Modeling and forecasting abnormal stock returns using the nonlinear Grey Bernoulli model

Purpose – This study aims to use gray models to predict abnormal stock returns. Design/methodology/approach – Data are collected from listed companies in the Tehran Stock Exchange during 2005-2015. The analyses portray three models, namely, the gray model, the nonlinear gray Bernoulli model and the Nash nonlinear gray Bernoulli model. Findings – Results show that the Nash nonlinear gray Bernoulli model can predict abnormal stock returns that are defined by conditions other than gray models which predict increases, and then after checking regression models, the Bernoulli regression model is defined, which gives higher accuracy and fewer errors than the other two models. Originality/value – The stock market is one of the most important markets, which is influenced by several factors. Thus, accurate and reliable techniques are necessary to help investors and consumers find detailed and exact ways to predict the stock market.Propósito – Este estudio tiene como objetivo utilizar modelos grises para predecir rendimientos anormales de acciones. Diseño / metodología / enfoque – Los datos se recopilan de empresas que cotizan en la Bolsa de Valores de Teherán durante 2005-2015. Los análisis muestran tres modelos, a saber, el modelo gris, el modelo Bernoulli gris no lineal y el modelo Bernoulli gris no lineal de Nash. Recomendaciones – Los resultados muestran que el modelo de Bernoulli gris no lineal de Nash puede predecir rendimientos de acciones anormales que se definen por condiciones distintas de los modelos grises que predicen aumentos, y luego, después de verificar los modelos de regresión, se define el modelo de regresión de Bernoulli, que proporciona una mayor precisión y menos errores que el modelo de regresión de Bernoulli. otros dos modelos. Originalidad / valor – El mercado de valores es uno de los mercados más importantes, en el que influyen varios factores. Por lo tanto, se necesitan técnicas precisas y confiables para ayudar a los inversores y consumidores a encontrar formas detalladas y exactas de predecir el mercado de valores

Exploiting multilingual lexical resources to predict MWE compositionality

Semantic idiomaticity is the extent to which the meaning of a multiword expression (MWE) cannot be predicted from the meanings of its component words. Much work in natural language processing on semantic idiomaticity has focused on compositionality prediction, wherein a binary or continuous-valued compositionality score is predicted for an MWE as a whole, or its individual component words. One source of information for making compositionality predictions is the translation of an MWE into other languages. This chapter extends two previously-presented studies – Salehi & Cook (2013) and Salehi et al. (2014) – that propose methods for predicting compositionality that exploit translation information provided by multilingual lexical resources, and that are applicable to many kinds of MWEs in a wide range of languages. These methods make use of distributional similarity of an MWE and its component words under translation into many languages, as well as string similarity measures applied to definitions of translations of an MWE and its component words. We evaluate these methods over English noun compounds, English verb-particle constructions, and German noun compounds. We show that the estimation of compositionality is improved when using translations into multiple languages, as compared to simply using distributional similarity in the source language. We further find that string similarity complements distributional similarity

Knowledge, attitude and practice of urban and ruarl households towards principles of nutrition in Iran: results of NUTRIKAP Survey.

Background: The aim of this study was to assess knowledge, attitude and practice of urban and rural households toward principles of nutrition in Iran. Methods: The study population was Iranian households who live in rural and urban areas in all provinces of the country. The sampling method at households’ level in each province was single stage cluster sampling with equal size clusters. The incumbent data was collected by a structured questionnaire and through the interview with the eligible subject in each household. Results: A total of 14,136 Iranian households were selected as total sample size, 9,149 urban households, and 4,987 rural households. Around 57.2% of urban and 49.5% of rural households was aware of food groups. Respectively in urban and rural households, about 35.1% and 39.7% had correct knowledge toward roles of food groups. Approximately 41.5% and 39.9% of households had accurate knowledge about reason of food eating in urban and rural areas, respectively. The results showed that 79.6% of them had favorable attitudes. The most of the households consumed red meat and poultry weekly whereas fish was eaten rarely. Fruits, vegetables and dairy were consumed daily in the most of households. Sugar intake was daily in the most of households and cream and butter intake was weekly. Conclusion: The most of households had moderate knowledge and good attitudes. Practice of families about food consumption was good. The results of this study can be used for proper intervention for improving of health society

Nutritional knowledge, attitude and practice of Iranian households and primary health care staff: NUTRIKAP survey

The aim of this study is to determine knowledge, attitude and practice of Iranian households and health staff on nutrition at province level. The sampling method in NUTRIKAP survey for households in each province is singlestage cluster sampling and the size of clusters is equal. The sampling method for health staff in each province is stratified random sampling. Samples are selected from physicians, health experts, health technicians, nutritionists and health assistants (Behvarz). Overall, 14136 people in 57 clusters in each province and 480 health staff over the country participate in this survey. The necessary data will be gathered by the structured questionnaire and the interview with the eligible person in each household. Data gathering from health staff will be carried out by selfadministered questionnaire. The results of this study can help the bureau of community nutrition to provide the proper interventions to improve nutritional health of households

Docking-Based Screening of Cell-Penetrating Peptides with Antiviral Features and Ebola Virus Proteins as a Drug Discovery Approach to Develop a Treatment for Ebola Virus Disease

Ebola drug discovery continues to be challenging as yet. Proteins of the virus should be targeted at the relevant biologically active site for drug or inhibitor binding to be effective. In this regard, by considering the important role of Ebola virus proteins in the viral mechanisms of this viral disease, the Ebola proteins are selected as our drug targets in this study. The discovery of novel therapeutic molecules or peptides will be highly expensive; therefore, we attempted to identify possible antigens of EBOV proteins by conducting docking-based screening of cell penetrating peptides (CPPs) that have antiviral potential features utilizing Hex software version 8.0.0. The E-value scores obtained in this research were very much higher than the previously reported docking studies. CPPs that possess suitable interaction with the targets would be specified as promising candidates for further in vitro and in vivo examination aimed at developing new drugs for Ebola infection treatment

Prevalence of Surgical Site Infection After Hip Arthroplasty; a Systematic Review and Meta-Analysis

Introduction: One of the worrisome complications of hip arthroplasty is surgical site infection (SSI). This study aimed to investigate the prevalence of SSI after hip arthroplasty. Methods: A comprehensive and systematic exploration was conducted across various international electronic databases, including Scopus, PubMed, and Web of Science, alongside Persian electronic databases such as Iranmedex and the Scientific Information Database (SID). This search strategy entailed the utilization of Medical Subject Headings-derived keywords such as "Prevalence," "Surgical wound infection," "Surgical site infection," and "Arthroplasty," spanning from the earliest records up to January 1, 2024. Each study’s weight was assigned based on its inverse variance. A forest plot visualization was used to assess the studies’ heterogeneity. Data on sample size and SSI frequency were compiled for each study to calculate the overall effect size. Results: The study encompassed a cumulative participant cohort of 1,070,638 hip arthroplasty procedures drawn from seventeen selected studies. Notably, the female gender constituted 59.10% of the overall participant demographic. The aggregate SSI among patients undergoing hip arthroplasty was estimated to be 1.9% (95% CI: 1.3% to 2.8%; I2=99.688%; P<0.001). The results of the meta-regression analysis unveiled a statistically significant correlation between the prevalence of SSIs after hip arthroplasty and the year of publication (Coefficient=-0.0020; 95% CI: -0.0021 to -0.0018; Z=-19.39, P<0.001). Conclusion: The study findings indicated a prevalence rate of 1.9% for SSI following hip arthroplasty. This prevalence underscores the importance of vigilance in infection prevention and management strategies within orthopedic surgery. However, it is essential to acknowledge the variability in SSI prevalence observed across diverse studies, which can be attributed to multifaceted factors, notably variances in patient populations and associated risk factors

Multiword expressions at length and in depth: Extended papers from the MWE 2017 workshop

The annual workshop on multiword expressions takes place since 2001 in conjunction with major computational linguistics conferences and attracts the attention of an ever-growing community working on a variety of languages, linguistic phenomena and related computational processing issues. MWE 2017 took place in Valencia, Spain, and represented a vibrant panorama of the current research landscape on the computational treatment of multiword expressions, featuring many high-quality submissions. Furthermore, MWE 2017 included the first shared task on multilingual identification of verbal multiword expressions. The shared task, with extended communal work, has developed important multilingual resources and mobilised several research groups in computational linguistics worldwide. This book contains extended versions of selected papers from the workshop. Authors worked hard to include detailed explanations, broader and deeper analyses, and new exciting results, which were thoroughly reviewed by an internationally renowned committee. We hope that this distinctly joint effort will provide a meaningful and useful snapshot of the multilingual state of the art in multiword expressions modelling and processing, and will be a point point of reference for future work

Multiword expressions at length and in depth: Extended papers from the MWE 2017 workshop

The annual workshop on multiword expressions takes place since 2001 in conjunction with major computational linguistics conferences and attracts the attention of an ever-growing community working on a variety of languages, linguistic phenomena and related computational processing issues. MWE 2017 took place in Valencia, Spain, and represented a vibrant panorama of the current research landscape on the computational treatment of multiword expressions, featuring many high-quality submissions. Furthermore, MWE 2017 included the first shared task on multilingual identification of verbal multiword expressions. The shared task, with extended communal work, has developed important multilingual resources and mobilised several research groups in computational linguistics worldwide. This book contains extended versions of selected papers from the workshop. Authors worked hard to include detailed explanations, broader and deeper analyses, and new exciting results, which were thoroughly reviewed by an internationally renowned committee. We hope that this distinctly joint effort will provide a meaningful and useful snapshot of the multilingual state of the art in multiword expressions modelling and processing, and will be a point point of reference for future work

Multiword expressions at length and in depth: Extended papers from the MWE 2017 workshop

The annual workshop on multiword expressions takes place since 2001 in conjunction with major computational linguistics conferences and attracts the attention of an ever-growing community working on a variety of languages, linguistic phenomena and related computational processing issues. MWE 2017 took place in Valencia, Spain, and represented a vibrant panorama of the current research landscape on the computational treatment of multiword expressions, featuring many high-quality submissions. Furthermore, MWE 2017 included the first shared task on multilingual identification of verbal multiword expressions. The shared task, with extended communal work, has developed important multilingual resources and mobilised several research groups in computational linguistics worldwide. This book contains extended versions of selected papers from the workshop. Authors worked hard to include detailed explanations, broader and deeper analyses, and new exciting results, which were thoroughly reviewed by an internationally renowned committee. We hope that this distinctly joint effort will provide a meaningful and useful snapshot of the multilingual state of the art in multiword expressions modelling and processing, and will be a point point of reference for future work