Consumption Trends Research Articles

IoT Sensor Network Electricity Consumption Behaviour Using ClusterAnalysis Algorithm for Network Environment

The Internet of Things (IoT) sensor network plays a crucial role in monitoring electricity consumption behavior by collecting real-time data from various connected devices. Utilizing cluster analysis algorithms, researchers can effectively segment and analyze consumption patterns within a network environment. By grouping similar usage behaviors, these algorithms reveal insights into energy efficiency, peak usage times, and anomalies in consumption. The paper presents a novel methodology for analyzing and classifying users' electricity consumption behavior, utilizing the Clustering Behavior Analysis Weighted Classification (CBAWC) algorithm. By leveraging cluster analysis techniques and weighted classification, the proposed approach allows for the segmentation of consumers into distinct clusters based on their consumption patterns. Through the application of CBAWC, the study provides insights into the diverse behaviors exhibited by consumers, ranging from moderate to high consumption levels, varied peak hours, and peak days. The classification results demonstrate the effectiveness of the algorithm in accurately assigning users to their respective clusters, enabling stakeholders to better understand consumption trends and tailor energy management strategies accordingly. Through the application of CBAWC, consumers are segmented into distinct clusters based on their consumption patterns. For example, clusters exhibit varying average daily consumption levels, with Cluster 1 consuming around 300 kWh, Cluster 2 consuming approximately 450 kWh, and Cluster 3 consuming about 280 kWh on average. Additionally, clusters display different peak hours per day and peak days per month. The classification results demonstrate the algorithm's effectiveness, with high accuracy scores ranging from 0.86 to 1.00 across different user groups.

Exploring the Nicotine-Parkinson's disease link – Insights from the UK Biobank

IntroductionEvidence suggests an inverse correlation between smoking and Parkinson's disease (PD), yet the mechanisms remain unclear. This study examines the changing risk relationship between smoking and PD diagnosis using alcohol consumption, another reward-driven behavior, as a comparative measure. MethodsA nested case-control study was conducted using the UK Biobank (UKBB) database. Participants in the prediagnostic phase of PD were identified, and self-reported data on tobacco and alcohol use were analyzed employing conditional binary logistic regression. Polynomial and piece-wise regression models were employed to discern shifting associations with PD over time. ResultsOf 502,304 participants (63 ± 5.3 years, 63 % male), 3049 prediagnostic PD cases were identified. Non-smokers had a heightened PD risk, and this association strengthened closer to diagnosis. The odds ratio (OR [95 % CI]) associated with PD in non-smokers was 2.02 [1.07–3.81] 1–4 years before diagnosis, compared to 1.36 [1.02–1.83] at >10-year intervals (linear trend, p = 0.012). The time trajectory of ORs was best depicted by a quadratic function, identifying a shift in risk 7.5 years before diagnosis documentation. Similar patterns emerged among alcohol non-consumers, with an 8.5-year interval inflection point. ConclusionThis study identified two disparate risk trajectories among non-smokers: an initial low-amplitude increased risk at prolonged prediagnostic intervals possibly related to genetic/personality factors, followed by a sharp escalation in risk association commencing 7–8 years before diagnosis, possibly propelled by reverse causality. Similar trends in alcohol consumption reinforce these conclusions. These findings could suggest that smoking cessation may serve as an early indicator of PD.

The regulatory mechanism of mannan from millet Huangjiu on flavor release

Huangjiu, the national wine of China, has the characteristics of low grain consumption, low alcohol content and high nutrition, which aligns with the world's beverage wine consumption trend. The research on glutinous rice huangjiu from southern China is more extensive, while millet huangjiu from northern China has received less attention, hindering the diversified development of huangjiu. Polysaccharides play an important role in huangjiu's health activity and flavor characteristics. In this study, the polysaccharide components in millet huangjiu were separated and identified for the first time, and the influence of polysaccharides on the flavor release was explored through sensory evaluation, SPME-GC–MS, threshold determination and isothermal titration calorimetry. The millet huangjiu polysaccharide HJ-1 was composed of →3)-α-Manp(1→, α-Manp-(1→, →2,6)-α-Manp-(1→, →3)-α-Xylp-(1→ and →3)-β-Arap-(1→. HJ-1 (0.1–1 mg/mL) could regulate the release performance of key flavor compounds in millet huangjiu, inhibit the herbal, fruity and alcoholic aromas, and promote the sweet and rice aromas. Further studies showed that HJ-1 formed complexes with ethyl acetate, acetic acid, 2,3-butanediol and γ-butyrolactone through van der Waals forces and hydrogen bonds, and the process was a spontaneous exothermic entropy reduction reaction. This study provides a new idea for optimizing the overall aroma of huangjiu and improving its sensory quality.

Fisheries management for food security in the Maldives

Purpose. The goal of the study was to assess trends in providing food security with a special emphasis on efficiency of the local fisheries and offer evidence-based improvements, which can improve resource management and protect disadvantaged population in the Maldives from nutrition insecurity aggravated by strong import dependency. Methodology / approach. The methodological basis of this study was a set of modern qualitative economic and quantitative mathematical methods including factor analyses, econometric and optimisation modelling. This approach ensured holistic research and reasonable recommendations on providing food security and maintaining efficient fisheries management compared to international experience and intrinsic aspects that determine a fish catch and consumption in the Maldives. Results. The conclusions of the paper relate to the further development of fisheries to combat the identified negative trends in domestic fish consumption and an unstable fish catch by atoll in the Maldives. The conducted factor analysis detected relationships between a fish catch, fishing trips, engaged vessels, fish species, and fishing methods. The study findings proved the optimal locations where it is advisable to establish additional fish storages and ice plants taking into account indicators of a poverty rate, a share of children, and a population density. The research conclusions allow the authors to state that the outlined scientific proposals can essentially improve food security in the Maldives, which is threatened by land shortage and overpopulation. Originality / scientific novelty. The scientific novelty of the work is in presenting a clarified picture of challenges that Maldivian fishermen face nowadays. The scientific novelty lies in the identification of weaknesses in fisheries management and the proposals of areas of potential growth that are crucial for ensuring food security not only on poor islands but for the whole country. Practical value / implications. The main practical value of the research results is the development of recommendations for increasing fish catch and organising post-harvest processing, which are vital for the Maldivian fishery, as it is the country's main export sector. In addition, the study contributes to food security in the Maldives through proposed interventions aimed at supporting the most disadvantaged populations in the scattered atolls.

Research on Construction of Offline Data Warehouse for Ship Shore Power based on DolphinScheduler and Hive

In today's networked, intelligent, and data-driven era, the shore power industry is facing the challenge of rapidly growing data. This paper presents the construction of a professional offline data warehouse system for shore power based on DolphinScheduler and Hive. Firstly, MySQL is adopted as the backend database, combined with Sqoop to synchronize business data to HDFS, ensuring data reliability and integrity. Secondly, the Flume-Kafka-Flume architecture is utilized to achieve real-time collection and caching of user behavior data, providing data support for subsequent analysis. Thirdly, HQL statements are written in Hive to clean, merge, and analyze shore power data, calculating key indicators such as electricity consumption and usage trends. Fourthly, data visualization is achieved through the integration of Superset, displaying data analysis results via a web interface. Fifthly, DolphinScheduler is employed for timed scheduling, ensuring dependency control among various tasks and the smooth operation of the project. This system fully leverages the replication mechanism of HDFS to enhance reliability, dynamically adds nodes to achieve system scalability, and fully utilizes the fault tolerance of the Yarn scheduler. It saves time and computational costs for the shore power industry, realizing higher value and benefits.

Dietary Shifts since COVID-19: A Study of Racial Differences.

The COVID-19 pandemic has fundamentally changed the quality and quantity of people's food consumption. This study aimed to explore the dietary shifts among different racial groups resulting from the COVID-19 pandemic, focusing on changes in consumption across various food categories. This cross-sectional study included a sample of 10,050 urban residents aged 40-100 years across the United States. Dietary patterns among African American, Asian, Hispanic, and White populations were assessed before and since the pandemic (retrospective condition) using the Dietary Screening Tool (DST). The DST investigates consumption trends in food groups aligned with the MyPlate guidelines, plus fat, sugar, and sweet (FSS) intake and processed meats. This study found significant shifts in food consumption patterns among racial groups since COVID-19. The data indicate that African American individuals largely reduced their consumption of several food groups compared to White individuals, with a 43% decrease in processed meats, 42% in dairy, 36% in lean protein, 21% in fruit, 17% in grains, and 15% in FSS, although their vegetable consumption did not significantly decrease. African American individuals also consumed 66% less processed meat, 57% less dairy, and 30% less lean protein in comparison to Asian individuals. Hispanic individuals also showed a tendency to reduce their consumption more than White individuals, with a 34% decrease in dairy, 28% in vegetables, and 24% in fruit. In contrast, Asian individuals consumed 37% less FSS and 34% less grains than White individuals. Additionally, when compared to Asian individuals, Hispanic individuals consumed 49% less dairy and 47% less processed meat. The findings also revealed that African American individuals were the most nutritionally vulnerable group since the pandemic. Specifically, they were 38% and 35% more likely to be considered at nutritional risk than Asians and White participants, respectively. These findings illuminate the considerable dietary shifts induced by the COVID-19 pandemic and emphasize the critical need to address the racial disparities in nutritional vulnerability and public health policy.

The dynamic nexus between economic growth, renewable energy use, urbanization, industrialization, tourism, green supply chain management, and CO2

Since 2012, China has pursued an “ecological civilization” policy to promote green energy, increase environmental protection, and transition to more sustainable growth models. The complicated positive trends in energy consumption, more sustainable economic growth, and ecological management are obscured by China's persistent, significant dependence on fossil fuels, particularly coal. The study aims to analyze how renewable energy use in China affects carbon dioxide emissions and how those impacts change over time, as well as urbanization, industrialization, tourism, and green supply chain management. The DOLS dynamic system method used historical data from 1995 to 2022. The DOLS results show a positive and statistically significant economic growth coefficient in the long term, suggesting that an increase of only one percent in CO₂ emissions rise would be proportional to a surge in economic growth.Furthermore, using renewable energy sources correlates with long-term sustainability negatively and significantly. The results show reducing CO₂ emissions and boosting renewable energy use by 1 %. Furthermore, the long-run coefficients for industrialization and urbanization are positive and statistically significant, indicating that a 1 % increase in either component results in a comparable increase in CO₂ emissions. Sustainable logistics and tourism have negative and statistically significant coefficients, meaning that a one percent increase will gradually decrease carbon dioxide emissions. The estimated findings hold up when using other estimators, such as the commonly used co-integrating regression (CCR) strategy and fully modified least squares (FMOLS). When Granger causality is coupled, the test also catches the variables' causal link. test. To achieve environmental sustainability, the essay suggests using robust regulatory policy tools to curb ecological deterioration.

A key factor in monitoring cannabis consumption trends through wastewater analysis: Partitioning of THC[sbnd]COOH between the liquid and solid phase of influent wastewater

Current wastewater-based epidemiology (WBE) studies are predominantly focused on the analysis of urinary biomarkers present in the liquid phase of influent wastewater (IWW). This approach systematically underestimates less polar metabolites, such as cannabis biomarkers. These biomarkers can potentially sorb to and desorb from suspended particulate matter (SPM) present in IWW. This study investigates the bidirectional partitioning of THCCOOH between the liquid phase and SPM of IWW by performing multiple sorption experiments using THCCOOH-D9 as a surrogate due to the unavailability of blank SPM and blank IWW. In addition, this study involves the analysis of IWW collected from eight wastewater treatment plants (WWTP) (n = 56) across four European countries, where raw IWW, the liquid phase and SPM were separately analysed to identify potential trends in the fraction of THCCOOH in the SPM between and withing the examined locations. Based on the performed sorption experiments, bidirectional partitioning of THCCOOH between the liquid phase and SPM was noted, showing partition between both phases when only one of the two phases was spiked. It was illustrated that the concentration of SPM had a notable influence on the THCCOOH partitioning between both phases. In addition to the inter location variability (average THCCOOH present in the SPM ranged 31–59 %), a substantial intra location variation was also observed, e.g., in one location ranged 17–58 %. While the determination of a correction factor for the amount of THCCOOH present in SPM would be ideal, this is challenging, since the amount of SPM is not fixed in all IWW samples. Although SPM has influence on the THCCOOH partition, no correlation (p value Spearman correlation = 0.3160) was observed between the SPM concentration and the fraction of THCCOOH in the solid phase. Moreover, the collection of homogenized samples is difficult, and the time required to reach an equilibration in partitioning of THCCOOH between both IWW phases remains unclear. Due to i) the large inter-and intra-location variation of THCCOOH present in the SPM, ii) the variability in SPM concentration in IWW samples, and iii) the time required to reach a partitioning equilibration, an analytical procedure based on liquid-liquid extraction (LLE) that considers both the liquid phase and SPM of IWW is recommended to reduce the overall uncertainty for THCCOOH measurement in IWW. It was illustrated that this extraction method is capable to recover the total concentration of THCCOOH in both phases.

Time Series Analysis to Estimate the Volume of Drinking Water Consumption in the City of Meoqui, Chihuahua, Mexico

Water is a vital resource for sustaining life and for numerous processes within the transformation industry. It is a finite resource, albeit one that can be renewed, and thus sustainable management is imperative. To achieve this objective, it is necessary to have the appropriate tools to assist with the planning policies for its management. This paper presents a time series analysis approach to measure and predict the pattern of water consumption by humans throughout subsectors (domestic, commercial, public sector, education, industry, and raw water) and total water consumption in Meoqui, Chihuahua, Mexico with data from 2011 to 2023, applying calibration model techniques to measure uncertainty in the forecasting. The municipality of Meoqui encompasses an area of 342 km2. The climate is semi-arid, with an average annual rainfall of 272 mm and average temperatures of 26.4 °C in summer and 9.7 °C in winter. The municipal seat, which has a population of 23,140, is supplied with water from ten wells, with an average consumption of 20 ± 579 m3 per user. The consumption of the general population indicates the existence of a seasonal autoregressive integrated moving average (SARIMA) (0,1,2)(0,0,2)12 model. (Sen’s Slope = 682.7, p < 0.001). The domestic sector exhibited the highest overall consumption, with a total volume of 17,169,009 m3 (13 ± 93). A SARIMA (2,1,0)(2,0,0)12 model was estimated, with a Sen’s slope of 221.65 and a p-value of less than 0.001. The second-largest consumer of total water was the “raw water” sector, which consumed 5,124,795 (30,146 ± 35,841) m3 and exhibited an SARIMA (0,1,1)(2,0,0)12 model with no statistically significant trend. The resulting models will facilitate the company’s ability to define water resource management strategies in a sustainable manner, in alignment with projected consumption trends.

Alcohol-associated liver disease - a current overview.

Alcohol-associated liver disease (ALD) remains a major and increasingly pressing concern in hepatology. ALD includes spectrum of conditions, each with unique diagnostic and therapeutic challenges. Excessive alcohol intake is a leading preventable cause of physical harm, including ALD. The pathogenesis of ALD involves oxidative stress, inflammation, and lipid metabolism disruptions, with genetic predispositions playing a major role. ALD progresses from hepatic steatosis to steatohepatitis, and finally liver cirrhosis, which is marked by severe fibrosis and impaired liver function. Advanced ALD stages, particularly alcoholic hepatitis and liver cirrhosis, are characterized by high mortality rates. Management of ALD primarily involves strict abstinence from alcohol, which can reverse early-stage disease or halt progression. Nutritional support, vitamin supplementation, and symptomatic treatment are also essential. Liver transplantation is the only definitive treatment for alcoholic liver cirrhosis, but it is difficult for patients with a history of alcohol abuse to qualify for the procedure. Epidemiological data indicate a growing burden of ALD, especially among younger populations, exacerbated by increased alcohol consumption trends and the COVID-19 pandemic's influence on drinking behaviors. Despite ALD's significant impact, current therapies are limited, highlight- ing the need for innovative treatments and comprehensive patient management strategies. Individualized care, enhanced epidemiological research, and new therapeutic approaches are crucial to improving outcomes for ALD patients.

Wastewater-based evaluation of the efficacy of oxycodone regulations in Australia.

Between 2018 and 2020, Australia implemented major policy changes to improve the quality and safety of opioid prescribing, with a specific focus on oxycodone. This study used wastewater-based epidemiology to assess the efficacy of Australia's regulatory reforms by measuring change in consumption of oxycodone via exploratory analysis. Wastewater analysis data on oxycodone consumption was from the National Wastewater Drug Monitoring Program. The program captures data from more than 50 wastewater treatment plant catchments across Australia, equivalent to more than 50% of the national population. Geographic trend analyses were conducted for both major cities and regional areas within all states and territories of Australia over a 6-year period between 2017 and 2023. Oxycodone consumption showed a statistically significant increase nationally from 78 mg/day/1000 people (95% confidence interval [CI] = 71, 84) in 2017 to 120 mg/day/1000 people in August 2019 (95% CI = 110, 120), an increase of 52% (95% CI = 42, 62, P < 0.0001). From August 2019 to December 2020, there was a statistically significant decrease from 120 to 65 mg/day/1000 people (95% CI = 60, 71), a decrease of 45% (95% CI = 40, 51), followed by a modest 2.4% increase to the end of the study period in April 2023 (95% CI [2.0,2.7]). A 45% reduction in oxycodone consumption in Australia from 2019 to 2020 coincided with national policy changes that aimed to reduce consumption of prescription opioids. The overall declining trend in consumption was suggestive of the effectiveness of national interventions in reducing pharmaceutical opioid use. Wastewater-based epidemiology provides an effective approach for assessing the effectiveness of controlled substances policy changes.

Technology for Green Hydrogen Production: Desk Analysis

The use of green hydrogen as a high-energy fuel of the future may be an opportunity to balance the unstable energy system, which still relies on renewable energy sources. This work is a comprehensive review of recent advancements in green hydrogen production. This review outlines the current energy consumption trends. It presents the tasks and challenges of the hydrogen economy towards green hydrogen, including production, purification, transportation, storage, and conversion into electricity. This work presents the main types of water electrolyzers: alkaline electrolyzers, proton exchange membrane electrolyzers, solid oxide electrolyzers, and anion exchange membrane electrolyzers. Despite the higher production costs of green hydrogen compared to grey hydrogen, this review suggests that as renewable energy technologies become cheaper and more efficient, the cost of green hydrogen is expected to decrease. The review highlights the need for cost-effective and efficient electrode materials for large-scale applications. It concludes by comparing the operating parameters and cost considerations of the different electrolyzer technologies. It sets targets for 2050 to improve the efficiency, durability, and scalability of electrolyzers. The review underscores the importance of ongoing research and development to address the limitations of current electrolyzer technology and to make green hydrogen production more competitive with fossil fuels.

Consumption Trends of Antifungal and Antiprotozoal Agents for Human Systemic Use in Kazakhstan from 2017 to 2023.

While multiple studies have investigated antibiotic consumption rates, there are few studies on the consumption of systemic antifungals and antiprotozoals. This study aims to fill this gap by providing a comprehensive analysis of nationwide consumption trends in Kazakhstan over a seven-year period (2017-2023). Defined daily doses per 1000 inhabitants per day were calculated for systemic antifungals (J02 code of the Anatomical Therapeutic Chemical Classification System (ATC)) and antiprotozoals (P01 code of the ATC). Time series analyses were applied to examine historical trends, evaluate the impact of the COVID-19 pandemic, and make future projections until 2030. The total consumption increased over the study period, with an average annual percent change of 1.11% for antifungals and 5.48% for antiprotozoals. Fluconazole was the most consumed antifungal agent, whereas metronidazole was the most consumed antiprotozoal agent. The COVID-19 pandemic had a positive but insignificant effect on the consumption of antifungals and a negative and also insignificant effect on the consumption of antiprotozoals. Forecast modeling indicates that the future trends in antifungal and antiprotozoal consumption until 2030 will largely remain stable, with the exception of antiprotozoal consumption in the hospital sector, which is projected to decline. These findings offer valuable insights into the development and implementation of targeted antimicrobial stewardship programs in Kazakhstan.

Talk to engage: The influence of smartphone voice assistants on consumers’ brand engagement

ABSTRACT This study aims to investigate how smartphone voice assistants (SVAs) impact brand engagement among Malaysian university students, who are leading in digital consumption trends. The study aims to investigate how brand characteristics such as appeal, attachment, perceived utility, and usability impact consumer behaviour by utilising Brand Identity and Technology Acceptance models. Analysed using structural equation modelling (SEM) in AMOS, data from 500 students experienced with voice assistants was collected through a survey. The results show strong connections in all studied relationships, emphasising the crucial importance of various brand attributes in promoting engagement. Usability and perceived utility are essential factors, indicating that businesses should focus on creating products that are both functional and user-friendly. The study provides distinctive perspectives on improving student engagement and brand partnerships in a competitive environment. It highlights the necessity for more research to help voice assistant professionals and academics enhance consumer-brand connections, especially among the digitally adept youth population in Malaysia.

Trends in alcohol consumption of adults with presumed atrial fibrillation in the United States, 2001 to 2018

Trends in alcohol consumption of adults with presumed atrial fibrillation in the United States, 2001 to 2018

Food Safety Knowledge, Attitudes, and Practices Among Food Handlers in Jeddah, Saudi Arabia

Background: Several outbreaks of foodborne illness have recently occurred in Saudi Arabia, posing a public health concern, especially considering the increasing trend of food consumption at restaurants. Objectives: This study evaluates the food safety knowledge, attitudes, and self-reported practices among food handlers in Jeddah, Saudi Arabia, and their association with the food handlers’ sociodemographic characteristics. Methods: A cross-sectional study was performed for a convenience sample of 80 food handlers (15–65 years old), followed by face-to-face interviews. A validated questionnaire with 65 questions across four sections (sociodemographic characteristics, knowledge, attitudes, and practice related to food safety) was utilized for data collection. Data entry and analysis were conducted using the SPSS version 26 software. Results: Most of the participants exhibited moderate levels of food safety knowledge and practices and a high level of food safety attitudes. Food safety knowledge was significantly greater among the oldest participants (&gt;45 years old), those holding valid health cards, and those who had attended training courses (p&lt;0.05). However, no significant differences in food safety attitudes and practices were found based on the sociodemographic characteristics of the participants (p&gt;0.05). Food safety knowledge was found to be significantly correlated with the food safety attitudes of the participants (r= 0.379, p= 0.001), and food safety attitudes were significantly correlated with practices (r= 0.264, p= 0.018). Conclusion: This study revealed that continuous implementation of food safety educational and training programs is necessary to improve the knowledge of food handlers and transform it into appropriate attitudes and practices.

Oral Health in Tanzania: Unmasking Its Neglected Dimension

ABSTRACTThe World Health Organization's definition of oral health underscores its holistic nature encompassing physiological, psychosocial, and functional dimensions. The current estimates of oral diseases in Tanzania and around the world underscore the urgency of intervention, particularly in light of rising sugar consumption trends. The unique challenges facing Tanzania, including inadequate knowledge, limited infrastructure, and disparities in oral healthcare access, are analyzed within the broader context of sub‐Saharan Africa's health priorities. This article addresses the multifaceted challenges of oral health neglect in Tanzania, emphasizing the imperative need for a comprehensive and integrated approach. The discussion offers a detailed exploration of determinants contributing to oral health neglect, spanning socioeconomic, behavioral, and commercial factors, with a focus on their implications for Tanzanian communities. Recommendations are presented as a strategic roadmap, encompassing public health education, integrated healthcare services, government intervention, educational programs, community engagement, financial accessibility, and research initiatives. By synthesizing these recommendations into a cohesive framework, a proactive and collaborative approach to mitigate the consequences of oral health neglect in Tanzania, emphasizing the need for transformative policies and cultural sensitivity, is therefore envisioned.

Trends of high fat, salt and sugar food consumption and its impact on nutritional status of school children

Background: India is currently in a phase of nutrition transition. Increased consumption of food high in fat, sugar and salt (HFSS) is contributing significantly to prevalence of triple burden of malnutrition among children. Methods: In this study, the nutritional status and frequency of HFSS food consumption were assessed in 360 school children (boys n=171, girls n=189) aged between 6-16 years using standard tools and techniques. Results: From the results, it was evident that the frequency of consumption of HFSS food among children of both the age group 6-10 years and 11-16 years was significantly high (p≤0.05). Children aged between 6-10 years were more prone to under nutrition (38%) and they were consuming more snacks such as chips, biscuits, packaged fruit juices high in sodium, saturated fat and sugar. On the other hand, adolescents aged between 11-16 years show a higher prevalence of both under nutrition and overweight (10%), potentially linked to their consumption of bakery products, street foods, sugary drinks and carbonated beverages. Conclusions: The nutrition transition shift is towards consumption of refined, ultra processed, high sodium and fat foods. The age-based trend is a nutritional challenge among children and adolescents. The findings of this study emphasized the urgent need for early screening of nutrient deficiencies, comprehensive interventions to address the triple burden of malnutrition among school children. Efforts should focus on promoting healthy dietary behaviours, enhancing nutritional literacy among children, parents and care givers, improving access to nutritious foods, and regulating the marketing and availability of HFSS foods.

Opioid consumption following foot and ankle surgery: a systematic review

Objective: Systematically review studies characterizing postoperative opioid consumption in patients submitted to foot and anklesurgery to identify trends in opioid consumption among regions of procedures (forefoot, midfoot, hindfoot/ankle) and ultimatelycreate prescribing guidelines that treat patient’s pain adequately while limiting leftover pills. Methods: A systematic review was performed following the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. Retrospective and prospective observational cohort studies that reported mean opioid consumption following foot and ankle surgery were included, as well as data from our institution that fit the review’s parameters. Studies that did not report this data or reported patients receiving alternative surgical techniques were excluded. The risk of bias in non-randomized studies – of exposure (ROBINS-E) tool and the Methodological index for non-randomized studies (MINORS) criteria were used to assess bias and study quality, respectively. Results: Three hundred ninety-five articles were identified, and six studies, including our institution’s, met inclusion criteria. Reported data from 2,445 patients were synthesized to show opioid consumption overall, by region of surgery, and by invasiveness of procedure. Four of five studies found significantly higher opioid use postoperatively in patients submitted to hindfoot/ankle surgery, and two of five studies found significantly higher consumption among those submitted to bony foot and ankle surgery. Conclusion: Prescribing physicians must approach foot and ankle patients on a case-by-case basis to ensure adequate pain management while mitigating excess opioid risk. For prescriptions of 5 mg oxycodone pills, we recommend 15-, 20-, and 25-pill prescriptions for patients submitted to forefoot, midfoot, and hindfoot/ankle surgery. Level of Evidence I; Systematic review.

Predicting and Forecasting of Vehicle Charging Station Using ECNN with DHFO Algorithm

The forecast of the optimal placement of a charging station (CS) according to the real-time consumption of electric vehicles is a subject of urgency in this new era. The demand of a charging station in an area based on the trend of consumption can be predicted by means of interpolation and the extrapolation of historical data using a linear function of prediction model. The prediction of the charging station system was performed with distance relevancy methods. An adaptive optimal learning model was proposed to enhance the prediction performance for charging station management and to represent the pattern of vehicles’ travelling directions. The proposed model uses Distributional Homogeneity Feature Optimization (DHFO) using artificial intelligence (AI) to categorize and forecast the charging station from the database. The prediction performance of this model is improved more than the conventional classification model by filtering the apt features from all the electric vehicular and charging station attributes in the database. The Enhanced Cladistic Neural Network (ECNN) is used to improve the pattern learning model and increase learning accuracy. By comparing statistical parameters with other state-of-the-art methodologies, the suggested model’s overall findings were verified.