Bivariate Doubly Inflated Poisson and Related Regression Models

Count data are common in observational scientific investigations, and in many instances, such as twin or crossover studies, the data consists of dependent bivariate counts. An appropriate model for such data is the bivariate Poisson distribution given in Kocherlakota and Kocherlakota (2001). However, in situations where inflated count of (0, 0) occur, Lee et al. (2009) proposed the zero-inflated bivariate Poisson distribution which accounts for the inflated count. In this research, we introduce and study a bivariate distribution that accounts for an inflated count of the (k, k) cell for some k\u3e0, in addition to the inflated count for the (0, 0) cell. This bivariate doubly inflated Poisson distribution (BDIP) is a parametric model determined by four parameters (p, λ 1, λ2, λ3). In this dissertation, we will first discuss the distributional properties such as identifiability, moments and conditional distributions and stochastic representation of the BDIP model. Next, we will discuss parameter estimation by the method of moments and maximum likelihood methods and a comparison of the methods via asymptotic relative efficiency calculations. We also discuss the BDIP regression model that incorporates covariates into the BDIP model. We illustrate applicability of the BDIP regression model to analyze a subset of the Australian health survey data. Finally we conclude with an introduction to BDIP2 distribution, defined by the parameters (p1, p 2, λ1, λ2, λ3), and corresponding regression models

De-SaTE: Denoising Self-attention Transformer Encoders for Li-ion Battery Health Prognostics

The usage of Lithium-ion (Li-ion) batteries has gained widespread popularity across various industries, from powering portable electronic devices to propelling electric vehicles and supporting energy storage systems. A central challenge in Li-ion battery reliability lies in accurately predicting their Remaining Useful Life (RUL), which is a critical measure for proactive maintenance and predictive analytics. This study presents a novel approach that harnesses the power of multiple denoising modules, each trained to address specific types of noise commonly encountered in battery data. Specifically, a denoising auto-encoder and a wavelet denoiser are used to generate encoded/decomposed representations, which are subsequently processed through dedicated self-attention transformer encoders. After extensive experimentation on NASA and CALCE data, a broad spectrum of health indicator values are estimated under a set of diverse noise patterns. The reported error metrics on these data are on par with or better than the state-of-the-art reported in recent literature.Comment: 8 pages, 6 figures, 3 table

Examining the predictors of successful Airbnb bookings with Hurdle models: evidence from Europe, Australia, USA and Asia-Pacific cities

Recent studies on Airbnb have examined the predictors of room prices, successful reservations and customer satisfaction. However, a preliminary investigation of the listings from twenty-two cities across four continents revealed that a significant number of Airbnb homes remained non-booked. Thus, Poisson count-regression techniques cannot efficaciously explain the effects of predictors of successful Airbnb bookings. To address this gap, we proposed a text mining framework using Hurdle-based Poisson and Negative Binomial regressions. We found that the superhost status, host response time, and communication with guests emerged as the most significant predictors irrespective of geographies. We also found that the instant booking option strongly influences the bookings across cities with incoming business visitors. Additionally, we presented a machine learning-based variable-importance scheme, which helps determine the top predictors of successful bookings, to design customized recommendations for attracting more guests and unique advertisement content on P2P accommodation platforms

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Discutindo a educação ambiental no cotidiano escolar: desenvolvimento de projetos na escola formação inicial e continuada de professores

A presente pesquisa buscou discutir como a Educação Ambiental (EA) vem sendo trabalhada, no Ensino Fundamental e como os docentes desta escola compreendem e vem inserindo a EA no cotidiano escolar., em uma escola estadual do município de Tangará da Serra/MT, Brasil. Para tanto, realizou-se entrevistas com os professores que fazem parte de um projeto interdisciplinar de EA na escola pesquisada. Verificou-se que o projeto da escola não vem conseguindo alcançar os objetivos propostos por: desconhecimento do mesmo, pelos professores; formação deficiente dos professores, não entendimento da EA como processo de ensino-aprendizagem, falta de recursos didáticos, planejamento inadequado das atividades. A partir dessa constatação, procurou-se debater a impossibilidade de tratar do tema fora do trabalho interdisciplinar, bem como, e principalmente, a importância de um estudo mais aprofundado de EA, vinculando teoria e prática, tanto na formação docente, como em projetos escolares, a fim de fugir do tradicional vínculo “EA e ecologia, lixo e horta”.Facultad de Humanidades y Ciencias de la Educació

A critical assessment of consumer reviews: a hybrid NLP-based methodology

Online reviews are integral to consumer decision-making while purchasing products on an ecommerce platform. Extant literature has conclusively established the effects of various review and reviewer related predictors towards perceived helpfulness. However, background research is limited in addressing the following problem: how can readers interpret the topical summary of many helpful reviews that explain multiple themes and consecutively focus in-depth? To fill this gap, we drew upon Shannon’s Entropy Theory and Dual Process Theory to propose a set of predictors using NLP and text mining to examine helpfulness. We created four predictors - review depth, review divergence, semantic entropy and keyword relevance to build our primary empirical models. We also reported interesting findings from the interaction effects of the reviewer’s credibility, age of review, and review divergence. We also validated the robustness of our results across different product categories and higher thresholds of helpfulness votes. Our study contributes to the electronic commerce literature with relevant managerial and theoretical implications through these findings

Road Map to Understanding SARS-CoV-2 Clinico-Immunopathology and COVID-19 Disease Severity

SARS-CoV-2, a novel coronavirus, was first identified in Wuhan, China in December 2019. The rapid spread of the virus worldwide prompted the World Health Organization (WHO) to declare COVID-19 a pandemic in March 2020. COVID-19 discontinuing’s a global health crisis. Approximately 80% of the patients infected with SARS-CoV-2 display undetectable to mild inflammation confined in the upper respiratory tract. In remaining patients, the disease turns into a severe form affecting almost all major organs predominantly due to an imbalance of innate and adaptive arms of host immunity. The purpose of the present review is to narrate the virus’s invasion through the system and the host’s reaction. A thorough discussion on disease severity is also presented regarding the behavior of the host’s immune system, which gives rise to the cytokine storm particularly in elderly patients and those with comorbidities. A multifaceted yet concise description of molecular aspects of disease progression and its repercussion on biochemical and immunological features in infected patients is tabulated. The summary of pathological, clinical, immunological, and molecular accounts discussed in this review is of theranostic importance to clinicians for early diagnosis of COVID-19 and its management

Psychological factors affecting social media usage: A U&G theory perspective

The COVID-19 pandemic has led to a significant increase in social media usage, raising concerns about its potential impact on mental health. The pandemic has created unique stressors and challenges that have worsened the mental health conditions of individuals due to prolonged social media usage. Hence, this study explores the psychological factors affecting social media usage post-pandemic. A mixed-method approach was utilized grounded on U&G theory to identify fear of missing out, peer pressure, self-esteem, loneliness, social comparison, and habit as factors affecting social media usage. The study found that those with higher levels of peer pressure, social comparison, habit and fear of missing out (FOMO) tend to use social media more frequently, suggesting its use as a coping mechanism. The study emphasizes the need for continued investigation to understand the complex relationship between social media usage and mental health post-pandemic