5% during the reopening period. In contrast, globally, non-residential customers have decreased their consumption 38% during full lockdown and 14.5% during the reopening period. However, referring to non-residential customers, five different consumption profiles were found with different short-term and mid-term behaviors during the COVID crisis. The different behavior found shows customers who have maintained their normal consumption during the lockdown, others who have reduced it (to a greater or lesser extent) and have not recovered it after the removal of the restrictions, and others who have reduced the consumption but then they recovered it when the restrictions were removed. The metadata of the customers in each behavior cluster found are highly correlated to the restrictions imposed to control the spread of the virus. This study shows evidence about the proposed approach usefulness to analyze the behavior and the impact at customer level during the COVID-19 crisis.The aim of this work was to compare the surface adsorption and lubrication properties of plant and dairy proteins. Whey protein isolate (WPI) and pea protein isolate (PPI) were chosen as model animal and plant proteins, respectively, and various protein concentrations (0.1-100 mg/mL) were studied with/without heat treatment (90 °C/60 min). Quartz crystal microbalance with dissipation monitoring (QCM-D) experiments were performed on hydrophilic (gold) and hydrophobic polydimethylsiloxane (PDMS) sensors, with or without a mucin coating, latter was used to mimic the oral surface. Soft tribology using PDMS tribopairs in addition to wettability measurements, physicochemical characterization (size, charge, solubility) and gel electrophoresis were performed. Soluble fractions of PPI adsorbed to significantly larger extent on PDMS surfaces, forming more viscous films as compared to WPI regardless of heat treatment. Introducing a mucin coating on a PDMS surface led to a decrease in binding of the subsequent dietary protein layers, with PPI still adsorbing to a larger extent than WPI. Such large hydrated mass of PPI resulted in superior lubrication performance at lower protein concentration (≤10 mg/mL) as compared to WPI. However, at 100 mg/mL, WPI was a better lubricant than PPI, with the former showing the onset of elastohydrodynamic lubrication. Enhanced lubricity upon heat treatment was attributed to the increase in apparent viscosity. Fundamental insights from this study reveal that pea protein at higher concentrations demonstrates inferior lubricity than whey protein and could result in unpleasant mouthfeel, and thus may inform future replacement strategies when designing sustainable food products.Understanding residents’ daily activity chains provides critical support for various applications in transportation, public health and many other related fields. Recently, mobile phone location datasets have been suggested for mining activity patterns because of their utility and large sample sizes. Although recently machine learning-based models seem to perform well in activity purpose inference using mobile phone location data, most of these models work as black boxes. To address these challenges, this study proposes a flexible white box method to mine human activity chains from large-scale mobile phone location data by integrating both the spatial and temporal features of daily activities with varying weights. We find that the frequency distribution of major activity chain patterns agrees well with the patterns derived based on a travel survey of Shenzhen and a state-of-the-art method. Moreover, a dataset covering over 16.5% of the city population can yield a reasonable outcome of the major activity patterns. The contributions of this study not only lie in offering an effective approach to mining daily activity chains from mobile phone location data but also involve investigating the impact of different data conditions on the model performance, which make using big trajectory data more practical for domain experts.The COVID-19 epidemic is influencing global population. Social media has become important platforms to acquire and exchange information during the outbreak of COVID-19. This study explores public attention on social media. Popular Weibo texts related to COVID-19 with “coronavirus” and “pneumonia” as the keywords during December 27, 2019 and May 31, 2020 were collected in our study for public attention analysis. By combining data mining and text analysis, the public attention level trend in different stages were presented. AZ20 research buy Then a correlation analysis between public attention level and COVID-19 related cases number, topic analysis, and sentiment analysis were conducted. Significant positive correlation between public attention level and COVID-19 related cases number was identified. Based on Latent Dirichlet Allocation model, topic extraction was implemented in different stages and 41 topics were identified totally. For a comprehensive understanding of public emotions, sentiment analysis was performed. This study provides valuable lessons for public response to COVID-19.The BBC Loneliness Experiment provided a unique opportunity to examine differences in the experience of lonelines across cultures, age, and gender, and the interaction between these factors. Using those data, we analysed the frequency of loneliness reported by 46,054 participants aged 16-99 years, living across 237 countries, islands, and territories, representing the full range of individualism-collectivism cultures, as defined by Hofstede (1997). Findings showed that loneliness increased with individualism, decreased with age, and was greater in men than in women. We also found that age, gender, and culture interacted to predict loneliness, although those interactions did not qualify the main effects, and simply accentuated them. We found the most vulnerable to loneliness were younger men living in individualistic cultures.Rice-wheat cropping system (RWCS) is the most important system occupying around 26 M ha spread over the Indo Gangetic Plains in South Asia and China. Many long-term trials were led to assess the agronomic productivity and economic profitability of various combinations of conservation agricultural (CA) practices (zero tillage, residue management and crop establishment) in RWCS of Eastern Indo-Gangetic Plains (EIGP) of India. The purpose of this study was to investigate the best management practices involving different tillage-based crop establishment and residue retention techniques and their contribution to agricultural system sustainability through improvement in soil health by developing soil quality index (SQI). We have used SQI as an instrument based on physical [macro aggregate stability (MAS), available water capacity (AWC) and soil penetration resistance (SPR)], chemical [soil organic carbon (OC), available N, available P and available K] and biological [microbial biomass carbon (MBC), fluorescein diacetate (FDA) and dehydrogenase activity (DHA)] properties of soil, because these are very useful indicators of soil’s functions for agronomic productivity and soil fertility.