Labeling Program Research Articles

Consumer-driven selection of low-emission vehicles for sustainable urban centers: An AHP-based approach

In densely populated urban areas, air quality is one of the main concerns, affecting human health and the environment. In developing and emerging countries, an alternative method for reducing the effects of air pollution is to select vehicles with lower pollutant emissions, as a way of making these large centers more sustainable. Since the complete elimination of vehicle emissions is not possible in the short term. The aim of this work is conducting a selectivity study of low-emission vehicles to increase the proportion of less polluting vehicles by using a dynamic combination of technical information while simultaneously meeting consumer preferences. An adapted Analytic Hierarchy Process (AHP), a multi-criteria technique, was applied on secondary data on vehicle characteristics. The Brazilian Labeling Program was the main resource used in this research. The research innovatively promotes vehicle selection considering environmental aspects. In addition to being adaptable, dynamic, and interactive, it facilitates its use in different vehicle markets and contributes to policies for reducing pollutant emissions. Thus, the study presents a strategy to minimize vehicle emissions in urban centers until the energy transition is consolidated, especially in emerging countries like Brazil.

Sugar-Sweetened Beverages and Diet Related Chronic Non-Communicable Diseases in Sub-Saharan Africa

abstract In Sub-Saharan Africa (SSA), diet-related chronic non-communicable diseases (DR-CNCDs) pose a growing public health threat, jeopardising progress towards achieving Sustainable Development Goal 3.4. One contributing factor to this rising threat is the recent surge in consumption of sugar-sweetened beverages (SSBs), across SSA in recent years. Using the socio-ecological model, this commentary explores the nexus between DR-NCDs and SSBs and offers interventions to address this public health challenge at the individual, interpersonal, community, organisational and policy levels. At the individual level, health education campaigns should target individuals and ensure an increase in awareness creation. The life-course approach underscores the importance of promoting healthier dietary choices from childhood to prevent diet-related non-communicable diseases in later life. Schools and workplaces enforcing local policies that discourage SSB consumption patterns will make a huge difference at the community and organisational levels. At the societal/policy level, comprehensive strategies such as sugar taxes, front-of-pack labelling, advertising restrictions and health education programmes are crucial.

Exploring the Influence of Reflectivity and Emissivity on Energy Consumption Across Varied Climate Zones in India

Abstract With a particular emphasis on sustainability, this research investigates the influence of reflectance and emissivity qualities on the amount of energy used by residential buildings of a mid-rise height located in various temperature zones in India. In the first part of the study, the impacts of highly reflecting, cool roofs were evaluated using base case and proposed simulations. In the second phase, a comparison was made between the possible energy savings that may be obtained by switching from low to high solar reflective roofs. An analysis of the reflectance and emissivity characteristics of the roof was carried out with the assistance of the eQUEST simulation tool. The study findings were validated using the Bureau of Energy Efficiency (BEE) schedule for the residential building energy labeling program. According to the results, highly reflecting roofs, which have a reflectivity of 0.8 and an emissivity of 0.9, dramatically lowered cooling loads by 38% and 20% in hot and dry areas, 21–25% in composite climates, 17–25% in warm and humid climates, and 37% in colder climates. These cost-effective solutions could be applied to existing and new constructions and have the potential to provide large energy and monetary savings by improving the performance of the building envelope, which in turn contributes to efforts to make the building more environmentally friendly.

YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition

ObjectivesIn the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and numbering of deciduous and permanent teeth in mixed dentition pediatric patients based on PRs.MethodsA total of 3854 mixed pediatric patients PRs were labelled for deciduous and permanent teeth using the CranioCatch labeling program. The dataset was divided into three subsets: training (n = 3093, 80% of the total), validation (n = 387, 10% of the total) and test (n = 385, 10% of the total). An artificial intelligence (AI) algorithm using YOLO-v5 models were developed.ResultsThe sensitivity, precision, F-1 score, and mean average precision-0.5 (mAP-0.5) values were 0.99, 0.99, 0.99, and 0.98 respectively, to teeth detection. The sensitivity, precision, F-1 score, and mAP-0.5 values were 0.98, 0.98, 0.98, and 0.98, respectively, to teeth segmentation.ConclusionsYOLO-v5 based models can have the potential to detect and enable the accurate segmentation of deciduous and permanent teeth using PRs of pediatric patients with mixed dentition.

A deep learning approach to detection of oral cancer lesions from intra oral patient images: A preliminary retrospective study

IntroductionOral squamous cell carcinomas (OSCC) seen in the oral cavity are a category of diseases for which dentists may diagnose and even cure. This study evaluated the performance of diagnostic computer software developed to detect oral cancer lesions in intra-oral retrospective patient images. Materials and methodsOral cancer lesions were labeled with CranioCatch labeling program (CranioCatch, Eskişehir, Turkey) and polygonal type labeling method on a total of 65 anonymous retrospective intraoral patient images of oral mucosa that were diagnosed with oral cancer histopathologically by incisional biopsy from individuals in our clinic. All images have been rechecked and verified by experienced experts. This data set was divided into training (n = 53), validation (n = 6) and test (n = 6) sets. Artificial intelligence model was developed using YOLOv5 architecture, which is a deep learning approach. Model success was evaluated with confusion matrix. ResultsWhen the success rate in estimating the images reserved for the test not used in education was evaluated, the F1, sensitivity and precision results of the artificial intelligence model obtained using the YOLOv5 architecture were found to be 0.667, 0.667 and 0.667, respectively. ConclusionsOur study reveals that OCSCC lesions carry discriminative visual appearances, which can be identified by deep learning algorithm. Artificial intelligence shows promise in the prediagnosis of oral cancer lesions. The success rates will increase in the training models of the data set that will be formed with more images.

Food, nutrition, and related policy issues: Evidence-based policy and the credibility crisis

Abstract In recent years, the push for evidence-based policy has gained momentum, particularly in the field of food and nutrition, where the stakes for public health are high, underscoring the need for aligning policy with robust scientific evidence. This paper examines the credibility crisis in evidence-based policymaking, highlighting challenges such as conflicts of interest and misuse of scientific findings. We delve into the complexities of food policies, including nutrition labeling and food assistance programs, and discuss the limitations of relying on single studies and short-term impacts for policymaking. The paper explores perverse incentives within the policy research ecosystem and illustrates potential bias and the lack of equipoise in research design. We conclude with recommendations for enhancing the credibility of evidence-based policy, emphasizing the importance of independence, relevance, and transparency in research citations and the need for dynamic policy responses informed by ongoing scientific developments.

Labels that limit: A framework for reducing sales of ornamental invasive plants

Invasive plants cost the US billions of dollars each year due to ecological and economic impacts as well as management costs. One of the most common pathways of introduction and spread of invasive plants is through ornamental plant sales. While solutions such as regulations and voluntary self-bans have been implemented in some instances to mitigate this problem, widespread adoption has not occurred. As such, new alternatives should be explored. Opt-in labeling programs are commonly used throughout the agricultural industry to better inform customers about the products they are purchasing. An opt-in labeling program that consists of a partnership between retailers and governments or non-profit organizations could help reduce the spread of invasive plants by influencing customer behavior. This approach would be less costly to retailers than regulations, create new invasive plant prevention opportunities for governments and nonprofits, and better inform consumers about specific invasive plant species.

The Detection of Pulp Stones with Automatic Deep Learning in Panoramic Radiographies: An AI Pilot Study.

This study aims to evaluate the effectiveness of employing a deep learning approach for the automated detection of pulp stones in panoramic imaging. A comprehensive dataset comprising 2409 panoramic radiography images (7564 labels) underwent labeling using the CranioCatch labeling program, developed in Eskişehir, Turkey. The dataset was stratified into three distinct subsets: training (n = 1929, 80% of the total), validation (n = 240, 10% of the total), and test (n = 240, 10% of the total) sets. To optimize the visual clarity of labeled regions, a 3 × 3 clash operation was applied to the images. The YOLOv5 architecture was employed for artificial intelligence modeling, yielding F1, sensitivity, and precision metrics of 0.7892, 0.8026, and 0.7762, respectively, during the evaluation of the test dataset. Among deep learning-based artificial intelligence algorithms applied to panoramic radiographs, the use of numerical identification for the detection of pulp stones has achieved remarkable success. It is expected that the success rates of training models will increase by using datasets consisting of a larger number of images. The use of artificial intelligence-supported clinical decision support system software has the potential to increase the efficiency and effectiveness of dentists.

BIM application in a Brazilian Building Labeling Program

BIM application in a Brazilian Building Labeling Program

Evaluating the efficiency of green innovation and marketing strategies for long-term sustainability in the context of Environmental labeling

In the contemporary landscape, as environmental concerns take center stage, consumers are increasingly urged to make ecologically friendly choices, facilitated by the prevalence of environmental labeling. This study undertakes a comprehensive investigation into the influence of environmental labeling on customer purchasing behavior, with a specific emphasis on evaluating the effectiveness of green marketing and innovation initiatives. Using the Fuzzy Analytical Hierarchy Process (AHP) and Fuzzy Weighted Aggregated Sum Product Assessment (WASPAS) methodologies, the research pinpoints the elements and tactics able to influence consumer behavior in connection to environmental labeling. Therefore, seven factors, twenty-one sub-factors, and six strategies are identified. The results of Fuzzy AHP show the prominent role of product attributes, marketing and promotion strategies, and sustainability practices while seeking to change consumer behavior with regard to environmental labeling. In addition, the findings from the Fuzzy WASPAS approach suggest that environmentally friendly packaging, product labeling, and partnerships and collaboration are the most successful green innovation and marketing strategies. The findings of this study can help shape the design of more effective environmental labeling programs and marketing plans for encouraging sustainable purchases.

The role of above-code labeling programs in reducing CO2e emissions in residential buildings

Residential buildings account for 15 % of total U.S. carbon dioxide (CO2) emissions. Voluntary, above-code labeling programs, such as the ENERGY STAR® and Zero Energy Ready Home™ programs, can play a crucial role in reducing the carbon footprint of the new construction residential building sector by encouraging adoption of more energy-efficient and lower emissions building practices and technologies. It’s important for the industry to understand the carbon dioxide equivalent (CO2e) and energy cost savings potential of these programs. To estimate the potential CO2e emissions savings for homes built to above-code labeling program levels, it is imperative to have reliable and accurate tools for estimating emissions from buildings. The open-source OpenStudio-ERI workflow employs detailed physics-based building energy modeling (BEM) to estimate energy usage and operational CO2e emissions. In this study we use the OpenStudio-ERI workflow and regional, time-varying emissions factors from the Cambium database to compare the CO2e emissions and energy costs of homes built to performance levels required by these above-code labeling programs relative to homes built to model energy codes. We present the results of our analysis and provide a range of emissions and annual energy cost savings for the above-code labeling programs.

State labeling's impact on geographic boundaries of local

AbstractLocal labeling is an important issue within the horticultural industry as many producers have devoted considerable resources to advertising their products as local. Further, all states have implemented some form of local labeling program to increase local purchasing. As states expand the reach of these programs, the programs begin to compete as awareness and perception move across state lines. Using an online survey to study consumers in the Southeastern United States, we look to identify how state labeling programs are viewed in nearby states. Notably, we assess awareness, perception, and its impact on the local definition of these programs in their “home” state as well as nearby states. Specifically, we evaluate the impact of nine Southeastern state‐level branding programs. Our results indicate that consumers are more aware of their home state's local label, but awareness does exist for other states' labels. We also find that a home state label moves some consumers to a more state‐level local boundary, while seeing another state's label increases the boundary for what is perceived as local [EconLit Citation: Q13].

Tooth numbering and classification on bitewing radiographs: an artificial intelligence pilot study

ObjectiveThe aim of this study is to assess the efficacy of employing a deep learning methodology for the automated identification and enumeration of permanent teeth in bitewing radiographs. The experimental procedures and techniques employed in this study are described in the following section. Study DesignA total of 1248 bitewing radiography images were annotated using the CranioCatch labeling program, developed in Eskişehir, Turkey. The dataset has been partitioned into 3 subsets: training (n = 1000, 80% of the total), validation (n = 124, 10% of the total), and test (n = 124, 10% of the total) sets. The images were subjected to a 3 × 3 clash operation in order to enhance the clarity of the labeled regions. ResultsThe F1, sensitivity and precision results of the artificial intelligence model obtained using the Yolov5 architecture in the test dataset were found to be 0.9913, 0.9954, and 0.9873, respectively. ConclusionThe utilization of numerical identification for teeth within deep learning-based artificial intelligence algorithms applied to bitewing radiographs has demonstrated notable efficacy. The utilization of clinical decision support system software, which is augmented by artificial intelligence, has the potential to enhance the efficiency and effectiveness of dental practitioners.

Energy Efficiency and Thermal Comfort Analysis in a Higher Education Building in Brazil

Thermal comfort is extremely important in architecture, especially in environments with more people spending longer time on studies or intellectual activities. This research describes a case study designed to investigate the energy and thermal performance of university buildings as part of the ANEEL programme. Because of this importance and the need to save energy in Brazilian public buildings, ANEEL—the Brazilian Energy Electricity Regulatory Agency—launched a national programme focusing on energy efficiency in public universities in 2016. University offices and classrooms sustain high intellectual effort; thus, environmental comfort is critical for maintaining their users’ physical and mental health. This study included a pre-diagnosis of the performance of the envelope, lighting, and air-conditioning systems and a survey about the quality of the environments from the user’s point of view. The Prescriptive Method of the Brazilian Labelling Program (PBE) for Commercial, Service, and Public Buildings (RTQ-C) was used to assess the building performance. Statistical analysis was applied to correlate the quality and thermal preference of the users, with reference to the predicted mean vote and the predicted percentage of dissatisfied (PMV-PPD). The results showed a high rate of thermal discomfort in both study environments, even when using air conditioning.

Evaluation of artificial intelligence for detecting periapical lesions on panoramic radiographs

Background/Aim: The aim of this study is to evaluate the function of diagnostic computer software designed for the detection of periapical lesions on panoramic images with deep learning methods. Material and Methods: In our study, more than 500 adult retrospective panoramic radiography images obtained randomly were evaluated, and periapical lesions were labeled on the radiographs with the ResultLab.Ai labeling program (ResultLab.AI, Istanbul, Turkey). All images have been rechecked and verified by Oral, Dental and Maxillofacial Radiology experts. The model used is a U-Net based architecture customized for dental radiographs and optimized for fast operation. What is meant by customization is the structures called "Attention Gate" added to the architecture to draw the model's attention to the lesions. Results: Mask estimation was made separately for each image and F1 and IoU scores were calculated by comparing them with the marked masks. A list was created from the calculated F1 and IoU scores. The F1 and IoU score of the entire data set was calculated by taking the average of the values in this list. In IoU score calculation, Keras library's Mean IoU metric was used to calculate for 2 classes. In the F1 score calculation, the F1 score metric of the SKLearn library was used to calculate the unweighted average for 2 classes. As a result, the IoU-Score was obtained as 0.8578 and the F1-Score as 0.8587. Conclusions: Detection of periapical lesions on panoramic radiographs with an artificial intelligence approach will help practitioners reach definitive conclusions even in lesions that are likely to be overlooked. In addition, success rates for deep learning methods improve as data set size grows. As the number of images rises, the training models' success rates will follow.

Improvement in the Energy Efficiency Label level – PBE Edifica: Case Study of the Community Library of the Federal University of São Carlos (UFSCar) – São Paulo - Brazil

This article describes the application of a method of environmental certification of buildings used in Brazil and named as Brazilian Labelling Program PBE–Edifica. The method was developed by Procel, National Electric Energy Conservation Program, which confers, four Labels of the Energy Efficiency levels of a building, covering the Envelope; the Lighting System; the Indoor Air Conditioning; and the building Overall, which includes all previous levels and bonuses. The levels and scope of the Label apply both in the design phase and after construction or existing building in accordance with the Technical Quality Regulation for the Level of Energy Efficiency for Commercial, Service and Public Buildings, RTQ-C. The efficiency level ranges from A to E, where A is the most efficient and E the least efficient. The method also considers Bonuses that allow improvements in the Overall Classification efficiency. Thus, the objective of the research carried out was to identify the classification of the level of energy efficiency of an existing building on the campus of the Federal University of São Carlos, the Community Library, called BCo, Brazil built in 1995 with an area of 6,947.48 m2. The guidelines of the PBE Edifica using the prescriptive method were applied, and the characterization of the level of energy efficiency was carried out based on data obtained from plans and technical drawings of the building, and through a technical visit to obtain the data for entry into an application, WebPrescriptive. To obtain specific information about the wall's thermal transmittance and roof solar absorbance the PROJETEE application was used. All information processed so the building obtained an Overall classification C. For envelope and lighting level E, and air conditioning level B. To improve the level of efficiency, through bonuses, improvements in the lighting system and the elevator were suggested, resulting in level B for both Lighting and Overall efficiency. Notice that the building, due to its design and construction system does not have the conditions to reach level A of efficiency. In conclusion, we would like to point out that it is feasible to submit the building to the formal labelling process, since the proposed improvements resulted in an Overall Energy Efficiency Rating B. As a contribution for EFFICACY Project, some guidelines to be considered for buildings rehabilitation will be stated, as simulation of thermal insulation in facades and others improvements.

European Heritage Label-based Education as an Essential Part of Memory Education – The Polish Case

The purpose of this article is to show how the EHL is used for the interpretation, awareness, and transmission of heritage and memory. As part of the EU European Heritage Label (EHL) program, Polish institutions received six distinctions in the years 2014-2019, which places Poland high in the ranking of the awarded Brands in Europe. Polish institutions are obligated to spreading knowledge about the cultural heritage of Europe. On the one hand, EHL’s task is to raise awareness and transmit common values, elements of European history, and heritage, which can be interpreted as promoting “uni memory.” On the other hand, EHL aims to deepen intercultural dialogue and synergy, recognizing the values of diversity, which can be understood as promoting the idea of plural memory. The EHL organizers in Poland are obliged (by the provisions of the EU Program) to conduct a European narrative, but in practice, they sometimes remain at the stage of restoring and refreshing the memory of the site, organizing local memory, and revising the regional policy due to “dormant memory” during the communist period.

CLEANING PRODUCTS REGULATION IN THREE WORLD MARKETS

The cleansers technological sophistication accompanied scientific, technological and market developments to meet the criteria for cleaning the environments. In addition to specific safety criteria for users, such products need to be safe for the environment, which requires appropriate regulations for society effective protection. This work aimed to identify the strengths and improvement points of the Brazilian regulatory procedures regarding household cleaning products compared to those applied in the US and Europe, based on the regulations imposed on such products and the administrative organization of the responsible agencies in each location. It was raised the Brazilian legislation referring to cleaning products and producers and their regulatory agency. It was searched the regulations for items similar to cleansers, which are pesticides and hazardous substances for the US and chemical substances for Europe. The collected information was analyzed through the comparative law method. The Brazilian regulatory procedures have the same technical and scientific rigor and regulatory practices to the USA and the European Union, in aspects such as the regulations curating and the issuing operation authorizations for companies. The lack of translation of the standards and an environment labeling program are improvement points.

Metamodel to predict annual cooling thermal load for commercial, services and public buildings: A country-level approach to support energy efficiency regulation

The energy sector significantly impacts the environment, with energy production contributing to greenhouse gases emissions and climate change. In Brazil, buildings account for a substantial portion of energy consumption, making energy efficiency essential for sustainable development. Building simulation is an efficient way to provide valuable insights into the thermal performance of buildings, but it requires expertise, time, and computational resources. To overcome these simulation constraints, metamodeling has emerged as an easy-to-use and fast-response alternative to analyse the thermal performance of buildings. This study focuses on developing a metamodel to predict cooling thermal loads in Brazil's commercial, services, and public buildings, supporting the country's building energy efficiency labelling program. It is expected from the metamodel a high capacity to reproduce the variability of climates, contexts, and heterogeneity of buildings from a country-level perspective. A parametric sampling process was used to develop a comprehensive simulated database considering several variations of building-related, occupancy patterns, and weather parameters. The metamodel was trained, validated and tested using optimisation techniques and an artificial neural network. Afterwards, it was compared with actual models, considering different typologies and climates. While the metamodel demonstrates high accuracy and generalisation, limitations were found regarding its application in warmer temperatures and complex building shapes. Further refinement is needed to improve its applicability and reliability in real-world scenarios. The proposed metamodel offers a practical and widely applicable tool for supporting energy code compliance and energy efficiency assessment in buildings.

Ready-to-use-item database to improve nutrition labeling program efficiency

Go for Green® (G4G 2.0) is an evidence-based, comprehensive dining facility nutrition program designed to improve access, availability, and knowledge of nutrient-dense foods for Service Members. A key component assigns codes to recipes and ready-to-use (RTU) foods and beverages to educate diners on performance impact (Green = high, Yellow = moderate, Red = low) and sodium content (Low, Moderate, High). The authors identified a gap in that RTU items were coded repetitively at local levels, which resulted in inefficient use of limited resources and coding inconsistencies. The objective was to create a database of common RTU items on the public G4G 2.0 website to decrease resources spent coding locally and maximize consistency among coded items. The authors compiled RTU items from nutrition colleagues, quality checked G4G 2.0 codes, then created a database. Stakeholders were informed via various communication methods about the database availability. New items can be coded upon request, with the database updated quarterly. The percentage of Green-coded items increased from 14 % to 18 % in one year. A publicly available, pre-coded database of RTU items streamlines G4G coding efforts, enhances coding consistency, showcases high-performance (Green) options, but also indicates the high-prevalence of Red-coded items available in military foodservice venues.