An improved flow-based procedure for microdetermination of total tannins in beverages with minimized reagent consumption

A flow system designed with solenoid micro-pumps is introduced for spectrophotometric determination of total tannins based on the Folin- Denis reaction. The procedure minimizes the main drawbacks related to the AOAC batch procedure, i.e. interferences from reducing species in the samples, high reagent consumption and waste generation, and low sampling rate. Linear response was observed for tannic acid concentrations in the range 2-100 mg L-1, with a detection limit (99.7% confidence level) of 0.3 mg L-1. The sampling rate and coefficient of variation (n = 10) were estimated as 75 measurements per hour and 1.1%, respectively. Results of determination of total tannin in tea, beer and wine samples were in agreement with those achieved by the batch reference procedure at the 95% confidence level. In comparison to the batch procedure, the reagent consumption and effluent generation were 83 and 60-fold lower, respectively.

Combined heat and power and consumption optimization in a SCADA-based system

The operation of power systems in a Smart Grid (SG) context brings new opportunities to consumers as active players, in order to fully reach the SG advantages. In this context, concepts as smart homes or smart buildings are promising approaches to perform the optimization of the consumption, while reducing the electricity costs. This paper proposes an intelligent methodology to support the consumption optimization of an industrial consumer, which has a Combined Heat and Power (CHP) facility. A SCADA (Supervisory Control and Data Acquisition) system developed by the authors is used to support the implementation of the proposed methodology. An optimization algorithm implemented in the system in order to perform the determination of the optimal consumption and CHP levels in each instant, according to the Demand Response (DR) opportunities. The paper includes a case study with several scenarios of consumption and heat demand in the context of a DR event which specifies a maximum demand level for the consumer.

The impact of driving styles on fuel consumption: a data-warehouse-and-data-mining-based discovery process

This paper discusses the results of applied research on the eco-driving domain based on a huge data set produced from a fleet of Lisbon's public transportation buses for a three-year period. This data set is based on events automatically extracted from the control area network bus and enriched with GPS coordinates, weather conditions, and road information. We apply online analytical processing (OLAP) and knowledge discovery (KD) techniques to deal with the high volume of this data set and to determine the major factors that influence the average fuel consumption, and then classify the drivers involved according to their driving efficiency. Consequently, we identify the most appropriate driving practices and styles. Our findings show that introducing simple practices, such as optimal clutch, engine rotation, and engine running in idle, can reduce fuel consumption on average from 3 to 5l/100 km, meaning a saving of 30 l per bus on one day. These findings have been strongly considered in the drivers' training sessions.

Quantum-based Particle Swarm Optimization Application to Studies of Aggregated Consumption Shifting and Generation Scheduling in Smart Grids

Demand response programs and models have been developed and implemented for an improved performance of electricity markets, taking full advantage of smart grids. Studying and addressing the consumers’ flexibility and network operation scenarios makes possible to design improved demand response models and programs. The methodology proposed in the present paper aims to address the definition of demand response programs that consider the demand shifting between periods, regarding the occurrence of multi-period demand response events. The optimization model focuses on minimizing the network and resources operation costs for a Virtual Power Player. Quantum Particle Swarm Optimization has been used in order to obtain the solutions for the optimization model that is applied to a large set of operation scenarios. The implemented case study illustrates the use of the proposed methodology to support the decisions of the Virtual Power Player in what concerns the duration of each demand response event.

Context-based identification of energy consumption in industrial plants

Nowadays, reducing energy consumption is one of the highest priorities and biggest challenges faced worldwide and in particular in the industrial sector. Given the increasing trend of consumption and the current economical crisis, identifying cost reductions on the most energy-intensive sectors has become one of the main concerns among companies and researchers. Particularly in industrial environments, energy consumption is affected by several factors, namely production factors(e.g. equipments), human (e.g. operators experience), environmental (e.g. temperature), among others, which influence the way of how energy is used across the plant. Therefore, several approaches for identifying consumption causes have been suggested and discussed. However, the existing methods only provide guidelines for energy consumption and have shown difficulties in explaining certain energy consumption patterns due to the lack of structure to incorporate context influence, hence are not able to track down the causes of consumption to a process level, where optimization measures can actually take place. This dissertation proposes a new approach to tackle this issue, by on-line estimation of context-based energy consumption models, which are able to map operating context to consumption patterns. Context identification is performed by regression tree algorithms. Energy consumption estimation is achieved by means of a multi-model architecture using multiple RLS algorithms, locally estimated for each operating context. Lastly, the proposed approach is applied to a real cement plant grinding circuit. Experimental results prove the viability of the overall system, regarding both automatic context identification and energy consumption estimation.

Associations between alcohol consumption and selected cytokines in a Swiss population-based sample (CoLaus study).

To assess the associations between alcohol consumption and cytokine levels (interleukin-1beta - IL-1β; interleukin-6 - IL-6 and tumor necrosis factor-α - TNF-α) in a Caucasian population. Population sample of 2884 men and 3201 women aged 35-75. Alcohol consumption was categorized as nondrinkers, low (1-6 drinks/week), moderate (7-13/week) and high (14+/week). No difference in IL-1β levels was found between alcohol consumption categories. Low and moderate alcohol consumption led to lower IL-6 levels: median (interquartile range) 1.47 (0.70-3.51), 1.41 (0.70-3.32), 1.42 (0.66-3.19) and 1.70 (0.83-4.39) pg/ml for nondrinkers, low, moderate and high drinkers, respectively, p<0.01, but this association was no longer significant after multivariate adjustment. Compared to nondrinkers, moderate drinkers had the lowest odds (Odds ratio=0.86 (0.71-1.03)) of being in the highest quartile of IL-6, with a significant (p<0.05) quadratic trend. Low and moderate alcohol consumption led to lower TNF-α levels: 2.92 (1.79-4.63), 2.83 (1.84-4.48), 2.82 (1.76-4.34) and 3.15 (1.91-4.73) pg/ml for nondrinkers, low, moderate and high drinkers, respectively, p<0.02, and this difference remained borderline significant (p=0.06) after multivariate adjustment. Moderate drinkers had a lower odds (0.81 [0.68-0.98]) of being in the highest quartile of TNF-α. No specific alcoholic beverage (wine, beer or spirits) effect was found. Moderate alcohol consumption is associated with lower levels of IL-6 and (to a lesser degree) of TNF-α, irrespective of the type of alcohol consumed. No association was found between IL-1β levels and alcohol consumption.

Local consumption and territorial based accounting for CO2 Emissions

We examine the complications involved in attributing emissions at a sub-regional or local level. Speci cally, we look at how functional specialisation embedded within the metropolitan area can, via trade between sub-regions, create intra-metropolitan emissions interdependencies; and how this complicates environmental policy implementation in an analogous manner to international trade at the national level. For this purpose we use a 3-region emissions extended input-output model of the Glasgow metropolitan area (2 regions: city and surrounding suburban area) and the rest of Scotland. The model utilises data on commuter flows and household consumption to capture income and consumption flows across sub-regions. This enables a carbon attribution analysis at the sub-regional level, allowing us to shed light on the signi cant emissions interdependencies that can exist within metropolitan areas.

Clustering of risk behaviors with cigarette consumption: a population-based survey.

OBJECTIVE: This study assessed clustering of multiple risk behaviors (i.e., low leisure-time physical activity, low fruits/vegetables intake, and high alcohol consumption) with level of cigarette consumption. METHODS: Data from the 2002 Swiss Health Survey, a population-based cross-sectional telephone survey assessing health and self-reported risk behaviors, were used. 18,005 subjects (8052 men and 9953 women) aged 25 years old or more participated. RESULTS: Smokers more frequently had low leisure time physical activity, low fruits/vegetables intake, and high alcohol consumption than non- and ex-smokers. Frequency of each risk behavior increased steadily with cigarette consumption. Clustering of risk behaviors increased with cigarette consumption in both men and women. For men, the odds ratios of multiple (> or =2) risk behaviors other than smoking, adjusted for age, nationality, and educational level, were 1.14 (95% confidence interval: 0.97, 1.33) for ex-smokers, 1.24 (0.93, 1.64) for light smokers (1-9 cigarettes/day), 1.72 (1.36, 2.17) for moderate smokers (10-19 cigarettes/day), and 3.07 (2.59, 3.64) for heavy smokers (> or =20 cigarettes/day) versus non-smokers. Similar odds ratios were found for women for corresponding groups, i.e., 1.01 (0.86, 1.19), 1.26 (1.00, 1.58), 1.62 (1.33, 1.98), and 2.75 (2.30, 3.29). CONCLUSIONS: Counseling and intervention with smokers should take into account the strong clustering of risk behaviors with level of cigarette consumption.

The association between cigarettes smoking, cannabis use and alcohol consumption in a population of young men: results of a population-based survey.

BACKGROUND: Cigarette smoking is often initiated at a young age as well as other risky behaviors such as alcohol drinking, cannabis and other illicit drugs use. Some studies suggest that cigarette smoking may have an influence on other risky behaviors but little is known about the chronology of occurrence of those different habits. The aim of this study was to assess, by young men, what were the other risky behaviors associated with cigarette smoking and the joint prevalence and chronology of occurrence of those risky behaviors. METHODS: Cross-sectional analyses of a population-based census of 3526 young men attending the recruitment for the Swiss army, aged between 17 and 25 years old (mean age: 19 years old), who filled a self reported questionnaire about their alcohol, cigarettes, cannabis and other illicit drugs habits. Actual smoking was defined as either regular smoking (¡Ý1 cigarette/day, on every day) or occasional smoking, binge drinking as six or more drinks at least twice a month, at risk drinking as 21 drinks or more per week, recent cannabis use as cannabis consumption at least once during the last month, and use of illicit drugs as consumption once or more of illicit drugs other than cannabis. Age at begin was defined as age at first use of cannabis or cigarette smoking. RESULTS: In this population of young men, the prevalence of actual smoking was 51.2% (36.5% regular smoking, 14.6% occasionnal smoking). Two third of participamnts (60.1%) declared that they ever used cannabis, 25.2% reported a recent use of cannabis. 53.8% of participants had a risky alcohol consumption considered as either binge or at risk drinking. Cigarette smoking was significantly associated with recent cannabis use (Odds Ratio (OR): 3.85, 95% Confidence Interval (CI): 3.10- 4.77), binge drinking (OR: 3.48, 95% CI: 3.03-4.00), at risk alcohol drinking (OR: 4.04, 95% CI: 3.12-5.24), and ever use of illicit drugs (OR: 4.34, 95% CI: 3.54-5.31). In a multivariate logistic regression, odds ratios for smoking were increased for cannabis users (OR 3.10,, 95% CI: 2.48-3.88), binge drinkers (OR: 1.77, 95% CI: 1.44-2.17), at risk alcohol drinkers (OR 2.26, 95% CI: 1.52-3.36) and ever users of illicit drugs (OR: 1.56, 95% CI: 1.20-2.03). The majority of young men (57.3%) initiated smoking before cannabis and mean age at onset was 13.4 years old, whereas only 11.1% began to use cannabis before smoking cigarettes and mean age at onset was slightly older (14.4 years old). 31.6% started both cannabis and tobacco at the same age (15 years old). About a third of participants (30.5%) did have a cluster of risky behaviours (smoking, at risk drinking, cannabis use) and 11.0% did cumulate smoking, drinking, cannabis and ever use of illegal drugs. More than half of the smokers (59.6%) did cumulate cannabis use and at risk alcohol drinking whereas only 18.5% of non-smokers did. CONCLUSIONS: The majority of young smokers initiated their risky behaviors by first smoking and then by other psychoactive drugs. Smokers have an increased risk to present other risky behaviors such as cannabis use, at risk alcohol consumtion and illicit drug use compared to nonsmokers. Prevention by young male adults should focus on smoking and also integrate interventions on other risky behaviors.

First consumption ever of multiple substances: applying an expert-based taxonomy to a Swiss national sample of adolescents.

The use of multiple legal and illegal substances by adolescents is a growing concern in all countries, but since no consensus about a taxonomy did emerge yet, it is difficult to understand the different patterns of consumption and to implement tailored prevention and treatment programs directed towards specific subgroups of the adolescent population. Using data from a Swiss survey on adolescent health, we analyzed the age at which ten legal and illegal substances were consumed for the first time ever by applying a method combining the strength of both automatic clustering and use of substance experts. Results were then compared to 30 socio-economic factors to establish the usefulness of and to validate our taxonomy. We also analyzed the succession of substance first use for each group. The final taxonomy consists of eight groups ranging from non-consumers to heavy drug addicts. All but four socio-economic factors were significantly associated with the taxonomy, the strongest associations being observed with health, behavior, and sexuality factors. Numerous factors influence adolescents in their decision to first try substances or to use them on a regular basis, and no factor alone can be considered as an absolute marker of problematic behavior regarding substance use. Different processes of experimentation with substances are associated with different behaviors, therefore focusing on only one substance or only one factor is not efficient. Prevention and treatment programs can then be tailored to address specific issues related to different youth subgroups.

Associations between fruit and vegetable consumption and psychological distress: results from a population-based study.

BACKGROUND: Several studies observed associations of various aspects of diet with mental health, but little is known about the relationship between following the 5-a-day recommendation for fruit and vegetables consumption and mental health. Thus, we examined the associations of the Swiss daily recommended fruit and vegetable intake with psychological distress. METHODS: Data from 20,220 individuals aged 15+ years from the 2012 Swiss Health Survey were analyzed. The recommended portions of fruit and vegetables per day were defined as 5-a-day (at least 2 portions of fruit and 3 of vegetables). The outcome was perceived psychological distress over the previous 4 weeks (measured by the 5-item mental health index [MHI-5]). High distress (MHI-5 score ≤ 52), moderate distress (MHI-5 > 52 and ≤ 72) and low distress (MHI-5 > 72 and ≤ 100) were differentiated and multinomial logistic regression analyses adjusted for known confounding factors were performed. RESULTS: The 5-a-day recommendation was met by 11.6 % of the participants with low distress, 9.3 % of those with moderate distress, and 6.2 % of those with high distress. Consumers fulfilling the 5-a-day recommendation had lower odds of being highly or moderately distressed than individuals consuming less fruit and vegetables (moderate vs. low distress: OR = 0.82, 95 % confidence interval [CI] 0.69-0.97; high vs. low distress: OR = 0.55, 95 % CI 0.41-0.75). CONCLUSIONS: Daily intake of 5 servings of fruit and vegetable was associated with lower psychological distress. Longitudinal studies are needed to further determine the causal nature of this relationship.