Efficient Scenario Research Articles

Blockchain based energy efficient multi-tasking optimistic scenario for mobile edge computing

Mobile edge computational power faces the difficulty of balancing the energy consumption of many devices and workloads as science and technology advance. Most related research focuses on exploiting edge server computing performance to reduce mobile device energy consumption and task execution time during task processing. Existing research, however, shows that there is no adequate answer to the energy consumption balances between multi-device and multitasking. The present edge computing system model has been updated to address this energy consumption balance problem. We present a blockchain-based analytical method for the energy utilization balance optimization problem of multi-mobile devices and multitasking and an optimistic scenario on this foundation. An investigation of the corresponding approximation ratio is performed. Compared to the total energy demand optimization method and the random algorithm, many simulation studies have been carried out. Compared to the random process, the testing findings demonstrate that the suggested greedy algorithm can improve average performance by 66.59 percent in terms of energy balance. Furthermore, when the minimum transmission power of the mobile device is between five and six dBm, the greedy algorithm nearly achieves the best solution when compared to the brute force technique under the classical task topology.

DISCOVERING CLUSTERING PATTERNS FROM E-COUNTER DATA STREAMS

Abstract. Clustering data streams has gained popularity in recent years due to their potential of generating relevant information for planning building automation, evaluating energy efficiency scenarios, and simulating emergency protocols in indoor spaces. In this paper, a Data Stream Affinity Propagation (DSAP) clustering algorithm is proposed for analyzing indoor localization data generated from e-counters systems. The data streams are a sequence of potentially infinite and non-stationary data points, arriving continuously where random access to the data is not feasible and storing all the arriving data is impractical. The DSAP model is developed based on a two-phase approach (i.e., online and offline clustering phases) using the landmark time window model. It is non-parametric in the sense of not requiring any prior knowledge about the number of clusters and their respective labels. The validation and performance of the DSAP model is evaluated using real-world data streams from a behavioural experiment aimed at finding new spatio-temporal patterns in stair usage due to an educational intervention campaign.

Efficient Energy Saving Scenarios for Indoor PM2.5 Management in an Apartment of South Korea.

Indoor PM2.5 must be effectively controlled to minimize adverse impacts on public health. Cooking is one of the main sources of PM2.5 in residential areas, and indoor air quality (IAQ) management methods such as natural and mechanical ventilation, range hood, and air purifier are typically used to reduce cooking-generated PM2.5 concentrations. However, studies on the combined effects of various IAQ management methods on indoor PM2.5 reduction and energy consumption are limited. In this study, a theoretical model was established to estimate the performance of various IAQ management methods for controlling indoor PM2.5 concentrations and energy consumption. The model was verified by comparative experiments in which, various IAQ management methods were operated individually or combined. Seasonal energy consumption was calculated through the verified model, and energy consumption saving scenarios were derived for maintaining indoor PM2.5 concentrations less than 10 μg/m3, a World Health Organization annual guideline, under fair and poor outdoor PM2.5 concentrations of 15 and 50 μg/m3, respectively. Based on our results, we found that energy consumption could be reduced significantly by applying natural ventilation in spring, autumn, and summer and mechanical ventilation in winter. Our study identified efficient energy saving PM2.5 management scenarios using various IAQ management methods by predicting indoor PM2.5 concentration and energy consumption according to the annual life patterns of typical residents in South Korea.

Planning feasible and efficient operational scenarios for a university hospital through multimethodology

The COVID-19 pandemic required managerial and structural changes inside hospitals to address new admission demands, frequently reducing their care capacity for other diseases. In this regard, this study aims to support the recovery of hospital productivity in the post-pandemic context. The major challenge will be to make use of all the resources the institution has obtained (equipment, beds, temporarily hired human resources) and to increase production to meet the existing repressed demand. To support evidence-based decision-making at a major university hospital in Rio de Janeiro, hospital managers and operations research analysts designed an approach based on multiple methodologies. Besides multimethodology, one important novelty of this study is the application of a productivity frontier function to future scenario planning through the quantitative DEA methodology. Concept maps were used to structure the problem and emphasize stakeholders’ perspectives. In sequence, data envelopment analysis (DEA) was applied, as it combines benchmarking best practices and assigns weights to inputs and outputs. To guarantee that the efficiency measurement considers all inputs and outputs before any inclusion of expert judgment, the scope was redirected to full dimensional efficient facet, if any, or to maximum efficient faces. The results indicate that production scenarios proposed by stakeholders based on the Ministry of Health parameters overestimate the viable production framework and that the scenario that maintains temporary human resource contracts is more compatible with quality in health provision, teaching, and research. These findings will serve as a basis for decision-making by the governmental agency that provided temporary contracts. The present methodology can be applied in different settings and scales.

Tutorial: Guide to error propagation for particle counting measurements

Forward error propagation is an established technique for uncertainty quantification (UQ). This article covers practical applications of forward error propagation in the context of particle counting measurements. We begin by presenting pertinent error models, including the Poisson noise model, and assess their role in UQ. Next, we describe several basic techniques for UQ, including Gauss’s formula, its generalization to the Law of Propagation of Uncertainty (LPU), and the use of Monte Carlo (MC) sampling. We conclude with demonstrations of increasing complexity, including total number concentration, total mass concentration, penetration, and mass-based filtration efficiency scenarios. These examples serve two functions: (1) providing examples in which theoretical concepts are practically applied to interpret particle counting data and (2) presenting expressions that can be used to compute uncertainties for specific problems in particle counting measurement.

Index Modulation Pattern Design for Non-Orthogonal Multicarrier Signal Waveforms

Spectral efficiency improvement is a key focus in most wireless communication systems and achieved by various means such as using large antenna arrays and/or advanced modulation schemes and signal formats. This work proposes to further improve spectral efficiency through combining non-orthogonal spectrally efficient frequency division multiplexing (SEFDM) systems with index modulation (IM), which can efficiently make use of the indices of activated subcarriers as communication information. Recent research has verified that IM may be used with SEFDM to alleviate inter-carrier interference (ICI) and improve error performance. This work proposes new SEFDM signal formats based on novel activation pattern designs, which limit the locations of activated subcarriers and enable a variable number of activated subcarriers in each SEFDM subblock. SEFDM-IM system designs are developed by jointly considering activation patterns, modulation schemes and signal waveform formats, with a set of solutions evaluated under different spectral efficiency scenarios. Detailed modelling of coded systems and simulation studies reveal that the proposed designs not only lead to better bit error rate (BER) but also lower peak-to-average power ratio (PAPR) and reduced computational complexity relative to other reported index-modulated systems.

First unmanned aerial vehicle airborne gravimetry based on the CH-4 UAV in China

As one of the important ways to obtain gravity fields, airborne gravimetry has been widely used because of its high efficiency and multiple scenario applicability. Compared with the manned aircraft commonly used in airborne gravimetry, unmanned aerial vehicle (UAV) platforms will provide a safer environment for pilots and surveyors. Based on the airborne gravimetry system integrated within a CH-4 UAV, this study carried out multiple gravimetry sorties in the survey area, including long-endurance gravimetry sorties. The test results show that the SGA-WZ04 gravimeter developed by the National University of Defense Technology (NUDT) can carry out gravimetry tests for >20 h on the modified CH-4 UAV platform and complete measurement tasks safely and reliably. From analysis of the gravity data processing results, the repeatability of the repeat lines is 0.35 mGal/3 km, and the external coincidence accuracy after fitting with the EGM2008 model is 0.47 mGal/3 km. The results provide strong support for the future expansion and layout of airborne gravimetry in areas where the use of manned aircraft is costly and operationally inefficient, such as medium and distant seas.

Impact of Green Fiscal Policy on Investment Efficiency of Renewable Energy Enterprises in Kazakhstan

Years of industrialization and reliance on energies such as oil and coal have resulted in global pollution on an unprecedented scale. Carbon footprint, destruction of forests, melting away of ice caps in the North Pole, and climate change have persuaded many to adopt a more sustainable growth. In the wake of this catastrophe, “green” energy is a phenomenon that takes a step toward sustained development. Green energy consists of innovative and cost-effective ways to address the global consciousness by switching to renewable energy, water resources management, waste reduction, etc. Green Fiscal Policy aims to align prices and mobilize resources for climate change and sustainable development. Many developed and developing countries like Kazakhstan are taking the initiative towards zero carbon emission and renewable energy. The challenges of implementing the required policies include overcoming the barriers of the status quo to establish a new Geo-political and economic system in Kazakhstan. Political and economic challenges persist despite its vision for 2050 that Green Economy influences. This report focuses on Kazakhstan's natural resources, topography, and renewable energy enterprises. The Green Fiscal Policy is closely examined over Kazakhstan's current investment efficiency scenario.

Deep Learning based DFWF Model for Audio Spoofing Attack Detection

One of the biggest threats in the speaker verification system is that of fake audio attacks. Over the years several detection approaches have been introduced that were designed to provide efficient and spoof-proof data-specific scenarios. However, the speaker verification system is still exposed to fake audio threats. Hence to address this issue, several authors have proposed methodologies to retrain and finetune the input data. The drawback with retraining and fine-tuning is that retraining requires high computation resources and time while fine-tuning results in degradation of performance. Moreover, in certain situations, the previous data becomes unavailable and cannot be accessed immediately. In this paper, we have proposed a solution that detects fake without continual-learning based methods and fake detection without forgetting in order to develop a new model which is capable of detecting spoofing attacks in an incremental fashion. In order to retain original model memory, knowledge distillation loss is introduced. In several scenarios, the distribution of genuine voice is said to be very consistent. In several scenarios, there is consistency in distribution of genuine voice hence a similarity loss is embedded additionally to perform a positive sample alignment. The output of the proposed work indicates an error rate reduction of up to 80% as observed and recorded.

Efficient scenario analysis for optimal adaptation of bridge networks under deep uncertainties through knowledge transfer

Due to deep uncertainties associated with climate change and socioeconomic growth, managing bridge networks faces the challenge to perform optimization for different scenarios. There exist a large number of scenarios when various sources of uncertainties, such as population growth and the increasing magnitude and frequency of natural hazards due to climate change, are compounded. Traditionally, scenarios are analyzed sequentially. However, when optimization for one single scenario is time-consuming, only a limited number of scenarios can be considered. To accelerate scenario analysis, this paper proposes a novel scheme through knowledge transfer between scenarios. Specifically, after finishing the optimization of a certain number of scenarios, the analyses of any new scenarios are accelerated by utilizing the knowledge obtained from optimization of previous scenarios. To implement the novel scheme, a proper definition of similar scenarios for adaptation of bridge networks under deep uncertainties is first given to stipulate the situation when knowledge transfer can occur. Then an approach based on surrogate modeling and meta-learning is used to realize the concept of knowledge transfer and perform the optimization. A bridge network is used as an illustrative example to demonstrate the computational efficiency of the proposed novel scheme.

Global perspectives on trends in health higher education.

Today, changes in political and economic conditions, epidemiological and sociological developments, and the advancement of science and technology have necessitated the health and medical education systems to change. Therefore, we conducted a study entitled "Global perspectives on trends in higher education in healthcare," to improve the quality of healthcare so that it can be used as a model for predicting future events related to medical education. This futures study applied the social, technological, economical, ecological, political, values (STEEPV) model to identify and analyze the trends that affect medical education at different levels. To collect and analyze the data, a scoping review of the articles published from the year 2000 was conducted on the World Health Organization (WHO), Web of Science, Scopus, PubMed/MEDLINE, EMBASE, Science Direct, Google Scholar, EBSCO, and Cochrane databases. The review process was performed in five stages: 1- Determining the research question, 2- Identifying relevant studies, 3- Selecting the studies, 4- Charting the data, and 5- Analyzing data. The preferred reporting item for systematic review and meta-analysis (PRISMA) statement was used in the selection and screening of articles. A total of 213 articles were included in the study for qualitative synthesis. A total of 154 trends were identified using the STEEPV model at seven levels of health behaviors and patients, diseases and health problems, healthcare system, medical education system, medical education institutions, medical curricula, and teaching and learning. Considering the results of this study, it is possible to formulate proper and efficient future scenarios for the higher health education system. Also, it will be helpful for medical education policymaking.

Residual RNN Models With Pruning for Digital Predistortion of RF Power Amplifiers

The paper presents several novel residual recurrent neural network (RNN) models for digital predistortion of radio-frequency power amplifiers. Then, unstructured and structured neural network (NN) pruning algorithms are proposed to reduce the per-sample computational complexity and the required memory, without compromising their modeling performance. The mathematical equations of the complexity and the memory are derived to analyze the hardware cost and design low cost models. An experimental platform is developed to measure the modeling performance in terms of adjacent channel leakage ratio (ACLR), normalized mean square error, error vector magnitude (EVM) and bit error rate. The platform uses an envelope tracking PA excited by 80 MHz IEEE 802.11ac signals. Compared with the state-of-the-art RNN model in (Sun <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2019), the proposed models reduce the per-sample complexity and required memory by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$94\hbox{--}99$</tex-math></inline-formula> %, they also improve the ACLR and EVM by at least 3 dB and 1 dB at 0.45 dB and 3.15 dB gain compression, respectively. Furthermore, an observation path with time alignment is used to remove the transmit latency in the state-of-the-art approach. At 3.15 dB gain compression, the proposed models outperform the state-of-the-art feedforward and convolutional NN models by at least 1.3 dB in EVM, outperform the general memory polynomial model by at least 3.3 dB in EVM. The above evidences indicate that the proposed RNN models can be implemented with relatively low hardware cost and achieve the best EVM in high power efficiency scenario.

Assessment of the round-trip efficiency of gravity energy storage system: Analytical and numerical analysis of energy loss mechanisms

Emerging large-scale energy storage systems (ESS), such as gravity energy storage (GES), are required in the current energy transition to facilitate the integration of renewable energy systems. The main role of ESS is to reduce the intermittency of renewable energy production and balance energy supply and demand. Efficiency considerations are critical when developing energy storage systems. In this paper, a novel multi-domain simulation tool is employed to determine the round-trip energy efficiency (RTE) of gravity energy storage system. The study considers analytical and numerical simulations to investigate the effect of the flow rate and the pressure on the energy losses i.e., hydraulic losses, mechanical equipment losses, and particularly friction and leakage induced energy losses. Different designs are compared to determine the most efficient storage scenario and to assess the scaling up effect on GES' efficiency of storage. The effect of the internal and external positioning of GES return pipe on the system's round-trip efficiency was also compared. Finally, the overall round-trip efficiency of GES system was calculated and compared to other energy storage technologies. The results obtained from the analytical and numerical models show that the round-trip energy efficiency depends on the pressure inside GES chambers, consequently, the operating scale. The obtained round-trip efficiency of GES system ranges between 65 % and 90 %. The amount of energy lost due to the sealing friction represents the largest share in comparison with other loss mechanisms, with a percentage of 21.1 % and 0.9 % for small-scale and large-scale operations, respectively. This study demonstrates that GES operates more effectively in large-scale applications.

Improving residential building energy simulations through occupancy data derived from commercial off-the-shelf Wi-Fi sensing technology

One of the main required actions for moving forward in zero-carbon cities’ mission is to increase building energy efficiency. Building energy simulations are key tools for HVAC control, building energy management, testing different building energy efficiency scenarios, and providing feedback to the occupants. Unfortunately, these tools have a significant weakness in occupant behavior modeling and prediction. Several recent studies used different occupancy measurements and methods to predict occupant behavior. However, they suffer from threatening the occupant's privacy and the high cost of their infrastructure and deployment. This paper aims to leverage passive WiFi sensing methods for occupant behavior estimation using commercial off-the-shelf WiFi devices, for which the infrastructure is already available in many buildings. Passive and device-free WiFi sensing does not threaten the occupant's privacy, and its deployment cost is low. Therefore, this method of occupancy measurement has the potential to be used on an urban scale and to play an essential role in zero carbon and smart cities in the future.Different machine learning algorithms have been applied to the collected, denoised, and preprocessed Channel State Information (CSI) data to estimate the occupant behavior. Five schedules of occupancy, occupant activity, lighting, electrical equipment usage, and thermostat set point temperature could be extracted and estimated from CSI data. In the last step, all estimated schedules were fed into EnergyPlus, an energy simulation software, along with other required data for accurate energy demand calculation to estimate heating and cooling demand, which can be used for HVAC control and occupancy feedback.

Моделирование влияния различных сценариев национальной политики и финансирования в области ВИЧ-инфекции на социальные и демографические показатели России

A number of governmental anti-HIV strategic programs are being implemented today in Russia; however, as of 2020, only 74.5% of the infected were aware of their status and only 67.4% of them were receiving the therapy. Under such conditions, it seems reasonable to assess future social and economic consequences depending on various scenarios of centralized financing of anti-HIV activities in the future. With the use of mathematic modelling, an economic analysis was performed for the “no change” scenario of maintaining costs for HIV control at the level of 2020, and a prognosis was made for 4 possible scenarios of national policy changes in relation to federal expenditures: a 25% and 50% increase in expenditures from the level of 2020, a “maximum extensive scenario” (which assumes reaching the upper limit of expenditures with approximately 60% growth while maintaining the current structure of costs), and an “intensive” scenario with an increase in nominal expenditures and shifting costs to purchasing newer third-generation antiretroviral therapy regimens. According to the modelling results, the current financial strategy (the “no change” scenario) in nominal terms will result in HIV mortality growth up to 25 thousand cases annually, the economic burden over the period of 2020–2030 will exceed RUB 4 bln and will not let achieving the national target of “90-90-90” by 2024. Under this scenario, Russia would remain the leader in HIV prevalence among the countries of Eastern Europe and Central Asia. The “no change” scenario would result in indirect economic losses of RUB 3,481.06 bln per year, with epidemiological parameters still high. The most efficient scenario turned out to be the “maximum extensive scenario” that suggests an increase in federal budget expenditures on combating HIV infection by approximately RUB 20 bln annually. This course of events would allow reducing HIV mortality to 5.4 cases per 100 thsd population by 2030, which is comparable to the current HIV mortality in Spain, Italy and Switzerland, and reducing HIV mortality by 111,9 thsd cases to a level comparable to that of the USA, Chile and New Zealand in 2019. The economic benefit from the implementation of this scenario in 2020–2030 would amount to RUB 1,663.02 bln due to decreased direct non-medical and indirect costs in the form of GDP growth and reduced payments of pensions and disability benefits.

Robust optimal capacity planning of grid-connected microgrid considering energy management under multi-dimensional uncertainties

Microgrid is considered an efficient paradigm for managing the massive number of distributed renewable generation and storage facilities. The optimal microgrid capacity planning is a non-trivial task due to the impact of randomness and uncertainties of renewable generation sources, and the adopted energy management strategies. In this paper, an optimal capacity planning model for the grid-connected microgrid is developed fully considering the renewable generation uncertainties through efficient scenario generation and reduction based on the deep convolutional generative adversarial network (DCGAN) and improved k-medoids clustering algorithm, as well as the microgrid energy management strategy. The proposed solution optimizes the capacity planning for the maximization of renewable energy utilization efficiency, and minimizes the economic cost and carbon emissions. The proposed solution is assessed using a case study of a microgrid (MG) project in northern China through a comparative study and the numerical results confirm the cost-effectiveness of the proposed solution.

Urban emissions and land use efficiency scenarios towards effective climate mitigation in urban systems

There are critical mitigation opportunities in urban areas that can be better understood through urban level analyses in the context of climate scenarios. This research work develops an approach for emissions scenarios for specific urban areas based on Shared Socioeconomic and Representative Concentration Pathways with a focus on the green growth paradigm at the lowest radiative forcing outcomes. The scenario characteristic for compact urban form is further complemented with improvements in existing land use efficiencies that are based on the Global Human Settlement Layer. Urban emissions scenarios for 420 urban areas among those with the highest emissions footprint totaling about 10.7 GtCO2eq in 2020 and land use efficiency scenarios for the top 10 urban areas in 9 regions are analysed. The 90 urban areas that represent about 6.2 GtCO2eq of urban emissions and 83.3 thousand km2 of built-up area in 2020 are then compared in a two-dimensional scenario space. Co-benefits are quantified for a new 100% renewable energy scenario that represents electrification combined with sector coupling. Based on Monte Carlo simulations, random performances that near the most stringent emissions pathways in a range of ±10% can still approach net-zero emissions by 2050 in alignment with the temperature goal of the Paris Agreement. Yet there is a penalty of about 0.95 GtCO2 even in the best land use efficiency scenario due to possible impacts of urban land expansion on the sequestration potential of local biomes. The results have widespread ramifications for guiding urban areas towards integrated action for reducing emissions, limiting the growth in urban extent, and providing co-benefits for urban inhabitants when effective action is needed urgently.

Energy efficiency in residential and non-residential buildings: Short-term macroeconomic implications

Investments in energy efficiency are vital for reducing greenhouse gas emissions by 2030 given that changes in the structure of the main energy sources cannot be expected in the short to medium term. The greatest potential for energy savings and cutting emissions lies in buildings due to their significant carbon intensity and rapid growth rate. However, gaps remain in what is known about integrating decarbonisation of the built environment into the economy-wide system. The paper addresses such gaps by examining the socio-macroeconomic implications of different scenarios related to building stock investments in a small open economy. The effectiveness of the measures implemented for various building types and the possibility of a rebound effect are also considered. The study is valuable for the peculiarities of the recursive dynamic computable general equilibrium model, GreenMod Slovenia, which was established to analyse the effects of decarbonisation. Two major contributions emerge: (1) projected energy efficiency in buildings is presented separately for commercial services, public services and households in two investment scenarios, namely, the business-as-usual scenario and the energy efficiency scenario with additional investments; and (2) the disaggregation of households into income quintiles to project their consumption of energy inputs. The study reveals vital macroeconomic benefits flowing from the energy efficiency scenario, including higher GDP and employment. Second, energy efficiency improvements in commercial services might encourage higher energy consumption. Finally, low-income households reduced the consumption of energy products the least. In this quintile group, energy efficiency improvement can lead to greater energy consumption, denoting policy failure.

Well-to-wheel emissions and abatement strategies for passenger vehicles in two Latin American cities.

More stringent standards for engines and fuels are progressively implemented as alternatives to reduce on-road vehicle emissions. While electric vehicles appear as a perfect alternative since their engines do not emit pollutants, wear and dust resuspension (W&R) and indirect emissions associated with electricity production remain significant sources of pollution. This work compares well-to-wheel emissions (WTW) and abatement strategies for various types of passenger vehicles in Bogotá and Santiago for different pollutants (CO, PM2.5, SO2, and NOx) and greenhouse gases like CO2 equivalent (CO2-Eq). Results show that WTW baseline emissions are more extensive in Bogotá than in Santiago (i.e., 58 and 30% for PM2.5 and CO2-Eq), mainly due to the higher vehicle activity and older state of Bogotá's fleet. We also evaluated extreme scenarios to assess the potential of a given vehicle technology or energy source to reduce emissions. We assessed, in particular, the replacement of all current vehicles by (1) conventional technologies with stricter emission standards and (2) battery electric vehicles powered with different energy resources. Our results indicate that replacing the current fleet with modern combustion technologies has a lower reduction potential than battery electric vehicles, but these reductions largely depend on the energy mix. Substitution by electric vehicles powered with electricity from renewable energies is the most efficient scenario in both cities. Finally, results also stress the importance of the resuspension of deposited road dust and brake and tire wear emissions in both cities as a crucial source of PM2.5, which must be better controlled.

Economic and environmental analysis of green transport penetration in Pakistan

The transport sector is going through a transition from a traditional to a sustainable system. Advanced countries have evaluated the costs and benefits of such transition, however, developing countries like Pakistan have rarely looked into evaluating such transition rigorously. This paper uses the transport sector of Pakistan as a case study and provides an economic evaluation of different scenarios for sustainable transportation in the region. The study has used the Long-range Energy Alternative Planning (LEAP) framework to evaluate the environmental and social costs of three scenarios, Business as Usual Scenario (BAUS), Efficient Combustion Scenario (ECS), and Hybrid Vehicle Scenario (HVS). It concluded that by 2040, the HVS and ECS will reduce carbon dioxide emissions by 303.7 and 213.3 million metric tons respectively compared to BAUS. These savings in terms of social cost will be US$ 10.1 billion in HVS and US$ 7.2 billion in ECS as compared to BAUS. By the year 2040, oil demand in the transportation system will also be possible to contain at the 2026 level. This research is anticipated to help discover the best policy decisions for increasing the share of green fuels in the transport sector of Pakistan.