Current Energy Level Research Articles

Next‐generation energy‐efficient optical networking: DQ‐RGK algorithm for dynamic quality of service and adaptive resource allocation

SummaryIn green optical networking, designing an adaptive energy‐saving scheme plays a vital role, in optimizing energy consumption by dynamically adjusting resources based on network traffic and environmental conditions, to a more sustainable and efficient optical communication infrastructure. Traditional methods in optical networking face challenges such as static resource allocation, limited adaptability, inefficient power usage, environmental insensitivity, and scalability issues. Therefore this article proposed a novel method named Dynamic Quality of Service based Random update Genghis Khan (DQ‐RGK) algorithm, the proposed model can tackle the abovementioned complexities. In this study, cluster head dynamic placement is utilized to optimize the network's performance by adapting the placement of cluster heads to the current topology, load distribution, and energy levels in the network nodes. Additionally, Dynamic Quality of Service (QoS) is employed to respond dynamically to changes in network conditions, adapting to varying traffic patterns and resource availability. In this work, the Genghis Khan Shark optimization with a random update strategy is implemented for hyperparameter optimization to enhance the performance of the DQ‐RGK method. The DQ‐RGK adjusts the parameters of QoS in real‐time, and this ensures that network resources based on the requirements changed and priorities of applications, which ultimately optimizes performance and enhances user experience. By dynamically assigning and reallocating resources based on the current demand the algorithm enhances overall network efficiency and reduces energy consumption. Then, this work analyzes the experimental results, where some evaluation measures estimate the DQ‐RGK method's performance. Routing efficiency, latency, scalability, spectral efficiency, Packet Delivery Ratio, throughput, network lifetime, energy consumption, jitter, and energy consumption are the measures employed by the DQ‐RGK model. In The results, other routing models that do not provide efficiency are utilized, a comparison of these other routing models is represented in results. The overall DQ‐RGK model's effectiveness is represented in the experimental results and its effectiveness is greater among other methods.

Distributed Energy-Based Estimation Over Harvesting-Constrained Sensor Networks.

This article investigates the distributed joint state and fault estimation issue for a class of nonlinear time-varying systems over sensor networks constrained by energy harvesting. It is assumed that data transmission between sensors requires energy consumption, and each sensor can harvest energy from the external environment. A Poisson process models the energy harvested by each sensor, and the sensor's transmission decision depends on its current energy level. One can obtain the sensor transmission probability through a recursive calculation of the probability distribution of the energy level. Under such energy harvesting constraints, the proposed estimator only uses local and neighbor data to simultaneously estimate the system state and the fault, thereby establishing a distributed estimation framework. Moreover, the estimation error covariance is determined to possess an upper bound, which is minimized by devising energy-based filtering parameters. The convergence performance of the proposed estimator is analyzed. Finally, a practical example is presented to verify the usefulness of the main results.

Optimizing dielectric strength and viscosity of synthetic ester with wide bandgap nano-additives

This study explores the potential of insulating oil-based nanofluids as a promising alternative to traditional insulating oils. Specifically, we employ a thermally stimulated depolarization current (TSDC) approach to characterize the effect of trapped charges on the dielectric strength of synthetic ester (SE) based oils (bare SE, SE/Co3O4, and SE/Al2O3). The TSDC findings indicate that nanofluids exhibit higher current density peaks and trap energy levels than bare oil. Nonetheless, the AC breakdown voltage (BDV) of SE oils is significantly enhanced when nanoparticles are well-dispersed. The wide bandgap of Co3O4 and Al2O3 nanoparticles might be responsible for the increase in trap levels, enabling nanofluids to withstand higher AC electric field than pure oils. Additionally, we conduct statistical analyses of the AC BDV values of SE-based oils. Lastly, we find that nanofluids have a lower kinematic viscosity than bare oil.

IoT adaptive threshold energy management algorithm based on energy harvesting

The Internet of Things (IoT) provides a promising solution to resolve pipeline conditions and oil and gas leakage detection problems. How to realize complex data monitoring and data uploading in energy-constrained sensor nodes is an urgent problem for oil and gas IoT. Energy harvesting technology and energy-efficient management have been introduced to the oil and gas IoT to improve energy utilization efficiency and good network performance. In this paper, we propose an energy management algorithm for oil and gas IoT. The scheme consists of: (1) the nodes mode switching strategy which allows sensor nodes to perform specific tasks based on the current energy level and (2) cluster head selection algorithm which considers energy harvesting and energy consumption ratios to achieve a balance between network performance and lifetime. Moreover, for the timely upload of essential data, we propose an adaptive threshold strategy that considers the energy level and importance level of sensor nodes to prioritize the transmission of important data under energy-limited conditions. The simulation results show that the proposed scheme can simultaneously prolong the network lifetime and improve energy utilization efficiency and is superior to existing schemes.

Distributed State Estimation Over Wireless Sensor Networks With Energy Harvesting Sensors.

This article is concerned with the distributed state estimation problem over wireless sensor networks (WSNs), where each smart sensor is capable of harvesting energy from the external environment with a certain probability. The data transmission between neighboring nodes is dependent on the energy level of each sensor, and the internode communication is deemed as a failure when the current energy level is inadequate to guarantee the normal data transmission. Considering the intermittent information exchange over WSNs, a novel distributed state estimator is first constructed via introducing a set of indicator functions, and then the evolution of the probability distribution of energy level and its steady-state distribution is systematically discussed by resorting to the eigenvalue analysis approach and the mathematical induction. Furthermore, the optimal estimator gain is derived by minimizing the trace of the estimation error covariance under known communication sequences. In addition, the convergence of the minimized upper bound of the expected estimation error covariance is analyzed under any initial condition. Finally, an illustrative example regarding the target tracking problem is provided to verify the validity of the obtained theoretical results.

Improving real-time energy decision-making model with an actor-critic agent in modern microgrids with energy storage devices

The hereby study combines a reinforcement learning machine and a myopic optimization model to improve the real-time energy decisions in microgrids with renewable sources and energy storage devices. The reinforcement learning-based agent is built as an actor-critic agent making the aggregated near-optimal charging/discharging energy decisions of the microgrid energy storage devices from a discrete action space relying on a reward related to the microgrid online optimal objective function value. The next step time energy levels of storage devices are then computed and provided to the myopic optimization-based decision-making model as parameters which optimally find the incurred power flow within the microgrid minimizing the real-time microgrid energy cost. The real-time measurement of stochastic parameters of the microgrid coupled with the current energy levels of electrical and heat storage are input to the artificially intelligent machine as observations states. The actor-critic agent approximators are modeled as deep neural networks optimized using the Adam gradient descent algorithm with a gradient threshold. Although the proposed model with a 2-kWh increment of the charging/discharging energy training is time-consuming, it has been able at 100% to optimally make microgrid energy decisions and improve online energy decisions by 90.98% compared to the myopic model alone.

The effect of metal rate on the gamma shielding parameters of hydroxyapatite at medical treatment energies

The hydroxyapatite (HAp) is a kind of biomaterial which is used for bone treatment applications. We have scrutinized the gamma attenuation parameters such as such as the effective atomic number (Zeff), electron density (Nel), mass attenuation coefficient (MAC), linear attenuation coefficient (LAC), half value layer (HVL), tenth value layer (TVL) and mean free path (MFP) for only single liquid gamma source and a narrow beam geometry for the energy (medical treatment energy) ranging from 778 keV to 1408 keV (Eu-152) for the animal bone, iron, cobalt, copper, and zinc decorated Nano hydroxyapatite (nFeHAp, nCoHAp, nCuHAp, and nZnHAp) artificial bone powders. The gamma-rays were counted with using Ultra Low Energy Germanium detection system with a resolution 150 eV at 5,95 keV and a high purity germanium detector with a resolution of 1.85 keV at 1.33 MeV experimentally. The gamma ray attenuation parameters are calculated for the metal doped hydroxyapatite and compare with the animal bone. The results were compared with the output XCOM NIST data. While the mass absorption coefficient values for animal bone range from 0.08 to 0.05 at current energy levels, the values for metal-added artificial bone powders range from 0.07 to 0.05. While the linear absorption coefficient values for existing energy values for animal bone range from 0.04 to 0.02, they range from 0.03 to 0.02 for metal-added artificial bone powders. Mean free path values for real bone range from 24 to 36 at current energies, while half value layer values range from 16 to 25 and tenth value layer values range from 56 to 83. For metal-doped artificial bone powders, these parameters range from 26 to 35, 18 to 24, and 61 to 80, respectively. The results points that, the data of the gamma ray attenuation parameters are very close to the value of the animal bone due to the removal of calcium atoms from the structure when metal is added.

Distributed Electric Vehicle Charging Mechanism: A Game-Theoretical Approach

To reduce the overloading of the power system due to the increasing use of electric vehicles (EVs), the power system operators employ dynamic cost plans depending on the number of EVs being charged. In this situation, the charging cost of an EV can be reduced by considering other EVs’ charging strategies and avoiding simultaneous charging. In this article, we develop a distributed EV charging mechanism (D-EVCM) where each EV possesses some information, such as the charging probabilities of other EVs, electricity price, current energy level, and expected departure time. Based on the information, an EV periodically decides whether to charge its battery in a distributed manner. To minimize the average charging cost of EV while avoiding the situation where an EV departs from a charging station with insufficient energy level, a constrained stochastic game model is formulated and its solution is obtained using a best response dynamics-based algorithm. The evaluation results show that D-EVCM can reduce the average charging cost over 48% with a sufficient low battery outage probability (e.g., 0.01%) compared to a threshold-based charging scheme.

Fundamentals of the Theory of a Hydrogen Atom for Problems of Synthesis of Quantum-Electronic Circuits

On the basis of the unconventional concept of the formation of the line spectrum of the hydrogen atom, an equation is obtained that is necessary for computer calculations of the energy emitted by an electron during the transition from a certain current energy level rλ,n to the receiver radius r0. The obtained solution of the energy equation made it possible to derive the formal relations necessary for calculating the values of the radii of the levels of the hydrogen atom and, accordingly, the velocities of the electron in these orbits. The applied aspect of the considered methodology consists in using the results of the performed calculations for the problems of synthesis and analysis of quantum-electronic circuits.

Analysis of a linear programming‐based decision‐making model for microgrid energy management systems with renewable sources

With the increasing share of renewable energy sources in microgrids, systems enhancing the power flexibility at the demand side have become mandatory in microgrid architecture. Thus, the electrical energy storage system has been mostly integrated into microgrids although its capital and operating costs are very high. Hence, diversifying microgrid energy storage based on the end-energy usage can improve the reliability, operating cost, and power demand flexibility of the microgrid. The hereby study integrates an absorption chiller and electrical heat pump coupled to a heat storage into a renewable energy resources-based microgrid and develops its centralized energy management model for both off-grid and on-grid operation modes. The model considers the current energy level of energy storages in the microgrid, current, and forecasted information state to compute the vector-valued decision minimizing a realistic microgrid operating cost. The operating cost includes the energy purchasing/generation, heating/electrical storage, and penalty cost due to load shedding. Hence, such an objective function leads to maximizing the consumption of the generated renewable power, minimizing the energy storage and load shedding cost. The load shedding has been allowed to increase the flexibility in microgrid energy management. However, a high penalty has been imposed on each electrical, heating, or cooling load shedding decision. The decision vector components are thermal and electrical power flow between each energy source to loads and energy storage systems. The problem has been modeled as a Linear Programming with forecasted multi-parametric inputs at different prediction horizons. The effect of the charging/discharging rate and capacity of energy storages, coefficient of performance of absorption chiller and heat pump, prediction horizon, and energy level of storage devices provided to the myopic energy management model has been evaluated for better integration of renewable sources and thermal storage systems into microgrids. Suggestions have been made to improve the microgrid self-consumption, energy efficiency, and myopic decision-making model using the energy level of storage devices obtained from the full look-ahead optimization model. Highlights The optimization problem combining electricity, heating, and cooling is formulated The electrical and thermal storage charging/discharging rate significantly affect the power supplied by the renewable power source decision of the microgrid energy management model. Accurate energy storage level information into the myopic energy management model improves the model decisions. The absorption chiller coefficient of performance greatly impacts the cooling and electrical loads shedding.

A rendezvous point‐based data gathering in underwater wireless sensor networks for monitoring applications

SummaryIn underwater wireless sensor networks (UWSNs), most of the mobile sink data gathering scheme assumes that sensor nodes are static. Also, very limited set of works aim to eliminate the data redundancy during data gathering. Moreover, the data collection latency, error rate and load have to be considered in addition to the buffer space and energy level of the nodes. Here, a mobile sink data gathering technique is designed where rendezvous points (RPs) and sojourn times are selected. The RPs, where the mobile sinks stop to gather the data, are selected based on the node density, current traffic load and energy level of nodes. The proposed scheme, that is, adaptive distributed mobile sink data gathering (ADMSDG), eliminates data redundancy even by considering mobility of all the nodes including sink node. Here, multihop data gathering is also considered to address the energy hole problem near the sink based on similarity index. To examine the result, proposed scheme and other state‐of‐the‐art schemes are implemented in NS2.30 having the same environment. Obtained results show that when proposed scheme is experimented with respect to number of nodes, the proposed ADMSDG technique achieves 18% high data collection accuracy with 5% reduced data collection delay and 5% less energy consumption. Similarly, when proposed scheme is experimented with respect to transmission range, the proposed ADMSDG technique achieves 18% high data collection accuracy with 12% reduced data collection delay and 8% less energy consumption.

Experimental damage tolerance evaluation of thick fabric carbon/epoxy laminates under low-velocity and high-velocity impact and compression-after-impact

Impact experiments of thick fabric carbon/epoxy laminate specimens, with small thickness ratio, are conducted at distinct energy levels and thicknesses to characterise the damage process. These specimens and loading conditions are representative of a new generation of critical structural components in aviation, such as wing spars, landing gear beams and fittings, that are increasingly being made entirely from composites. The tests address the need to better understand the damage process for specimens with a small thickness ratio since existing experimental impact data for large thickness ratio (thin laminates) may not be directly applicable. Two energy levels, two different fabric layups and two impact methods (drop-weight and gas-cannon) were used. Data from high-speed cameras were processed in a novel way, providing the force during impact. C-scans and micrographs were used to characterise damage. The results show that specimens with a thickness ratio of 5 (20 mm thick) experience more bending compared to specimens with a ratio 2.5 (40 mm thick). For gas-cannon impacts, this results in a higher delaminated area. The drop-weight impacts show almost no differences in damage size for the thickness range analysed. The influence of layup on the global impact response is negligible, but locally it can result in significant variations in dent depth. The dent depth scales linearly with the impact energy and the delaminated area linearly with the impact velocity. There is no clear correlation between the compression-after-impact failure mechanisms and the residual strength. Impact damage, at the current energy levels, showed a minimal reduction of residual strength.

Drivers of the increasing water footprint in Africa: The food consumption perspective

In Africa, water resources pervade multiple sustainable development goals (SDGs), which mainly focus on eliminating poverty (SDG 1) and hunger (SDG 2), promoting good health and well-being (SDG 3) and supporting clean water and sanitation (SDG 6). Africa's water scarcity problems have been worsened by population growth and climate change. Agriculture is the largest consumer of water in Africa, and a clear understanding of the water-food nexus is necessary to effectively alleviate water-related pressures on food security. Water footprint (WF) accounts and decompositions provide insights into water management planning for policy-makers. We investigated the WF of food consumption from 2000 to 2018 in 23 African countries and used the logarithmic mean Divisia index (LMDI) to decompose its driving forces into consumption structure, per capita food consumption, water intensity and population effect. The WF of food consumption increased from 609.8 km3 in 2000 to 1212.9 km3 in 2018, with an average annual growth rate of 3.7%. The population effect contributed most to this change (64.6%), followed by per capita food consumption (28.3%) and consumption structure (7.1%). Cereals (46.7%) and livestock (24.4%) were the major contributors to the increase in the total WF. Our findings highlight that controlling population growth and improving water efficiency are effective measures to relieve water-related pressures on food consumption. However, a healthy dietary structure must also be promoted because Africa's current dietary energy level is below the global average. Moreover, nine countries in the research area have an inadequate supply of dietary energy; this will inevitably drive the WF of food, as calories increase and diets change. This study is helpful for understanding the water-food nexus in Africa and provides strategies to conserve water and enhance food production.

Age-of-Information With Information Source Diversity in an Energy Harvesting System

Age of information (AoI) is a key performance metric for the Internet of things (IoT). Timely status updates are essential for many IoT applications; however, they often suffer from harsh energy constraints and the unreliability of underlying information sources. To overcome these unpredictabilities, one can employ multiple sources that track the same process of interest, but with different energy costs and reliabilities. We consider an energy-harvesting (EH) monitoring node equipped with a finite-size battery and collecting status updates from multiple heterogeneous information sources. We investigate the policies that minimize the average AoI, formulating a Markov decision process (MDP) to choose the optimal actions of either updating from one of the sources or remaining idle, based on the current energy level and the AoI at the monitoring node. We analyze the structure of the optimal solution for different cost/AoI distribution combinations, and compare its performance with an aggressive policy that transmits whenever possible.

540-P: Predictors of Daily Diabetes Distress in Type 1 Diabetes

Aim of this analysis was to analyse daily factors that lead to diabetes distress using data from an ecological momentary assessment study in combination with CGM. In the DIA-LINK study, 203 people with type 1 diabetes were asked four times a day over 17 days (EMA) while using unblinded CGM. They rated their sleep quality and the anticipated stress in the morning and their current mood, energy, and stress level four times a day. Daily diabetes distress was assessed in the evening using five adapted items from the Problem Areas in Diabetes (PAID) scale (feeling overwhelmed, alone, guilty, deprivation, diabetes takes up too much mental/physical energy). Glucose data from the 12-hour period prior to each distress rating were extracted. Using multilevel regression analysis, the sum score of daily distress was entered as dependent variable (person as nested factor). The following independent variables were used: sleep quality, anticipated stress level, mean level and coefficient of variation (CV) of mood, energy, and stress, CV of glucose, time in range, and % < 70 mg/dl; controlled for study day and age, sex, diabetes duration, HbA1c, diabetes distress, depressive symptoms (all assessed at baseline). Higher time in range and lower glucose fluctuations were predictive of lower diabetes distress, as was daily stress and energy (Table 1). Daily mood and elevated depressive symptoms had no effect. These results may help to explain the sources of diabetes distress.View largeDownload slideView largeDownload slide DisclosureD. Ehrmann: Speaker’s Bureau; Self; Berlin-Chemie AG. A. J. Schmitt: None. L. Priesterroth: None. B. Kulzer: Advisory Panel; Self; Berlin-Chemie AG, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Research Support; Self; Berlin-Chemie AG, Novo Nordisk, Speaker’s Bureau; Self; Berlin-Chemie AG, Dexcom, Inc., Novo Nordisk. N. Hermanns: Advisory Panel; Self; Abbott, Research Support; Self; Berlin-Chemie AG, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH.FundingGerman Center for Diabetes Research (DZD) (82DZD01102)

An Efficient and Secure Data Forwarding Mechanism for Opportunistic IoT

Internet of Things (IoT) is a heterogeneous network of interconnected things where users, smart devices and wireless technologies, collude for providing services. It is expected that a great deal of devices will get connected to the Internet in the near future. Opportunistic networks(OppNet) are a class of disruption tolerant networks characterized by uncertain topology and intermittent connectivity between the nodes. Opportunistic Internet of Things(OppIoT) is an amalgamation of the OppNet and IoT exploiting the communication between the IoT devices and the communities formed by humans. The data is exposed to a wide unfamiliar audience and the message delivery is dependent on the residual battery of the node, as most of the energy is spent on node discovery and message transmission. In such a scenario where a huge number of devices are accommodated, a scalable, adaptable, inter-operable, energy-efficient and secure network architecture is required. This paper proposes a novel defense mechanism against black hole and packet fabrication attacks for OppIoT, GFRSA, A Green Forwarding ratio and RSA (Rivest, Shamir and Adleman) based secure routing protocol. The selection of the next hop is based on node’s forwarding behavior, current energy level and its predicted message delivery probability. For further enhancing the security provided by the protocol, the messages are encrypted using asymmetric cryptography before transmission. Simulations performed using opportunistic network environment (ONE) simulator convey that GFRSA provides message security, saves energy and outperforms the existing protocols, LPRF-MC (Location Prediction-based Forwarding for Routing using Markov Chain) and RSASec (Asymmetric RSA-based security approach) in terms of correct packet delivery by 27.37%, message delivery probability is higher by 34.51%, number of messages dropped are reduced by 15.17% and the residual node energy is higher by 14.08%.

Particle Filtering for Nonlinear/Non-Gaussian Systems With Energy Harvesting Sensors Subject to Randomly Occurring Sensor Saturations

In this paper, the particle filtering problem is investigated for a class of nonlinear/non-Gaussian systems with energy harvesting sensors subject to randomly occurring sensor saturations (ROSSs). The random occurrences of the sensor saturations are characterized by a series of Bernoulli distributed stochastic variables with known probability distributions. The energy harvesting sensor transmits its measurement output to the remote filter only when the current energy level is sufficient, where the transmission probability of the measurement is recursively calculated by using the probability distribution of the sensor energy level. The effects of the ROSSs and the possible measurement losses induced by insufficient energies are fully considered in the design of filtering scheme, and an explicit expression of the likelihood function is derived. Finally, the numerical simulation examples (including a benchmark example for nonlinear filtering and the applications in moving target tracking problem) are provided to demonstrate the feasibility and effectiveness of the proposed particle filtering algorithm.

Experimental energy levels of 12C14N through marvel analysis.

The cyano radical (CN) is a key molecule across many different factions of astronomy and chemistry. Accurate, empirical rovibronic energy levels with uncertainties are determined for eight doublet states of CN using the marvel (Measured Active Rotational-Vibrational Energy Levels) algorithm. 40 333 transitions were validated from 22 different published sources to generate 8083 spin-rovibronic energy levels. The empirical energy levels obtained from the marvel analysis are compared to current energy levels from the mollist line list. The mollist transition frequencies are updated with marvel energy level data which brings the frequencies obtained through experimental data up to 77.3percent from the original 11.3percent, with 92.6percent of the transitions with intensities over 10-23 cm molecule-1 at 1000K now known from experimental data. At 2000K, 100.0percent of the partition function is recovered using only marvel energy levels, while 98.2percent is still recovered at 5000K.

RETRACTED ARTICLE: Dishonesty is more affected by BMI status than by short-term changes in glucose

There is evidence that human decision-making is affected by current body energy levels and physiological states. There is less clear evidence linking decision-making to long-term changes in energy, as those associated with obesity. We explore the link between energy, obesity and dishonesty by comparing the behaviour of obese and lean subjects when hungry or sated while playing an anonymous die-under-cup task. Participants performed the task either before or after breakfast. We find that short-term switches in energy have only a mild effect on dishonesty, as only lean females lie less when sated. By contrast, obese subjects lie more than lean subjects in both conditions, and they lie more to avoid the lowest payoff than to get the highest payoff. Our findings suggest that the observed patterns are more likely mediated by factors associated with obesity than by short term energy dynamics, and call for a better integration of the psychological, economic and biological drivers of moral behaviour.

Mainstreaming Energetic Resilience by Morphological Assessment in Ordinary Land Use Planning. The Case Study of Moncalieri, Turin (Italy)

Energetic resilience is seen as one of the most prominent fields of investigation in the upcoming years. The increasing efficiency of urban systems depends on the conversion of energetic production of buildings, and therefore, from the capacity of urban systems to be more rational in the use of renewable resources. Nevertheless, the integration of the energetic regulation into the ordinary urban planning documents is far from being reached in most of planning processes. In Italy, mainstreaming energetic resilience in ordinary land use planning appears particularly challenging, even in those Local Administrations that tried to implement the national legislation into Local Building Regulation. In this work, an empirical methodology to provide an overall assessment of the solar production capacity has been applied to selected indicators of urban morphology among the different land use parcel-zones, while implementing a geographic information system-based approach to the city of Moncalieri, Turin (Italy). Results demonstrate that, without exception, the current minimum energy levels required by law are generally much lower than the effective potential solar energy production that each land use parcel-zone could effectively produce. We concluded that local planning processes should update their land use plans to reach environmental sustainability targets, while at the same time the energetic resilience should be mainstreamed in urban planning by an in-depth analysis of the effective morphological constraints. These aspects may also represent a contribution to the international debates on energetic resilience and on the progressive inclusion of energy subjects in the land use planning process.