure Dislocations in the brittle and ductile materials are greatly different. Gallium arsenide is a typical brittle crystal, and the dislocations are concentrated near the machined surface. However, dislocations in ductile materials like copper will extend and slip deep into the workpiece and form a high-density dislocation cluster during the cutting process , as showFigure The lattice structure of the material may change because of high stress during nano-cutting. By studying the phase transformation process, a better understanding of cutting gallium arsenide can be achieved, which is useful for optimizing the process parameters or in designing the pretreatment experiment before nano-cutting.Monocrystalline gallium arsenide has a zincblende structure under normal pressure and temperature. However, the structure changes into a six-coordinate GaAs-II when the compressive stress reaches 17 GPa . The phaFigure It is obvious that some atoms under the machining position turn to a five-coordination structure. Similar to the bct-5 structure of silicon crystal forming with compressive stress, this five-coordination structure is considered as an intermediate in the formation of the six-coordinate GaAs-I. Hydrostatic stress increases and causes the distortion of lattice with the tool cutting. However, as is shown in hydrostatic stress distribution of Fig. Figure The surface/subsurface damage formation is influenced by many factors, and different cutting parameters will affect the formation of the dislocation.The dislocation distributions with different cutting speeds are compared in Fig. Anisotropy is a serious problem in the nano-cutting process of the crystal , 27, incmentclasspt{minimamentclasspt{minimamentclasspt{minimaFigure Comparing the stress and dislocation distributions, the models with a higher dislocation density may have lower stress in the machined subsurface. Figure us study , when thIn the cutting process, the material near the cutting tool receives high stress and severe compression, which will cause the material structural changes such as phase transformation and amorphization. Under the amorphous layer, slip will occur in a larger area and dislocations will form in the single crystal due to the widespread stress effects. These are the main ductile deformation in the nano-cutting of gallium arsenide. Different cutting condition will affect the local stress state and the ease of dislocation formation. When the dislocations is difficult to generate due to the high processing speed or anisotropy, the mode of ductile deformation will tends to be amorphous dominated because of the amorphization caused by unreleased stress. The crack will also easy to form in this situation. On the contrary, dislocations will be the main component of ductile deformation if the conditions are suitable for the material slip.In the nano-cutting of gallium arsenide, the dislocation and structural transformation are found as the main deformation mechanism in the machined subsurface.Dislocations and stacking faults are observed in the machined subsurface groove, and the formations of stacking faults and partial dislocations are consistent.An intermediate phase with five-coordination is found in front of the tool because hydrostatic stress is close to but not higher than the transition threshold (17 GPa). An amorphous layer forms in the machined subsurface.With increasing cutting speed, the dislocation density decreases because of the high strain rate. Anisotropy has a great effect on the dislocation type and density. Partial dislocations are easier to form in the (010) [MD simulation is used to study the mechanism of damage formation in the nano-cutting process of gallium arsenide crystal. The atomic motion and cutting force of the process are also analyzed. The dislocation, stacking fault, and phase transformation are mainly studied as the surface/subsurface damages. The conclusions can be summarized as follows:


When reading interesting content or searching for information on a website, the appearance of a pop-up advertisement in the middle of the screen is perceived as irritating by a recipient. Interrupted cognitive processes are considered unwanted by the user but desired by advertising providers. Diverting visual attention away from the main content is intended to focus the user on the appeared disruptive content. Is the attempt to reach the user by any means justified? In this study, we examined the impact of pop-up emotional content on user reactions. For this purpose, a cognitive experiment was designed where a text-reading task was interrupted by two types of affective pictures: positive and negative ones. To measure the changes in user reactions, an eye-tracker  and an iMotion Platform  were used. The results confirm the impact of the type of emotional content on users’ reactions during cognitive process interruptions and indicate that the negative impact of cognitive process interruptions on the user can be reduced. The negative content evoked lower cognitive load, narrower visual attention, and lower irritation compared to positive content. These results offer insight on how to provide more efficient Internet advertising. Intrusive web advertising, such as animated, pop-up, layer ads, and other informational content appearing in any part of the screen, drag the users’ attention away from their primary task: reading or navigating through the main content of web pages. This approach is a standard procedure used by advertisement designers who use their marketing activities to ensure advertisements are delivered and noticed. Therefore, increasing the effectiveness of ads delivery is often accompanied by an increase in intrusiveness ,2,3. HowThe distractions introduced by online ads are being perceived as annoying by an increasing number of users, which has created a negative attitude toward the ads. Irritation is associated with most forms of intensive advertising, especially with forced exposure such as pop-up, layer , and preWith the above assumptions, adding emotional appeal to video pre-roll ads has received special focus. Li et al. reported the results of an experiment where a level of intrusiveness was measured with a scale , showingEmotional load is also known as a factor modifying visual attention. The influence of affective pictures on attention is reported in ,16,17. MA separate study investigating how the arousal level of a stimulus influences visual attention assumed that highly arousing stimuli capture attention, regardless of their emotional valence . Thus, aThe studies presented above analyzed the influence of the emotional content on users’ visual attention or their emotional response. In this study, our aim was to study both factors simultaneously and to determine whether such an approach allows for a more compact and unified answer to the question on how to prepare the marketing content to reduce user irritation and limit ad avoidance. Hence, the aim of this study was to measure the impact of affective disruptive content and its emotional type on changes in user visual attention, cognitive load, and emotional states.To achieve our aim, we designed a cognitive experiment. During the experiment, a subject’s task was to read and understand the text displayed on the screen. The text-reading task was suddenly interrupted by affective pictures  ) appeariThe results of our experiment confirm that the choice of emotional load of the disruptive content is an important factor when designing ads. The correct type of emotional content might reduce the negative impact of interrupting the user’s ongoing cognitive process. As we found, the more negative the content, the lower the cognitive load, and the narrower the visual attention, and the lower the irritation.The paper is structured as follows. The goal of the experiment was to measure the impact of affective disruptive content and its emotional type on user visual attention, cognitive load, and emotional states change . The expThe experiment was performed using an NEC monitor with a native resolution of 1680 × 1050 pixels. The monitor display was calibrated to the sRGB color space using a Minolta CS-200 colorimeter and a Specbos 1201 spectroradiometer. During the experiment, the gaze points and eye movements were recorded with a Tobii Pro X3 120 Hz eye-tracker, and the movements of the user’s face muscles were recorded with an iMotions platform. The recording device (HP webcam HD 3300 720P) was attached to the upper part of the stimulus screen.The main respondent task was to read a set of 10 different texts; each text was followed by a short questionnaire testing a text understanding. The reading process was interrupted by visual stimuli with a positive or negative emotional load. The examples of negative and positive stimuli displayed during the experiment, as well as the text reading task, are presented in The two-level randomization procedure was applied in the process of selecting the stimuli for each respondent. First, the order of the condition (negative/positive) was randomly chosen; second, the pictures in one emotional group were randomly arranged. Such an experimental plan enabled us to find out whether there are statistically significant differences between the two experimental conditions, that is, between positive and negative stimuli.The disrupting stimuli were presented for seven seconds with random intervals (13 and 26 s) between each two of them. In total, 30 stimuli were displayed for each user. After displaying the whole set of 30 stimuli (15 negative and 15 positive), the remaining part of the reading task was free from interruptions. The average experiment time was about 15–20 min.The affective pictures used in the experiment were obtained from the IAPS database , which wEye movement characteristics provide indirect access to cognitive processes, e.g., decision making , attentiA number of studies ,34,35 haIn our research, to measure visual attention change, when interruption from a cognitive task occurred, we chose two metrics: fixation duration, in the form of fixation duration in the area of interest (AOI) of a given stimuli compared to the fixation duration in the experiment window for the total time the stimuli was displayed (fixation duration percent (FDP); and the saccade velocity (SCV). Notably, according to , informai is the observer number zi,jk, and k was presented).The fixation duration percent  , electroFacial expression analysis is usually based on the facial actions coding system (FACS) published in 1978 by Ekman and Friesen . The sysAlthough the manual assignment of scores to different AUs to recognize underlying emotional expressions is possible for static pictures or short video clips, the emotional tagging of longer video material is ultimately a tedious task. Therefore, automated recognition systems are usually used to deal with the task. Apart from different algorithms and approaches proposed by scientists from leading research centers ,58,59, fAlthough the Facial Expression Analysis module provides both the raw information about the activation of single action units and their translation to core emotions  together with some additional indexes, only nine features were stored for offline analysis. Seven of them represented the core emotions mentioned above, and two represented more general mental characteristics: attention and engagement. Each of these features was provided as an intensity score measured in a normalized scale from 0 to 100.All nine features were processed according to the same processing pipeline, which was composed of three steps. The first step was performed over the whole set of features, which was accompanied by the stimulus vector. The task of this step was to remove all empty records that were introduced during the recording process as a result of massive changes in a subject’s position that temporarily broke the contact between the camera and the subject. Usually, empty records appeared when the picture box was removed from the screen at the beginning of the next text-reading period. The average number of empty records was about 10% of the total.All the next steps from the processing pipeline were performed individually on each feature. The task of step 2 was to remove the influence of the outlier data. This step was necessary because each sudden movement of the subject’s head introduced a large change in the feature value. To deal with the outlier problem, we calculated the 5th and 95th percentiles over the feature time series and replaced all the feature values falling under the 5th percentile or exceeding the 95th percentile with the value of the 5th or 95th percentile, respectively. In step 3, we applied 250 ms nonoverlapping windows on the feature time series. For each window, a corresponding label  was assigned. Next, each window was described by its means value.p-value set to 0.05 was used to test the significance of between-groups differences.At the end of the processing pipeline, about 260 samples characterizing negative and 260 samples characterizing positive picture periods were obtained for each subject (10 pictures × (4 windows per second × 7 s of 1 picture presentation − 2 possible borders periods)). Regarding the text-reading periods, the number of samples characterizing those periods varied among subjects. This number of samples was much higher since (i) the text was presented before each picture and (ii) the text presentation time was longer. On average, about 3560 samples characterizing text periods (20 text periods × (4 windows per second × 45 s of 1 text presentation − 2 possible border periods)) were obtained for each subject. Hence, the total size of the feature matrix of one subject was about 9 features × 4080 samples. To ensure the comparability of the feature matrixes between subjects, each feature in the matrix was standardized to have zero mean and unit standard deviation. The feature matrices (and the stimulus vectors) calculated for all the subjects were concatenated and submitted to statistical analysis. Since the data distribution in the three analyzed groups significantly deviated from the normal distribution for most features, in all analyses, the non-parametric Kruskal–Wallis test with a The following section discusses results from the perceptual experiment with the goal to determine whether, during advertising design, both users’ visual attention and their emotional response can be considered to reduce the user irritation and limit ad avoidance.The user’s natural reactions such as gaze point change, eye movements, and resulting oculometrics of eyesight behavior were deeply analyzed, as well the user’s natural face expression. To reliably measure the user’s natural responses, the keys were the eye-tracker and the iMotions Facial Expression Analysis Module employed during the experiment.In order to determine if the negatively loaded content attracted more focal user attention and incurred lower cognitive load compared to positively loaded content, an eye-tracking signal was acquired from the experiment and analyzed with two metrics: ratio of fixation duration in the area of interest  to fixation duration on the slide of the experiment where the stimuli was displayed (FDP); and saccade velocity (SCV) when the affective picture was displayed.To analyze the eye-tracker signal in relation to the displayed stimuli (text and picture), two areas of interest were defined: AOI 1 (covering the part of the screen where only the text was displayed) and AOI 2 (covering the part of the screen where the disruptive affective pictures were suddenly displayed during the text reading). The defined AOIs for en example screen from the experiment are depicted in Fixations are the most common feature recorded by eye trackers, providing inferences about cognitive processes or states that users are interested in probing. Fixations are the times when a user’s eyes essentially stop scanning the scene, holding the central foveal vision in place so that the visual system can obtain detailed information about what is being looked at.Saccades are voluntary eye movements between two fixations, and they can be visualized as scan paths. Saccadic eye movements are identified with their amplitude, duration, and velocity. The average duration of a saccade is very short, typically between 20 and 80 ms.The measurements of saccade velocity help observing the pattern of a scan path and exploring the cognitive effort. Saccade velocity is highly correlated to discriminatory parameters in terms of cognitive performance .The oculometrics used in the analysis of eye-tracking data (FDP and SCV oculometrics) are based on the main eye movements:The first oculometrics analyzed was fixation duration percent (FDP): the longer the fixation time, the more often the component attracts the user’s attention. Therefore, we analyzed the relation between fixation duration in the area of interest of a given stimuli and the fixation duration in the experiment window during the period when the stimuli was displayed (see Equation ). To visWhen analyzing the eyesight focus for text reading, AOI 1 was considered. For emotional pictures that interrupted the text-reading process, AOI 2 was analyzed respectively. First, we compared the text and affective pictures without division of positive and negative pictures , which was measured when emotional stimulus was disrupted. Saccadic eye movements bring the line of sight to details of interest in the visual scene. In earlier research ,35,36,37Given the above, we used the saccade velocity to prove that the emotional characteristic of disruptive stimuli (positive or negative) has an impact on user attention, especially sight stability, and on cognitive load when the cognitive task was disrupted. As the characteristics of saccades differ for text reading and looking at pictures, for the analysis of saccadic metrics, we only considered the affective stimuli. When examining the sight behavior during text reading, the saccade velocity is high, which is the nature of reading. During picture watching, the metrics should be much lower if the user is in focus.Analyzing the results obtained from the experiment  testing the p-value levels, are presented in To identify which emotions were evoked when the subject’s cognitive task (text-reading task) was interrupted with emotional pictures, we used a one-way statistic test. In this test, we were not interested in picture valence: we only wanted to check whether the emotional features significantly differed between the two conditions: the text-reading condition and the picture presentation condition. To prepare data for this analysis, the samples labeled as negative and positive were joined together to form one group, which was named Five of the significant effects found in the analysis agreed with our preliminarily expectations and are straightforward to explain. The text-reading period required more attention than the picture-presentation period because the subjects were forced to read and understand the text in order to provide the correct answers to the questions following each section of the text. The higher disgust in the picture presentation period was also expected, since some of the negative pictures were perceived as revolting for most subjects, which contrasted the neutral tone of the text. The significant increase in the anger feature in the picture presentation period is also easy to explain. The pictures appearing in front of the subject’s eyes and obscuring the text interrupted the current cognitive task performed by the subject and were hence perceived as annoying. The higher anger was negatively correlated with the next significant feature, joy, which explains its smaller value in the picture presentation period.The next effect that we detected was less sadness in the picture presentation period. Once again, the explanation of this effect is straightforward. Sadness is a negative emotion that reflects a person’s avoidance tendency. Anger is also a negative emotion, but it reflects the motivation to engage (approach tendency). Since a person cannot withdraw and engage at the same time, one emotion has to prevail. In the case of interrupting a person’s ongoing cognitive process, anger is usually much stronger than eventual sadness; as a result, the pop-up pictures induced more anger and less sadness.The emotional feature that was the most unexpected to us was the feature depicting the subject’s surprise. At the beginning of the experiment, we were sure that since the picture presentation time was random, the subject should be surprised each time when a picture appeared on the screen. Our anticipation was found to be false: the subjects were significantly more surprised by the text than by the pictures. One possible explanation of this effect might be that the symptoms of surprise appeared in both the text-reading and picture presentation periods. However, during the text-reading period, the surprise emotion had chances to develop; in the picture presentation period, it was quickly overcome by anger associated with the interruption of the cognitive task.The last significant effect that we identified during the analysis was the higher fear in the text-reading period. This effect was the opposite of our expectation. The set of negative pictures used in our experiment also included pictures with fearful content. Hence, we expected those pictures to evoke fear in the subjects. However, the fearful pictures induced some fear but not enough to counteract the nonfearful positive pictures compare .p-value: 0.05), one test per feature. This time, we tested all periods when the positive pictures were presented  significantly differed between the two analyzed conditions. The comparison of the grand means obtained for three of those features showed the assumed tendency: the negative pictures were perceived as more surprising, more contemptuous, and sadder than positive ones.Three other significant effects, higher anger and engagement and lower joy for positive pictures, were the opposite of expected. At the beginning of the experiment, we assumed that positively loaded content would be able to attenuate the irritation induced by interrupting the user’s main task: the text-reading task. Hence, we assumed that anger would be significantly higher for negative than for positive pictures. However, the more anger in the positive picture presentation period was one of the most vivid effects in the experiment. Since engagement and joy are highly correlated with anger (engagement presented positive and joy presented negative correlation), higher anger during the positive picture presentation period also directly explains the two remaining effects: higher engagement and lower joy.Despite marketing activity being associated with digital environments and commercial aspects being important for portals, games, and social platforms, user experience makes it important to keep those environments as user-friendly as possible. The types of content and techniques used have an impact on the obtained effects as well as on user perception and attitudes toward brands. Earlier attempts showed that the level of intrusiveness could be reduced by adding emotional appeal to video ads . EarlierFor the reduction in the negative impact due to disruptive content, the intensity of stimuli and the level of attention induced during interruptions observed for negative content were more effective than positive emotional load. This supports earlier results  showing Hence, one of the conclusions of our experiment is that user attention was dependent on the valence of the emotional content. In the case of positive pictures, the attention was more ambient than for the negative ones, which were characterized with more focal attention. This conclusion is supported by the fixation time. The long fixation time corresponded to the pictures with a negative load, opposite to the short fixation time observed for the positive pictures. The analysis of saccadic eye movements (saccades velocity) provided more insight into the users’ cognitive states and emotions: it showed greater cognitive load and irritation (which is consistent with the results in ); surpriThese results are consistent with those of earlier studies , emphasiOur experiment was based on several methodological choices. Firstly, we used affective pictures from a standardized database instead of real advertisements. We decided to go in this direction, since those pictures were accompanied by an identified emotional load. Moreover, they allowed us to keep an emotional load not disturbed by marketing content, brand names, or product altitudes. On the other hand, our choice might narrow the scalability of our results, as true-marketing stimuli can have a slightly different influence on the users.Secondly, we used an eye-tracking system and facial emotion recognition software to measure the emotional reaction of respondents. Both approaches are usually the first choice in the experiments focused on emotion recognition, although the methods that measure directly the biosignals generated by the human nervous system, such as electroencephalography, electrocardiography, electromyography, or galvanic skin response, usually provide more precise measurements. However, those methods, are characterized by higher invasiveness and smaller applicability, which makes them less practical and harder to use in outside-laboratory conditions.Thirdly, we focused only on one cognitive task, a text-reading task that is typical for portals or blogs being the main target for marketing activity. This choice might influence the transition of our results to non-text content that provides different levels of user engagement. Moreover, we used neutral texts, while texts within websites might have a positive or negative emotional load. Consistency or inconsistency between text and content used during interruption of user cognitive process can also have an impact on differences in users’ reactions.One possible limitation of the results presented in this paper is related to the scaling intensity of both positive and negative content. We used two classes of images, and in both classes, different levels of positive or negative impact could be distinguished. It opens additional directions for future research on the impact of content type and task difficulty on user emotions.The study whose results are presented in the paper was designed to provide a better understanding of the impact of emotional content used in the process of creating marketing content on the users’ perception. To achieve this aim, we conducted the cognitive experiment with affective pictures that enabled us to study users’ visual attention and their emotional response to visual stimuli.By studying both factors simultaneously, we were able to find a more compact and unified answer to the question on how to prepare the marketing content to reduce a user irritation and limit ad avoidance.Our experiment clearly shows that the user attention depends on the valence of the emotional content. In the case of positive pictures, the attention was more ambient than for the negative ones, which were characterized by more focal attention. This conclusion was also supported by the fixation time, which was higher for negative pictures. The conclusions formulated on the basis of eye patterns were confirmed also by the facial features analysis. Here, irritation, measured by anger level, was much higher when pictures with positive valence were presented.The presented results show the possibility of replacing emotions induced by interruptions using content with stronger emotional load such as shocking or scary pictures. This creates implications for practice when adjusting the emotional intensity of the content used for the marketing technique and intensity of other stimuli within the user’s environment. We do not suggest using shocking content in typical campaigns; instead, we propose searching for methods of attracting user attention by emotionally intensifying the disruptive content, because this might lower user irritation.


Trend anomaly detection is the practice of comparing and analyzing current and historical data trends to detect real-time abnormalities in online industrial data-streams. It has the advantages of tracking a concept drift automatically and predicting trend changes in the shortest time, making it important both for algorithmic research and industry. However, industrial data streams contain considerable noise that interferes with detecting weak anomalies. In this paper, the fastest detection algorithm “sliding nesting” is adopted. It is based on calculating the data weight in each window by applying variable weights, while maintaining the method of trend-effective integration accumulation. The new algorithm changes the traditional calculation method of the trend anomaly detection score, which calculates the score in a short window. This algorithm, SNWFD–DS, can detect weak trend abnormalities in the presence of noise interference. Compared with other methods, it has significant advantages. An on-site oil drilling data test shows that this method can significantly reduce delays compared with other methods and can improve the detection accuracy of weak trend anomalies under noise interference. Data stream trend anomaly detection, a branch of data-flow research, has been used in online real-time data in the following fields: intelligent engineering  and systMany scholars have carried out studies on the difficulty of detecting trend anomalies under noise interference. For example, Li  adopted The online data stream is incremental and based on endless data, so it is necessary to intercept the current moment and a small piece of historical data to be the object of analysis. Many real-time processing models have often used a sliding window length of 1, in which only the data of all parameters at the current moment is used, not historical data. For example, Tang  used deeNoise, though, makes the detection of trend anomalies difficult. For example, Thomson  has fourThe above literature shows that the detection of data-flow trend anomaly in noisy environments is widely used in industry, aerospace, and other fields. Based on the sliding nesting algorithm, this paper adopted the forgetting strategy to build a trend anomaly detection algorithm under the influence of incremental data-stream noise and improved the timeliness of algorithm detection without affecting the accuracy of trend anomaly detection. The flow trend was abnormal to ensure the accuracy and timeliness of the real-time online data-flow analysis.This article first introduces the research background and the problems to be solved. Then it proceeds to discuss the trend anomaly detection algorithm and the problems to be solved, followed by the verification of the algorithm using a real industrial data stream and conclusions.The sliding window is commonly used to process data streams. It is a window of a pre-established length that moves as new (current) data enters from one end and pushes old  data out the other. In this way, current and historical data are always changing, and this affects the data being analyzed. For example, Wang  proposedIn addition to the sliding window, there are two other commonly used methods to processing data streams. One is the landmark model. It uses the memory of important moments and uses each moment as a calibration point to analyze the data that comes after it, as proposed by Leung . A landmThe other method is the snapshot model, which intercepts and analyzes the data stream. For example, Krämer  developeThe sliding window is the most commonly used data stream processing model because it meets the requirements of real-time data analysis and consider both current data and historical data. It is even used in the noise environment of oil drilling because it can detect abnormal changes in trends in the presence of noise interference. Because valuable information is hidden under the noise, real-time data filtering has become a prerequisite. The sliding window nesting method divides the historical period into short-time and relatively long-time windows. Two types of secondary variable parameters, or multiple derivative variable parameters, are calculated in each, and target detection is calibrated through these extracted feature parameters. For example, Du  accurateThe facet-kernel filter enhances the center layer target, while the background is suppressed by calculating the grey similarity difference between the center and surrounding layers. Li  proposedThe so-called sliding nested window is a small sliding window nested inside a larger one. As shown in According to its algorithm, trend anomaly detection can be known only after coming in contact with several data with abnormal trends. Therefore, for real-time systems, the shorter the time it takes to determine an abnormal trend the better. However, due to the limitations of the trend anomaly detection algorithm, especially for interference data, the accuracy of detection cannot be guaranteed because of the gradual-trend anomaly interference noise. In order to ensure detection accuracy, the sliding window is combined with other trend anomaly detection algorithms.More and more applications, such as Riss  proposedThe above trend abnormality detection methods all faced the same problem: timeliness of detection; that is, the distance between the time that the trend is detected and the current moment. The push-back continuation is a common method for trend anomaly detection. It uses the current time to delay the period of calculating the current trend change. Meng  used theIt can be seen from As shown in From the above analysis, the trend anomaly detection algorithm does not consider the time of the trend anomaly because detection has a paradox within a certain range; that is, the shorter the detection time, the lower the detection accuracy, but the reverse is not true. The restricted is the result of the design of the algorithm and the processing data environment. Therefore, discussing the accuracy of the algorithm and the detection time is an important aspect of trend anomaly detection.It can be seen from p is a slope.The new algorithm still uses the sliding nesting method, but the data weights in the two windows are updated and changed in real-time: the closer the data to the current time the greater the weight; the farther the data, the smaller the weight.In the original and prediction models, (Formulas (2) and (3), respectively) the initial time is The deviation between the two is the benchmark for trend anomaly detection, as shown in Formula (4).The sum of the cumulative deviations in the two windows of length is shown in Formula (5).In Formulas (6) and (7), the stop time is The determination formula for weak trend abnormality isThe formula for abnormally drastic changes in the trend isThe formula for abnormal changes in the later trend isFrom the basic formula of the above trend, the sliding nested window fastest detection of data stream (SNWFD–DS) algorithm was based on the sliding nest window chart anomaly detection, which was based on the data stream algorithm (SNWCAD–DS) , which iAlgorithm 1 Sliding Nest Window Weights Changed AlgorithmSNWWCA: initialize LW, SWSet weights range: Compute weights step: (b − a)/LW; (b − a)/SW;Update: t;Update: P * (b − a)/LW; S * (b − a)/SW;Update: P * (b − a)/LW; S * (b − a)/SW;Algorithm 1 is the algorithm of real-time weight transformation.LW is the length of the long sliding window; SW is the length of the short sliding window; T is the number of anomalies; Algorithm 2: SNWFD-DSDCUSUM-DS: initialize LW, SW, T, Compute weights of SW and LWCompute: Compute: Compute: Whether Compute: Compute: Box(Score(Result))Whether Score > Output: label VAIt can be seen that Algorithm 2, SNWFD–DS, performed two average calculations: ®) Core(™) i5-4570 @ 3.2 GHz 3.2 GHz, the memory was 16 G, and the operating system was Window10 64-bit.The data used in this paper came from the Tarim Oil field. The specific parameters were the total pool volume relative to the gradual change in the trend of the drilling leakage overflow. The total pool volume sensor detected the height of the total pool volume and was affected by surroundings. There was noise and other influences in the working environment, so detecting the gradual trend of parameters such as the total pool volume was the objective. The program software used in this article was matlab2020a, the CPU was Intel(As shown in As shown in It can be seen from The data in From the analysis of the figures and tables in the fourth section, the new algorithm SBWFD–DS had higher anomaly detection accuracy and shorter time delay compared to DCUSUM–DS. When the SNWFD–DS algorithm was not lower than the DCUSUM–DS in trend anomaly detection performance, it shortened the time for determining the trend anomaly. Although SNWFD–DS was compared with SNWCAD–DS, the delay time did not have obvious advantages, but the accuracy of trend anomaly detection was significantly higher than that of SNWCAD–DS. From the above analysis, the newly proposed algorithm SNWFD–DS had obvious trend anomaly detection accuracy and had the shortest time delay. It not only met the requirements of on-site trend anomaly detection, but also met the characteristics of on-site real-time operation.The algorithm proposed in this article was verified with real data from dozens of wells in the Tarim Oil field, and the results are shown in The trend anomaly detection algorithms listed in this article had to meet certain constraints. The degree of anomaly trend could not be too weak because a weak trend requires a long time, and the amount of data to be calculated for each engineering parameter was relatively large. However, there were hundreds of engineering parameters on site, and the calculation of so many parameters prevented the on-site computer from working. Limited by the calculation conditions, the long window of a single parameter of oil drilling is limited to 500 pieces of data. Therefore, very weak trend anomalies could not be detected by the algorithm proposed in this article. In addition, the detection time of the trend anomaly cannot be overly short because often that leads to the inability to distinguish between noise interference and a true trend anomaly.Because the trend anomaly detection of the oil drilling data stream was the thinking mode of the simulated field experts, the algorithm proposed in this article was applicable to the engineering, mud, and gas measurement parameters for oil drilling. However, as the position of the sensor moved downward, more and more sensors were installed near the drill bit, and more and more sensors with higher sampling frequencies appeared. Whether the algorithm in this article was effective at detecting trend anomalies remains to be seen.The fastest detection algorithm of sliding nesting was adopted. Based on the principle of sliding nesting, the data in each window was calculated with variable weights––higher for the data at the current moment and then reduced with increasing distance––which greatly reduced the complexity of the algorithm, and shortened the time for detecting weak trend abnormalities under noise interference by about half. This strategy can sensitively detect trend changes at the current moment, and can effectively eliminate noise interference. This method can improve the accuracy of trend anomaly detection. Compared with the previous trend anomaly detection method, this method adopts the integration method, which can make the trend anomaly detection be detected faster. Adopting a weighted data strategy improved prediction accuracy; the point of accumulation and function integral to data accumulation was retained; the method of abnormal judgment was changed; the forecasting time was advanced; and the timeliness of forecasting was ensured.The new algorithm proposed in this paper can improve the accuracy of trend anomaly detection under noisy environments, and reduce the detection time. Faint abnormal trends often correspond to various engineering accidents or changes in working conditions. If these engineering accidents are not dealt with in time, they will often have very serious consequences or produce more difficult problems to deal with. For example, the occurrence of overflow in oil drilling is manifested as a slight decline in the outlet flow. If the weak anomaly of the outlet flow trend is not detected and no further treatment is performed, it will cause a blowout, or even cause damage to the derrick, or death. Thus, the fastest detection time can give more time for later processing. Therefore, the algorithm in this paper is of great significance to reduce equipment damage and deaths in industrial production.The trend anomaly algorithm mentioned in this article has some limitations. It is limited by the characteristics of the real-time trend anomaly detection algorithm, which is a general problem of trend anomaly detection to detect the abnormal change of the trend in the shortest time period possible. Therefore, the amount of data processed in a single time is within the ability of the computer, but the algorithm must be able to reflect the change of the trend. Therefore, the algorithm proposed in this article is powerless for particularly slow abnormal changes in the trend. Second, particularly sensitive trend changes are often drowned in noise. Therefore, the trend anomaly detection algorithm proposed in this article is invalid for particularly sensitive trend anomaly detection.Features correspond to various engineering accidents or status changes, so feature extraction in industrial data streams is very important. Because the data stream has the characteristics of real-time update and concept drift, the feature extraction of traditional static data will no longer be used. This article is aimed at the trend anomaly detection of a single data stream; however, in industrial production, engineering accidents or status changes often correspond to the trend anomaly detection of multiple data streams. Our next work will concern the selection method for machine-learning features based on concept drift, research on the reduction methods for high-dimensional data streams, and the applicability of original data features to deep-learning algorithms.


Human anxiety is a grave mental health concern that needs to be addressed in the appropriate manner in order to develop a healthy society. In this study, an objective human anxiety assessment framework is developed by using physiological signals of electroencephalography (EEG) and recorded in response to exposure therapy. The EEG signals of twenty-three subjects from an existing database called “A Database for Anxious States which is based on a Psychological Stimulation (DASPS)” are used for anxiety quantification into two and four levels. The EEG signals are pre-processed using appropriate noise filtering techniques to remove unwanted ocular and muscular artifacts. Channel selection is performed to select the significantly different electrodes using statistical analysis techniques for binary and four-level classification of human anxiety, respectively. Features are extracted from the data of selected EEG channels in the frequency domain. Frequency band selection is applied to select the appropriate combination of EEG frequency bands, which in this study are theta and beta bands. Feature selection is applied to the features of the selected EEG frequency bands. Finally, the selected subset of features from the appropriate frequency bands of the statistically significant EEG channels were classified using multiple machine learning algorithms. An accuracy of 94.90% and 92.74% is attained for two and four-level anxiety classification using a random forest classifier with 9 and 10 features, respectively. The proposed state anxiety classification framework outperforms the existing anxiety detection framework in terms of accuracy with a smaller number of features which reduces the computational complexity of the algorithm. Anxiety is a mental health problem that can cause serious damage to our bodies . AnxietyHuman stress and anxiety have been used as interchangeable terms in the acedemic literature because they share many common symptoms like faster heartbeat and breathing, diarrhea, or constipation, but there exists a distinction between the two emotions. Stress is a sentiment of being unable to handle the psychological or emotional burden caused by an external stimulus. Some of the symptoms precisely linked with stress include anger, loneliness, dizziness, general unhappiness, and a feeling of being overwhelmed. The cause of stress can be short-term  or long-term . On the other hand, anxiety originates due to factors that are persistent and continues to exist even after the stressful situation is over. Symptoms explicitly connected with anxiety are restlessness, tension, sweating, nervousness, and a feeling of uneasiness. Chronic stress has been found to be a key factor in developing depression and anxiety symptoms with an effect on sleep, mood, and appetite of an individual –9.Anxiety can be categorized into two main types i.e., state anxiety and trait anxiety . State aHuman anxiety is a common mental health concern for a very large segment of the world population. According to the statistics of a survey conducted in the United States of America (USA), approximately 18% of the total population  is affected by an anxiety disorder and only 36.9% of these individuals from among these anxiety patients are receiving treatment . MoreoveDiagnoses of anxiety have been a challenge both for researchers and clinicians. Clinical analyses mainly depend on the documented symptoms frequently detected in the anxiety patients but there exists a significant overlap in these diagnostic criteria for stress, anxiety, and depression . It is pMoreover, the psychologists consider the standard psychometric questionnaires as a dependable measure of anxiety. These questionnaires have been used for quite a long time and have provided an accurate assessment of human anxiety, but they are not able to provide any insight into the physiological changes occurring within the body of the individual affected by the anxiety. Some of the regularly used questionnaires for the quantification of anxiety include State-Trait Anxiety Inventory (STAI) questionnaire , HamiltoA diverse range of human anxiety measurement schemes using physiological signals has been proposed in the literature which can be characterized into two lines i.e., state-anxiety and trait-anxiety. The footsteps for the general framework of human anxiety quantification are outlined beneath. Firstly, the physiological signals of the participants are acquired in the rest-state (for measurement of trait anxiety)  or in reHuman state anxiety detection based on features extracted from the EEG signals recorded in response to face-to-face psychological stimuli is presented in . The stuAnother type of state anxiety i.e., social anxiety is measured using heartbeat signals recorded in response to two different social evaluative situations i.e., public speaking and thesis defending in a study conducted in . StatistAn objective anxiety measurement framework using the fusion of features extracted from the EEG and the PPG signals recorded in response to two scenarios i.e., riding a bike at their most comfortable speed and while imagining that they had to contend with somebody who is riding at 80km/hr. is discussed in . PrincipHuman state anxiety assessment schemes available in the literature have used a diverse range of anxiety-inducing stimuli’s  , Cold PrThe main contribution of the proposed anxiety classification scheme as compared to the existing research is elaborated below.Firstly, all the existing state anxiety classification schemes that have been developed using the EEG datasets which are not publicly available for future research and hence cannot be used to further improve the results of their proposed scheme. A fair comparison is not possible with their methods, whereas, in our proposed scheme a publicly available EEG signals database named DASPS” has been used and we have worked on improving the results of the study in which the DASPS database has been developed.Secondly, we have examined the effect of EEG electrodes and frequency band selection for the optimum classification of anxiety into multiple levels.To the best of our knowledge, only one existing study discussed in  has explThe organization of the rest of the paper is as follows. The proposed human anxiety quantification framework comprising of details about data acquisition protocol, pre-processing, feature extraction, frequency band, and feature selection, and classifiers adopted for the anxiety classification is elaborated in Section 3. Section 4 focuses on the experimental results and their analysis followed by the comparison of the proposed algorithm with the state-of-the-art schemes available in the literature. Finally, the conclusion of the proposed scheme is outlined in Section 5 of the paper.The proposed anxiety detection framework is presented in In this study, the DASPS database consisting of EEG signals recorded in response to exposure therapy is used. Exposure therapy is a popular type of Cognitive Behavioral Therapy (CBT) that involves stating situations that prompt anxiety to a level that is both comfortable and tolerable . The detThe total score of the questionnaire can range from 0 to 56. This prior anxiety severity computation helps the psychotherapist to determine the amount of exposure a particular person can withstand. Following the subjective anxiety assessment, the subject is asked to be seated in a comfortable chair in a closed eye manner avoiding speech and gesture movements as much as possible. The psychotherapist starts the recital of the first scenario and aids the subject of the experiment to imagine it to elicit anxiety. Both the recital and the imagination phase are composed of 15 seconds each. The Self-Assessment Manikin (SAM) scale is used to inquire the subject that how he felt during the stimulation. SAM scale consists of two parameters i.e., valance which ranges from negative to positive, and arousal that runs from calm to excited state. Both valance and arousal have a nine points scale for measuring the subject’s emotion. This sequence of provoking anxiety by psychotherapists and evaluation of valance and arousal by the subject is executed six times in the experiment. Finally, at the end of the experiment, HAM-A was once more answered by the subject to report his current anxiety level. After this, the participant was allowed to leave the experiment room. The timeline diagram for the experimental protocol is presented in EEG data acquisition is performed using Emotiv EPOC wireless headset. The headset consists of 14 electrodes and 2 mastoids placed according to the 10-20 electrode positioning system . The eleThe reliability of the Emotiv EPOC headset as compared to medical grade EEG data acquisition equipment has been explored in several studies in the literature to validate the findings of the consumer grade EEG devices. In the studies conducted in  and 72]72], the Emotive EPOC headset records the raw EEG data for all the 14 electrodes and saves it in the form of an “.edf” file which can be converted to a “.csv” or “.mat” file for further processing. EEG data were recorded for a duration of 6 minutes with each trial consisting of 1 minute which is composed of 30 seconds for the recitation of the situation by psychotherapist and imagination by the subject and 30 seconds for answering the SAM rating scale. The sampling rate of the EPOC headset is 128 Hz and the impedance was kept at 7 kilo-ohms. Raw experimental output for the EEG signal acquired from the Emotiv EPOC headset is shown in In order to analyze the effect of stimuli in eliciting anxiety, the authors in , analyzeIn biomedical signal processing, it is necessary to pre-process the data to suppress the noise and artifacts from the acquired signals so that a clean signal is reached which could be fed to the feature extraction stage to realize decent classification accuracy. EEG signals contaminated by the physiological artifacts generated by parts of the human body other than the human brain include electrooculography (EOG) artifacts (having frequency range less than 4 Hz) and electromyography (EMG) artifacts (having frequency range greater than 30 Hz). Moreover, other than the physiological artifacts generated by different parts of the human body, there also exist artifacts caused by the power line interference having a frequency range of around 50 Hz.EOG artifacts can be subdivided into eye blinking and eye movement artifacts. Both types of artifacts belong to low frequencies i.e., less than 4 Hz, but eye blinking artifact has significantly higher amplitude as compared to eye movement artifact. Moreover, the eye blinking artifacts are mainly identified in the EEG data from the frontal electrodes of the headband whereas, on the other hand the eye movement artifacts are characterized by the difference in the symmetry of the left and right brain hemisphere. EMG artifacts can be categorized in two ways i.e., forehead movement and jaw clenching artifacts. Both forehead movement and jaw clenching are primarily an activity consisting of high frequency components i.e., less than 13 Hz with some exception of low frequencies. The presence of these frequencies can significantly deteriorate the diagnosis criterion and can lessen the usefulness of the recorded EEG data significantly. Therefore, these artifacts need to be eliminated from the EEG data to make the diagnoses more effective and reliable. In addition to applying the artifact removal techniques the participants in the experiment were instructed to keep muscles and eye movement minimum to achieve good quality EEG data.emg_psd which defines a threshold for the average power ratio of the recorded EEG signal and the components which have a value less than the defined threshold are regarded as EMG artifacts and are discarded. The estimator used for the valuation of power spectral density of the EEG signal is hamming window-based Welch periodogram. For the removal of EOG artifacts, the EEGLab uses improved weighted adjusted second-order blind identification method (iWASOBI) and fractal dimension parameter [To remove EOG and EMG artifacts from the recorded EEG signals, an automatic artifact removal tool of the EEGLab  was emplarameter . As a ruhttps://bit.ly/31ARYYo.In the second phase of the pre-processing stage, channel selection is performed by application of student t-test and analysis of variance (ANOVA) for two and four-level, respectively on the EEG data obtained from Emotiv headset. Power spectral density is computed using the Welch algorithm with an overlapping window of 50%. The decision about statistically significant channels of the Emotiv EPOC headset is made on the basis of p-value and the channels are considered statistically significant if the p ≤ 0.05. Filtering and signal processing implementation for the proposed anxiety classification scheme are available at Brainstorm3 toolbox in MATLAB. Welch method uses an overlapping window of 50% for computing power spectrum followed by the application of hamming window to each of the overlapped sequences. Next to this, the Fast Fourier Transform (FFT) of each segment was computed and the power of the FFT coefficients of all overlapping windows was averaged out. Hence the raw EEG data is represented by the power spectrum of its frequency bands [In this study, the analysis of the recorded EEG data is performed in the frequency domain by computing the power spectral density of the raw EEG signals. Power spectral density is realized using the Welch method of the cy bands . ObtaineThe reason for using fast fourier transform (FFT) as a feature extraction method over discrete wavelet transform (DWT) are manifold. Firstly, the computational power required to compute the DWT is higher as compared to the FFT algorithm, thus creating computational overhead. Secondly the additional information provided by the DWT over FFT is the time at which a particular frequency occurred in the EEG signal and in this study, we are not interested in locating the events in time, therefore use of DWT instead of FFT only adds to the computational cost. Moreover, when choosing FFT in comparison to discrete cosine transform (DCT) for feature extraction is due to the reason that FFT considers the harmonically related complex exponential functions whereas, DCT only takes real valued cosine functions. Therefore, DCT is more suited over FFT for applications where compression is needed.Frequency domain features extracted from the EEG data of the selected channels of the Emotiv EPOC headset include rational asymmetry (RASM), mean power (MP), and asymmetry index (AI). RASM is the ratio of the power spectrum for the selected asymmetric channels of the left and right cerebral hemispheres. MP feature is represented by the average of the power spectrum of each band for each selected channel of the Emotiv EPOC headband.After extraction of the features from the selected EEG channels, the next phase is to apply a frequency band selection algorithm to select the optimum frequency band for the classification of human anxiety. To this end, we have applied the frequency band selection algorithm proposed in  to our EAfter EEG frequency band selection, the features from the selected EEG frequency bands are applied upon with the wrapper method for feature selection to select the optimum subset of features. The wrapper method is a recursive feature elimination technique that removes the features which are least correlated with the class labels.Recursive feature reduction is a highly efficient optimization technique that extracts the feature subset yielding the highest accuracy. Like any recursive function, this technique calls itself again and again keeping track of the best and the worst performing feature subsets and removing the worst-performing feature subset at the end of the iteration. In the next iteration, the feature selection is performed only with the remaining feature subset. This process continues recursively till the point reaches when there is no change in the feature subset in the subsequent iterations and the algorithm converges resulting in the final optimum subset of features.The feature vector obtained after EEG frequency band and feature selection is subjected to classification step via different machine learning techniques. A variety of machine learning classifiers with different sets of optimization parameters were used for our proposed anxiety classification scheme. The parameters used for the classification models were chosen using grid search hyperparameter tuning method. KNN is termed as a lazy learning algorithm because it stores all the examples of the training set and computes the distance of the test sample from each example of the training data and the test data is assigned to the class having a smaller Euclidean distance. For the KNN classifier we performed the grid search using values of k = 1,3,5,7,9 but the highest classification accuracy was obtained using k = 5. DT is another non-parametric supervised machine learning technique that is based on entropy measure of the data and it classifies the data by building a tree and predicting the output variable for the test data by simple decision rules deduced from the data features. Similarly, for the decision tree classifier we tried different depth values for the tree and found the depth value of 32 to be the optimum. Increasing the tree depth further does not improved the classification performance therefore a tree depth of 32 is used in the current study. RF is an extension of the DT classifier in which we have multiple decision trees (hundred or even thousands of them) and the final classification result is achieved by majority rule i.e., the final decision is the decision of the majority of the decision trees. For the random forest classifier, we used different number of decision trees and a value of 500 trees with a depth of 32 was found to be the best among all and hence it is used in this study. MLP is a back-propagation feed-forward neural network. The simplest kind of MLP network consists of single input, hidden, and output layers. An activation function that transforms the input to the output is implemented in the hidden layer of the network. The Multilayer perceptron classifier was tested with different optimizer and activation functions and the optimum classification results were obtained using 4 hidden layers, ‘Adam optimizer’ and ‘Relu activation function’ with a momentum and learning rate of 0.3 and 0.4, respectively. SVM is one of the most well-known algorithms used for supervised machine learning. It classifies the data points by building an N-dimensional hyperplane between the two classes. Radial basis function (RBF) produced the best results and hence is used as a SVM kernel in this study. In the proposed anxiety classification scheme, we have reported the classification results obtained from the best tuned parameters. The recently proposed deep learning methods were not used for the current study because the number of subjects involved in the study were only 23 and therefore the available EEG data was not sufficient to train the deep learning model. In future we intend to acquire EEG data from a larger number of subjects to be able to obtain results for anxiety classification using deep learning models.This section presents the anxiety classification result of both two and four levels based on the selected subset of features from the optimum frequency band combination for the statistically significant channels of the Emotiv EEG headset. The analysis is performed using a trial duration of 1 second which was found to be the most optimum for anxiety classification in the studies conducted in , 39.The labeling of the subjects is performed in two different manners i.e., based on SAM rating and HAM-A scale. For the SAM rating-based labeling, trials of the experiment have valance < 5, and arousal > 5 are labeled as Normal whereas, the trials with valance scores in the range of 0 to 2 and arousal score in the range of 7 to 9 are labeled as Severe. Moreover, the subjects having valance scores between 2 to 4 and arousal scores between 6 and 7 are labeled as having Moderate anxiety, and the subjects with valance scores in the interval of 4 to 5 and arousal scores in the interval of 5 to 6 are labeled as having Light anxiety. The number of instances based on SAM-based labeling for a trial duration of 1 second for two-level anxiety detection are Light Anxiety: 2640 and Severe Anxiety: 1500. Similarly, the number of instances for four-level anxiety detection are Normal Anxiety: 2340, Light Anxiety: 300, Moderate Anxiety: 150, and Severe Anxiety: 1350.For labeling based on HAM-A based scale recorded at the end of the experiment, trials having HAM-A scores between 0 to 12 are labeled as normal anxiety, trials with scores between 12 to 20 are termed as having light anxiety. Moderate anxiety ranges between 20 to 25 and severe anxiety is labeled above the score of 25. Based on this labeling the number of instances for a trial duration of 1 second for two-level anxiety detection are Light Anxiety: 1260 and Severe Anxiety: 2880. Similarly, for four-level anxiety detection, the number of instances are Normal Anxiety: 360, Light Anxiety: 900, Moderate Anxiety: 540, and Severe Anxiety: 2340. PSDN is the normalized power spectral density, xi is the ith sample of PSD, min(x) in the minimum value for PSD of a particular EEG channel and max(x) is the maximum value for the PSD of a particular EEG channel. It can be inferred from the graph that only the significantly different EEG channels have visually dissimilar patterns from each other for both two and four-level.Channel selection is conducted using student t-test for two-level and ANOVA for four-level anxiety classification. T-test and ANOVA are applied on the power spectral density (PSD) of the raw EEG data obtained from each channel of the Emotiv EPOC headset. PSD of the EEG data is computed using the Welch method with an overlapping window of 50%. The results of the statistical test based on both SAM and HAM-A rating reveals the fact that channels AF3, AF4, FC5, FC6, P7 and P8 are significantly different for both two  and four  level anxiety detection. Apart from these six channels, no other channels of the Emotiv EPOC headset are statistically significant for either two or four-level anxiety detection. Figs Feature extraction is performed from the selected subset of EEG channels. Mean power resulted in a total of 30 features from the five frequency bands of six selected channels of the Emotiv EPOC headset. Similarly, each of the RASM and AI feature groups resulted in a total of 15 features extracted from the EEG data of the selected channels of the Emotiv headset. Hence, the cumulative EEG feature vector obtained is composed of 60 values.One of the main contributions of this study is to identify the optimum subset of the EEG frequency band which results in the highest anxiety classification accuracy. To this end, the frequency band selection algorithm proposed in  was usedβAF3, βAF4, βP7, θAF3, θAF4, θP8), RASM , and AI . Similarly, the selected subset of features for four level anxiety classification includes MP , RASM , and AI  resulting in a feature vector of length 8. For HAM-A based labeling, the selected subset of features for two level anxiety detection include MP , RASM , and AI  resulting in a feature vector of 9. Moreover, the optimum subset of features from the selected frequency bands for four level anxiety detection using HAM-A based labeling constitutes a feature vector of length 10, which includes MP , RASM , and AI .The extracted features from the selected EEG frequency bands are subjected to the feature selection phase via a recursive feature elimination technique to obtain the best subset of features from the selected frequency bands. The recursive feature selection algorithm when applied on two level anxiety classification problem using SAM based labeling, it resulted in a feature vector length of 10 consisting of MP , F-value (F) and kappa statistics (K). The accuracy of a classification algorithm depicts the correct number of predictions from among the total number of predictions. Mathematically it is given as,Tables This section compares the proposed scheme with the other state-of-the-art methods available in the literature on the basis of the number of participants involved in the experiment, modalities, number of EEG electrodes, classifiers used, number of classes, and the accuracy achieved. The results are presented in In this paper, a two and four-level anxiety classification framework is developed by using EEG data of 23 subjects recorded during exposure therapy and using Emotiv EPOC headset. Channel selection is performed to select the statistically significant channels using t-test for two-level and ANOVA for four-level on the power spectral density of the data from the EEG electrodes. Channels AF3, AF4, FC5, FC6, P7, and P8 are found to be statistically significant from among 14 channels of the Emotiv EPOC headset. Frequency domain features which include mean power, rational asymmetry, and asymmetry index are extracted from the selected EEG channels. Next, the frequency band selection algorithm is applied to identify the appropriate EEG frequency bands which in this study are theta and beta bands. The wrapper method is applied for feature selection from all the features of the selected frequency bands. Finally, the selected subset of features is fed into five different machine learning algorithms i.e., DT, kNN, SVM, MLP, and RF for anxiety classification. RF classifier achieved the highest classification accuracy both for two and four-level anxiety classification. An accuracy of 94.90% and 92.74% is achieved for two and four-level anxiety classification using HAM-A based labeling and RF classifier with a feature vector length of 9 and 10, respectively using data only from 6 selected EEG channels. Many clinical applications which can improve the quality of life of an individual in the society can be derived from the findings of the current study. The limitations of the proposed anxiety classification scheme include a lesser number of participants which restricts us from applying the deep learning model for classification. Moreover, the anxiety elicitation protocol was applied on participants with an average age of 30 years. The results of the experiment with subjects having higher average age e.g., around 50 years still need to be examined to further validate the findings of the proposed scheme. Furthermore, the effect of EEG data recorded from participants with different ethnical background also needs to be examined.


This article uses the “Green Credit Guidelines” promulgated in 2012 as an example to construct a quasi-natural experiment and uses the double difference method to test the impact of the implementation of the “Green Credit Guidelines” on the green innovation activities of heavy-polluting enterprises. The study found that, in comparison to non-heavy polluting enterprises, the implementation of green credit policies inhibited the green innovation of all heavy-polluting enterprises. In the analysis of heterogeneity, this restraint effect did not differ significantly due to the nature of property rights and the company’s size. The mechanism test showed that green credit policy limits the efficiency of business investment and increases the cost of financing business debt. Eliminating corporate credit financing, particularly long-term borrowing, negatively impacts the green innovation behavior of listed companies. Over the last few years, due to the impact of greenhouse gas, particularly carbon dioxide emissions, global warming and climate change issues have become increasingly severe, attracting the attention of many countries. Green technological innovation is considered to be an important means to achieve sustainable development. Technological change, financial liberalization and globalization have reinforced the boundaries among countries on technological capabilities and competitiveness . Within Through the guidance of green technology innovation policies, companies could improve resource utilization efficiency and achieve a win–win development of social energy conservation and emission reduction ,9. The gBefore the implementation of green credit in China, the International Finance Corporation (IFC) first proposed the concept of green credit in 2002, the “Equator Principles”. The “Equator Principles” aims to evaluate the environmental and social risks in project financing. To reduce pollution emissions and support high-quality economic development, the Chinese government announced a series of green finance policies aimed at reducing environmental degradation. Green credit is one of the most important green financial instruments implemented by the government. Financial institutions are encouraged to direct capital to cleaner production . The “OpThe objective of this paper is to analyze the effectiveness of green credit policies. This article therefore draws up an econometric model. Variables include the level of green innovation, the effectiveness of investments, the level of debt financing and other factors. This study examines the impact of green credit policies on the level of green innovation of high-polluting companies. The marginal contributions of this article are as follows: First, concerning the effects of green credit policies, this article focuses on samples of highly polluting firms. This study will provide a practical basis for further investigation. Second, the green credit policy plays an important role in environmental oversight. Previous research on green credit policies has been subject to numerous omissions and shortcomings. However, this paper should not only look at the effects of green credit policies, but also discuss the mechanism of other possible factors which may work. Third, this paper enriches the research topics related to environmental regulation and pollution reduction. On the one hand, there is a lack of research on the level of green innovation in heavy polluters. On the other hand, this paper fully considers the problem of policy failure under the conditions of “strong government and weak society”, and provides a research basis for the full implementation of pollution reduction policies.In recent years, in the face of an increasingly severe ecological environment, the Chinese government has paid more attention to the use of financial products in environmental governance and introduced a series of policies in the field of green credit. The subject of this paper is the impact of the implementation of green credit policies on the green innovation capacity of heavily polluting enterprises. What the green credit policy is actually trying to achieve is an improvement in society’s pollution reduction results. The improvement of the green innovation capacity of heavy polluters is only one path to achieving improved environmental performance of enterprises. As the indicators of the results of pollution reduction by enterprises have only been emphasized in recent years, the indicators are cumbersome. And the indicators are not of a uniform form. This has caused difficulties in the collation of data. This paper therefore focuses its attention on the impact of the implementation of green credit policies on the green innovation capacity of heavily polluting enterprises.Current research on green credit policy is mainly divided into two main topics: theoretical analysis and effect assessment. More specifically, early in the study research, researchers were most interested in the need to implement green credit policies and the barriers to implementation. Ref.  elaboratWith the deepening of the operation of the policy, researchers are gradually focusing on the quantitative evaluation of the application effect of the green credit policy. Furthermore, research on this subject can be discussed from both a macro and a micro perspective.At the macroscopic impact level, scholars have conducted in-depth discussions on the impact of green credit on the overall environmental quality. The authors of  found thAt the micro-impact level, most existing research is conducted around the two main bodies of companies and banks. For commercial banks, academics focus primarily on the impact of green credit implementation on bank performance. Ref.  used theAt the enterprise level, most scholars evaluate the policy effect of green credit from enterprises’ investment and financing status. Liu et al.  concludeAn analysis of many pieces of literature shows that research on the political impact of green credit is abundant at the macro-economic level. Furthermore, from a microeconomic perspective, the discussion on the effect of this policy on the performance of banks and investment and corporate finance is also very comprehensive. The fundamental objective of implementing the green credit policy is to encourage enterprises to proactively develop production and operation while respecting the environment. Green innovation is clearly an essential element of green transformation and company modernization. Unfortunately, at this point, little literature focuses on the specific effects of green credit policy on green innovation and its impact mechanism, and there are some differences in the conclusions drawn by existing studies. Ling et al.  used a dTo sum up, the analysis of the micro effect of green credit in the existing literature is relatively limited, and there is a lack of relevant discussion on whether green credit can induce enterprise innovation transformation. At the same time, whether the implementation of this policy can promote enterprise green innovation has yet to be unified. In terms of research content, the above papers pay more attention to the direct impact of green credit policies on enterprises and banks, and neglect to fully sort out the policy mechanism. This article attempts to make up for these shortcomings. Concerning research samples, the scope of research in the existing literature is broader, and there is a lack of research on specific industries. This article uses highly polluting Chinese companies as a research sample to analyze the effects of policies at the industry level. In terms of empirical research, this article adopts the difference-in-difference method, which is appropriate for analyzing policies implemented in different regions and at different times.At present, there is no unified opinion in the academic circles on the impact of environmental supervision policies represented by the “Green Credit Guidelines” on the innovation capabilities of enterprises. Pigou proposed the “Pigou Tax” in 1932, establishing a precedent for environmental control. Subsequently, Coase criticized Pigou’s method of correcting externalities. This is due to the fact that the “Pigou tax” restricts economic choices and stresses the important role of property rights and property rights transactions in environmental monitoring. According to using data on firms in major European countries, Rubashkina et al.  found thWhaller and Whitehead  argues tHypothesis 1 (H1).Following the promulgation of the “Green Credit Guidelines” in relation to non-polluting companies, highly polluting companies’ level of green innovation decreased considerably.First of all, in the process of market-oriented reforms, the degree of local government intervention in enterprises within its jurisdiction also shows obvious differences across industries, which will also affect the innovation behavior of enterprises. An important feature in regions and industries with relatively high investment efficiency is that local governments have less improper and excessive intervention in enterprises within their jurisdiction. Moreover, local governments can also provide more convenient institutional infrastructure conditions for innovative financing of enterprises within their jurisdiction. For example, building enterprise information databases, setting up regional policy innovation guidance funds, implementing scientific and technological innovation guidance plans, and introducing preferential fiscal and tax policies for scientific and technological innovation, etc. All of this helps to increase opportunities for companies to obtain external loans, boosting entrepreneurial innovation . ConversSecondly, the allocation of credit resources also shows obvious differences between sectors and regions. Banks in various areas have “relaxing forces”, and the distribution of credit resources will influence whether firms can obtain adequate credit financing support when carrying out investment activities . Where tHypothesis 2 (H2).When firms’ investment efficiency is weak, the inhibiting effect of green credit policy on firms’ innovation capacity will be reinforced.Supply factors in financial markets influence corporate funding decisions ,53. The Hypothesis 3 (H3).Green credit policy may increase the debt financing cost of enterprises and then reduce the green technology innovation of enterprises.http://pss-system.cnipa.gov.cn/sipopublicsearch/portal/uiIndex.shtml accessed on 9 December 2021). We specifically determine green patents based on the international patent classification in the “Green List of International Patent Classifications” issued by the World Intellectual Property Organization (WIPO) in 2010 (https://www.wipo.int/Classification/ipc/greeninventory/home accessed on 9 December 2021). Then, the financial data in the control variables, such as company size, asset-liability ratio, company life, ROA, proportion of tangible assets, cash holding rate, ownership concentration, earnings volatility, Tobin’s Q, stock return rate, and whether to disclose social responsibility report, are mainly from the CSMAR database.This article mainly selects the data of China’s listed companies from 2010 to 2019. Based on the previous search objectives, this article selects the relevant data and tries to explain how policy works by analyzing mechanisms. In accordance with previous research objectives, this document selects relevant data. There are three data sources: First, the number of green invention patent applications mainly comes from the State Intellectual Property Office  of the quantile. To alleviate the endogenous problem, the control variables of the investment and financing equation ) in the regression analysis are all lagging one period . At the same time, the standard deviation of the test results is the adjustment of the cluster at the company level. The impact of green credit policy on the green technology innovation of heavily polluting companies is mainly achieved through political shocks. Specifically, first, the implementation of the green credit policy has reduced the ability of high-polluting companies to secure loans for green innovation. At the same time, high-polluting companies have spent more on reducing corporate pollution to cope with public monitoring of corporate environmental effects. To some degree, fewer funds are used to research companies’ green patents ,61.A higher level of green credit will raise the threshold for companies to access green credit funds and reduce green technology innovation among companies. On the contrary, as the level of innovation in green technologies decreases, it will be harder for companies to obtain green credit funds from banks. In this way, there is a mutual causality between the explanatory variable and the explained variable. This will establish a correlation between the explanatory variable and the error term, resulting in endogenous problems and inconsistent parameter estimates . The polOn this basis, we use the “Green Credit Guidelines” incident released by the China Banking Regulation Commission on 24 February 2012, as a political shock. Before the publication of the “Green Credit Guidelines” in 2012, credit policies of banking financial institutions paid little attention to green development issues. After 2012, banking financial institutions are required to clarify the direction and focus areas of green credit support. This paper uses the DID model to evaluate green credit guidelines’ impact on green technological innovation. The specific model is as follows:Among them, In this paper, the impact of green credit on the technological innovation of heavily polluting enterprises is estimated by using the differential difference model of panel data. This paper further examines whether the moderating effect of investment efficiency exists. Therefore, based on model (2), investment efficiency is added as an intermediary variable.Moreover, the implementation of green credit policies may indirectly impact green innovation capacities by altering the debt financing costs of high-polluting companies. In other words, the cost of debt finance has a mediating effect on developing the green innovation capacity of heavy polluting enterprises inhibited by green credit. As shown in This article uses the DID method to measure the impact of green credit on the level of corporate green innovation. At the same time, in order to determine whether the fixed-effect model or the random-effect model should be used, this paper uses the LM test and the Hausman test. The test results show that the two-way fixed-effects model should be used. This paper used two methods to verify the accuracy of the conclusions. The first method was parallel trend testing. The second method was the placebo test.The assumption of parallel trend is the premise of using the time-varying DID model, requiring the lngp of the treatment group and the control group to have the same changing direction before implementing the green credit policy. Therefore, this paper adopts the time trend diagram of the treatment group and the control group to conduct a parallel trend test, as shown in The basic idea behind the double-difference placebo test is to estimate the duration of the dummy treatment group or dummy policy. If the coefficient of the “pseudo-political fictitious variable” remains significant in the fictitious situation, this means that the result of the initial estimation is likely to be biased. Changes in the level of green innovation of heavy pollutant companies may have been influenced by other political or random factors. This paper randomly selected the interaction term 500 times to check whether the coefficient is significantly different from the baseline estimation result and draws the distribution diagram of the regression coefficient. As shown in The nature and scope of company ownership may result in differences in the effectiveness of green credit policies. In general, compared to state-owned enterprises, non-state enterprises are less sensitive to environmental impacts in their day-to-day production and operations, and their consciousness of social responsibility is relatively weak. At the same time, enterprises of different sizes may have some differences in operational efficiency and financing channels. Therefore, differences in corporate ownership and size can lead to different characteristics of green innovation under the influence of green credit policies.In order to explore the micro effects of green credit policies under different enterprise attributes, sample companies are divided into state-owned enterprises and non-state-owned enterprises according to different ownership attributes of enterprises. The sampled enterprises were divided into three quartiles based on differences in the size of the enterprise. The first third of the largest companies are defined as large companies, and the last two thirds are defined as small and medium-sized companies. The DID model is used to analyze the heterogeneous impact of green credit policies on green technological innovation. The empirical results are shown in the table below.The results of In order to further identify the internal mechanism of green credit policies, it is necessary to test the mediation effect of debt financing costs. According to the stepwise regression method, the models 4~6 are regressed. It can be seen from After implementing green credit, financial institutions reduced the long-term debt of polluting companies to avoid risks. This will impede investment in R and D and the generation of business innovation. The implementation of green credit will harm the company’s green innovation. Mechanism analysis found that the credit constraints caused by the reduction of credit scale and the increase of credit costs are the main mechanism of action .This study makes a significant contribution to exploring the relationship between green credit policies and the green innovation capacity of highly polluting firms. Based on the implementation of green credit policies in China, this paper uses a difference-in-difference approach to identify policy impacts. This paper finds that the implementation of green credit policies inhibits the ability of heavily polluting firms to innovate green. This paper adopts a similar research methodology to , but theIn this paper, investment efficiency is considered the moderating variable and the cost of debt financing is considered the mediating variable in the analysis of the mechanism. Some existing studies concentrate on the unique impact of green credit policies. For example, Li et al.  argued tIn the meantime, this article breaks down the research sample into small and medium-sized enterprises (SMEs) and large enterprises. This article argues that the disincentive effect of green credit on corporate green innovation does not differ by company size. This differs from the conclusions reached by . Singh eThere are several limitations to this study. Firstly, since the enactment of the green credit policy, it has been more difficult for the heavily polluting enterprises to access social capital. Therefore, it is worth considering whether this social capital has gone into non-heavily polluting enterprises and improved environmental benefits. This is a direction that may require further consideration going forward. Then, the ability of companies to innovate in green is only one way for them to reduce their pollution levels. Innovation is not the ultimate goal. Reducing pollution is a desired result of green credit policies. Future research will focus on indicators that represent the level of pollution reduction of high-polluting enterprises. However, due to the complexity of the current corporate pollution reduction indicators, there is no uniformity. As a result, data collection is more difficult.Based on the panel data of China’s A-share non-financial listed companies from 2010 to 2019, this paper constructs a DID model to study the impact of green credit guidelines on the green innovation capabilities of heavily polluting companies. With respect to the impact of environmental regulation on business technological innovation, Porter’s weak hypothesis remains controversial. This study uses Chinese listed companies as a sample to empirically test the inhibitory effect of green credit standards on companies’ green innovation capabilities, which are manifested in three aspects: (1) Under the green credit policy, it is difficult for heavily polluting enterprises to obtain more credit support and better loan interest rates for a short time, leading to difficulty for enterprises to have enough funds to research green innovative products and technologies, (2) The implementation of green credit policy leads to the restriction of green credit from commercial banks for heavily polluting enterprises, which stimulates enterprises to increase commercial credit instead of debt financing. However, due to the differences in the ability of different heavy polluting enterprises to obtain commercial credit, many heavy polluting enterprises find it difficult to obtain enough funds to supplement the credit gap of commercial banks , (3) Pil