Labelling Criteria Research Articles

Assessing Compliance: Violations of WHO Code in Breast Milk Substitute Marketing, Ecuador.

Breast milk substitute (BMS) marketing significantly influences global infant feeding practices. Ecuador, like many countries, seeks to regulate these promotions under the WHO's International Code of Marketing of Breast-Milk Substitutes. This cross-sectional analysis assessed BMS marketing compliance with WHO's Code in Ecuador. Surveys were conducted with mothers (n = 330) and healthcare professionals (n = 66), complemented by observations at health facilities (n = 33) and retail outlets (n = 44). Media monitoring and product labelling evaluations were also conducted. The study revealed widespread exposure to BMS marketing outside health facilities (91.21% of mothers). Promotional activities targeted healthcare professionals, with significant interactions involving free supplies (26.09%) and gifts (21.74%). Retail outlets prominently displayed BMS promotions, often featuring discounts (95%). Compliance with labelling criteria was notably low, particularly concerning nutrition and health claims (39%). TV emerged as the dominant platform for BMS advertising, with 2884 ads aired over 16 h and 24 min, totalling $1,876,915.50 in expenditures. Digital platforms also featured BMS ads, with significant engagement on social media (533,845 interactions). This study reveals widespread violations of the WHO Code in Ecuador, emphasizing the need for stronger regulations and targeted education for healthcare professionals and the public to protect infant health and promote breastfeeding. Despite existing regulations, the pervasive advertising and substantial investment in BMS marketing across various media underscore significant enforcement gaps. To effectively safeguard maternal and child health, Ecuador must fully incorporate and rigorously enforce all Code recommendations within its national legislation.

Minimum labelling requirements for dermatology artificial intelligence-based Software as Medical Device (SaMD): A consensus statement.

Artificial intelligence (AI) holds remarkable potential to improve care delivery in dermatology. End users (health professionals and general public) of AI-based Software as Medical Devices (SaMD) require relevant labelling information to ensure that these devices can be used appropriately. Currently, there are no clear minimum labelling requirements for dermatology AI-based SaMDs. Common labelling recommendations for AI-based SaMD identified in a recent literature review were evaluated by an Australian expert panel in digital health and dermatology via a modified Delphi consensus process. A nine-point Likert scale was used to indicate importance of 10 items, and voting was conducted to determine the specific characteristics to include for some items. Consensus was achieved when more than 75% of the experts agreed that inclusion of information was necessary. There was robust consensus supporting inclusion of all proposed items as minimum labelling requirements; indication for use, intended user, training and test data sets, algorithm design, image processing techniques, clinical validation, performance metrics, limitations, updates and adverse events. Nearly all suggested characteristics of the labelling items received endorsement, except for some characteristics related to performance metrics. Moreover, there was consensus that uniform labelling criteria should apply across all AI categories and risk classes set out by the Therapeutic Goods Administration. This study provides critical evidence for setting labelling standards by the Therapeutic Goods Administration to safeguard patients, health professionals, consumers, industry, and regulatory bodies from AI-based dermatology SaMDs that do not currently provide adequate information about how they were developed and tested.

A review of the machine learning datasets in mammography, their adherence to the FAIR principles and the outlook for the future

The increasing rates of breast cancer, particularly in emerging economies, have led to interest in scalable deep learning-based solutions that improve the accuracy and cost-effectiveness of mammographic screening. However, such tools require large volumes of high-quality training data, which can be challenging to obtain. This paper combines the experience of an AI startup with an analysis of the FAIR principles of the eight available datasets. It demonstrates that the datasets vary considerably, particularly in their interoperability, as each dataset is skewed towards a particular clinical use-case. Additionally, the mix of digital captures and scanned film compounds the problem of variability, along with differences in licensing terms, ease of access, labelling reliability, and file formats. Improving interoperability through adherence to standards such as the BIRADS criteria for labelling and annotation, and a consistent file format, could markedly improve access and use of larger amounts of standardized data. This, in turn, could be increased further by GAN-based synthetic data generation, paving the way towards better health outcomes for breast cancer.

An improved YOLOv5-based method for multi-species tea shoot detection and picking point location in complex backgrounds

Accurate detection of tea shoots and precise location of picking points are prerequisites for automated, intelligent and accurate tea picking. A method was developed for the detection of tea shoots and key points and the localisation of picking points in complex environments. Images of four types of tea shoots were collected from multiple fields of view in a tea plantation over two months and labelling criteria were established. The YOLO-Tea model was developed based on the YOLOv5 network model, which uses a content-based upsampling operator (CARAFE) with a larger field of perception to implement the tea shoot feature upsampling operation, adds a convolutional attention mechanism module (CBAM) to focus the model on both channel and spatial dimensions to detect and localise important areas of tea shoots in a large field of view. The Bottleneck Transformers module was used to inject global self-focus for residuals to create long-distance dependencies on the tea shot feature images, and a six-point landmark regression head was added. The experimental results demonstrated that the YOLO-Tea model improved the mean Average Precision (mAP) value of tea shoots and their key points by 5.26% compared to YOLOv5. Finally, we use image processing methods to locate picking point positions based on key point information during the model inference phase. This study has theoretical and practical implications for the detection of tea shoots and their key points, tea shoot alignment, phenotype identification, pose estimation and picking locations of premium teas in complex environments.

Real-world eligibility for vericiguat according to trial, guideline, and labelling eligibility criteria: data from the Swedish Heart Failure Registry

Abstract Introduction The VICTORIA trial demonstrated the efficacy and safety of vericiguat in patients with worsening heart failure (HF) and an ejection fraction (EF) of <45%. Trial selection criteria might limit the external validity, and the perceived low generalisability may be an underlying reason for poor implementation of new treatments. Purpose We calculated the proportions of patients who would be eligible for vericiguat according to trial, guideline, and labelling scenarios in a large and unselected real-word HF population, and compared eligible to ineligible populations for patient characteristics and outcomes. Methods From the Swedish HF Registry (SwedeHF), 41,635 patients with EF<40% recorded between May 2000 and December 2018 were considered. We applied the VICTORIA trial's selection criteria, the 2021 European Society of Cardiology (ESC) guidelines' recommendation, and the regulatory labelling of vericiguat according to the Food and Drug Administration and European Medicines Agency. Outcome rates (cardiovascular [CV] and non-CV death, and first HF hospitalisation [HFH]) were compared between eligible and ineligible patients using exact Poisson test with a significance level of 5%. We also analysed 60,351 patients with EF<50% for the trial scenario. Results Eligibility for vericiguat based on the trial, guideline, and labelling criteria were 21.2%, 25.7%, and 44.5%, respectively (Figure 1). The criteria with major impacts on eligibility were: 1) in the trial scenario - inclusion criteria: recent HFH (within 6 months) and elevated NT-proBNP (met by 47.5% and 74.4% of the population, respectively), exclusion criteria - nitrate use (14.0%); 2) in the guideline scenario: recent HFH and HF duration longer than 6 months (as a proxy for optimal medical therapy) (47.5% and 57.6%, respectively); 3) in the labelling scenario: recent HFH (47.5%). In patients with EF<50%, trial eligibility was slightly lower, and the impact of selection criteria was overall similar to those with EF<40%. Overall, eligible patients were older, less likely to be female, had more severe HF and comorbidities, and had greater mortality and morbidity (Figure 1). Conclusion In a large and contemporary real-world cohort of HF with EF<40%, eligibility for vericiguat according to the trial and guideline scenarios was limited, i.e. ∼20–25%, whereas it was higher according to the labelling scenario, i.e. ∼45%. The VICTORIA trial's selection criteria successfully selected a HF population enriched for CV events but also with a higher risk of non-CV events, which is consistent with more severe HF and comorbidities in eligible vs. ineligible patients, but the requirement for recent worsening HF also rendered many patients ineligible. Our findings might provide insights for designing more inclusive and feasible future trials and might help stakeholders assess the consequences of future trial eligibility. Funding Acknowledgement Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Bayer AG

Automating Visual Blockage Classification of Culverts with Deep Learning

Blockage of culverts by transported debris materials is reported as the salient contributor in originating urban flash floods. Conventional hydraulic modeling approaches had no success in addressing the problem primarily because of the unavailability of peak floods hydraulic data and the highly non-linear behavior of debris at the culvert. This article explores a new dimension to investigate the issue by proposing the use of intelligent video analytics (IVA) algorithms for extracting blockage related information. The presented research aims to automate the process of manual visual blockage classification of culverts from a maintenance perspective by remotely applying deep learning models. The potential of using existing convolutional neural network (CNN) algorithms (i.e., DarkNet53, DenseNet121, InceptionResNetV2, InceptionV3, MobileNet, ResNet50, VGG16, EfficientNetB3, NASNet) is investigated over a dataset from three different sources (i.e., images of culvert openings and blockage (ICOB), visual hydrology-lab dataset (VHD), synthetic images of culverts (SIC)) to predict the blockage in a given image. Models were evaluated based on their performance on the test dataset (i.e., accuracy, loss, precision, recall, F1 score, Jaccard Index, region of convergence (ROC) curve), floating point operations per second (FLOPs) and response times to process a single test instance. Furthermore, the performance of deep learning models was benchmarked against conventional machine learning algorithms (i.e., SVM, RF, xgboost). In addition, the idea of classifying deep visual features extracted by CNN models (i.e., ResNet50, MobileNet) using conventional machine learning approaches was also implemented in this article. From the results, NASNet was reported most efficient in classifying the blockage images with the 5-fold accuracy of 85%; however, MobileNet was recommended for the hardware implementation because of its improved response time with 5-fold accuracy comparable to NASNet (i.e., 78%). Comparable performance to standard CNN models was achieved for the case where deep visual features were classified using conventional machine learning approaches. False negative (FN) instances, false positive (FP) instances and CNN layers activation suggested that background noise and oversimplified labelling criteria were two contributing factors in the degraded performance of existing CNN algorithms. A framework for partial automation of the visual blockage classification process was proposed, given that none of the existing models was able to achieve high enough accuracy to completely automate the manual process. In addition, a detection-classification pipeline with higher blockage classification accuracy (i.e., 94%) has been proposed as a potential future direction for practical implementation.

An integrated methodology for real-time driving risk status prediction using naturalistic driving data

Real-time driving risk status prediction is critical for developing proactive traffic intervention strategies and enhance driving safety. However, the optimal observation time window length and prediction time window length, which should be the prerequisite for the timeliness and accuracy of real-time driving risk status prediction model, have been rarely explored in previous studies. In this study, a methodology which integrates driving risk status identification, rolling time window-based feature extraction, real-time driving risk status prediction and driving risk influencing factors analysis was proposed to accurately evaluate and predict real-time driving risk status. The methodology was tested based on 1,440 car-following events from Shanghai Naturalistic Driving Study. Results show that four driving risk statuses (safe, low-risk, median-risk and high-risk) are most appropriate to establish risk labelling criteria. In addition, results from driving risk status prediction show that when the observation time window length is 0.5 s, the accuracy rate of predicting medium-risk or high-risk status occurring in the next 0.7 s is higher than 85 % using multi-layer perceptron model. Meanwhile, the results from the analysis of influencing factors show that the input variables related to the risk status score higher in the ranking of feature importance. A part from that, speed difference, headway distance, speed and acceleration are still important in predicting driving risk status. The proposed methods in this paper can be applied in connected and autonomous vehicle (CAV) to reduce driver cognitive workload and hence improve driving safety fed with naturalistic driving data collected using in-vehicle systems.

New homework for environmental labelling to implement the Paris Agreement to address climate change

Environmental labelling is one of the non-market approaches (NMA) to reduce energy consumption and mitigate carbon emissions to meet the Paris Agreement (PA) objectives. Furthermore, observing mitigation actions in the nationally determined contributions (NDC) is a way to ascertain whether the parties have determined environmental labelling. This study examines whether the contribution of mitigation to the NDC as a tool for implementing the PA has used environmental labelling as an NMA approach to reduce GHG emissions. This study proposes the NDC feature guideline. The proposed NDC feature guideline consists of determining labelling objectives, determining where to label the product, choosing a suitable labelling standard, determining suitable environmental aspects for labelling, selecting suitable labelling criteria, choosing sustainable testing standards, choosing a competent laboratory or validation and verification body, performing labelling verification actions and conduct monitoring actions.

Nutrition Information on Packaged Foods Marketed in India: Disclosure Adequacy, Relevance, Use and Health Implications

Health problems that stem from poor diet increase the need of nutrition labelling on food packages as it conveys meaningful information to consumers at the point of purchase and enables them to fulfill their dietary requirements. Nutrition labelling is relatively new to Indian markets, thus, the aim of this paper is to analyze the nature and extent of disclosure of nutrition information on packaged foods in India, its adequacy, nutrition quality of marketed foods according to UK traffic light food labelling criteria and consumer use of nutrition information. Primary data collected from 230 food packages have been content analyzed which reveals that although nutrition labels are present on the majority of sampled foods, yet, disclosure of mandatory nutrients is not adequate. Also, the majority of packaged foods are found to be unhealthy. Further, from a sample of 600 consumers, information was collected about nutrition knowledge, use and the reasons for not using nutrition information on food labels. It was found that consumers possess knowledge about total fat, protein, vitamins and minerals. The present study adds to existing knowledge by providing an understanding level of disclosure of nutrition information, its effectiveness when used by consumers and health implications of these disclosures to gauge the extent to which the disclosures are adequate in promoting healthful choices amongst consumers. Policy implications have also been identified that will help regulatory authorities to draft measures required for promoting health of Indian consumers.

Giraffe taxonomy, geographic distribution and conservation

Abstractxxxxxx.

Monitoring and capturing patient identification errors in laboratory medicine

Specimen labelling and patient identification are significant contributors to the rate of error in the preanalytical phase of laboratory medicine. This study aimed to investigate the prevalence and nature of preanalytical quality monitoring practices for patient identification and specimen labelling errors in Irish clinical laboratories. A survey was developed by the Clinical Biochemistry Unit, Trinity College Dublin and the Irish External Quality Assessment Scheme (IEQAS), with the intention of gathering key information from each laboratory. Thirty-nine questions were organized into seven subsections covering general information, labelling requirements, information availability, rejection criteria, error monitoring, error reporting and interest in participation in an external quality assessment scheme. The survey was sent electronically to 63 laboratory quality managers at 55 laboratories in Ireland. A total of 39 responses (61% response rate) provided information on 94 separate laboratory departments or disciplines. Laboratories reported varying practices and requirements for labelling specimens and all accepted handwritten preprinted request forms. All (100%) respondents had defined rejection criteria both for specimen labelling and request form completion. Unsurprisingly, the rejection criteria differed between the various laboratory disciplines. Almost all respondents provided information to clinical staff on labelling requirements, but just over half provided training on the same. A large percentage of laboratories (74%) monitored the rate of specimen-labelling errors; however, only 46% had defined target limits for acceptable rates of error. The survey observed a wide variation in collection, recording and monitoring of errors but also confirmed significant interest in improving preanalytical monitoring and data collection.

The utility of linking National Food Ingredient Databases to National Food Consumption surveys: a pilot study on fibre and sugar

AbstractIntroduction:Current dietary recommendations encourage increased fibre and reduced sugar consumption. In the UK, specific targets and benchmarks have been established for the sugar content of some foods but not for fibre. National Food Consumption Surveys provide comprehensive information of all foods consumed by representative population samples. The Irish national food surveys as completed by the Irish Universities Nutrition Alliance (IUNA) capture dietary data at brand level with all details as gathered on pack entered into a discrete but inter-linked database, the Irish National Food Ingredient Database (INFID). The aim of this study was to profile the carbohydrate quality of a convenience sub-sample of packaged foods as eaten by Irish children during the National Children's Food Survey II (2017/2018) as entered into INFID.Materials and Methods:All on-pack details from 385 available foods in the categories ‘white breads and rolls’; ‘brown breads and rolls’; ‘other breads and scones’; ‘ready to eat breakfast cereals (RTEBC)’; ‘biscuits’; and ‘cakes, buns and pastries’ were entered in to INFID and quality control completed. The carbohydrate profile of the products was assessed with respect to fibre labelling criteria and UK sugar guidelines and targets. SPSS Version 25 was used for all analyses.Results:Although 56% (n210) of all products entered were eligible to make a ‘source of’ or ‘high’ fibre claim, only 20% (n78) made such a claim. Of this, 46% stated ‘high fibre’ and 32% ‘source’, predominately in the ‘brown breads and rolls’ and ‘RTEBC’ groups. When compared to UK Department of Health guidance for ‘low’, ‘medium’ and ‘high’ sugar, 65% of all products examined (n250) were either ‘low’ or ‘medium’ sugar. Comparison of median sugar contents with Public Health England sugar reformulation targets revealed different responses in each category, with all categories other than foods deemed as “morning goods” yet to meet the 2020 target of 20% reduction in sugar content.Discussion:This small pilot study of a convenience sample of foods suggests that for the limited number of foods examined, for some there remains challenges to reduce sugar and increase fibre contents. Strategies such as reformulation, change in portion size, flexibility in labelling and/or a shift in sales portfolios could be considered but only alongside technological and safety considerations. Further research to broaden this analysis and to link nutrient levels as listed on pack with actual consumption patterns could help ensure all recent initiatives including reformulation are recognised.

Eligibility of sodium-glucose co-transporter-2 inhibitors among patients with diabetes mellitus admitted for heart failure.

AimsSodium–glucose co‐transporter (SGLT)‐2 inhibitors have been shown to reduce the risk of cardiovascular death and heart failure (HF) hospitalization in patients with type 2 diabetes mellitus (DM) and high cardiovascular risk in two large clinical outcome trials: empagliflozin in EMPA‐REG OUTCOME and canagliflozin in CANVAS. The scope of eligibility for SGLT‐2 inhibitors (empagliflozin and canagliflozin) among patients with type 2 DM and HF, based on clinical trial criteria and current US Food and Drug Administration (FDA) labelling criteria, remains unknown.Methods and resultsUsing data from the US Get With The Guidelines (GWTG)—Heart Failure registry, we evaluated the proportion of patients with DM and HF eligible for SGLT‐2 inhibitor therapy based on the clinical trial criteria and the US FDA labelling criteria. The GWTG‐HF registry is a quality improvement registry of patients admitted in hospital with HF in the USA. We included GWTG‐HF registry participants meeting eligibility criteria hospitalized between August 2014 and 30 June 2017 from sites fully participating in the registry. The initial inclusion time point reflects when both drugs had FDA approval. Among the 139 317 patients (out of 407 317) with DM hospitalized with HF (in 460 hospitals; 2014 to 2017), the median age was 71 years, 47% (n = 65 685) were female, and 43% (n = 59 973) had HF with reduced ejection fraction. Overall, 43% (n = 59 943) were eligible for the EMPA‐REG OUTCOME trial, 45% (n = 62 818) were eligible for the CANVAS trial, and 34% (n = 47 747) of patients were eligible for either SGLT‐2 inhibitors based on the FDA labelling criteria. Among the FDA‐eligible patients, 91.5% (n = 43 708) were eligible for either the EMPA‐REG OUTCOME trial or the CANVAS trial. Patients who were FDA eligible, compared with those who were not, were younger (70.0 vs. 72.0 years of age), more likely to be male (57.7 vs. 50.3%), and had less burden of co‐morbidities.ConclusionsThe majority of patients with DM who are hospitalized with HF are not eligible for SGLT‐2 inhibitor therapies. Ongoing studies evaluating the safety and efficacy of SGLT‐2 inhibitors among patients with HF may potentially broaden the population that may benefit from these therapies.

A Block Object Detection Method Based on Feature Fusion Networks for Autonomous Vehicles

Nowadays, automatic multi‐objective detection remains a challenging problem for autonomous vehicle technologies. In the past decades, deep learning has been demonstrated successful for multi‐objective detection, such as the Single Shot Multibox Detector (SSD) model. The current trend is to train the deep Convolutional Neural Networks (CNNs) with online autonomous vehicle datasets. However, network performance usually degrades when small objects are detected. Moreover, the existing autonomous vehicle datasets could not meet the need for domestic traffic environment. To improve the detection performance of small objects and ensure the validity of the dataset, we propose a new method. Specifically, the original images are divided into blocks as input to a VGG‐16 network which add the feature map fusion after CNNs. Moreover, the image pyramid is built to project all the blocks detection results at the original objects size as much as possible. In addition to improving the detection method, a new autonomous driving vehicle dataset is created, in which the object categories and labelling criteria are defined, and a data augmentation method is proposed. The experimental results on the new datasets show that the performance of the proposed method is greatly improved, especially for small objects detection in large image. Moreover, the proposed method is adaptive to complex climatic conditions and contributes a lot for autonomous vehicle perception and planning.

Advertising of Over-the-Counter Codeine-Containing Medicines in the EU: Differences in the Regulation of Advertising Between Member States

The availability of over-the-counter (OTC) medicines containing codeine has generated worldwide debate with increased focus on its safe use. Medicine agencies across the European Union (EU) have responded to the public health concern by placing restrictions and warnings on codeine medicines sold OTC in High Street and internet pharmacies. These restrictions include direct-to-consumer advertising; however, there are few published studies examining conditions of advertising across member states. A review of the conditions of advertising was conducted by accessing information pertaining to each medicine’s regulatory agency allowing the sale of codeine in the EU. Each agency was asked to respond to three topics: (1) level and type of restrictions placed on all forms of direct-to-consumer advertising of OTC medicines containing codeine; (2) labelling criteria on packaging including indication related to the risk of addiction and any other patient information specific to codeine; and (3) complaints received by the medicines agency related to direct consumer advertising of OTC medicines containing codeine. Data were collated and tabulated in Excel to demonstrate its position on OTC codeine advertising. In the EU, 12 countries allow the sale of OTC codeine, while 16 do not. Of the 12 countries permitting its sale OTC, 4 countries prohibited direct-to-consumer advertising of codeine. The majority of the countries permitting advertisements did not have any additional or special restrictions or warnings for codeine-containing products with the exception of the UK where advertising codeine was only permitted under certain restrictions including product endorsement and special warnings including its indication of use for a maximum of 3 days. There is wide disparity in advertising of OTC codeine in the EU. Safeguards for OTC codeine use are likely to continue to remain a priority in the interest of public safety.

Formaldehyde emissions from seams and cut edges of laminate flooring: Implications for emission testing protocols and exposure estimation

Concern about high formaldehyde emission from specific models of laminate flooring (LF) products has recently drawn wide public attention in the U.S. This raises questions about how to best test emissions of LF products and how to make appropriate model assumptions when conducting relevant lifetime exposure and risk assessments. This paper presents a preliminary study focused on these questions. Formaldehyde emissions were measured in 20-L environmental chambers for 6 months for two LF products made with low- and high-emitting composite wood core. Tests were conducted for specimens with and without exposed seams (“click-joints” at the sides and ends of each flooring plank) and perimeter cut edges. Results demonstrate that exposed seams and cut edges can contribute significantly to overall emissions, especially for the product with a high-emitting core, and this effect could last long after flooring installation. Testing with no exposed seams and perimeter cut edges might allow a finished flooring product with a high-emitting core and high emissions after installation to meet low-emitting labelling criteria inappropriately.

Nutritional value of the Tunisian mussel Mytilus galloprovincialis with a special emphasis on lipid quality

This study reports, for the first time, data on nutritional quality parameters in Tunisian mussels, Mytilus galloprovincialis, with an especial emphasis on lipid compounds. Mussel condition index (CI), proximate composition and fatty acid profiles were investigated for a one year period in order to identify the best harvesting period. Analysis revealed that polyunsaturated fatty acids (PUFA) were the dominant fatty acids with a prevalence of n-3 over n-6 forms. Pearson’s correlation indicated a strong relationship between CI and PUFA compound and Principal Components Analysis suggested that, from winter to summer, the product maintained a condition sufficiently good for marketing. The best CI and lipid quantity/quality occurred during summer and this may be used as criteria for product labelling. The study also included a thorough literature review that allowed data comparison on mussels from various Mediterranean sites and allowed the mussels from the Bizerte lagoon (North of Tunisia) to be differentiated.

Traffic-light labels could reduce population intakes of calories, total fat, saturated fat, and sodium.

Traffic-light labelling has been proposed as a public health intervention to improve the dietary intakes of consumers. OBJECTIVES: to model the potential impact of avoiding foods with red traffic lights on the label on the energy, total fat, saturated fat, sodium, and sugars intakes of Canadian adults. METHODS: Canadian adults aged 19 and older (n = 19,915) who responded to the Canadian Community Health Survey (CCHS), Cycle 2.2. The nutrient levels in foods consumed by Canadians in CCHS were profiled using the United Kingdom’s criteria for traffic light labelling. Whenever possible, foods assigned a red traffic light for one or more of the profiled nutrients were replaced with a similar food currently sold in Canada, with nutrient levels not assigned any red traffic lights. Average intakes of calories, total fat, saturated fat, sodium, and sugars under the traffic light scenario were compared with actual intakes of calories and these nutrients (baseline) reported in CCHS. RESULTS: Under the traffic light scenario, Canadian’s intake of energy, total fat, saturated fat, and sodium were significantly reduced compared to baseline; sugars intakes were not significantly reduced. Calorie intake was reduced by 5%, total fat 13%, saturated fat 14%, and sodium 6%. CONCLUSION: Governments and policy makers should consider the adoption of traffic light labelling as a population level intervention to improve dietary intakes and chronic disease risk.

Moving beyond probabilities – Strength of knowledge characterisations applied to security

Many security experts avoid the concept of probability when assessing risk and vulnerabilities. Their main argument is that meaningful probabilities cannot be determined and they are consequently not useful for decision-making and security management. However, to give priority to some measures and not others, the likelihood dimension needs to be addressed in some way; the question is how. One approach receiving attention recently is to add strength of knowledge judgements to the probabilities and probability intervals generated. The judgements provide a qualitative labelling of how strong the knowledge supporting the probability assignments is. Criteria for such labelling have been developed, but not for a security setting. The purpose of this paper is to develop such criteria specific to security applications and, using some examples, to demonstrate their suitability.

Analysis of energy development sustainability: The example of the lithuanian district heating sector

Today, sustainable energy development is one of key issues on European development agenda. The article describes one of sustainable energy development promoting tool - the eco-labelling scheme for district heating and cooling systems elaborated within the framework of Intelligent Energy for Europe program project “Ecoheat4cities” and partially funded by European Agency for Competitiveness and Innovation. The scheme is based on measured energy and environmental performance data of the district heating and cooling system and considers primary non-renewable energy usage together with the share of renewable energy and carbon dioxide emissions calculated using life-cycle analysis methodology. The “power bonus” approach is used for performance indicators of the heat generated in cogeneration installations. An analysis of a number of Lithuanian district heating companies using elaborated labelling criteria shows positive trends towards fulfilling Lithuania's energy policy goals. The labelling scheme gives opportunity for policy makers and urban planners to compare different heat supply options and decide upon exploiting district heating advantages and benefits for reaching EU energy and environment policy goals.