bluuebunny/crossref_metadata_2025 huggingface dataset:
Roles of Heart Rate Variability in Assessing Autonomic Nervous System in Functional Gastrointestinal Disorders: A Systematic Review
Authors: Ali M. Khawar , Chen Jiande D. Z.
Published: January 2023

Functional gastrointestinal disorders (FGID) and gastroesophageal reflux (GERD) disease affect a large global population and incur substantial health care costs. Impairment in gut-brain communication is one of the main causes of these disorders. The central nervous system (CNS) provides its inputs to the enteric nervous system (ENS) by modulating the autonomic nervous system (ANS) to control the gastrointestinal functions. Therefore, GERD and FGID’s might be associated with autonomic dysfunction, which can be identified via heart rate variability (HRV). FGIDs may be treated by restoring the autonomic dysfunction via neuromodulation. This article reviews the roles of HRV in the assessment of autonomic function and dysfunction in (i) gastroesophageal reflux (GERD), and the following FGIDs: (ii) functional dyspepsia (FD) and gastroparesis, (iii) irritable bowel syndrome (IBS) and (iv) constipation. The roles of HRV in the assessment of autonomic responses to various interventions were also reviewed. We used PUBMED, Web of Science, Elsevier/Science direct and Scopus to search the eligible studies for each disorder, which also included the keyword ‘heart rate variability’. The retrieved studies were screened and filtered to identify the most suitable studies using HRV parameters to associate the autonomic function with any of the above disorders. Studies involving both human and animal models were included. Based on analyses of HRV, GERD as well as the FGIDs were found to be associated with decreased parasympathetic activity and increased sympathetic nervous system activity with the autonomic balance shifted towards the sympathetic nervous system. In addition, the HRV methods were also reported to be able to assess the autonomic responses to various interventions (mostly neuromodulation), typically the enhancement of parasympathetic activity. In summary, GERD and FGIDs are associated with impaired autonomic dysfunction, mainly due to suppressed vagal and overactive sympathetic tone, which can be assessed noninvasively using HRV.
Antiretroviral Levels in the Cerebrospinal Fluid: The Effect of Inflammation and Genetic Variants
Authors: Cusato Jessica , Avataneo Valeria , Antonucci Miriam , Trunfio Mattia , Marinaro Letizia , Palermiti Alice , Manca Alessandra , Di Perri Giovanni , Mula Jacopo , Bonora Stefano , D’Avolio Antonio , Calcagno Andrea
Published: January 2023

Neurocognitive impairments are common in people living with HIV. Some conditions, such as chronic inflammation, astrocyte infection and an impaired blood–brain barrier (BBBi), along with host genetic variants in transporter genes, may affect antiretroviral (ARV) exposure in the cerebrospinal fluid (CSF). The aim of this study was to evaluate ARV CSF penetration according to compartmental inflammation, BBB permeability and single-nucleotide polymorphisms (SNPs) in drug transporter encoding genes. CSF neopterin (ELISA), plasma and CSF ARV concentrations (HPLC) and host genetic variants in ABCC2, HNF4α, SLCO1A2 and SLC22A6 (real-time PCR) were measured. Bi- and multivariate analyses were performed for single ARV and classes. We included 259 participants providing 405 paired plasma and CSF samples. CSF/plasma ratios (CPR) showed an increase for NRTIs and nevirapine with low penetrations for the majority of ARVs. At bi-variate analysis, several associations, including the effect of BBBi (emtricitabine, raltegravir), age (zidovudine and darunavir), and high CSF neopterin (NRTIs and border-line for PIs) were suggested. An association was found between genetic variants and integrase strand transfer (ABCC2 and HNF4α), non-nucleoside reverse transcriptase inhibitors (SLCO1A2), and protease inhibitors (SLC22A6). At multivariate analysis age, gender, BMI, and altered BBB were independent predictors of nucleoside reverse transcriptase CSF concentrations; age (for protease inhibitors) and body mass index and altered BBB (integrase strand transfer inhibitors) were also associated with ARV CSF exposure. We describe factors associated with CSF concentrations, showing that demographic, BBB integrity and, partially, genetic factors may be predictors of drug passage in the central nervous system.
Lightweight Separable Convolution Network for Breast Cancer Histopathological Identification
Authors: Nneji Grace Ugochi , Monday Happy Nkanta , Mgbejime Goodness Temofe , Pathapati Venkat Subramanyam R. , Nahar Saifun , Ukwuoma Chiagoziem Chima
Published: January 2023

Breast cancer is one of the leading causes of death among women worldwide. Histopathological images have proven to be a reliable way to find out if someone has breast cancer over time, however, it could be time consuming and require much resources when observed physically. In order to lessen the burden on the pathologists and save lives, there is need for an automated system to effectively analysis and predict the disease diagnostic. In this paper, a lightweight separable convolution network (LWSC) is proposed to automatically learn and classify breast cancer from histopathological images. The proposed architecture aims to treat the problem of low quality by extracting the visual trainable features of the histopathological image using a contrast enhancement algorithm. LWSC model implements separable convolution layers stacked in parallel with multiple filters of different sizes in order to obtain wider receptive fields. Additionally, the factorization and the utilization of bottleneck convolution layers to reduce model dimension were introduced. These methods reduce the number of trainable parameters as well as the computational cost sufficiently with greater non-linear expressive capacity than plain convolutional networks. The evaluation results depict that the proposed LWSC model performs optimally, obtaining 97.23% accuracy, 97.71% sensitivity, and 97.93% specificity on multi-class categories. Compared with other models, the proposed LWSC obtains comparable performance.
Entropy, Graph Homomorphisms, and Dissociation Sets
Authors: Wang Ziyuan , Tu Jianhua , Lang Rongling
Published: January 2023

Given two graphs G and H, the mapping of f:V(G)→V(H) is called a graph homomorphism from G to H if it maps the adjacent vertices of G to the adjacent vertices of H. For the graph G, a subset of vertices is called a dissociation set of G if it induces a subgraph of G containing no paths of order three, i.e., a subgraph of a maximum degree, which is at most one. Graph homomorphisms and dissociation sets are two generalizations of the concept of independent sets. In this paper, by utilizing an entropy approach, we provide upper bounds on the number of graph homomorphisms from the bipartite graph G to the graph H and the number of dissociation sets in a bipartite graph G.
Can One Series of Self-Organized Nanoripples Guide Another Series of Self-Organized Nanoripples during Ion Bombardment: From the Perspective of Power Spectral Density Entropy?
Authors: Li Hengbo , Li Jinyu , Yang Gaoyuan , Liu Ying , Frost Frank , Hong Yilin
Published: January 2023

Ion bombardment (IB) is a promising nanofabrication tool for self-organized nanostructures. When ions bombard a nominally flat solid surface, self-organized nanoripples can be induced on the irradiated target surface, which are called intrinsic nanoripples of the target material. The degree of ordering of nanoripples is an outstanding issue to be overcome, similar to other self-organization methods. In this study, the IB-induced nanoripples on bilayer systems with enhanced quality are revisited from the perspective of guided self-organization. First, power spectral density (PSD) entropy is introduced to evaluate the degree of ordering of the irradiated nanoripples, which is calculated based on the PSD curve of an atomic force microscopy image (i.e., the Fourier transform of the surface height. The PSD entropy can characterize the degree of ordering of nanoripples). The lower the PSD entropy of the nanoripples is, the higher the degree of ordering of the nanoripples. Second, to deepen the understanding of the enhanced quality of nanoripples on bilayer systems, the temporal evolution of the nanoripples on the photoresist (PR)/antireflection coating (ARC) and Au/ARC bilayer systems are compared with those of single PR and ARC layers. Finally, we demonstrate that a series of intrinsic IB-induced nanoripples on the top layer may act as a kind of self-organized template to guide the development of another series of latent IB-induced nanoripples on the underlying layer, aiming at improving the ripple ordering. The template with a self-organized nanostructure may alleviate the critical requirement for periodic templates with a small period of ~100 nm. The work may also provide inspiration for guided self-organization in other fields.
Willow Catkin Optimization Algorithm Applied in the TDOA-FDOA Joint Location Problem
Authors: Pan Jeng-Shyang , Zhang Si-Qi , Chu Shu-Chuan , Yang Hong-Mei , Yan Bin
Published: January 2023

The heuristic optimization algorithm is a popular optimization method for solving optimization problems. A novel meta-heuristic algorithm was proposed in this paper, which is called the Willow Catkin Optimization (WCO) algorithm. It mainly consists of two processes: spreading seeds and aggregating seeds. In the first process, WCO tries to make the seeds explore the solution space to find the local optimal solutions. In the second process, it works to develop each optimal local solution and find the optimal global solution. In the experimental section, the performance of WCO is tested with 30 test functions from CEC 2017. WCO was applied in the Time Difference of Arrival and Frequency Difference of Arrival (TDOA-FDOA) co-localization problem of moving nodes in Wireless Sensor Networks (WSNs). Experimental results show the performance and applicability of the WCO algorithm.
On Divided-Type Connectivity of Graphs
Authors: Zhou Qiao , Wang Xiaomin , Yao Bing
Published: January 2023

The graph connectivity is a fundamental concept in graph theory. In particular, it plays a vital role in applications related to the modern interconnection graphs, e.g., it can be used to measure the vulnerability of the corresponding graph, and is an important metric for reliability and fault tolerance of the graph. Here, firstly, we introduce two types of divided operations, named vertex-divided operation and edge-divided operation, respectively, as well as their inverse operations vertex-coincident operation and edge-coincident operation, to find some methods for splitting vertices of graphs. Secondly, we define a new connectivity, which can be referred to as divided connectivity, which differs from traditional connectivity, and present an equivalence relationship between traditional connectivity and our divided connectivity. Afterwards, we explore the structures of graphs based on the vertex-divided connectivity. Then, as an application of our divided operations, we show some necessary and sufficient conditions for a graph to be an Euler’s graph. Finally, we propose some valuable and meaningful problems for further research.
A Critical Examination of the Beam-Squinting Effect in Broadband Mobile Communication: Review Paper
Authors: Abdalrazak Mariam Q. , Majeed Asmaa H. , Abd-Alhameed Raed A.
Published: January 2023

Due to the fact that the quality of the received signal is adversely affected by the beam squint phenomenon, which is especially pertinent to the millimeter wave (mmwave) bands, many studies have been utilised by other researchers to provide light on some of the challenges that come with analysing this type of occurrence. Squint causes several issues, the most important of which are its detrimental effects on gain, line of sight, angle of arrival, progressive phase, usable bandwidth, and fading effect. As a result of these obstacles, the advantages of adopting a high-frequency band such as mmwave in modern wireless communication systems are severely limited. Squint-phenomena-related difficulties, such as decreased channel capacity, increased bit error rate (BER), and lowered quality of services, may have a substantial detrimental impact on channel performance. The squint phenomenon and associated issues become more pressing with the expansion of the frequency spectrum and the deviation of the arrival angle from boresight. The purpose of this article is to provide a comprehensive overview of the relevant literature and to compare and contrast various methods in order to identify the most fruitful lines of inquiry for future research.
Indoor Localization Using Positional Tracking Feature of Stereo Camera on Quadcopter
Authors: Firdaus Ahmad Riyad , Hutagalung Andreas , Syahputra Agus , Analia Riska
Published: January 2023

During the maneuvering of most unmanned aerial vehicles (UAVs), the GPS is one of the sensors used for navigation. However, this kind of sensor cannot handle indoor navigation applications well. Using a camera might be the answer to performing indoor navigation using its coordinate system. In this study, we considered indoor navigation applications using the ZED2 stereo camera for the quadcopter. To use the ZED 2 camera as a navigation sensor, we first transformed its coordinates into the North, East, down (NED) system to enable the drone to understand its position and maintain stability in a particular position. The experiment was performed using a real-time application to confirm the feasibility of this approach for indoor localization. In the real-time application, we commanded the quadcopter to follow triangular and rectangular paths. The results indicated that the quadcopter was able to follow the paths and maintain its stability in specific coordinate positions.
LACE: Low-Cost Access Control Based on Edge Computing for Smart Buildings
Authors: Huang Haifeng , Tan Hongmin , Xu Xianyang , Zhang Jianfei , Zhao Zhiwei
Published: January 2023

With the explosive growth in personal mobile devices, offloading computation through nearby mobile user devices as opportunistic edge servers to support complex applications with limited computation resources is receiving increasing attention. In this paper, we first establish the optimal opportunistic offloading problem using the statistical properties of user movement speed and CPU load of mobile edge servers. We then determine the amount of computation to be offloaded to individual mobile edge servers. Moreover, we design an adaptive mechanism based on PID to realize the function of continuing large data packets from breakpoints, mainly used to adjust the size of data packets automatically. It efficiently avoids data loss and reduces the cost of resources through the latency deviation as the variable of the gain function to estimate the packet size. Finally, an access control system based on edge computing is designed and developed to make full use of the mobile phones of nearby users. It can address the shortcomings of traditional schemes with high latency to some extent, and it makes latency lower and data reliability higher.
Integrated Circuit Design of Fractional-Order Chaotic Systems Optimized by Metaheuristics
Authors: Valencia-Ponce Martin Alejandro , González-Zapata Astrid Maritza , de la Fraga Luis Gerardo , Sanchez-Lopez Carlos , Tlelo-Cuautle Esteban
Published: January 2023

Nowadays, a huge amount of research is done on introducing and implementing new fractional-order chaotic systems. In the majority of cases, the implementation is done using embedded hardware, and very seldom does it use integrated circuit (IC) technology. This is due to the lack of design automation tools ranging from the system level down to layout design. At the system level, the challenge is guaranteeing chaotic behavior by varying all parameters while optimizing dynamical characteristics, such as the Lyapunov spectrum and the Kaplan–Yorke dimension. Using embedded hardware, the implementation is straightforward, but one must perform a scaling process for IC design, in which the biases may be lower than 1 volt but the amplitudes of the state variables of the chaotic systems can have values higher than one. In this manner, this paper describes three levels of abstraction to design fractional-order chaotic systems: The first one shows the optimization of a case study, the mathematical model of the fractional-order Lorenz system to find the fractional-orders of the derivatives, and the coefficients that generate better chaotic behavior. The second level is the block description of a solution of the mathematical model, in which the fractional-order derivatives are approximated in the Laplace domain by several approximation methods. The third level shows the IC design using complementary metal–oxide–semiconductor (CMOS) technology. The transfer functions approximating the fractional-order derivatives are synthesized by active filters that are designed using operational transconductance amplifiers (OTAs). The OTAs are also used to design adders and subtractors, and the multiplication of variables is done by designing a CMOS four-quadrant multiplier. The paper shows that the simulation results scaling the mathematical model to have amplitudes lower than ±1 are in good agreement with the results using CMOS IC technology of 180 nm.
Part-I: State-of-the-Art Technologies of Solar Powered DC Microgrid with Hybrid Energy Storage Systems-Architecture Topologies
Authors: Raveendhra Dogga , Poojitha Rajana , Narasimharaju Beeramangalla , Dreglea Aliona , Liu Fang , Panasetsky Daniil , Pathak Mukesh , Sidorov Denis
Published: January 2023

In the case of microgrid (MG) systems, the choice of the right configuration plays a vital role to meet grid/load necessities when integrating low voltage, non-linear and highly sensitive (to environmental conditions) power sources such as solar PV modules, batteries and supercapacitors (SCs), etc. In the case of MG systems, the choice of the right configuration and the appropriate type of power converters in any application can have a significant impact on the optimum performance. Numerous architectures have been proposed for the integration of various energy sources to achieve optimum performance. A large number of research articles have been published in these areas. In this article, the detailed organization of various architectures based on the arrangement of various sources and detailed analyses is presented along with a discussion on those architectures. Moreover, the suitability of all the reviewed architectures based on driving factors such as (a) high conversion gain, (b) good power decoupling, (c) high efficiency, (d) isolation, (e) power-handling capabilities and (f) compact design is presented in the discussions section. The critical examination and comparative study presented in this work can assist both industry personnel and academicians in selecting the best architectural and power converter topologies required for optimum performance.
Nowcasting Hourly-Averaged Tilt Angles of Acceptance for Solar Collector Applications Using Machine Learning Models
Authors: Nemalili Ronewa Collen , Jhamba Lordwell , Kiprono Kirui Joseph , Sigauke Caston
Published: January 2023

Challenges in utilising fossil fuels for generating energy call for the adoption of renewable energy sources. This study focuses on modelling and nowcasting optimal tilt angle(s) of solar energy harnessing using historical time series data collected from one of South Africa’s radiometric stations, USAid Venda station in Limpopo Province. In the study, we compared random forest (RF), K-nearest neighbours (KNN), and long short-term memory (LSTM) in nowcasting of optimum tilt angle. Gradient boosting (GB) is used as the benchmark model to compare the model’s predictive accuracy. The performance measures of mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE) and R2 were used, and the results showed LSTM to have the best performance in nowcasting optimum tilt angle compared to other models, followed by the RF and GB, whereas KNN was the worst-performing model.
Thermal Effects in the End-Winding Region of Electrical Machines
Authors: Vukotić Mario , Lutovski Stefan , Šutar Nina , Miljavec Damijan , Čorović Selma
Published: January 2023

The main heat transfer mechanism in the end-winding region of electrical machines is convection. In order to increase the air motion, the rotor is equipped with a series of blades. Their geometry is reflected in the fanning factor, i.e., the ratio between the rotor peripheral speed and air velocity. An accurate calculation procedure for the fanning factor has not yet been given. Knowing its value is crucial for the determination of air velocity and heat transfer coefficient (HTC), as the latter describes the end-winding heat removal capability. In this study, the convective heat transfer phenomena between the end winding and air inside the end-winding region were analyzed, with the heat generated only in the end winding, mimicked with a custom designed coil, and air moved by the blades. The analysis was performed by experimental testing and computational fluid dynamics (CFD) modeling. Measurements data were used to build a reliable CFD model. Further on, CFD results were used to derive a generalized analytical equation for calculation of the end-winding HTC, related to blade geometry and rotor rotational speed. The developed analytical model significantly improves the quality of real-time lumped circuit thermal modeling of electrical machines and, thus, enriches this field of science.
An Overview of Non-Intrusive Load Monitoring Based on V-I Trajectory Signature
Authors: Lu Jiangang , Zhao Ruifeng , Liu Bo , Yu Zhiwen , Zhang Jinjiang , Xu Zhanqiang
Published: January 2023

Non-intrusive load monitoring (NILM) can obtain fine-grained electricity consumption information of each appliance by analyzing the voltage and current data measured at a single point on the bus, which is of great significance for promoting and improving the efficiency and sustainability of the power grid and enhancing the energy efficiency of users. NILM mainly includes data collection and preprocessing, event detection, feature extraction, and appliance identification. One of the most critical steps in NILM is signature extraction, which is the basis for all algorithms to achieve good state detection and energy disaggregation. With the generalization of machine learning algorithms, different algorithms have also been used to extract unique signatures of appliances. Recently, the development and deployment of the voltage–current (V-I) trajectory signatures applied for appliance identification motivated us to present a comprehensive review in this domain. The V-I trajectory signatures have the potential to be an intermediate domain between computer vision and NILM. By identifying the V-I trajectory, we can detect the operating state of the appliance. We also summarize existing papers based on V-I trajectories and look forward to future research directions that help to promote the field’s development.
Numerical Simulation on Electromagnetic Energy Harvester Oscillated by Speed Ripple of AC Motors
Authors: Kato Masayuki
Published: January 2023

The suppression of torque ripples in an interior permanent magnet synchronous motor (IPMSM) is essential to improve its efficiency and responsiveness. Here, we report on the development of an electromagnetic energy harvester incorporated into an IPMSM to suppress its torque ripples. The proposed harvester is driven to oscillations by the speed ripple of the AC motor. We derived the motion and circuit equations for the motor and the harvester according to Euler–Lagrange equations. We discussed the principle of electrical power generation and used MATLAB/Simulink numerical simulations to investigate the dynamic behavior of the proposed harvester. Our findings revealed that the active Coriolis force unnecessarily reduces the motor’s original torque, leading to unsuccessful power generation. Nevertheless, our results demonstrated that the reactive Coriolis force successfully suppresses the motor torque ripple.
The Effect of Flue Gas Recirculation on CO, PM and NOx Emissions in Pellet Stove Combustion
Authors: Polonini Luigi Francesco , Petrocelli Domenico , Lezzi Adriano Maria
Published: January 2023

Pellet stoves are popular appliances because they are an affordable technology and because the fuel is easy to store and to use. The increasing concern for environmental issues, however, requires a continuous effort to reduce pollutant levels in the atmosphere. This experimental work focuses on flue gas recirculation (FGR) as a possible way to improve combustion and decrease the emissions of carbon monoxide CO, particulate matter PM, and nitrogen oxides NOx in order to fulfill European and Italian emission requirements, for NOx in particular. A pellet stove has been tested in several experimental sessions with and without FGR. Pollutant emissions have been measured and analyzed in terms of statistical summaries and instantaneous trends. With FGR, the average CO and PM emissions were found to be 80% and 45% lower than the corresponding emissions without FGR. Results for PM are significant since FGR reduces emissions well below the most restrictive limits enforced in Italy. The analysis of instantaneous emissions in relation to excess air indicated that FGR can considerably reduce emissions, especially at the extremities of the oxygen O2 content range. Optimal ranges of excess air, in terms of O2 in flue gas, were identified for both the tested configurations, in which CO and PM emissions are minimized. The optimal range is 8–9% without FGR, and it decreases to 5–7% with FGR. Finally, a reduction in NOx emissions by about 11% has been observed in the configuration with FGR. Although this reduction seems modest as compared to CO and PM, it is important in that it lowers the emission level to the most severe limit in Italian regulations and indicates an improved FGR system as the solution for further reduction.
Barriers to the Expansion of Sugarcane Bioelectricity in Brazilian Energy Transition
Authors: Soares Munir Younes , Ramos Dorel Soares , de Oliveira Pavan Margareth , Diuana Fabio A.
Published: January 2023

This article evaluated bioelectricity’s evolving competitiveness and systemic complementarity benefits, both in comparison with other renewable sources. To do so, the results of several energy auctions were analysed, and a modelling exercise was developed using an optimisation model based on stochastic dual dynamic programming. The results indicate that wind and solar energies became the least cost expansions, and sugarcane bioelectricity lost significance and competitiveness in this environment. At the same time, the study shows that wind power’s potential to be complementary to hydropower generation is greater than bioenergy in Brazil. These findings have relevant policy implications regarding the power sector and whether bioelectricity from sugarcane should still be incentivised along with wind power sources. It is worthwhile to point out that although the Brazilian case is explored in the article, it can be used as an example by other countries, especially developing ones, that can take advantage of Brazilian expertise on biomass exploitation aiming at integration with the power sector.
A Fault Diagnosis Method of the Shearer Hydraulic Heightening System Based on a Rough Set and RBF Neural Network
Authors: Liu Min , Liu Zhiqi , Cui Jinyuan , Kong Yigang
Published: January 2023

The hydraulic heightening system is the core component of the shearer, and its stable operation directly affects the safety and reliability of the equipment, so it is of great significance to realize an efficient and accurate fault diagnosis. This paper proposes a fault diagnosis method combining a rough set and radial basis function neural network (RS-RBFNN). Firstly, the RS is used to discretize the original fault data set and attribute reduction, remove the redundant information, and mine the implicit knowledge and potential rules. Then, the topology structure of the RBFNN is determined. The mapping relationship is established between the fault symptom and category. The fault diagnosis is carried out with Python language. Finally, the method is compared with two diagnostic methods including a back propagation neural network (BPNN) and RBFNN. The research results show that the RS-RBFNN has the highest fault diagnosis accuracy, with an average of 98.68%, which verifies the effectiveness of the proposed fault diagnosis method.
WACC for Electric Power Transmission System Operators: The Case of Colombia
Authors: Bedoya-Cadavid Jorge Armando , Lanzas-Duque Ángela María , Salazar Harold
Published: January 2023

In emerging countries, energy service users generally pay high rates of return to transmission system operators (TSOs). One of the causes of this situation is the application of the CAPM with the comparable beta method when estimating the cost of equity in the WACC. The purpose of this article is to present a new methodology for calculating the cost of equity of TSOs in Colombia. To achieve this objective, a multifactor model has been built to explain the variation in returns on the electric utilities portfolio in Colombia between April 2008 and March 2022 and then recalculate the WACC approved by the country’s regulatory commission. It was found that, in addition to the estimated market risk in the CAPM, there is a risk due to the regulatory framework and changes in interest rates, which helps to explain 73% of the variations in the electric utilities portfolio, resulting in a lower cost of equity, and therefore a lower WACC of 5.28% compared to the WACC estimated by the regulator of 11.79% in 2019. These results can support regulatory commissions in emerging countries in establishing a more accurate rate of return for users.
Forecasting Energy Consumption of a Public Building Using Transformer and Support Vector Regression
Authors: Huang Junhui , Kaewunruen Sakdirat
Published: January 2023

Most of the Artificial Intelligence (AI) models currently used in energy forecasting are traditional and deterministic. Recently, a novel deep learning paradigm, called ‘transformer’, has been developed, which adopts the mechanism of self-attention. Transformers are designed to better process and predict sequential data sets (i.e., historical time records) as well as to track any relationship in the sequential data. So far, a few transformer-based applications have been established, but no industry-scale application exists to build energy forecasts. Accordingly, this study is the world’s first to establish a transformer-based model to estimate the energy consumption of a real-scale university library and benchmark with a baseline model (Support Vector Regression) SVR. With a large dataset from 1 September 2017 to 13 November 2021 with 30 min granularity, the results using four historical electricity readings to estimate one future reading demonstrate that the SVR (an R2 of 0.92) presents superior performance than the transformer-based model (an R2 of 0.82). Across the sensitivity analysis, the SVR model is more sensitive to the input close to the output. These findings provide new insights into the research area of energy forecasting in either a specific building or a building cluster in a city. The influences of the number of inputs and outputs related to the transformer-based model will be investigated in the future.
Life Cycle Assessment of Greenhouse Gas (GHG) and NOx Emissions of Power-to-H2-to-Power Technology Integrated with Hydrogen-Fueled Gas Turbine
Authors: Song Guohui , Zhao Qi , Shao Baohua , Zhao Hao , Wang Hongyan , Tan Wenyi
Published: January 2023

Hydrogen is expected to play an important role in renewable power storage and the decarbonization of the power sector. In order to clarify the environmental impacts of power regenerated through hydrogen-fueled gas turbines, this work details a life cycle model of the greenhouse gas (GHG) and NOx emissions of the power regenerated by power-to-H2-to-power (PHP) technology integrated with a combined cycle gas turbine (CCGT). This work evaluates the influences of several variables on the life cycle of GHG and NOx emissions, including renewable power sources, hydrogen production efficiency, net CCGT efficiency, equivalent operating hours (EOH), and plant scale. The results show that renewable power sources, net CCGT efficiency, and hydrogen production efficiency are the dominant variables, while EOH and plant scale are the minor factors. The results point out the direction for performance improvement in the future. This work also quantifies the life cycle of GHG and NOx emissions of power regenerated under current and future scenarios. For hydro, photovoltaic (PV) and wind power, the life cycle of the GHG emissions of regenerated power varies from 8.8 to 366.1 gCO2e/kWh and that of NOx emissions varies from 0.06 to 2.29 g/kWh. The power regenerated from hydro and wind power always has significant advantages over coal and gas power in terms of GHG and NOx emissions. The power regenerated from PV power has a small advantage over gas power in terms of GHG emissions, but does not have advantages regarding NOx emissions. Preference should be given to storing hydro and wind power, followed by PV power. For biomass power with or without CO2 capture and storage (CCS), the life cycle of the GHG emissions of regenerated power ranges from 555.2 to 653.5 and from −2385.0 to −1814.4, respectively, in gCO2e/kWh; meanwhile, the life cycle of NOx emissions ranges from 1.61 to 4.65 g/kWh, being greater than that of coal and gas power. Biomass power with CCS is the only power resource that can achieve a negative life cycle for GHG emissions. This work reveals that hydrogen-fueled gas turbines are an important, environmentally friendly technology. It also helps in decision making for grid operation and management.
Capacity Mechanisms in Europe and the US: A Comparative Analysis and a Real-Life Application for Greece
Authors: Simoglou Christos K. , Biskas Pandelis N.
Published: January 2023

This paper presents a comparative analysis of various capacity mechanisms that are either in force or under approval in key countries/regions in Europe and the US. A detailed analysis on the necessities that led to the establishment of the capacity mechanisms, along with various fundamental technical and operational features associated with the design and operation of different capacity mechanisms, mainly in Europe (Italy, France, Germany, Belgium, Poland, Great Britain, Ireland, Cyprus) and complementarily in the US (PJM, New England), are presented. This analysis is complemented by a real-life application regarding the long-term capacity remuneration mechanism that is expected to be established in Greece in the near future. A detailed simulation of the envisaged capacity mechanism auctions under differentiated scenarios has been performed, regarding the future Greek power system operating conditions during the forthcoming decade (2022–2031). Test results illustrate that the outcome of the auctions is heavily dependent on the future energy generation mix and the market participants’ bidding strategy. Whereas, the total cost that will have to be undertaken by the electricity supply companies and, ultimately, by the end-consumers for the financing of the proposed capacity mechanism lies in the range of 5.5–8.7 €/MWh for the entire study period.
Electric Vehicle Charging Station Layout for Tourist Attractions Based on Improved Two-Population Genetic PSO
Authors: Che Shuang , Chen Yan , Wang Longda
Published: January 2023

In this paper, the optimization issue of electric vehicle charging station layout (EVCSL) for tourist attractions is addressed, and an improved PSO is used to solve the optimization issue. Specifically, the improved particle swarm optimization (PSO) is proposed to obtain an appreciative planning solution of EVCSL, and dynamic weight adjustment strategy and integration into the two-population genetic mode are proposed to improve the optimization quality for PSO. Simulation results show that the proposed improvement strategies can increase the optimization quality for PSO effectively so that a more appreciative planning solution of EVCSL can be obtained.
Thermal Performance and Durability Evaluation of Arundo Donax towards an Improvement in the Knowledge of Sustainable Building Materials
Authors: Malheiro Raphaele , Morillas Aurora , Ansolin Adriana , Fernandes Jorge , Camões Aires , Amorim Maria Teresa , Silva Sandra Monteiro , Mateus Ricardo
Published: January 2023

Bio-based and natural building materials can support the building sector to reduce its environmental impact. In this study, experimental research was developed to evaluate the performance of giant reed when used as a building thermal insulation. Reed was chosen due to its high availability and close relationship to Portuguese vernacular architecture. The role of the reed’s origin in this context, its physical characteristics and its durability aspects were evaluated. The results showed that the performance of the reed boards was compatible with their use as a thermal insulation material, regardless of the reed’s origin. Denser boards showed better results. Under the favourable conditions related to the reed’s water content, mould growth could occur.
Neural Load Disaggregation: Meta-Analysis, Federated Learning and Beyond
Authors: Bousbiat Hafsa , Himeur Yassine , Varlamis Iraklis , Bensaali Faycal , Amira Abbes
Published: January 2023

Non-intrusive load monitoring (NILM) techniques are central techniques to achieve the energy sustainability goals through the identification of operating appliances in the residential and industrial sectors, potentially leading to increased rates of energy savings. NILM received significant attention in the last decade, reflected by the number of contributions and systematic reviews published yearly. In this regard, the current paper provides a meta-analysis summarising existing NILM reviews to identify widely acknowledged findings concerning NILM scholarship in general and neural NILM algorithms in particular. In addition, this paper emphasizes federated neural NILM, receiving increasing attention due to its ability to preserve end-users’ privacy. Typically, by combining several locally trained models, federated learning has excellent potential to train NILM models locally without communicating sensitive data with cloud servers. Thus, the second part of the current paper provides a summary of recent federated NILM frameworks with a focus on the main contributions of each framework and the achieved performance. Furthermore, we identify the non-availability of proper toolkits enabling easy experimentation with federated neural NILM as a primary barrier in the field. Thus, we extend existing toolkits with a federated component, made publicly available and conduct experiments on the REFIT energy dataset considering four different scenarios.
A Multi-Agent Approach to Binary Classification Using Swarm Intelligence
Authors: Grimes Sean , Breen David E.
Published: January 2023

Wisdom-of-Crowds-Bots (WoC-Bots) are simple, modular agents working together in a multi-agent environment to collectively make binary predictions. The agents represent a knowledge-diverse crowd, with each agent trained on a subset of available information. A honey-bee-derived swarm aggregation mechanism is used to elicit a collective prediction with an associated confidence value from the agents. Due to their multi-agent design, WoC-Bots can be distributed across multiple hardware nodes, include new features without re-training existing agents, and the aggregation mechanism can be used to incorporate predictions from other sources, thus improving overall predictive accuracy of the system. In addition to these advantages, we demonstrate that WoC-Bots are competitive with other top classification methods on three datasets and apply our system to a real-world sports betting problem, producing a consistent return on investment from 1 January 2021 through 15 November 2022 on most major sports.
Default Matters in Trust and Reciprocity
Authors: Zhang Yao , Zhang Yushu , Wu Yan , Krueger Frank
Published: January 2023

Trust and reciprocity promote cooperation and are key elements of a successful social life. This study investigated the framing effects on trust and reciprocity behaviors. Using an iterated one-shot within-subjects design, this study explored how trust and reciprocity decisions changed when the game was framed in terms of a give (i.e., using a standard trust game with a default of no trust) and a take (e.g., using a distrust game with a default of full trust) frame. Participants of both genders first completed the scenario version of the game (Session 1), and then played the roles of trustors (Session 2) and trustees (Session 3) with human and computer-mediated human partners either in the give or take frame. Our results showed increased trust in the give than in the take frame, but only pronounced in direct interaction with human (vs. indirect computer-mediated) partners. Participants also showed higher expectations of return in the give than in the take frame. The actual reciprocity was higher in the give than in the take frame when interacting both with human and computer-mediated human partners. The results contribute to our understanding of the factors that shape trust and reciprocity and emphasize the impact of framing the default.
Demonstration and Suggestion on the Communication Efficiency of New Media of Environmental Education Based on Ideological and Political Education
Authors: Ren Huiyu , Zhao Liang
Published: January 2023

With the rapid development of the economy, we are facing more and more problems, and the construction of ecological civilization has become the focus of our national concern. With the rapid development of network technology, the immediacy of the new media and the huge audience have brought new development trends to the dissemination of environmental information. The number of new environmental media is increasing, but there are still some problems, such as formality, rigid content and lack of innovation, which make it difficult to achieve better communication effects. However, the research on new environmental media is still in its infancy, and there is not yet a set of targeted and specialized new media evaluation systems. Based on the social function of new environmental news media and the social responsibility of media as the entry point, the article establishes a set of index systems to measure the efficiency of environmental news dissemination and proposes corresponding improvement measures accordingly. The results of the study show that the best use of environmental education publicity is at 81.3%. In terms of cognitive efficiency and attitudinal efficacy, the scores of environmental education weibo public numbers were not high, at 60.7% and 71.5%, respectively. From the perspective of ideological and political education, the environmental protection class of WeChat plays a good role in attracting the attention of college students, and can provide ideological and political education to them and improve their ideological awareness. In terms of cognition, new media is responsible for conveying environmental knowledge and concepts to college students, so the development of new media centers on environmental information, and the content directly affects the cognitive level of college students, fully reflecting the importance of cognitive efficacy in new media of environmental education. On attitude efficacy, only one indicator is set for identity shaping, which has the highest score, but the lowest is 4.0, showing that the public number is still not obvious enough in terms of identifying with college student groups, influencing college students’ emotions and attitudes. Based on this, this paper points out the problems of the current communication efficiency of environmental education new media through the analysis of the evaluation results and puts forward suggestions to improve its communication efficiency in this regard.
Identification of Phenolics Profile in Freeze-Dried Apple Peel and Their Bioactivities during In Vitro Digestion and Colonic Fermentation
Authors: Zahid Hafza Fasiha , Ali Akhtar , Ranadheera Chaminda Senaka , Fang Zhongxiang , Ajlouni Said
Published: January 2023

Freeze-dried apple peel powder (Fd-APP) was subjected to in vitro digestion and colonic fermentation to evaluate the variations in its phenolic composition, bioactivities (antioxidant activity, α-amylase, and α-glucosidase inhibition), and fecal metabolic outputs. A total of 88 phenolics were tentatively identified, of which 51 phenolic compounds were quantitated in Fd-APP sample extracts before digestion, and 34 were released during subsequent phases of digestion. Among these, phenolic acids showed the highest bio accessibility index (BI) of 68%, followed by flavonoids (63%) and anthocyanins (52%). The inhibitory functions of Fd-APP extract against α-amylase and α-glucosidase pre- and post-digestion were moderate and ranged from 41.88 to 44.08% and 35.23 to 41.13%, respectively. Additionally, the antioxidant activities revealed a significant (p ≤ 0.05) decline during the in vitro digestion. However, the colonic fermentation stage presented different products where the intact parent phenolic compounds present in Fd-APP were utilized by gut microbes and produced various phenolic metabolites such as 3- hydroxyphenyl acetic acid (3-HPAA), ferulic acid (FA), 3-(4-hydroxyphenyl) propionic acid (3,4 HPPA) and 4- hydroxybenzoic acid (4-HBA). Furthermore, colonic fermentation of Fd-APP accelerated the production of short-chain fatty acids (SCFAs), with acetic acid being the most prevalent (97.53 ± 9.09 mM). The decrease in pH of fermentation media to 4.3 significantly (p ≤ 0.05) enhanced counts of Bifidobacterium (10.27 log CFU/mL), which demonstrated the potential prebiotic effects of Fd-APP. These findings indicated that the consumption of apple peel as a constituent of novel functional foods may support and protect the intestinal microbiota and consequently promote human health.
Metabolomics Profiles Reveal New Insights of Herpes Simplex Virus Type 1 Infection
Authors: Huang Pu , Wang Xu , Lei Mengyue , Ma Ying , Chen Hongli , Sun Jing , Hu Yunzhang , Shi Jiandong
Published: January 2023

Herpes simplex virus type 1 (HSV-1) is a ubiquitous human pathogen that can cause significant morbidity, primarily facial cold sores and herpes simplex encephalitis. Previous studies have shown that a variety of viruses can reprogram the metabolic profiles of host cells to facilitate self-replication. In order to further elucidate the metabolic interactions between the host cell and HSV-1, we used liquid chromatography-tandem mass spectrometry (LC-MS/MS) to analyze the metabolic profiles in human lung fibroblasts KMB17 infected with HSV-1. The results showed that 654 and 474 differential metabolites were identified in positive and negative ion modes, respectively, and 169 and 114 metabolic pathways that might be altered were screened. These altered metabolites are mainly involved in central carbon metabolism, choline metabolism, amino acid metabolism, purine and pyrimidine metabolism, cholesterol metabolism, bile secretion, and prolactin signaling pathway. Further, we confirmed that the addition of tryptophan metabolite kynurenine promotes HSV-1 replication, and the addition of 25-Hydroxycholesterol inhibits viral replication. Significantly, HSV-1 replication was obviously enhanced in the ChOKα (a choline metabolic rate-limiting enzyme) deficient mouse macrophages. These results indicated that HSV-1 induces the metabolic reprogramming of host cells to promote or resist viral replication. Taken together, these observations highlighted the significance of host cell metabolism in HSV-1 replication, which would help to clarify the pathogenesis of HSV-1 and identify new anti-HSV-1 therapeutic targets.