scholarly journals Using person-specific networks in psychotherapy: challenges, limitations, and how we could use them anyway

BMC Medicine ◽  
2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Lino von Klipstein ◽  
Harriëtte Riese ◽  
Date C. van der Veen ◽  
Michelle N. Servaas ◽  
Robert A. Schoevers
Keyword(s):  
Time Series Data ◽  
Series Data ◽  
Case Conceptualization ◽  
Symptom Profiles ◽  
Starting Point ◽  
Context Variables ◽  
Consistent Manner ◽  
Collaborative Exploration ◽  
Insight Into

Abstract Background The complexity of psychopathology is evident from its multifactorial etiology and diversity of symptom profiles and hampers effective treatment. In psychotherapy, therapists approach this complexity by using case conceptualization. During this process, patients and therapists closely collaborate on a personalized working theory of the patient’s psychopathology. This is a challenging process and shows low reliability between therapists. With the experience sampling method (ESM), time-series data—valuable for case conceptualization—can be systematically gathered in a patient’s normal daily life. These data can be analyzed and visualized in person-specific networks (PSNs). PSNs may support case conceptualization by providing a schematic representation of association patterns between affective, cognitive, behavioral, and context variables. Main text We adopt a clinical perspective in considering how PSNs might be implemented to serve case conceptualization and what their role could be in psychotherapy. We suggest PSNs to be based on personalized ESM assessment to capture the unique constellation of variables in each patient. We reflect on the lack of a gold standard for creating PSNs, which may result in substantially different PSNs and thereby disparate information for case conceptualization. Moreover, even if PSNs are created in a consistent manner, results remain ambiguous as they are subject to multiple interpretations. Therefore, associations in PSNs do not allow for firm conclusions about a patient’s psychopathology, but they may nevertheless be valuable in the process of case conceptualization. PSNs are based on systematically gathered, ecologically valid ESM data and provide a unique personalized perspective. When used responsibly, PSNs may be able to support case conceptualization by generating questions that serve as a starting point for a dialog between therapists and patients. Well-targeted questions are an essential tool for therapists to gain insight into the patients’ psychopathology patterns and improve the quality of case conceptualization. Conclusions PSNs have limitations in terms of the reliability of the insights they provide directly. However, taking these challenges into account, we believe they have potential as a tool to help therapists and patients in their collaborative exploration of a patient’s psychopathology. Clearly, this would need to be validated in future clinical research.

Real Time Analysis Based on Reproducing Kernel Henderson Filters

2021 ◽  
Vol 28 (3) ◽  
pp. 553-574
Author(s):  
Silvia Bianconcini ◽  
Benoit Quenneville
Keyword(s):  
Reproducing Kernel ◽  
Time Series Data ◽  
Reproducing Kernels ◽  
Series Data ◽  
Linear Filter ◽  
Nonparametric Estimators ◽  
Real Time Analysis ◽  
Bandwidth Parameter ◽  
Starting Point ◽  
Kernel Hilbert Spaces

Recently, reproducing kernel Hilbert spaces have been introduced to provide a common approach for studying several nonparametric estimators used for smoothing functional time series data (Dagum and Bianconcini, 2006 and 2008). The reproducing kernel representation is based on the derivation of the density function (i.e. a second order kernel) embedded on the linear filter. This is the starting point for deriving higher order kernels, which are obtained from the product of the density and its orthonormal polynomials. This paper focuses on the Henderson filter, for which two density functions and corresponding hierarchies have been derived. The properties of the Henderson reproducing kernels are analyzed when the filters are adapted at the end of the sample period. The optimality criterion satisfied as well as the influence of the kernel order and bandwidth parameter are studied.

scholarly journals Growth Limits: A Conceptual Analysis for Sustainable Development in Nigeria

SAGE Open ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 215824402091827
Author(s):  
Oluwabunmi O. Adejumo
Keyword(s):  
Time Series Data ◽  
Optimal Growth ◽  
Series Data ◽  
Data Sets ◽  
African Countries ◽  
Limits To Growth ◽  
Development Indicators ◽  
Long Run ◽  
Sub Saharan

In the school of development thought, growth has been identified as a viable alternative to the challenge of poverty and economic backwardness. However, the ecologists have continuously challenged the growth position in relation to environmental degradation and depletion. It is against this background; this study examined the limits to growth in Nigeria beyond which there will be inimical consequences for the environment. The study employed time series data that spanned between 1970 and 2014. These data sets were sourced from the World Development Indicators. Based on the assimilation model, threshold estimates were used to identify optimal growth regions, whereas regression estimates were used to measure growth effects. It was discovered that below the identified growth limit, there are currently significant negative impacts on the quality of the environment in Nigeria via economic growth. This study is a single-country case, that is, Nigeria; hence, the study can be expanded to include other sub-Saharan African countries. The study adds to knowledge by establishing the prospects for sustainability in the quality of the environment in the long run; therefore, policies designed in this areas have higher likelihood of attaining sustainability.

scholarly journals Accounting Implications of Micro-Fiscal Measures and Quality of Real Gross National Goods and Services: Empirical Evidence From Nigeria

2020 ◽  
Vol 11 (6) ◽  
pp. 155
Author(s):  
Akabom I. Asuquo ◽  
Arzizeh Tiesieh Tapang ◽  
Uwem E. Uwah ◽  
Nicholas O. Dan ◽  
Ashishie Peter Uklala
Keyword(s):  
Empirical Evidence ◽  
Inverse Relationship ◽  
Time Series Data ◽  
Series Data ◽  
Government Agencies ◽  
Exploratory Research ◽  
Direct Effects ◽  
Goods And Services ◽  
The Government

The study explored into accounting implications of micro-fiscal measures and quality of real gross national goods and services: empirical evidence from Nigeria for a period of thirty years. The objective was to examine how micro-fiscal measures affect real gross national goods and services using thirty years’ time-series data. The exploratory research methodology was applied and data collected were analysed using multiple regression and other statistical techniques. Findings of the study revealed that significant and direct effects were exerted on gross national goods and services by all the known and identified micro-fiscal measures in the review, except swap and levy ratios which had inverse relationship as revealed by their coefficients obtained from the analysis. Therefore, the government and government agencies have a duty to control macro-fiscal activities in terms of creation of national goods, wealth and services using the identified micro-fiscal mechanisms as the basis for decisions and policies making besides implementation.

scholarly journals Blazhko effect in Cepheids and RR Lyrae stars

2013 ◽  
Vol 9 (S301) ◽  
pp. 241-248
Author(s):  
Róbert Szabó
Keyword(s):  
Time Series Data ◽  
Period Doubling ◽  
High Order ◽  
Series Data ◽  
Theoretical Point ◽  
Rr Lyrae Stars ◽  
Rr Lyrae ◽  
Starting Point ◽  
Blazhko Effect ◽  
Lyrae Stars

AbstractThe Blazhko effect is the conspicuous amplitude and phase modulation of the pulsation of RR Lyrae stars that was discovered in the early 20th century. The field of study of this mysterious modulation has recently been invigorated thanks to the space photometric missions providing long, uninterrupted, ultra-precise time-series data. In this paper I give a brief overview of the new observational findings related to the Blazhko effect, such as extreme modulations, irregular modulation cycles and additional periodicities. I argue that these findings together with dedicated ground-based efforts provide us now with a fairly complete picture and a good starting point to theoretical investigations. Indeed, new, unpredicted dynamical phenomena have been discovered in Blazhko RR Lyrae stars, such as period doubling, high-order resonances, three-mode pulsation and low-dimensional chaos. These led to the proposal of a new explanation to this century-old enigma, namely a high-order resonance between radial modes. Along these lines I present the latest efforts and advances from the theoretical point of view. Lastly, amplitude variations in Cepheids are discussed.

scholarly journals Forecast Quality of Physics-Based and Data-Driven PV Performance Models for a Small-Scale PV System

2021 ◽  
Vol 9 ◽  
Author(s):  
Moritz Stüber ◽  
Felix Scherhag ◽  
Matthieu Deru ◽  
Alassane Ndiaye ◽  
Muhammad Moiz Sakha ◽  
...  
Keyword(s):  
Smart Grids ◽  
Time Series Data ◽  
Short Term Memory ◽  
Data Driven ◽  
Series Data ◽  
Small Scale ◽  
Pv System ◽  
Advantages And Disadvantages ◽  
Forecast Quality

In the context of smart grids, the need for forecasts of the power output of small-scale photovoltaic (PV) arrays increases as control processes such as the management of flexibilities in the distribution grid gain importance. However, there is often only very little knowledge about the PV systems installed: even fundamental system parameters such as panel orientation, the number of panels and their type, or time series data of past PV system performance are usually unknown to the grid operator. In the past, only forecasting models that attempted to account for cause-and-effect chains existed; nowadays, also data-driven methods that attempt to recognize patterns in past behavior are available. Choosing between physics-based or data-driven forecast methods requires knowledge about the typical forecast quality as well as the requirements that each approach entails. In this contribution, the achieved forecast quality for a typical scenario (day-ahead, based on numerical weather predictions [NWP]) is evaluated for one physics-based as well as five different data-driven forecast methods for a year at the same site in south-western Germany. Namely, feed-forward neural networks (FFNN), long short-term memory (LSTM) networks, random forest, bagging and boosting are investigated. Additionally, the forecast quality of the weather forecast is analyzed for key quantities. All evaluated PV forecast methods showed comparable performance; based on concise descriptions of the forecast approaches, advantages and disadvantages of each are discussed. The approaches are viable even though the forecasts regularly differ significantly from the observed behavior; the residual analysis performed offers a qualitative insight into the achievable forecast quality in a typical real-world scenario.

scholarly journals Inferring phage-bacteria infection networks from time series data

2016 ◽  
Author(s):  
Luis F. Jover ◽  
Justin Romberg ◽  
Joshua S. Weitz
Keyword(s):  
Time Series ◽  
Time Course ◽  
Community Dynamics ◽  
Time Series Data ◽  
Plaque Assay ◽  
Interaction Network ◽  
Series Data ◽  
Factors Affecting ◽  
Evolutionary Changes

In communities with bacterial viruses (phage) and bacteria, the phage-bacteria infection network establishes which virus types infects which host types. The structure of the infection network is a key element in understanding community dynamics. Yet, this infection network is often difficult to ascertain. Introduced over 60 years ago, the plaque assay remains the gold-standard for establishing who infects whom in a community. This culture-based approach does not scale to environmental samples with increased levels of phage and bacterial diversity, much of which is currently unculturable. Here, we propose an alternative method of inferring phage-bacteria infection networks. This method uses time series data of fluctuating population densities to estimate the complete interaction network without having to test each phage-bacteria pair individually. We use in silico experiments to analyze the factors affecting the quality of network reconstruction and find robust regimes where accurate reconstructions are possible. In addition, we present a multi-experiment approach where time series from different experiments are combined to improve estimates of the infection network and mitigate against the possibility of evolutionary changes to infection during the time-course of measurement.

scholarly journals Persepsi Kualitas Mahasiswa Program Studi DIII Akuntansi Polines Selama Praktek Kerja Lapangan (PKL) di Industri

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Moh.Hasanudin Marliyati ◽  
Sri Murtini ◽  
Resi Yudhaningsih ◽  
Retno Retno
Keyword(s):  
Information Technology ◽  
Time Series ◽  
Communication Skills ◽  
Time Series Data ◽  
Soft Skills ◽  
Series Data ◽  
Central Java ◽  
Hard Skills ◽  
Main Components

<p>This research aimed at exploring the quality of accounting diploma <br />students during their internship program in industries. The term of student’s <br />quality described in this research isexplained using 5 main components as follows: (1) communication skills (2) teamwork (3) independence (4) creativity (5) accounting and information technology (IT)-related skills. The research’s sample is industries where students of Diploma in Accounting of State Polytechnic of Semarang (SPS) took their intership and the students themselves whom have completed their internship program for three months in various institutions such as private enterprises, state owned enterprises, local government offices spread out around Central Java. The data on this research is time series data taken from 2015 to 2016 and was collected using questionnaires from the corresponding industries about the students competencies both hard skills and soft skills. <br />Data was scored using Likert scale, ranges from Poor (1) to Excellent (5) and <br />analyzed using statistic descriptive. The result showed that average students’ <br />quality during their internship was good. Among the 5 skills observed, the <br />corresponding industries ranked teamwork skills as the highest, followed by <br />independence, creativity, communication skills and the accounting and IT -related skills. It is expected that the result can be used for future development of Accounting Program Study of SPS.</p>

scholarly journals A Model Visualization-based Approach for Insight into Waveforms and Spectra Learned by CNNs

2021 ◽  
Author(s):  
Charles A Ellis ◽  
Robyn L Miller ◽  
Vince D Calhoun
Keyword(s):  
Neural Networks ◽  
Time Series Data ◽  
Sleep Stage ◽  
Series Data ◽  
Spectral Features ◽  
Classifier Performance ◽  
Sleep Stage Classification ◽  
Significant Step ◽  
Model Visualization ◽  
Insight Into

Recent years have shown a growth in the application of deep learning architectures such as convolutional neural networks (CNNs), to electrophysiology analysis. However, using neural networks with raw time-series data makes explainability a significant challenge. Multiple explainability approaches have been developed for insight into the spectral features learned by CNNs from EEG. However, across electrophysiology modalities, and even within EEG, there are many unique waveforms of clinical relevance. Existing methods that provide insight into waveforms learned by CNNs are of questionable utility. In this study, we present a novel model visualization-based approach that analyzes the filters in the first convolutional layer of the network. To our knowledge, this is the first method focused on extracting explainable information from EEG waveforms learned by CNNs while also providing insight into the learned spectral features. We demonstrate the viability of our approach within the context of automated sleep stage classification, a well-characterized domain that can help validate our approach. We identify 3 subgroups of filters with distinct spectral properties, determine the relative importance of each group of filters, and identify several unique waveforms learned by the classifier that were vital to the classifier performance. Our approach represents a significant step forward in explainability for electrophysiology classifiers, which we also hope will be useful for providing insights in future studies.

scholarly journals The Role of Cooperatives, Remittances, and Infrastructure in Export Performance of Nepal: ARDL Approach of Cointegration

2020 ◽  
pp. 151-159
Author(s):  
Ramesh C. Paudel ◽  
Chakrapani Acharya ◽  
Resham Thapa-Parajuli
Keyword(s):  
Time Series Data ◽  
Export Performance ◽  
Series Data ◽  
Supply Side ◽  
Policy Makers ◽  
Ardl Approach ◽  
Negative Role ◽  
Supply Side Factors

Cooperatives, remittances, and foreign direct investment (FDI) are crucial source of funds required for better entrepreneurships, which combinedly along with the quality of infrastructure can contribute to enhance the supply side factors of the export performance. Due to the well perceived role of cooperatives, Nepal’s constitution 2015 mentions this sector as one of the three pillars of the national economy while around 30 percent of Nepal’s GDP comes from remittances. As the country lacks the domestic sources for investment, FDI has become an indispensable part of the development sources of the developing countries in the recent decades. This paper analyzes the role of cooperatives, remittances, FDI and infrastructure in export performance of Nepal using the Autoregressive Distributive Lag (ARDL) approach of cointegration as suggested by the properties of the time series data for the period of 26 years from 1993 to 2018. The major finding shows that the cooperatives have not contributed to export performance as expected, however the role is positive. The remittances have a strong negative role on export performance, which is largely impacted by the number and quality of the infrastructure. The role of FDI is also negative and might be due to insufficient volume to contribute substantially. This fact seeks the urgent attention from the policy makers to make the country more investment friendly.

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