Meaning in life mediates the effects of sense of self and prosocial behaviours on anhedonia: a path analysis

Background Anhedonia, the loss of interest and pleasure, is a core symptom of depression that is resistant to treatment. Anhedonic young people describe a weakened sense of self and reduced meaning in life. Knowing if these experiences predict anhedonia could reveal novel targets for intervention development. Methods We recruited young people (N = 429, mean age: 20 years) with a range of depression scores. Using path analysis, we examined anhedonia, sense of self, meaning in life, and prosocial behaviours cross-sectionally and longitudinally at ~5-month follow-up (N = 160). Results Cross-sectionally, sense of self (β =. 81, p < .001) and prosocial behaviours (β = 0.37, p < .001) had direct effects on meaning in life, and meaning in life had a direct effect on anhedonia (β = −0.11, p < .001). Sense of self (β = −0.09, p < .001) and prosocial behaviours (β = −0.04, p < .001) had indirect effects on anhedonia, mediated by meaning in life. In the longitudinal analysis, sense of self at T1 had a direct effect on meaning in life at T2 (β = 0.36, p < .01) and an indirect effect on anhedonia at T2 (β = −0.05, p < .01), mediated by meaning in life. Limitations Approximately 70 % of the participants were female. Future studies should include equal numbers of males and females. Conclusion We provide novel evidence that targeting meaning in life, sense of self, or prosocial behaviours in psychotherapeutic interventions could be effective in alleviating anhedonia

How does small-scale mining stabilize rural livelihoods in Sub-Saharan Africa? the case of Mozambique

This paper examines the linkages between subsistence farming and artisanal and small-scale mining (ASM) – lowtech, labor-intensive mineral extraction and processing – in sub-Saharan Africa, focusing on the case of Mozambique. While the body of literature on this subject is burgeoning, it is comprised mostly of conceptual pieces and country case studies that rely heavily on qualitative data. Focusing on Manica Province, long an epicentre of small-scale gold mining activity in Mozambique, the paper showcases the value of including complementary quantitative data in analyses of ASM-farming linkages in rural sub-Saharan Africa. In particular, quantitative data that provide detail on the demographical composition of communities engaged in both ASM and agriculture, and which shed light on the spending patterns of households involved, could go a long way toward enriching dialogues on this subject, and, in the process, yield more effective (and, indeed, representative) rural development and poverty alleviation strategies in the region. The data gathered in Manica Province provide a more nuanced picture of how the ages and educational levels of household heads, and the sizes of their families, shape views on ASM and agriculture in gold-rich sections of Mozambique. Studies exploring the linkages between ASM and agriculture in sub-Saharan Africa that feature both qualitative and quantitative data provide greater clarity on the role each activity could play in tackling some of the region’s broader development challenges, including food insecurity and (building) community resilience

Personality matters in consumer preferences for cultured meat in China

This study extends our current knowledge of consumer preferences for cultured meat. We explored if personality traits have a role in affecting Chinese urban consumer choice behavior for cultured meat. We performed a choice experiment (CE) and used cultured chicken breast as a case study. The results indicate that personality traits (i.e., agreeableness, neuroticism, and conscientiousness) influence consumer preference for cultured meat. Our findings provide valuable insights into the psychology of consumer preferences and attitudes that can help to effectively communicate the nature of cultured meat to the public. They also have relevant implications for cultured-meat producers, and policy makers

Multi-response kinetic modelling of the formation of five Strecker aldehydes during kilning of barley malt

Control of aroma formation during production of barley malt is critical to provide consistent and high-quality products for the brewing industry. Malt quality can be affected by the inherent variability of raw material and processing conditions, leading to inconsistent and/or undesirable profiles. Dried green malts were cured isothermally at 65, 78 and 90 °C for 8.4 h, and characteristic aroma compounds (Strecker aldehydes), precursors and intermediate compounds were analysed over time. By kinetic modelling of Strecker aldehydes, based on fundamental chemical pathways, we showed that degradation of Amadori rearrangement products and short-chain dicarbonyls was more sensitive to temperature change due to their higher activation energies compared to other kinetic steps. This study can help maltsters to manipulate formation of Strecker aldehydes, via raw material screening and process control, and hence optimise the organoleptic quality of malts and their products, such as non-alcoholic beers, where these aldehydes play a key role

Improved urban flood detection in deeper floods using synthetic aperture radar double-scattering intensity and interferometric coherence

Detection of flooding in rural areas is now commonly performed by high resolution Synthetic Aperture Radar (SAR) sensors. However, flooding in urban areas causes the greater risk to lives and property. Urban flood detection is more challenging due to SAR shadow, layover and double scattering effects. Nevertheless, it may often be identified using the fact that, in a post-flood image, double scattering between building walls and adjacent floodwater generally exceeds that in a pre-flood image, where double scattering occurs between buildings and adjacent ground. However, in the event of the urban region being deeply flooded, only a part of the building walls may remain above the flood level, so that the post-flood double scattering may reduce and a flooded region may be misclassified as non-flooded. We investigate whether flood detection can be improved for deeper urban floods by using interferometric coherence as an adjunct to double scattering. An urban area that is not flooded should often exhibit high coherence between image pairs, whereas if there is flooding in one of the images the coherence should be low. An urban flood in Japan that contained deep-flooded, shallow-flooded and non-flooded areas was used as a test example. It was imaged by Sentinel-1, and WorldDEM Digital Surface Model data was used to estimate flood depth and building orientation. An analysis of double scatterers of low post-/pre-flood brightness ratio was carried out for deeply flooded and non-flooded urban double scatterers. It was shown that, using coherence, 58% of the deeply flooded buildings could be detected at the cost of a 16% false positive rate. Without the use of coherence to supplement brightness ratio, all these deeply flooded buildings would be misclassified as non-flooded. This finding could be of use in automating the detection of urban flooding as an aid to flood risk management

An efficient multimodal attentional principal component analysis for continual learning-based dynamic process monitoring

Traditional multimode process monitoring methods extract features from time series data. Due to the catastrophic forgetting effect, data-driven multimode dynamic process monitoring is challengingbased on a single monitoring model paradigm, i.e. the learned knowledge from previous modes may diminish as operating conditions undergo changes between modes, yet it is impractical to access all past data to retrain the model. In this work, a novel efficient method of multimodal attentionalprincipal component analysis (M-APCA) with continual learning ability is introduced. Under the assumption that data from successive modes are received sequentially, dynamic process data are modeled using an attention mechanism to capture the relationship between data and the latent space, whereby meaningful information is concentrated as dynamic features which are extracted via a vector autoregressive model. In order to overcome the catastrophic forgetting problem, the idea of replay continual learning is employed. Specifically, past modes’ data which are significant to reflect the operating conditions, are selected and stored. These are repeatedly used in tandem with sequential data as replay data. Two types of attention mechanisms are considered and analyzed, each of which is specifically designed to learn from data in an unsupervised manner, so the overall algorithm is efficient both in time and storage costs. The proposed attentional principal component analysis and M-APCA are analyzed against several state-of-the-art methods to highlight the virtues of the proposed method. Compared with multimode monitoring methods, the effectiveness is demonstrated through case studies of: a continuous stirred tank heater, the Tennessee Eastman process and a practical coal pulverizing system

A benchmark of industrial polymerization process for thermal runaway process monitoring

Polymer production is of paramount importance in the chemical manufacturing industry. However, safety concerns are prevalent due to the exothermic nature of polymerization reactions, which can cause thermal runaway. The limitations of the current industry-standard monitoring methods underscore the need for novel techniques to detect faults early. To facilitate the development and evaluation of such algorithms, benchmarks that enable direct comparisons of performance are required. Addressing this gap, the present work first introduces an open-source polymerization benchmark model and associated datasets. Derived from reaction kinetics, mass balance, and energy balance analysis, the differential equation forms the basis of our model. By manipulating relative parameters, we intentionally induce five typical faults that can lead to thermal runaway. As a result, our benchmark model serves as an invaluable tool for advancing and validating algorithms for thermal runaway process monitoring, significantly enhancing the safety of the polymerization process. The effectiveness of the model and dataset is demonstrated by testing multivariate statistical process monitoring algorithms and deep learning algorithms

The hidden costs of imposing minimum contributions to a global public good

We study how different types of individuals respond to being forced to make a minimum contribution to a global public good. Participants in our experiment decide how much of their endowment to contribute towards offsetting CO2 emissions. We elicit their contributions when they are free to spend any amount of their endowment on carbon offsets and when they are forced to spend a certain minimum amount on it. We find that those who contribute more than the minimum before it is imposed contribute less overall once the minimum comes into effect. This is true for both a low and a high level of the minimum and appears to be driven in part by pessimistic beliefs about the contributions of others. We show that the lower minimum also reduces overall contributions relative to a situation with no minimum. We do not find evidence that having the level of the minimum determined through a majority vote rather than an exogenous procedure has any material impact on these results