Anemia is a significant public health problem among children, especially school-age children because their body quickly produces red blood cells to provide sufficient blood volume with plasma expansion to maintain blood concentration. This research aimed to study the anemia situation, knowledge about anemia, food consumption behaviors, and the association between knowledge, food consumption behaviors, and hematocrit level among school-age children in primary school, in Thasala district, Nakhon Si Thammarat Province. This is a descriptive study among 408 students, Grades 4 to 6, aged 9-12 years. Research instruments included the demographic data of the children and their knowledge about anemia, food consumption behaviors, and hematocrit assessment. Data were analyzed using descriptive, Spearman’s rank correlation coefficient, and logistic regression statistics. The results revealed that 23.2% of the samples had anemia, whereas 22.22% had mild anemia and 0.98% had moderate anemia. The children knew about anemia, where the mean score was at a moderate level (Mean= 6.63; SD= 2.51) out of 10. The mean score on food consumption behaviors was in the moderate level (Mean= 17.49; SD= 3.68) out of 24. There were significantly positive correlations between the knowledge about anemia and the hematocrit level at a moderate level (r= 0.45, p< 0.001). Correlation analysis revealed moderate food consumption behaviors with the hematocrit level (r= 0.40, p< 0.001). When confirm with Logistic regression found that knowledge about anemia (OR = 9.15, 95% CI: 4.57-18.34), and food consumption behaviors (OR = 19.09, 95% CI: 9.71-37.53) were significantly associated with the hematocrit level. Conclusions: This study showed that knowledge and appropriate food consumption behaviors are associated with hematocrit levels. Enhancing knowledge about anemia and food consumption behaviors may be reduced the prevalence of anemia in school-age children. The health care providers in primary care should be encouraged children to have health education and eat sufficient food.

In this study is considered the design and implementation of pilots in desalination plants, in a systematic way, to evaluate energy improvements in water desalination processes. Pilot tests with the latest generation of reverse osmosis membranes in large desalination plants are becoming increasingly common, as small-scale experiments are carried out on the operation of new generation membranes with better salt rejection and lower energy consumption, to achieve the best water quality required for each case at the lowest possible cost. In this way, pilot tests are being carried out before the decision is made whether or not to change the reverse osmosis membranes, as this represents a significant investment and we minimize the risk of making a mistake in order to ensure the best results in terms of energy consumption, operating costs and reducing the environmental impact while complying with the required water quality. In this case, the Pareto analysis is used to highlight the 2 or 3 most significant causes whose treatment affects more than 80% of the possible energy improvement to be implemented. In this sense, figure 3 below shows the Pareto diagram for the energy improvement of N plants in a given territory. A series of factors that can affect this energy improvement have been numbered from 1 to 7, such as the seawater intake, the decision on the membranes, the energy recuperators, the high-pressure pump, the decisions on the pre-treatment, the feed pump or the post-treatment. The Pareto diagram has been studied at territorial level with a total of 180 seawater desalination plants. The novelty of this article is to apply Pareto diagram to a study of a total of 180 seawater desali-nation plants at territorial level in Canary Islands.

The present research aimed to identify if consumption values influence the attitudes of Millennials who intend to purchase organic products. The research was quantitative, with a correlational scope and a cross-sectional design, and it was conducted on 509 Peruvian millennials. A questionnaire consisting of 23 questions was administered, and responses were quantified using a five-point Likert scale. The results were processed through an Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM) conducted using Statistical Package for Social Sciences 24 and AMOS 24. The study identified that environmental attitudes of Millennials do influence their intentions to purchase organic products, and the antecedents of these attitudes are functional and social values, whereas emotional, conditional, and epistemic values do not. The study confirmed that consumption values, either as a second-order variable or as a single construct, do not influence the attitudes of Millennials who intend to purchase organic products, thus confirming that these dimensions should be measured independently.

Sustainable consumption became the community’s attention as the respond of worrying consumption growth direction that tends to excessively exploit nature without considering the continuity of the next generation. Take into account the growing coffee consumption in Indonesia, this article tries to prove whether the connoisseur consumers (CCs) are capable to mediate dynamics capabilities (DCs) of single origin coffee shops (SOCSs) and to encourage the consumption sustainability. In-depth interview to 30 SOCS managers, 60 baristas and 450 consumers, it is found that CCs are capable of supporting the business continuity of SOCSs because they are loyal consumers with the contribution of more than 20% from the total consumers. Correlation testing between connoisseurs’ attributes and the number of visits also shows positive value. It means there is a significant relationship. In addition, related to the sustainability attribute, the existence of CCs has encouraged the practice of consumption sustainability. At the same time, CCs are capable to moderate SOCSs to improve sensing, seizing, and transforming shop management to put up with the competition. Based on the above findings, CCs are moderating SOCSs in improving their DCs to be more potential in improving the sustainable consumption of coffee commodity in the future.

Reducing and time-shifting household energy demand is important for a sustainable transition of energy systems, and dynamic electricity pricing, like real-time pricing (RTP), is a popular instrument to incite households to change energy demand. However, the effect of such products relies on the assumption that dynamic price signals incorporate into everyday routines and rhythms. This paper investigates the flexibility of households’ everyday routines and rhythms. The study provides three insights based on 11 Danish household visits and interviews with electricity customers who are new to RTP. First, the interviews indicated confusion over terminology for dynamic pricing products and low interest in flexible energy demand. Second, everyday practices (already) appeared flexible but for other reasons than energy demand flexibility. Third, energy habits were important for providing meaning to everyday practices. In addition, the study shows that energy flexibility is not just about energy demand, but rather about all the other activities that form routines and rhythms in everyday life. We suggest 1) designing products that reflect ‘natural’ or ‘societal’ daily rhythms, 2) focusing on the engaged households, and 3) being aware that for some households, changes in pricing, including new payment schemes, might put extra pressure on already harried everyday routines.

This study assesses the impact of militarization, industrialization, technological innovation, and energy consumption on CO2 emissions in NATO countries, divided into "traditional" (Group 1) and "new member" states (Group 2), using data from 1985 to 2019. It identifies heterogeneity in effects and dependencies between the groups. Employing Pedroni and Kao panel cointegration tests, a long-term equilibrium relationship among the variables is confirmed. The analysis, using panel ARDL and GMM methods, reveals distinct outcomes for each group. In Group 1, military spending and energy consumption significantly increase CO2 emissions, while industrialization and technological innovation reduce them. Conversely, in Group 2, technological innovation and energy consumption raise emissions, whereas military spending and industrialization decrease them. The findings suggest Group 1 should strategize military expenditure and energy consumption to cut emissions, whereas Group 2 should focus on eco-friendly technology and efficient energy use to achieve environmental sustainability.

A number of sustainable and environmental issues are caused by consumer's behavior. Consumers' consumption pattern plays an increasingly important role in environmental conservation and adopting to a more sustainable community. This study used planned behavior theory and structural equation model to quantifies individual purchasing behavior of electronic vehicles based on perceived health risk cased by air pollution in Kunming, China. The findings showed that (1)for residents in Kunming, the perceived air pollution risk, the willingness to purchase new energy vehicles, and actual purchase behavior are low; (2) perception of health risks has a significant positive impact on purchase willingness, behavioral attitude and subjective norms play a significant positive mediating role between perception of health risks and purchase willingness, and perception of behavioral control plays a significant negative role, purchase willingness has significant positive impact on purchase behavior, the direct effect of perceived behavioral control on purchase behavior is insignificant; (3) for highly educated groups, risk controllability has a significant impact on the perception of health risk, subjective norms and behavioral attitude have significant impacts on purchase willingness, while for low-income gourp, risk controllability has significant impacts on the perception of health risk.

The level of technological development of any civilization can be gaged in large part by the amount of energy they produce for their use, but also encompasses that civilization’s stewardship of their home world. Following the Kardashev definition, a Type I civilization is able to store and use all the energy available on its planet. In this study, we develop a model based on Carl Sagan’s K formula and use this model to analyze the consumption and energy supply of the three most important energy sources: fossil fuels (e.g., coal, oil, natural gas, crude, NGL and feedstocks), nuclear energy and renewable energy. We also consider environmental limitations suggested by United Nations Framework Convention on Climate Change, the International Energy Agency, and those specific to our calculations to predict when humanity will reach the level of a Kardashev scale Type I civilization.

Innovative practices in irrigation systems can bring improvements in terms of economic efficiency and in the same time can reduce environmental impact. Investment in high tech technologies frequently involves additional costs, but an efficient water management can increase the lifetime of the equipment. The main objective of this article is to reduce the energy consumption by one thousand cubic meters pumped and automatically to increase the economic efficiency of the pumping groups. This paper develops a new operating algorithm that ensures the operation of the pumping group at safe operating intervals and in the same time identifies the equivalent pump operating points for the entire flow range and pumping height of the pumping group. This methodology is based on the principles of an Expert System to perform the optimization process of the energy consumption in pumping groups. The resulting methodology avoids the combinatorial explosion of the solutions to be analyzed and determines the point of maximum efficiency without violation of any of the system constraints under any operating condition. The proposed methodology is tested on an irrigation system that includes a pumping group with 5 pumps, showing its effectiveness in obtaining the optimal solution with a relatively low computational burden.

The U.S. Energy Information Administration (EIA) provides crucial data on monthly and annual fuel consumption for electricity generation. This data covers significant fuels such as coal, petroleum liquids, petroleum coke, and natural gas. Fuel consumption patterns are highly dynamic, influenced by diverse factors. Understanding these fluctuations is essential for effective energy planning and decision-making. This study outlines a comprehensive analysis of fuel consumption trends in electricity generation. Utilizing advanced statistical methods, including time series analysis and autocorrelation, our objective is to uncover intricate patterns and dependencies within the data. This paper aims to forecast fuel consumption trend for electricity generation using data from 2015 to 2022. Several time-series forecasting models, including all four benchmark methods (Mean, Naïve, Drift, and seasonal Naïve), Seasonal and Trend Decomposition using Loess (STL), Exponential Smoothing (ETS), and Autoregressive Integrated Moving Average (ARIMA) methods, have been applied. The best-performing models are determined based on Root Mean Squared Error (RMSE) values. For Natural Gas (NG) consumption, the ETS model achieves the lowest RMSE of 20,687.46. STL demonstrates the best performance for coal consumption with an RMSE of 5,936.203. The seasonal Naïve (SNaïve) model outperforms others for petroleum coke forecasting, yielding an RMSE of 99.49. Surprisingly, the Mean method has the lowest RMSE of 287.34 for petroleum liquids, but the ARIMA model is reliable for its ability to capture complex patterns. Residual plots are analyzed to assess the models' performance against statistical parameters. Accurate fuel consumption forecasting is very important for effective energy planning and policymaking. The findings from the study help policymakers strategically allocate resources, plan infrastructure development and support economic growth.

Introduction Our aim was to determine the impact of antimicrobial stewardship tools (AST) and COVID-19 pandemic on antibiotic consumption (AC). Methods We used the national software Consores to determine AC in DDD / 1000 days of hospitalization from 2017 to 2022 in voluntary private hospitals in France. The AST considered were 1/ internal guidelines 2/ list of antibiotics with restricted access 3/ presence of an antibiotic referent or 4/ an ID specialist 5/ proof of annual meeting on antimicrobial resistance. Institutions with dedicated units for COVID-19 patients were specified. Results In 30 institutions total AC varied from (means) 390 to 405 DDD/1000 DH from 2017 to 2022. Fluoroquinolones and amoxicillin / clavulanate consumption decreased from 50 to 36 (p = 0.003) and from 112 to 77 (p = 0.025) respectively, but consumption of piperacillin / tazobactam increased from 9 to 21 (p < 0.001). Over the study period, 10 institutions with ≤ 2 AST had lower AC compared to 20 institutions with ≥ 3 AST (p < 0.01). COVID-19 units opened in 10 institutions were associated with a trend toward higher macrolide consumption from 15 to 25 from 2017 to 2020 (p = 0.065) and with an acceleration of piperacillin / tazobactam consumption from 2020 to 2022 (p ≤ 0.003). Conclusion Antibiotic consumption in 30 private hospitals in France was inversely related to the number of AST. The COVID-19 pandemic was associated with limited impact on AC but special attention should be paid on piperacillin / tazobactam consumption.

Co-integration and Causality was built to conduct studies on causality relation between carbon intensity and coal consumption leading to providing important basis for the transition to a low carbon economy. The EG two-step method was performed to study the relation between carbon intensity and coal consumption of China during 1990-2015 and the co-integration and Granger test was constructed to build up the co-integration and error correction models for analysis of the interaction between carbon intensity and coal consumption. The results showed that in long term there is a stable co-integration relation and a positive correlation between carbon intensity and coal consumption; whereas fluctuations exist in short term and there is a one-way Granger causality of carbon intensity with respect to coal consumption.

The growing energy consumption in the cryptocurrency and artificial intelligence (AI) industries is becoming an increasingly critical issue. This paper discusses methods for addressing this problem using data compression algorithms and the optimization of computational processes. Specifically, we examine research results that demonstrate the potential for significantly reducing energy consumption through mathematical solutions, such as the Riemann Hypothesis. We also present the notarized test results of an image compression program developed by the author. The work was presented at a UN panel session and an ECO webinar, receiving recommendations for implementation in ECO member countries. Additionally, modern technologies for accelerating cryptographic methods and algorithms are considered.The growing energy consumption in the cryptocurrency and artificial intelligence (AI) industries is becoming an increasingly critical issue. This paper discusses methods for addressing this problem using data compression algorithms and the optimization of computational processes. Specifically, we examine research results that demonstrate the potential for significantly reducing energy consumption through mathematical solutions, such as the Riemann Hypothesis. We also present the notarized test results of an image compression program developed by the author. The work was presented at a UN panel session and an ECO webinar, receiving recommendations for implementation in ECO member countries. Additionally, modern technologies for accelerating cryptographic methods and algorithms are considered.

Consumer trust in supply chain certifications plays a pivotal role in shaping contemporary consumer behavior and marketing effectiveness. This qualitative research investigates consumer perceptions of supply chain certifications and their impact on purchasing decisions and brand loyalty. Through semi-structured interviews and focus group discussions with 30 participants, the study explores themes such as credibility, transparency, perceived value, and the influence of certifications on consumer trust. Findings reveal that consumers value certifications that signal ethical sourcing practices and sustainability commitments, viewing them as assurances of product quality and social responsibility. Credibility and transparency in certification processes emerged as critical factors influencing consumer trust, while perceived benefits such as environmental sustainability and fair labor practices positively influenced purchase intentions. Moreover, certifications were found to enhance brand loyalty by aligning with consumers' ethical values and beliefs. However, the study identifies challenges including high certification costs, complexity in compliance, and consumer skepticism, which hinder broader adoption and effectiveness of certifications. The implications of these findings suggest that businesses can enhance consumer trust and market competitiveness by investing in rigorous certification standards, clear communication strategies, and sustainable practices. Addressing barriers through improved transparency and consumer education is crucial for maximizing the impact of certifications on ethical consumption. Future research directions include longitudinal studies on certification impacts and comparative analyses across industries and global markets to further enrich understanding of consumer behavior and certification influences.

Urbanization and industrialization processes in Mongolia have been significant and rapid for the last half-century. During this period, changes in political and economic systems, growth in the population, and the occasional harsh climate conditions were subject to fluctuations in the natural resource usage. The total Ecological Footprint (EF) in Mongolia has increased from 6.8 million global hectares (gha) in 1961 to 14.6 million gha in 2012. However, Biocapacity (BC) has decreased from 50.6 million gha in 1961 to 39.0 million gha in 2012. The study shows that grazing land Footprint and carbon uptake land Footprint are the two major contributors of the recent intensified use of biological resources. To ensure stable economic development and sustainable use of natural resources, environmental planning is required to consider both the population’s pressure on the environment and the ecosystem’s regeneration capacity, simultaneously. We have proposed a few possible strategies for sustainable utilization of grazing land Footprint and carbon Footprint. For grazing land Footprint, efficient management of both herding practice and number of animals should be considered. In case of carbon Footprint, it is estimated that with the improved combustion efficiencies of coal-based power plants and the maximum use of renewable energy, carbon dioxide (CO₂) emissions in Mongolia can be reduced up to 30% compared to the base line business as usual case in 2030.

This paper addresses the difficulty of balancing real-time response and low power consumption in intelligent manhole cover application scenarios. It proposes a method to distinguish normal and abnormal events by segmenting the boundary where the acceleration of the intelligent manhole cover deviates from a set threshold and lasts for a certain period, based on the difference in vibration patterns of the intelligent manhole cover when a normal event and an abnormal event occur. This paper uses digital output motion sensor data autonomous data fusion to implement the pattern mentioned above recognition algorithm, which reduces the MCU computing and working time and the overall power consumption of the system while meeting the real-time response requirements. The test results demonstrate that the method has a high rate of anomaly recognition accuracy. The method ensures the system's real-time response capability, and the actual low power consumption test demonstrates that the device can operate continuously for 9.5 years. The low power consumption index exceeds the requirements of the existing national standard, thereby resolving the issue that it is challenging to balance intelligent manhole cover abnormality recognition and low power consumption.

The pre-chamber technologies can improve the ignition performance of IC engines by more than two orders in magnitude and thereby substantial economic benefits. Compared with the common pre-chamber, a plug-and-play passive scheme is suitable for quick retrofit, which is getting more attention from the automobile industry. Good scavenging is the precondition for improving turbulent jet ignition performance for a passive pre-chamber. Therefore, detailed evaluations of the scavenging process and turbulent jet ignition deserve investigations for new pre-chamber schemes. In this paper, the effects of design parameters on ignition processes of plug-and-play passive pre-chamber were numerically studied, allowing for the lateral angle, orifice diameter, and vertical angle design. Seven pre-chamber schemes were evaluated, and four optimal ones were selected for bench tests. The characteristics of the scavenging process, turbulent jet ignition, and main-chamber combustion were investigated and analyzed. The results show that allowing for the trade-off between ignition energy and scavenging efficiency, the volume ratio of the pre-chamber to clearance is recommended to be 0.2~0.7%, and the corresponding area-volume ratio is 0.003~0.006 mm-1. Compared with the original natural gas engine, the pre-chamber retrofit can save up to 13.2% fuel consumption, which presents a significant improvement in fuel economy.

Elevated indoor CO2 levels might have adverse effects on human health. However, the introduction of outdoor air to lower indoor CO2 concentrations results in significant HVAC energy consumption. Aligning with office hours and the natural light cycle, the utilization of photosynthesis in living walls offers an energy-efficient and sustainable solution for the mitigation of high CO2 levels in office spaces. This study experimentally investigates the impacts of the carbon fixation pathways, light intensity, and substrate moisture content on the CO2 removal rate of living walls at the room scale. Furthermore, the fresh air energy-saving effects of living walls under different scenarios are accurately simulated in EnergyPlus. The results demonstrate that choosing C3 plants over CAM plants in living walls yields higher CO2 removal efficiency. In a 30-m2 office room accommodating 2-3 occupants, living walls can reduce the demand for fresh air by 12.3%-27.8% and decrease fresh air energy consumption by 11.2%-28.2%. The city with the highest energy savings has energy savings that are 4.5 times greater than those of the city with the lowest energy savings. The findings of this research promote the application and development of living walls, thus providing a viable solution for improving indoor air quality.

Direct electric curing (EC) is a new green curing method for cement-based materials that improves the early mechanical properties via the uniform high temperature produced by Joule heating. To understand the effects of EC and steam curing (SC) on the mechanical properties and microstructure of cement-based materials, the mortar was cured at different temperature-controlled curing regimes (40°C, 60°C and 80°C). Meanwhile, mechanical properties, hydrate phase and pore structure of specimens were investigated. The energy consumption of two curing methods was compared and analyzed. The results show that the EC specimens have better and more stable growth of mechanical strength. The pore structure of EC specimen is also better than that of SC specimen at different maintenance ages. However, the hydration degree and products of samples cured by EC are similar to that SC samples. The energy consumption of EC is lower than SC. This study provides an important technical support for the EC in the production of energy-saving and high early-strength concrete precast components.

Building energy consumption is the main urban energy consumption component, which mainly serves people-centered work and living energy demands. Based on the physical requirements of humans in urban buildings and to determine the comfortable body temperature in each season, this paper establishes a sizing optimization model of building-type integrated energy systems (IES), where the cooling and heating loads required to maintain indoor somatosensory body comfortable temperature are calculated. Depending on the external energy price, internal power balance and other constraints, the model develops an optimal sizing and capacity panning method of energy conversion and storage unit in a building-type IES with PV generation. The operating principle is described as follows: the PV generation is fully consumed, a gas engine satisfies the electric and thermal base load requirements, while the power system and a heat pump supply the remaining loads. The gas price, peak-valley electricity price gap and heat-topower ratio of gas engines are considered as important factors for the overall techno-economic analysis. The developed method is applied to optimize the economic performance of building-type IES and verified by practical examples. The results show that using the complementary characteristics of different energy conversion units is important to the overall IES cost.

This study empirically investigates the effect of productive safety net programme (PSNP) on household food consumption and dietary diversity in Ethiopia. The study applied random effects with instrumental variable to estimate the effect of PSNP membership. The result of the study indicates that though PSNP membership improves household food consumption, it reduces household dietary diversity score. Household food consumption and dietary diversity are also significantly influenced by sex, age, education status of household head, household size, livestock ownership, distance to the nearest market and participation in non-farm activities. The findings of this study suggest that PSNP membership should be reinforced by building household awareness of the benefits of consuming a variety of foods. In addition, PSNP membership should be designed to endow the households to accumulate essential assets, especially livestock.

Brazil, Russia, China, India, and the Republic of South Africa (BRICS) represent developing economies facing different energy and economic development challenges. The current study aims to forecast energy consumption in BRICS at aggregate and disaggregate levels using the annual time series data set from 1992 to 2019 and to compare results obtained from a set of models. The time-series data are from the British Petroleum (BP-2019) Statistical Review of World Energy. The forecasting methodology bases on a novel Fractional-order Grey Model (FGM) with different order parameters. This study contributes to the literature by comparing the forecasting accuracy and the forecasting ability of the FGM(1,1) with traditional ones, like standard GM(1,1) and ARIMA(1,1,1) models. Also, it illustrates the view of BRICS's nexus of energy consumption at aggregate and disaggregates levels using the latest available data set, which will provide a reliable and broader perspective. The Diebold-Mariano test results confirmed the equal predictive ability of FGM(1,1) for a specific range of order parameters and the ARIMA(1,1,1) model and the usefulness of both approaches for energy consumption efficient forecasting.

The rapid development of China's economy has led to a rapid increase in energy production and use. Among them, the excessive consumption of coal in fossil energy consumption is the leading cause of air pollution in China. This paper incorporates renewable energy innovation, fossil energy consumption and air pollution into a unified analysis framework, and uses spatial measurement models to investigate the spatial effects of renewable energy green innovation and fossil energy consumption on air pollution in China, and decomposes the total impact into direct and indirect effects. influences. The empirical results show that China's air pollution, renewable energy green innovation and fossil energy consumption are extremely uneven in geographical space, generally showing the characteristics of high in the east and low in the west, and showing a strong spatial aggregation phenomenon. Fossil energy consumption will lead to increased air pollution, and the replacement of fossil fuels with clean and renewable energy is an important means of controlling pollution emissions. The direct and indirect effects of renewable energy green innovation on air pollution are significantly negative, indicating that renewable energy green innovation not only suppresses local air pollution, but also suppresses air pollution in neighboring areas. The consumption of fossil energy will significantly increase the local air pollution, and the impact on the SO2 and Dust&Smoke pollution in the adjacent area is not very obvious. It is recommended to strengthen investment in renewable energy green innovation, reduce the proportion of traditional fossil energy consumption, and pay attention to the spatial connection and spillover of renewable energy green innovation.

The article is focused mainly on verifying the suitability of data from experimental milling heat-treated beech wood and on investigation of the effect of technical and technological parameters of milling on the energy consumption of this process. The independent parameters of machining process are cutting speed, feed speed, rake angle, and hydrothermal modification of experimental wood material. Based on analysis of variance it can be argued that the greatest statistically significant effect on energy consumption have cutting speed and rake angle of the tool while the feed speed had the least influence. The measured data of cutting power during milling were used to build a regression model and validate it, while the most suitable type of model with correlation of 87 % is Classification and Regression Tree followed by model created by Random Forests method.

The growing global threat of multidrug-resistant (MDR) bacteria has been exacerbated by inadequate antibiotic consumption and the difficulty in developing new therapeutic strategies. Selective Digestive Decontamination (SDD) is presented as an encouraging measure to prevent the appearance and colonization of MDR bacteria. We compared the pattern of antibiotic consumption in the Intensive Care Unit (ICU) of our hospital before and after the implementation of the SDD protocol. This quasi-experimental study was conducted in an ICU from June-2021 to June-2023. The incidence of nosocomial infections, colonizations, and the number of isolated MDR bacteria, as well as the defined-daily-dose (DDD), were monitored. A total of 3266 patients were included, with 1532 in the pre-intervention group and 1734 in the post-intervention group. The median age was 62 years, 66% were male, and the average APACHE II and SOFA scores were 15 and 6.5, respectively. There was a significant reduction in overall antibiotic consumption in the post-SDD group (p= 0.028), highlighting a decrease in Carbapenemase-Producing Enterobacteriaceae (CPE) (p= 0.0099). There was also a reduction in colonization incidence density and nosocomial infections. Implementing the SDD protocol significantly reduced antibiotic consumption in the ICU, suggesting important implications for infection control and antimicrobial resistance management.

The use of energy resources is essential to the survival and development of human civilization. However, inefficient energy use not only has an impact on the economy, but it also has a significant impact on climate change. Studying and analyzing the factors that increase energy consumption is crucial for improving energy management. The aim of this study is to analyze the factors influencing energy savings in a building taken as a study model under two different climatic conditions. Measurements of the building's energy performance were carried out using the EnergyPlus calculation engine, which enabled us to analyze the strengths of building parameters such as the number of lights, the equipment used, occupancy, temperature and the relationships between the factors influenced. EnergyPlus is an important software tool in the field of building energy efficiency, enabling the precise simulation of building energy consumption. Simulation scenarios based on actual consumption data carried out by researchers have made it possible to develop energy management strategies in buildings in order to optimize energy efficiency, and in particular to promote the thermal performance of these buildings. In the present work, EnergyPlus building energy simulation software is used to simulate the energy consumption of the building of the Faculty of Electrical Engineering at the "Gheorghe Asachi" Technical University in Iasi, in order to determine the factors influencing building energy consumption. It has been shown that occupancy and heating and cooling systems are the key factors influencing energy consumption in buildings. The advantage of this study is that it can be used to model the most influential factors in predicting future energy consumption in buildings.

The emergence of Distributed Ledger Technology (DLT) in the past decade has challenged our imagination to discover new, innovative and disruptive solutions to problems in domains ranging from finance and healthcare to supply chain and smart cities. However, the enormous energy consumption that has been observed in some of the most successful DLT applications raises the question of their long term sustainability. This article reviews the standardization efforts of the International Telecommunications Union (ITU) to provide guidelines to regulators and policy makers for making informed decisions on the applicability and sustainability of DLT architectures from the point of view of energy consumption.

Resource pricing in cloud computing has become one of the main challenges for cloud providers. The challenge is determining a fair and appropriate price to satisfy users and resource providers. To determine a fair price, it is necessary to consider the conditions and needs of both parties: provider and user. This research tries to provide a pricing mechanism for cloud computing based on game theory. The proposed mechanism takes into account three factors including fault possibility, interaction between virtual machines, and energy consumption to reach a fair price. In the proposed game, the provider decides on the price of the virtual machine which can be offered to the user on each physical machine and the user decides between the virtual machines offered to run its application. The whole game is implemented as a function of the resource broker component. The proposed mechanism is simulated and evaluated using the CloudSim simulator. Its performance is compared with several previous recent mechanisms. According to the results, the proposed mechanism has been able to determine a more reasonable price for the user and the provider which increases the overall profitability of the cloud system.

As Solar Photovoltaics in buildings reaches maturity, grid integration and economic yield are topics of greater interest. The traditional design of photovoltaic installations has considered the optimal orientation of photovoltaic modules to be that which yields the maximum annual energy production. The influence of the consumption patterns and the hourly-variable electricity prices implies that this traditional optimal design might not be the most profitable. Using a full-year dataset for a residential installation, alternative installations using canopies and modules attached to the façades are simulated. Simulating the energy balances for different annual consumptions, it is found that the canopy and façade installations offer better self-consumption of the PV produced energy, reflected in a 9% higher self-consumption degree using modules on façades and a 5% using canopies. The economic evaluation under the new electricity tariffs in Spain shows a better profit for PV self-consumption, reducing by more than 2 years the time of return on investment. The analysis of different alternatives for an industrial PV has allowed us to identify several benefits for these orientations, such as an increase in annual energy production of up to 59% over the optimal-producing orientation, that are confirmed after several months of operation.

The purpose of this paper is to evaluate the acceptance and the utilization of GPS/GPRS-based telematics technology in road transport companies registered in Poland. Telematics technologies are essential for management of energy saving and emissions reduction in road transport. It is in line with the European Union policy of sustainable transportation. The evaluation is based on a survey designed and carried out in 2020. The issues concerning the scope of telematics systems utilization as well as the internal and external factors affecting their use are analysed. The methodology is based on Technology Acceptance Model (TAM) and Structural Equation Modelling (SEM). The results are checked for robustness. Based on the results, it can be reasoned that as a result of the COVID19 pandemic, the companies started to use telematics systems more widely than they did before. Furthermore, the companies employing more people recognize the higher usefulness of telematics systems and are motivated to have the systems more than smaller enterprises; however, TAMs estimated separately for small and medium-sized enterprises did not reveal any significant differences in the parameter estimates.

Economic globalization has increased interdependence, particularly among the developing economies. This has increased the potential of migration across the national borders. In a similar context, the Indian economy has witnessed a rapid growth in the number of migrants after the phase of globalization. The rise in the migrant stock has led to a massive increase in the income generated through international borders in the form of remittances. India has become one of the top recipients of remittances with 79$ billion inflows in 2018. The major factor driving the growth of migrant remittances has been the monetary incentives that raise the standard of living of the recipient’s households. The rise in the income level of these households also affects other economic parameters including consumption and investment. Apart from this, the pattern of migration has also changed since the past few years with skilled workers migrating to the developed economies and unskilled ones migrating to the Gulf economies. In this context, the present study examines the trend and pattern of remittance inflows in India for the period of 1975-2017. Additionally, the study explores how remittance inflows affect the level of household consumption and investments. This relationship was examined using a two-stage least squares method by framing a set of simultaneous equations. The findings of the two-stage least square estimates indicate that though personal remittances do not impact the gross domestic product of the economy directly. But, an increase in the inflow of personal remittances leads to a rise in consumption and investment which in turn plays an important role in determining the Gross Domestic Product of the economy.

The present research focuses on the endogenous development theory and investigates the relationship between economic growth (dependent variable) and renewable energy consumption, technological innovation, and export diversification (independent variables) in seven emerging economies known as the E-7. Previous studies have examined these factors individually but have not explored their combined impact on the E-7 economies. Therefore, this study contributes to the existing literature on the effects of renewable energy consumption, technological advancement, and export diversification on economic development. The study analyses the dynamic connection among these variables in seven selected emerging countries: Brazil, China, Indonesia, India, Mexico, Russia, and Turkey. Panel data from 1990 to 2022 is utilized, and various methodologies, including panel cointegration, the PMG-ARDL estimator, and robustness tests such as FMOLS and DOLS, are employed. Empirical inferences are drawn using the Dumitrescu-Hurlin Panel Causality (DHC) test, and the long-run relationships among the variables are validated using the Westerlund residual cointegration tests. The results from the PMG-ARDL estimator show that renewable energy consumption, technological advancement, and export diversification have a significant and positive impact on economic expansion, confirming the validity of the endogenous growth model in the E-7 countries. The control variable of the financial sector has a positive but insignificant effect on economic growth, while trade openness has a negative and significant effect. The DHC test results indicate a neutral feedback effect of renewable energy consumption on economic growth. The findings also reveal a unidirectional causal relationship between technological innovation and economic growth. Overall, these findings provide valuable insights for economic policymakers in the E-7 countries. By removing barriers to renewable energy consumption, technological innovation, and export diversification, policymakers can promote sustainable economic development.

Rotary tillage is a main management tillage practices and widely applied in the North China Plain. However, the long term rotary tillage (depth of 20 cm) results in soil compaction and plow pan formation, which reduces water use efficiency and nutrient uptake, and then impedes the yield increase. In this study, a 3-year field experiment was conducted to investigate the influence of different depths of tillage on soil bulk density, field capacity, water use, photosynthetic rate, nutrients and maize yields in the North China Plain. Three depths of tillage (D20, depth of 20 cm; D25, depth of 25 cm; D30, depth of 30 cm) were assessed. D25 and D30 significantly reduced soil bulk density and improved field capacity in 10-20 and 20-30 cm soil layer, compared to D20. Soil water consumption for D25 was significantly higher 10.12% and 6.61% than that for D20 and D30, respectively. Photosynthetic rate for D25 significantly improved than that for D20 and D30. Total nitrogen in 0-20cm soil layer decreased with the depths of tillage. The maize yields for D25 significantly increased by 20.92% and 21.56% compared to that for D20 and D30, respectively. Structural equation models showed that the total effects of tillage, total nitrogen, photosynthesis and soil water consumption on yields were 0.019, -0.628, 0.121, and 0.895 (path coefficients λ), respectively. The results demonstrated that D25 could improve maize yields, water use efficiency, photosynthetic rate by improving soil water consumption. Depth of 25 cm is optimal tillage practice for the maize production in the North China Plain.

Earth’s climate cannot be ignored any longer. Policies are vital in order to mitigate the negative effects of climate change. The energy crisis created by the Russo-Ukrainian war and pandemic affected the EU and its member states. The focus is more than ever on its energy policies and independence. The EU revised the energy strategy in response to Russia’s blackmail, and it sped up all the processes for energetic independence from Russia. This benefited the climate change policies the most, as all the measures involved reducing energy consumption and increasing renewables, thus contributing to reducing greenhouse gas emissions. As a member state of the EU, Romania is committed to complying with EU regulations. With a high degree of energy independence compared with the other EU members, Romania plans to become a regional energy provider and modernize the energy infrastructure internally as a response to Russia’s unprovoked aggression. The measures that the EU and Romania implemented after the war started in 2022 have come to fruition, and the effects are becoming visible a year later. This study aims to study the energy strategy of Romania in correlation with the EU strategy in the turbulent period of pandemics and war between 2019 and 2023, with the latest available data.

Diet modification may be a potential contributor in the prevention of age-related cognitive decline. The association between dairy product consumption and cognitive function remains unknown in older people. We investigated whether cheese intake is associated with lower cognitive function (LCF) in community-dwelling older adults. This cross-sectional study included 1503 adults aged over 65 years. Analyzed data were obtained from face-to-face interviews and functional ability measurement. Cognitive function was assessed using mini-mental state examination (MMSE), and a score ≤ 23 was defined as LCF. The prevalence of LCF was 4.6% and this group had smaller calf circumference, slower usual walking speed, and more frequent history of anemia than subjects with MMSE scores ≥ 24. After adjusting for confounding factors, logistic regression analysis revealed cheese intake (odds ratio (OR) = 0.404, 95% confidence interval (CI) = 0.198-0.824), age (OR = 1.170, 95% CI = 1.089-1.256), usual walking speed (OR = 0.171, 95% CI = 0.062-0.472) and calf circumference (OR = 0.823, 95% CI = 0.747-0.908) as significant factors associated with LCF. Although the present study was an analysis of cross-sectional data of Japanese community-dwelling older adults, the results suggest that cheese intake is inversely associated with LCF.

Wireless Sensor Network (WSN) and the sensing devices are considered part of the core components of the Internet of Things (IoT). Hence, the modeling of IoT-WSNs is fundamental to having a better understanding, management, and deployment of this kind of technology. A fundamental issue in WSN is energy consumption due to the inherent limitations of this resource in the sensor devices. On the other hand, several issues arise in heterogeneous scenarios due to the coexistence of different sensor devices. Therefore, the modeling process becomes challenging and must consider the different transmission and operation cycles and modes that the transmission protocol implements to assure successful functioning in terms of transmission, synchronization, and energy savings. A duty-cycled MAC WSN protocol (PSA-MAC) has been modeled based on a pair of two-dimensional Discrete-Time Markov Chain (2D-DTMC), whose solution in terms of stationary probability distribution is used by expressions that have been derived to evaluate the energy consumption of sensor devices that could be used for WSN-based IoT applications. Analytical results of average energy consumption are obtained, where the synchronization, data, and sleep periods of a full transmission cycle are considered in a heterogeneous scenario with different node classes and priority assignments. Also, the normal and awake operation cycles are considered to assure synchronization and energy savings. Moreover, the transmission schemes of SPT (transmission of single packets) and APT (transmission of packets in aggregation) are also considered in the model. These results are validated through discrete event-based simulations, and the obtained results are accurate.

In the Italian context, the diffusion of online fake news about food is becoming increasingly fast-paced and widespread, making it more difficult for the public to recognize reliable information. Moreover, this phenomenon is deteriorating the relation with public institutions and industries. The purpose of this article is to provide a more advanced understanding of the individual psychological factors and the social influence contribute to the belief in food-related online fake news and the aspects that can increase or mitigate this risk. Data were collected with a self-report questionnaire between February and March 2019. We obtained 1004 valid questionnaires filled out by a representative sample of Italian population, extracted by stratified sampling. We used structural equation modelling (SEM) and the multi-group analyses to test our hypothesis. The results show that self-evaluation negatively affects the social-influence, which in turn positively affects the belief in online fake news. Moreover, this latter relationship is moderated by the readiness to change. Our results suggest that individual psychological characteristics and social influence are important to explain the belief in online fake news in the food sector; however, a pivotal role is played by the motivation of change lifestyle. This should be considered to engage people in clear and effective communication.

Traffic source emissions inventories for the rapidly growing West African urban cities are necessary for better local characterization of vehicle emissions released into these cities atmosphere. This study based on local field campaign in a representative site of anthropogenic activities over West African cities such as Yopougon (Abidjan, Côte d'Ivoire) during 2016, provided useful information on vehicle type and age, traveling time, fuel type and amount for fuel consumption estimation. Also, high traffic flow of personal car were recorded on highway, boulevard and backstreet whereas high flows of intra-communal sedan taxi are recorded on main and secondary roads. In addition, the highest daily fuel consumption value of 56 L.day^-1 was recorded in heavy vehicle while the lowest value of 15 L.day^-1 is recorded for personal car using gasoline. This study will be useful for the improvement of uncertainties related to the different databases used to estimate inventories emissions either national or international reports. This work provides useful information for future studies on urban air quality, climate and health impacts assessment in African cities. It may also be useful for policy makers to support implementation of emission reduction policy in West African cities.

The utilization of eco-fonts for office printing is a sustainable, “green” printing concept, which has obvious economic benefits. As a result, it has a significant effect on environmental sustainability. This practice's fundamental problem is the decreased quality of text printed using eco-fonts compared to those printed with regular fonts. The aim of this research is eco-font efficiency estimation, i.e. determination of toner usage reduction level of inkjet-printed documents typed with this font type, as well as estimation of the extent humans perceive differences between text printed with eco-font and the one printed by its “non-eco“ equivalent. Combining the instrumental measuring method and digital image analysis, it was found that this simple principle (eco-font utilization) enables substantial toner usage reduction for an inkjet printing system. At the same time, a visual test showed that the visual experience of text printed using eco-font was sufficient. In addition, awareness of the benefits that eco-font utilization brings, change users’ attitude towards eco-font quality. The concept of removing the black pixel from this commonly used Thai font has a great potential for the sustainability printing process, and this simple solution could be applied to other languages as part of the GIT campaign.

Purposes: Engaging in regular physical activity has been consistently linked to improved physical health and academic performance. Despite its known benefits, there is a concerning trend of decreased physical activity among children globally. The study primarily aims to investigate the level of physical activity among junior high school students in Taiyuan and analyze the main affecting factors from a socio-ecological perspective. Methods: A cross-sectional study was conducted, involving 650 junior high school students from 7 schools in Taiyuan, and 648 valid questionnaires were collected. Physical activity levels were assessed using the Children’s Leisure Activities Study Survey Questionnaire. Factors influencing physical activity were evaluated through the Student Perceived Factors Affecting Physical Activity Questionnaire. Results: In this study, students from the 7th, 8th, and 9th grades participated in physical activities, averaging 214.5 minutes per week in moderate-intensity and 25 minutes in high-intensity activities, cumulatively averaging 280 minutes weekly. Notably, a significant disparity (p<0.05) was observed in the combined duration of moderate and high-intensity activities, with male students engaging more time compared to their female counterparts (307.500 vs 255.000 minutes). This variance extended across different grades, particularly evident in 8th graders who recorded the highest weekly high-intensity activity duration (31.000 minutes) and overall physical activity time (320.500 minutes), surpassing the 7th graders. Furthermore, an exploratory factor analysis of a 32-item questionnaire, designed to identify determinants of physical activity, revealed six principal components. These components were found to positively correlate with both moderate and high-intensity physical activities. Conclusion: This study emphasizes the necessity for educational institutions and community programmes to provide engaging physical activities, especially during weekends. It highlights the importance of tailoring physical education curricula to address gender and grade differences and investing in quality sports equipment and facilities by schools and local governments.

The aim of this work was to determine the potential of using a pulsed electric field (PEF) and ultra-sound (US) apparatus to produce mitragynine extracts from Mitragyna speciosa dried leaves. Four modes of the device were tested: PEF, US, US + PEF, and PEF + US. The results were compared with extracts obtained using a conventional technique (maceration, as the control). Changes in the mitragynine content were determined with the liquid chromatography/mass spectrometry (LC-MS/MS) method. The LC-MS/MS analysis showed that the mitragynine contents from kratom extracts using four different modes were different. The highest extraction (106.63 ± 0.85 mg/L) of mitragynine was achieved by the PEF + US procedure, followed by US + PEF (97.27 ± 1.33 mg/L), with increased extraction efficiencies of 45.81 ± 0.59% and 33.00 ± 1.85%, respectively. Moreover, the total energy consumption under the combination technique was 25.0% lower than that with PEF assistance. Furthermore, scanning electron microscopy (SEM) and Fourier Transform Infrared Spectroscopy (FTIR) provided confirmatory evidence for the extraction of M. speciosa by the prototype. This study confirmed that PEF and US devices can be considered a green alternative method and may contribute to the application of agricultural products.

This study aims at improving the forecast accuracy of primary energy consumptions in China, Japan and South Korea and verifying the correlation in primary energy consumptions among the neighboring countries. Considering the diversity of primary energy composition, this study selects 6 components of primary energy, including oil, coal, natural gas, nuclear energy, hydropower and renewable energy as characteristic variables. A collaborative prediction model based on SVR for primary energy consumption prediction is proposed to explore the correlation of primary energy consumption among three countries in China, Japan and South Korea. The results show that there is a strong correlation between primary energy consumption when multiple countries make collaborative prediction, among which the primary energy consumption of South Korea has the largest impact on the primary energy consumption of China and Japan. In the primary energy cooperation of China-Japan-South Korea, a primary energy cooperation system with the South Korea as the link should be established through regional coordination to alleviate the shortage of traditional fossil energy.

To enhance local culinary offerings, it is imperative to comprehend visitor behavior prior to, during, and subsequent to their visit. This study utilizes a quantitative methodology. The sample for this study consisted of 482 tourists who indulged in traditional cuisine in Yogyakarta and Solo. Data was collected between February and March 2021 through the distribution of Google forms both directly and online via WhatsApp broadcast messages. The quantitative data analysis technique employed utilizes descriptive statistics and structural equation modeling (SEM). The variables investigated in this study demonstrate a favorable impact on consumer buying choices, with intrinsic variables (individual, motivational, and gastronomic experience) accounting for 52.5% or greater than extrinsic variables (social media and socio-cultural influences), which account for 41.4%. The behavior model is structured into three sequential stages: pre-consumption, consumption, and post-consumption. Tourists place experience as their foremost incentive for engaging in local cuisine excursions. This research has theoretical implications in terms of the uniqueness of visitor consumer behavior theory, specifically in the context of local culinary tourism. The behavior model undergoes a transformation into a comprehensive approach for assessing tourist behavior. The practical implications of this research can be utilized by many stakeholders, such as destination managers and the government, to sustain local culinary tourism destinations. These implications are based on market factors and involve the incorporation of information technology.

Industry 4.0 brings advanced technologies and automation, enabling greater productivity and efficiency. However, it also increases energy consumption and carbon emissions. Sustainable manufacturing in the era of Industry 4.0 is vital for addressing the environmental impact by minimizing resource consumption, optimizing energy efficiency, and adopting eco-friendly practices. CNC (Computer Numeric Control) 5-axis milling plays a significant role in the machining of precision molds and dies, aerospace, consumer electronics, etc. This research aims to explore the potential of the artificial intelligence (AI) technique in improving energy efficiency on CNC 5-axis milling process for sustainable manufacturing. The experiments were carried out using a CNC 5-axis milling machine with various machining parameters, toolpath planning, and dry cutting condition. Data on energy consumption of CNC 5-axis milling machine were collected, then the relationship between the machining parameters, toolpath planning, and energy consumption was analyzed and built. Furthermore, an AI-based optimization algorithm was developed that uses machine learning and optimization techniques to identify energy-efficient machining strategies. The developed algorithm was implemented within a simulation framework, the algorithm was rigorously assessed using machine learning analysis to effectively reduce energy consumption, all while ensuring the accuracy of machining result. Finally, verification experiments were carried out to evaluate the applicability and performance of the AI-based energy optimization approach on a CNC 5-axis milling machine. By integrating AI into the CNC machining process, manufacturers can achieve significant energy savings, leading to reduced carbon emissions and improved sustainability.

With an increasing concern for global warming, there have been many attempts to reduce greenhouse gas emissions. About 30 % of total energy has been consumed by buildings and much attention has been paid to reducing building energy consumption. While there are many ways of reducing building energy consumption, accurate energy consumption prediction becomes more significant. As mechanical systems are the most energy-consuming components in the building, the present study developed the energy consumption prediction model for a direct-fired absorption chiller by using the ANN technique for the short term. The ANN model was optimized and validated with the actual data collected through a BAS. For the optimization, the numbers of input variables and neurons, and the data size of training were applied. By changing these parameters, the predictive performance was analyzed. In sum, the outcome of the present study can used to predict the energy consumption of the chiller as well as improve the efficiency of the energy management. The outcome of the present study can be used to develop a more accurate prediction model with a few datasets in that it can improve the efficiency of building energy management.

In the literature, it is well known the correlation between poor semen quality and DNA sperm integrity, which can turn into negative outcomes in terms of embryo development and clinical pregnancy. Sperm selection plays a pivotal role in clinical practice and the most widely used methods are mainly based on sperm motility and morphology. The cumulus oophorus complex (COC) during natural fertilization represents a barrier that spermatozoa must overcome to reach the zona pellucida and fertilize the oocyte. Spermatozoa that can pass through the COC have better structural and metabolic characteristics as well as enhanced acrosome reaction (AR). The present study aimed to evaluate the exposure of sperm to cumulus cell secretome during swim-up treatment (SUC), compared to the routinely used swim-up method (SU). To determine the effectiveness of this method biological factors critical for the ability of sperm to fertilize an oocyte, including capacitation, AR, tyrosine phosphorylation signature, DNA integrity, and mitochondrial functionality were assessed. The SUC selection assures recovery of high-quality spermatozoa, with enhanced mitochondrial functionality and motility compared to both SU-selected or unselected (U) sperm. Furthermore, by this modified swim-up procedure significantly reduced sperm DNA damage (p<0.05) was detected. In conclusion, the SUC approach is a more physiological and integrated method for sperm selection that deserves further investigation for its translation into clinical practice.

Children under two years old are at risk for anemia because young children have an increased need for iron for their physical growth and brain development. The purpose of this study was designed to evaluate the effects of a caregiver potential support program on anemia prevention in children six months to two years old attending the subdistrict health-promoting hospital in Thasala District, Nakhon Si Thammarat Province. This study was quasi-experimental. The sample included children aged six months to two years old and their caregivers, who were selected by random sampling and allocated to either the experimental or control group, with 40 pairs per group. The experimental group received a potential support program, while the control group received regular care. Both groups were followed for 12 weeks. The instruments used were the potential caregiver assessment, children’s anemia assessment, and a program to support the potential of primary caregivers. Descriptive statistics, chi-square, and t-tests were used to analyze the data. The results revealed that 11.4% of the children had anemia, and a hematocrit count of less than 33% (range = 30-40, M = 34.89, SD = 1.97). The mean scores of knowledge about anemia and iron supplementation after using the program in the experimental group and control group were significantly different (p < 0.001). The mean scores of knowledge about anemia in the experimental and control group were 15.75, SD = 0.54, and 13.28, SD = 1.43 respectively. The mean scores of knowledge about iron supplementation in the experimental and control group were 10.75, SD = 0.49, and 8.15, SD = 1.54 respectively. It was found that the experimental group had a higher mean score on food care behaviors than the control group for 6-11 months and 1-2 years, with statistical significance. The experimental group also had a higher mean score of care behaviors concerning children’s consumption of medicine than the control group (M = 58.20, SD = 4.05; M = 45.78, SD = 9.66, respectively), (p < 0.001). The mean score of the hematocrit level for the experimental group was found to be higher than the control group after receiving the program (M = 35.80, SD = 1.55; M = 34.83, SD = 2.14, respectively), (p < 0.05). Therefore, healthcare providers should support caregivers' capacity to provide continued care for children to prevent anemia.

The aim were to determine the drinking water preferences of people applied to a family health center. This cross-sectional study was carried out from April 01st to May 31st, 2022. The data were evaluated using the chi-square test and percent ratios with a significant of P < 0.05. The mean age of all 432 respondents was 48.03±15.86. It was determined that those aged 31-45 had drunk more bottled water (p<0.01) and more spring water (p<0.001), that those aged 65 and over more purified water (p<0.001), that women more tap water (p<0.001), that married people had drunk more demijohn and tap water (p<0.001, each one), that divorced/widows had drunk more packaged and purified water (p<0.001, each one), that the illiterate/literate bottled water (p<0.001, per one), that those who had no income/people who lived on the state or municipal assistance only carboy water (p<0.001). This study suggests that the biggest factor that positively affected the drinking water preference was the packaging of the water. However, the drinking rates of mains water and spring water were quite low. Convincing the authorities to make the mains water drinkable is of great importance in terms of overcoming the public's distrust of mains water.

Reducing office buildings’ energy consumption can contribute significantly towards carbon reduction commitments since it represents 10% of total energy consumption. Major components are lighting (40% of consumption), electrical equipment (35%), and heating and central cooling systems (25\%). Occupants’ behaviours impact these energy consumption components, with solid evidence on the role of individual behaviours. In this work, we propose a methodology that uses virtual choreographies to identify and prioritize behaviour-change interventions towards office users based on the potential impact on energy consumption. The data shows that some behaviours with significant consumption have little potential for behavioural change impact, while other behaviours hold substantial potential for lowering energy consumption via behavioural change.

Since the most important constituent of the cost of cryptocurrency production is energy bills, use of illegal electricity in cryptocurrency mining farms is very common. For this reason, many countries prohibit cryptocurrency production or restrict it with some legal regulations. This situation raises the problem of how to detect illicit cryptocurrency mining. Therefore, innovative approaches are needed to detect illicit cryptocurrency mining farms. In this study, it has been shown that unique noise and/or harmonic characteristics of machines producing cryptocurrency can be used to detect illicit cryptocurrency mining farms. The unique harmonic characteristics formed by data centers on neutral line have been detected by field measurements performed. Electricity distribution companies can make risk assessments with this approach by monitoring energy quality data. As a result, the necessary legal permissions can be obtained to investigate suspicious high-risk consumers. Illicit cryptocurrency mining farms can be detected by investigating at high-risk consumer facilities.

Demographic and socio-economic developments couple with other requirements to satisfy human needs result in rapid urban expansion sometimes with increasing rate of surface extent greater than the rate of growth of population, that result in continuous sealing of ground surface thereby affecting ecosystem services. This study applied remote sensing toward achieving the progress of SDGs and stage to determine the ratio of the rate of land consumption to the rate of population growth of Gombe metropolis. QGIS 2.18 was used for the image processing and classification analysis for the key Landsat ETM+ (Enhanced Thematic Mapper), Impervious Surface Indices and population data to inform on the urban trend and LCR/PGR for the periods 2000-2005, 2005-2010, and 2010-2015. The result appears that the LCR/PGR for the periods of study show split trends. During 2000-2005 the result shows that the study area expanded outward with LCR/PGR of 1.2. The result also indicate that during 2005-2010, the study area densified with little expansion with the LCR/PGR of 0.8. The result further reveals that during 2010-2015 the LCR/PGR reached 1.8. Pointing that the study area expanded outward with the rate of ground sealing getting high.

A The agri-food sector is in full renovation, continuously demanding new systems that allow farmers to facilitate their work. Efficient agricultural practices are essential for increasing farm profitability. Efficient agricultural practices can be increased by understanding and managing water neeeds. However, real-time monitoring of water needs is currently slow, and irrigation decisions are based on previous data or physical evidence. Furthermore, the prices of automatic systems for collecting data from several sources (soil and climate) are expensive and the autonomy is very low. Emerging Internet of Things (IoT) technologies such as wireless sensor networks can be used to collect vast amounts of data recorded via apps. By means of an LPWAN communication the farmer can know in real time the state of his crops thanks to a large number of sensors connected wirelessly and distributed across his farm. This paper presents a low consumption solution based on LoRaWAN technology. The wireless sensor node, called BoX, exhibits very low power since it has been optimized both in terms of hardware and software. The result is a higher degree of autonomy than commercial motes. That will allow the farmer to have all the information necessary to achieve an efficient management of his crops with full autonomy.

This study presents a novel approach for predicting hierarchical short time series. In this article, our objective was to formulate long-term forecasts for household natural gas consumption by considering the hierarchical structure of territorial units within a country's administrative divisions. For this purpose, we utilized natural gas consumption data from Poland. The length of the time series was an important determinant of the data set. We contrast global techniques, which employ a uniform method across all time series, with local methods that fit a distinct method for each time series. Furthermore, we compare the conventional statistical approach with a machine learning (ML) approach. Based on our analyses, we devised forecasting methods for short time series that exhibit exceptional performance. We have demonstrated that global models provide better forecasts than local models. Among ML models, neural networks yielded the best results, with the MLP network achieving comparable performance to the LSTM network while requiring significantly less computational time.

The construction field uses up over one-third of the global energy consumption and contribute to 40% of CO2 emissions according to the International Energy Agency (IEA) and the 2020 annual reporter of United Nation, Goal 11 (Make cities inclusive, safe, resilient and sustainable) which discusses sustainable, safe and efficient buildings. Therefore, Morocco has a commitment to this program by publishing the law 47-09 of energy efficiency. This work aims to study the energy efficiency of two types of building, a conventional and a natural building. Conventional building is constructed using concrete, while the natural one uses sand clay and straws. As for the technique of making the natural building, it perpetually follows the same approach accustomed in rural zones of Atlas Mountains in Morocco. In this research we also simulate, temperature and humidity variation inside these buildings using TRNSYS software. Sketch Up software was employed to design these houses. The weather database is used for a typical meteorological year (TMY). In the case of natural building, many building configurations were simulated: roof insulation, floor insulation, different types of glazing and sun protection. What's more, the thermal comfort is revealed to be more conspicuous in the case of natural building.

This paper evaluates norms and assesses the level of knowledge in air conditioning project management within the construction industry. A total of 25 questions were distributed to multiple candidates, who were filtered based on pre-established inclusion and exclusion criteria. Thirty-nine candidates were ultimately approved to participate in the survey. The questions were designed to address five hypotheses, with each set of five questions corresponding to one hypothesis. The results were obtained after pre-processing the data using Matlab software. The data was pre-processed using Matlab software, and the results were analyzed using the Delphi method. The analysis revealed that only two hypotheses were approved: No matter whether there are nationalized safety rules or not, the impact of data sciences and smart technologies, including air conditioning management systems, is critical for human life in the building business.

The social confinement resulting from the COVID-19 crisis temporarily reduced greenhouse gas emissions. Although experts consider that the decrease in pollution rates was not drastic, some surveys detect a growth in social concern about the climate. In this new climate-conscious environment, municipalities and local governments are promoting a new way of living and caring for cities, even before they can regain national and international freedom of movement. This work analyzes the connection between the new climate awareness arising from the COVID-19 crisis, the proposals of sustainable citizenship around the world, and its communication on Twitter to educate the new eco-conscious audience. The methodology mixes quantitative and qualitative analysis, using the Twitonomy Premium tool and the Twitter research tool, with data extracted at the end of December 2020. Among the top 10 most influential and active accounts, the results show educational institutions, local institutions, companies, neighborhood, associations, and influencers. The impossibility of living the city, has not prevented citizen education and commitment to make real change for when that city and its citizens return to normality. Although this new normality must be different: more ecological, more responsible, more sustainable and practiced from early childhood.

Integrating a grid-connected battery into a Renewable Energy Community boosts Collective-Self-Consumption of photovoltaic produced energy and allow to perform Energy Arbitrage in the day-ahead and intraday electricity market. However, is it a cost-effective investment? The proposed analysis relay on Net Present Value assessment. Ageing affects are integrated and sensitivity analyses are conducted: considering community customers number, electricity mar-ket prices, battery cost, size and the decision to engage in energy arbitrage. Each scenario under-goes a 20-year hourly simulation using an ageing aware rolling-horizon 24-hours-looking-ahead scheduling, optimized with Mixed-integer linear Programming. Forecast errors, provid-ing of ancillary services, real time control and grid constrains are not taken into account. Simulations conducted on the Italian market indicates that a battery dedicated solely to collec-tive self-consumption is not economically appealing. However, integrating Energy Arbitrage, despite impact on battery lifespan, halves the payback period and enhances the attractiveness of larger battery investments. The Net Present Value is contingent upon battery size, customer number and market prices. However, if battery cost does not exceed 200 €/kWh the investment become cost-effective across all scenarios.

In 2023, residential and commercial sectors together consumed approximately 27.6% of total U.S. energy, equivalent to about 20.6 quadrillion Btu. Factoring in the electrical system energy losses, the residential sector represented approximately 19.7% of total U.S. energy consumption during that time. There were approximately 144 million housing units in the United States in 2023, which is increasing yearly. In this study, information on energy usage in the United States (U.S.) residential sector has been analyzed and then represented as energy intensities to establish benchmark data and to compare energy consumption of varying sizes and locations. First, public sources were identified, and data from these previously published sources were aggregated to determine the energy use of the residential sector within the US. Next, as part of this study, the energy data for seven houses/apartments from five different United States climate zones were collected firsthand. That data was analyzed, and the energy intensity of each home was calculated and then compared with the energy intensities of the other homes in the same states using RECS data. The energy intensity for each facility was calculated based on the actual energy bills. Finally, the study evaluated the carbon footprint associated with residential energy consumption in all 50 states to reinforce the importance of sustainable development initiatives.

In the field of embedded systems, energy efficiency is a critical requirement, particularly for battery-powered devices. RISC-V processors have gained popularity due to their flexibility and open-source nature, making them an attractive choice for embedded applications. However, not all RISC-V processors are equally energy-efficient, and it is important to evaluate their performance in specific use cases. This paper evaluates the energy consumption and resource utilization of a new RISC-V processor, RisCO2, and four existing processors - Zero-riscy, Micro-riscy, Ri5cy, and CV32E40P - in a signal demodulation application for NDIR CO2 sensors. The processors were implemented in the PULPino SoC and synthesized using Vivado IDE. The processor named RisCO2 is based on the RV32E_Zfinx instruction set and was designed from scratch by the authors specifically for low-power signal processing applications such as signal demodulation in CO2 NDIR sensors. The other processors are Ri5cy, Micro-riscy, and Zero-riscy, developed by the PULP Platform team, and CV32E40P (derived from Ri5cy) from the OpenHW Group, all of them widely used in the RISC-V community. Our experiments showed that RisCO2 had the lowest energy consumption among the five processors, with a 53.5% reduction in energy consumption compared to CV32E40P and a 94.8% reduction compared to Micro-riscy. Additionally, RisCO2 had the lowest FPGA resource utilization compared to the best-performing processors, CV32E40P and Ri5cy, with a 46.1% and a 59% reduction in LUTs, respectively. Our findings suggest that RisCO2 is a highly energy-efficient RISC-V processor for NDIR CO2 sensors that require signal demodulation. The results also highlight the importance of evaluating processors in specific use cases to identify the most energy-efficient option. This paper provides valuable insights for designers of energy-efficient embedded systems using RISC-V processors.

Advances in communication technologies have made the interaction of small devices, such as smartphones, wearables, and sensors, scattered on the Internet, bringing a whole new set of complex applications with ever greater task processing needs. These Internet of Things (IoT) devices run on batteries with strict energy restrictions. They tend to offload task processing to remote servers, usually to Cloud Computing (CC) in datacenters geographically located away from the IoT device. In such a context, this work proposes a dynamic cost model to minimize energy consumption and task processing time for IoT scenarios in Mobile Edge Computing environments. Our approach allows for a detailed cost model, with an algorithm called TEMS that considers energy, time consumed during processing, the cost of data transmission, and energy in idle devices. The task scheduling chooses among Cloud or Mobile Edge Computing (MEC) server or local IoT devices to better execution time with lower cost. The simulated environment evaluation saved up to 51.6% energy consumption and improved task completion time up to 86.6%.

This work aims to integrate the circular economy (CE) into learning experiences to enhance learning outcome development in Higher Education (HE). Building on existing active learning research, this work aligns CE initiatives—particularly waste reduction and elimination— with the Sustainable Development Goals (SDGs). The study proposes an experiential learning framework to cultivate citizenship commitment, sustainability impact, and engineering learning outcomes through realistic problem-solving in the supply chains of small and medium enterprises (SMEs). Using the ADDIE instructional design model and Kolb’s experiential learning cycle, the approach is illustrated in a case study within an undergraduate industrial engineering module. Based on student opinion surveys and achievement metrics, results indicate that the experience significantly enhanced student engagement and motivation. This research contributes to the broader dissem-ination of CE concepts in HE and supports the advancement of SDG #4 Quality Education and #12 Sustainable Consumption and Production. Future research will explore the application of this learning experience across diverse industries and assess its impact on student learning outcomes in various educational contexts.

This study examines the prospective association of egg consumption with multiple domains of cognitive function in older, community-dwelling men and women followed 16.3 years. Participants were 617 men and 898 women from the Rancho Bernardo Cohort aged 60 and older who were surveyed about egg intake/week in 1972-74, and attended a 1988-91 research visit when cognitive function was assessed with 12 tests. Analyses showed that egg intake ranged from 0-24/week (means: men=4.23.2, women=3.52.7, p<0.0001). In men, covariate-adjusted regressions showed egg intake was associated with better performance on Buschke total (p=0.04), long-term (p=0.02) and short-term (p=0.05) recall. No significant associations were observed in women (p’s>0.05). Analyses in those <60y at in 1972-74 showed egg intake was positively associated with scores on Heaton copying(p<.04) and the Mini-Mental Status Exam(MMSE;p<.02) in men, and category fluency(p<0.05) in women. Egg intake was not significantly associated with odds of poor performance on MMSE, Trails B, or category fluency in either sex. These reassuring findings suggest there are no long-term detrimental effects of egg consumption on multiple cognitive function domains, and for men, there may be beneficial effects for verbal episodic memory. Egg consumption in middle-age may also be related to better cognitive performance later in life.

A comprehensive evaluation system of rural building energy consumption from the innovative composite perspective was established, which was suitable for southwest of China. The index system was established by Brainstorming method and Delphi method, the weights of the comprehensive evaluation model were calculated by Network Process (ANP) method, the scoring criteria of all evaluation indexes were leveld based on fuzzy evaluation theory. The system model was verified by case analysis, at the countryside around Chengdu Second Circle. With the highest weight, lowest comprehensive score, and broadest range of comprehensive scores taken into consideration, three key factors affecting the target layer, namely "Percentage of Clean Energy Use", "Thermal Performance of Exterior Walls", and "Implementation Rate of Energy saving Measures". The distribution of comprehensive indicators and evaluation factors has certain spatial distribution characteristics, and the overall spatial distribution shows a characteristic of "high in the southeast and low in the northwest". Finally, Based on key factors and regional distribution characteristics, energy-saving measures have been proposed from three aspects: increasing sunrooms, adding wall insulation layers, and standardizing air conditioning temperature settings.

A comprehensive evaluation system of rural building energy consumption from a innovative composite perspective is established, which is suitable for southwest China. the brainstorming method and Delphi method were used to establish the indicator system, the ANP method was applied to calculate the weights of the comprehensive evaluation model, the scoring criteria of all evaluation indexes are constructed based on fuzzy evaluation theory, the applicability of the model is verified by an example of the countryside around Chengdu. The results showed that the score of some factors is low, whole or part area, just like the percentage of clean energy use(C24), the thermal performance of external walls(E21), the implementation of energy-saving measures(S22), were key factors affecting the target level, which has greater potential for improvement. The distribution of comprehensive indicators and evaluation factors have certain spatial distribution characteristics, the overall spatial distribution shows the characterwastic of "high in the southeast and low in the northwest". The key factors and spatial distribution characteristics found can be used as an important basis for the improvement of green energy-saving promotion measures, energy-saving retrofit programmes were proposed in terms of additional solar room, energy-saving of external walls and air-conditioning temperature setting guidance.

Many techniques have been and are being made to find alternatives to water-saving practices. Among them, Partial root drying (PRD), one of the effective approaches, plays a major role in reducing the harmful effects of water deficit stress. An experiment was carried out using irrigation strategies [I1 (100% full irrigation “FI”), I2 (75%FI), I3 (50%FI), and I4 (PRD (50%FI)] and soil organic mulch [L0 (Zero layer organic soil mulch” control”), L1 (Single layer organic soil mulch), L2 (Two layers of organic soil mulch), and L3 (Three layers of organic soil mulch)] to inspect the impact of those treatments for increasing yield, water productivity and saving energy under arid region conditions. To meet the study's objective, two field experiments were carried out at a private farm. Our results demonstrated a general decrease in water stress and salt accumulation inside root-zone area with PRD with L3. The PRD strategy increased fruit yields by 3.7 and 7.3% and water productivity by 51.9 and 53.1% compared with I1 during 2020/2021 and 2021/2022, respectively, while reducing the applied irrigation water by 50 %. The PRD strategy showed superior results in increasing mango yield and water productivity. In general, PRD can be used as good technique to save water and energy up to 50% and enhance productivity along with using organic mulching, ultimately improving mango yield under arid climatic regions. It may prove a good adaptation strategy for current and future water shortage scenarios of climate change.

In the context of carbon neutrality and carbon peaking, in order to achieve low carbon emissions and promote the efficient utilization of wind energy, hydrogen energy as an important energy carrier is proposed to mix hydrogen and natural gas to form hydrogen enriched compressed natu-ral gas (HCNG). It is also injected into the natural gas pipeline network to realize the transmission and utilization of hydrogen energy. At the same time, the participation of demand response is considered, the load peak and trough period is adjusted, and large-scale consumption of renewa-ble energy and the reduction of carbon emissions are realized. First of all, a fine model of hydro-gen production and hydrogen use equipment is established to analyze the impact of adding hy-drogen mixing on the economy and low-carbon of the system. With green certificates and demand response, the utilization rate of hydrogen energy is improved to further explore the energy utiliza-tion rate and emission reduction capacity of the system. Secondly, on the basis of modeling, the optimal scheduling strategy is proposed with the sum of energy purchase cost, equipment opera-tion cost, carbon emission cost, wind curtailment cost and green certificate income as the lowest objective function. Considering the constraints such as hydrogen blending ratio and flexible load ratio of the pipeline network, a low-carbon economic scheduling model of hydrogen mixed natu-ral gas was established. The model was linearized and solved by using MATLAB and CPLEX solver. By comparing different scenarios, the superiority of the model and the effectiveness of the strategy are verified.

The UK Government is committed to reducing greenhouse gas (GHG) emissions by 80% by 2050. Buildings are responsible for 37% of the total GHG emissions in the UK and the need to reduce their emissions has resulted in more stringent building regulations in the recent past. The regulations, energy rating systems and voluntary guidelines — all are primarily aimed at reducing the need for heating and associated energy use by increasing insulation and air-tightness. However, future climates are projected to be warmer than the present day. Internal gains dominated non-domestic buildings will likely overheat, the adaptation to which will require energy-intensive cooling solutions, thus defeating the purpose of heating-focused regulations. This research investigated the effects of warming climate on overheating, and energy use and resulting emissions in representative urban office spaces in London in the present-day and future climates using hourly dynamic thermal simulations. Findings suggest that more airtight and highly—insulated office buildings designed for heating—dominated temperate UK climate will overheat in the 2050s. Heating demand reduces but electricity consumption increases by 121% when hybrid cooling is adopted to ameliorate overheating. Despite the rise, adopting a mixed-mode ventilation strategy was one of the ways of achieving overall energy efficiency while meeting benchmark overheating and carbon emissions target in present and future climatic contradictions. Current heating-focused legislations need to be urgently re-evaluated to account for the effects of climatic variability and overheating risks.

Introduction: Adolescence is a critical period for the development of affectivity and sexual-ity. Adolescents begin to explore their sexual identity, form intimate relationships, and learn to manage their emotions in new and complex contexts. This study aims to analyze the prevalence of habits and behaviors related to the affective-sexual health of adolescents in the 4th year of ESO, identifying risk factors, comparing behaviors and risks, monitoring these behaviors, analyzing pornography consumption, and evaluating the use of the in-ternet as a source of sexual information. Method: Descriptive cross-sectional study using questionnaires. Participants are 4th-year ESO students from a school in the Baix Llobregat province (Catalonia) aged between 14 and 18 years. Descriptive and bivariate statistical analyses were conducted using the Chi-square test and p-value calculations with R Project software. Frequency and percentage analyses were also used to describe the health behav-iors of the participants. Results: The study reveals that girls have better knowledge of the contraceptive pill and male condom than boys. Sexual initiation and condom use man-agement vary between genders, with girls being more capable of persuading their partners to use them. Pornography consumption also shows significant differences in age of initia-tion and frequency between boys and girls. Conclusions: The study on adolescent affectiv-ity and sexuality reveals knowledge about contraceptives, early sexual initiation, and high pornography consumption, highlighting the need for early and diverse affective-sexual education, youth empowerment, and media misinformation management to promote safer and healthier behaviors in municipal public health.

Our analysis of previous studies shows that solar energy and wind can consume negligible amount of water to generate one megawatt of electricity.

Agricultural tractors are high fuel-consuming and polluting machines, thus the introduction of new sustainable technologies, as hybridization, can be very impactful for the development of electric hybrid agricultural tractors. These vehicles combine the classic internal combustion engine with an electric machine, to reduce the environmental impact and improve the efficiency. This paper reports the modeling and the simulation conducted, using a simulation software typically used for on-road vehicles, of a two wheel-drive agricultural tractor in three different configurations: the conventional one, the series and the parallel electric hybrid powertrain. The simulated task is the trailing of a big square baler during the process of straw wrapping and baling. The evaluation and the comparison of the fuel consumption, CO2 emissions and the depth of discharge of the different configurations has been carried out to determine if it is possible to downsize the ICE, maintaining the same performances. This study points out that the hybridization of agricultural tractors powertrains is one of the most promising approaches to reduce pollutant emissions and fuel consumption.

In order to study the strength changes of concrete specimens under different loading conditions, based on the principle of minimum energy dissipation and from the Angle of concrete failure energy per unit area, the finite element numerical simulation software was applied to concrete specimens with different failure modes of tension, compression, bending and torsion, and the double-broken line damage constitutive model was adopted to simulate and analyze the failure forms of concrete specimens under different loading conditions. The failure area and failure energy of each specimen during loading were quantitatively calculated under each failure mode, and the relationship between the failure area and failure energy consumed under different failure modes was analyzed. The results show that under different failure modes, the failure area of concrete specimens is different, the energy consumed during failure is different, and the strength is different. However, in the process of failure, no matter how the failure mode changes, the failure energy W per unit area remains unchanged and is a constant, which is related to the physical properties of concrete itself.

Scavenging of atmospheric trace gases has been recognized as one of the lifestyle-defining capabilities of microorganisms in terrestrial polar ecosystems. Several metagenome-assembled genomes of as-yet-uncultivated methanotrophic bacteria, which consume atmospheric CH4 in these ecosystems, have been retrieved in cultivation-independent studies. In this study, we isolated and characterized a representative of these methanotrophs, strain D3K7, from a subarctic soil of Northern Russia. Strain D3K7 grows on methane and methanol in a wide range of temperatures, between 5 and 30°C. Weak growth was also observed on acetate. The presence of acetate in the culture medium stimulated growth at low CH4 concentrations (~100 p.p.m.v.). The finished genome sequence of strain D3K7 is 4.15 Mb in size and contains about 3700 protein-encoding genes. According to the result of phylogenomic analysis, this bacterium forms a common clade with metagenome-assembled genomes obtained from the active layer of a permafrost thaw gradient in Stordalen Mire, Abisco, Sweeden, and the mineral cryosol at Axel Heiberg Island in the Canadian high Arctic. This clade occupies a phylogenetic position in between characterized Methylocapsa methanotrophs and representatives of the as-yet-uncultivated Upland Soil Cluster Alpha (USCα). As shown by the global distribution analysis, D3K7-like methanotrophs colonize mostly northern terrestrial ecosystems, such as polar deserts, subarctic soils, permafrost environments, but also occur in boreal peatlands or high-latitude habitats.

In this paper, particular attention is paid to an advanced variant of the plug-in hybrid engine, known as PHEV, which combines two functionalities: the vehicle's internal combustion engine (ICEV) and the electric engine (BEV). The paper also presents the assumptions of the DEVS model, which allows for the estimation of vehicle’s energy consumption. The study described herein also presents the influence of factors such as ambient temperature and constant speed on the PHEV’s energy consumption. It has been shown that the vehicle’s range estimated based on its electronic control module (ECU) is about 20% shorter per annum on average for its year-round operation in every-day driving conditions. When analyzing the energy consumption based on the vehicle’s unit energy consumption model, attention was paid to values that are strongly correlated with the traffic volume and weather conditions affecting the moving vehicle. In addition, the authors emphasized that the estimated total energy consumption of an electric (BEV) or hybrid (PHEV) vehicle, relative to the normative values arising from the type approval test cycle, deviate from the actual values arising from real driving conditions and often vary substantially. In extreme cases, the actual range of a hybrid vehicle is 75% shorter.

Nowadays Air-Source Heat Pumps (ASHP) in combination with a Photovoltaic (PV) installation are a very promising option for a necessary and urgent energy transformation in European Union (EU). It is extremely important to develop solutions that will help maximize the use of energy generated from renewable energy sources (RES). Such issues include the problem of insufficient use of generated electricity in PV on-grid microinstallations in residential buildings. This paper's aim is to analyze the results of one-year-round operation of a PV array grid-connected hybrid installation with ASHP for domestic hot water (DHW) preparation in a residential building in Cracow, Poland in the context of increasing self-consumption (SC) of PV energy. Models of systems are built and simulated in Transient System Simulation Tool 18 (TRNSYS) software. Simulations were carried out for different scenarios involving different building electricity consumption profiles, PV system capacity and specified runtime management of ASHP. The novelty of this study lies in the evaluation of the impact of a certain range of conditions on the energy performance of the system, in particular on SC. The results showed that the use of ASHP, with specified runtime management, results in an increase in monthly SC values from 7 to 18%, and annual SC values up to 13%. Also determining the appropriate size of the used PV system depending on whether it is present ASHP in the installation is crucial to increase the value of the SC parameter. Overall, this study provides valuable insights into the potential benefits of PV panels and ASHP operating together, in particular on SC values.

The COVID-19 pandemic introduced risks and challenges to global food and nutrition security. In this paper, we examine the impact of the COVID-19 pandemic on the nutritional intake of China's rural residents using panel data and a fixed effect model. The data were collected in 2019 and 2020 and covered nine provinces and 2,631 households in rural China. The results reveal that an increase of 100 confirmed cases in a county resulted in a 1.48% (p<0.01), 1.46% (p<0.01), 1.77% (p<0.01), and 1.23% (p<0.01) decrease in per capita intake of dietary energy, carbohydrates, fats, and proteins, respectively. Moreover, the COVID-19 pandemic only had a significant and negative effect on dietary energy intake in the low-income group at the 5% level of significance. Our study indicates that the potential insufficient nutrition situation, nutritional imbalance, and dietary imbalance of low-income rural residents should be addressed appropriately.

Burn-induced cardiac dysfunction is thought to involve mitochondrial dysfunction although the mechanisms responsible are unclear. In this study, we used our established model of in vivo burn injury to understand the genetic evidence of burn-induced mitochondrial metabolism confusion by describing cardiac mitochondrial metabolism-related gene expression after burn. Cardiac tissue was collected at 24 hours after burn injury. An O2K respirometer system was utilized to measure cardiac mitochondrial function. Oxidative phosphorylation complex activities were determined using enzyme activity assays. RT Profiler PCR array was used to identify differential regulation of genes involved in mitochondrial biogenesis and metabolism. Quantitative qPCR and Western Blotting were applied to validate differentially expressed genes. Burn-induced cardiac mitochondrial dysfunction was supported by the finding of decreased state 3 respiration and decreased mitochondrial electron transport chain activity in complex I, III, IV, and V following burn injury. Eighty-four mitochondrial metabolism-related gene profiles were measured. The mitochondrial gene profile showed that one third of genes related to mitochondrial energy and metabolism was differentially expressed. Of these 28 genes, 15 were more than 2-fold upregulated and 13 were more than 2-fold downregulated. All genes were validated using qPCR; 4 genes had a protein level which correlated with the observed change in gene expression. This study provides preliminary evidence that a large percentage of mitochondrial metabolism-related genes in cardiomyocytes were significantly affected by burn injury.

Although food retailers have embraced organic certified food products as a way to reduce their environmental loading, organic sales only make up a small proportion of total sales worldwide. Most consumers have positive attitudes towards organic food, but attitudes are not reflected in behaviour. This article addresses consumers’ attitude-behaviour gap regarding their purchase organic food and reports on how visualization of personal shopping data may encourage them to buy more organic food. Through the design of the visualization tool, the EcoPanel, and through an empirical study of its use, we provide evidence on the potential of the tool to promote sustainable food shopping practices. Sixty-five users tested the EcoPanel for five months and interviews were made with ten of these. The test users increased their purchase of organic food with 23%. The informants used the EcoPanel to reflect on their shopping behaviour and to increase their organic shopping. We conclude that the visualization of food purchases stimulates critical reflection and the formation of new food shopping practices. This implies that food retailers may increase sales of organic food through using a visualization tool available for their customers. In this way these retailers may decrease their environmental impact.

In the Kingdom of Saudi Arabia (KSA), residential buildings’ energy consumption accounts for almost 50% of the building stock electricity consumption. The electricity generation consumes over one-third of the daily oil production. KSA was ranked as one of the highest countries in fossil fuel consumption per capita in 2014. Moreover, the KSA’s economy heavily relies on fossil fuel sources, namely oil reservoirs, whereby depletion will negatively affect the future development of the country. The total electricity consumption is annually growing by approximately 5-8%, which would lead to identical oil consumption to oil production in 2035. Currently, the KSA government is concerned to generate more renewable energy using large renewable energy plants. The government is investing in energy generation through renewable sources, by financing large scale photovoltaic farms to stop an economic crisis that may occur in 2035. The existing building stock consumes around 80% of the total current Saudi electricity that is generated. According to the Saudi energy efficiency report, the primary energy consumption per capita is over three times higher than the world average. Therefore, the residential buildings need further assessment as to their current energy consumption. This research used a survey to explore current user behaviour in residential buildings energy performance in the city of Jeddah, KSA. The findings of the survey showed: • The buildings thermal properties were found to be poorly designed • The majority of users within the buildings prefer a room temperature of below 24 °C, which requires a massive amount of cooling • Due to the climate conditions and the cultural aspects of KSA, housing units are occupied for more than 18 hours per day • An increase in user awareness has helped to slightly improve residential buildings energy efficiency Knowing the current high energy consumption sources and causes and being able to define available opportunities for further developments on building thermal properties enhancements and how to increase user awareness to reach self-sustaining buildings is essential.

NIRS uses the relative absorption of light at 850nm and 760nm, to determine skeletal muscle oxygen saturation. Previous studies have used the ratio of both signals to report muscle oxygen saturation. Purpose: To evaluate the different approaches used to represent muscle oxygen saturation, and to evaluate the pulsations of the O₂heme and Heme signal. Method: Twelve participants, ages 20-29years were tested on the forearm flexor muscles using continuous wave NIRS at rest. Measurements were taken during 2-3mins rest, during physiological calibration (5-minuts Ischemia) and during reperfusion. Results: There was a significant difference in pulse size between O₂heme and Heme signal at the three locations (p < 0.05). Resting oxygen saturation was 58.8+9.2%, 69.6+3.9%, and 89.2+6.9% when calibrated using O₂heme, TSI, and Heme, respectively. Conclusion: The difference in magnitude of O₂heme and Heme pulse with each heartbeat might suggest different anatomical locations of these signals, which propose calibrating with just one of the signals instead of the ratio of both. Calculations of physiological calibration must account for increased blood volume in the tissue, because of the changes in blood volume which appear to be primarily from the O₂heme signal. Resting oxygen levels calibrated with Heme agrees with theoretical oxygen saturation.

The use of Smart Antenna Systems (SAS) in pervasive environments such the Mobile Ad hoc Networks (MANET) has been promoted as the best choice to improve Spatial Division Multiple Access (SDMA) and throughput. Although directional communications are expected to provide great advantages in terms of network performance, directional MAC (Medium Access Control) protocols introduce several issues. One of the most known problems in this context is represented by the fact that, attempting to solve or at least mitigate the problems introduced by these kinds of antennas especially at MAC layer, a large amount of energy consumption is achieved ; for example, due to excessive retransmissions introduced by very frequently issue such as deafness and handoff. The expedients proposed in order to reduce these drawbacks attempting to limit beamforming time of nodes in cooperation with a Round-Robin scheduling, can grant high performance in terms of fairness and throughput. However the overall energy consumption in the network is not efficent due to the static approach. In view of this, we propose an Adaptive Beamforming Time with Round-Robin MAC providing for a dynamic assignment of the beamforming time with the purpose to limit the waste of energy of nodes.

An energy diagnosis is a tool used to seek the improvement of energy saving measures, environmental conservation and energy efficiency, making relevant its implementation in any kind of buildings. For this article, an energy diagnosis of third level was carried out in buildings of the Instituto de Energías Renovables (IER) from Universidad Nacional Autónoma de México (UNAM) through survey and census of the 36 buildings in the IER, in order to characterize current patterns of energy consumption and demand, and generating specific strategies towards savings and energy efficiency, such as indicators and corrective proposals within and non-financial investment.

The purpose of this paper is to provide empirical evidence to water policy-makers and the evaluation of the application of economic instruments such as price and other factors affecting demand, they contribute to improved decision-making around water management in the Biosphere Reserve El Vizcaino, Mexico. It was estimated a dynamic function with average price specification and other price perception specification. The findings show that consumers react to average price perceived and not the marginal price; long-run price elasticity is higher than short-run and both elasticities are inelastic. Elasticities inelastic with rising prices generate high incomes to improve water planning, supply quality and expand service coverage. The results suggest that the level of knowledge of users on the price is key to take into account restructuring rates in a scenario where the consumer has asymmetric information and themselves achieve goals of economic efficiency, social equity and environmental sustainability.

This study examines the influences of live streaming servicescape on Chinese consumers' luxury purchase intention. Interestingly, for the sample without luxury purchase experience, the mediating effect of the perceived value of luxury goods is significant but effect of consumer trust is not. For the sample with luxury purchase experience, the result of mediating effect is just the opposite. The interaction between conspicuous consumption and live streaming servicescape plays a certain role in regulating the perceived value of luxury goods. These results show that luxury brands can adopt innovation in the live streaming servicescape for different types of consumers to promote live streaming marketing. This study enriches the servicescape theory of live streaming.

Hybrid reverse osmosis electrodialysis systems make it possible to obtain solutions with a higher concentration of components compared to single electrodialysis treatment, i.e. more efficient separation of brine and pure water. The article proposes experimental and pilot-industrial studies of a hybrid membrane system operation using industrial wastewater for the disposal of liquid waste from an ion-exchange chemical-desalting water treatment plant of a thermal power plant, followed by a calculation of economic efficiency and an analysis of the environmental feasibility of their use. The developed technological scheme offers separate processing of acidic and alkaline waste regeneration solutions using calcium carbonate reagent and desalination on baromembrane and electromembrane units to obtain clean water and dry residue. The hybrid system includes a booster filter press and an evaporator. The hybrid system makes it possible to provide a thermal power plant with a "zero discharge" with a minimum consumption of reagents and electricity, as well as return all water from wastewater to the power plant cycle.

Keywords: Academic Trends; Energy Consumption; Higher Education Institutions; Systematic Literature Review; Scholarly output; University buildings

Cause-Related Marketing (CRM) has emerged as a new concept that links CSR activities to marketing, enabling corporations to pursue both building company-consumer partnership and gaining sustainable mutual benefits that are elicited from the partnership. Among various cause-related marketing activities, on-line cause-related advertising is the most commonly used one. The purpose of this study is to examine the effects of on-line cause-related advertising and moderating role of ethical consumption propensity. In particular, this study intends to contribute to successful development of on-line cause-related advertising strategy by focusing on the relationship between the four factors: customers' perception of on-line cause-related advertising, brand-consumer relationship, brand attachment and loyalty. The results of this study are as follows: First, customers' perception of on-line cause-related advertising was related positively to consumer-brand- relationship. Second, brand-consumer relationship was related positively to brand attachment and customer loyalty. Third, brand attachment was related positively to customer loyalty. Furthermore, from the consumer’s psychological viewpoint, ethical consumption propensity had a significant moderating effect to between customers' perception of cause-related advertising and brand-consumer relationship as well as brand attachment. The results of this study may contribute to the existing literature as an new empirical attempt to examine the effect of the on-line cause-related advertising.

Recent years have shown that traditional regulatory techniques alone are not effective in achieving behavior change in important fields such as environmental sustainability. Governments all over the world have been progressively including behaviourally informed considerations in policy and law-making with the aim of improving the acceptance and impact of sustainability-oriented measures. This led to the arrival of alternative regulatory tools, such as nudges. The effectiveness of nudges for environmental sustainability (green nudges) has been largely reported but the practical and ethical implications are still largely neglected by academic research. In this contribution, “nudges” are conceptually distinguished from “boosts” and their ethical briefly explained. The analysis is made at the light of the current mostly European and US American academic literature.

Almost 6% of cancers worldwide are attributable to alcohol consumption. Approximately half of them occur in tissues highly exposed to ethanol, such as the oral cavity, pharynx, upper larynx and esophagus. However, since ethanol is not mutagenic and the carcinogenic metabolite of ethanol (acetaldehyde) is mainly produced in the liver, it is unclear why alcohol consumption preferentially causes a local carcinogenic effect. We recently hypothesized that the cytotoxic activity of ethanol could explain the high risk of these cancers in alcohol users. Here we report that short-term exposures (2-3 seconds) to ethanol concentrations between 10% and 15% start to cause a marked cytotoxic effect on human epithelial keratinocytes in a concentration-dependent manner. After discussing new evidence that cancer is the end-result of the accumulation of cell divisions in stem cells, we explain why regular alcohol consumption imposes a high risk of cancer on these tissues. Briefly, the cytotoxicity of ethanol reduces the lifespan of the cells lining these tissues. The stem cells located in deeper layers need to divide more often than usual to renew the damaged epithelia. The accumulation of cell divisions in stem cells leads to the accumulation of cancer-promoting errors (e.g., mutations arising during DNA replication) that increase their risk of malignant transformation. Cell division also exposes the DNA of the stem cells to the genotoxic activity of acetaldehyde and tobacco carcinogens. We propose that choosing alcoholic beverages containing non-cytotoxic concentrations of ethanol, or diluting ethanol to non-cytotoxic concentrations, is a simple way to reduce the risk of cancer of the oral cavity, pharynx, larynx and esophagus in alcohol users. This preventive strategy may also abolish the known synergistic effect of alcohol drinking and tobacco smoking on the risk of these cancers.

BackgroundIt is unclear what underpins the large global variations in the prevalence of fluoroquinolone resistance in gram-negative bacteria. We tested the hypothesis that different intensities in the use of quinolones for food-animals plays a role. MethodsWe used Spearman’s correlation to assess if the country-level prevalence of fluoroquinolone resistance in human infections with Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa was correlated with the use of quinolones for food producing animals. Linear regression was used to assess the relative contributions of country-level quinolone consumption for food-animals and humans on fluoroquinolone resistance in these 4 species. ResultsThe prevalence of fluoroquinolone resistance in each species was positively associated with quinolone use for food-producing animals (E. coli [ρ=0.55; P<0.001], K. pneumoniae [ρ=0.58; P<0.001]; A. baumanii [ρ=0.54; P=0.004]; P. aeruginosa [ρ=0.48; P=0.008]). Linear regression revealed that both quinolone consumption in humans and food animals were independently associated with fluoroquinolone resistance in E. coli and A. baumanii. ConclusionsReducing quinolone use in food-producing animals may help retard the spread of fluoroquinolone resistance in various gram negative bacterial species.

Water resource is considered as a significant factor in development of regional environment and society. Water consumption prediction can provide important decision basis for the regional water supply scheduling optimisations. According to the periodicity and randomness nature of the daily water consumption data, a Markov modified autoregressive moving average (ARIMA) model is proposed in this study. The proposed model, combined with the Markov chain, can correct the prediction error, reduce the continuous superposition of prediction error, and improve the prediction accuracy of future daily water consumption data. The daily water consumption data of different monitoring points are used to verify the effectiveness of the model, and the future water consumption is predicted, in the study area. The results show that the proposed algorithm can effectively reduce the prediction error compared to the ARIMA.

Background: Data on the benefits of rapid microbiological testing on antimicrobial consumption (AC) and antimicrobial resistance patterns(ARP) are scarce. We evaluated the impact of a protocol based on rapid techniques on AC and ARP in intensive care (ICU) patients. Methods: Retrospective pre(2018) and post-intervention(2019-21) study in ICU patients. A rapid diagnostic algorithm was applied from 2019 in patients with lower respiratory tract infection. Incidence of nosocomial infection, ARP, and AC as DDD(defined daily dose) were monitored. Results: A total of 3635 patients were included, 987(pre-intervention) and 2648(post-intervention). A median age was 60 years, 64% male, with APACHE II of 19 points and SOFA of 3 points. Overall ICU mortality of 17.2% without differences between the periods. An increase in the number of infections was observed in the post-intervention period(44.5% vs 17.9%, p<0.01), especially due to an increase in ventilator-associated pneumonia(44.6% vs 25%,p<0.001). AC decreased from 128.7 DDD in 2018 to 66.0 DDD in 2021(Rate Ratio=0.51). A recovery of P.aeruginosa susceptibility of 23% for Piperacillin/tazobactam and 31% for Meropenem was observed. Conclusion: The implementation of an algorithm based on rapid microbiological diagnostic techniques allowed a significant reduction in AC and ARP without affecting the prognosis of critically ill patients.

In this study, a novel two-layer control scheme aimed at enhancing the dynamic performance of a nonlinear vehicle through the utilization of an optimal control approach grounded in predictive control principles is presented. The first layer of our control system focuses on the computation of an optimized rotational torque that ensures lateral dynamic stability. This torque is subsequently translated into differential forces acting on the wheels, employing the inverse tire model to derive the desired longitudinal slips. These slip values are then transmitted to the second layer of our control system. In the second layer, the electric motors associated with the vehicle's wheels dynamically adjust the input torque to accurately track the desired slip, thereby ensuring the overall stability of the electric vehicle. In view of the paramount importance of energy consumption in electric vehicles, we adopt optimal control strategies to substantially minimize battery utilization. To this end, careful selection of appropriate weighting coefficients in the control laws enables us to maintain the electric motors within their permissible operational range while simultaneously minimizing battery energy consumption for desired slip tracking. Extensive simulation results validate the effectiveness of our proposed control system in proficiently managing nonlinear effects and safeguarding the vehicle's stability.

Road congestion in Amman city increased yearly, due to the increase in private car ownership and traffic volumes. This study aims to (a) assess the social and economic impacts of the toll road in Amman, Jordan through a survey questionnaire using statistical software (SPSS), (b) identify the optimal price at selected road using micro-simulation (VISUM). Traffic, geometric and cost data of toll technique of two sections on the Airport Highway (From Ministry of Foreign Affairs to Madaba Interchange.; and From Madaba Interchange to Queen Alia International Airport (QAIA) interchange) were used for simulation purpose. The toll road (across 7 distinct scenarios at various price) were assessed for an optimal revenue. The survey questionnaire was made based on all scenarios including AM peak hour. The operation cost for toll road was determined based on greater Amman Municipality (GAM). The optimal scenario was measured based on the value ¬¬of revenue (JD), and type of payment. The results indicate that users found the most charging method to be based on traveled distance (54.02%) and the optimal value of the toll to be equal 0.25 JD (34.08%). In Addition, the total cost of Manual Toll Collection method (MTC) in 2025 is 126,935JD and the revenue is 1122.6 JD so this indicates to positive result. Eventually, the result of applying road pricing on the airport road in 2025 indicate to be effective and economically feasible only by using Manual Method.

Background: Humankind have faced unexperienced pandemic events since 2020. After COVID-19 pandemic is calming down, we felt the need to verify whether in-hospital mortality had worsened compared to pre-pandemic conditions due to the COVID-19 pandemic. Objective: To test the hypothesis that daily food consumption is associated with in-hospital mortality during hospitalization and to provide baseline data to examine the effects of COVID-19 in post-pandemic exists or not. Methods: All hospitalized patients staying in a single institution on the third Thursday of May, August, November, and the following next February were included. Compared data: 1) among four seasons, 2) between age < vs.

Environmental preservation presents numerous challenges, particularly in capturing carbon di-oxide. Despite multiple studies on this topic, there remains a need for additional capture plants, particularly in the energy sector. The power industry emits flue gas during its post-combustion process, and capturing carbon dioxide from this gas would make power plants more environ-mentally friendly while aiding the preservation of the planet. The work conducted in This study aimed to investigate the gap in knowledge to contribute to the development of a better world without harming it. The identified research gap reveals no established optimal monoethanola-mine (MEA) solvent concentration for the process. A simulation was performed using Aspen Hysys to analyze the absorption efficiency at higher solvent concentrations and address this is-sue. The input data was obtained from a flue gas capture plant in the UK. The results obtained from discrete increments indicate an optimal solid result for an MEA concentration of 36 wt.%. Further analysis of temperature and pressure revealed that other solvents, such as diethanola-mine (DEA) and triethanolamine (TEA), are more stable at higher concentrations and that DEA and TEA at the same concentration as MEA consumed significantly less energy to capture carbon dioxide.

To reduce the redundant counting of TDC in high-speed applications and thus reduce power consumption, a TDC architecture is proposed to quantify the difference between two adjacent rows of pixel signals. This structure can remove the identical part between two rows of pixel signals by adjusting the start and stop signal of the TDC, which will reduce the number of flipping of D flip-flops in the TDC. In the 110nm CMOS process, simulation results show that this design achieves an effective number of bits (ENOB) of 4.72 bits and a Figure of Merit (FoM) of 104.7 fJ/step - 162.3 fJ/step with a power consumption ranging from 60 µW to 93 µW. Compared with traditional counting methods, the proposed TDC can reduce counting power consumption by 30%.

The benchmarking of hotel energy use comprehensively identifies the controllable and uncontrollable factors affecting energy performance, including building characteristics, management strategies, operations, and maintenance systems. Other factors include climatic conditions, floor areas, operating hours, occupancy rates, and guest populations. A benchmarking study on energy consumption patterns in significant hotels (each with less than 100 rooms and an average staff strength of 40 employees), situated in the university town of Nsukka (longitude 70 23' E, latitude 60 52' N), Nigeria, was performed using the data envelopment analysis (DEA) methodology. DEA, a linear programming technique that measures the relative performances of units, was chosen as a benchmarking methodology due to its ability to handle multiple inputs and outputs. Following a correlation test, energy use intensity, diesel consumption, and the number of employees were selected as the analysis inputs, while the occupancy rate was chosen as the output variable. Data on these variables spanning 12 months were collected using questionnaires, interviews, site visits, and oral conversations with hotel managers to ensure validity. Grid-supplied electricity accounted for most of the hotels' energy needs, followed by diesel used in generators. More than 70% of the electricity use was for HVAC. From the DEA, Hotel 3 (DMU H3) had a technical efficiency score of 1, whereas adjustments were recommended for improving the efficiency scores of the other hotels, which were deemed inefficient. DMU H7 had the lowest efficiency score (0.474) and the highest identified savings for electricity and diesel. The analysis also revealed that occupancy rates were generally low in the months of June and July, coinciding with the high rainfall season with its accompanying decline in outdoor activities. Consistent with this, electricity consumption was highest in the Christmas and Easter holiday months of December, January, and April following increased travel-related activities.

Increasing of renewable energy share in total energy production is a direction that leads towards European Union’s aims of carbon neutrality by 2050, as well as increases energy self-sufficiency and independence. Some of the main challenges to increase renewable energy share while providing efficient and secure energy supply are related to optimization and profitability of de-centralized energy production systems. Integration of energy storage systems in addition to decentralized renewable energy production, for example, by solar panels, leads to more effective electricity supply and smart energy solutions. The modelling of such complex dynamic system can be performed using system dynamics method. The aim of the article is to forecast the practice of electricity storage in Latvia in the coming decades. A system dynamics model predicting the implementation of battery storage in private households was created for the case study of Latvia. Modelling results reveal that under the right conditions for electricity price, investment costs and with the right policy interventions battery storage technologies combined with PV panels have high potential for utilization in household sector. Model results show that in baseline scenario with no additional policies up 21 422 households or 10.8 % of Latvian households could have combined PV and battery system installed in 2050. Moderate subsidy policy can help to increase this number up to 25 118.

Currently, a wide variety of velocity profiles are used in motion control. Thus, this paper compares the energy consumption of three parabolic, trapezoidal, and S-curve profiles. We studied the case of using embedded systems since they are a powerful solution to monitor and coordinate the movement of devices and machinery in various industries combined with motion control applications. These integrated systems allow precise and efficient control of motors and actuators through control and feedback algorithms. In addition, we propose an alternative methodology for implementing motion controllers using an Advanced RISC Machine (ARM) microcontroller, which computes the trajectory and the control action in real-time. We conducted an experiment using a linear plant composed of a direct current (DC) motor coupled to an endless screw where a carriage is mounted that can move mechanically along a rail a distance of 1.16m. In addition, a 4096 pulses per revolution (PPR) encoder is connected to the motor to measure position and angular velocity, which compute the distance the carriage has moved in meters. A current sensor is used to assess the energy consumption, and 40 tests for each profile were carried out to compare the energy consumption for the three-motion profiles considering cases with and without load on the carriage.

This research aims to contribute to the growing body of knowledge of Collaborative Consumption (CC) by exploring the motivations that influence the use of Peer-to-Peer (P2P) accommodation platforms from the demand side. There has been a significant increase in interest in sustainable choices regarding P2P accommodation in recent years. To address this gap and contribute to the collaborative consumption and P2P accommodation theory a quantitative study was conducted through a survey yielding 235 responses from both users and non-users of P2P platforms. The findings of this study suggest that P2P accommodation platform usage is influenced by multiple motivations, including sustainability, belonging, economic benefits, and convenience. Interestingly, the study also found that individuals with higher levels of academic qualifications are more likely to adopt this mode of consumption. Moreover, the study is the first of its kind to quantitatively address this topic and, therefore, contributes significantly to the field of collaborative consumption and P2P accommodation theory. By understanding the motivations of P2P platform users, policymakers and practitioners can develop targeted strategies to promote sustainable choices in this sector. Overall, this study provides a useful framework for future research on collaborative consumption and sustainable P2P accommodation.