Actual Consumption Research Articles

Reinforcement learning for occupant behavior modeling in public buildings: Why, what and how?

Effective control of energy consumption in public buildings holds paramount significance for global sustainable development. However, uncertainty in occupant behavior during operational phase often leads to a substantial discrepancy between the designed and actual energy consumption. While there is existing research on occupant behavior in public buildings utilizing stochastic modeling, statistical modeling, data mining, and agent-based modeling methods, the connotations and formation mechanisms have not been systematically revealed, rendering these developed models weak in applicability and reliability in practical scenarios. Reinforcement learning, which can perceive aspects of external environment and learn from stochastic interactions spontaneously, has been distinguished as a promising method for occupant behavior modeling. This research aims to develop a paradigm for reinforcement learning application in occupant behavior modeling in public buildings. Semi-structured interviews and literature review were combined to collect relevant information, while reasons for using reinforcement learning and applicable algorithms were discerned through qualitative analysis. Finally, detailed frameworks were proposed and validated through focus groups, offering a comprehensive guide that will serve as a reference for future research. This includes steps for collecting occupant-related data, selecting appropriate algorithms, and training and deploying reinforcement learning agents effectively. This research innovatively proposed a “panorama” of reinforcement learning for occupant behavior modeling in public buildings, laying a solid foundation for indoor environment and energy efficiency improvement in public building operations.

Comparison of energy consumption prediction models for air conditioning at different time scales for large public buildings

Large public buildings have complex functions and high air conditioning energy consumption, and the prediction of energy consumption for air conditioning in different time scales can realise different energy saving management needs. Taking a large public building in Guangzhou as a research case, the long and short-term memory neural network (LSTM) is used as the basis to establish the machine learning model for energy consumption prediction for air conditioning at different time scales in terms of 10 min, hourly and daily, and a comparative analysis is carried out. The original data were analysed and processed by Z-Score and Local Outlier Factor (LOF) outlier detection methods. The relationship between meteorological parameters for Typical Meteorological Year (TMY) and the actual local meteorological parameters and energy consumption for air conditioning was analysed using linear regression, Pearson and Spearman correlation coefficients, taking meteorological parameters as input variables. The results show that comparing energy consumption for air conditioning with the meteorological parameters in TMY and actual local meteorological parameters, the actual meteorological parameters had a stronger correlation energy consumption for air conditioning. When the actual local meteorological parameters are taken as input variables, the mean absolute percentage error (MAPE) is reduced by about 0.85 %, and the coefficient of determination (R2) is increased by about 0.01. At the same time, the MAPE and R2 of the 10-min energy consumption prediction model are about 6.43 % and 0.9738, respectively, the MAPE and R2 of the hourly prediction model are about 9.29 % and 0.9568, respectively, while the MAPE and R2 of the daily prediction model are about 25.76 % and 0.7998. Meanwhile, an improved energy consumption prediction model is proposed to reduce the daily energy consumption prediction error to 6.36 % for MAPE, and the R2 of this model is 0.9927.

Development and Validation of the Meiji Nutritional Profiling System per Serving Size.

Serving size may be the appropriate reference for calculating food nutritional value. We aimed to assess the nutritional values of Japanese foods based on serving sizes rather than per 100 g by adapting the Meiji Nutritional Profiling System (Meiji NPS). Given the variability in serving sizes across countries, we used Japanese serving sizes to calculate the Meiji NPS scores. We confirmed the convergent validity of the Meiji NPS scores per serving size with the Nutrient-Rich Food Index 9.3 using Spearman's correlation coefficients (r = 0.51, p < 0.001). Food groups recommended by official guidelines, such as pulses, nuts and seeds, fish and seafood, fruits, vegetables, and milk and milk products, scored relatively high. Furthermore, the nutrient density scores of food items with small serving sizes, such as mushrooms, algae, seasonings, and fats and oils, were moderated when calculated by per serving size, despite having considerably higher or lower scores per 100 g. These results indicate that calculating NPS per serving size allows for the assessment of the nutritional value of food items in accordance with actual consumption quantities. Therefore, the Meiji NPS calculated per serving size, alongside the per 100 g version, may be useful for dietary management depending on specific purposes.

Impact of Non-Residential Users on the Energy Performance of Renewable Energy Communities Considering Clusterization of Consumptions

Renewable energy communities foster the users’ engagement in the energy transition, paving the way to the integration of distributed renewable energy sources. So far, the scientific literature has focused on residential users in energy communities, thus overlooking the opportunities for industrial and commercial members. This paper seeks to bridge this gap by extending the analysis to the role of non-residential users. The proposed methodology develops an effective clustering approach targeted to actual non-residential consumption profiles. It is based on the k-means algorithm and statistical characterization based on relevant probability density function curves. The employed clusterization procedure allows for effectively reducing a sample of 49 real industrial load profiles up to 11 typical consumption curves, whilst capturing all the relevant characteristics of the initial population. Furthermore, a peer-to-peer sharing strategy is developed accounting for distributed and shared storage. Three scenarios are considered to validate the model with different shares of non-residential users, and the results are then evaluated by means of shared energy, self-consumption, and self-sufficiency indices. Moreover, the results show that the integration of a large non-residential prosumer in a REC may increase the self-sufficiency of residential members by 8.2%, self-consumption by 4.4%, and the overall shared energy by 37.3%. Therefore, the residential community consistently benefits from the presence of non-residential users, with larger users inducing more pronounced effects.

A novel scheme for shore power data to enhance containership-at-berth emission estimation

Ship-at-berth emissions significantly affect air quality and health of human beings in a port and its neighbourhood. However, it is challenging to estimate these emissions precisely due to stringent data requirements. Shore Power (SP) data, including its actual energy consumption and duration, offers useful insights to refine these estimates, but has yet to be fully explored. This study proposes a novel scheme incorporating SP data to improve the accuracy of containership-at-berth emission estimates and evaluate emission reduction measures. The findings reveal substantial differences among existing emission estimates from identical case studies, highlighting the importance of SP data. Additionally, it demonstrates significant emissions from low-load main engines and confirms the efficacy of SP in emission reduction. These findings provide valuable insights into emission estimation methods and their potential applications in estimating emission reduction measures, underlining the importance of policy support in facilitating the SP implementation.

ASSESSMENT AND CURRENT TRENDS OF ENSURING FOOD SECURITY IN UKRAINE

The research on Ukraine's food security is of paramount importance, as it directly impacts the national agro-food systems. The article delves into the current state of internal food self-sufficiency in the Ukrainian population, conducting an analytical assessment of export-import transactions with agricultural raw materials and food products. The findings reveal a significant trend of increasing food exports in recent years, along with an evaluation of the export-import coverage for the main types of food products. Ukraine's place in the world rating of food security based on the Global Food Security Index was studied. The fundamental problems and reasons that determined Ukraine's decrease in the rating positions and the level of food security of its population were identified. In addition to the military conflict, Ukraine has a low economic availability of food products for the population and a low level of sustainability in the domestic agro-food system. Despite the war's consequences, the agricultural business remains one of the most profitable economic activities in Ukraine. This analysis of individual indicators of the effectiveness of the country's agrarian sector as a basis for ensuring food security underscores the sector's potential. The modern production structure of the main types of agricultural products by categories of producers was studied. The key trends accompanying ensuring food security in Ukraine under modern conditions are summarized. It was revealed that food security in the crop sector is achieved at the expense of the public sector agrarian formations, mainly at the expense of households in the livestock sector. Based on the average values ​​of the actual consumption indicators, the state of providing the population with the main types of food was analyzed. The results showed problematic aspects in providing the country's population with meat and dairy products, fish, fruits, berries, and grapes. The priority directions for ensuring and strengthening food security on a national scale are substantiated. Among the main ones, the following are highlighted: the need to implement sustainable farming and animal husbandry practices, further improvement of the food quality management and control system, protection of biodiversity of ecological agrosystems, optimization and reduction of value chains, increasing the level of social responsibility of food producers and consumers.

Assessing radon hazard in drinking water: A comprehensive approach integrating deterministic and probabilistic methods with water consumption routines

The research aimed to determine the spatiotemporal distribution patterns of radon activity concentrations in tap water of Yerevan city and assess radon-associated hazards using both deterministic and probabilistic approaches. This was accomplished by integrating one-year monitoring data of radon in water with water consumption habits among adult population clusters, which were identified through food frequency questionnaire in Yerevan.The study findings indicated variations in radon activity levels across administrative districts. The highest average activity concentrations were detected in Davtashen (7.07 Bq/L), while the lowest average was observed in Kanaker-Zeytun (1.57 Bq/L). The overall pattern of spatiotemporal variation during monitoring period revealed higher prevalence of radon in water in the northern and western parts of the city compared to the east and south, indicating different sources of drinking water. The radon-associated hazard assessment from water, using a deterministic approach (e.g., inhalation, ingestion, radon dissolution in blood, total effective dose), revealed values below the individual dose criterion (IDC) of 0.1 mSv/y. Monte Carlo simulation revealed a probability of exceeding IDC in specific water consumption-based groups. Residents of Yerevan who drink more than 3 L water daily with the highest observed activity concentration of 11.4 Bq/L, have an 86.26 % chance of exceeding IDC. Residents consuming 2.1 L water daily have a 7.02 % chance of exceeding IDC.The study highlights the importance of applied principles and methodologies for radon monitoring, particularly considering actual water consumption data and different risk assessment approaches. Considering the worst-case scenario results, it is recommended to keep tap water consumption up to 3 L per day, keeping the tap open longer to reduce radon levels. It also emphasizes the need for continued monitoring, given the variations in radon activity. The study provides valuable insights into radon exposure assessment, mitigation, and action plans in terms of water safety and public health.

Underwhelming pleasures: Toward a self-regulatory account of hedonic compensation and overconsumption.

Hedonic overconsumption (e.g., overconsumption of gratifying behaviors, e.g., eating, gaming) is common in daily life and often problematic, pointing to the need for adequate behavioral models. In this article, we develop a self-regulatory framework proposing that when an actual consumption experience falls short of hedonic expectations-such as when being distracted-people will want to consume more to compensate for the shortfall. In a preliminary meta-analysis, a small-scale field experiment on distraction during lunch and subsequent afternoon snacking (Study 1), and a preregistered experience sampling study (Study 2) involving more than 6,000 consumption episodes in everyday life across multiple consumption domains, we investigated the predictions from our hedonic compensation model. There was clear and consistent evidence across studies and analyses for the prediction that distraction during consumption compromises the actual enjoyment of a given consumption experience. Both empirical studies yielded consistent evidence for a positive association between actual enjoyment and consumption satisfaction but inconsistent and weaker evidence for the expected role of actual-expected enjoyment discrepancies for this part of the model. There was also consistent evidence for the expected negative association between consumption satisfaction and the need for further gratification. Finally, there was moderate and inconsistent support linking the need for further gratification to subsequent consumption across Study 1 (amount and frequency of snacking in the afternoon) and Study 2 (shorter duration to subsequent consumption). Taken together, the present framework provides initial support for the proposed link among compromising consumption contexts, consumption enjoyment, and subsequent hedonic compensation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Knowledge, Attitudes, and Practices towards Antibiotics, Antimicrobial Resistance, and Antibiotic Consumption in the Population of Kazakhstan.

During the COVID-19 pandemic, a ban on inspections of small businesses, including pharmacies, was imposed in Kazakhstan, which relaxed law enforcement efforts regarding the prohibition of over-the-counter antibiotic (AB) sales. This study aimed to investigate how this affected the knowledge, attitudes, and practices (KAP) related to AB and antimicrobial resistance (AMR), as well as to assess actual AB consumption at the community level. The study comprised two cross-sectional sub-studies: the first involved a KAP survey conducted in 2022 and 2024, utilizing the Special Eurobarometer questionnaire on AMR. The second sub-study analyzed AB consumption in 2021 and 2023, measured in defined daily doses per 1000 inhabitants. Results revealed an increase in the percentage of individuals reporting receipt of information about ABs and AMR in the past year (37.3% in 2022 vs. 52.9% in 2024, p < 0.001) and an increase in the percentage of individuals reporting AB use in the past year (49.0% in 2022 vs. 54.0% in 2024, p = 0.056). The most consumed ABs were from the Watch group, with azithromycin and ceftriaxone ranking highest. These findings support the hypothesis that the relaxation of law enforcement contributed to an increase in AB consumption and emphasize the need for public health policies to address this issue.

Impacts of carbon emissions allowance limitations on carbon price with power generation rights trading in China

Carbon pricing is a crucial mechanism for achieving carbon peaking and carbon neutrality through the carbon trading market. Recently, the price of carbon emissions allowances in China's national carbon market has shown a gradual upward trend. The limitations on carbon emissions allowances have also become increasingly stringent. Power generators, which primarily rely on thermal power generation, have a substantial demand for carbon emissions allowances. The rising prices and stricter carbon emissions constraints have increased operational costs. Therefore, exploring the carbon pricing mechanism under more stringent carbon emissions allowance constraints is vital. It encourages thermal power generators to engage in carbon trading and reduce carbon emissions. This paper focuses on an energy trading platform supply chain comprising three power generators, one energy trading platform, and one power retailer. We consider stringent carbon emissions constraints and power generation rights trading. Through the analysis of three scenarios, the study explores optimal pricing decisions for carbon emissions allowances to facilitate carbon emission reduction and promote renewable energy generation and consumption. The findings indicate that, under certain conditions (i.e., the carbon quotas allocation coefficient b1∈[0.73,0.8]), scenario 3, which excludes generation rights trading, yields the highest optimal carbon price. The energy trading platform can adjust various commissions to influence the carbon price, as well as the price and quantity of generation rights trading. Introducing power generation rights trading proves beneficial in reducing carbon emissions (nearly 400 tons less than that in scenario 3 without generation rights trading, when the realized total power demand is around 8600 MWh) and fostering renewable power generation and consumption (the actual renewable power consumption weight is 3% higher than that without generation rights trading). When considering generation rights trading, the high-emission generator and renewable power generator achieve the highest power profits, while for the low-emission generator, the profit in this case is the second best among these scenarios. Moreover, by decreasing the coefficient of carbon quotas allocation and increasing the reduction rate simultaneously, the carbon cost will pass through to the electricity price more sufficiently (e.g., when b1=0.76, the pass-through rate increases from 0.5015 to 0.7984), particularly within a specific range of the carbon quotas allocation coefficient.

An Overview of Livestock Role in Ensuring Food and Nutrition Security: The Case of Ethiopia

This review aimed to deliver and document summarized information on the contribution of livestock to food and nutrition security, both generally and specifically in the case of Ethiopia. Livestock have made both positive and negative contributions to food and nutrition security. Positively, they provide high biological value, palatable, and nutrient-dense food sources such as milk, meat, and eggs for human consumption. They also offer quick income, employment, source of fuel, social functions, raw materials for industry (hide and skin…. etc.), input for soil fertility (manure) and draft power, indirectly contributing to stable food cultivation. However, livestock also pose negative impacts on food and nutrition supply by competing with humans for grains, utilizing fodder crop lands, emitting greenhouse gases, and transmitting zoonotic diseases to humans. In Ethiopia, trends in livestock populations and food production levels reveal disparities between actual consumption and productivity compared to available resources. Energy, protein, and fat supply from the livestock sector has increased from 2010 to 2021. Yet, the country's per capita consumption of dairy, meat, and eggs remains below global averages, exacerbating undernutrition, particularly in rural areas. Livestock products are rich in high-quality protein and essential micronutrients crucial for growth and development, especially in children and infants. Enhancing nutritional outcomes in Ethiopia necessitates addressing barriers to livestock output and promoting the consumption of livestock-derived foods. Tackling underlying issues impeding livestock productivity is equally vital as efforts to enhance productivity and ensure access to livestock-based foods.

Comparative Analysis Of Cost Calculation With Activity-Based Costing And Traditional Methods

In the modern business world, accurate cost calculation is crucial for effective decision-making. Traditional methods often fail to reflect actual resource consumption, leading to inaccurate pricing and resource allocation. This research aims to analyze the use of Activity-Based Costing (ABC) and compare it with traditional methods. The research approach is qualitative, employing literature review methods. The findings show that ABC, which allocates costs based on activities performed, provides more realistic and accurate cost estimates. ABC helps companies identify non-value-added activities, improve efficiency, and reduce waste. Although ABC is more complex and expensive to implement, it provides more precise cost information, supporting optimal decision-making regarding pricing, product strategy, and resource allocation. Conversely, traditional methods are simpler but often result in less accurate cost allocation and do not reflect variations in activity consumption. Therefore, ABC is superior in terms of accuracy and effectiveness in identifying waste and inefficient activities, despite requiring technological support and higher initial costs.

Predictive Analysis of a Building’s Power Consumption Based on Digital Twin Platforms

Colleges and universities are large consumers of energy, with a huge potential for building energy efficiency, and need to reduce energy consumption to build a low-carbon, energy-saving campus. Predicting the energy consumption of campus buildings can help to accurately manage the electricity consumption of buildings and reduce the energy consumption of buildings. However, the electricity consumption of a building’s operation is affected by many factors, and it is difficult to establish a model for analysis and prediction. Therefore, in this study, the training building of the BIM education center on campus was selected as the research object, and a digital twin O&amp;M platform was established by integrating IoT, digital twin technology (DDT), smart meter monitoring devices, and indoor environment monitoring devices. The O&amp;M management platform can monitor real-time changes in indoor power consumption data and environmental parameters, and organize data on multiple influencing factors and power consumption. Following training, validation, and testing, the machine learning models (back propagation neural network, support vector model, and multiple linear regression model) were assessed and compared for accuracy. Following the multiple linear regression and support vector models, the backpropagation neural network model exhibited the highest accuracy. Consistent with the actual power consumption detection results in the BIM education center, the backpropagation neural network model produced results. Consequently, the BP model created in this study demonstrated its dependability and ability to forecast campus building power usage, assisting the university in organizing its energy supply and creating a campus that prioritizes conservation.

Strengthening sustainable consumption behavior in high school students: Evaluation of an experiential training intervention targeting psychological determinants

Designing effective educational measures constitutes a key area of action for achieving global sustainability goals. Therefore, the present study examined a training addressing sustainable consumption practices in plastic reduction, fashion and food. An interactive training was held in a German comprehensive high school with grade 11 students, addressing psychological drivers of sustainable consumption behavior, namely, awareness and knowledge, emotional, rational and value-based factors. The training was evaluated in a pretest(t1)/posttest(t2)/follow-up(t3) design. Final self-report data was obtained from 77 pupils in t1 and t2 and 56 pupils for all three points of measurement. Results of a repeated measures MANOVA indicate significantly higher levels of all outcome variables directly after the training. Significant long-term effects emerge on the knowledge of sustainable consumption alternatives, anticipated feelings of guilt, perceived norms in the individual’s social environment, perceived control over one’s own behavior, as well as sustainable consumption behavior in everyday life. Taken together, results suggest the training to be an effective instrument for furthering actual sustainable consumption practices of young pupils. Its implementation during school hours with regular-sized classes offers a low expenditure educational approach for sustainable consumption.

Food security in Ukraine today’s conditions

The system-forming element of national security is food security, which guarantees effective management of the food system, ensuring the food needs of the appropriate quantity and quality of each country’s citizen. Despite the significant potential of the agricultural sector, which forms the basis of food security, the need to analyse the state of food security in the current situation is due to military operations on the territory of Ukraine, which led to its negative global results. In this regard, the need to develop effective strategies and legal regulation is essential to ensure national and global food security, which in turn requires coordination of efforts of all countries and international organisations through international cooperation. The study aims to establish a pattern regarding the current state of food security by analysing the impact of availability, access, usefulness and stability of food supply on food security. Research methods: comparative analysis; taxonomic analysis; systematisation, generalisation. The survey found that Ukraine’s position in the Global Food Security Index (FSI) among 113 countries in 2022 worsened by 13 points, where the worst indicators for domestic food security are sustainability and adaptability (43.5 points out of 100 and 94th place globally) and economic affordability of food (48.1 points out of 100 and 93rd place in the world). It was found that the actual consumption of vegetables, melons, potatoes, and oil in 2021 was slightly higher than the rational norm, indicating sufficient consumption of these products, while the actual consumption of products that are lower than the rational norm includes meat and meat products, milk and dairy products, fish and fish products, and fruits, berries, and grapes. It has been determined that since 2016, the number of kilocalories in the daily diet of the population of Ukraine has not reached the minimum norm of consumption of basic foodstuffs for the working population. It has been established that in 2020 and 2021, the value of the taxonomic indicator for assessing the level of food security (I) has been decreasing, i.e. the distance to the reference vector has been increasing, which indicates a decrease in the level of food security. The forecasting results suggest a downward trend in the food security index for the period 2022–2027, and there is an urgent need to take measures to eliminate this trend and improve the value of the index, which is extremely important for the security of the state, especially in times of war.

The impact of codeine rescheduling on non-opioid analgesic use by people who regularly use codeine: a prospective cohort study

BackgroundCodeine was rescheduled in Australia to prescription only in February 2018. Initial studies reported an increase in population level paracetamol and ibuprofen sales following codeine upscheduling. However, to date no study has been able to investigate changes in non-opioid analgesic use at the individual patient level to determine if sales data reflect actual consumption patterns.AimTo address this gap, we aimed to determine the impact of codeine rescheduling on non-opioid analgesic use in people who regularly used over-the-counter codeine, primarily for pain, prior to the rescheduling change.MethodWe conducted a prospective cohort study with 260 participants who reported regular over-the-counter codeine consumption at cohort entry. Surveys were completed at baseline (November 2017, 3 months before rescheduling) and at 1 month (February 2018), 4 months (June 2018), and 12 months (February 2019), following rescheduling. The primary outcomes were mean daily doses of non-opioid analgesics, captured through a 7 day medication diary.ResultsThe mean daily paracetamol dose decreased from 1754.4 mg (95% CI 1300.5–2208.3) at baseline to 1023.8 mg (95% CI 808.5–1239.1) at the final time-point (+ 12 months) (p = .009). The mean daily ibuprofen dose decreased from 305.1mg (95% CI 217.9–392.4) at baseline to 161.2 mg (95% CI 98.5–224.0) 12 months after rescheduling (p = .03). No significant change in doses of other medications remained was found.ConclusionIn people who regularly consumed over-the-counter codeine, doses of non-opioid analgesics either reduced or remained stable following codeine rescheduling, suggesting concerns of medication substitution or overuse following the change were not realised.

The role of cognitive biases involving selective interrogation of taste-based information in sugar-sweetened beverage consumption

Background and objectivesConsumption of sugar-sweetened beverages (SSBs) is a global public health issue. Consequently, there is strong interest in advancing understanding of the cognitive mechanisms that underpin excessive SSB consumption. This work proposes that selective interrogation of taste-based characteristics (e.g., flavour), rather than health-based characteristics (e.g., sugar content), of beverages is associated with greater SSB choice and consumption. MethodsTo evaluate this novel hypothesis, undergraduate students (n = 210) were recruited to complete self-report measures of SSB consumption, motivation and success in reducing SSB consumption and unhealthy eating more broadly, as well as a novel selective interrogation assessment task. This task provided participants with the opportunity to selectively interrogate taste-based and health-based characteristics of various mystery beverages. ResultsResults supported the hypothesis that greater selective interrogation of taste-based characteristics, rather than health-based characteristics, of beverages is associated with greater SSB choice and consumption. Additionally, results revealed that greater selective interrogation of taste-based characteristics was associated with decreased motivation to reduce SSB consumption and decreased motivation and success to reduce unhealthy consumption more broadly. LimitationsThe current study did not evaluate whether selective interrogation of taste-based characteristics of beverages predicted actual SSB consumption. ConclusionThese findings provide novel evidence for the potential role of selective interrogation of taste-based characteristics of beverages in SSB choice and consumption. An important implication of these findings is that in order for interventions focusing on front-of-package nutrition labels to be effective in reducing unhealthy consumption, it is necessary that individuals are selectively interrogating this ‘health-based’ information.

Indicators for suitability and feasibility assessment of flexible energy resources

Recommender systems play a critical role in optimizing building energy consumption by providing personalized advice based on data analytics and user preferences. However, the literature highlights the need for systems that can justify their recommendations, as many of these systems use non-transparent machine-learning techniques. This research introduces two distinct types of indicators with three main goals: to identify patterns of flexible consumption behavior using transparent and straightforward methods suitable for remote decision support systems, thereby eliminating the need for extensive databases; to evaluate the feasibility of installing solar panels on building facades, rooftops, and structures using high-resolution 3D models; and to enhance understanding through a quantitative assessment of the feasibility and suitability of integrating renewable energy sources, particularly photovoltaic systems. Flexible prosumers are scored by assessing their energy consumption level, consistency, and variability through the Flexible Consumption Indicators. Topology Indicators perform a quantitative assessment of the feasibility of support surfaces for installing photovoltaic panels, taking into account rooftop pitch angles, orientations, and surrounding and internal structures, identifying those areas exposed to sufficient levels of irradiation. This study, which uses actual consumption profiles and similar households' buildings 3D models, demonstrates how the proposed indicators can aid identifying users with flexible consumption profiles that reside in buildings compatible with renewable energy sources, aiding in decision-making process within the energy transition.

Development of a new environmentally friendly and efficient centrifugal variable diameter metering device.

The design of the maize metering device involves centrifugal variable diameter pneumatic and cleaning mechanisms, aiming to enhance the performance and power efficiency of pneumatic maize metering devices. Leveraging the impact of changes in centrifugal diameter and the guidance and positioning of airflow, we optimize the hole insert, seeding plate, seed limit board, and integrated front shell. This optimization facilitates the adjustment of both the quantity and posture of seed filling. As a result, seeds can form a uniform flow within the annular cavity, reducing the wind pressure necessary for regular operation and decreasing power consumption. A quadratic regression orthogonal rotation combination experiment is conducted using a self-made experiment bench, considering ground speed, wind pressure, and seeding rate as the experiment factors. Furthermore, a comparative experiment involving a novel centrifugal variable-diameter type metering device. The results indicate optimal seeding performance when the ground speed is 13.2 km/h, the wind pressure is 1.2 kPa, and the feeding rate is 25 seeds/s. Under these conditions, the quality of feed index reaches 95.20%, the multi-index is 3.87%, and the miss index is 0.93%. Findings reveal that the developed seed metering device achieved a quality of feed index exceeding 93.00% across varying speeds of 12~18 km/h, aligning with the production requirements. Moreover, the actual power consumption of Type B and C is about 85.00% and 98.00% lower than Type A, standing at only 32.90 W at 18 km/h. The COP of Type C is about 86 times and 12 times that of Type A and B, respectively, meeting the demands for efficient production of maize seed metering devices. In comparison to traditional design and structural parameter optimization methods for maize seed metering device, this study is helpful to the sustainable development of maize industry and reduce environmental pollution.

To play or not to play, that is the question: an interview study with amateur football coaches on perceptions of pain during sports participation

Amateur football coaches play a key role in preventing, assessing and treating pain among their players, as they are often the first point of contact and may be the main...