The COVID‐19 Pandemic and Families in Japan

This paper, which is authored by members of the Japanese Association of Family Therapy (JAFT), describes the COVID‐19 pandemic in Japan from a family systems perspective. The authors are active members of JAFT and include current and past presidents and officers. We describe the course of the pandemic and the ways in which government policies to mitigate the pandemic have affected Japanese families. Challenges that affect Japanese families include the inability to participate in family and social rituals, prescribed gender roles that specifically affect women, high suicide rates, and prejudice against those who are at risk of spreading the infection. The need to shelter in place has also forced family homes to function as a workplace for parents, classrooms for children, and day care services for frail elders, which has resulted in psychological distress among individuals and conflicts among families. We discuss ways that therapists have worked with Japanese families using online therapy.     

Children's experiences with an intervention aimed to prevent further physical abuse

Although many children across cultures are victims of physical abuse, few treatment models target these children and their parents. In Sweden, Combined Parent–Child Cognitive Behavioural Therapy for families at risk for child physical abuse has been successfully used according to pretreatment and posttreatment studies. However, few studies have explored how physically abused children experience treatment. This study includes 20 physically abused children aged 9–17 who completed Combined Parent–Child Cognitive Behavioural Therapy. Children had a positive overall impression of the treatment and highlighted addressing the abuse, as well as processing their experiences as particularly essential. Children described a positive transformation in their family life as a result of treatment, including violence cessation and bonding among family members. Children experienced the intervention as inclusive and child‐friendly. The implications of the promising findings are discussed.     

Conditional Akaike Information Criteria for a Class of Poisson Mixture Models with Random Effects

Focusing on the model selection problems in the family of Poisson mixture models (including the Poisson mixture regression model with random effects and zero‐inflated Poisson regression model with random effects), the current paper derives two conditional Akaike information criteria. The criteria are the unbiased estimators of the conditional Akaike information based on the conditional log‐likelihood and the conditional Akaike information based on the joint log‐likelihood, respectively. The derivation is free from the specific parametric assumptions about the conditional mean of the true data‐generating model and applies to different types of estimation methods. Additionally, the derivation is not based on the asymptotic argument. Simulations show that the proposed criteria have promising estimation accuracy. In addition, it is found that the criterion based on the conditional log‐likelihood demonstrates good model selection performance under different scenarios. Two sets of real data are used to illustrate the proposed method.     

Can Carbon Nanomaterials Improve CZTS Photovoltaic Devices? Evaluation of Performance and Impacts Using Integrated Life‐Cycle Assessment and Decision Analysis

In emergent photovoltaics, nanoscale materials hold promise for optimizing device characteristics; however, the related impacts remain uncertain, resulting in challenges to decisions on strategic investment in technology innovation. We integrate multi‐criteria decision analysis (MCDA) and life‐cycle assessment (LCA) results (LCA‐MCDA) as a method of incorporating values of a hypothetical federal acquisition manager into the assessment of risks and benefits of emerging photovoltaic materials. Specifically, we compare adoption of copper zinc tin sulfide (CZTS) devices with molybdenum back contacts to alternative devices employing graphite or graphene instead of molybdenum. LCA impact results are interpreted alongside benefits of substitution including cost reductions and performance improvements through application of multi‐attribute utility theory. To assess the role of uncertainty we apply Monte Carlo simulation and sensitivity analysis. We find that graphene or graphite back contacts outperform molybdenum under most scenarios and assumptions. The use of decision analysis clarifies potential advantages of adopting graphite as a back contact while emphasizing the importance of mitigating conventional impacts of graphene production processes if graphene is used in emerging CZTS devices. Our research further demonstrates that a combination of LCA and MCDA increases the usability of LCA in assessing product sustainability. In particular, this approach identifies the most influential assumptions and data gaps in the analysis and the areas in which either engineering controls or further data collection may be necessary.     

Completing the Circuit: Routine,Reflection, and Ethical Consumption

Efforts to explain why some people incorporate ethical concerns into everyday shopping for food and household goods, while many do not, have so far left significant variation in “ethical consumption” unexplained. Seeking to move beyond explanations that rely mainly on differences in consumers' social class, gender, and political engagement, I draw on concepts associated with “practice theory” to argue that ethical consumption is closely tied to people's willingness and ability to spend time, while shopping, on distinct activities associated with breaking old routines and establishing new ones. The central insight of practice theory is that most consumption is the product of unconscious routine. And it is precisely because consciously departing from routine is, according to my study, a fundamentally time‐consuming process, that lack of time emerges as a crucial obstacle to translating abstract ethical concerns into concrete action as a consumer.     

Open Data on a Closed Shop? Assessing the potential of transparency initiatives with a focus on efforts to strengthen capacity development support

Despite strong political commitment, actual gains in improving accountability in development co‐operation have been limited. Main reasons for this include the low level of transparency and high number of actors, as well as the limitations of the self‐reported and input‐oriented OECD reporting system. Efforts through the International Aid Transparency Initiative (IATI) and related transparency initiatives are ill‐equipped to change this given their decision to adhere to the same system. This article uses the specific area of support to capacity development to illustrate this. While further disaggregation of reporting on technical co‐operation could respond to a transparency gap, overcoming the accountability gap requires moving beyond promoting ‘supply‐side’ reform and re‐engaging in efforts to promote collective action.     

Campylobacter QMRA: A Bayesian Estimation of Prevalence and Concentration in Retail Foods Under Clustering and Heavy Censoring

A Bayesian statistical temporal‐prevalence‐concentration model (TPCM) was built to assess the prevalence and concentration of pathogenic campylobacter species in batches of fresh chicken and turkey meat at retail. The data set was collected from Finnish grocery stores in all the seasons of the year. Observations at low concentration levels are often censored due to the limit of determination of the microbiological methods. This model utilized the potential of Bayesian methods to borrow strength from related samples in order to perform under heavy censoring. In this extreme case the majority of the observed batch‐specific concentrations was below the limit of determination. The hierarchical structure was included in the model in order to take into account the within‐batch and between‐batch variability, which may have a significant impact on the sample outcome depending on the sampling plan. Temporal changes in the prevalence of campylobacter were modeled using a Markovian time series. The proposed model is adaptable for other pathogens if the same type of data set is available. The computation of the model was performed using OpenBUGS software.     

Multiplicative consistency analysis for interval fuzzy preference relations: A comparative study

To examine the multiplicative consistency of interval fuzzy preference relations (IFPRs), this paper first analyzes the limitations associated with the previous consistency concepts. Accordingly, a new consistency concept is defined that is an extension of the crisp case and overcomes limitations in the previous concepts. Next, a linear programming model to judge the consistency of IFPRs is constructed, and an approach to derive multiplicative consistent IFPRs is introduced. Furthermore, goal-programming models to determine missing values in an incomplete IFPR are constructed that have the highest consistent level with respect to known values. Moreover, a multiplicative consistency and consensus based method for group decision making with IFPRs is developed that can address incomplete and inconsistent cases. Finally, two practical decision-making problems are offered to demonstrate the feasibility and efficiency of the new method, and an analysis of a numerical and theoretical comparison with several related methods is performed.