Consumption Patterns Research Articles

A strategic and social analytics model for sustainable packaging in the cosmetic industry

Every day, we use cosmetic products that are not only focused on beauty but also with everything related to personal care and hygiene. The impact that these products have on sustainability cannot be overlooked. Many cosmetics contain unsustainable ingredients that can cause environmental damage and loss of biodiversity. In addition, fossil-based packaging contributes greatly to environmental pollution and increases waste in the absence of a circular supply chain. This work has a dual objective. The first is to provide a strategic analysis based on a multi-criteria approach to evaluate the most sustainable alternatives to traditional packaging that manufacturers could adopt based on the opinions of experts from different categories of stakeholders. In this study, the multi-criteria approach was employed, as it has been widely recognized in the literature for its effectiveness in evaluating and comparing alternatives across multiple, often conflicting criteria. The second is to provide a social analysis to assess consumers’ views, habits, preferences, and willingness to pay toward sustainable packaging. The results show divergence among experts who prefer refillable packaging while consumers prefer recyclable packaging. In contrast, a convergence in selling price and production costs is verified, highlighting the strategic importance of the economic dimension is for sustainable packaging, and the willingness to pay for sustainable packaging is about twice that of traditional packaging. The implications of this work suggest that circular supply chains covering the entire life cycle of products, based on a pragmatic approach, can drive the convergence of consumption and production patterns toward sustainable development.

Multiscale Spatio-Temporal Feature Fusion Based Non-Intrusive Appliance Load Monitoring for Multiple Industrial Industries

The appliance types and power consumption patterns vary greatly across different industries. This can lead to unstable identification results of traditional appliance load monitoring methods in different industries. A non-intrusive appliance load monitoring (NIALM) method for multiple industries based on multiscale spatio-temporal feature fusion has been proposed. Firstly, the ConvNeXt Block with efficient channel attention has strong feature extraction capability. Spatial features of appliance state changes and micro-variations generated during operation can be extracted from mixed industrial load information by it. Meanwhile, the bidirectional gated recurrent neural network is used to learn the bidirectional dependencies of the load data, obtaining temporal features. Then, the multi-scale feature extraction module is used to extract temporal and spatial features from different depths of the network layers. And the extracted multi-scale temporal and spatial features are fully integrated. Finally, the proposed model is optimized using the Stochastic Weight Averaging method. During the training process, a certain number of model weights are randomly averaged, which can improve the model's generalization ability and identification accuracy. The experiment was conducted on six different industries. The evaluation indexes such as accuracy, F1 score, and Wasserstein distance are also used to verify the effectiveness and superiority of the method.

Revisiting the Alcohol-Aggression Link: The Impact of Alcohol Consumption Patterns

Laboratory studies have repeatedly reported a link between alcohol and aggression, yet many rely on single-dose administration methods and overlook variations in alcohol consumption patterns. The present study investigates the effects of alcohol on aggressive behavior using a double-blind, placebo-controlled cumulative drinking administration approach that mirrors the natural drinking behaviors often observed in pubs within a laboratory setting. This study also pioneers the examination of how alcohol consumption patterns (light or heavy) moderate the relationship between precise Breath Alcohol Concentration (BrAC) levels and extreme aggressive behavior. Seventy-five light (N=38) and heavy (N=37) alcohol drinkers were randomly assigned to alcohol (N=33) or placebo (N=42) conditions. Participants drank four drinks successively. Taylor's aggression paradigm was completed twenty minutes after each drink. Multilevel modeling was applied to analyze the effects of precise BrAC and account for within-person variability. Alcohol showed a dose-dependent effect on aggression; as alcohol levels rose, so did aggression (p<.001). Alcohol consumption pattern moderated the effect of cumulative BrAC on aggression, such that the effect was more substantial for light as compared with individuals who drink heavily (p=.03). The current study highlights the moderating role of alcohol consumption pattern in the association between precise BrAC levels and extreme aggression, offering insights into individual differences in susceptibility to alcohol-induced aggression, and supporting the I3 meta-theory (Finkel, 2014). The Findings underscore the importance of investigating the interplay between acute and chronic alcohol use on behavior, challenging conventional thresholds for hazardous drinking classification. Implications for future studies, legislators, and policymakers are discussed.

Contribution of ultra-processed food and animal-plant protein intake ratio to the environmental impact of Belgian diets

There is growing concern about the various impacts of food consumption, both on human and planetary health. Given the context-specific nature of consumption patterns, evaluating their national-level impacts is crucial for proactive policy development. This research aims to evaluate the environmental impact of current Belgian diets, with particular attention to the contribution of food groups, ultra-processed foods (UPF), and the animal-to-plant protein ratio. The methodology consists of three key stages. Firstly, the Belgian diet was summarised, based on data from the Belgian National Food Consumption Survey 2014/2015. Secondly, the origin of the most frequently consumed foods was traced using trade databases. Finally, a cradle-to-grave life cycle assessment was conducted to determine the impact of Belgian diets on climate change, water use, land use, and fossil resource scarcity. In this third step, an iterative procedure for selecting the food items to be included in the study was performed. The iterative approach resulted in the inclusion of 227 food items in the analysis. The results indicate greenhouse gas (GHG) emissions of 4.4 [4.27–4.54] kg CO2-equivalent per person per day. Red meat (35 %), beverages (16 %), dairy products (16 %) and snacks (10 %) are identified as primary contributors to climate change. Similar results were observed for land use impacts. Water use and fossil resource scarcity exhibited different trends, with beverages being the most impactful food group. Moreover, UPF account for 50 % of the total climate change and land use impacts, with a linear relationship observed between increased UPF consumption and GHG emissions and land use. A similar linear trend is observed between the ratio of animal-to-plant protein intake and both climate change and land use impact categories. A shift from the current protein ratio to a ratio of 40/60, as suggested in the Flemish Green Deal Protein Shift has been shown to result in a reduction in GHG emissions of the diet by 31 %. This study emphasises the need to target the consumption of high-impact foods such as UPF and animal-based products. Future research will investigate the relationship between environmental and health impacts.

Quantitative microbial risk assessment of antibiotic-resistant Salmonella enterica contaminating hydroponic leafy vegetables

Hydroponics plays an important role in addressing food security concerns, particularly in countries aiming to increase food self-sufficiency. However, it is vulnerable to microbial contamination, and biofilms formed in hydroponic facilities may promote horizontal gene transfer (HGT) of antibiotic resistance genes (ARGs). Eventually, the bacteria are internalized into the edible parts of the vegetable through the roots, which can lead to human exposure to antibiotic-resistant pathogenic bacteria. Microbial risk assessment can play a pivotal role in microbial risk management; however, it has not been conducted for hydroponic systems. In this study, a quantitative microbial risk assessment of hydroponic vegetables was performed using literature values regarding the concentration of Salmonella spp. in hydroponics, efficiency of HGT, probability and rate of internalization, vegetable consumption patterns, and dose-response relationships. Furthermore, a sensitivity analysis was performed to identify the factors that had a significant impact on the infection probability per single exposure event for all Salmonella spp. by calculating Spearman's correlation coefficients. The estimated annual probability of infection per person by all Salmonella spp. was 2.04×10−1, while the estimated probability of infection from Salmonella spp. that acquired ARGs was 2.54×10−6. Our sensitivity analysis showed the correlation between the occurrence of internalization and hydroponic contamination levels, highlighting the need for increased awareness and regulatory action.

Examining the Energy-Growth Dynamics in Saudi Arabia: Insights and Policy Implications

The empirical relationship between energy consumption and economic growth is complex and varies by country and model specification. This study focuses on Saudi Arabia, one of the largest producers and exporters of crude oil, to explore this relationship. Our analysis refutes the Energy-Kuznets Curve hypothesis for Saudi Arabia, finding neither a linear nor a curvilinear relationship between economic growth and energy consumption. Instead, we observe an asymmetric impact of economic growth on energy consumption: the rate of increase in energy consumption with rising per capita income differs from the rate of decrease when per capita income falls. Additionally, the study reveals a positive impact of investment on energy consumption, indicating a dynamic interplay between economic growth, industrial activity, and improved energy access. These findings suggest that energy consumption patterns in Saudi Arabia are intricately linked to economic fluctuations but are not uniformly predictable. Policy recommendations are provided to address these complexities and promote sustainable growth.

Translation Evaluation of Social Media Auto-Translate Feature toward News Headline

Machine translation has become an everyday part of society's news consumption patterns. Unfortunately, important news content is poorly mediated by social media, especially on the output of the translation system feature on the site. Therefore, further research is needed on the evaluation of the auto-translate system on social media. This study focuses on the results of linguistic evaluation on the output of social media machine translation in translating news headlines. The data were taken from news coverage by the Reuters channel in the period of September 1st–7th, 2023 and from social media such as Facebook and Twitter. The translation evaluation used linguistic elements consisting of orthography, morphology, lexical, semantic, and syntax. This study found various kinds of linguistic non-equivalence. Orthographic non-equivalence includes punctuation, capitalization, and apostrophes. Morphological non-equivalence in the two systems was found in number concordance, inflection, derivation, and compounding. Lexical errors occur in both systems in the form of no correspondence and missing target words, as well as untranslated words and proper noun. In terms of semantics, both systems had difficulties with polysemy, homonymy, and expression. Finally, syntactic errors occur with missing articles, prepositions, and inappropriate arrangements of syntactic elements.

Big Data and Machine Learning-Based Iot Models for Sustainable Energy Prediction

Integrating Big Data and Internet of Things (IoT) platforms is the focus of this research, which aims to improve energy management. The problem statement is centered on the potential for development through advanced technologies and the inefficiencies in traditional energy management methods. The objectives are to analyze energy consumption patterns, develop an innovative Home Energy Management System (HEMS) architecture, and offer energy-saving solutions. Synthetic energy consumption data is generated, normalized, and divided into training and testing sets from a methodological perspective. K-nearest neighbors, Decision Trees, Support Vector Regression, and Random Forest are the machine learning models trained and evaluated. The Random Forest model outperforms other models in terms of the accuracy of its predictions of energy consumption. The integration of renewable energy sources with cutting-edge technology to revolutionize energy management practices is the essence of novelty. In conclusion, this investigation underscores the importance of utilizing advanced technologies to promote sustainable energy management, providing practitioners and policymakers with practical insights.

Optimal dimensioning of renewable energy generation and storage systems

With high energy demand and large available area, agricultural farms offer significant potential for renewable energy investments, like photovoltaic systems and electrical energy storages. However, the profitability of such investments depends strongly on self-consumption, so accurate planning requires computation-intensive simulation and optimization considering local consumption. This study presents a novel methodology for the optimal dimensioning and configuration of photovoltaic systems and electrical energy storages using efficient techniques from continuous non-linear optimization. Combining physical and economic models with measured consumption data, an investment’s net present value over 20 years is maximized. Using gradient-based solver WORHP, the simulation, optimal control, and optimal dimensioning of the local energy system are calculated simultaneously, allowing for efficient computation over an entire year of hourly data to capture both daily and seasonal variations.The approach is demonstrated with simple use cases, including an exemplary day of a dairy farm’s consumption, for which optimal systems with and without storage achieve 77% and 43% of autarky, respectively. Saturation effects of optimal plant size can be observed when sizes are large enough for optimal self-consumption but not expanded further for grid export. With energy storage, this saturation is reached at higher values. Optimizing photovoltaic plants with different orientations to match the specific consumption patterns characteristic of the dairy farm achieves similar autarky as a single plant while reducing investment costs by more than 20%. While thorough validation and comparison against heuristic methods predominantly used in the field is part of ongoing research, the presented use cases demonstrate the flexibility and efficiency of the proposed method and highlight its promise as a planning tool in the agricultural domain and beyond.

Determinants of malnutrition among adolescent females of age 15–19 years in an urban slum of Raipur city

ABSTRACT Context: Adolescent girls suffer the brunt of malnutrition due to increased nutritional needs to facilitate rapid growth spurt including menstruation and increased physical activity. This vulnerable group has low social power and is subjected to peer and social pressures related to body image and weight. All these factors along with the sociocultural milieu may lead to a spectrum of nutritional problems in them. Aims: To determine factors associated with malnourishment among adolescent females of age 15–19 years in an urban slum of Raipur City. Settings and Design: The study setting was the urban field practice area of the institute in Raipur. The study was an analytical cross-sectional study done from January 2022 to June 2023 among 401 adolescent girls of age 15–19 years. Methods and Material: The sampling was done using the consecutive sampling method. The study tools used were a semi-structured questionnaire on sociodemographic variables and details of the respondents, anthropometric measurements, dietary assessment using the 24-hour food recall method, and food consumption patterns using a food frequency questionnaire. Statistical Analysis Used: The Chi-square test of significance was applied to find the association between malnourishment with the input variables. For calculating the strength of the association, the odds ratio was estimated for those variables with P value &lt; 0.2 in the Chi-square test, and later the odds ratio was adjusted for confounding with the same variables, using multivariable logistic regression. Results: According to BMI-for-age Z scores, 2% were severely thin (&lt; −3 SD), 9.7% were thin (−3 to − 2 SD), 5% were overweight (1 to 2 SD), and 1.5% were obese (&gt;2SD). While measuring the height-for-age Z scores, 5.5% had severe stunting and 21.9% had moderate stunting. Those respondents with educational status of high school or below [AOR 3.5 (1.8–6.8), P &lt; 0.001] and those who did not consume a non-vegetarian diet [AOR 2.1 (1.1–4.1), P = 0.02] had significantly higher adjusted odds of being undernourished (severely thin or thin) as compared to those with educational status above high school and those who consumed a mixed diet, respectively. The respondents who are currently not going to school/college had significantly higher adjusted odds [AOR 2.2 (1.2–3.8), P = 0.007] of being stunted when compared to those who are attending school/college. Conclusions: The significant levels of malnutrition observed in this study among late adolescent girls aged 15–19 years—11.7% undernourishment, 6.5% overnourishment, and 27.4% stunting—highlight the concerning vulnerability of this population. The higher prevalence of stunting throws light onto the chronicity of malnutrition. As seen from the significant associations, proper education, and a mixed diet will help nurture a healthy female adolescent population.

Metabolic Versatility of acetogens in syngas Fermentation: Responding to varying CO availability.

Syngas fermentation using acetogenic bacteria offers a promising route for sustainable chemical production. However, gas-liquid mass transfer limitations and efficient co-utilization of CO and H2 pose significant challenges. This study investigated the kinetics of syngas conversion to acetate by Acetobacterium wieringae and Clostridium species in batch conditions under varying initial CO partial pressures (19 - 110 kPa) in batch cultures. A. wieringae strains, exhibited superior growth in all gas compositions, with a maximum growth rate of 0.104 h-1. The distinct CO, H2, and CO2 consumption patterns revealed metabolic flexibility and adaptation to varying syngas compositions. Notably, A. wieringae strains and C. autoethanogenum achieved complete CO and H2 conversion, with C. autoethanogenum also exhibiting net CO2 uptake. These findings provide valuable insights into the distinct metabolic capabilities of these acetogens and contribute to the development of efficient and sustainable syngas fermentation processes.

Redefining realistic and stochastic occupancy schedules and patterns for residential buildings in Jordan

Occupant behavior significantly influences building energy consumption, underscoring the importance of understanding the interplay between energy usage and occupant behavior. This integration is vital for evaluating how occupants and their interactions impact building energy consumption patterns. Despite widespread recognition of the importance of behavior-centric approaches in achieving sustainable architecture, there remains a gap in understanding human behavior, particularly in performance-based design. This study presents realistic and stochastic occupancy schedules for residential buildings in Jordan. This was achieved using a mixed-method approach involving a survey, data classification, clustering processes, and a co-simulation approach combining Rhino Grasshopper and Python. The results contributed to refining the general characteristics of each cluster within different age groups, encompassing various patterns of interaction, tolerance levels or levels of sensing, energy conservation consciousness, and occupancy patterns. A significant correlation was also observed between age groups, occupancy schedules, and energy consumption behaviors. The simulation also evaluated the effects of stochastic behavior patterns on the prediction of the energy performance of residential buildings. The results confirm the significant variation in energy use intensity levels between realistic occupancy schedules and fixed schedules for residential buildings. The results also prove that using realistic occupancy schedules for each age group significantly affects energy consumption behavior through occupant-based behaviors. This confirms the pivotal role of dynamic schedules and occupant behaviors in understanding and shaping energy consumption patterns and realistically determining the efficiency of building systems.

Determination of Average Water Demand and Diurnal Consumption Pattern for a Multifamily Apartment Complex in Peshawar

Abstract: Accurately estimating and understanding domestic water demand and consumption patterns is essential for the efficient planning and management of drinking water supply systems. Traditional methods for estimating water demand are labor-intensive and often rely on assumptions. To overcome these limitations, this study employed a novel approach using highprecision data loggers to monitor water levels continuously in rooftop tanks at 5-minute intervals within a residential apartment complex in Peshawar, Pakistan. The findings indicate that per capita water consumption varies between 97 and 209 liters per capita per day (lpcd), with an average consumption of 173 lpcd. This methodology offers enhanced accuracy in quantifying water usage, providing a robust alternative to conventional estimation techniques.

Strategi Sukses Wirausaha Perempuan Menghadapi Tantangan dan Peluang di Bulan Ramadhan

Ramadan is a special month for Muslims around the world, including Indonesia. During the month of Ramadan, Muslims will fast and increase their worship and good deeds. However, the month of Ramadan can also provide opportunities and challenges for the business world. especially for female entrepreneurs so that female entrepreneurs need to understand the cultural and religious context that surrounds the month of Ramadan, while still adapting to the ever-changing dynamics of business. This involves culturally sensitive marketing strategies, adjustments to operating hours, and innovations in offering products or services that suit consumer needs during the fasting month. In this study, a descriptive qualitative approach was used, data was obtained using observation, interview, and documentation methods. Furthermore, the data was analyzed through several stages starting from data collection, data reduction, data presentation and drawing conclusions. Then data triangulation was carried out to test the validity of the data. The results of this study indicate that women have great potential to achieve their success. Although faced with various challenges and obstacles such as changes in consumption patterns and limited time, women can take advantage of opportunities by implementing smart and innovative strategies. Some strategies that can help women face challenges and maximize business opportunities during the month of Ramadan include: a deep understanding of consumer needs and preferences during the month of Ramadan, flexibility in adjusting operating hours and marketing strategies, creativity in developing products and services that are in line with the Ramadan theme.

A Study of Consumer Awareness Regarding Health Issues Caused by Consumption of Cold Drinks in Ahmedabad City

This study explores consumer awareness regarding health issues caused by the consumption of cold drinks in Ahmedabad city. The research examines associations between age, reasons for cold drink consumption (e.g., taste, habit, socializing), and the occurrence of health problems such as diabetes, digestive issues, and dental problems. It also investigates how consumers gather information about the health impacts of cold drinks, with a focus on sources such as social media, family/friends, schools, and health professionals. The findings reveal weak associations between age and reasons for consumption, suggesting that cold drink preferences are not strongly age-dependent. However, digestive issues are more significantly associated with cold drink consumption compared to other health risks like diabetes and dental problems.The study also highlights the importance of labeling and natural ingredients in influencing consumer choices, as well as the role of government campaigns in raising health awareness. Practical implications include the need for targeted public health campaigns, clearer product labeling, and promoting healthier beverage alternatives. The findings suggest that age-specific interventions, especially leveraging social media for younger consumers, can be effective in raising awareness and promoting healthier behaviors. Future research should explore psychological and behavioral drivers, evaluate the effectiveness of public health interventions, and investigate long-term health impacts to provide deeper insights into cold drink consumption patterns and their health consequences

Driving Thailand healthcare communication policy on e-cigarettes through social network analysis

E-cigarettes pose a significant public health concern, particularly for youth and young adults. Policymaking in this area is complicated by changing consumption patterns, diverse user demographics, and dynamic online and offline communities. This study uses social network analytics to examine the social dynamics and communication patterns related to e-cigarette use. We analyzed data from various social media platforms, forums, and online communities, which included both advocacy for e-cigarettes as a safer smoking alternative and opposition due to health risks. Our findings inform targeted healthcare policy interventions, such as educational campaigns tailored to specific network clusters, regulations based on user interaction and influence patterns, and collaborations with key influencers to spread accurate health information.

POLA KONSUMSI DAN STATUS NUTRISI PADA REMAJA

Various health problems in adolescents are related to meeting balanced nutritional needs. For both boys and girls, nutritional problems that often occur in other teenagers include underweight, overweight/obesity, and anemia. This study identified the food consumption patterns of male and female students and their nutritional status using a cross-sectional design. The sample calculation used total sampling and obtained 33 students. The collection technique is carried out using the convenience method. The instrument used was the Student Consumption Patterns questionnaire and the data was analyzed using chi-square. The research results showed that 57.6% had poor consumption patterns. A nutritional monitoring program for adolescents is needed which includes maintaining nutritional status, consumption patterns and risk factors related to nutrition. Persuasive health education is needed by utilizing electronic media which is currently widely accessed, especially by teenagers.

Unveiling Regional Dynamics and Factors Influencing Fish Purchase and Consumption in Odisha, India

Despite India being a major global fish producer, per capita fish consumption remains notably low in several regions, raising concerns regarding the nutritional adequacy of diets. This study explores the factors influencing fish purchase and consumption patterns in the Cuttack and Khordha districts of Odisha, India. A comprehensive survey (n=400)was conducted, and a trans-regression analysis was applied to identify key factors. The results reveal that fish quality, with an emphasis on freshness, taste, and texture, is the most crucial factor influencing consumer preferences in both districts. Affordability, represented by fish prices, ranked second, while dressing facilities unexpectedly held significant importance, reflecting consumer preference for convenience in fish preparation. Sociocultural factors, including local traditions and dietary customs, also played a role but were less significant compared to other factors. The study highlights apromising trend of fish consumption exceeding recommended levels, contributing positively to nutritional goals. A comparative analysis betweenregions showed variability in meat consumption, with fish standing out as a primary dietary staple. These findings underscore the need for region-specific strategies to promote sustainable fish consumption, considering both economic and socio-cultural influences. The study contributes to the understanding of regional variations in fish consumption patterns and provides a foundation for targeted interventions to address nutritional challenges and promote sustainable dietary practices.

Quantitative Risk Assessment of Aflatoxin M1 Related to the Consumption of Raw Cow's Milk in Daloa (Côte d’Ivoire)

Cow's milk represents a potential health risk for consumers. The objective of this work is to assess the sanitary quality of raw cow's milk produced and consumed by the populations of Daloa. For this purpose, a total of 30 raw milk samples were collected from 3 raw cow's milk extraction points in the city of Daloa. Mycotoxins were determined using gas chromatography. The results showed that 100% of the analyzed samples were contaminated with aflatoxin M1 (AFLM1). These results indicated that the aflatoxin M1 levels in the analyzed samples exceeded the maximum limit (ML) set by European regulations. To assess the risk of contamination for the population of Daloa, a survey was conducted among consumers of raw cow's milk. A total of 388 individuals were interviewed to gather their opinions. The survey involved a form with questionnaires related to the objectives of the study. This form provided information on identity, marital status, consumption patterns, frequency, quantity, and the socio-economic characteristics of the consumers. The results showed that the risk of liver cancer related to the consumption of raw cow's milk in the city of Daloa is approximately 36.08% to 36.16%; representing 36,080 to 36,150 cases per 100,000 inhabitants. Thus, to ensure consumer safety, good hygiene practices in the preservation of feed for cattle and the construction of enclosures for them must be implemented.

Neurostructural features predict binge drinking in emerging adulthood: Evidence from a 5-year follow-up study

BackgroundBinge drinking (BD) involves consuming large amounts of alcohol within a short timeframe, leading to a blood alcohol concentration of 0.08g/dL or above. This pattern of alcohol consumption is prevalent among young adults and has significant implications for brain structure and subsequent drinking behaviors. MethodsIn this prospective longitudinal study, we employed zero-inflated negative binomial regression models to examine whether various neurostructural features (i.e., volume, surface area, cortical thickness) of brain regions involved in executive and emotional/motivational processes at the age of 18–19 could predict number of BD episodes five years later, at ages 23–24, once participants were expected to complete their university degree. Specifically, we recorded magnetic resonance imaging (MRI) data from 68 students who completed both the baseline MRI and follow-up alcohol use assessment, with the aim of analyzing the predictive value of these neurostructural characteristics five years later. ResultsThe analysis revealed that a larger surface area in the caudal division of the right middle frontal gyrus was significantly associated with a higher incidence rate of BD episodes (IRR = 2.24, 95 % CI = 1.28–3.91, p = 0.005). Conversely, a smaller surface area in the right caudal anterior cingulate cortex was associated with a higher incidence rate of BD episodes (IRR = 0.61, 95 % CI = 0.44–0.85, p = 0.004). ConclusionsThese findings suggest that specific neurostructural characteristics during adolescence can predict BD behaviors in young adulthood. This highlights the potential of neuroimaging to identify individuals at risk for developing problematic alcohol use.