Although its roots can be traced to the 19th century, progress in the study of nonlinear dynamical systems has taken off in the last 30 years. While pertinent source material exists, it is strewn about the literature in mathematics, physics, biology, economics, and psychology at varying levels of accessibility. A compendium research methods reflecting the expertise of major contributors to NDS psychology, Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data examines the techniques proven to be (...) the most useful in the behavioral sciences. The editors have brought together constructive work on new practical examples of methods and application built on nonlinear dynamics. They cover dynamics such as attractors, bifurcations, chaos, fractals, catastrophes, self-organization, and related issues in time series analysis, stationarity, modeling and hypothesis testing, probability, and experimental design. The analytic techniques discussed include several variants of the fractal dimension, several types of entropy, phase-space and state-space diagrams, recurrence analysis, spatial fractal analysis, oscillation functions, polynomial and Marquardt nonlinear regression, Markov chains, and symbolic dynamics. The book outlines the analytic requirements faced by social scientists and how they differ from those of mathematicians and natural scientists. It includes chapters centered on theory and procedural explanations for running the analyses with pertinent examples and others that illustrate applications where a particular form of analysis is seen in the context of a research problem. This combination of approaches conveys theoretical and practical knowledge that helps you develop skill and expertise in framing hypotheses dynamically and building viable analytic models to test them. (shrink)

We suggest that the network approach to comorbidity (Cramer et al.) is best examined by using longitudinal, multi-measurement, intra-individual data. Employment of time-series analysis to the examination of the generalized anxiety disorder and major depressive disorder comorbidity enables a detailed appreciation of fluctuations and causal trajectories in terms of both symptoms and cognitive vulnerability.

Due to the complexity of research objects, theoretical concepts, and stimuli in media research, researchers in psychology and communications presumably need sophisticated measures beyond self-report scales to answer research questions on media use processes. The present study evaluates stimulus-dependent structure in spontaneous eye-blink behavior as an objective, corroborative measure for the media use phenomenon of spatial presence. To this end, a mixed methods approach is used in an experimental setting to collect, combine, analyze, and interpret data from standardized participant (...) self-report, observation of participant behavior, and content analysis of the media stimulus. T-pattern detection is used to analyze stimulus-dependent blinking behavior, and this structural data is then contrasted with self-report data. The combined results show that behavioral indicators yield the predicted results, while self-report data shows unpredicted results that are not predicted by the underlying theory. The use of a mixed methods approach offered insights that support further theory development and theory testing beyond a traditional, mono-method experimental approach. (shrink)

The temporal structure of behavior contains a rich source of information about its dynamic organization, origins, and development. Today, advances in sensing and data storage allow researchers to collect multiple dimensions of behavioral data at a fine temporal scale both in and out of the laboratory, leading to the curation of massive multimodal corpora of behavior. However, along with these new opportunities come new challenges. Theories are often underspecified as to the exact nature of these unfolding interactions, (...) and psychologists have limited ready-to-use methods and training for quantifying structures and patterns in behavioral time series. In this paper, we will introduce four techniques to interpret and analyze high-density multi-modal behavior data, namely, to: (1) visualize the raw time series, (2) describe the overall distributional structure of temporal events (Burstiness calculation), (3) characterize the nonlinear dynamics over multiple timescales with Chromatic and Anisotropic Cross-Recurrence Quantification Analysis (CRQA), (4) and quantify the directional relations among a set of interdependent multimodal behavioral variables with Granger Causality. Each technique is introduced in a module with conceptual background, sample data drawn from empirical studies and ready-to-use Matlab scripts. The code modules showcase each technique’s application with detailed documentation to allow more advanced users to adapt them to their own datasets. Additionally, to make our modules more accessible to beginner programmers, we provide a “Programming Basics” module that introduces common functions for working with behavioral timeseries data in Matlab. Together, the materials provide a practical introduction to a range of analyses that psychologists can use to discover temporal structure in high-density behavioral data. (shrink)

Many individuals with disabling conditions have difficulty with gait and balance control that may result in a fall. Exoskeletons are becoming an increasingly popular technology to aid in walking. Despite being a significant aid in increasing mobility, little attention has been paid to exoskeleton features to mitigate falls. To develop improved exoskeleton stability, quantitative information regarding how a user reacts to postural challenges while wearing the exoskeleton is needed. Assessing the unique responses of individuals to postural perturbations while wearing an (...) exoskeleton provides critical information necessary to effectively accommodate a variety of individual response patterns. This report provides kinematic and neuromuscular data obtained from seven healthy, college-aged individuals during posterior support surface translations with and without wearing a lower limb exoskeleton. A 2-min, static baseline standing trial was also obtained. Outcome measures included a variety of 0 dimensional measures such as center of pressure RMS, peak amplitude, velocities, pathlength, and electromyographic RMS, and peak amplitudes. These measures were obtained during epochs associated with the response to the perturbations: baseline, response, and recovery. T-tests were used to explore potential statistical differences between the exoskeleton and no exoskeleton conditions. Time series waveforms of the COP and EMG data were also analyzed. Statistical parametric mapping was used to evaluate the 1D COP and EMG waveforms obtained during the epochs with and without wearing the exoskeleton. The results indicated that during quiet stance, COP velocity was increased while wearing the exoskeleton, but the magnitude of sway was unchanged. The OD COP measures revealed that wearing the exoskeleton significantly reduced the sway magnitude and velocity in response to the perturbations. There were no systematic effects of wearing the exoskeleton on EMG. SPM analysis revealed that there was a range of individual responses; both behaviorally and among neuromuscular activation patterns. Using both the OD and 1D measures provided a more comprehensive representation of how wearing the exoskeleton impacts the responses to posterior perturbations. This study supports a growing body of evidence that exoskeletons must be personalized to meet the specific capabilities and needs of each individual end-user. (shrink)

Baumard proposes a model to explain the dramatic rise in innovation that occurred during the Industrial Revolution, whereby rising living standards led to slower life history strategies, which, in turn, fostered innovation. We test his model explicitly using time series data, finding limited support for these proposed linkages. Instead, we find evidence that rising living standards appear to have a time-lagged bidirectional relationship with increasing innovation.

Arguments about the nature of teaching have depended principally on naturalistic observation and some experimental work. Additional measurement tools, and physiological variations and manipulations can provide insights on the intrinsic structure and state of the participants better than verbal descriptions alone: namely, time-series analysis, and examination of the role of hormones and neuromodulators on the behaviors of teacher and pupil.

Legislative scholars have debated what factors (e.g. divided government) account for the number of important laws a legislative body passes per year. This paper presents a monopoly model for explaining legislative production. It assumes that a legislature adjusts its law production so as to maximize its utility. The model predicts that socio-economic and political changes increase the marginal benefit of law production, whereas low negotiation costs and ample legislative resources decrease the marginal cost of law production. The model is tested (...) in two ways. The first approach compares the legislatures of 42 developed and developing countries. The second analyzes Japanese lawmaking from 1949 to 1990, using an appropriate method for event count time series data. Both empirical investigations support the model's predictions for legislative production. (shrink)

Source: Author: Ahamed Lebbe Mohamed Aslam, S.M. Ahamed Lebbe There is a relationship between the fiscal deficit and inflation, which was confirmed empirically in several studies conducted in many countries. Sri Lanka has been encountering the problem of inflation for the recent years. But in Sri Lanka, this proposition has not yet been studied scientifically. Therefore, this study was going to fill this gap. The objective of this study was to test the impact of fiscal deficit on inflation in Sri (...) Lanka. For this study, the annual time series data were used during the period of 1959 to 2013. The fiscal deficit, exchange rate, government expenditures and import outflow were used as independent variables while the Colombo consumer price index was considered as dependent variable. In addition, the multiple regressions model was used to test the impact of fiscal deficit on inflation. Based on the regression results, the fiscal deficit preserved the positive relationship with inflation in Sri Lanka at one percent significant level. Therefore, this study confirmed that the fiscal deficit accelerates the inflation in Sri Lanka. ]]>. (shrink)

When attempting to predict future events, people commonly rely on historical data. One psychological characteristic of judgmental forecasting of time series, established by research, is that when people make forecasts from series, they tend to underestimate future values for upward trends and overestimate them for downward ones, so-called trend-damping. Events in a time series can be experienced sequentially, or they can also be retrospectively viewed simultaneously, not experienced individually in real time. In one (...) experiment, we studied the influence of presentation mode on two sorts of judgment: predictions of the next event and estimation of the average value of all the events in the presented series. Participants' responses in dynamic mode were anchored on more recent events than in static mode for all types of judgment but with different consequences; hence, dynamic presentation improved prediction accuracy, but not estimation. These results are not anticipated by existing theoretical accounts; we develop and present an agent-based model—the adaptive anchoring model —to account for the difference between processing sequences of dynamically and statically presented stimuli. ADAM captures how variation in presentation mode produces variation in responses in both forecasting and judgment tasks. ADAM's model predictions for the forecasting and judgment tasks fit better with the response data than a linear-regression time series model. Moreover, ADAM outperformed autoregressive-integrated-moving-average and exponential-smoothing models, while neither of these models accounts for people's responses on the average estimation task. (shrink)

An emerging perspective on human cognition and performance sees it as a kind of self-organizing phenomenon involving dynamic coordination across the body, brain and environment. Measuring this coordination faces a major challenge. Time series obtained from such cognitive, behavioral, and physiological coordination are often complicated in terms of non-stationarity and non-linearity, and in terms of continuous vs. categorical scales. Researchers have proposed several analytical tools and frameworks. One method designed to overcome these complexities is recurrence quantification analysis, (...) developed in the study of non-linear dynamics. It has been applied in various domains, including linguistic data or motion data. However, most previous studies have applied recurrence methods individually to categorical or continuous data. To understand how complex coordination works, an integration of these types of behavior is needed. We aimed to integrate these methods to investigate the relationship between language and motion directly. To do so, we added temporal information to categorical data, and applied joint recurrence analysis methods to visualize and quantify speech-motion coordination coupling during a rap performance. We illustrate how new dynamic methods may capture this coordination in a small case-study design on this expert rap performance. We describe a case study suggesting this kind of dynamic analysis holds promise, and end by discussing the theoretical implications of studying complex performances of this kind as a dynamic, coordinated phenomenon. (shrink)

Flooding is one of the most damaging natural hazards globally. During the past three years, floods have claimed hundreds of lives and millions of dollars of damage in Iran. In this study, we detected flood locations and mapped areas susceptible to floods using time series satellite data analysis as well as a new model of bagging ensemble-based alternating decision trees, namely, bag-ADTree. We used Sentinel-1 data for flood detection and time series analysis. We employed (...) twelve conditioning parameters of elevation, normalized difference’s vegetation index, slope, topographic wetness index, aspect, curvature, stream power index, lithology, drainage density, proximities to river, soil type, and rainfall for mapping areas susceptible to floods. ADTree and bag-ADTree models were used for flood susceptibility mapping. We used software of Sentinel application platform, Waikato Environment for Knowledge Analysis, ArcGIS, and Statistical Package for the Social Sciences for preprocessing, processing, and postprocessing of the data. We extracted 199 locations as flooded areas, which were tested using a global positioning system to ensure that flooded areas were detected correctly. Root mean square error, accuracy, and the area under the ROC curve were used to validate the models. Findings showed that root mean square error was 0.31 and 0.3 for ADTree and bag-ADTree techniques, respectively. More findings illustrated that accuracy was obtained as 86.61 for bag-ADTree model, while it was 85.44 for ADTree method. Based on AUC, success and prediction rates were 0.736 and 0.786 for bag-ADTree algorithm, in order, while these proportions were 0.714 and 0.784 for ADTree. This study can be a good source of information for crisis management in the study area. (shrink)

Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm (...) maze (RAM) task. This task has been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of the task, as defined by the experimenter, namely, the encoding/retrieval, choice, reward and delay stages, can be statistically discriminated from the BOLD time series in brain areas relevant for decision making and working memory. Discrimination of these task stages was significantly reduced during poor behavioral performance in dorsolateral prefrontal cortex (DLPFC), but not in the primary visual cortex (V1). Experimenter-defined dissection of time series into class labels based on task structure was confirmed by an unsupervised, bottom-up approach based on Hidden Markov Models. Furthermore, we show that different groupings of recorded time points into cognitive event classes can be used to test hypotheses about the specific cognitive role of a given brain region during task execution. We found that whilst the DLPFC strongly differentiated between task stages associated with different memory loads, but not between different visual-spatial aspects, the reverse was true for V1. Our methodology illustrates how different aspects of cognitive information processing during one and the same task can be separated and attributed to specific brain regions based on information contained in multivariate patterns of voxel activity. (shrink)

Mining time series data is of great significance in various areas. To efficiently find representative patterns in these data, this article focuses on the definition of a valid dissimilarity measure and the acceleration of partitioning clustering, a common group of techniques used to discover typical shapes of time series. Dissimilarity measure is a crucial component in clustering. It is required, by some particular applications, to be invariant to specific transformations. The rationale for using the (...) angle between two time series to define a dissimilarity is analyzed. Moreover, our proposed measure satisfies the triangle inequality with specific restrictions. This property can be employed to accelerate clustering. An integrated algorithm is proposed. The experiments show that angle-based dissimilarity captures the essence of time series patterns that are invariant to amplitude scaling. In addition, the accelerated algorithm outperforms the standard one as redundancies are pruned. Our approach has been applied to discover typical patterns of information diffusion in an online social network. Analyses revealed the formation mechanisms of different patterns. (shrink)

Data mining is on the interface of Computer Science and Statistics, utilizing advances in both disciplines to make progress in extracting information from large databases. It is an emerging field that has attracted much attention in a very short period of time. This article highlights some statistical themes and lessons that are directly relevant to data mining and attempts to identify opportunities where close cooperation between the statistical and computational communities might reasonably provide synergy for further progress (...) in data analysis. (shrink)

The paper sets out the challenges facing the Police in respect of the detection and prevention of the volume crime of burglary. A discussion of data mining and decision support technologies that have the potential to address these issues is undertaken and illustrated with reference the authors’ work with three Police Services. The focus is upon the use of “soft” forensic evidence which refers to modus operandi and the temporal and geographical features of the crime, rather than “hard” evidence (...) such as DNA or fingerprint evidence. Three objectives underpin this paper. First, given the continuing expansion of forensic computing and its role in the emergent discipline of Crime Science, it is timely to present a review of existing methodologies and research. Second, it is important to extract some practical lessons concerning the application of computer science within this forensic domain. Finally, from the lessons to date, a set of conclusions will be advanced, including the need for multidisciplinary input to guide further developments in the design of such systems. The objectives are achieved by first considering the task performed by the intended systems users. The discussion proceeds by identifying the portions of these tasks for which automation would be both beneficial and feasible. The knowledge discovery from databases process is then described, starting with an examination of the data that police collect and the reasons for storing it. The discussion progresses to the development of crime matching and predictive knowledge which are operationalised in decision support software. The paper concludes by arguing that computer science technologies which can support criminal investigations are wide ranging and include geographical information systems displays, clustering and link analysis algorithms and the more complex use of data mining technology for profiling crimes or offenders and matching and predicting crimes. We also argue that knowledge from disciplines such as forensic psychology, criminology and statistics are essential to the efficient design of operationally valid systems. (shrink)

The study of expertise is difficult to do in a laboratory environment due to the challenge of finding people at different skill levels and the lack of time for participants to acquire mastery. In this paper, we report on two studies that analyze naturalistic gameplay data using cohort analysis to better understand how skill relates to practice and habit. Two cohorts are analyzed, each from two different games. Our work follows skill progression through 7 months (...) of Halo matches for a holistic perspective, but also explores low-level in-game habits when controlling game units in StarCraft 2. Players who played moderately frequently without long breaks were able to gain skill the most efficiently. What set the highest performers apart was their ability to gain skill more rapidly and without dips compared to other players. At the beginning of matches, top players habitually warmed up by selecting and re-selecting groups of units repeatedly in a meaningless cycle. They exhibited unique routines during their play that aided them when under pressure. (shrink)

Can green growth policies help protect the environment while keeping the industry growing and infrastructure expanding? The City of Kitakyushu, Japan, has actively implemented eco-friendly policies since 1967 and recently inspired the pursuit of sustainable development around the world, especially in the Global South region. However, empirical studies on the effects of green growth policies are still lacking. This study explores the relationship between road infrastructure development and average industrial firm size with air pollution in the city through the Environmental (...) Kuznets Curve (EKC) hypothesis. Auto-Regressive Distributed Lag (ARDL) and Non-linear Auto-Regressive Distributed Lag (NARDL) methods were applied on nearly 50-years’ time series data, from 1967 to 2015. The results show that the shape of the EKC of industrial growth, measured by average firm size, depends on the type of air pollution: inverted N-shaped relationships with NO2 and CO, and the U-shaped relationships with falling dust particle and Ox. Regarding infrastructure development, on the one hand, our analysis shows a positive effect of road construction on alleviating the amount of falling dust and CO concentration. On the other hand, the emissions of NO2 and Ox are shown to rise when plotted against road construction. The decline of CO emission, when plotted against both industrial growth and road development, indicates that the ruthlessness of the local government in pursuing green growth policies is effective in this case. However, the story is not straightforward when it comes to other air pollutants, which hint at limits in the current policies. The case of Kitakyushu illustrates the complex dynamics of the interaction among policy, industry, infrastructure, and air pollution. It can serve as an important reference point for other cities in the Global South when policies are formed, and progress is measured in the pursuit of a green economy. Finally, as an OECD SDGs pilot city and the leading Asian green-growth city, policymakers in Kitakyushu city are recommended to revise the data policy to enhance the findability and interoperability of data as well as to invest in the application of big data. (shrink)

Electricity is an indicator that shows the progress of a civilization; it is a product that has greatly changed the way we think about the world. Electricity price forecasting became a fundamental task in all countries due to the deregulation of the electricity market in the 1990s. This work examines the effectiveness of using multiple variables for price prediction given the large number of factors that could influence the price of the electricity market. The tests were carried out over four (...) periods using data from Spain and deep learning models. Two different attribute selection methods based on Pearson’s correlation coefficient have been used to improve the efficiency of the training process. The variables used as input to the different prediction models were chosen, considering those most commonly used previously in the literature. This study attempts to test whether using time series lags improves the non-use of lags. The results obtained have shown that lags improve the results compared to a previous work in which no lags were used. (shrink)

Cramer et al.'s account of comorbidity comes with a substantive philosophical view concerning the nature of psychological disorders. Although the network account is responsive to problems with extant approaches, it faces several practical and conceptual challenges of its own, especially in cases where the individual differences in network structures require the analysis of intra-individual time-series data.

The way digital news platforms represent migration issues can significantly impact intergroup relations and policymaking. A recurring question in the debate on the role of news platforms is whether they merely transmit information on migration, or actively hype specific issues. Drawing on a comprehensive set of socioeconomic statistics on migrants in Denmark, and employing a longitudinal automated content analysis of migration news content, we utilize time-series analysis to understand how four distinct categories of threat (security, economic, cultural, and (...) generalized) relate to socioeconomic data on terror attacks, migrant crime levels, economic performance, and demographic trends. The results reveal a direct effect of terror attacks, economic performance, and demographic trends on migration news. We discuss the implications of socioeconomic and demographic developments as factors in digital media content to understand the role of media and substantiate contemporary debates. (shrink)

About 30 years ago, the Dynamical Hypothesis instigated a variety of insights and transformations in cognitive science. One of them was the simple observation that, quite unlike trial-based tasks in a laboratory, natural ecologically valid behaviors almost never have context-free starting points. Instead, they produce lengthy time series data that can be recorded with dense-sampling measures, such as heartrate, eye movements, EEG, etc. That emphasis on studying the temporal dynamics of extended behaviors may have been the trigger (...) that led to a rethinking of what a “representation” is, and then of what a “cognitive agent” is. This most recent and perhaps most revolutionary transformation is the idea that a cognitive agent need not be a singular physiological organism. Perhaps a group of organisms, such as several people working on a joint task, can temporarily function as one cognitive agent – at least while they're working adaptively and successfully. (shrink)

The Sciences of the Qurʾān contain information about the process of Qurʾān and its structural characteristics, language and stylistic features, as well as statistical data on the content of the Qurʾān. This information, which contributes significantly to the understanding of the Qurʾān, is generally classified within the relevant narratives and the classifications are sometimes associated with verses. In this context, the way in which the Sciences of the Qurʾān explain the verses, which do not act solely on methodical premises, (...) differs from the Qurʾānic exegesis books. In the Sciences of the Qurʾān, the subject of revelation forms are classified within the frame of the narrations, and the 51st verse of the Sūrat al-Shūrā is frequently referred to. In other words, the related classifications are matched with the 51st verse of the Sūrat al-Shūrā. In the exegesis, this verse is handled more comprehensively considering its context and the literal possibilities. There are two remarkable comments about this verse in terms of its reflection in the revelation classifications. However, on the contrary, it is possible to encounter examples where the verse is subject to specific discussions. One of them belongs to Jār Allāh al-Zamakhsharī (d.538/1144) and the other to Nāṣir al-Dīn al-Bayḍāwī (d. 685/1286). When the exegesis of al-Zamakhsharī and al-Bayḍāwī are examined, it is seen that this verse is the subject of ru’yat Allah (seeing Allah) discussion between Muʿtazila and Ahl al-Sunnah and the classifications are made in this context. However, it is interesting to note that the classification of al-Zamakhsharī is more suitable for the perspective of the Sciences of the Qurʾān. In this study, the codes of the determinations made by al-Zamakhsharī and al-Bayḍāwī related to the 51st verse of Sūrat al-Shūrā was read on the commentaries. While al-Zamakhsharī explains this verse as a proof for the argument that God cannot be seen, al-Bayḍāwī develops an example of reactionary interpretation upon Zamakhsharī’s view. The Sciences of the Qurʾān (as a discipline), which opens a separate title to the ways of revelation, uses this verse as a template and takes it away from the discussion. In the study, the subjective dimensions of the discussion and classifications made in the context of this verse were evaluated. As a result, it is concluded that a classification similar to classification of al-Zamakhsharī has been made in the literature of the Sciences of the Qurʾān as it provides more comprehensive framework. Summary: During the period of revelation of the Holy Qurʾān not only the sunnah and actions of the Prophet which explained the Holy Qurʾān, but also various sources of information based on language and consultation, were used in order to understand the Holy Qurʾān. The first people introduced to the revelation used their own language/oratory skills, which were natural them, to understand the Qurʾān and were also able to observe the events and problems that brought about the revelation of the verses. These elements, used by those first people for understanding the Qurʾān, were readily available in their society. Therefore, understanding of the Qurʾān while the Prophet was still alive did not require a codification of those resources or treatment of the matter on a theoretical level. Subsequent generations mostly compensated the lack of those advantages by the use of the Sciences of the Qurʾān books penned individually after 2nd Century (A.H.), and those written systematically under the collective name "the Sciences of the Qurʾān" starting from the 6th century (A.H.). These works covered many subjects such as the beginning of the revelation, the accentuation of the Qurʾān, the physical development process of the Qurʾānic text, information based on observation as well as the tone and content of the Qurʾān and provided information details for Qurʾānic interpretations. Over time, this information was classified in the Sciences of the Qurʾān literature, organized under various relevant titles and enhanced with examples, numerical and qualitative information. One of the main topics of the Sciences of the Qurʾān is the subject of “revelation and its forms”. In the literature of the Sciences of the Qurʾān, the classifications made under this topic are generally associated with the verse 51 of Sūrat al-Shūrā. In this verse, the forms of communication between Allah (Jalla Jalaluhu) and the servant/prophet are categorized. The context of the verse 51 of the Sūrat al-Shūrā in the tafsīr literature is different from the context in the Sciences of the Qurʾān literature. As a matter of fact, while this verse is examined in detail with respect to the context, concept analysis, and the possibilities of i‘rāb (classical Arabic grammar), it is merely referred to in the Sciences of the Qurʾān. This is due to the fact that the determinations and classifications made in the Sciences of the Qurʾān are shaped mostly in the light of the historical context and narrations of the subject.It is also possible to encounter examples of tafsīr where the 51st verse is made the topic of detailed discussions. There are two remarkable comments about this verse in terms of its reflection in the revelation classifications. One of them belongs to Jār Allāh al-Zamakhsharī (d.538/1144) and the other belongs to Nāṣir al-Dīn al-Bayḍāwī (d. 685/1286). The tafsīr s of al-Zamakhsharī and al-Bayḍāwī make it clear that this verse is the subject of ru’yat Allah discussion between the Muʿtazila and Ahl al-Sunnah and the classifications are made accordingly. The fact that i‘rāb examinations of the two authors are similar and both took into account other linguistic possibilities despite their preferences, which also prove that the points of conflict are not literal. Al-Zamakhsharī thinks that this verse determines the way Allah speaks not only with his prophets but also with all people. According to al-Zamakhsharī, the 51st verse refers to the three forms of communication that takes place between God and any of His servants. Allah communicates with His servants; a) through inspiration, inspiration to heart or through dreams, b) by making His words heard through certain channels, c) by sending one of his angels as a messenger. In the verse, the first is expressed as revelation, the second is to speak behind a veil, and the third is to send a messenger. According to al-Zamakhsharī, Allah's inspiration to the mother of Moses, the dream that He showed to Abraham regarding the sacrificing of his son and inspiration of Psalms to David, all relate to the revelation concept mentioned in the first option. Al-Zamakhsharī believes that revelation from behind a veil is an adage. This adage can be compared to a king speaking with one of those at his service behind a veil/curtain, where the person hears the voice of the king but cannot see him. Allah only spoke to Moses and angels in that manner, and sent angels to speak to His other servants/prophets.As a response to al-Zamakhsharī, al-Bayḍāwī interpreted the verse concisely explaining the 'revelation' (وحيا) as a secret word that is quickly understood. Because, in terms of its nature, revelation is not a word made up of sound waves that come one after another to make words. In the verse, this word refers both to the Night Journey (Mi‘rāj) and the talk mentioned in the hadiths regarding the ru’yat Allah, and Allah speaking to Moses without Moses seeing Allah. However, in the verse min verâi ḥicâb/attributed to the word 'revelation 'من وراء حجاب makes it clear that the expression refers to a form of speech in which Allah is heard but not seen. Therefore, the word 'revelation' refers to a mutual talk and according to al-Bayḍāwī, this is proof that ru’yat Allah is possible. The fact that the classifications made in the Sciences of the Qurʾān literature agrees with the classification of al-Zamakhsharī, not al-Bayḍāwī. This result is closely related to the structural features of the Sciences of the Qurʾān in which authors strive to make general explanations that are beyond sectarian preferences and views. Understanding the verse as a conversation between Allah and man, and not between Allah and His prophet, provides a more comprehensive classification in terms of the use of revelations (not only the use of the word revelation) in the Qurʾān. Thus, the address that was made to Abraham in his dream, the revelation of to Moses' mother, and inspiration to our holy Prophet and other people can be interpreted according to this verse. In addition, the ru’yat Allah debate was excluded from the subject in the classifications made in the Sciences of the Qurʾān and implication of the verse was limited to the talks made in a worldly manner. (shrink)

I am a behavioral economist, who is interested in both behavioral sciences and economic behavior. By the term “economic behavior” I refer to the calculative reasoned domain of economic analysis, whereas by “behavioral economics” I address aspects of human feelings, emotions and everything that is not captured by the “rational” paradigm. Evidently, erroneous calculations, as well as unhinged sentiments lead to economic losses, and every change in the economics of the world has both calculative and behavioral (...) sides to it. In what follows, we examine these two sides of behavior in relation to the COVID-19 pandemic by drawing on contemporaneous data, forecasts, and images. Each side of behavior reveals distinctive insights, and intriguingly, not all outcomes count as loss. As we face this crisis, there is a clear environmental gain that we would be smart to preserve post pandemic. (shrink)

Aquaculture plays a significant role in both economic development and food production. Maintaining an ecological environment with good water quality is essential to ensure the production efficiency and quality of aquaculture. Effective management of water quality can prevent abnormal conditions and contribute significantly to food security. Detecting anomalies in the aquaculture environment is crucial to ensure that the environment is maintained correctly to meet healthy and proper requirements for fish farming. This article focuses on the use of deep learning techniques (...) to detect anomalies in water quality data in the aquaculture environment. Four deep learning anomaly detection techniques, including Autoencoder, Variational Autoencoder, Long-Short Term Memory Autoencoder, and Spectral-Residual Convolutional Neural Network, were analysed using multiple real-world sensor datasets collected from IoT aquaculture systems. Extensive experiments were conducted for temperature, dissolved oxygen, and pH parameters, and the evaluation analysis revealed that the Long-Short Term Memory Autoencoder anomaly detection method showed promising results in detecting anomalies for the temperature and oxygen datasets, while the Spectral-Residual Convolutional Neural Network demonstrated the best performance on the pH datasets. (shrink)

Success in online and blended courses requires engaging in self-regulated learning (SRL), especially for challenging STEM disciplines, such as physics. This involves students planning how they will navigate course assignments and activities, setting goals for completion, monitoring their progress and content understanding, and reflecting on how they completed each assignment. Based on Winne & Hadwin’s COPES model, SRL is a series of events that temporally unfold during learning, impacted by changing internal and external factors, such as goal orientation and (...) content difficulty. Thus, as goal orientation and content difficulty change throughout a course, so might students’ use of SRL processes. This paper studies how students’ SRL behavior and achievement goal orientation change over time in a large (N = 250) college introductory level physics course taught online. Students’ achievement goal orientation was measured by repeated administration of the achievement goals questionnaire-revised (AGQ-R). Students’ SRL behavior was measured by analyzing their clickstream event traces interacting with online learning modulesviaa combination of trace clustering and process mining. Event traces were first divided into groups similar in nature using agglomerative clustering, with similarity between traces determined based on a set of derived characteristics most reflective of students’ SRL processes. We then generated causal nets for each cluster of tracesviaprocess mining and interpreted the underlying behavior and strategy of each causal net according to the COPES SRL framework. We then measured the frequency at which students adopted each causal net and assessed whether the adoption of different causal nets was associated with responses to the AGQ-R. By repeating the analysis for three sets of online learning modules assigned at the beginning, middle, and end of the semester, we examined how the frequency of each causal net changed over time, and how the change correlated with changes to the AGQ-R responses. Results have implications for measuring the temporal nature of SRL during online learning, as well as the factors impacting the use of SRL processes in an online physics course. Results also provide guidance for developing online instructional materials that foster effective SRL for students with different motivational profiles. (shrink)

Elliot Sober ([2001]) forcefully restates his well-known counterexample to Reichenbach's principle of the common cause: bread prices in Britain and sea levels in Venice both rise over time and are, therefore, correlated; yet they are ex hypothesi not causally connected, which violates the principle of the common cause. The counterexample employs nonstationary data—i.e., data with time-dependent population moments. Common measures of statistical association do not generally reflect probabilistic dependence among nonstationary data. I demonstrate the inadequacy (...) of the counterexample and of some previous responses to it, as well as illustrating more appropriate measures of probabilistic dependence in the nonstationary case. A challenge to the principle of the common causeSober's argument and the attempts to rescue the principleProbabilistic dependenceNonstationary time seriesProbabilistic dependence in nonstationary time seriesDo Venetian sea levels and British bread prices violate the principle of the common cause? (shrink)

This paper proposes a personalized music recommendation method based on multidimensional time-series analysis, which can improve the effect of music recommendation by using user’s midterm behavior reasonably. This method uses the theme model to express each song as the probability of belonging to several hidden themes, then models the user’s behavior as multidimensional time series, and analyzes the series so as to better predict the use of music users’ behavior preference and give reasonable recommendations. Then, (...) a music recommendation method is proposed, which integrates the long-term, medium-term, and real-time behaviors of users and considers the dynamic adjustment of the influence weight of the three behaviors so as to further improve the effect of music recommendation by adopting the advanced long short time memory technology. Through the implementation of the prototype system, the feasibility of the proposed method is preliminarily verified. (shrink)

Recent advances in wireless data transmission technology have the potential to revolutionize clinical neuroscience. Today sensing-capable electrical stimulators, known as “bidirectional devices”, are used to acquire chronic brain activity from humans in natural environments. However, with wireless transmission come potential failures in data transmission, and not all available devices correctly account for missing data or provide precise timing for when data losses occur. Our inability to precisely reconstruct time-domain neural signals makes it difficult to (...) apply subsequent neural signal processing techniques and analyses. Here, our goal was to accurately reconstruct time-domain neural signals impacted by data loss during wireless transmission. Towards this end, we developed a method termed Periodic Estimation of Lost Packets. PELP leverages the highly periodic nature of stimulation artifacts to precisely determine when data losses occur. Using simulated stimulation waveforms added to human EEG data, we show that PELP is robust to a range of stimulation waveforms and noise characteristics. Then, we applied PELP to local field potential recordings collected using an implantable, bidirectional DBS platform operating at various telemetry bandwidths. By effectively accounting for the timing of missing data, PELP enables the analysis of neural time series data collected via wireless transmission—a prerequisite for better understanding the brain-behavior relationships underlying neurological and psychiatric disorders. (shrink)

We employ data envelopment analysis on a series of experiments performed in Fermilab, one of the major high-energy physics laboratories in the world, in order to test their efficiency (as measured by publication and citation rates) in terms of variations of team size, number of teams per experiment, and completion time. We present the results and analyze them, focusing in particular on inherent connections between quantitative team composition and diversity, and discuss them in relation to other factors (...) contributing to scientific production in a wider sense. Our results concur with the results of other studies across the sciences showing that smaller research teams are more productive, and with the conjecture on curvilinear dependence of team size and efficiency. (shrink)

This study aimed to identify real-life experts working for a port authority and lay people (students) who played The Sustainable Port, a serious game aiming to simulate the dynamics occurring in a port area. To achieve this goal, we analyzed eye gaze data collected noninvasively using low-grade webcams from 28 participants working for the port authority of the Port of Rotterdam and 66 students. Such data were used for a classification task implemented using a MiniRocket classifier, an algorithm (...) used for time-series classification. The classifier reached an F1 score of 0.75 (SD = 0.07), a PR AUC of 0.73 (SD = 0.14), and an ROC AUC of 0.75 (SD = 0.15) providing evidence that it is possible to identify real-life experts about maritime port management using data that can be obtained from a webcam. We speculate that the gaze direction used to train the MiniRocket may contain relevant information about the cognitive processes and decisions occurring throughout the gameplay. We suggest that the methods here presented not only can be used to detect experts playing simulations, such as serious games, but also to identify experts tackling screen-presented tasks. (shrink)

This article considers the temporal dimension of data processing and use and the ways in which it affects the production and interpretation of knowledge claims. I start by distinguishing the time at which data collection, dissemination, and analysis occur from the time in which the phenomena for which data serve as evidence operate. Building on the analysis of two examples of data reuse from modeling and experimental practices in biology, I then argue that Dt (...) affects how researchers select and interpret data as evidence and identify and understand phenomena. (shrink)

Recent accounts of the speed/accuracy relation for motor tasks have focused on the concept of motor output variability. We outline the advantages of this approach and the limitation of Plamondon's model in explaining movement error. We also examine and present complimentary data for rapid timing tasks. While these tasks do not meet the presented assumptions, the data still fit the model predictions.

In early 2020, in-person data collection dramatically slowed or was completely halted across the world as many labs were forced to close due to the COVID-19 pandemic. Developmental researchers who assess looking time were forced to re-think their methods of data collection. While a variety of remote or online platforms are available for gathering behavioral data outside of the typical lab setting, few are specifically designed for collecting and processing looking time data in (...) infants and young children. To address these challenges, our lab developed several novel approaches for continuing data collection and coding for a remotely administered audiovisual looking time protocol. First, we detail a comprehensive approach for successfully administering the Multisensory Attention Assessment Protocol, developed by our lab to assess multisensory attention skills. The MAAP is administered from a distance by using Zoom, Gorilla Experiment Builder, an internet connection, and a home computer. This new data collection approach has the advantage that participants can be tested in their homes. We discuss challenges and successes in implementing our approach for remote testing and data collection during an ongoing longitudinal project. Second, we detail an approach for estimating gaze direction and duration collected remotely from webcam recordings using a post processing toolkit and demonstrate its effectiveness and precision. However, because OpenFace derives gaze estimates without translating them to an external frame of reference, we developed a machine learning approach to overcome this limitation. Thus, third, we trained a ML algorithm [] to classify gaze estimates from OpenFace with respect to areas of interest on the participant's screen. We then demonstrate reliability between this approach and traditional coding approaches. The combination of OpenFace and ML will provide a method to automate the coding of looking time for data collected remotely. Finally, we outline a series of best practices for developmental researchers conducting remote data collection for looking time studies. (shrink)

The present study aims at proposing a time series-based network public opinion emergency management decision support algorithm for the problems of low decision accuracy and long decision time in traditional similar algorithms. In this proposed algorithm, after the time series data are preprocessed, the association rules of the original indicator data of network public opinion emergencies are mined, the original indicator data matrix of NPOEs will be constructed, and the improved local linear (...) embedding approach will be employed to obtain the original indicator data of NPOEs. After carrying out the preprocessing according to the preprocessing results, the objective weight of the emergency decision index is calculated through the interval value fuzzy information entropy measurement of the emergency decision index, the emergency management decision support model is constructed, and the emergency management decision support of the NPOE is realized. The simulation experiment results show that the proposed algorithm has a better effect on the decision-making effect of the management of NPOEs, and a higher decision-making efficiency is achieved. (shrink)

Similarity profiled association mining from time stamped transaction databases is an important topic of research relatively less addressed in the field of temporal data mining. Mining temporal patterns from these time series databases requires choosing and applying similarity measure for similarity computations and subsequently pruning temporal patterns. This research proposes a novel z-space based interest measure named as Krishna Sudarsana for time-stamped transaction databases by extending interest measure Srihass proposed in previous research. Krishna Sudarsana is (...) designed by using the product based fuzzy Gaussian membership function and performs similarity computations in z-space to determine the similarity degree between any two temporal patterns. The interest measure is designed by considering z-values between z = 0 and z = 3.09. Applying the Krishna Sudarsana requires moving the threshold value given by user to a different transformation space which is a defined as a function of standard deviation. In addition to proposing interest measure, new expressions for standard deviation and equivalent z-space threshold are derived for similarity computations. For experimental evaluation, we considered Naïve, Sequential and Spamine algorithms that applies Euclidean distance function and compared performance of these three approaches to Z-Spamine algorithm that uses Krishna Sudarsana by choosing various test cases. Experiment results proved the performance of the proposed approach is better to Sequential approach that uses snapshot database scan strategy and Spamine approach that uses lattice based database scan strategy. (shrink)

This paper presents the concept and algorithm of data mining and focuses on the linear regression algorithm. Based on the multiple linear regression algorithm, many factors affecting CET4 are analyzed. Ideas based on data mining, collecting history data and appropriate to transform, using statistical analysis techniques to the many factors influencing the CET-4 test were analyzed, and we have obtained the CET-4 test result and its influencing factors. It was found that the linear regression relationship between the (...) degrees of fit was relatively high. We further improve the algorithm and establish a partition-weighted K-nearest neighbor algorithm. The K-weighted K nearest neighbor algorithm and the partition algorithm are used in the CET-4 test score classification prediction, and the statistical method is used to study the relevant factors that affect the CET-4 test score, and screen classification is performed to predict when the comparison verification will pass. The weight K of the input feature and the adjacent feature are weighted, although the allocation algorithm of the adjacent classification effect has not been significantly improved, but the stability classification is better than K-nearest neighbor algorithm, its classification efficiency is greatly improved, classification time is greatly reduced, and classification efficiency is increased by 119%. In order to detect potential risk graduating students earlier, this paper proposes an appropriate and timely early warning and preschool K-nearest neighbor algorithm classification model. Taking test scores or make-up exams and re-learning as input features, the classification model can effectively predict ordinary students who have not graduated. (shrink)

The psychological factors influencing a return to sport has gained increased research attention. In the current investigation, we explored professional basketball players’ perceptions of the psychological factors facilitating a return to performance equal to or exceeding previous performance standards. We also sought to describe athletes’ experiences – both positive and negative – of returning to sport following injury recovery. Ten Italian professional male basketball players (age range 22-36 years), were retrospectively interviewed in relation to three time-periods: (1) from the (...) commencement of rehabilitation to their first official competition, (2) the first official competition, and (3) the six-months following the initial competition. Hierarchical content analysis of the data revealed numerous themes across the three time periods. In regards to Period 1, participants indicated that social support, investment in rehabilitation and training programs, coping skills and motivation were fundamental in reaching pre-injury performance levels. During their first official game (i.e. Period 2), athletes reported that realistic performance expectations, focusing on the performance, positive emotions, motivation, arousal and social support facilitated their return to sport. Athletes however, also described a predominance of factors that hindered their return to pre- injury levels (i.e., low confidence in personal abilities, decrements in skill execution and dysfunctional physical sensations). Moreover, participants typically described a substandard level of performance during their first competition back following injury. In recounting experiences during the six months following their first official game, basketballers reported improvements in skill execution and highlighted the importance of coping skills, motivation and social support. The process of restoring self-confidence in one’s ability to successfully perform was perceived as crucial in enabling participants to move beyond a mere return to sport to a return to high performance ─ that is, to reach a level of proficiency equal to or exceeding previous performance standards. Findings support the relevance of cognitive, emotional and behavioral responses highlighted in the Integrated Model and suggest the importance of addressing psychological factors throughout the return-to-sport process. Finally, results from the present study hold a number of practical implications for athletes’ aiming to achieve a return to pre-injury levels. (shrink)

In this study we analyze daily data on television viewing in the Netherlands. We postulate hypotheses on supply and demand factors that could impact the amount of daily viewing time. Although the general assumption is that supply and demand often correlate, we see that for television this is only marginally the case. Especially diversity of program supply, often deemed very important in media markets, does not affect (positively or negatively) television viewing behavior. Most variation in television viewing can (...) be attributed to habit and to regular events (e. g. weekends, Christmas) and to unexpected events (e. g. the 9/11 WTC attack). We also find that weather conditions interact with program types, so that, for example, in winter times people favor entertainment programs even more, suggesting that people use television for mood management. (shrink)

Predicting stock movements is a valuable research field that can help investors earn more profits. As with time-series data, the stock market is time-dependent and the value of historical information may decrease over time. Accurate prediction can be achieved by mining valuable information with words on social platforms and further integrating it with actual stock market conditions. However, many methods still cannot effectively dig deep into hidden information, integrate text and stock prices, and ignore the (...) temporal dependence. Therefore, to solve the above problems, we propose a transformer-based attention network framework that uses historical text and stock prices to capture the temporal dependence of financial data. Among them, the transformer model and attention mechanism are used for feature extraction of financial data, which has fewer applications in the financial field, and effective analysis of key information to achieve an accurate prediction. A large number of experiments have proved the effectiveness of our proposed method. The actual simulation experiment verifies that our model has practical application value. (shrink)

In this study we analyze daily data on television viewing in the Netherlands. We postulate hypotheses on supply and demand factors that could impact the amount of daily viewing time. Although the general assumption is that supply and demand often correlate, we see that for television this is only marginally the case. Especially diversity of program supply, often deemed very important in media markets, does not affect (positively or negatively) television viewing behavior. Most variation in television viewing can (...) be attributed to habit and to regular events (e. g. weekends, Christmas) and to unexpected events (e. g. the 9/11 WTC attack). We also find that weather conditions interact with program types, so that, for example, in winter times people favor entertainment programs even more, suggesting that people use television for mood management. (shrink)

Telecommunications is one of the most dynamic sectors in the market, where the customer base is an important pawn in receive safe revenues, so is important to focus attention is paid to maintaining them with an active status. Migrating customers from one network to another varies among telecommunication companies depending on different factors such as call quality, pricing plan, minute consumption, data, sms facilities, customer billing issues, etc. Determining an effective predictive model helps detect early warning signals when churn (...) occurs and assigns to each customer a score called “churn score” that indicates the likelihood that the individual might migrate to another network over a predefined time period. To this extent, the present paper uses more than 10k customers sample of a telecommunication company and tries to analyse the churn behavior. The aim of the paper is both to test the efficiency and performance of the most commonly used data mining techniques to predict the churn behavior and to underline the main indicators that can be used when conducting such analyses. Knowing the magnitude of the churn phenomenon, the company can prevent the instability that is going to occur by applying a series of measure in order to increase the retention of the current customers. (shrink)

In this paper, a size-independent modification of the general detrended fluctuation analysis method is introduced. With this modified DFA, seismic time series pertaining to most seismically active regions of the world from the year1972up to the year2016are comparatively analyzed. An eminent homogeneity of spatial clustering behaviors in worldwide range is detected and DFA scaling exponents coincide with previous results for local regions. Furthermore, universal nontrivial spatial clustering behaviors are revealed from shallow to intermediate-depth earthquakes by varying the depth (...) of the hypocenter: in shallow earthquakes, the depth range corresponding to the minimum spatial clustering is first verified to be spatiotemporal and identical; in intermediate-depth earthquakes, strong weakening long-range correlations compared to shallow earthquakes are unveiled to strengthen the recent findings in seismology. Our work aims to suggest a possible statistical approach to explore the dynamic mechanisms implied in the seismicity as well as in other analogous dynamic evolution processes. (shrink)

A new layer of complexity, constituted of networks of information token recurrence, has been identified in socio-technical systems such as the Wikipedia online community and the Zooniverse citizen science platform. The identification of this complexity reveals that our current understanding of the actual structure of those systems, and consequently the structure of the entire World Wide Web, is incomplete, which raises novel questions for data science research but also from the perspective of social epistemology. Here we establish the principled (...) foundations and practical advantages of analyzing information diffusion within and across Web systems with Transcendental Information Cascades, and outline resulting directions for future study in the area of socio-technical systems. We also suggest that Transcendental Information Cascades may be applicable to any kind of time-evolving system that can be observed using digital technologies, and that the structures found in such systems comprise properties common to all naturally occurring complex systems. (shrink)