PAR Model Research Articles

Advancing Astropharmacy: PAR Model and Space Pharmacy Council to Preparing Pharmacists for Space Missions

Advancing Astropharmacy: PAR Model and Space Pharmacy Council to Preparing Pharmacists for Space Missions

Do non-linearity and non-Gaussianity truly matter in streamflow forecasting? A comparative study between PAR(p) and vine copula for Brazilian streamflow time series.

This study evaluates the joint impact of non-linearity and non-Gaussianity on predictive performance in 23 Brazilian monthly streamflow time series from 1931 to 2022. We consider point and interval forecasting, employing a PAR(p) model and comparing it with the periodic vine copula model. Results indicate that the Gaussian hypothesis assumed by PAR(p) is unsuitable; gamma and log-normal distributions prove more appropriate and crucial for constructing accurate confidence intervals. This is primarily due to the assumption of the Gaussian distribution, which can lead to the generation of confidence intervals with negative values. Analyzing the estimated copula models, we observed a prevalence of the bivariate Normal copula, indicating that linear dynamic dependence is frequent, and the Rotated Gumbel 180°, which exhibits lower tail dependence. Overall, the temporal dynamics are predominantly shaped by combining these two types of effects. In point forecasting, both models show similar behavior in the estimation set, with slight advantages for the copula model. The copula model performs better during the out-of-sample analysis, particularly for certain power plants. In interval forecasting, the copula model exhibits pronounced superiority, offering a better estimation of quantiles. Consistently demonstrating proficiency in constructing reliable and accurate intervals, the copula model reveals a notable advantage over the PAR(p) model in interval forecasting.

CADRE ASSISTANCE IN PROMOTIVE AND PREVENTIVE DENTAL HEALTH PROGRAMS IN TRADITIONAL COMMUNITIES OF KAMPUNG NAGA

Kampung Naga is one of the traditional villages in Neglasari Village, Salawu District, Tasikmalaya Regency. The Kampung Naga Traditional Community is known for its strength in maintaining and preserving the traditions of its ancestral heritage. As a traditional village, the lives of its people are inseparable from traditional rules, including regarding health. Self-cleaning activities such as bathing are carried out in the "pacilingan" (MCK) which is above the fish pond. Interestingly, people here do not use cosmetics such as soap, shampoo and toothpaste when cleaning themselves. The ingredients used for bathing and shampooing are "taneuh porang" (clay), crushed orang-aring leaves or aloe vera and some also use lime. Meanwhile, to brush your teeth, use "eurih jeung lebu" (fiber from weed leaves and rubbing ash). The aim of the research is to determine the effect of cadre training in promotive and preventive dental health programs for the Naga village indigenous community in Tasikmalaya Regency. The qualitative research method is a quasi-experimental one group pre and post test design method using an observational approach and implementation of the PAR model which is supported by a literature review. The research results show that training local cadres is very effective in the promotive efforts carried out by cadres in indigenous communities. The results show that the level of pre-post knowledge of indigenous communities has increased to good with a result of p=0.000, and for the preventive results of OHIS measurements, the majority of indigenous community members in Dental hygiene maintenance has implemented what they got from the promotive cadres so that the pre-post OHIS results are good, p = 0.000, there is a significant change in the promotive and preventive efforts above, according to statistics, it is very significant. Conclusion: that there is an influence of cadre training in the promotional and preventive dental health program efforts of the Naga village indigenous community in maintaining the cleanliness of the teeth and mouth of local residents.

Artificial Intelligence based wrapper for high dimensional feature selection

BackgroundFeature selection is important in high dimensional data analysis. The wrapper approach is one of the ways to perform feature selection, but it is computationally intensive as it builds and evaluates models of multiple subsets of features. The existing wrapper algorithm primarily focuses on shortening the path to find an optimal feature set. However, it underutilizes the capability of feature subset models, which impacts feature selection and its predictive performance.Method and ResultsThis study proposes a novel Artificial Intelligence based Wrapper (AIWrap) algorithm that integrates Artificial Intelligence (AI) with the existing wrapper algorithm. The algorithm develops a Performance Prediction Model using AI which predicts the model performance of any feature set and allows the wrapper algorithm to evaluate the feature subset performance in a model without building the model. The algorithm can make the wrapper algorithm more relevant for high-dimensional data. We evaluate the performance of this algorithm using simulated studies and real research studies. AIWrap shows better or at par feature selection and model prediction performance than standard penalized feature selection algorithms and wrapper algorithms.ConclusionAIWrap approach provides an alternative algorithm to the existing algorithms for feature selection. The current study focuses on AIWrap application in continuous cross-sectional data. However, it could be applied to other datasets like longitudinal, categorical and time-to-event biological data.

AMALAN KEPIMPINAN MORAL GURU BESAR LEPASAN PROGRAM PRIME DALAM ELEMEN NILAI MERENTASI ORGANISASI: SATU KAJIAN TINDAKAN PARTISIPATIF

This study aims to explore the moral leadership practices that exist among principals who have graduated from the Prime program (Principals Residency and Immersion Programme), from three primary schools in Malaysia. The element of leadership that is studied among head teachers is the practice of moral leadership that exists in the aspect of Organizational Values. This study was carried out in a qualitative approach using a participatory action research design (Participatory Action Research or PAR). A three-loop PAR model adapted from Crane and Richardson (2000)3 was used. A total of 21 people, from 3 national schools (SK) have become study participants. Those who became study participants consisted of head teachers, senior administrators and academic teachers. Data for this study were collected through interview techniques, observation and journal entries. The collected data was analyzed using thematic analysis method. The findings of the study show that head teachers who completed PRIme program, in each school studied, are able to develop moral leadership practices in the management of school organizations in planning element of Cross-Organizational Values. The implications of this study provide important information to the moral leadership of head teachers, school organization management and the PRIme module in more efficient moral leadership.

ANALYSIS OF RISK MANAGEMENT IN TERRITORIAL PLANNING IN AREAS SUSCEPTIBLE TO SLOW FLOODING. CASE STUDY RURAL SETTLEMENT “EL PLAYÓN” BAJO SINÚ (CÓRDOBA, COLOMBIA).

As a result of this research, it is proposed the incorporation of risk management as an environmental determinant in flood areas of the lower Sinú River basin for territorial planning. This was carried out through the analysis of the total flood risk, determining the threat and vulnerability of the target community, likewise, the risk drivers were identified under approaches of the integral analysis of holistic and systemic risk using a PAR model through which it is established the underlying reason, the dynamic relationship of the environment, the unsafe conditions of geography, and the socio-economic characterization of the community. In this way, it was not only obtained the analysis of the areas affected by the floods by identifying the causes and establishing a level of risk tolerance, but also the risk factors with their effects in the study area. This highlighted the importance of developing immediate and priority activities in the study area

Causal inference for the covariance between breeding values under identity disequilibrium

BackgroundThe covariance matrix of breeding values is at the heart of prediction methods. Prediction of breeding values can be formulated using either an “observed” or a theoretical covariance matrix, and a major argument for choosing one or the other is the reduction of the computational burden for inverting such a matrix. In this regard, covariance matrices that are derived from Markov causal models possess properties that deliver sparse inverses.ResultsBy using causal Markov models, we express the breeding value of an individual as a linear regression on ancestral breeding values, plus a residual term, which we call residual breeding value (RBV). The latter is a noise term that accounts for the uncertainty in prediction due to lack of fit of the linear regression. A notable property of these models is the parental Markov condition, through which the multivariate distribution of breeding values is uniquely determined by the distribution of the mutually independent RBV. Animal breeders have long been relying on a causal Markov model, while using the additive relationship matrix as the covariance matrix structure of breeding values, which is calculated assuming gametic equilibrium. However, additional covariances among breeding values arise due to identity disequilibrium, which is defined as the difference between the covariance matrix under the multi-loci probability of identity-by-descent (varvec{Sigma}) and its expectation under gametic phase equilibrium, i.e., A. The disequilibrium term varvec{Sigma}−A is considered in the model for predicting breeding values called the “ancestral regression” (AR), a causal Markov model. Here, we introduce the “ancestral regression to parents” (PAR) causal Markov model, which reduces the computational burden of the AR approach. By taking advantage of the conditional independence property of the PAR Markov model, we derive covariances between the breeding values of grandparents and grand-offspring and between parents and offspring. In addition, we obtain analytical expressions for the covariance between collateral relatives under the PAR model, as well as for the inbreeding coefficient.ConclusionsWe introduced the causal PAR Markov model that captures identity disequilibrium in the covariances among breeding values and produces a sparse inverse covariance matrix to build and solve a set of mixed model equations.

Generating customer’s credit behavior with deep generative models

Banks collect data x1 in loan applications to decide whether to grant credit and accepted applications generate new data x2 throughout the loan period. Hence, banks have two measurement-modalities, which provide a complete picture about customers. If we can generate x2 conditioned on x1 keeping the relationship between these two modalities, credit and behavior scoring may be enabled simultaneously (at the time x1 is obtained) to support cross-selling, launching of new products or marketing campaigns. Therefore, we develop a novel conditional bi-modal discriminative (CBMD) model for credit scoring, which is able to generate x2 based on x1 and can classify the outcome of loans in an unified framework. The idea behind CBMD is to learn joint (among modalities) latent representations that are useful to generate x2 using the available data x1 during the application process. The classifier model introduced in CBMD encourages the generative process to generate x2 accurately. Further, CBMD optimizes a novel objective function introduced in this research, which maximizes mutual information between the latent representation z and the modality x2 to improve the generative process in the model. We benchmark the generative process of our proposed model and CBMD outperforms other multi-learning models. Similarly, the classification performance of CBMD is tested under different scenarios and it achieves higher or on a par model performance compared to the state-of-the-art in multi-modal learning models.

PAR(1) model analysis: a web-based shiny application for analysing periodic autoregressive models

In this paper, a web-based shiny application called the ‘PAR(1) Model Analysis’— that allows the modelling, estimation and prediction of a periodic autoregressive time series with scale mixtures of skew-normal innovations, a general and quite flexible class of error distributions—is presented. The class of scale mixtures of skew-normal distributions is often used for statistical procedures of analysing symmetrical and asymmetrical data. The formulation of the scale of a mixture of skew-normal periodic autoregressive models and the estimating methods, which will be applied in the web application, is briefly explained. The embedded tools in the web application and their applications on data analysis are fully described, and finally, a real data example is completely analysed by using this app to illustrate its handling.

Combination of Models to Generate the First PAR Maps for Spain

This work addresses the development of a PAR model in the entire territory of mainland Spain. Thus, a specific model is developed for each location of the study field. The new PAR model consists of a combination of the estimates of two previous models that had unequal performances in different climates. In fact, one of them showed better results with Mediterranean climate, whereas the other obtained better results under oceanic climate. Interestingly, the new PAR model showed similar performance when validated at seven stations in mainland Spain with Mediterranean or oceanic climate. Furthermore, all validation slopes ranged from 0.99 to 1.00; the intercepts were less than 3.70 μmol m−2 s−1; the R2 were greater than 0.988, while MBE was closer to zero percent than −0.39%; and RMSE were less than 6.21%. The estimates of the PAR model introduced in this work were then used to develop PAR maps over mainland Spain that represent daily PAR averages of each month and a full year at all locations in the study field.

Fractional lower-order covariance (FLOC)-based estimation for multidimensional PAR(1) model with $$\alpha -$$stable noise

Fractional lower-order covariance (FLOC)-based estimation for multidimensional PAR(1) model with $$\alpha -$$stable noise

Modelling Tourism in Australia Based on Periodic Autoregressive Time Series Models

Tourism has become very important in international commerce and also represents one of the main sources of income for some developing countries in the world. It is the act of people travelling and staying in a place outside their usual environment for leisure, business or other purposes, and this may include sightseeing, camping, retreats, etc Tourism is one of the key economic growth contributors and has contributed towards complete growth and development of Australia by bringing numerous economic value and benefits to her, and also building her brand value, image and identity. This paper seeks to generate a periodic autoregressive PAR model that could be used to make reliable forecast for tourism in Australia. Periodic autoregressive models are for seasonally observed data, particularly quarterly and monthly. Its’ parameters take different values across the seasons. The data used in this work was extracted from the official website of the Australian Bureau of Statistics (ABS), (www.abs.gov.au). It consists of monthly historical data of the number of short term visitors in Australia from January 1998 to December 2017 and was analysed using R- statistical software. The result revealed the order of the PAR model, and also verified that there is periodic variation (periodicity) in the tourism data. It was also verified that there is existence of single unit root and periodic integration which led to the fitting of periodic integrated autoregressive (PIAR(2)) model as the suitable model for the Australian tourism data. Finally, the residual generated from the model was subjected to statistical test and it showed a white noise behavior. Based on these findings, it is concluded that periodic autoregressive time series model can be used to generate reliable forecast for Australian tourism data. Further research on the stochastic nature and seasonality of Australian tourism data was recommended.

Emprego de protocolo de avaliação rápida no diagnóstico ambiental de sistemas fluviais: estudo de caso em áreas urbanas de Iconha e Piúma (ES)

Bacias hidrográficas são exemplos de recortes espaciais que integram uma visão conjunta do comportamento das condições naturais e das atividades humanas nelas compreendidas, onde ganham destaque a urbanização e suas materializações que, muitas vezes, têm desdobramentos negativos nos sistemas fluviais. Atualmente, o desenvolvimento de ferramentas rápidas e simples para realizar o diagnóstico ambiental preliminar desses sistemas vêm ganhando espaço frente às estações de monitoramento, a exemplo dos Protocolos de Avaliação Rápida (PARs). Logo, este artigo discute as potencialidades de aplicação de um modelo de PAR em áreas urbanas de Iconha e Piúma (ES), no contexto da Bacia Hidrográfica do Rio Novo. Os resultados apontam que em quatro dos cinco pontos analisados a qualidade ambiental foi classificada como “intermediária”, enquanto no outro restante foi “boa”. A utilização do protocolo mostrou-se, em geral, satisfatória, expondo as vantagens do seu uso para aferir a qualidade ambiental dos rios analisados.
 
 Abstract
 
 USE OF RAPID EVALUATION PROTOCOL IN THE ENVIRONMENTAL DIAGNOSIS OF FLUVIAL SYSTEMS: CASE STUDY IN URBAN AREAS OF ICONHA AND PIÚMA (ES)
 
 Drainage basins are examples of spatial arrengements that integrate a joint vision of the behavior of natural conditions and human activities within them, where urbanization and its materializations are highlighted, which often have negative consequences in the fluvial systems. Nowadays, the development of rapid and simple tools to accomplish the preliminary environmental diagnosis of such systems has been conquering space against monitoring stations, like the Rapid Evaluation Records (Protocolos de Avaliação Rápida – PARs). Thus, this study discuss the potentialities of applying a PAR model in urban areas of the municipalities of Iconha and Piúma (ES), in the context of the Novo River Drainage Basin. The results show that in four out of five analyzed points, the environmental quality was classified as “intermediate”, while in the remaining one it was “good”. The use of the PAR model was generally satisfactory, exposing the advantages of its use to designate the environmental quality of the analyzed rivers.

Genes involved in stress signals: the CBLs-CIPKs network in cold tolerant Solanum commersonii

Several studies revealed the important contribution of calcineurin B-like (CBLs) and CBL-interacting kinase (CIPKs) genes in transmitting stress signals in plants. Taking advantage from the genome sequences of the cultivated potato Solanum tuberosum and its wild relatives S. commersonii and S. chacoense, we identified for the first time 10 CBLs and 26 CIPKs genes in each species. The CBLs and CIPKs derived from tandem duplications indicate that these gene families in potato mainly arise through amplification mechanisms. Once annotated, we compared the par excellence model of Arabidopsis thaliana with S. commersonii, the potato model species for studying cold tolerance. We found that four ScCBL proteins (ScCBL1, ScCBL4a, ScCBL4b, and ScCBL9) started with a conserved N-myristoylation motif (MGXXXS/T), which might function in membrane targeting of the CBLs-CIPKs complex. Additionally, expression analyses of S. commersonii CBL and CIPK genes based on RNAseq revealed diverse expression patterns following various abiotic and biotic stresses and in the four tissues analyzed (flowers, leaf, roots, and tubers). Data also suggest that the ScCBLs-ScCIPKs complex may be more responsive to abiotic rather than biotic stimuli. Overall, the results described in the present work will be useful for future investigations and for functional characterization of individual CBLs and CIPKs in Solanum.

SUFISM MEANINGS IN THE BRAI ART IN CIREBON

This article aims to discuss a Sufi-inspired traditional art and performance popular in Cirebon, the so-called “Brai”. The Brai is a traditional Islamic Sufism music popular among the Cirebonese. This traditional music combines sounds, lyrics, and dance that invite the practitioners and audiences to exercise the spiritual stages through music. The Brai performance follows the hierarchy of Sufi-state of minds and spiritual stages (suluk and raka’at). Thus, as this article argues, the Brai is a par excellence model for the entanglement between Islam and local culture. The Brai is indeed a way of the Cirebonese both to introduce Sufi-form of Islam and provides a performance where the profane arts and spiritual-Islamic Sufims are mutually entangled.

SUFISM AND HYBRID SPIRITUALITY

This article aims to discuss a Sufi-inspired traditional art and performance popular in Cirebon, the so-called “Brai”. The Brai is a traditional Islamic Sufism music popular among the Cirebonese. This traditional music combines sounds, lyrics, and dance that invite the practitioners and audiences to exercise the spiritual stages through music. The Brai performance follows the hierarchy of Sufi-state of minds and spiritual stages (suluk and raka’at). Thus, as this article argues, the Brai is a par excellence model for the entanglement between Islam and local culture. The Brai is indeed a way of the Cirebonese both to introduce Sufi-form of Islam and provides a performance where the profane arts and spiritual-Islamic Sufims are mutually entangled.

On the estimation problem of periodic autoregressive time series: symmetric and asymmetric innovations

ABSTRACTPeriodic autoregressive (PAR) models with symmetric innovations are widely used on time series analysis, whereas its asymmetric counterpart inference remains a challenge, because of a number of problems related to the existing computational methods. In this paper, we use an interesting relationship between periodic autoregressive and vector autoregressive (VAR) models to study maximum likelihood and Bayesian approaches to the inference of a PAR model with normal and skew-normal innovations, where different kinds of estimation methods for the unknown parameters are examined. Several technical difficulties which are usually complicated to handle are reported. Results are compared with the existing classical solutions and the practical implementations of the proposed algorithms are illustrated via comprehensive simulation studies. The methods developed in the study are applied and illustrate a real-time series. The Bayes factor is also used to compare the multivariate normal model versus the multivariate skew-normal model.

A Structural Model for Optimal Selection of Maturity and Timing of Callable Bond Issuance: Help in Incorporating the Impact of Federal Reserve Policy and other Factors

A major shortcoming of previous structural models of bonds is that they ignore how the complex terms of a call feature interact with default to determine term structures at issuance. A term structure of par coupon yields is different and superior to a term structure based upon the option adjusted spread and, furthermore, is the most useful term structure for firms issuing bonds because it is the most accurate measure of cost of debt. We present a par coupon structural model that can be used as an aid in timing bond issues because some of parameters of the model have been proven to exhibit predictability. One can use the model to incorporate the impact of alternative future Federal Reserve policies for short-term interest rates upon the optimal timing and maturity of corporate bond issuance. We report that par coupon term structures can have distinctly different shapes than other simultaneous term structures, such as those derived from option adjusted spreads where the difference depends on default risk, alternative call features, and the interaction of default risk and call features. As one finding among many, we find that call option value (at issuance) declines as credit quality declines.

Application of a PAR Model for Assessing Vulnerability to Drought Hazard in Kondoa District

The Tanzania Disaster Management act of 2015 defines Vulnerability as the extent to which a community, structure, services, or geographical areas is prone to a particular hazard. The objective of this paper to document the extent of which Kondoa District is vulnerable to drought. The district is located along the border of two regions, namely Manyara and Arusha. The paper employed secondary data as main sources of data. These data were obtained from the district files, profile, documents report and research report written about the district. The study was based on the Pressure and Release Model (PAR) which developed by author Blaikie, et al. and modified by Wisner, et al. The finding reveals that the population is vulnerable to drought because of land degradation, low production, poor transportation and infrastructure, low price of commodities, poor market and a poor method of cultivation and a weather-related problem such as shortage of rainfall. No significant measures were taken by authorities to address the matter.

Vulnerability factors of shellfisherwomen in the face of oil spill events: An analysis of the Prestige case

Increasingly frequent disasters involving oil spills in coastal zones highlight the vulnerability faced by women's groups that are highly dependent on the marine environment. Based on the theoretical contribution of feminist environmentalism and the interpretative approach of the pressure and release model (PAR model), this paper verifies the progressive vulnerability of Galician shellfisherwomen in the period prior to the 2002 Prestige disaster in Spain. The main findings reveal the marginal position occupied by shellfisherwomen within the fishing sector. Moreover, they demonstrate the shellfisherwomen's dependency on a fragile coastal zone constantly threatened by pollution yet lacking adequate protection measures. This combination of factors exposed the social-environmental system maintained by shellfisherwomen to the risk of an oil disaster, increasing the vulnerability of women in that region. This study employs a gender vulnerability analysis to better understand oil disasters, and makes recommendations to improve future disaster preparedness and response, thereby benefiting people at risk.