Contribuições de hedonomia e design na produção e comercialização de frutas, legumes e verduras (FLVs) / Hedonomics and design contributions in the production and commercialization of fruits and vegetables (FVs)

No Brasil, o consumo de Frutas, Legumes e Verduras (FLVs) tem sido abaixo do recomendado pela Organização das Nações Unidas (ONU); mais da metade da população apresenta o índice de massa corporal acima do recomendado pela Organização Mundial de Saúde (OMS) e 18,9% dos brasileiros estão obesos. Apenas 1/3 da população consome regularmente cinco ou mais porções de frutas e hortaliças em cinco dias ou mais da semana (BRASIL. MINISTÉRIO DA SAÚDE. SECRETARIA DE VIGILÂNCIA EM SAÚDE, 2017). Diante dos benefícios que a alimentação saudável pode proporcionar para o bem-estar da população brasileira, este trabalho tem como objetivo analisar os aspectos de produção e comercialização de FLVs relacionando-os às emoções sob o viés da Hedonomia a partir de cinco tendências de consumo de alimentos da pesquisa FIESP/IBOPE Food Trends para os anos de 2010 a 2020 no Brasil (Conveniência e Praticidade; Confiabilidade e Qualidade; Sensorialidade e Prazer; Saudabilidade e Bem-estar; e Sustentabilidade e Ética). Através de referencial bibliográfico, faz-se uma análise dos desafios do setor e verifica-se como o Design pode ser utilizado neste cenário para gerar resultados aos produtores rurais e aumentar o consumo destes alimentos saudáveis. Portanto, diante das dificuldades de produção e comercialização das FLVs, pretende-se que este trabalho contribua na tomada de decisão com relação ao desenvolvimento destes produtos

A Scenario-Based Model Comparison for Short-Term Day-Ahead Electricity Prices in Times of Economic and Political Tension

In recent years, energy prices have become increasingly volatile, making it more challenging to predict them accurately. This uncertain market trend behavior makes it harder for market participants, e.g., power plant dispatchers, to make reliable decisions. Machine learning (ML) has recently emerged as a powerful artificial intelligence (AI) technique to get reliable predictions in particularly volatile and unforeseeable situations. This development makes ML models an attractive complement to other approaches that require more extensive human modeling effort and assumptions about market mechanisms. This study investigates the application of machine and deep learning approaches to predict day-ahead electricity prices for a 7-day horizon on the German spot market to give power plants enough time to ramp up or down. A qualitative and quantitative analysis is conducted, assessing model performance concerning the forecast horizon and their robustness depending on the selected hyperparameters. For evaluation purposes, three test scenarios with different characteristics are manually chosen. Various models are trained, optimized, and compared with each other using common performance metrics. This study shows that deep learning models outperform tree-based and statistical models despite or because of the volatile energy prices

Антропогенное влияние на лавинную и селевую активность

The paper presents examples of the change in snow avalanches and debris flows activity due to the anthropogenic pressure on vegetation and relief. The changes in dynamical characteristics of selected snow avalanches and debris flows due to the anthropogenic activity are quantified. The conclusion is made that the anthropogenic effects on the snow avalanches and debris flows activity are more pronounced than the possible effects of the climate change. The necessity is expressed on the unavoidable changes of the natural environment as the result of a construction and of use of the constructed infrastructure to be account for in corresponding planning of the protection measures.Приведены примеры изменения лавинной и селевой активности при антропогенном воздействии на растительность и рельеф. Даётся количественная оценка изменения характеристик отдельных лавин и селей в результате деятельности человека. Делается вывод о преобладающем влиянии человеческого воздействия на лавинную и селевую активность по сравнению с возможными последствиями изменения климата. Высказана необходимость учёта неизбежных изменений природной среды, происходящих в процессе строительства и использования объектов инфраструктуры, при соответствующем планировании противолавинных и противоселевых мероприятий

Changes in the CFC inventories and formation rates of upper Labrador Sea Water, 1997-2001

Chlorofluorocarbon (component CFC-11) and hydrographic data from 1997, 1999, and 2001 are presented to track the large-scale spreading of the Upper Labrador Sea Water (ULSW) in the subpolar gyre of the North Atlantic Ocean. ULSW is CFC rich and comparatively low in salinity. It is located on top of the denser “classical” Labrador Sea Water (LSW), defined in the density range σΘ = 27.68–27.74 kg m−3. It follows spreading pathways similar to LSW and has entered the eastern North Atlantic. Despite data gaps, the CFC-11 inventories of ULSW in the subpolar North Atlantic (40°–65°N) could be estimated within 11%. The inventory increased from 6.0 ± 0.6 million moles in 1997 to 8.1 ± 0.6 million moles in 1999 and to 9.5 ± 0.6 million moles in 2001. CFC-11 inventory estimates were used to determine ULSW formation rates for different periods. For 1970–97, the mean formation rate resulted in 3.2–3.3 Sv (Sv ≡ 106 m3 s−1). To obtain this estimate, 5.0 million moles of CFC-11 located in 1997 in the ULSW in the subtropical/tropical Atlantic were added to the inventory of the subpolar North Atlantic. An estimate of the mean combined ULSW/LSW formation rate for the same period gave 7.6–8.9 Sv. For the years 1998–99, the ULSW formation rate solely based on the subpolar North Atlantic CFC-11 inventories yielded 6.9–9.2 Sv. At this time, the lack of classical LSW formation was almost compensated for by the strongly pronounced ULSW formation. Indications are presented that the convection area needed in 1998–99 to form this amount of ULSW exceeded the available area in the Labrador Sea. The Irminger Sea might be considered as an additional region favoring ULSW formation. In 2000–01, ULSW formation weakened to 3.3–4.7 Sv. Time series of layer thickness based on historical data indicate that there exists considerable variability of ULSW and classical LSW formation on decadal scales

The early bee catches the flower - circadian rhythmicity influences learning performance in honey bees, Apis mellifera

Circadian rhythmicity plays an important role for many aspects of honey bees’ lives. However, the question whether it also affects learning and memory remained unanswered. To address this question, we studied the effect of circadian timing on olfactory learning and memory in honey bees Apis mellifera using the olfactory conditioning of the proboscis extension reflex paradigm. Bees were differentially conditioned to odours and tested for their odour learning at four different “Zeitgeber” time points. We show that learning behaviour is influenced by circadian timing. Honey bees perform best in the morning compared to the other times of day. Additionally, we found influences of the light condition bees were trained at on the olfactory learning. This circadian-mediated learning is independent from feeding times bees were entrained to, indicating an inherited and not acquired mechanism. We hypothesise that a co-evolutionary mechanism between the honey bee as a pollinator and plants might be the driving force for the evolution of the time-dependent learning abilities of bees