Climate Change-Induced Flood Disaster Policy Communication Issues for Local Community Adaptation Resilience Management in Uganda

Effective climate change and disaster policy communication services are vital for enhancing the adaptive resilience capacity of the vulnerable local communities in poor countries like Uganda. This chapter focuses on the effectiveness of the Ugandan national climate change and disaster policy information communication strategies in addressing national flooding disaster risks, highlights the recent trends of knowledge based responses to climate change induced floods, assesses the impact of the flood on the socio-economic well-being of local households and communities, and determines the vulnerability issues with corresponding adaptation strategies to floods in the flood prone country. Climate change flood risks have continued to exact huge socio-economic loss and damage effects due to the vulnerability and weak adaptation strategies to floods. The national meteorological services tend to forecast seasonal flood events; some flood forcing factors; and the impact of floods on social, economic, ecological, and physical infrastructure are on the rise in some parts of the country.

Parental leave policy information during residency interviews

Background During interviews, medical students may feel uncomfortable asking questions that might be important to them, such as parental leave. Parental leave policies may be difficult for applicants to access without asking the program director or other interviewers. The goal of this study is to evaluate whether parental leave information is presented to prospective residents and whether medical students want this information. Methods Fifty-two program directors (PD’s) at 3 sites of a single institution received a survey in 2019 to identify whether parental leave information is presented at residency interviews. Medical students received a separate survey in 2020 to identify their preferences. Fisher exact tests, Pearson χ2 tests and Cochran-Armitage tests were used where appropriate to assess for differences in responses. Results Of the 52 PD’s, 27 responded (52%) and 19 (70%) indicated that information on parental leave was not provided to candidates. The most common reason cited was the belief that the information was not relevant (n = 7; 37%). Of the 373 medical students, 179 responded (48%). Most respondents (92%) wanted parental leave information formally presented, and many anticipated they would feel extremely or somewhat uncomfortable (68%) asking about parental leave. The majority (61%) felt that these policies would impact ranking of programs “somewhat” or “very much.” Conclusions Parental leave policies may not be readily available to interviewees despite strong interest and their impact on ranking of programs by prospective residents.

Artificial intelligence-controlled pole balancing using an Arduino board

Automation Process (AP) is an important issue in the current digitized world and, in general, represents an increase in the quality of productivity when compared with manual control. Balance is a natural human capacity as it relates to complex operations and intelligence. Balance Control presents an extra challenge in automation processes, due to the many variables that may be involved. This work presents a physical balancing pole where a Reinforcement Learning (RL) agent can explore the environment, sense its position through accelerometers, and wirelessly communicate and eventually learns by itself how to keep the pole balanced under noise disturbance. The agent uses RL principles to explore and learn new positions and corrections that lead toward more significant rewards in terms of pole equilibrium. By using a Q-matrix, the agent explores future conditions and acquires policy information that makes it possible to maintain stability. An Arduino microcontroller processes all training and testing. With the help of sensors, servo motors, wireless communications, and artificial intelligence, components merge into a system that consistently recovers equilibrium under random position changes. The obtained results prove that through RL, an agent can learn by itself to use generic sensors, actuators and solve balancing problems even under the limitations that a microcontroller presents.

Family Policy Awareness and Marital Intentions: A National Survey Experimental Study

Despite extensively examining the effects of family policies on marriage and fertility rates, previous research has paid little attention to the process of policy implementation and has implicitly assumed that individuals are fully aware of the policy information when making marital and fertility decisions. Challenging this assumption, we theorize policy awareness as an important mechanism for understanding the potential influence of family policies on individuals' marital intentions, an understudied yet crucial determinant of family formation behavior. In an experiment using a national survey of young unmarried individuals in Japan, respondents were randomly assigned to treatment and control groups. The treatment group was informed about 17 Japanese family policy benefits, but most of the respondents knew none or only a few of these benefits. After exposure to the policy information, the treatment group had significantly higher marital intentions than the control group, which had similar baseline characteristics but no information exposure. Crucially, such positive effects were particularly pronounced among high-educated women and high- and low-educated men, reflecting the differentiated effects of policy awareness under Japan's traditional gender role norms. Overall, these findings highlight the pivotal role of policy awareness during the family formation process and contribute to the debate over whether and how family policies may influence different subpopulations.

Unexplained mortality during the US COVID-19 pandemic: retrospective analysis of death certificate data and critical assessment of excess death calculations

ObjectivesCause-of-death discrepancies are common in respiratory illness-related mortality. A standard epidemiological metric, excess all-cause death, is unaffected by these discrepancies but provides no actionable policy information when increased all-cause mortality is unexplained by reported specific causes. To assess the contribution of unexplained mortality to the excess death metric, we parsed excess deaths in the COVID-19 pandemic into changes in explained versus unexplained (unreported or unspecified) causes.DesignRetrospective repeated cross-sectional analysis, US death certificate data for six influenza seasons beginning October 2014, comparing population-adjusted historical benchmarks from the previous two, three and five seasons with 2019–2020.Setting48 of 50 states with complete data.Participants16.3 million deaths in 312 weeks, reported in categories—all causes, top eight natural causes and respiratory causes including COVID-19.Outcome measuresChange in population-adjusted counts of deaths from seasonal benchmarks to 2019–2020, from all causes (ie, total excess deaths) and from explained versus unexplained causes, reported for the season overall and for time periods defined a priori: pandemic awareness (19 January through 28 March); initial pandemic peak (29 March through 30 May) and pandemic post-peak (31 May through 26 September).ResultsDepending on seasonal benchmark, 287 957–306 267 excess deaths occurred through September 2020: 179 903 (58.7%–62.5%) attributed to COVID-19; 44 022–49 311 (15.2%–16.1%) to other reported causes; 64 032–77 054 (22.2%–25.2%) unexplained (unspecified or unreported cause). Unexplained deaths constituted 65.2%–72.5% of excess deaths from 19 January to 28 March and 14.1%–16.1% from 29 March through 30 May.ConclusionsUnexplained mortality contributed substantially to US pandemic period excess deaths. Onset of unexplained mortality in February 2020 coincided with previously reported increases in psychotropic use, suggesting possible psychiatric or injurious causes. Because underlying causes of unexplained deaths may vary by group or region, results suggest excess death calculations provide limited actionable information, supporting previous calls for improved cause-of-death data to support evidence-based policy.

Coupling of Inference and Access Controls to Ensure Privacy Protection

This article is devoted to the topic of coupling access and inference controls into security policies. The coupling of these two mechanisms is necessary to strengthen the protection of the privacy of complex systems users. Although the PrivOrBAC access control model covers several privacy protection requirements, the risk of inferring sensitive data may exist. Indeed, the accumulation of several pieces of data to which access is authorized can create an inference. This work proposes an inference control mechanism implemented through multidimensional analysis. This analysis will take into account several elements such as the history of access to the data that may create an inference, as well as their influence on the inference. The idea is that this mechanism delivers metrics that reflect the level of risk. These measures will be considered in the access control rules and will participate in the refusal or authorization decision with or without obligation. This is how the coupling of access and inference controls will be applied. The implementation of this coupling will be done via the multidimensional OLAP databases which will be requested by the Policy Information Point, the gateway brick of XACML to the various external data sources, which will route the inference measurements to the decision-making point.

Analysis of the impact of central bank communications on money market indicators

Press releases on monetary policy play an important role in the communication policy of the central bank. These press releases explain key rate decisions and provide signals about the future direction of the central bank’s monetary policy. Information signals can influence the expectations of financial market participants and increase the predictability and effectiveness of monetary policy. There are not enough research papers dedicated to the text analysis of the Bank of Russia press releases and the assessment of information signals. Hence, this article examines the impact of information signals about monetary policy on the money market rate, term and credit spreads. First, we estimate latent Dirichlet allocation to determine the topics of information signals. Second, we use sentiment analysis to construct signals about easing or tightening of the monetary policy. Third, the impact of signals about the future monetary policy on the money market indicators is assessed using the exponential GARCH model. Empirical research has shown that signals of future monetary policy easing are associated with lower money market rates and term spreads, and an increase in the credit spread. The result proved to be resistant to various ways of vectorizing the text of press releases. The article was prepared as a part of the state assignment research of Russian Presidential Academy of National Economy and Public Administration.

News media coverage of COVID-19 public health and policy information

During a pandemic, news media play a crucial role in communicating public health and policy information. Traditional newspaper coverage is important amidst increasing disinformation, yet uncertainties make covering health risks and efforts to limit transmission difficult. This study assesses print and online newspaper coverage of the coronavirus disease COVID-19 for March 2020, when the global pandemic was declared, through August 2020 in three countries: Canada (with the lowest per-capita case and death rates during the study timeframe), the United Kingdom (with a pronounced early spike), and the United States (with persistently high rates). Tools previously validated for pandemic-related news records allow measurement of multiple indicators of scientific quality (i.e., reporting that reflects the state of scientific knowledge) and of sensationalism (i.e., strategies rendering news as more extraordinary than it really is). COVID-19 reporting had moderate scientific quality and low sensationalism across 1331 sampled articles in twelve newspapers spanning the political spectrums of the three countries. Newspapers oriented towards the populist-right had the lowest scientific quality in reporting, combined with very low sensationalism in some cases. Against a backdrop of world-leading disease rates, U.S. newspapers on the political left had more exposing coverage, e.g., focused on policy failures or misinformation, and more warning coverage, e.g., focused on the risks of the disease, compared to U.S. newspapers on the political right. Despite the generally assumed benefits of low sensationalism, pandemic-related coverage with low scientific quality that also failed to alert readers to public-health risks, misinformation, or policy failures may have exacerbated the public-health effects of the disease. Such complexities will likely remain central for both pandemic news media reporting and public-health strategies reliant upon it.

Putting Policy First

The vast potential of Twitter means that it can be used as a tool for policy information. This chapters shows that a majority of senators adopt a policy-wonk style for their rhetorical agenda. While institutions and constituent expectations constrain the issues senators may act on, what they communicate in their rhetorical agenda is an opportunity to gain agency over their policy messaging. Senators with policy-focused constituencies, particularly committee leaders and Democrats with policy coalitions, act as legislative entrepreneurs by taking advantage of Twitter’s expansive network to link their legislative activity and policy-focused rhetorical agenda with a diverse constituency of followers networked by shared attention to issue priorities. Additionally, this chapter introduces the concept of a dual process of policy prioritization where lawmakers not only have to decide to become a policy wonk but also make a second decision about which policies define their time in office.

Party or Policy? Testing the Elaboration Likelihood Model in the context of voting

<p>In this study, I experimentally tested if the Elaboration Likelihood Model applies to a voting context. Participants rated their likelihood to vote for hypothetical candidates where the candidates’ associated policy and party affiliation were both manipulated. Participants also completed a quiz as a measure of their political sophistication. As expected, those who demonstrated high political sophistication used policy information more often when rating candidates. Contrary to expectations, there was no evidence that low politically sophisticated individuals used party cues more often to guide their ratings of candidates. The findings provide partial support for the Elaboration Likelihood Model, and future adaptations to the experimental design are discussed.</p>