Mean Energy Consumption Research Articles

Energy Consumption and Energy Costs Based on Time-of- Day Tariffs in Seafood Processing Units in Kerala, India

Energy conservation in seafood processing is crucial due to the highly energy-intensive nature of its main stages such as pre-processing, processing, and storage, which must operate under cold chain conditions. The variability of energy tariffs based on Time of Day (TOD) significantly impacts the economic sustainability of these operations. This study evaluates the electrical energy consumption patterns of seafood processing units in South India, categorized by production capacity. Primary data were collected from 10 seafood processing units, assessing energy consumption during Normal Load Time (NLT), Peak Load Time (PLT), and Off-Peak Load Time (OLT) over a one-year period. Resultsrevealed that smaller capacity units (<30 T/D) accounted for 56% of total production during PLT, despite energy tariffs being highest during this time.These low-capacity units also exhibited higher mean energy consumption and costs compared to larger units, suggesting inefficiencies. The findings highlight the need for optimized energy management strategies, such as rescheduling production to lower-tariff periods and adopting more energy-efficient technologies. Implementing such measures could reduce energy costs and improve the overall profitability of seafood processing operations, particularly for smaller units.

HTSA: A novel hybrid task scheduling algorithm for heterogeneous cloud computing environment

Cloud computing provides users and programs with scalable resources and on-demand services virtually in real time, making it a fundamental paradigm in modern computing. The concept for using remote computing resources is novel. Cloud computing relies on task scheduling to boost system performance, reduce execution time, and optimise resource use. Due to exponential task increase and problem complexity, the search space is huge. Optimization tasks like this are NP-hard. This work aims to find a near-optimal solution for a multi-objective task scheduling problem in the cloud while lowering search time. Using the Genetic Algorithm (GA) and Gravitational Search Algorithms (GSA) benefits while avoiding their drawbacks, we offer a standard cloud computing task scheduling method to improve system performance and optimise the Quality of service (QoS) parameters like energy, makespan, resource utilization and throughput. We use CloudSim to test standard functions, real-time, and synthetic workloads. The obtained results are compared to other similar, metaheuristic-based techniques that were evaluated under the same conditions. The designed technique outperforms Gravitational Search Algorithms (GSA), Ant Colony Optimizaion(ACO), and Particle Swarm optimization(PSO) in Degree Of Imbalance (12%), resource utilization (9%), Mean Response Time (7%) and energy consumption (6%).

Osmotic assisted convective drying of pomegranate arils: Process optimisation, structural characterisation, and bioactive compound evaluation

Convective drying of pomegranate arils is time-consuming, energy-intensive, and affects the quality. Osmotic pre-treatment has been resorted prior to convective drying to improve drying performance and quality. In the present work, the osmosis factors such as temperature (30 - 60°C), time (50 - 250 min), and total soluble solids (TSS) (40 - 60°B) were studied using Box-Behnken design of response surface methodology. The quadratic models obtained adequately explained the process, and the optimum conditions were temperature (48.52°C), time (209.65 min), and TSS (51.31°B). The osmotic pre-treatment at optimum conditions followed by the convective tray drying reduced the drying time by nine hours, and the mean energy consumption by 0.172 MJ/g. Light microscopy revealed rupture and breakage of the honeycomb-like cellular structure of the pomegranate aril. Quality analysis of dried arils revealed that the texture (softness) and TSS improved by 11.75 N and 4.2°B, respectively. Likewise, sensorial quality parameters such as taste, mouth feel, and overall acceptability of the OATD (osmo-assisted tray-dried) arils significantly improved over the TD (tray-dried). However, there was a minor higher loss of 15.48, 12.52, and 15.88% in anthocyanin, phenols, and antioxidant capacity in OATD compared to TD. The OATD arils can be stored for up to six months in a modified atmosphere package (MAP).

Energy consumption of data transfer: Intensity indicators versus absolute estimates

AbstractThe assessment of energy consumption of data traffic for Internet services usually relies on energy intensity figures (in Wh/GB). In this paper, we argue against using these indicators for evaluating the evolution of energy consumption of data transmission induced by changes in Internet usage. We describe a model that estimates global impacts for different scenarios of Internet usages and technological hypothesises, and show that it can overcome some limitations of intensity indicators. We experiment the model on four use‐cases: basic usage, video streaming, large downloads, and video conferencing. Results show that increasing the resolution of videos does increase the total energy consumption while misleadingly decreasing the power intensity indicator at the same time. In other words, a more efficient network does not necessarily mean less energy consumption.

A Security-Enhanced Energy Conservation with Enhanced Random Forest Classifier for Low Execution Time Framework (S-2EC-ERF) for Wireless Sensor Networks

Wireless sensor networks (WSNs) play a pivotal role in diverse applications such as environmental monitoring, industrial automation, healthcare, and smart cities. The motivation behind the development of WSNs stems from their impact in providing real-time data on various environmental parameters. The challenge for WSNs is to achieve strong security and efficient energy saving together. Traditional methods sought to find solutions either through security or energy. In response, this study proposed a secure and energy-efficient framework for enhancing security measures in WSNs while minimizing the impact on energy resources by using the Enhanced Consumed Energy Leach (ECP-LEACH) protocol and the Enhanced Random Forest Classifier for Low Execution Time (ERF-LET) algorithm for attack detection named Security-Enhanced Energy Conservation with ERF-LET (S-2EC-ERF). The integration of the detection algorithm at the node level played a pivotal role in fortifying the security posture of individual nodes by detecting and mitigating potential security threats. Leveraging a comprehensive dataset obtained from NS3 simulations, the ERF-LET algorithm demonstrated its proficiency in differentiating between normal and attack packets, thereby laying a strong foundation for subsequent evaluations, where it achieved an accuracy of 98.193%. The proposed methodology was further validated through real-time simulations conducted on the NS3. The results demonstrated the superiority of the proposed S-2EC-ERF in terms of the packet delivery ratio (PDR), average throughput, end-to-end delay, and mean energy consumption compared to the Security-Enhanced Energy Conservation with Logistic Regression (S-2EC-LR), Security-Enhanced Energy Conservation with Decision Tree (S-2EC-DT), and Security-Enhanced Energy Conservation with AdaBoost (S-2EC-Ada) algorithms.

The Nutrition Counseling Improve Volleyball Athelets Knowledge

The dietary intake of athletes is crucial for attaining peak performance, sustaining physical fitness, preventing the risk of injury, and speeding post-competition recovery. A prevalent issue among athletes is insufficient food consumption relative to their nutritional requirements, resulting from a limited understanding of proper dietary practises. The objective of this study is to increase athletes' understanding of dietary consumption through the provision of nutritional guidance to athletes. The study was conducted on ten volleyball atheletes who were in the process of training for the PORPROV (Pekan Olahraga Provinsi) Jawa Timur VIII tournament in 2023. This research used the pre-experimental design method . The research design uses one group pre-test post-test design. The data collected includes body weight, height, food intake and nutritional knowledge of athletes. The analysis used a one sample t-test to see the athletes' food intake and knowledge before and after being given nutritional counseling. The results of this study were a significant increase in athletes' knowledge and energy intake (p <0.05), the average athlete knowledge score before nutritional counseling was 52.5 and after nutritional counseling it was 74.5. The mean energy consumption before to receiving nutritional counselling was 1794.6 kilocalories (kcal), while after receiving nutritional counselling, it increased to 2141.8 kcal. Providing nutrition counselling to volleyball athletes can improve their understanding of dietary requirements and improve their food consumption, finally leading to optimal performance. The government should provide nutritional counseling services for athletes

Population-Density Bounds for 100% Domestic Renewable Energy Generation

The long-running discussion on the global theoretical and practical potential of renewable energy is strongly affected by the size and connectivity of power networks. Small regional and national networks driven predominantly by intermittent and variable energy sources will require significantly more back-up power than continental networks containing a variety of weather zones. Thus, large networks will require less back-up but this comes with the drawback that some nations or regions rely on import from others. For example, in Europe in 2022, energy dependence on gas imports from Russia led to energy shortages and escalating prices. In this work, we estimate the limits of primary energy supply based on renewables, mainly solar photovoltaic (PV) sources and wind, in this setting. Taking as a starting point an average of 14 European countries, we find the mean energy consumption per unit time in what we argue is sufficient and necessary power per capita to maintain a sustainable life. The required energy generation is then translated into an area of energy production that, together with estimated area requirements for biological diversity, food, and infrastructure, constitutes a given fraction of the land area. When compared with typical human population densities, we find that before reaching one person per hectare, the area requirements exceed what is available. The results support the part of the scientific community that claims that a 100% renewable global power supply is unrealistic, especially as the world population steadily grows toward 10×109 people. Published by the American Physical Society 2024

Pulsed Thulium:YAG laser – What is the lithotripsy ablation efficiency for stone dust from human urinary stones? Results from an in vitro PEARLS study

BackgroundThe novel pulsed thulium:yttrium–aluminum–garnet (p-Tm:YAG) laser was recently introduced. Current studies present promising p-Tm:YAG ablation efficiency, although all are based on non-human stone models or with unknown stone composition. The present study aimed to evaluate p-Tm:YAG ablation efficiency for stone dust from human urinary stones of known compositions.MethodsCalcium oxalate monohydrate (COM) and uric acid (UA) stones were subjected to lithotripsy in vitro using a p-Tm:YAG laser generator (Thulio®, Dornier MedTech GmbH, Germany). 200 J was applied at 0.1 J × 100 Hz, 0.4 J × 25 Hz or 2.0 J × 5 Hz (average 10W). Ablated stone dust mass was calculated from weight difference between pre-lithotripsy stone and post-lithotripsy fragments > 250 µm. Estimated ablated volume was calculated using prior known stone densities (COM: 2.04 mg/mm3, UA: 1.55 mg/mm3).ResultsMean ablation mass efficiency was 0.04, 0.06, 0.07 mg/J (COM) and 0.04, 0.05, 0.06 mg/J (UA) for each laser setting, respectively. This translated to 0.021, 0.029, 0.034 mm3/J (COM) and 0.026, 0.030, 0.039 mm3/J (UA). Mean energy consumption was 26, 18, 17 J/mg (COM) and 32, 23, 17 J/mg (UA). This translated to 53, 37, 34 J/mm3 (COM) and 50, 36, 26 J/mm3 (UA). There were no statistically significant differences for laser settings or stone types (all p > 0.05).ConclusionTo our knowledge, this is the first study showing ablation efficiency of the p-Tm:YAG laser for stone dust from human urinary stones of known compositions. The p-Tm:YAG seems to ablate COM and UA equally well, with no statistically significant differences between differing laser settings.

Enhancement of Precise Underwater Object Localization

AbstractUnderwater communication applications extensively use localization services for object identification. Because of their significant impact on ocean exploration and monitoring, underwater wireless sensor networks (UWSN) are becoming increasingly popular, and acoustic communications have largely overtaken radio frequency broadcasts as the dominant means of communication. The two localization methods that are most frequently employed are those that estimate the angle of arrival and the time difference of arrival. The military and civilian sectors rely heavily on UWSN for object identification in the underwater environment. As a result, there is a need in UWSN for an accurate localization technique that accounts for dynamic nature of the underwater environment. Time and position data are the two key parameters to accurately define the position of an object. Moreover, due to climate change there is now a need to constrain energy consumption by UWSN to limit carbon emission to meet net‐zero target by 2050. To meet these challenges, we have developed an efficient localization algorithm for determining an object position based on the angle and distance of arrival of beacon signals. We have considered the factors like sensor nodes not being in time sync with each other and the fact that the speed of sound varies in water. Our simulation results show that the proposed approach can achieve great localization accuracy while accounting for temporal synchronization inaccuracies. When compared to existing localization approaches, the mean estimation error (MEE) (MEE) and energy consumption figures, the proposed approach outperforms them. The MEEs is shown to vary between 84.2154 and 93.8275 m for four trials, 61.2256 and 92.7956 m for eight trials, and 42.6584 and 119.5228 m for 12 trials. Comparatively, the distance‐based measurements show higher accuracy than the angle‐based measurements.

Effect of tip path overlap on ring debarker performance of frozen and unfrozen black spruce logs

ABSTRACT The effect of the tip path overlap on debarking quality of unfrozen and frozen black spruce logs was studied. The power, energy consumption, and torque were also measured for both temperature conditions. The experiment consisted of debarking logs using three overlaps (8, 27, and 43%) at two temperatures (−12°C and +20°C). Debarking quality was evaluated by the proportion of bark remaining on log surfaces and by the amount of wood in bark residues. Log characteristics (dimensions, eccentricity, and knot features), bark/wood shear strength (BWSS), moisture content, and basic densities of sapwood, inner, and outer barks were measured and treated as covariates. Experiments revealed that tip path overlap affects debarking quality for both temperature conditions: the amount of bark remaining on log surfaces decreased, and the proportion of wood in bark residues increased as tip path overlap increased. In addition, the effects of bark/wood shear strength, the proportion of knot area and sapwood, inner and outer barks densities, and moisture content on debarking quality criteria were analyzed. The mean power, energy consumption, and torque increased as the tip path overlap increased. These parameters were also positively affected by the log diameter and BWSS for frozen logs. The results give helpful information to improve debarking quality within the studied range of overlaps, log characteristics, and debarking parameters with efficient energy use.

Effects of wood species on the energy requirements and size distribution of strands produced by a strander-canter

The effects of wood species on the performance of the strander-canting process were studied. Logs of balsam fir (Abies balsamea (L.) Mill.), black spruce (Picea mariana (Mill.) B.S.P.), and jack pine (Pinus banksiana Lamb) were processed under two temperature conditions (-13.3 °C and 22.3 °C). The cutting and feed speeds, rake angle, cutting width, and strand thickness were kept constant. The strander-canting process was evaluated by the strand dimensions and yield, as well as by the energy requirements. The results showed that wood species significantly affected the proportions of strands and fines, maximum power, mean energy consumption, and specific cutting energy when processing the logs under frozen conditions. In unfrozen conditions, wood species only affected the strand width and the maximum power. Unfrozen logs produced higher proportions of strands and a lower volume of pin chips and fines than frozen logs. The maximum power, mean energy consumption, and specific cutting energy were, on average, 2 to 4 times higher for the processing frozen logs than for unfrozen logs.

GHG emissions and energy consumption of residential buildings-a systematic review and meta-analysis.

Residential buildings generate significant greenhouse gas (GHG) emissions and consume energy throughout their life cycle. In recent years, research on GHG emissions and energy consumption of buildings has developed rapidly in response to the growing climate change and energy crisis. Life cycle assessment (LCA) is an important method for evaluating the environmental impacts of the building sector. However, LCA studies of buildings show widely varying outcomes across the world. Besides, environmental impact assessment from a whole life cycle perspective has been undeveloped and slow. Our work presents a systematic review and meta-analysis of LCA studies on GHG emissions and energy consumption in the preuse, use, and demolition stages of residential buildings. We aim to examine the differences among the results of diverse case studies and demonstrate the spectrum of variations under contextual disparities. Results show that residential building emits about 2928kg GHG emission and consumes about 7430 kWh of energy per m2 of gross building area on average throughout the life cycle. Residential buildings have an average GHG emission of 84.81% in the use phase, followed by the preuse phase and demolition phase; the mean energy consumption in the use stage occupied the largest share of 84.52%, followed by preuse stage and demolition stage. GHG emissions and energy use vary significantly in different regions due to different building types, natural conditions, and lifestyles. Our study stresses the compelling requirement to slash GHG emissions and optimize energy consumption from residential buildings by use of low carbon building materials, energy structure adjustment, consumer behavior transformation, etc.

Sizing of community centralized battery energy storage system and aggregated residential solar PV system as virtual power plant to support electrical distribution network reliability improvement

Virtual Power Plants (VPP) have been seen as one of the techniques to integrate more decentralized and distributed variable renewable energy systems into the grid. They will thus facilitate the greater democratization of the energy systems. VPP is an aggregation of renewable energy technologies, such as solar PV. It is usually operated with battery energy storage system (BESS) facilities to solve the variability issue of solar PV. However, BESS used as part of VPP has to be appropriately sized to work in the electrical network. While VPP operated with BESS may provide significant technical and financial advantage, most of the BESS sized to be used as VPP in the literature are large scale, operated with large scale VRE’s in the grid. Some of the literatures also provide the use of BESS sizing with Solar PV but for shared residential community use only. The context of BESS sizing for VPP from a residential community is not prevalent in the literature. In this paper, the context of a sizing BESS for VPP from a residential neighbourhood is studied. It is aimed that by optimally sizing BESS for a residential community with rooftop solar PV can supply power to the community and any excess energy may be used for VPP application for the grid. This paper provides a practical process for evaluating the proper size of a centralized BESS in a community with rooftop Solar PV by considering the energy consumption and peak demand profiles of the residential units in three types of communities with rooftop Solar PV, namely low-cost, medium-cost, and high-end community, by considering the mean and maximum energy consumption and peak demand profiles of each residential unit. Such values are used to allocate for the Solar PV system per residential unit and the centralized BESS at mean, 75% of maximum, maximum, and 125% of maximum energy consumption, to be used for the community and for any excess energy to support the electrical networks reliability improvement. The result shows that using the maximum and 125% of maximum energy consumption can provide more than enough energy capacity for VPP application, particularly electrical networks reliability improvement by supplying for the Energy not Supplied (EnS), EN. The result provides that for a High-end community, the stored energy in the BESS can reach up to 24.578 MW h using 125% of maximum sizing at either mean or maximum energy consumption. While for Medium-cost and Low-cost communities, the stored energy can reach up to 17.549 MW h and 14.527 MW h using 125% of maximum sizing at either mean or maximum energy consumption, respectively. Moreover, the result also shows that to alleviate Energy not Supplied (EnS) and contribute to reliability improvement, more residential units are needed if sizing is based on maximum energy consumption, while a lesser number of residential units if the size is based on 125% of the maximum energy consumption.

LoRaWAN Gateway Placement in Smart Agriculture: An Analysis of Clustering Algorithms and Performance Metrics

The use of Wireless Sensor Networks (WSN) in smart agriculture has emerged in recent years. LoRaWAN (Long Range Wide Area Networks) is widely recognized as one of the most suitable technologies for this application, due to its capacity to transmit data over long distances while consuming little energy. Determining the number and location of gateways (GWs) in a production setting is one of the most challenging tasks of planning and building this type of network. Various solutions to the LoRaWAN gateway placement problem have been proposed in the literature, utilizing clustering algorithms; however, few works have compared the performance of various strategies. Considering all these facts, this paper proposes a strategy for planning the number and localization of LoRaWAN GWs, to cover a vast agricultural region. Four clustering algorithms were used to deploy the network GWs: K-Means and its three versions: Minibatch K-Means; Bisecting K-Means; and Fuzzy c-Means (FCM). As performance metrics, uplink delivery rate (ULDR) and energy consumption were used, to provide subsidies for the network designer and the client, with which to choose the best setup. A stochastic energy model was used to evaluate power consumption. Simulations were performed, considering two scenarios: Scenario 1 with lower-medium concurrence, and Scenario 2 with higher-medium concurrence. The simulations showed that the use of more than two GWs in Scenario 1 did not lead to significant improvements in ULDR and energy consumption, whereas, in Scenario 2, the suggested number of GWs was between 11 and 15. The results showed that for Scenario 1, the FCM algorithm was superior to all alternatives, regarding the ULDR and mean energy consumption, while the K-Means algorithm was superior with respect to maximum energy consumption. In relation to Scenario 2, K-Means caused the best ULDR and mean consumption, while FCM produced the lowest maximum consumption.

Optimal energy management system for residential buildings considering the time of use price with swarm intelligence algorithms

Revolutions in human activities and lifestyles result in a transition from conventional to intelligent residential building infrastructure. Conventional heating, ventilation, and air conditioning (HVAC), refrigerator and lighting system challenges are addressed without taking into account building heat gains, outdoor illuminance, and temperature. Based on these parameters, a mathematical model for cost estimation of residential building energy consumption, considering indoor heat gains, outdoor temperature, outdoor illuminance, and TOU price has been developed. A total of 46 swarm intelligence based optimization algorithms are used to optimize different building parameters. These swarm intelligence algorithms (SIA) are compared using the convergence curves, statistical and box-plot analysis and the Bald Eagle search (BES) algorithm is found to be the best algorithm among all 46 SIAs. The mean energy consumption costs of the best algorithm, BES, and the worst algorithm, fireworks algorithm (FA) are found to be Rs. 8.85 and Rs. 12.98, respectively. In addition, economic analysis has been conducted for the proposed study and it is compared with the existing models with building energy management systems (BEMS) and conventional model (without BEMS). It is observed that, based on this analysis, the cost savings achieved by the proposed study are nearer to 34% and 57% as compared to existing and conventional models.

Crop diversification improves cropping system sustainability: An 8-year on-farm experiment in South-Western France

CONTEXTConventional cropping systems in south-western France contribute greatly to the degradation of environmental resources. Crop diversification is considered to be an effective mechanism to increase the sustainability of cropping systems and promote their transition to agroecology. To test this hypothesis, farmers, agricultural advisers and scientists developed a participative co-design project. OBJECTIVEThe main objective was to co-design cropping systems to reduce the use of inputs, experiment with them on farms and assess their sustainability over several years. METHODSEight diversified cropping systems were designed during multi-actor co-innovation workshops. These systems were established and monitored for eight years (2010–2017) on two fields on each of eight farms located in areas with different soil and climate conditions. At the end of the eight-year study, the performance of these cropping systems was evaluated using 15 economic, environmental and social indicators. RESULTS AND CONCLUSIONSCrop diversification improved most of the environmental indicators. Pesticide use decreased by 20–64% in five of the eight systems but increased in the other three, due to production contracts that required systematic applications or in order to control high levels of pest or weed pressure. In parallel, mean energy consumption (−30%), greenhouse gas emissions (−36%) and irrigation water consumption (−43%) decreased significantly after diversification, which helped the systems mitigate and adapt to climate change. The economic performance, however, was more contrasted, with four of the eight farms showing a decrease in semi-net margin of 10–35% compared to those of the initial systems, but the other four showing an increase of 5–190%. Production of food energy also generally decreased (by up to 40%) after diversification, mainly due to a decrease in the amount of cereals produced (especially maize). Thus, crop diversification usually improves the environmental sustainability of cropping systems; however, for certain specialised high-value cropping systems, which often have high environmental impacts, it tends to reduce their economic performances.

Particle size distribution, energy consumption, nutrient composition and in vitro ileal digestion characteristics of hammer milled maize and soybean meal affected by moisture content

Grinding is an important feed processing technology, determining physical and nutritional characteristics of ground materials, which affects nutrient digestion in animals and their growth performance. This study aimed to clarify if differences in moisture content (MC) lead to differences in nutrient composition over various particle size fractions after grinding that have potential to affect feed manufacturing characteristics or animal performance. Maize and soybean meal (SBM) with targeted MC of 120, 140 and 160 g/kg (adding no (0), 30 and 60 g/kg of tap water, respectively) were hammer-milled and the physical and chemical characteristics as well as in vitro apparent ileal digestibility (AID) of particle size fractions were determined. The mill was fitted with a 6-mm (maize) or 2-mm (SBM) sized screen, with milled material subsequently separated by dry sieving (size ranging from < 0.075 to > 3.36 mm) and each fraction was analyzed for its nutrient composition, morphology characteristics and in vitro AID of organic matter (OM) and crude protein (CP). For the latter assay, specific particle size fractions were additionally ground using a laboratory mill (1 mm screen). Geometric mean particle size diameter and energy consumption increased with increasing MC (P < 0.05). Chemical composition, physical characteristics and in vitro AID of particle size fractions were significantly different (P < 0.001). Moisture addition had no significant effect on nutritional and physical parameters except for solidity in maize, ash content, projected area, circularity, and solidity in SBM. Physical characteristics of particles, especially particle size affected in vitro AID of OM and CP most (P < 0.05). Additional grinding of samples before determination of in vitro AID increased the OM digestibility up to 0.684 in maize (P < 0.001). Additional grinding of particles larger than 0.595 mm increased in vitro AID of OM and CP (P < 0.05). In summary, increasing MC has limited effect on the breakage behavior of maize and SBM, but increased energy consumption during hammer-milling. In vitro AID measurement of fractionated particles appears to require material should be ground to pass a 0.595 mm sieve rather than the prescribed 1 mm.

MO302: The Correlation Between Serum and Urinary NGAL With Redox Biomarkers in Post-Ultramarathon Amateur Athletes

Abstract BACKGROUND AND AIMS Few studies have investigated the physiological alterations provoked by ultramarathon competition, particularly those related to redox balance, kidney function and NGAL regulation. A better understanding of the dynamics of these biomarkers during ultramarathons could be useful in the search of new biomarkers in the context of exercise physiology. Thus, the aim of this study was to evaluate the relationship between oxidative stress and NGAL levels in blood and urine of amateur athletes before and after a 100-km ultramarathon. METHOD The sample was composed of seven athletes between the ages of 21 and 60 years, submitted to anthropometric evaluation, ergoespirometric testing, urine and blood sampling, biochemical and hormonal measurements, body weight measurement and subjective perception of effort (SPE)—–the perception was evaluated through a scale from 6 to 19 [1], along with verification of competition duration, besides the heart rate (HR), energy expenditure and oxygen consumption (VO2) during the event. The duration, the energy expenditure and the average HR of the competition were measured by a frequency meter (Polar, m200). The data provided was analyzed by the Polar Flow app platform. RESULTS The sample had an average BMI of 25.75 ± 3.20, a body fat percentage of 18.54% ± 4.35% and a VO2max of 48.87 ± 4.78. In the pre-competition period, no significant correlations (P &amp;gt; .05) were found between NGAL levels, in blood and urine and blood and urine biomarkers. In relation to the results obtained after competition, there were no significant correlations among of the NGAL levels and plasma biomarkers, except for the serum creatinine level (P &amp;lt; .05). The average duration of the competition (the average finishing time of the competitors) was 820.60 min (±117.00), during which the mean energy consumption was 2209.72 kcal (+951.97) and energy expenditure was 7837.16 kcal (+195.71), with an average HR of 127.85 (±12.02). CONCLUSION The data presented here did not support our initial hypothesis that the ultramarathon would cause oxidative stress and acute kidney injury. In addition, there was no significant correlation between oxidative stress biomarkers and NGAL in serum and urine, which suggests that NGAL is more sensitive to the inflammatory process than ROS levels.

Effects of log temperature, moisture content, and cutting width on energy requirements for processing logs by a chipper–canter

ABSTRACT Four matched groups of 15 black spruce logs were processed with a chipper–canter at temperatures of 20, 0, −10, and −20°C. Each log was transformed at two moisture contents (MCs) (green and air-dried) using two cutting widths (CWs) (12.7 and 25.4 mm). Mean MC for each CW was assessed from a sample of the obtained chips. The results showed that mean power, energy consumption, and specific cutting energy were significantly affected by the temperature and CW, and in a lesser degree by the MC. These parameters were greater when cutting frozen logs. Mean power and energy consumption were higher at the larger CW, as a result of the higher volume of removed wood. However, specific cutting energy increased as CW decreased. This behavior was related to the higher level of fragmentation occurred with the smaller CW. These results are useful to estimate changes in energy requirements occurred within the range of cutting conditions studied here.

Energy performance of healthcare facilities in 3 climatic zones in Cyprus

Safeguarding affordable and energy-efficient medical services has emerged as a crucial necessity to sustain national healthcare systems, especially in view of the current pandemic. While a set of national standards and guidelines aim to set minimum energy requirements for the building facilities, more insight into the healthcare sector’s energy consumption in Cyprus is deemed necessary, since it is the 4th most energy-demanding sector per square meter in this country. This is the first extended research on energy consumption in healthcare facilities across the different climatic zones of Cyprus and the types of fuel favoured in each zone, which marks the novelty of this work. The findings of the study highlight that the coastal and inland climatic zones are the most energy-challenging regions, with more than 60% of the final energy needs covered by electricity. On the contrary, in the mountainous region, approximately 80% of the final energy needs are covered by oil and attributed mainly to heating purposes. The average national primary energy consumption of healthcare facilities was found to be 497 kWh/m2 in air-conditioned spaces. Therefore, this study highlights that the mean primary energy consumption per building surface is approximately 4 times higher than the national threshold for nZEBs; compelling critical consideration of intervention for their energy enhancement. Moreover, healthcare facilities in mountainous regions in Cyprus are heavily reliant on conventional fuel; an unreliable and highly polluting energy option. The work demonstrates the prominent challenge and high potential for energy retrofit of the examined facilities and their upgrading to nZEB-Hospitals, towards the endeavour to a climate-neutral energy transition.