Consumer Information Research Articles

A pilot study of the contribution of energy-dense Caribbean diets to acrylamide exposure with associated health risks for a population of university students in Trinidad and Tobago

Previous studies on university students have indicated a significant decline in the consumption of fruits and vegetables complemented by an increase in energy-dense foods. The food toxicant, acrylamide, typically occurs in carbohydrate-rich, energy-dense foods that have been heated. Hence, this work presents an estimated dietary acrylamide exposure for university students in Trinidad and Tobago. A 2-day dietary recall method was used to obtain the food consumption information from 683 university students of differing sociodemographic backgrounds. The acrylamide exposure was estimated using a deterministic approach. The median acrylamide intake was estimated to be 1.39 µg/kg bw/day. The estimated mean acrylamide dietary intakes for the female and male population were 1.40 and 1.37 µg/kg bw/day, respectively. Coffee was determined to be the major dietary contributor to acrylamide exposure. However, bread was the food item that was most frequently consumed among the students. Using multiple linear regression, a possible correlation was detected between the acrylamide exposure and these variables: dietary habits (mostly eat out; p < 0.05), and Indian ethnicity (p < 0.10). Using the margin of exposure approach, dietary acrylamide exposure was found to be a health concern with regards to neurotoxicity and carcinogenicity. An evaluation of the procedures and results from this pilot study was carried out for the potential of conducting a full-scale research project.

Application of deep reinforcement learning in electricity demand response market: Demand response decision-making of load aggregator

With the large-scale integration of renewable energy generation, developing the potential of demand response is becoming more and more significant. However, the number of consumers is huge, the electricity consumption behaviors are various and the information of consumers is incomplete, thus there are great difficulties in modeling and processing demand response by the conventional mathematical methods. Therefore, how to accurately predict the response behaviors of consumers and select the appropriate consumers for demand response is worthy of in-depth discussion. In this paper, the decision-making method of the load aggregators who participant in demand response market on behalf of the small and medium-sized consumers based on Deep Q-network algorithm is proposed, supporting the aggregators to properly guide the potential demand response consumers. At the same time, a dynamic consumer classification method based on self-organizing maps algorithm is also proposed, supporting the aggregators to accurately predict the response behaviors of the consumers. Simulation results show that the proposed method can effectively realize the classification of the consumers and result in a more beneficial demand response.•The proposed method does not require complete information, such as demand response behavior parameters of the consumers.•Self-organizing maps can realize a dynamic classification of demand response consumers as the case may be.•DQN algorithm can effectively realize the demand response decision-making of aggregators under the condition of incomplete information.

Evaluating the usability of public health data dashboards as information sources for professionals and the public: Findings from a case study with domain experts.

Recently, public health data dashboards have gained popularity as trusted, up-to-date sources of health information. However, their usability and usefulness may be limited. To identify the requirements of usable public health data dashboards through a case study with domain experts. Paired-user virtual data collection sessions were conducted with 20 experts in three steps: (1) a monitored use of an existing dashboard to complete tasks and discuss the usability problems, (2) a survey rating user experience, and (3) an interview regarding the users and use cases. Data analysis included quantitative analysis of the survey findings and thematic analysis of the audio transcripts. Analyses yielded several findings: (1) familiar charts with clear legends and labels should be used to focus users' attention on the content; (2) charts should be organized in a simple and consistent layout; (3) contextual information should be provided to help with interpretations; (4) data limitations should be clearly communicated; (5) guidance should be provided to lead user interactions. The identified requirements guide health librarians and information professionals in evaluating public health data dashboards. Public health data dashboards should be designed based on users' needs to provide useful up-to-date information sources for health information consumers.

RASPBERRY EMPLOYED POWER THEFT CONTROLLER AND IOT BASED LOAD CONTROLLING FOR SMART ENERGY METER SYSTEM

This paper discusses the significance of right monitoring and controlling power consumption, specifically in detecting strength theft. It suggests using an Internet of Things (IoT) and Arduino-based totally clever energy meter to deal with energy waste and theft. The Raspberry Pico is applied in this undertaking to control strength theft via controlling loads connected to a 4xl channel relay and presenting strength intake details to the IoT interface. This statistics may be used by purchasers to make knowledgeable choices about their strength usage. Keywords: Energy consumption tracking, Power theft detection, Internet of Things (IoT), Arduino- based, smart energy-meter, Raspberry Pico, Load manipulate, Relay, Energy consumption information, Consumer consciousness.

ПРАВОВЫЕ АСПЕКТЫ ДОГОВОРА ПРЕДОСТАВЛЕНИЯ ПЛАТНЫХ МЕДИЦИНСКИХ УСЛУГ

Relevance. The new Rules for the provision of paid medical services by medical organizations, approved by Decree of the Government of the Russian Federation of March 11, 2023 No. 736, are aimed at developing and improving the legal framework for the provision of medical services. In this regard, it seems relevant to draw the attention of doctors dentists on the legal aspects of contractual relations. The main goal of the work. The purpose of the study is to draw the attention of dentists to certain legal aspects of contracts for the provision of paid medical services, which are essential for the effective protection of their interests in the event of disputes regarding the proper performance of contractual obligations. Approaches used. The provisions of legislative and by-laws related to paid medical services have been studied. A survey of 125 respondents (72 dentists, 53 consumers of medical services) was conducted on some legal aspects of the provision of paid medical (dental) services. Results. Using the survey method, the attitude of dentists and consumers to pre-contractual relations, to information about medical workers involved in the provision of paid medical services, as well as to the provision in written form of rules for using the results of the service or their storage is shown. Main conclusions. When providing paid medical (dental) services, it is necessary to pay special attention to: 1) pre-contractual notification to consumers in writing about the consequences of failure to comply with the instructions (recommendations) of the performer (medical worker providing a paid medical service; 2) notification to the consumer ( to the customer) information about medical workers involved in the provision of paid medical services, about the level of their professional education and qualifications; 3) providing the consumer with written rules for using the results of the service or storing them.

CONSUMER INFORMATION SYSTEM IN WEB-BASED COOPERATIVES: CASE STUDY OF DIRA JAYA SINAR RESKI COOPERATIVE

This study aims to determine the results of the development of consumer information systems at the Dira Jaya Sinar Reski cooperative as well as find out user responses to the system to be developed. This research uses the type of R&amp;D (Research and development) research and prototype development model that uses data collection techniques such as interviews, observations, and questionnaires. Based on the results of the research, a Web-Based Consumer Information System was developed at the Dira Jaya Sinar Reski Cooperative which has been tested using ISO 25010 testing using 8 aspects of testing that have been validated by content experts. The final result of the overall assessment is that the Consumer Information System at the Web-Based Dira Jaya Sinar Reski Cooperative was declared valid by the validator lecturers, and declared effective based on functional suitability aspects, and declared practical based on user responses that have been tested from the usability aspect.

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.

Comparasion of HVAC Energy Consumption Prediction in an Academic Building using LSTM and DNN

Energy consumption information is a collection of information obtained from datasets that is useful for making decisions for energy conservation. In this paper, we proposed a modern approach based on LSTM and DNN. While many researchers have employed these methods for predicting energy consumption, this paper seeks to compare their efficacy to determine which is superior. The comparative analysis in question employs accuracy metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) values. Furthermore, the accuracy metric outcomes indicate that the LSTM method surpasses the DNN approach in terms of the R-squared (R2) value, with respective scores of 0.931 and 0.782. Meanwhile, for other accuracy metrics, the DNN method outperforms LSTM. Nevertheless, the performance of the two proposed methods is excellent, as evidenced by the R-squared (R2) value exceeding 0.75, which aligns with modeling standards observed in numerous research studies.

Assessing precautionary allergen labeling management in China: A comprehensive analysis of unintended allergen presence and VITAL® 3.0 evaluation

Precautionary allergen labeling (PAL) is used to warn consumers about potential unintended allergen presence (UAP) due to cross-contact in food processing. This study aimed to reveal the UAP levels in food products with PAL and to assess the current implementation of PAL in China based on Voluntary Incidental Trace Allergen Labeling (VITAL®) 3.0 program. A total of 127 pre-packaged snack foods containing egg PAL and 114 pre-packaged snack foods containing milk PAL were collected through stratified random sampling in Nanchang, Jiangxi, China, for subsequent detection and analysis. ELISA analysis indicated that 18.11 % of products had egg protein and 29.82 % had milk protein at levels exceeding 2.5 ppm, with the majority of these positives concentrated in baked products. Meanwhile, high UAP levels in certain products, particularly those from small and micro manufacturers, posed a non-negligible allergy risk. VITAL® 3.0 assessments found that over 80 % of these products were deemed risk-free and did not require PAL. These findings highlighted that the current PAL approach in China led to excessive labeling which diminished information and value for consumers. Therefore, it is necessary to strengthen the risk assessment and management of cross-contact allergens in food industry, establish a PAL system to effectively communicate risks to consumers, and ultimately build a more robust food management framework in China.

Applying user-centered design to improve drinking water consumer confidence reports: A Baltimore case study

The US Environmental Protection Agency is revising its policy on drinking water quality reports for consumers. These reports are intended to enhance the public’s “right to know” and to spur action to protect and promote safe water. However, these reports are known to be highly technical and difficult to access compromising their communication value. This study engaged a 10-person focus group to gather evidence on how these reports can be improved. We applied user-centered design principles to understand public drinking water consumer information needs and preferences and to develop new communication tools and methods. Through a set of in-depth interviews, we learned that most participants were unaware of the report until introduced to it during the study. The focus group participants voiced preferences for: better ways to convey technical information; more health information; a clearer understanding of costs and billing; and neighborhood or household level water quality information. Following the interviews, we convened two rounds of small group meetings to create new report designs and to review and refine the designs. The focus group developed a one-page summary statement, water contaminant trend charts, an interactive map, and other recommendations on ways to improve dissemination of the report. The project results, focus group recommendations and designs were submitted to the US Environmental Protection Agency for consideration as the policy for these reports is finalized. We believe these findings provide valuable insights into water quality communication challenges and that this approach is widely applicable and can be informative for water utilities as they prepare future reports.

Effects of the added sugar labeling on consumers' willingness to pay: The case of cranberry products under different nutrition‐related information treatments

AbstractThe Food and Drug Administration announced a rule update to the Nutrition Facts Panel (NFP) requiring the declaration of added sugars on the NFP starting in 2020. This study measures the impact of these changes by estimating the willingness to pay for added sugars in cranberry products under different nutrition‐related information treatments. We found significant discounts for increases in added sugars that vary across information treatments and consumer subsamples. A positive information frame about the health benefits of cranberries was not found to consistently offset the impact of additional information on the recommended daily intake limits for added sugars.

Grounded Theory Analysis of Online Shopping Reviews Based on Text Clustering

Grounded Theory Analysis of online shopping reviews can improve the quality and accuracy of review data analysis, providing valuable information and suggestions for relevant businesses and consumers. This article focuses on analyzing mobile phone reviews on JD.com as coding objects, effectively employing machine learning algorithms such as Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), and text clustering in natural language processing to extract thematic keywords from online shopping review texts and perform axial coding. Subsequently, DBSCAN clustering algorithm is applied to categorize the main categories of design, service, and production under the perspective of businesses and consumers as consumption attraction, while categorizing price and brand under consumption intention, forming selective coding. By applying Grounded Theory to the analysis of online shopping reviews, this study accurately identifies consumer needs and feedback, with the aim of helping businesses improve product and service quality, enhance customer satisfaction and loyalty, and engage in personalized marketing and customized services.

The Impact of Data Media on Customer Spending Behavior and Consumer Psychology

In the epoch of big data and digital media, consumer behavior has experienced impressive changes. This paper explores the impact of data media on consumer behavior and psychology, particularly concerning social media and digital platforms. The transition from traditional data analytics to the diverse world of big data and digital media is analyzed in this paper as well as how this shift has changed the way companies understand consumer needs and adjust their marketing strategies. The paper also explores how big data enables companies to accurately predict consumer behavior and raises key questions about customers' and firms privacy, data security, and the ethical use of consumer information. In addition, the paper discusses how the COVID-19 pandemic is accelerating digital transformation and changing consumer behavior. Understanding these changes is critical for businesses, marketers, and researchers to gain deeper insights into the current and future trends in consumer behavior in the digital age.

Evaluating compliance with local and International Food Labelling Standards in urban Tanzania: a cross-sectional study of pre-packaged snacks in Dar Es Salaam

BackgroundUrbanization influences food culture, particularly in low- and middle-income countries where there is an increasing consumption of processed and pre-packaged foods. This shift is contributing to a rise in non-communicable diseases. Food labelling standards are crucial for regulating manufacturing practices and helping consumers make healthy food choices. We aimed to assess the compliance of local and imported pre-packaged snacks with Tanzanian and international labelling standards in Dar es Salaam, Tanzania.MethodologyA cross-sectional study was conducted on 180 snack products. A checklist based on Tanzanian and Codex labelling standards was used to evaluate adherence. We also examined factors influencing adherence, such as product origin, price, category, purchase location, and package size.ResultsThe majority of the snacks demonstrated partial adherence to Tanzania (n = 97; 54%) and International (Codex) (n = 120; 67%) labelling standards. Imported products showed significantly better adherence to both Tanzanian (n = 46; 53%) and international (n = 42; 48%) standards. Notably, more than half (n = 110; 66.7%) of the products used English for labelling, and infrequently (n = 74; 41.4%) used the recommended World Health Organization Front-of-Pack Nutrition Labelling. Product category, origin, and package size were significantly associated with higher levels of international standard adherence (p < 0.05).ConclusionThe inadequate adherence to mandatory labelling standards and the scarce use of Swahili and FoPL highlight the need to strengthen labelling practices and potential challenges faced by consumers in understanding nutritional information. Thus, strengthening and emphasizing good labelling practices are urgently needed as we seek to address diet-related noncommunicable diseases.

Decoding the information quantity-quality paradox: How eWOM volume influences consumption value uncertainties

This study explored how the type, quantity, and polarity of information shape customer uncertainty in consumption values, utilizing longitudinal and large-scale user-generated data collected from Yelp. Two types of curvilinear relationships between information quantity and consumer uncertainty were identified. Pattern (a) exhibits an inverted U-shaped trend where uncertainty rises with information volume (under scarcity) but diminishes after a threshold (indicating sufficiency). In contrast, Pattern (b) shows recurrent uncertainty surges after surpassing a volume threshold, signaling information overload. Moreover, the study revealed that the polarity of visual information significantly moderates the impact of information quantity on customer uncertainty. The findings suggest that restaurant managers, within the eWOM context, should tailor information management strategies corresponding to different stages of information diffusion. Lastly, the study underscores the importance of monitoring conflicting information for restaurant managers to effectively mitigate customer uncertainty, emphasizing the utility of the novel big data-based approach to measure customer uncertainty.

Design and Implementation of an SGX Based Electricity Information Collection and Management System

With the rapid growth of the number and scale of smart grid users, traditional data encryption transmission methods can no longer meet the performance requirements of data aggregation. In response, a power consumption information collection and management system based on SGX software protection extension is proposed. The system mainly consists of three parts: user electricity data acquisition terminal, SGX data security processing and distributed storage module on the chain, and data monitoring management display platform. The user electricity data collection terminal collects electricity data from various buildings, residences, rooms, and other smart meters, analyzes and uploads it. After calling the trusted function of SGX technology, it enters the security zone provided by SGX for data processing. Finally, the data security processing results and data are uploaded to the blockchain for storage. In order to visually display user electricity usage data, an intelligent monitoring platform for user electricity collection and management has been established. This system can reduce the workload of user electricity data collection, ensure the accuracy of data collection, and provide an efficient and highly reliable system platform for user electricity data management.

Question 65 What on‐farm techniques can increase water productivity? General report

AbstractTechnological interventions in tackling water scarcity in agriculture delve into the water productivity issue through available alternative water resources and on‐farm techniques including both structural measures and agronomic practices accompanied by a sound water management approach. On‐farm water management has been addressed under Congress Question 65 through deliberations on irrigation efficiency, soil moisture forecasts and hydrological predictions, efficient and timely distribution of water, controlling non‐beneficial evaporation and reducing non‐returnable losses of irrigation water at existing and state‐of‐the‐art levels using technologies such as SCADA (Supervisory Control and Data Acquisition) and sensor technology. A wide range of land, water and crop‐related important parameters such as soil degradation, soil moisture, zero tillage, conservation agriculture, water demand and supply, future irrigation demand, biologically degradable mulching materials, cost‐effective design of drip irrigation, water quality and cultivation practices through simulation for evaluating environmental impact and adaptation capacity of farmers were addressed. The major outcomes inferred increasing water productivity through optimizing agricultural water use that led to higher crop yields. IoT (Internet of Things) technologies can provide accurate and dynamic information of water consumption and rainfall in real time and have the potential of adapting to climate change scenarios.

Healthier Food Choices: From Consumer Information to Consumer Empowerment in EU Law

This article evaluates the European Union’s efforts to regulate consumer food information with the aim of improving consumer nutrition as part of its broader consumer protection agenda. With nearly a million deaths attributed to unhealthy diets annually, the EU’s New Consumer Agenda, its Farm to Fork Strategy, and Europe’s Beating Cancer Plan all highlight the objective of empowering consumers to make informed, healthy food choices. This underscores the EU’s longstanding emphasis on regulating information to protect consumers, an approach which is even stronger with nutrition more specifically. This article shows that the EU food information law is based on two beliefs. First, consumers are given food information that is sufficient, accurate, non-misleading, clear and easy to understand and they are, therefore, well-informed. Second, well-informed consumers are empowered. This article argues that both of these beliefs are misguided. The current food information rules do not truly inform consumers well and do not, on their own, empower consumers to make healthy dietary decisions. Consequently, this article expresses scepticism about the ability of the current rules to promote improved nutrition. In light of the critiques of the current rules, this article offers suggestions for an improved food information paradigm. In addition to rules that would more effectively inform consumers, such a paradigm would acknowledge the limitations of information to empower healthier food choices and, therefore, give greater weight to more substantive interventions. This would align food law with broader trends in consumer protection law and thereby genuinely promote healthier nutrition.

Novel AMI in Zigbee Satellite Network Based on Heterogeneous Wireless Sensor Network for Global Machine-to-Machine Connectivity

This study endeavored to enhance the efficiency and utility of microcomputer meters. In the past, their role was predominantly confined to remote meter reading, entailing high construction and communication transmission costs, coupled with subsequent maintenance and operational expenditures. These factors collectively impacted the enthusiasm of various stakeholders to invest in this realm. Hence, in alignment with the smart city development initiative, the natural gas industry has pioneered the establishment of an advanced metering infrastructure with heterogeneous wireless sensor networks (HWSNs) at its core. This visionary leap incorporates global machine-to-machine connectivity (G-M2MC) technology, interconnecting all facets of its operations, thereby positioning itself as a trailblazer within the industry. While advancing this endeavor, the project’s scheduling aligns with the enterprise’s sustainability goals in the early stages of digital transformation. This strategic allocation of resources is responsive to government policies and aspires to cultivate a digitally connected smart green energy hub, thereby expediting the transformation of the living environment. The objective is to provide a stable, secure, cost-effective, and reliable system that can be shared among peers. Furthermore, this study delved into the analysis of congestion avoidance in intelligent Zigbee satellite transport networks based on the HWSNs-GM2MC of non-synchronous satellite orbit system (NGSO) pivotal technologies, utilizing them to integrate the smart LNGas management system (SGMS). Concurrently, it developed application services through the smart meter application interface (SMAPI), distinct from conventional microcomputer meters. However, it is imperative to acknowledge that cloud computing, while processing sensitive data, grapples with issues of latency, privacy, efficiency, power consumption, and zero-trust security risk information management and ethical authority management capabilities in the defense of disaster relief responses.

Tracking and tracing water consumption for informed water sensitive intervention through machine learning approach

To develop a water conscious strategy, it is critical to track and trace water from its source to the end users, understand water conservation behaviors, and identify the factors that influence water consumption. However, in developing nations, little research has been done to provide a quantitative picture of how water is consumed and transformed in urban households, as well as the water sensitive interventions needed to improve access to clean water. Hence, the main objective of the study was to determine the most significant residential water consumption variables and to predict residential water consumption in a way that can generate water consumption information for water sensitive intervention decision making using the case study of Adama city in Ethiopia. A combination of top down and bottom up data collection techniques were employed as the data collection instrument. Machine learning was integrated with spatial and socioeconomic analytic techniques to estimate daily household water consumption and identify the factors that significantly influence household water consumption. The results show that there is only “one source option” for the city’s clean water supply and that different water harvesting methods are not likely to be developed. The average daily water consumption per person is 69 liters which falls below the national standard of 80 liters allocated per person per day. The result reveals that the water distribution network covers only 45% of the city master plan. About 38% of the water demand is unmet and 30% of households only receive water once every three days or fewer. This shows that the city is experiencing physical and economic water scarcity. The results demonstrated that family size, housing quality, income, number of rooms, legal status of the parcel, supply reliability, climate, and topographical features are the most important factors in predicting residential water consumption. This study further demonstrates how well supervised machine learning models, such as the Random Forest Regression algorithm, can predict the household’s daily water consumption. The findings also showed that there is a need for significant improvements in water saving habits of the households. Another conclusion that can be drawn is that as long as the city’s business as usual water consumption practice doesn’t change, the water supply problem will worsen over time.