Neuro-cognitive virtual environment for children with autism (VECA).

Autism a neurological disorder which is often diagnosed during early childhood and can cause significant social, communication, and behavioral challenges over a lifetime. It is increasing day by day and people are inclining from clinical and psychological therapies to assistive technologies. We have developed an interactive virtual environment VECA that aims to enhance the cognitive skills and creativity in children with autism by playing games and interacting with the environment. The setup also incorporates the feedback of the child that whether he/she is comfortable with the environment or not. This solution is cost effective, with no side effects unlike traditional therapies, and can provide valuable insight to the behavior analysis of the autism patients

Neural Network DPD for Aggrandizing SM-VCSEL-SSMF-Based Radio over Fiber Link Performance

This paper demonstrates an unprecedented novel neural network (NN)-based digital predistortion (DPD) solution to overcome the signal impairments and nonlinearities in Analog Optical fronthauls using radio over fiber (RoF) systems. DPD is realized with Volterra-based procedures that utilize indirect learning architecture (ILA) and direct learning architecture (DLA) that becomes quite complex. The proposed method using NNs evades issues associated with ILA and utilizes an NN to first model the RoF link and then trains an NN-based predistorter by backpropagating through the RoF NN model. Furthermore, the experimental evaluation is carried out for Long Term Evolution 20 MHz 256 quadraturre amplitude modulation (QAM) modulation signal using an 850 nm Single Mode VCSEL and Standard Single Mode Fiber to establish a comparison between the NN-based RoF link and Volterra-based Memory Polynomial and Generalized Memory Polynomial using ILA. The efficacy of the DPD is examined by reporting the Adjacent Channel Power Ratio and Error Vector Magnitude. The experimental findings imply that NN-DPD convincingly learns the RoF nonlinearities which may not suit a Volterra-based model, and hence may offer a favorable trade-off in terms of computational overhead and DPD performance

The relationship between Prenatal Stress, Depression, Cortisol and Preterm Birth: A review

Preterm birth is one of the most common adverse pregnancy outcomes. Maternal risk factors such as stress and depression have been associated with preterm birth. Preterm infants are at a higher risk of poor growth and neuro developmental outcomes. The objective of this paper is to examine the relationship between maternal stress, depression, cortisol level, and preterm birth. Preterm birth is one of the most common adverse pregnancy outcomes with a global prevalence of 9.6% and one of the major contributors to infant mortality and morbidity. The association between psychosocial stress and preterm birth, although examined for more than 25 years, has not yet been fully established. A systemic review was conducted in which research studies and review articles from 1970 to 2012, published in English, focusing on human subjects, and addressing the relationship between stress, depression, cortisol and preterm birth were included in this review. The studies examining the relationship between stress, cortisol levels and preterm birth have shown inconsistent findings that may be explained by varied study designs, differences in defining and measuring stress, timing of stress measurement, sample characteristics, and study designs. The relationship between stress, cortisol levels and preterm birth may be multifactorial and complex with premature birth being the final common pathway. A longitudinal cohort study, with a large sample size and multiple measures of stress, depression, and cortisol level, as well as a measure of anxiety and other stress hormone biomarkers may add new knowledge and enhance our understanding about the contribution of psychosocial stress to preterm birth

Big data assisted CRAN enabled 5G SON architecture

The recent development of Big Data, Internet of Things (IoT) and 5G network technology offers a plethora of opportunities to the IT industry and mobile network operators. 5G cellular technology promises to offer connectivity to massive numbers of IoT devices while meeting low-latency data transmission requirements. A deficiency of the current 4G networks is that the data from IoT devices and mobile nodes are merely passed on to the cloud and the communication infrastructure does not play a part in data analysis. Instead of only passing data on to the cloud, the system could also contribute to data analysis and decision-making. In this work, a Big Data driven self-optimized 5G network design is proposed using the knowledge of emerging technologies CRAN, NVF and SDN. Also, some technical impediments in 5G network optimization are discussed. A case study is presented to demonstrate the assistance of Big Data in solving the resource allocation problem

Determinantes dos honorários de auditoria para Portugal e Espanha

A liberalização dos serviços de auditoria em Portugal , com a eliminação em 2005, da tabela que fixava honorários mínimos baseados em padrões de dimensão da empresa auditada desperta o interesse em percecionar quais são os fatores que originam a determinação dos honorários de auditoria. Em Espanha, o conjunto de escândalos fina nceiros de que tem sido alvo tem colocado os honorários de auditoria no foco principal. A análise inclui uma amostra de 40 empresas cotadas em Portugal e 113 empresas cotadas em Espanha p ara o ano de 2013 utilizando a regressão dos mínimos quadrados (OSL). Os resultados indicam que em Espanha, os honorários são fixados principalmente em função da dimensão, complexidade e risco de auditoria, sendo que quanto maior a dimensão, a complexidade e o risco da empresa auditada, maiores os honorários de auditoria. E m Portugal, a dimensão da empresa auditada foi considerado o único fator que contribui para a determinação dos honorários em Portugal. Os resultados obtidos permitem - nos assim concluir que a determinação dos honorários de auditoria em Portugal é muito dife rente da dos honorários das empresas cotadas em Espanh

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Performance of Land Revenue Administration in Faisalabad (2001-2013): An Overview of Collection of Water Rate Abiana

Every government needs resources to run the country. In an agrarianeconomy like Pakistan collection of revenue from landed property becomesmore significant. Land revenue generated from the landed property used to bethe main source of revenue in the united Punjab during colonial era. That erawitnessed unprecedented rise in revenue by means of digging up a network ofan extensive canal system from rivers thereby rendering vast plains of Punjabcultivable. However, it is noted that this quite efficient and effective system ofcollection of land revenue has been underperforming. This paper tries to findand analyze the reasons of institutional deterioration and underperformance(of revenue department) through its focus on the revenue receipt andexpenditure in Faisalabad