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.

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.

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.

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.

In March 2020 the World Health Organization declared the “pandemic state” due to COVID-19 imposing strict confinement of the world population. People were forced to spend more time at home, changing some daily routines, including social interactions, the possibility to perform sports, and diet habits. These changes could exert a greater impact on patients suffering from chronic diseases, such as endocrine patients. This study aimed to assess the effects of Covid-19 induced quarantine on daily habits in a group of patients with endocrine disorders, focusing on food consumption, eating, and sleep habits during the confinement. Eighty-five endocrine patients were enrolled. A structured interview was administered investigating: socio-demographic information, general medical conditions and habits adopted during the quarantine. All patients underwent the Spielberger State Anxiety Inventory (STAI-Y1) to assess state anxiety. Subjects had mainly a sedentary lifestyle. We found a significant increase in the number of cigarettes in smokers, an increase of meals consumed during the confinement and a high rate of sleep disorder occurrence, especially insomnia. The changes of daily habits were, probably, due to the alterations of routine, that determined more bore and inactivity during the day.

The growth of sustainable apparels provides grounding for the emergence of a new consumer market. Surprisingly, this movement has not only taken place in developed countries but also in emerging countries. To the best of the authors’ knowledge, no systematic reviews have been carried out on sustainable apparels, focused specifically on emerging countries. The goal of this systematic review is to analyse the existing literature on various factors that influence sustainable apparel consumption in emerging countries, as well as presenting future research directions. This detailed literature review is guided by the PRISMA Statement (Preferred Reporting Items for Systematic reviews and Meta-Analyses) review method, and its searches encompass sources such as Web of Science, Scopus and Google Scholar databases which, in result, identified 19 related studies. Furthermore, the reviewers segregated the factors into four major domains which include value, subjective norm, perceived behavioural control and knowledge. The results of this review demonstrate that most customers in the emerging countries have already experienced a growing interest in sustainable apparel with a strong development of knowledge, great concern, values and social circle. In sum, this systematic literature review provides an insightful information about sustainable apparel purchase behaviour and helps academicians, manufacturers and retailers to position and market their sustainable clothing brands.

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.

This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things computing. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSN. Then, using the CUDA Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes’ diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services.

Energy consumption is a critical issue in the energy sector, and recent events such as the global energy crisis, costs, the need to reduce greenhouse emissions, and extreme weather conditions have increased the demand for energy efficiency. Accurately predicting energy consumption is one of the key steps to addressing inefficiency in energy consumption, and its optimization. In this regard, accurate predictions of energy consumption would not only help to minimize wastage, but also to save cost. In this article, we propose intelligent models using an ensemble of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) neural networks for predicting energy consumption in smart buildings. The proposed model outperforms other state-of-the-art deep learning models for predicting minute energy consumption, with a mean square error of 0.109. The proposed model also accurately captures latent trends in the data that other models struggle to capture. The results highlight the potential of using hybrid deep learning models for improved energy efficiency management in smart buildings.

Survivability is a critical concern in WSNs, heavily influenced by energy efficiency. Addressing severe energy constraints in WSNs requires solutions that meet application goals while prolonging network life. This paper presents an energy optimization approach (EOAMRCL) for WSNs, integrating the Grey Wolf Optimization (GWO) for enhanced performance. EOAMRCL aims to enhance energy efficiency by selecting the optimal duty-cycle schedule, transmission power, and routing paths. The proposed approach employs a centralized strategy using a hierarchical network architecture. During the cluster formation phase, an objective function, augmented with GWO, determines the ideal cluster heads (CHs). The routing protocol then selects routes with minimal energy consumption for data transmission to CHs, using transmission power as a metric. In the transmission phase, the MAC layer forms a duty-cycle schedule based on cross-layer routing information, enabling nodes to switch between active and sleep modes according to their network allocation vectors (NAV). This process is further optimized by an enhanced CSMA/CA mechanism, which incorporates sleep/activate modes and pairing nodes to alternate between active and sleep states. This integration reduces collisions, improves channel assessment accuracy, and lowers energy consumption, thereby enhancing overall network performance. EOAMRCL was evaluated in a MATLAB environment, demonstrating superior performance compared to EEUC, DWEHC, and CGA-GWO protocols, particularly in terms of network lifetime and energy consumption. This highlights the effectiveness of integrating GWO and the updated CSMA/CA mechanism in achieving optimal energy efficiency and network performance.

This study examines the aggregate consumption function of Saudi Arabia from 2000 to 2022, focusing on identifying key determinants of household consumption and evaluating the impacts of disposable income, household wealth, government expenditure, interest rates, and oil revenues. The research uses advanced econometric methods, including the Autoregressive Distributed Lag (ARDL) model and Johansen cointegration test, to analyze the relationships among these variables. The findings reveal that disposable income, household wealth, and government expenditure significantly and positively influence consumption, whereas interest rates show a negative correlation. Oil revenues also play a critical role, reflecting the country's economic reliance on oil. The study highlights the necessity for economic diversification to reduce the impact of oil price volatility on household income and consumption stability. The results offer crucial insights for policymakers, emphasizing the need for strategies that enhance household income and wealth, maintain robust public sector spending, and effectively manage interest rates. These findings also support the importance of consistent and predictable income sources for sustaining consumption. Additionally, this study suggests directions for future research, including developing sophisticated forecasting models to predict consumption trends and exploring other influencing factors such as demographic shifts and technological progress.

Although promoting healthy eating is a policy objective, the controllability of dietary habits remains uncertain. The personal dietary patterns reflect many factors, of which some are relatively controllable for individual, and others are not. In this article, assuming that some sort of information about controllability of dietary habits is contained in the observed pattern of food consumptions, we focused on dietary pattern on its own. We introduced a statistical descriptive model for data on food frequency questionnaire, estimated strength of pairwise linkage between foodstuffs, and grouped foodstuffs by applying community detection to the networks of the estimated inter-food linkages. Those linkages represent co-movement of pairs of foods consumption. Furthermore, we demonstrated an analysis on the relationship between health of mind and dietary habits considering the aspect of controllability of dietary habits. Using an observational study in Japan, we obtained the following results: 115 foodstuffs were divided into 3 groups for both of genders, but the compositions were different by gender; in the analysis of mental health, some stress response items were associated with the dependence on some of those food groupings. As the grouping of foodstuffs based on our estimation depicted the internal structure of dietary habit that a healthy eating policy should regard as constraint, it follows that we should design the policy along the line with that grouping.

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.

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.

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.

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.

This paper presents a novel approach of welding thick steel plates that offers time and energy savings compared with conventional techniques. The combination of gas metal arc welding (GMAW) and hot-wire technology simplifies the joint configuration and enhances the process tolerance. In this study, a square butt joint was prepared with as-cut edges and a thickness of 15 mm. The relationship between the welding current and the deposition rate of solo GMAW showed limitations and low process tolerance. Increasing the welding current led to a larger deposited volume with unnecessary weld penetration. An independent deposition volume due to hot-wire insertion was used to improve process tolerance. This approach provided an additional volume without increasing the welding current and reduced unnecessary penetration. With optimized parameters, full-penetration single-pass welding was achieved. Compared with the formation of a typical single-v butt joint at a similar welding speed of 30 cm/min, the proposed process reduced the minimum arc time and power consumption by approximately 83% and 62%, respectively. Moreover, a single pass at a travel speed of 60 cm/min was achieved with approximately 91% and 81% less arc time and power consumption, respectively. In summary, the combined process simplifies the joint configuration, enables full-penetration single-pass welding, and reduces time and energy requirements.

Background and objectives: Alzheimer's disease (AD) is a progressive neurodegenerative illness, responsible for 60-70% of all dementias, affecting over 50 million people worldwide, and nearly 11 million in European countries. Several putative factors are identified in the literature as causative agents or risk factors for the development of AD. The amyloid cascade hypothesis has been the main hypothesis about the pathophysiology of AD for decades. Recent studies raised the possible role of dysbiosis in the development of AD which prevents memory loss. The amyloid-β (Aβ) deposition might be considered as an inflammatory reaction to certain molecular products arising from the altered microbiome. Based on the above observations, it has been suspected, that antibiotic consumption patterns of different antibiotic classes might be associated with the prevalence of AD in European countries. Methods: Antibiotic consumption (ECDC) for 1997-2007, 2008-2018, and as the whole 1997-2018 period, have been compared to the AD prevalence for 2018 expressed in percentage of the population and statistically analyzed by Pearson calculation. Results: A significant positive correlation has been found between the AD prevalence (2018) and the average quinolone consumption for the year 1997-2007 (p: 0.044). A similar association was not observed for the entire 22 years (1997-2018) of the average quinolone consumption, and the years 2008-18, indicating 10-20 years of time-lapse between the antibiotic exposure and the development of AD. The ratio of broad-spectrum and narrow-spectrum antibiotics (B/N) estimated in the ECDC database for the years of 2008-2018 showed a strong positive association with AD prevalence (2018) (p: 0.026) and a positive correlation tendency for the entire 22 years 1997-2018 (p: 0.063), but none for the years 1997-2007 (p: 0.241). Broad-spectrum, beta-lactamase sensitive penicillin (J01CA) consumption showed a positive (non-significant) correlation with the prevalence of AD for the years 2008-2018 (p:0.080).Discussion: Our study indicated the possible sequential role of certain classes of antibiotics in the development of dysbiosis leading to amyloid deposits of AD, which strengthen the possible role of different mediator molecules (short-chain fatty acids, lipopolysaccharides, etc.) produced by the altered microbiome in the development of AD.

Private vehicle travel is the most basic mode of transportation, and the effective control of the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic development as well as achieving a green low-carbon society. Therefore, the impact factors of individual carbon emission must be elucidated. This study builds five different models to estimate real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the Light Gradient Boosting Machine (LightGBM) model performs better than the linear regression, Naïve Bayes regression, Neural Network regression, and Decision Tree regression models, with mean absolute error of 0.911 L/100 km, mean absolute percentage error of 10.4%, mean square error of 1.536, and R squared (R2) of 0.642. This study also assesses a large number of factors, from which three most important factors are extracted, namely, reference fuel consumption rate value, engine power and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with greater impact on real-world fuel consumption rate are vehicle brand, engine power, and engine displacement. Average air pressure, average temperature, and sunshine time are the three most important climate factors.

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%.

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.

In contrast to physical sustainable measures carried out in homes, such as insulation, the installation of a Home Energy Management System (HEMS) has no direct and immediate energy-saving effect. A HEMS gives insight into resident behaviour regarding energy use. When this is linked to the appropriate feedback, the resident is in a position to change his or her behaviour. This should result in reduced gas and/or electricity consumption. The aim of our study is to contribute towards the effective use of home energy management systems (HEMS) by identifying types of homeowners in relation to the use of HEMS. The research methods used were a literature review and the Q-method. A survey using the Q-method was conducted among 39 owners of single-family homes in various Rotterdam neighbourhoods. In order to find shared views among respondents, a principal component analysis (PCA) was performed. Five different types of homeowner could be distinguished: the optimists, the privacy-conscious, the technicians, the sceptics, and the indifferent. Their opinions vary as regards the added value of a HEMS, what characteristics a HEMS should have, how much confidence they have in the energy-saving effect of such systems, and their views on the privacy and safety of HEMS. The target group classification can be used as input for a way in which local stakeholders, e.g. a municipality, can offer HEMS that is in line with the wishes of the homeowner.

The present context regarding development of the food products trade at European Union level presumes manifestation of some tendencies and challenges meant to claim at the highest level the adaptation capacity of economical agents - both producers and traders. Among these we can take into account their need to comply with organic product labeling standards as an integral part of food products safety policies at Union level. In order to be able to implement a proper label type, not only from the point of view of standards but especially from the perspective given by the consumers perception and motivation toward a healthy sustainable food consumption, producers have to constantly innovate, to find new design solutions for food products labels. This article propose an exploratory research in the form of a field survey that highlights the perceptions of Romanian and Serbian consumers, altogether from the urban environment in relation to organic food labeling and how they influence purchasing decision-making. A random number of 373 Serbian consumers’ questionnaires, respectively 1262 Romanian consumers’ questionnaires have been validated, the results showing interesting results from the point of view of national consumption profile differences due to different cultural, economic and social factors as well as different levels of integration within European economy for the both countries. Main conclusions of the research stress the fact that perceptions regarding organic food products labels are constructed around variables like: type of information to appear within the label, category of information that are missing from labels, degree of producers interest in proper labeling of organic food products, relevance of the information on the organic food labels as regards the decision to purchase the products etc. Conclusions propose some possible developments for innovative future characteristics for organic food products labels.

(1) Background: This study aimed to analyze the temporal dynamics of blood lactate concentration and oxy-gen consumption following supra-maximal efforts, providing insights into recovery kinetics and physiologi-cal responses. (2) Methods: Ten highly trained 100-meter sprinters participated. Each athlete completed a graded exercise test (GXT) and a 30-second Wingate Anaerobic Test. Blood lactate concentrations were meas-ured before, immediately after, and at intervals up to 20 minutes post-exercise. Oxygen consumption (VO2) was continuously monitored during and after the GXT. (3) Results: Peak blood lactate concentration was reached around 3 minutes post-exercise, averaging 14.9 ± 3.5 mmol/L. Lactate clearance followed a bi-exponential decay, with a rapid initial phase (τ1 = 60 seconds) and a slower secondary phase (τ2 = 300 seconds). Peak VO2 values were high (45.4 ± 4.1 ml/kg/min), reflecting significant aerobic capacity. VO2 re-covery also showed a bi-exponential pattern, suggesting efficient recovery mechanisms. (4) Conclusion: The findings support previous research by demonstrating a high anaerobic glycolytic contribution during sprint-ing and rapid lactate clearance due to effective recovery. The bi-exponential models for both lactate and VO2 kinetics provide deeper insight into metabolic responses and recovery profiles, which are crucial for optimiz-ing training in competitive sports. This study offers valuable data for improving recovery strategies and per-formance in sprint athletes.

The clothing industry is a hotbed for exploitative working conditions and environmental damages, of which affluent global North countries remain a substantial driver. The circular economy is a potential solution to these issues, but its social implications remain unclear. Using the UK as a case study, this article analyses the global social impacts of a transition to circular clothing economies in the global North, finding these to be (almost) entirely dependent upon the intentions and design of the associated politics. Some aspects of a transition will be unambiguously good for some people; others will only be beneficial with careful governance. Moreover, entrenched global economic inequalities leave trade-offs borne largely by the global South, highlighting the need for circular economy research – its drive for localisation notwithstanding – to look globally to consider how a fair transition can be achieved.

Without an established energy management plan, human lifestyle improvement is incomprehensible. Adequate energy assets are the key to human lifestyle development, however, energy assets are restricted and exorbitant too. In this paper, an energy control system for a green environment called PMC (Power Management and Control) is proposed. The system is based on hybrid energy optimization, energy prediction, and multi-preprocessing. The blend of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) is utilized to make a fusion methodology to improve occupant comfort index (OCI) and decrease energy utilization. The main theme of the proposed PMC technique is to improve OCI and decrease energy utilization. The proposed framework gives better OCI when compared with its counterpart Ant Bee Colony along with Knowledge Base framework (ABCKB), GA-based prediction framework (GAP), Hybrid Prediction with Single Optimization framework (SOHP), and PSO-based power consumption framework. Compared with the existing AEO framework, the proposed PMC methodology gives practically the same OCI, but consumes less energy. The proposed PMC methodology additionally accomplished the most extreme OCI (i-e 1) when compared with the existing model FA-GA (i-e 0.98). The proposed PMC model consumed less energy as compared to existing models ABCKB, GAP, PSO and AEO. The proposed model consumed more energy than SOHP but provided better OCI. The comparative outcomes show the viability of the proposed PMC framework in lessening energy utilization and improving the OCI. Unlike other existing mythologies except for the AEO framework, the proposed PMC technique is additionally confirmed through a simulated climate by controlling indoor climate using actuators, like Fan, light, AC and boiler.

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 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.

We propose a new methodology for long-time biopotential recording based on MEMS multisensor integrated platform featuring a commercial electrostatic charge transfer sensor. That family of sensors was originally intended for presence tracking in automotive, so the existing setup has been engineered for the acquisition of electrocardiogram, electroencephalogram, electrooculogram and electromyography, designing a dedicated front-end and writing a proper firmware for the specific application. Systematic tests on controls and nocturnal acquisitions from patients in domestic environment will be discussed in detail. The excellent results candidate this technology to provide a low power, unexplored solution to biopotentials acquisition. The technology breakthrough is in that it enables adding this type of functionality to existing MEMS boards at near-zero additional power consumption. For these reasons, they open to additional possibilities for wearable sensing and strengthen the role of MEMS technology in medical wearable applications for long time synchronous acquisition of a wide range of signals.

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.

Ever since oil was discovered in the Persian Gulf Region in the 1920s, the oil and gas industries have been dominant in the area. These countries are responsible for generating approximately 35 percent of the world's natural gas and 25 percent of its crude oil, making them the largest crude oil producers globally. The release of carbon dioxide (CO2) into the atmosphere is directly linked to the use of fossil fuels. CO2 accounts for 58.8 percent of all greenhouse gas emissions caused by human activities and has a detrimental impact on urban residents through the urban heat island effect. A significant portion of these emissions can be attributed to residential buildings. Some nations exacerbate the problem by relying excessively on fossil fuels. Oil-rich countries provide energy subsidies to the public in order to lower energy costs for their citizens. However, this policy can have negative consequences, including wasteful energy usage in countries like Iran.

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.

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.

In the U.S. 44% of low-income households struggle to pay their utility bills, affecting their ability to afford necessities such as food and health expenses. Several government and utility funded energy efficiency programs exist to assist those experiencing energy insecurity. In Salt Lake City, Utah, there is a high demand for, but low availability of, energy efficiency services in underserved neighborhoods creating an opportunity for creative community-based programs to fill this inherent gap. This pilot project, involving the exchanging of LED bulbs in Salt Lake City, highlights the development of a community-based energy efficiency program that aims to bring energy savings to a uniquely targeted portion of the city and determines its feasibility in addressing energy insecurity at a larger scale. Through the 8-month project duration, 1,432 bulbs were exchanged at 23 events reaching 181 households in low-income areas. Through a year of use, these bulbs are estimated to save residents approximately 18,219 USD in electricity bills and reduce CO2 emissions from power plants by 122.23 metric tons, in addition to a savings of 4,400 USD in social cost of carbon as defined by the U.S. Environmental Protection Agency. Since this pilot reached less than 1% of households, we extrapolated a reach of 2%, 5%, and 7.5% and found substantial potential decreases in power plant emissions and financial savings. As this project is ongoing and being expanded, we discuss relevant findings that will help shape future community-based models so that they are appropriately deployed and more effective in alleviating local energy insecurity.

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.

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.

The present study propose an innovative turn-boring operation method and focuses on finding optimal turn-boring process parameters for Ti-6Al-4V by considering multiple performance characteristics using Taguchi orthogonal array with the grey relational analysis, the effect of machining variables such as, feed rate, depth of cut and cutting speed are optimized with considerations of multiple performance characteristics namely surface roughness, roundness error, material removal rate and power consumption the optimal values were found out from the Grey relational grade. The result of the Analysis of Variances (ANOVA) is shown that the most significant factor is cutting speed, followed by feed rate, radial depth of cut. Finally, confirmation tests were carried out to make a comparison between the experimental results. Experimental results have shown that machining performance in the turn-boring process can be improved effectively through this approach.

A space charge density wireless measurement system based on the idea of distributed measurement is proposed for collecting and monitoring the space charge density in an ultra-high-voltage direct-current (UHVDC) environment. The proposed system architecture is composed of a number of wireless nodes connected with space charge density sensors and a base station. The space charge density sensor based on atmospheric ion counter method is elaborated and developed, and the ARM microprocessor and Zigbee radio frequency module are applied. The wireless network communication quality and the relationship between energy consumption and transmission distance in the complicated electromagnetic environment is tested. Based on the experimental results, the proposed measurement system demonstrates that it can adapt to the complex electromagnetic environment under the UHVDC transmission lines and can accurately measure the space charge density.

Several studies have shown that physical activity (PA) is related to physical and mental health. Yet, there are few studies on the frequency of PA as it relates to health and a healthy lifestyle. The purpose of this study was to investigate the relationship between the frequency of practicing different types of physical activities (dependent variables), living a healthy lifestyle (BMI, smoking and alcohol consumption), physical health (sum of all doctor visits except psychiatrists) and mental health (a sum of visits to psychiatrists) at different ages (independent variables). We fo-cused on three types of PA: (1) medium to high-intensity aerobic exercises; (2) low to medium intensity relaxing and stretching exercises, (3) outdoor leisure PA. 9,617 participants (ages: 19 -81) were included in the study (with health registries over a period of 10 years prior to a cross-sectional survey). Descriptive statistics and multinomial logistic regression on frequencies of three types of PA and factors related to health and healthy lifestyles, as well as age and sex, were performed in this study. The results indicate that a higher frequency of practicing PA had a higher probability of association with the following factors: lower BMI, less or non-smoking behavior (types 1 & 3); higher education (types 1 & 2); higher age (types 2 & 3) and better physical health (type 1). Occasional (practicing sometimes) PA, type 2, was positively associated with poorer mental health (higher number of psychiatrist visits). Women were more likely to practice PA type 2, and men – PA types 3 & 1. Conclusion: In general, a higher frequency of PA is related with better health and healthy life styles; with the exception of PA type 2 that is related to poorer mental health.

With firm commitment to energy labelling, it is not a question of if but only of when the European Union will introduce an energy label for electric cars. To support such policy efforts, we conducted a scoping analysis of energy consumption and efficiency trade-offs for 342 electric car models sold in Europe. The results suggest that certified and real-world energy consumption average 19 ± 4 kWh/100 km and 21 ± 4 kWh/100 km, translating into drive ranges of 440 ± 120 km and 380 ± 110 km, respectively. Energy consumption is correlated with vehicle mass, frontal area, and battery capacity, but less so with rated power and vehicle price. Each 100 kg of vehicle mass and 0.1 m2 of frontal area tend to increase energy consumption by 0.20 ± 0.06 kWh/100 km and 0.72 ± 0.05 kWh/100 km. Raising battery capacity by 10 kWh increases vehicle mass by 145 kg, energy consumption by 0.6 kWh/100 km, and drive range by 45 km. Efficient vehicles are available at any price but drive range has a cost. Our findings point to considerable efficiency trade-offs that could be tapped by a dedicated energy label. We propose several options for categorizing vehicles on an efficiency scale from A to G, with and without additional utility parameters. Our analysis provides rationale for energy labelling of electric cars in Europe and could inspire similar analyses for other vehicle categories such as e-scooters, lightweight three- and four-wheelers, e-busses, e-trucks, and electric non-road machinery.

Water is a fundamental resource for ecosystems, humans, and the development of all economic sectors; it is necessary to identify and evaluate its environmental pressures or impacts. The water footprint (WF) is an appropriate indicator for the consumption of water used to produce a product. The present study uses this tool to evaluate the green and blue water requirements and the sustainability of irrigation water use for agroindustrial avocado production in Ziracuaretiro, Michoacán (2012-2021). Our analysis was based on aggregating weather and soil data at the municipal level and official government databases of avocado cultivated surface, fruit production, and water rights concessions. The analysis considers the homogeneity of information throughout the study area. We estimated that rainfed plantations require 839.03 m³/ton and irrigated plantations require 2355.80 m³/ton, with an average of 1597.47 m³/ton. In addition, we determined that avocado cultivation can demand up to 124.3% of agricultural water concessions in this municipality. In addition to the WF estimates, the analysis indicates that such studies are fundamental for decision-makers to develop and implement water use-efficiency strategies and shows the need for further research related to water consumption of avocado as a crop at more detailed scales to reduce uncertainty.

In the face of escalating climate concerns and the push for sustainable development, the global shift towards renewable and decentralized energy systems presents new challenges and opportunities. This study investigates integrating decentralized energy production, particularly photovoltaic (PV) systems, into national energy planning, aiming to optimize energy strategies that balance local production and consumption with national objectives. By analyzing the Swiss energy model, the research employs the EnergyScope and REHO models to assess the strategic implications of decentralized versus centralized energy systems. Results show that a decentralized approach can significantly reduce PV installation needs to 35 GW, about 23% of potential capacity, and decrease annual system costs by 10% to CHF 1230 per capita. This strategy emphasizes local consumption, minimizes grid reinforcement demands, and leverages economic advantages while addressing overproduction challenges through effective energy storage and grid management. Conclusions underline the strategic value of combining centralized and decentralized methods for resilient and sustainable energy planning. The study contributes to the discourse on energy policy and infrastructure planning, advocating for a hybrid model that accommodates both local conditions and broader energy objectives, urging further research into climate impacts and technology integration for a comprehensive energy future.

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.

Modeling and assessing the sustainability of machining systems has been considered to be a crucial approach to improving the environmental performance of the machining processes. As the most common machining system, the computer numerical control (CNC) milling system is a typical man-machine cooperative system where the activities of the machine tool and operator generate material and energy consumption. However, the energy consumption of the operator in the CNC milling system has often been ignored in most existing research. Therefore, existing methods fail to provide a comprehensive understanding of the sustainability of the CNC milling system. To fill this gap, an exergy loss assessment method is proposed to investigate the sustainability of the CNC milling system, where the energy consumption of the operator, the energy consumption of the machine tool, and material consumption are taken into consideration. The key performance indexes of the energy consumption of the operator, the energy consumption of the machine tool, the exergy loss, and the specific exergy loss (SEL) are analyzed and modeled. To demonstrate the feasibility of the proposed method, a case study was carried out on a three-axis machining center (XH714D), in which SEL was found to be 88.04J/mm3. The proposed method is effective to assess the sustainability of the CNC milling system, and the established exergy loss models build a good basis for exergy efficiency optimization.

The short-term intake (ESTI) of Hungarian consumers to pesticide residues was assessed based on 2331 test results obtained during 2017-2021 monitoring program in the frequently analyzed apples, sour cherries, table grapes, peaches, nectarines, peppers and strawberries (23.5% of all samples taken from 119 crops). The age-specific consumption data were obtained from two representative national food consumption surveys (2009 and 2018-2020). The exposure was characterized with Hazard Quotient and Hazard Index considering the acute reference doses of pesticide residues detected in the samples. When ESTI was calculated with all detected “single“ residues and variability factor of 3.6, recommended for evaluation of monitoring results, the HI only exceeded 1 for children &lt;3 years old eating grape (1.50-1.81). HI was &lt;1 when any of the 6 foods were eaten together within one day. Between forty and fifty percent of samples contained 2-23 residues. Though, the individual residue concentrations were below the corresponding MRLs, multiple residues being present in one sample resulted in maximum HI values in apples (1.14); grapes (6.57); peaches and nectarines (2.57); strawberries (2.74); peppers (10.44). Residues with low ARfD values contributed most. Applying HI is simple, but provides only point estimates; therefore, it should only be used in first-tier risk assessment.

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.

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.

From the perspective of manufacturing companies, the political, media and economic discourse on decarbonisation of the recent years manifests itself as an increasing social expectation of action. In Germany in particular, this discourse is also being driven forward by powerful companies, respectively sectors, most notably the automotive industry. Against this background, it was examined how German manufacturing companies react to rising societal pressure and emerging policies. It is examined which measures the companies have taken or plan to take to reduce their footprint, which aspirations are associated with this and by which structural characteristics (company size, energy intensity, sector) these are influenced. A mix methods approach was applied, utilising data gathered from approx. 900 companies in context of the Energy Efficiency Index of German Industry (EEI), along with media research focusing on decarbonisation plans and initiatives announced. We demonstrate that one-size-serves-all approaches are not suitable to decarbonise industry as the situation and ambitions differ considerably depending on size, energy intensity and sector. Even though the level of ambition and urgency is high, particularly micro and energy intensive companies are challenged. The research uncovers a series of questions that call for attention to materialise the ambitions and address the challenges outlined.

The rise of Asian and the stagnation of Western middle classes over the last thirty years have resulted in gradual convergence of income of large parts of the world’s population. Recent global crises ‐ the Great Recession and the COVID-19 pandemic ‐ have led to a decline in income and increase in income uncertainty. Rise in consumption of lower quality goods of shorter durability and an overall decline in demand and economic activity resulted as challenges to the global economy. In this paper, we argue that generational responsibility in consumption can be an environmentally sustainable response to crises which enables the economies to overcome the crisis of confidence and reaffirms community ties. As an element of long‐term orientation in consumption, generational responsibility is a cultural phenomenon dependent on solidarity within family and the wider community. It is characterized by consideration of consequences of consumption choices on the environment, and the abundance of savings and the usability of goods to be inherited by future generations. For companies, willing to revisit their traditional business models and incorporate principles of sustainability in their competitive strategies, promotion of generational responsibility can become a new source of competitive advantage and a driver of economic recovery.

One of the robust explanation related to sociological educational inequality is Bourdieu’s cultural reproduction theory. Admittedly, prior work investigated that children’s cultural capital (CC) have significant effects on educational success. Although, it remain unclear in the context of developing countries specifically Vietnam. Also, how children’s conversion mechanism of CC works and drive educational success. Meanwhile, Bourdieu theory argue that CC effects educations outcomes largely via teachers biasness by using support of school grades, in contrast some of the scholars argue that absorption of children’s CC may directly transmit into educational abilities and skill development. This study employed partial least square structural equation modelling to analyze 5th grades students’ data collected from educational policy and data center. This study argue that conversion mechanism grounded on CC dimensions. This study provide implications for the policy makers and future researchers.

The present study propose an innovative turn-boring operation method and focuses on finding optimal turn-boring process parameters for AA7050-T7451 by considering multiple performance characteristics using Taguchi orthogonal array with the grey relational analysis, the effect of cutting variables such as, feed rate, depth of cut and cutting speed are optimized with considerations of multiple performance characteristics namely surface roughness, roundness error, material removal rate and power consumption the optimal values were found out from the Grey relational grade. The result of the Analysis of Variances (ANOVA) is proved that the most significant factor is cutting speed, followed by feed rate, radial depth of cut. Finally, confirmation tests were performed to make a comparison between the experimental results. Experimental results have shown that machining performance in precision turn-boring process can be improved effectively through this approach

The article considers the issues of ensuring operational performance and environmental sustainability of marine diesel engines by using biodiesel fuel. The research was conducted on 5S60ME-C8 MAN-B&W Diesel Group and 6DL-16 Daihatsu Diesel marine diesel engines, which were operated using RMG380 petroleum fuel and B10 and B30 biodiesel fuels. The efficiency of biofuel usage was assessed based on environmental (reduced nitrogen oxide concentration in exhaust gases) and economic (increased specific effective fuel consumption) criteria. It was found out that the use of B10 and B30 biofuels provides reduction of nitrogen oxides concentration in exhaust gases by 14.71–25.13 %, but at the same time increases specific effective fuel consumption by 1.55–6.01 %. Optimum fuel injection advance angles were determined that ensure the best thermal energy, economic and environmental performance of diesel engines. The optimum angle of biofuel supply advance is determined experimentally and should correspond to the limits recommended by the diesel engine operating instructions. It has been proven experimentally that the use of biofuel increases the environmental sustainability of marine diesel engines by 13.75–29.42 %. It increases the diesel engines environmental safety in case of emergency situations, as well as accidental and short-term emissions of exhaust gases with an increased content of nitrogen oxides into the atmosphere - phenomena that are possible in starting modes of diesel engine operation, as well as in modes of sudden load changes. It is the increase in the environmental friendliness of marine diesel engines in the case of using biofuel that is the most positive criterion and contributes to the intensity of biofuel use in power plants of sea vessels.

Consuming fish sustainably means consuming less and better, considering environmental, social, and economic impacts. This demands more knowledge to make informed decisions. In this study, the main goal was to understand how sustainability education can be used as a tool to raise awareness and promote new habits regarding the sustainable consumption of fish in 4th-grade students at primary school. During the COVID-19 pandemic, face-to-face and online learning methodologies were used in two different groups of students to understand whether the teaching method impacted learning. A sequential explanatory mixed methodology was applied, with a pre-test/post-test design, combined with focus group sessions. Between the pre and post-tests three learning modules were performed. The results showed that the students were unaware of many aspects related to fish sustainable consumption, but the developed activities significantly increased their knowledge of the subject and impacted students to change their consumption behaviours. It was also found that the construction of knowledge by the students after the activities was achieved either by face-to-face or online learning. Interviews from focus group sessions showed that students got to know new fish species, learned about the concept of minimum landing size and closure season and that fish are labelled like other types of food.

Nowadays, the increasingly serious environmental pollution and energy problems urgently require internal combustion engine-based transportation vehicles to upgrade or replace, so the new energy transportation vehicles based on hybrid power and fuel cells have gradually stepped on the stage and continue to innovate. The shipping industry is one of the main sources of global greenhouse gas emissions. The development of clean energy ships represented by fuel cells has attracted wide attention. Fuel cell has the advantages of clean, pollution-free and low noise, but it has some disadvantages such as insufficient dynamic response performance and fast performance decay. Limited by the characteristics of a single energy source, a variety of energy sources and bidirectional DC converters are usually mixed together to form a hybrid ship to improve the flexibility, stability and economy of the ship, and enhance its adaptability to complex sea conditions through the reconfiguration of power system energy. Taking fuel cell ferry "FCS Alsterwasser" as the research object, this study proposed an improved equivalent minimum hydrogen consumption energy management strategy based on fuzzy logic control. First, the power system of the modified mother ship was simulated, and a hybrid power system including fuel cell, lithium iron phosphate battery and supercapacitor was proposed. Then, the dynamic system simulation model and double closed loop PI control model are established in MATLAB/Simulink, and the reliability of the model is verified by the simulation analysis of charge and discharge characteristics. Then, the feasibility of the proposed method is demonstrated by designing simulation experiments based on typical working conditions of the mother ship. The simulation results show that under the premise of meeting the load requirements, the control strategy designed in this paper has a better optimization effect than the S-type penalty function in terms of lithium battery power, lithium battery SOC, bus voltage stability and equivalent hydrogen consumption, which improves the stability and economy of the power system, and has certain engineering practical value.

To ensure sustainable production and consumption in the agricultural sector, it is necessary to assess the contribution of each element of the nexus in the agricultural production chain. The aim of this study is to make a quantitative and qualitative analysis of the contributions of each element of the energy, water, waste and land nexus to agricultural products. A composite method approach combining aspects based on input-output model, Location Quotient (LQ) as well as competitive position is adopted. A database of nexus elements over a period from 2009 to 2018 is used for Cameroon, with ten regions considered. The results show proportions of around 0.42% energy, 67.88% water withdrawal, 11.91% harvested area, 97.81% waste for agricultural products. The geolocation of harvested areas shows that the largest portion is in the far north (1,373,829 ha) and the smallest in Adamawa (224,038 ha). Maximum production is in the central region (4,334,095 tons) and minimum in the Adamawa region (915,841 tons). The central, littoral and west regions are more representative of agricultural products. The analysis of the competitive position of agricultural products contributes to a better orientation of national strategies for agricultural sustainability according to existing potentials.

Objective: The objective of our study was to explore the potential benefits of combining moderate alcohol consumption with regular vigorous physical activity on HDL-cholesterol levels and its potential positive impact on cardiovascular health. Material and method: The study comprised two groups: group A (102 patients) who consumed 1 gram of alcohol per day, specifically red wine, and increased their exercise levels in addition to their standard medical treatment, and group B (108 patients) who adhered only to their standard medical treatment. The patients were monitored for a year, during which various clinical parameters were observed. Results:Our study showed that moderate alcohol consumption, particularly red wine, along with increased physical activity levels, was associated with enhancements in exercise tolerance and elevated levels of HDL-cholesterol. Our findings suggest the efficacy of the intervention in improving lipid profiles, with the group that consumed alcohol as part of their treatment scheme, generally experiencing more pronounced positive effects. Statistically significant differences between groups in terms of angina evolution, AMI recurrence, or arrhythmias evolution were not found. However, there were important differences in terms of EF improvement and quality of life, with the alcohol consuming group displaying greater improvements in both aspects. Conclusions:In conclusion, our study emphasizes the potential benefits of combining moderate alcohol consumption, specifically red wine, with increased physical activity levels for improving exercise tolerance and enhancing HDL-cholesterol levels. However, further research and clinical trials are essential to fully understand the mechanisms and optimize the integration of these lifestyle elements for improved cardiovascular health outcomes.

Objective: The objective of our study was to explore the potential benefits of combining moderate alcohol consumption with regular vigorous physical activity on HDL-cholesterol levels and its potential positive impact on cardiovascular health. Material and method: The study comprised two groups: group A (102 patients) who consumed 1 gram of alcohol per day, specifically red wine, and increased their exercise levels in addition to their standard medical treatment, and group B (108 patients) who adhered only to their standard medical treatment. The patients were monitored for a year, during which various clinical parameters were observed. Results:Our study showed that moderate alcohol consumption, particularly red wine, along with increased physical activity levels, was associated with enhancements in exercise tolerance and elevated levels of HDL-cholesterol. Our findings suggest the efficacy of the intervention in improving lipid profiles, with the group that consumed alcohol as part of their treatment scheme, generally experiencing more pronounced positive effects. Statistically significant differences between groups in terms of angina evolution, AMI recurrence, or arrhythmias evolution were not found. However, there were important differences in terms of EF improvement and quality of life, with the alcohol consuming group displaying greater improvements in both aspects. Conclusions:In conclusion, our study emphasizes the potential benefits of combining moderate alcohol consumption, specifically red wine, with increased physical activity levels for improving exercise tolerance and enhancing HDL-cholesterol levels. However, further research and clinical trials are essential to fully understand the mechanisms and optimize the integration of these lifestyle elements for improved cardiovascular health outcomes.

Since a large amount of sewage sludge (WSS) has been generated daily, it is necessary to explore effective methods for utilizing WSS. Although a photo-fermentation system sometimes alters the characteristics of microbial functions, there are no attempts to perform the photo-fermentation using WSS which is regularly treated under a dark-fermentation style. In this study, the effect of photo-fermentation (photo-irradiation) on anaerobic digestion using WSS was revealed. Photo-irradiation during the anaerobic digestion of WSS significantly reduced the amount of organic acids and hydrogen sulfide and methane production was also suppressed. However, it was shown that the activity of sulfate-reducing bacteria in WSS under the light treatment increased. Photo-irradiation also stimulated the growth of green-sulfur bacteria and induced anoxygenic photosynthesis, by which the fermented samples turned green linking with the consumption of hydrogen sulfide. Finally, a dark/light switching fermentation was able to reduce only hydrogen sulfide while methane production remained the same.

The fishmeal production industry is essential for providing protein for animal feed in the aquaculture sector. However, the industry faces challenges related to energy consumption and environmental sustainability. This study evaluates the energy efficiency and environmental benefits of waste heat recovery (WHR) technologies in a fishmeal production plant in Vietnam. Data were collected from the plant between 2016 and 2022, and a specific energy consumption (SEC) indicator and a comprehensive methodology were utilized. Implementing an economizer as a WHR technology resulted in a 55.5% decrease in SEC compared to the state before installation. The enhanced energy efficiency also translated to reduced energy consumption per output unit. Moreover, the economizer contributed to annual energy savings of 4,537.57 GJ/year and cost savings of $26,474.49. Additionally, carbon dioxide (CO2) emissions associated with producing one ton of fishmeal decreased by 58.37%. These findings highlight the potential for WHR technologies to improve energy efficiency and reduce the environmental footprint of fishmeal production. The study's results provide valuable insights for practitioners and policymakers in promoting energy efficiency practices and reducing environmental impact in the industry.

The ubiquitous connectivity for the space-air-ground integrated network (SAGIN) of the beyond fifth generation of communication and sixth generation of communication (B5G/6G) is envisaged to meet the needs for the demanded quality of service (QoS), green communication, and "dual carbon" target. However, the offloading and computation of massive latency-sensitive tasks dramatically increases the energy consumption of the network. Furthermore, the traditional power supply technology of the network base stations (BSs) enhances the carbon emission. To address these issues, we first propose a SAGIN architecture with energy harvesting devices, where the BS is powered by both renewable energy (RE) and the conventional grid. The BS explores wireless power transfer (WPT) technology to power the unmanned aerial vehicle (UAV) for stable network operation. RE sharing between neighbouring BSs is designed to fully utilize RE for reduce carbon emission. Secondly, on the basis of task offloading decision, UAV trajectory, and RE sharing ratio, we construct cost functions with joint latency-oriented, energy consumption, and carbon emission. Then, we develop a twin delayed deep deterministic policy gradient (TD3PG) algorithm based on deep reinforcement learning to minimize the cost function. Finally, simulation results demonstrate that the proposed algorithm outperforms the benchmark algorithm in terms of reducing latency, energy saving, and lower carbon emission.

Existing research lacks studies that explore the impact of government fiscal support on the high-quality development of regional economy and society from the perspective of livelihood-based infrastructure construction. The investigation in this paper explores the economic and social effects of government fiscal support behavior from the perspective of livelihood infrastructure construction. On the basis of the TOPSIS-Entropy Method, this paper constructs an evaluation indicator that closely conforms to the denotation and requirements of high-quality development and then carries out empirical regressions in combination with government expenditure on people's livelihood to verify the theoretical hypothesis, whose transmission mechanism and moderating effect are further empirically tested. The results reveal that increasing government expenditure on people's livelihoods can effectively improve regional high-quality development, but the marginal improvement degree varies with different periods and locations. The mechanism test proves that the regional consumption level plays a significant part in the mediating effect, accounting for 24.27% of the total effect. Urbanization is also the critical impetus for regional high-quality development and positively moderates the promotion effect of people's livelihood fiscal expenditure. Our study presents a theoretical basis and policy inspiration for improving residents' quality of life and welfare level and improving people's livelihood in the midst of high-quality economic development.

The environmental issues we are currently facing require long-term prospective efforts for sustainable growth. Renewable energy sources seem to be one of the most practical and efficient alternatives in this regard. Understanding a nation's pattern of energy use and renewable energy production is crucial for developing strategic plans. No previous study has been performed to explore the dynamics of power consumption with the change in renewable energy production on a country-wide scale. In contrast, a number of deep learning algorithms demonstrated acceptable performance while handling sequential data in the era of data-driven predictions. In this study, we developed a scheme to investigate and predict total power consumption and renewable energy production time series for eleven years of data using a Recurrent Neural Network (RNN). The dynamics of the interaction between the total annual power consumption and renewable energy production are investigated through extensive Exploratory Data Analysis (EDA) and a feature engineering framework. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors and Root Mean Squared Error (RMSE) value of 0.084. Higher performance is achieved through the increase in the number of epochs and hyperparameter tuning. The proposed framework can be used and transferred to investigate the trend of renewable energy production and power consumption and predict the future scenarios for different communities. Incorporation of the cloud-based platform into the proposed pipeline may lead to real-time forecasting.

Fog computing is a potential solution to overcome the shortcomings of the cloud computing processing of IoT tasks. These drawbacks can be high latency, location awareness and security, and it is attributed to the distance between IoT devices and servers, network congestion and other variables. Although fog computing has evolved as a solution to these challenges, it is known for having limited resources that need to be consciously utilised, or any of its ad-vantages would be lost. Computational offloading and resource management are critical concerns to be considered to get maximum benefit of the available resource at fog computing systems and benefit from its advantages. Computational offloading and resource management are important issues to be considered to get maximum benefit of the available resource at fog computing systems and benefit from its advantages. In this article, in vehicular traffic applications, we introduce a dynamic online offloading scheme that involves the execution of delay-sensitive ac-tivities. This paper proposes an architecture of a fog node that enables a fog node to adjust its offloading threshold dynamically (i.e., the criteria by which a fog node decides whether tasks should be offloaded rather than executed locally) using two algorithms: dynamic task scheduling (DTS) and dynamic energy control (DEC). These algorithms seek to solve an optimisation problem aimed at minimising overall delay, improving throughput, and minimising energy consumption at the fog layer, while maximising the use of resource-constrained fog nodes. Compared with other benchmarks, our approach can reduce the delay by up to 95.38% and reduce energy consumption by up to 67.71% in fog nodes. Additionally, this approach enhances throughput by 71.08%.

This work studies a novel low voltage (<60 V) hybrid system that supports engine boosting and downsizing in addition to start-stop, regenerative braking, and constrained torque assist/regeneration. The hybrid power split supercharger (PSS) shares a 9 kW motor between supercharging the engine or providing hybrid functionalities through a planetary gear set, a brake and a bypass valve. The PSS operation is limited to only one of the parallel hybrid or boosting modes at a time, necessitating a highly optimized decision making algorithm to select the device mode and power split ratio. In this work an adaptive equivalent consumption minimization strategy (A-ECMS) is developed for energy management of the PSS. The A-ECMS effectiveness is compared against a dynamic programming (DP) solution with full drive cycle preview through hardware-in-the-loop experiments on an engine dynamometer testbed. The experiments show that the PSS with A-ECMS reduces a vehicle fuel consumption by 18.4% over standard FTP75 cycle compared to a baseline turbocharged engine, while global optimal DP solution decreases the fuel consumption by 22.8% compared to baseline.

Real data obtained from ship in operation are processed and analyzed in this paper. The intention was to provide software which would predict ship’s fuel consumption in some future time instant. It is showed that it is possible to develop such software based on numeric fitting of known data. In order to check how well the prediction of future fuel consumption is, we used only the first half of data for obtaining prediction curve. The second part of data was used to compare different prediction curves goodness. Hence, the presented research used actually a “real future data” and forecasted future data, which are used to numerically evaluate goodness of prediction. The research is of interest for companies logistics, to provide adequate fuel for fleet when and where actually needed. It is concluded that there are several prediction functions which satisfy used statistical quality measures.

Tourism, and particularly residential tourism, has led to a change in the urban and demographic model of towns along the European Mediterranean coastline. Water as a limited and limiting resource for the growth of tourism is a popular topic in the scientific literature. However, the incorporation of non-conventional resources (desalination) has meant, in theory, that this limitation has been overcome. The aims of this paper are: a) to identify the different tourism models implanted in this territory and describe them from the point of view of their consumption of water in the demand cycle from 2002 to 2017; b) analyse the hydrosocial cycle, highlighting the measures aimed at satisfying water demand; and c) identify the limitations associated with these hydrosocial systems. To this end, different types of information will be processed, and various complex indicators produced. The results show the importance that demand management and the use of desalinated water in increasing the resilience of this territory to aridity. However, this has generated other problems associated with a tsunami of construction and the continuity of a non-sustainable territorial model.

A tailored model for the assessment of environmental benefits achievable by “light-weighting” in the automotive field is presented. The model is based on the Fuel Reduction Value (FRV) coefficient, which expresses the Fuel Consumption (FC) saving involved by a 100 kg mass reduction. The work is composed of two main sections: simulation and environmental modelling. Simulation modelling performs an in-depth calculation of weight-induced FC whose outcome is the FRV evaluated for a wide range of Diesel Turbocharged (DT) vehicle case studies. Environmental modelling converts fuel saving to impact reduction basing on the FRVs obtained by simulations. Results show that for the considered case studies, FRV is within the range 0.115–0.143 and 0.142–0.388 L/100 km × 100 kg, respectively, for mass reduction only and powertrain adaptation (secondary effects). The implementation of FRVs within the environmental modelling represents the added value of the research and makes the model a valuable tool for application to real case studies of automotive lightweight LCA.

With consumers increasingly aware of the effects of human activity on the environment, tourism products and destinations are increasingly marketed as sustainable and socially responsible. As most sustainable practices lead to additional costs, and tourists’ decisions tend to be price sensitive, achieving sustainability goals necessarily involves understanding how much tourists are willing to pay extra for sustainable practices, as well as the antecedents of such Willingness to Pay (WTP). The present study aims to advance knowledge on the antecedents of WTP for Sustainable Destinations (WTP-4-SD), for which it builds on previous studies employing the Theory of Planned Behavior (TPB) and the New Environmental Paradigm (NEP). In this context, a theoretical model involving Ecotourism Attitudes, Environmental Beliefs, Climate Change-Related Risk Perceptions (CC-RRP), Environmental Concern During trip (ECDT), and Sustainable Consumption Behavior (SCB) as antecedents of WTP-4-SD is proposed. The model was tested based on data collected through an online survey, form a sample of 1,545 Spanish and Portuguese tourists, and analyzed through Structural Equations Modelling (SEM). The findings suggest that a cohesive set of attitudes and beliefs regarding the man-nature relationship, the risks of climate change and the role of tourism – which have been labelled “No Planet-B Attitudes” – is the main driver of WTP-4-SD.

Accurate, rapid, and stable prediction of electrical energy consumption is essential for decision-making, energy management, efficient planning, and reliable power system operation. Errors in forecasting can lead to electricity shortages, wasted resources, power supply interruptions, and even grid failures. Accurate forecasting enables timely decisions for secure energy management. However, predicting future consumption is challenging due to the variable behavior of customers, requiring flexible models that capture random and complex patterns. Existing forecasting methods, both traditional and modern, have limitations and do not fully meet accuracy expectations. To address these issues, this research introduces a hybrid models that combine FCRBM based forecaster, and GWDO based optimizer, namely FS-FCRBM-GWDO approach to enhance the model performance in STLF have been developed. While some models excel in accuracy and others in convergence rate, both aspects are crucial. The main objective is to create a forecasting model that provides reliable, consistent, and precise predictions for effective energy management. This led to the development of a novel two-stage hybrid model. The first stage predicts electrical energy usage through four modules using deep learning, support vector machines, and optimization algorithms. The second stage optimizes energy management based on predicted consumption, focusing on reducing costs, managing demand surges, and balancing electricity expenses with customer inconvenience. This approach benefits both consumers and electricity corporations by lowering bills and enhancing power system stability. Simulation results validate the proposed model's efficacy and efficiency compared to benchmark models.

This study considers ways to increase the consumption of sustainable fashion, given the significant environmental and social damages, associated with the industry. A series of experiments were conducted examining the impacts of choice architecture (nudges) under field conditions in collab-oration with one of Israel’s largest shopping centers. The study sought to identify which inter-ventions at the retail level successfully motivate sustainable fashion behavioral change regarding purchases and willingness to pay more, along with agreement with several statements regarding the climate crisis and sustainable fashion. Among the types of nudges examined in this field study were providing information, increasing accessibility to sustainable alternatives and appealing to social identity, in relation to demographics and green self-image. The study found that offering alterna-tives to consumers constitutes the most effective way to “nudge” consumers toward more sus-tainable purchasing behavior. Nonetheless, this does not negate the contribution of providing in-formation and strengthening social norms regarding sustainable fashion. Additionally in all groups, most participants reported that they do not know how to distinguish between sustainable and non-sustainable fashion, nor do they believe that the clothes they purchased were actually sustainable. The findings emphasize the need for policies that will increase the accessibility of sustainable fashion.

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.

The MAPK signaling pathway with BRAF mutations has been shown to drive the pathogenesis of 40-60% of melanomas. Inhibitors of this pathway's BRAF and MEK components are currently used to treat these malignancies. However, responses to these treatments are not always successful. Therefore, identifying noninvasive biomarkers to predict treatment responses is essential for personalized medicine of melanoma patients. Using noninvasive 1H magnetic resonance spectroscopy (1H MRS), we previously showed that BRAF inhibition reduces lactate and alanine tumor levels at early stages of effective therapy and could be construed as metabolic imaging biomarkers for drug response. The present work aimed to demonstrate if these metabolic changes and those assessed by 31P MRS are also found in preclinical human melanoma models treated with MEK inhibitors. Apart from 1H and 31P MRS, additional supporting in vitro biochemical analyses are described. Our results indicate significant early metabolic correlations with response levels to MEK inhibition in the melanoma models and are consistent with our previous study of BRAF inhibition. Given these results, our study supports the potential clinical utility of noninvasive MRS to objectively image metabolic biomarkers for early predicting melanoma response to MEK inhibition.

The problem of energy poverty, when energy becomes unaffordable for some population group, is not only a problem for developing countries, but this phenomenon is appearing more and more often in European countries as well. In Europe, it is estimated that 50 to 125 million people are at risk of energy poverty. We hear more and more about energy poverty in connection with the current energy crisis and rising energy prices, but also as a result of insufficient use of renewable energy sources. Due to rising energy prices, we are increasingly hearing about deepening energy poverty in Slovakia as well. The aim of the contribution is to examine the development of energy poverty in Slovakia compared to other EU countries. The situation is studied from the view of the number of days of heating and cooling, the percentage of the population that cannot maintain adequate heat at home, the percentage of the population that has a lack of heat and the percentage of residents without enough heat. During the research, we used distribution analysis, trend analysis, analysis of variance and one-way analysis. The main results show that HDD index recorded decrease, CDD index recorded increase, and Energy poverty is most obvious in low-income group of inhabitants, having shortage of heat, when RES use contribute to mitigation of energy poverty. Solving the unfavorable situation with energy poverty is possible by increasing the share of Renewable energy sources in the gross final energy consumption for heating and cooling, primarily in residential buildings. The results provide information for policy makers in area of economic, social and environmental decision-making.

Abstract: This article reports the results of an ecological study of cancer incidence rates by state in the US for the period 2016-2020. The goals of this study were to determine the extent to which solar UVB doses still reduced cancer risk compared to findings reported in 2006 for cancer mortality rates for the periods 1950–1969 and 1970-1794 as well as cancer incidence rates for the period 1998–2002 and to determine which factors were recently associated with cancer risk. The cancer data for non-Hispanic white (European American) men and women were obtained from the Centers for Disease Control and Prevention. Indices were obtained for solar UVB at the surface for July 1992, and alcohol consumption, and diabetes and obesity prevalence near the 2016–2020 period. Lung cancer incidence rates were also used in the analyses as a surrogate for smoking, diet, and air pollution. The cancers for which solar UVB is significantly associated with reduced incidence are bladder, brain (males), breast, corpus uteri, esophageal, gastric, non-Hodgkin’s lymphoma, pancreatic, and renal cancer. Lung cancer was significantly associated with colorectal, laryngeal, and renal cancer. Diabetes was also significantly associated with breast, liver, and lung cancer. Obesity prevalence was significantly associated with breast, colorectal and renal cancer. Alcohol consumption was associated with bladder and esophageal cancer. Thus diet has become a very important driver of cancer incidence rates. The role of solar UVB in reducing risk of cancer has been reduced due to people spending less time outdoors, wearing sunscreen that blocks UVB but not UVA radiation, and population increases in overweight and obesity, which lowers 25-hydroxyvitamin D concentrations and generates systemic inflammation, a risk factor for cancer. The dietary approach that would reduce the risk of diabetes, obesity, lung cancer, and, therefore cancer, would be one based mostly on whole-plants and restrictions on red and processed meats and ultraprocessed foods. Solar UVB exposure for a few minutes before applying sunscreen and taking vitamin D supplements would also help reduce risk of cancer.Keywords: alcohol consumption; cancer incidence;

In this work, optimal consumption and investment strategies are derived for risk-averse investors under the 4/2 stochastic volatility class of models. We work under an expected utility (EUT) framework and consider a Constant Relative Risk Aversion (CRRA) investor, who might also be ambiguity-averse. The corresponding Hamilton-Jacobi-Bellman (HJB) and HJB-Isaacs (HJBI) equations are solved in closed-form for a subset of the parametric space and under some restrictions on the portfolio setting, for complete markets. Conditions for proper changes of measure and well defined solutions are provided. These are the first analytical solutions for the 4/2 stochastic volatility model and the embedded 3/2 model for the type of excess returns established in .

Through the use of machine learning algorithms like the Support Vector Machine, it has been show that burn extent can be accurately mapped from hyperspatial drone imagery in both grasslands and forests. Despite these successes, hyperspatial imagery must be acquired via drones, requiring large amounts of time and resources to capture areas much smaller than the large catastrophic fires which result in the majority of the lands burned each year by wildland fires. To overcome this difficulty, high spatial resolution satellite imagery from Worldview2 can be substituted for hyperspatial drone imagery, allowing for larger regions of images to be acquired more easily and efficiently. Additionally, Worldview2 trades spatial resolution for spectral resolution and extent, capturing images in 8 multispectral bands as opposed to 3 band imagery in the visible spectra. This research examines the utility of each of the 8 bands observed in Worldview2 imagery using an Iterative Dichotomiser 3 decision tree, then uses these bands to map burn extent and biomass consumption. Several classifications of burn extent and biomass consumption are produced and compared based on the bands used as inputs. The results show that using Worldview2 imagery to map burn extent and biomass consumption results in highly accurate maps, with slight improvements when additional bands are added.

This paper develops a Green Light Optimal Speed Advisory (GLOSA) system for buses (B-GLOSA). The proposed B-GLOSA system is implemented on diesel buses, and field tested to validate and quantify the potential real-world benefits. The developed system includes a simple and easy to calibrate fuel consumption model that computes instantaneous diesel bus fuel consumption rates. The bus fuel consumption model, a vehicle dynamics model, the traffic signal timings, and the re-lationship between vehicle speed and distance to the intersection are used to construct an optimi-zation problem. A moving-horizon dynamic programming problem solved using the A-star algo-rithm is used to compute the energy-optimized vehicle trajectory through signalized intersections. The Virginia Smart Road test facility was used to conduct the field test on 30 participants. Each participant drove three scenarios including a base case uninformed drive, an informed drive with signal timing information communicated to the driver, and an informed drive with the recom-mended speed computed by the B-GLOSA system. The field test investigated the performance of using the developed B-GLOSA system considering different impact factors, including road grades and red indication offsets, using a split-split-plot experimental design. The test results demonstrated that the proposed B-GLOSA system can produce smoother bus trajectories through signalized in-tersections producing fuel consumption and travel time savings. Specifically, compared to the uninformed drive, the B-GLOSA system produces fuel and travel time savings of 22.1% and 6.1% on average, respectively.

Food consumption in Europe is changing. Red meat consumption has been steadily decreasing in the past decades. The rising interest of consumers for healthier and more sustainable meat products provide red meat producers with the opportunity to differentiate their offers by ecolabels, origin and health claims. This international study analyses the European consumer preferences for red meat (beef, lamb and goat) in seven countries: Finland, France, Greece, Italy, Spain, Turkey and the United Kingdom. Through a choice experiment, 2.900 responses were collected. Mixed multinomial logit models were estimated to identify heterogeneous preferences among consumers at the country level. Results indicate substantial differences between the most relevant attributes for the average consumers, as well as their willingness to pay for them in each country. Nevertheless, national origin and organic labels were highly valued in most countries.

Hong Kong is the biggest consumer of eggs in the world and ranks on the top with an average consumption of 24.52kg/capita/year. The egg consumption trend in Hong Kong has been increasing specifically; a drastic increase of about 100% has been observed from 2008 to 2020. One of the reasons behind this significant increase is the increase in the gross national income of Hong Kong in addition to the population growth rate. However, there was a slight decline in egg consumption trend in the year 2020 amid the COVID-19 pandemic. It has been estimated that there was a bulk of 17,100 tons of recoverable waste eggshells in 2019 (pre-COVID-19 situation), having a daily yield of 47 tons per day. Based on the linear regression, it may be expected that if the pre-COVID-19 trend of egg consumption remains sustained, there will be a big bulk of more than 45,000 tons per year and up to 125 tons per day of waste eggshells till 2050. In addition, a public survey was conducted across the city targeting individuals, families, and restaurants majorly on egg consumption and reuse. The survey outcomes revealed that Chinese eggs have an extensive market in Hong Kong, and people have a positive attitude towards eggshell recycling. Also, a massive population disposes of the waste eggshells while only a trace population reuses them. Therefore, Hong Kong has a very high potential to improve the circular economy by reusing waste eggshells on a commercial level in cementitious products.

To address the problems of wind abandonment in the regional integrated energy system (RIES), which occurs when cogeneration unit operates in a heat-determined electricity operation mode, and the low efficiency of the energy storage system in a low-temperature environment, we propose an optimal scheduling method for RIES based on fine energy storage and wind power dissipation. First, a fine energy storage model is established on the source side and the conditional value at risk(CvaR) theory is used to quantify the uncertainty of wind power; then a combined heat and power demand response mechanism is introduced on the load side to reduce the peak-to-valley difference between heat and power loads, and to promote the consumption of wind power. Finally, with the objective of minimizing the total cost of RIES optimal dispatch, the example is solved on the MATLAB platform. The simulation results show that compared with the traditional model, the proposed model is not only more adaptable to the low-temperature environment, but also can effectively improve the system economy and wind power consumption.

Through a partnership with three Federally Qualified Health Centers (FQHCs), the local health department in Los Angeles County implemented a produce prescription program (PPR) to increase fresh fruit and vegetable purchases among patients with diabetes or prediabetes. The PPR, which began in 2020, provided eligible patients with a $40 voucher every month for 6 months to promote the purchase of fresh produce at large chain grocery stores. This qualitative assessment describes staff experiences with the PPR, documenting the facilitators and barriers they encountered while implementing the program. Fifteen clinic staff (i.e., PPR implementers) were interviewed for this assessment. Thematic analysis and coding were conducted using the ATLAS.ti software; the coding was carried out by two separate coders. Interviewees discussed the importance of having preexisting partnerships and programs to support the PPR at their clinic site. Hidden costs related to implementing the program included a large and unexpected amount of staff time devoted to enrolling patients into the program. Collecting quality data and having limited expertise to rigorously evaluate the program were other challenges. Because patients often share their food with their family, the monthly $40 incentive was generally not enough to support their needs; interviewees suggested giving a higher inventive amount to those with a larger household. Future PPR efforts and similar food incentive programs should consider these and other facilitators and barriers to implementation and sustainability, especially when making adjustments to these programs to improve services and access to food resources.

Plums are primarily marketed for fresh consumption, canning, freezing, jam and jelly. Unfortunately, plum consumption has remained steady or declined. Consumers complain about a lack of flavor quality but are willing to pay for higher quality. Thus, lack of flavor and cold storage disorders are the main barriers to consumption. Plum cultivars are susceptible to gel breakdown, flesh browning and ‘off flavors’. Consumer acceptance and postharvest life are highly dependent on genotype, quality attributes, harvest date and proper postharvest handling. A consumer quality index (CQI) based on soluble solids concentration (SSC) and minimum firmness is proposed to maximize flavor and postharvest life. In most cases, late harvest increases quality attributes. Our work and industry experience demonstrated that using critical bruising thresholds (CBT) based on minimum firmness measured at harvest acts as a reliable predictor of how late to harvest safely for maximum visual and sensory quality. Plums are well adapted to late harvest because of their low susceptibility to bruising damage, but proper postharvest temperature management and marketing within the potential market life are necessary to maintain flavor and avoid the onset of storage disorders. Thus, to maximize flavor and postharvest life, a CQI based on SSC and minimum firmness measured at consumption is proposed. This article provides guidance on using this CQI, combined with proper postharvest handling techniques such as correct harvest date determination and temperature management, to maintain quality and increase consumption.

This study aims to explore the link between public investment and private investment in South Africa, using time series data spanning 40 years (1980–2020). Private investment is subdivided into credit to private sector (CPS) and foreign direct investment (FDI). Several econometric methodologies were used in the study, including the unit root test, cointegration test, and Error Correction Method (ECM). The Phillips-Perron (PP) test results point out that all the variables are stationary at levels with the exception of public investment (PI) which is stationary at first difference. The co-integration test reveals that the variables have a long-run equilibrium relationship. According to the findings of the ECM, public investment has a negative relationship with private investment (as measured by credit to private sector and foreign direct investment). The conclusion implies that in South Africa, public investment crowds out private investment. Other results revealed that, RGDP crowds in credit to private sector while crowding out foreign direct investment. Finally, the ECM findings show that government consumption expenditure crowds out credit to private sector and foreign direct investment. The residuals are homoskedastic and show no serial correlation, indicating that the model is adequate, according to the test for adequacy.

In order to meet the needs of an increasingly complex research landscape, researchers engage in “collaborative prosumption” through open data sharing and reuse. Although significant gains have been achieved in this regards because of growing requirements from funding agencies, governments and journals, the question of how reuse of openly available data for new research contribute to sustainability is yet to be appropriately addressed in the literature. Therefore, relying on a three stage stratified clustered random sampling of the Journal of Applied Econometrics data archive (JAEDA), the present research provides a case study of the value of research data recycling for sustainable research and economic development. More specifically our analysis show that reformatting from wide to long format, openly shared equity price index data on eleven European countries’ extracted from JAEDA, and augmented with country level geospatial Meta data, provides a new basis for interesting descriptive analytics and spatio-temporal econometric modeling and inference. Given the ever-increasing volume of openly available research data, our study provides a first-hand insight on open data reuse, which should benefit all stakeholders in the research community, as they seek sustainable solutions for scientific productivity and progress.

In this paper, the methodology using Insect Intelligent Building (I^2B) technology for establishing energy consumption monitoring system of public buildings is prevailed. The computing process node and distributed algorithm are utilized to implement the energy consumption collection and data transmission and data pre-processing. Taking a commercial building as a case study, CPNs are applied to set up the building energy consumption monitoring system, with the Spanning Tree Algorithm for generating network topology，and BPNN method for solving abnormal data and recovering missing data. The research results demonstrate the proposed method can effectively improve the performance of plug-and-play and self-identified and self-configuration of energy consumption monitoring system.

The application of thermoelectric generators (TEG) on the nozzle of an aviation jet engine was studied by finite element TEG-simulations. Against the background of system-level requirements of the reference aircraft this work reports the resulting requirements on the TEG design with respect to applied thermoelectric (TE) element lengths and fill factors (F) within the TE modules in order to maintain a positive effect on the specific fuel consumption. Assuming a virtual optimized TE material and varying the convective heat transfer coefficients at the nozzle surfaces this work reports the achievable power output. System-level requirement on the gravimetric power density (> 100 Wkg-1) can only be met for F ≤ 21%. Extrapolating TEG coverage to the full nozzle surface, the power output reaches 1.65 kW per engine. Assessment of further potential is demonstrated by a parametric study on the fill factor, convective heat transfer coefficients, and materials performance. This study confirms a feasible design range for TEG installation on the aircraft nozzle with a positive impact on the fuel consumption. This application translates into a reduction of operational costs, allowing for an economically efficient installation of TEG in consideration of the cost-specific power output of modern thermoelectric materials.

India, the world’s most populous country, is the world's third-largest emitter of greenhouse gases (GHGs). Despite employing several energy sources, it still relies heavily on coal, its primary energy source. Given India’s swiftly rising energy demand, this challenges meeting emission reduction targets. India has significantly increased investments in renewables like solar and hydrogen in recent years. While commendable, these initiatives alone cannot meet the country's expanding energy demands. In the short term, India must rely on both domestic and imported fossil fuels, with natural gas being the most environmentally friendly option. In this context, this paper attempts to forecast energy consumption, natural gas production, and consumption in India till 2050 using both univariate and multivariate forecasting methods. For multivariate forecasting, we have assumed two alternative possibilities for GDP growth: the business-as-usual and the high-growth scenarios. Each of our forecasts indicates a notable shortfall in the projected production of natural gas compared to the expected demand, implying our results are robust. Our model predicts that nearly 30-50 percent of India’s natural gas consumption will be met by imports, mainly in the form of LNG. Based on these findings, this paper recommends that Indian government policies emphasize increasing domestic natural gas production, importing LNG, and expanding renewable energy resources.

The spillover of a virus from an animal reservoir to humans requires both molecular and ecological risk factors to align. While extensive research both before and after the emergence of SARS-CoV-2 in 2019 implicates horseshoe bats as the significant animal reservoir for the new human coronavirus, it remains unclear why it emerged at this time. One massive disruption to animal-human contacts in 2019 is linked to the on-going African swine fever virus (ASFV) pandemic. Pork is the major meat source in the Chinese diet. We hypothesize that the dramatic shortage of pork following large-scale culling and restrictions of pig movement (resulting in marked price increases) led to alternative sources of meat and unusual animal and meat movements nationwide, e.g., involving wildlife, and thus greatly increased opportunities for human-sarbecovirus contacts. Pork prices were particularly high in southern provinces (Guangdong, Guangxi, Fujian, Jiangxi, Hunan, and Hubei), where wildlife is farmed and more frequently consumed. Major wildlife farming provinces are spread from Northern to Southern China, which overlaps with horseshoe bat host ranges, potential hosts of the proximal SARS-CoV-2 ancestor, and wildlife sourcing provinces of Wuhan Huanan market and possibly other markets. Trading of SARS-CoV-2 susceptible wildlife in these markets, such as minks, raccoon dogs, foxes and palm civets in Wuhan markets, could have increased the risk of SARS-CoV-2 from an intermediary host. Moreover, large quantities of animals raised for fur could have entered the human food chain undetected and significantly increased risks of animal-human contact. Performing retrospective testing of stored susceptible animals and their meat sold before December 2019 may be helpful in the next stage of tracing the animal origin of SARS-CoV-2 as spillover events are more likely to have taken place in 2019 when China was experiencing the worst effects of the ASFV pandemic.

The Smart Energy Research Lab (SERL) Observatory dataset described here comprises half-hourly and daily electricity and gas data, SERL survey data, Energy Performance Certificate (EPC) input data and 24 local hourly climate reanalysis variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) for over 13,000 households in Great Britain (GB). Participants were recruited in September 2019, September 2020 and January 2021 and their smart meter data are collected from up to one year prior to sign up. Data collection will continue until at least August 2022, and longer if funding allows. Survey data relating to the dwelling, appliances, household demographics and attitudes was collected at sign up. Data are linked at the household level and UK-based academic researchers can apply for access within a secure virtual environment for research projects in the public interest. This is a data descriptor paper describing how the data was collected, the variables available and the representativeness of the sample compared to national estimates. It is intended as a guide for researchers working with or considering using the SERL Observatory dataset, or simply looking to learn more about it.

This study aims to put forth a new method to study the socio-spatial boundaries by using georeferenced community-authored reviews for restaurants. In this study, we show that food choice, drink choice, and restaurant ambience can be good indicators of socio-economic status of the ambient population in different neighborhoods. To this end, we use Yelp user reviews to distinguish different neighborhoods in terms of their food purchases and identify resultant boundaries in 10 North American metropolitan areas. This data-set includes restaurant reviews as well as a limited number of user check-ins and rating in those cities. We use Natural Language Processing (NLP) techniques to select a set of potential features pertaining to food, drink and ambience from Yelp user comments for each geolocated restaurant. We then select those features which determine one’s choice of restaurant and the rating that he/she provides for that restaurant. After identifying these features, we identify neighborhoods where similar taste is practiced. We show that neighborhoods identified through our method show statistically significant differences based on demographic factors such as income, racial composition, and education. We suggest that this method helps urban planners to understand the social dynamics of contemporary cities in absence of information on service-oriented cultural characteristics of urban communities.

This study investigates the effect of different variables in the optimization of a hybrid air charging systems for marine diesel engines, exploring the potential for energy recovery and assistance using electric machines. The optimization parameters include turbine speed, compressor power, start of injection, turbomachine size, and valve timing. At low engine speeds, the larger improvements come from increasing compressor power, which increases air-to-fuel ratio compared with the baseline turbocharging. As free-floating turbocharged engines are known to have low air-to-fuel ratio at low engine speeds, the relative improvement of the hybrid architecture is large at low engine speeds, being near to 10% less BSFC. At high engine speeds, the largest influence is marked by the SOI, an early injection obtains a faster combustion, which improves gross efficiency. However, the relative improvement compared with baseline turbocharger is less than 1% at high engine speeds. Due to the distribution of relative improvement of BSFC, this architecture has a deeper impact on fuel consumption in vessel operating cycles that focus on low engine speeds and loads, such as those of tugboats.

Power consumption prediction is a crucial component in enhancing the efficiency and sustainability of building operations. This study investigates the impact of data collection frequency and model selection on the predictive accuracy of power consumption in two distinct building types: an Academic one with 15-minute interval data and an Industrial one with hourly data. Various machine learning models, including Support Vector Machine (SVM) with Radial and Sigmoid kernels, Random Forest (RF), and Deep Neural Networks (DNN), across different data splits and feature sets. Our analysis reveals that higher data collection frequency generally improves model performance, as indicated by lower RMSE, MAPE, and CV values, alongside higher R² scores. The inclusion of more historical power consumption features has also been found to have a more significant impact on the accuracy of predictions than including climate condition features. Moreover, the SVM-Radial model consistently outperformed others, particularly in capturing complex, non-linear patterns in the data. However, the DNN model, while competent in some metrics, showed elevated MAPE values, suggesting potential overfitting issues. These findings suggest that careful consideration of data frequency, features and model selection is essential for optimizing power prediction, contributing to more efficient power management strategies in building operations.

As an irreplaceable ecological barrier, the ecological conservation developing area (ECDA) is vital for the integrated construction of urban and rural areas, and optimization and adjustment of industrial structure. Developing green agriculture is the foundation and guarantee for improving the rural ecological environment and meeting the increasing needs of farmers′ income. Issued by the Chinese government, No. 1 Central Document stated the necessity of promoting agricultural green development (AGD) and the related technologies in dealing with ecological pressure and resource shortages, especially for large cities like Beijing. However, few empirical studies have conducted on spatiotemporal variations of AGD in ECDA of large cities. Based on the green agricultural traits of Beijing and the accessible data, we evaluate AGD and analyze its spatial and temporal heterogeneity of Beijing ECDA by constructiing a framework with 13 indicators. The weight coefficients of AGD indicators were calculated by the projection pursuit method, with the district panel data from 2006 to 2016. The results demonstrated that energy consumption is a vital factor of green agriculture production, and agricultural output value per unit of the arable land area is the key to the green agricultural revenue. From 2006 to 2016, the AGD index of ECDA had an increasing trend till 2012, followed by a decreasing tendency. The AGD index of the northern region is higher than the southern of ECDA. The obstacle degree model was used to verify AGD limiting factors. They were poor infrastructure, slow agritourism, low labor productivity, and low resource utilization efficiency that varied by districts in ECDA. Given these findings, our study is conducive to the AGD evaluation at the district (county) level for ECDA of large cities and also provides important policy implications.

The paper presents the final results from the research project “Urban Abacus of Building Energy performances (Abaco Urbano Energeticodegli Edifci – AUREE)” aimed at supporting the renovation and energy efficiency enhancement of urban building heritage financed by the “Ricerca Sistema Elettrico (MISE) program”. The crux of AUREE project is a Web – GIS GeoBlog portal with customized semantic dashboards aimed at sharing information on urban built environment and promoting the participation of local stakeholders to its improvement. As the latest development of this research, a workflow integrating the AUREE portal with BIM authoring and an open source IoT platform has been implemented and applied to an experimental case study concerning a public building in Carbonia (Italy). The headquarters of the sustainable energy research center Sotacarbo was selected as case study. The presented results proved that it was possible to create an open system, which could be easily consulted by "non-expert" users as well, to make available real-time monitoring data obtained from IoT sensors measuring the energy consumption, the indoor environmental conditions and the usage profiles of public buildings. The proposed open system could also be suitable to be later used as an effective tool to support the rising “energy communities”.

Primary energy consumption is one of the key drivers of global CO₂ emissions that, in turn, heavily depend on the efficiency of involved technologies. Either the improvement in technology efficiency or the expansion of non-fossil fuel consumption require large investments. The planning and financing of such investments, by policy makers or global energy firms, require, in turn, reliable measures of associated global spreads and their evolution in time. In this paper, our main contribution is the introduction of index measures for accessing global spreads (that is, measures of inequality or inhomogeneity in the statistical distribution of a related quantity of interest) of technology efficiency and CO₂ emission in primary energy consumption. These indexes are based on the Gini index, as used in economical sciences, and generalised entropy measures. Regarding primary energy sources, we consider petroleum, coal, natural gas and non-fossil fuels. Between our findings, we attest some stable relations in the evolution of global spreads of technology efficiency and CO₂ emission, and a positive relation between changes in global spreads of technology efficiency and use of non-fossil fuel.

Machine learning (ML) methods has recently contributed very well in the advancement of the prediction models used for energy consumption. Such models highly improve the accuracy, robustness, and precision and the generalization ability of the conventional time series forecasting tools. This article reviews the state of the art of machine learning models used in the general application of energy consumption. Through a novel search and taxonomy the most relevant literature in the field are classified according to the ML modeling technique, energy type, perdition type, and the application area. A comprehensive review of the literature identifies the major ML methods, their application and a discussion on the evaluation of their effectiveness in energy consumption prediction. This paper further makes a conclusion on the trend and the effectiveness of the ML models. As the result, this research reports an outstanding rise in the accuracy and an ever increasing performance of the prediction technologies using the novel hybrid and ensemble prediction models.

Nowadays, there is an increasing concern about environmental challenges ranging from emissions to overconsumption. Most of the environmental problems today are direct consequences of human consumption. Understanding the factors that affect consumer purchasing decisions is critical to developing demand for and commitment to sustainable consumption. In this regard, this study aims to explore the environmentally sustainable consumption behavior of Ethiopian consumers by using an extended model of the theory of planned behavior. Data were collected using a self-administered questionnaire having open and closed-ended questions. A total of 523 Ethiopian consumers were selected using convenience sampling technique. Structural equation modeling with AMOS V.26.0. was applied for data analysis. The findings show that Ethiopian consumers have very low awareness about the impact of their clothing consumption and disposal. Their consideration of sustainability attributes during purchasing decisions is also low. The structural equation model analysis showed that all variables had a positive and significant effect on the dependent variables at p<0.05, except the effect of personal norms on an environmentally responsible purchasing intention. These results provide additional evidence on the role of environmental labels in purchasing sustainable apparel products. The results of the study allow a better understanding of the factors determining sustainable consumption behavior and suggest practical solutions to their producers. As theory-building activities, with implications for the conceptualization of sustainable consumption behavior are sparse in the literature, this work has also been able to detect sustainable consumption behavior trends and practices by using the availability of a sustainable apparel product and consumers price perceptions of environmentally responsible apparel as a mediating effect of intention and behavior in developing countries, particularly Ethiopia.

Additive Manufacturing (AM) has been proving suitable to support or even replace traditional manufacturing in several industries, offering many advantages such as delivery time and reduction in terms of material waste, energy consumption and greenhouse gas (GHG) emissions. This study aimed to carry out a comparative assessment of the life cycle, from gate to gate, in the production of a low alloy carbon steel flange part using ER-90 wire. The methods utilized were Wire and Arc Additive Manufacturing (WAAM) and conventional manufacturing (CM) by forging, and comparative factors were energy demands, GHG emissions and generated solid waste. The total energy consumption in WAAM was 10,239.40 MJ, total carbon footprint in CO2 equivalent (CO2e) was 714.1 kgCO2e kg-1, and generated solid waste was 68.6 kg, respectively, 90%, 95% and 76% lower than consumption calculated in conventional manufacturing.

The aim of this research was to investigate the mass, substances and energy flow through two-stroke low speed Diesel engines. For this reason, a zero-dimensional model of the combustion in the engine was developed with a calculated amount and composition of exhaust gases. Due to the large amount of oxygen in the exhaust gases, a ratio of real air consumption and stoichiometric amount of air required for combustion of injected fuel was set. The calculated ratio showed that the engine consumes four times more air than needed for combustion in AFR_stoich. In this work it is called the Air Consumption Factor or Ratio and has not been mentioned in scientific literature before. Air Consumption Ratio is defined as a factor of dry or humid air. To be more comprehensive, a modified diagram of composition of the flow in and out of a two-stroke fuel injection engine and the cylinder was drawn.

Building energy consumption plays an essential role in urban sustainability. The prediction of the energy demand is also of particular importance for developing smart cities and urban planning. Machine learning has recently contributed to the advancement of methods and technologies to predict demand and consumption for building energy systems. This paper presents a state of the art of machine learning models and evaluates the performance of these models. Through a systematic review and a comprehensive taxonomy, the advances of machine learning are carefully investigated and promising models are introduced.