Internal Control Quality: The Role of Critical Audit Matters Reporting

We examine whether critical audit matter (CAM) reporting in audit reports improves issuers’ internal controls over financial reporting. We propose that increased scrutiny by auditors on CAM-related matters lead to early identification and remediation of material weaknesses in internal control (ICMW). Analyses show that compared to control companies, companies with CAM reporting experience a statistically significant decrease in both the likelihood of having an ICMW and the number of ICMWs. This result is driven primarily by account-level ICMWs rather than entity-level ICMWs. We also find that issuers with revenue-recognition CAMs have significantly fewer revenue-related ICMWs, suggesting that ICMWs related to revenue recognition are identified and remediated through the CAM evaluation process. For those that reported ICMW at year end, we find a positive and significant association between CAM reporting and the likelihood of disclosing ineffective internal control in SOX 302 reports. This finding supports that CAM reporting leads to early identification of internal control problems. Overall, our evidence suggests that by focusing auditor attention on areas of potential concern, CAM reporting leads to improvements in internal control quality. Our findings have important policy implications as they show that CAM reporting improves financial reporting quality by affecting auditor and management behavior

Willingness to accept monkeypox vaccine and its correlates among men who have sex with men in Southern China: a web-based online cross-sectional study

BACKGROUND: The May 2022 global outbreak of monkeypox (MPX) poses a threat to the health of men who have sex with men. However, there is limited data on the willingness of MSM to receive monkeypox vaccination in Southern China. This study aimed to assess the knowledge of MPX, concerns regarding MPX, and willingness to receive monkeypox vaccination, as well as their correlates, among MSM in China. METHODS: We conducted a Web-based online survey of MSM in Southern China from August to September 2022. Data were collected on the socio-demographic characteristics, knowledge, worries, concerns regarding MPX and willingness to receive monkeypox vaccination. Multivariate logistic regression was employed to explore the factors associated with willingness to receive monkeypox vaccination. RESULTS: A total of 1903 participants completed the survey. Among them, approximately 69.9% reported being aware of MPX awareness, 94.1% of the participants supported the promotion of monkeypox vaccination. The majority of participants (91.4%) expressed their willingness to receive monkeypox vaccination. Participants who considered monkeypox vaccination safe [adjusted odds ratio (aOR) = 4.82, 95% CI: 1.35-17.18], agreed on the necessity of government promotion of monkeypox vaccination in China (aOR = 6.03, 95% CI: 1.07-33.93), believed in prioritizing monkeypox vaccination for MSM (aOR = 5.01, 95% CI: 1.10-22.71), and had friends or sexual partners who had already received the monkeypox or smallpox vaccination (aOR = 10.37, 95% CI: 2.11-50.99) are more likely to be vaccinated. Conversely, married individuals (aOR = 0.13, 95% CI: 0.03-0.47), those engaging in anal sex 4-6 times per week in the past 3 months (aOR = 0.26, 95% CI: 0.09-0.77) expressed hesitancy toward monkeypox vaccination. CONCLUSION: There was a high willingness to receive monkeypox vaccination among MSM in China. The hesitancy toward the monkeypox vaccine can be effectively mitigated by addressing concerns about its safety and potential adverse reactions. Moreover, increasing acceptance of the monkeypox vaccination among MSM and their peers is crucial, as social influence significantly impacts vaccine attitudes and behaviors

The vertical cloud structure of the West African monsoon: a 4 year climatology using CloudSat and CALIPSO

The West African summer monsoon (WAM) is an important driver of the global climate and locally provides most of the annual rainfall. A solid climatological knowledge of the complex vertical cloud structure is invaluable to forecasters and modelers to improve the understanding of the WAM. In this paper, 4 years of data from the CloudSat profiling radar and CALIPSO are used to create a composite zonal mean vertical cloud and precipitation structure for the WAM. For the first time, the near-coincident vertical radar and lidar profiles allow for the identification of individual cloud types from optically thin cirrus and shallow cumulus to congestus and deep convection. A clear diurnal signal in zonal mean cloud structure is observed for the WAM, with deep convective activity enhanced at night producing extensive anvil and cirrus, while daytime observations show more shallow cloud and congestus. A layer of altocumulus is frequently observed over the Sahara at night and day, extending southward to the coastline, and the majority of this cloud is shown to contain supercooled liquid in the top. The occurrence of deep convective systems and congestus in relation to the position of the African easterly jet is studied, but only the daytime cumulonimbus distribution indicates some influence of the jet position

Prevalence of depressive symptoms and correlates among individuals who self-reported SARS-CoV-2 infection after optimizing the COVID-19 response in China

BACKGROUND: The burden of depression symptoms has increased among individuals infected with SARS-CoV-2 during COVID-19 pandemic. However, the prevalence and associated factors of depressive symptoms among individuals infected with SARS-CoV-2 remain uncertain after optimizing the COVID-19 response in China. METHODS: An online cross-sectional survey was conducted among the public from January 6 to 30, 2023, using a convenience sampling method. Sociodemographic and COVID-19 pandemic-related factors were collected. The depression symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9). Logistic regression analysis was performed to explore the associated factors with depressive symptoms. RESULTS: A total of 2,726 participants completed the survey. The prevalence of depression symptoms was 35.3%. About 58% of the participants reported experiencing insufficient drug supply. More than 40% of participants reported that they had missed healthcare appointments or delayed treatment. One-third of participants responded experiencing a shortage of healthcare staff and a long waiting time during medical treatment. Logistic regression analysis revealed several factors that were associated with depression symptoms, including sleep difficulties (OR, 2.84; 95% CI, 2.34-3.44), chronic diseases (OR, 2.15; 95% CI, 1.64-2.82), inpatient treatment for COVID-19 (OR, 3.24; 95% CI, 2.19-4.77), with COVID-19 symptoms more than 13 days (OR, 1.30, 95% CI 1.04-1.63), re-infection with SARS-CoV-2 (OR, 1.52; 95% CI, 1.07-2.15), and the increased in demand for healthcare services (OR, 1.32; 95% CI, 1.08-1.61). CONCLUSION: This study reveals a moderate prevalence of depression symptoms among individuals infected with SARS-CoV-2. The findings underscore the importance of continued focus on depressive symptoms among vulnerable individuals, including those with sleeping difficulties, chronic diseases, and inpatient treatment for COVID-19. It is necessary to provide mental health services and psychological interventions for these vulnerable groups during the COVID-19 epidemic

Recent Walker Circulation strengthening and Pacific cooling amplified by Atlantic warming

An unprecedented strengthening of Pacific trade winds since the late 1990s (ref. 1) has caused widespread climate perturbations, including rapid sea-level rise in the western tropical Pacific, strengthening of Indo-Pacific ocean currents, and an increased uptake of heat in the equatorial Pacific thermocline. The corresponding intensification of the atmospheric Walker circulation is also associated with sea surface cooling in the eastern Pacific, which has been identified as one of the contributors to the current pause in global surface warming. In spite of recent progress in determining the climatic impacts of the Pacific trade wind acceleration, the cause of this pronounced trend in atmospheric circulation remains unknown. Here we analyse a series of climate model experiments along with observational data to show that the recent warming trend in Atlantic sea surface temperature and the corresponding trans-basin displacements of the main atmospheric pressure centres were key drivers of the observed Walker circulation intensification, eastern Pacific cooling, North American rainfall trends and western Pacific sea-level rise. Our study suggests that global surface warming has been partly offset by the Pacific climate response to enhanced Atlantic warming since the early 1990s

Towards a fair comparison of statistical and dynamical downscaling in the framework of the EURO-CORDEX initiative

Both statistical and dynamical downscaling methods are well established techniques to bridge the gap between the coarse information produced by global circulation models and the regional-to-local scales required by the climate change Impacts, Adaptation, and Vulnerability (IAV) communities. A number of studies have analyzed the relative merits of each technique by inter-comparing their performance in reproducing the observed climate, as given by a number of climatic indices (e.g. mean values, percentiles, spells). However, in this paper we stress that fair comparisons should be based on indices that are not affected by the calibration towards the observed climate used for some of the methods. We focus on precipitation (over continental Spain) and consider the output of eight Regional Climate Models (RCMs) from the EURO-CORDEX initiative at 0.44? resolution and five Statistical Downscaling Methods (SDMs) ?analog resampling, weather typing and generalized linear models? trained using the Spain044 observational gridded dataset on exactly the same RCM grid. The performance of these models is inter-compared in terms of several standard indices ?mean precipitation, 90th percentile on wet days, maximum precipitation amount and maximum number of consecutive dry days? taking into account the parameters involved in the SDM training phase. It is shown, that not only the directly affected indices should be carefully analyzed, but also those indirectly influenced (e.g. percentile-based indices for precipitation) which are more difficult to identify. We also analyze how simple transformations (e.g. linear scaling) could be applied to the outputs of the uncalibrated methods in order to put SDMs and RCMs on equal footing, and thus perform a fairer comparison.We acknowledge the World Climate Research Programme’s Working Group on Regional Climate, and theWorking Group on CoupledModelling, former coordinating body of CORDEX and responsible panel for CMIP5. We also thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. We also acknowledge the Earth System Grid Federation infrastructure and AEMET and University of Cantabria for the Spain02 dataset (available at http: //www.meteo.unican.es/en/datasets/spain02). All the statistical downscaling experiments have been computed using theMeteoLab software (http://www.meteo.unican.es/software/meteolab), which is an open-source Matlab toolbox for statistical downscaling. This work has been partially supported by CORWES (CGL2010-22158-C02) and EXTREMBLES (CGL2010-21869) projects funded by the Spanish R&D programme. AC thanks the Spanish Ministry of Economy and Competitiveness for the funding provided within the FPI programme (BES-2011-047612 and EEBB-I-13-06354), JMG acknowledges the support from the SPECS project (FP7-ENV-2012-308378) and JF is grateful to the EUPORIAS project (FP7-ENV-2012-308291). We also thank three anonymous referees for their useful comments that helped to improve the original manuscript

Investigating the role of prior and observation error correlations in improving a model forecast of forest carbon balance using Four Dimensional Variational data assimilation

Efforts to implement variational data assimilation routines with functional ecology models and land surface models have been limited, with sequential and Markov chain Monte Carlo data assimilation methods being prevalent. When data assimilation has been used with models of carbon balance, prior or “background” errors (in the initial state and parameter values) and observation errors have largely been treated as independent and uncorrelated. Correlations between background errors have long been known to be a key aspect of data assimilation in numerical weather prediction. More recently, it has been shown that accounting for correlated observation errors in the assimilation algorithm can considerably improve data assimilation results and forecasts. In this paper we implement a Four-Dimensional Variational data assimilation (4D-Var) scheme with a simple model of forest carbon balance, for joint parameter and state estimation and assimilate daily observations of Net Ecosystem CO2 Exchange (NEE) taken at the Alice Holt forest CO2 flux site in Hampshire, UK. We then investigate the effect of specifying correlations between parameter and state variables in background error statistics and the effect of specifying correlations in time between observation errors. The idea of including these correlations in time is new and has not been previously explored in carbon balance model data assimilation. In data assimilation, background and observation error statistics are often described by the background error covariance matrix and the observation error covariance matrix. We outline novel methods for creating correlated versions of these matrices, using a set of previously postulated dynamical constraints to include correlations in the background error statistics and a Gaussian correlation function to include time correlations in the observation error statistics. The methods used in this paper will allow the inclusion of time correlations between many different observation types in the assimilation algorithm, meaning that previously neglected information can be accounted for. In our experiments we assimilate a single year of NEE observations and then run a forecast for the next 14 years. We compare the results using our new correlated background and observation error covariance matrices and those using diagonal covariance matrices. We find that using the new correlated matrices reduces the root mean square error in the 14 year forecast of daily NEE by 44% decreasing from 4.22 gCm−2 day−1 to 2.38 gCm−2 day−